Information and Communication Technologies: International Conference, ICT 2010, Kochi, Kerala, India, September 7-9, 2010, Proceedings (Communications in Computer and Information Science)

Communications in Computer and Information Science 101 Vinu V Das R. Vijaykumar Srinivasa K. G. Hatim A. Aboalsamh M...

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Communications in Computer and Information Science

101

Vinu V Das R. Vijaykumar Srinivasa K. G. Hatim A. Aboalsamh Mohammad Hammoudeh Vahid Salmani Dinesh Kumar Tyagi Anjali Mohapatra Bharatheesh Jaysimha Eliathamby Ambikairajah Jon Blackledge (Eds.)

Information and Communication Technologies International Conference, ICT 2010 Kochi, Kerala, India, September 7-9, 2010 Proceedings

13

Main Editors Vinu V Das Engineers Network, Trivandrum, Kerala, India E-mail: [email protected] R. Vijaykumar NSS College of Engineering, Palakkadu, India E-mail: [email protected]

Library of Congress Control Number: 2010934002 CR Subject Classification (1998): H.3, H.4, F.1, C.2, H.2.8, J.3 ISSN ISBN-10 ISBN-13

1865-0929 3-642-15765-3 Springer Berlin Heidelberg New York 978-3-642-15765-3 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. springer.com © Springer-Verlag Berlin Heidelberg 2010 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper 06/3180 543210

Volume Editors

Editor-in-Chief Vinu V Das Engineers Network, India

Editor R Vijaykumar NSS College of Engineering Palakkadu, India

Associate Editors Srinivasa K G Hatim A Aboalsamh Mohammad Hammoudeh Vahid Salmani Dinesh Kumar Tyagi Anjali Mohapatra Bharatheesh Jaysimha Eliathamby Ambikairajah Jon Blackledge

M. S. Ramaiah Institute of Technology, India King Saud University, Saudi Arabia University of Wolverhampton, UK University of California, USA BITS-Pilani, India IIIT-Bhubaneswar, India ABIBA Systems, India University of New South Wales, Australia Dublin Institute of Technology, Ireland

Preface

The International Conference on Advances in Information and Communication Technologies, ICT, is organized every year with the aim of promoting research and developmental activities in computer science, information technology, computational engineering, and communication. Another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in India and abroad. The conference provides an ideal platform for people to share views and experiences in information technology, telecommunication, computing techniques and related areas. ICT 2010 was organized by the Association of Computer Electronics and Electrical Engineers (ACEEE), during September 7–9, 2010 in Kochi, Kerala, India. This volume contains 124 papers selected through a rigorous reviewing process. The contributions reflect the richness of research on topics within the scope of the conference and represent several important developments, specifically focused on theoretical foundations and methods for information processing and communications. Organizing a conference like this one is not possible without the assistance and continuous support of many people and institutions. Particularly I would like to express my gratitude to the organizers of sessions on dedicated topics that took place during the conference. I also thank Stefan Goeller, Janahanlal Stephen, R Vijay Kumar, and Nessy Thankachan for their constant support and guidance. I would like to express my gratitude to Springer’s LNCS-CCIS editorial team, especially Leonie Kunz, for producing such a wonderful proceedings book.

July 2010

Vinu V Das

ICT 2010 – Organization

Technical Chair Pascal Lorenz Abdelhamid Mellouk Joel Rodrigues

University of Haute Alsace, France University of Paris Est, France University of Beira Interior, Portugal

Technical Co-chair R Vijayakumar Ford Lumban Gaol William M. Mongan

NSS College of Engineering, India University of Indonesia Drexel University, USA

Organizing Chair Janahan Lal PM Thankachan

Viswajyuothi College of Engineering and Technology, India Mar Ivanious, India

Organizing Co-chair Srinivasa K G Hatim A Aboalsamh

M. S. Ramaiah Institute of Technology, India King Saud University, Saudi Arabia

General Chair Vinu V Das

Tabor Engineers Network P. Ltd., India

Publicity Chair Mohammad Hammoudeh

University of Wolverhampton, UK

Publication Chair Mohamed Jamaludeen

SRM University, India

X

Organization

Advisory Committee Sudarshan TSB Sumeet Dua Nirwan Ansari

BITS Pilani, India Louisiana Tech University, USA New Jersey Institute of Technology, USA

Programm Committee Shelly Sachdeva Pradheep Kumar K Rupa Ashutosh Fadnavis Muhammad Nubli Zhenyu Y Angz Keivan Navi Anil K Jacob B. Kannan Liviu Vladutu Malabika Basu Pritimoy Sanyal J.Arunadevi Vahid Salmani Dinesh Kumar Tyagi Anjali Mohapatra Bharatheesh Jaysimha Eliathamby Ambikairajah Jon Blackledge Chandan Debasish Kundu Jims Marchang R Sanjeev Kunte Rama Shankar Yadav Smriti Agrawal Vandana Bhattacherjee R.D. Sudhaker Samuel Amitabha Sinha

Jaypee Institute of Information and Technology University, India SEEE, India Yeshwantrao Chavan College of Engineering, India University Malaysia, Pahang, Malaysia Florida International University, USA Shahid Beheshti University, Iran MES College of Engineering, India CUSAT, India Dublin City University, Ireland Dublin Institute of Technology, Ireland West Bengal University of Technology, India Thiagarajar School of Management, India University of California, USA BITS-Pilani, India IIIT-Bhubaneswar, India ABIBA Systems, India University of New South Wales, Australia Dublin Institute of Technology, Ireland IIT Kharagpur, India IIT Kharagpur, India NIT Silchar, India J N N College of Engineering, India MNNIT, India MNNIT, India BITS Mesra, India S J College of Engineering, India West Bengal University of Technology, India

Table of Contents

Full Paper Design and Simulation of Security Sub-layer of WMAN IEEE 802.16 Standard (Wi-Max Compliant) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajat Sheel Jain, Neeraj Kumar, and Brijesh Kumar

1

Speech Recognition of Assamese Numerals Using Combinations of LPC - Features and Heterogenous ANNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manash Pratim Sarma and Kandarpa Kumar Sarma

8

Design and Modeling of Power Eﬃcient, High Performance 32-bit ALU through Advanced HDL Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Dhanumjaya, G. Kiran Kumar, M.N. Giriprasad, and M. Raja Reddy A New Multi-language Encryption Technique for MANET . . . . . . . . . . . . Prasenjit Choudhury, Rajasekhar Gaddam, Rajesh Babu Parisi, Manohar Babu Dasari, and Satyanarayana Vuppala

13

22

Bi-variate Polynomial Approximation of Fuzzy Controller Using Genetic Algorithm for Trajectory Control of PUMA560 . . . . . . . . . . . . . . . A. Mona Subramaniam, A. Manju, and Madhav J. Nigam

29

Protecting Digital Images Using DTCWT-DCT . . . . . . . . . . . . . . . . . . . . . . K. Ramani, E.V. Prasad, and S. Varadarajan

36

A Novel High Speed Dynamic Comparator Using Positive Feedback with Low Power Dissipation and Low Oﬀset . . . . . . . . . . . . . . . . . . . . . . . . . Silpakesav Velagaleti, Pavankumar Gorpuni, and K.K. Mahapatra

45

VLSI Design of Four Quadrant Analog Voltage-Mode Multiplier and Its Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ankita Tijare and Pravin Dakhole

50

An Optimizing Technique for MDGT Using DRSA Algorithm Association with IP Traceback Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Karthik, V.P. Arunachalam, T. Ravichandran, and M.L. Valarmathi Designing a Promotor for a Novel Target Site Identiﬁed in Caspases for Initiating Apoptosis in Cancer Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mala S. Kumar, K.L. Lainu, V. Aghila, Dhanya Purushothaman, K. Varun Gopal, P.K. Krishnan Namboori, and Vrinda Harishankar

55

62

XII

Table of Contents

Learning Classiﬁer Systems Approach for Automated Discovery of Hierarchical Censored Production Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . Suraiya Jabin

68

Feature Based Watermarking Algorithm by Adopting Arnold Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S.S. Sujatha and M. Mohamed Sathik

78

Inference of Gene Networks from Microarray Data through a Phenomic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rio G.L. D’Souza, K. Chandra Sekaran, and A. Kandasamy

83

A Novel Lattice Based Research Frame Work for Identifying Web User’s Behavior with Web Usage Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . V.V.R. Maheswara Rao and V. Valli Kumari

90

Design and Analysis of Speciﬁcation Based Ids for Wireless Networks Using Soft Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vydeki Dharmar and K. Jayanthy

100

A New Image Content-Based Authenticity Veriﬁcation Procedure for Wireless Image Authentication Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Lokanadham Naidu, K. Ramani, D. Ganesh, Sk. Munwar, and P. Basha

108

Enhanced Substitution-Diﬀusion Based Image Cipher Using Improved Chaotic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Shatheesh Sam, P. Devaraj, and R.S. Bhuvaneswaran

116

Network Forensic Analysis by Correlation of Attacks with Network Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atul Kant Kaushik, Emmanuel S. Pilli, and R.C. Joshi

124

Robust and Real Time Data Delivery in Wireless Sensor Networks . . . . . Deepali Virmani and Satbir Jain

129

Multiple QoS Guided Heuristic for Independent Task Scheduling in Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sameer Singh Chauhan and R.C. Joshi

136

A Framework for Network Forensic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . Emmanuel S. Pilli, Ramesh C. Joshi, and Rajdeep Niyogi

142

A New Trust Model Based on Time Series Prediction and Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sarangthem Ibotombi Singh and Smriti Kumar Sinha

148

A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashutosh K. Dubey and Shishir K. Shandilya

157

Table of Contents

XIII

CDPN: Communicating Dynamic Petri Net for Adaptive Multimedia Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A.P. Sarath Chandar, S. Arun Balaji, G. Venkatesh, and Susan Elias

165

Developing a Web Recommendation System Based on Closed Sequential Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Utpala Niranjan, R.B.V. Subramanyam, and V. Khanaa

171

Nearest Neighbour Classiﬁcation for Trajectory Data . . . . . . . . . . . . . . . . . Lokesh K. Sharma, Om Prakash Vyas, Simon Schieder, and Ajaya K. Akasapu

180

Security-Aware Eﬃcient Route Discovery for DSR in MANET . . . . . . . . . Sanjeev Rana and Anil Kapil

186

Regression Modeling Technique on Data Mining for Prediction of CRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manisha Rathi

195

Congestion Games in Wireless Channels with Multipacket Reception Capability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Debarshi Kumar Sanyal, Sandip Chakraborty, Matangini Chattopadhyay, and Samiran Chattopadhyay

201

Secure and Revocable Multibiometric Templates Using Fuzzy Vault for Fingerprint and Iris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V.S. Meenakshi and G. Padmavathi

206

High Speed Cache Design Using Multi-diameter CNFET at 32nm Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aminul Islam and Mohd. Hasan

215

Dynamic Load Balancer Algorithm for the Computational Grid Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajkumar Rajavel, Thamarai Selvi Somasundaram, and Kannan Govindarajan

223

Instance-Based Classiﬁcation of Streaming Data Using Emerging Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohd. Amir and Durga Toshniwal

228

Modiﬁed Go-Left Balls and Bins Algorithm for Server Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prasun Banerjee, Stephan D’Costa, and Sukriti Bhattacharya

237

Three Layered Adaptation Model for Context Aware E-Learning . . . . . . . Minu M. Das, Manju Bhaskar, and T. Chithralekha

243

XIV

Table of Contents

AFDEP: Agreement Based CH Failure Detection and Election Protocol for a WSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amarjeet Kaur and T.P. Sharma

249

Problem Area Identiﬁcation with Secure Data Aggregation in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paresh Solanki, Gaurang Raval, and Srikant Pradhan

258

Operational Transconductance Ampliﬁer Based Two-Stage Diﬀerential Charge Ampliﬁers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dinesh B. Bhoyar and Bharati Y. Masram

267

Graceful Degradation in Performance of WaveScalar Architecture . . . . . . Neha Sharma and Kumar Sambhav Pandey

274

ICT 2010 – Short Paper Dual Tree Complex Wavelet Transform Based Video Object Tracking . . . Manish Khare, Tushar Patnaik, and Ashish Khare

281

Design of New Indexing Techniques Based on Ontology for Information Retrieval Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Saruladha, G. Aghila, and Sathish Kumar Penchala

287

Identifying the Attack Source by IP Traceback . . . . . . . . . . . . . . . . . . . . . . . K.C. Nalavade and B.B. Meshram

292

An Approach towards Secure and Multihop Time Synchronization in Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arun Kumar Tripathi, Ajay Agarwal, and Yashpal Singh

297

Improving Dynamic Diﬃculty Adjustment to Enhance Player Experience in Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Joy James Prabhu

303

A Novel Method for Cross-Language Retrieval of Chunks Using Monolingual and Bilingual Corpora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tayebeh Mosavi Miangah and Amin Nezarat

307

Application of Kohonan SOM in Prediction . . . . . . . . . . . . . . . . . . . . . . . . . Sathya Ramadass and Annamma Abhraham

313

Evaluation of the Role of Low Level and High Level Features in Content Based Medical Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K.S. Arun and K.S. Sarath

319

A Reinforcement Learning Approach for Price Oﬀer in Supplier Selection Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vali Derhami, Mohammad Ali Saadatjoo, and Fatemeh Saadatjoo

326

Table of Contents

XV

Generation of k-ary and (k,m)-ary Trees in A-order Using z-Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N.A. Ashraﬁ Payaman

330

Wavelet and Hadamard Transforms for Image Retrieval Using Color Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanjay N. Talbar and Satishkumar L. Varma

336

A Rough Set Integrated Fuzzy C-Means Algorithm for Color Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Byomkesh Mandal and Balaram Bhattacharyya

339

Local Monitoring Based Reputation System with Alert to Mitigate the Misbehaving Nodes in Mobile Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . K. Gopalakrishnan and V. Rhymend Uthariaraj

344

An Energy Eﬃcient Cluster Based Broadcast Protocol for Mobile Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Kalpana and M. Punithavalli

350

A Hybridized Graph Mining Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sadhana Priyadarshini and Debahuti Mishra

356

Dynamic Contract Generation and Monitoring for B2B Applications with Composite Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kanchana Rajaram and S. Usha Kiruthika

362

Synchronization of Authorization Flow with Work Object Flow in a Document Production Workﬂow Using XACML and BPEL . . . . . . . . . . . . Subrata Sinha, Smriti Kumar Sinha, and Bipul Syam Purkayastha

365

Virtual Nodes for Self Stabilization in Wireless Sensor Networks . . . . . . . Deepali Virmani and Satbir Jain

371

Small Square Microstrip Antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Lolit Kumar Singh, Bhaskar Gupta, and Partha P. Sarkar

376

Extraction of Optimal Biclusters from Gene Expression Data . . . . . . . . . . J. Bagyamani, K. Thangavel, and R. Rathipriya

380

Analysis of Data Warehouse Quality Metrics Using LR . . . . . . . . . . . . . . . Rolly Gupta and Anjana Gosain

384

Similar - Dissimilar Victor Measure Analysis to Improve Image Knowledge Discovery Capacity of SOM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Chenthalir Indra and E. RamaRaj

389

Cluster-Base Directional Rumor Routing in Wireless Sensor Network . . . Parvin Eftekhari, Hamid Shokrzadeh, and Abolfazl Toroghi Haghighat

394

XVI

Table of Contents

On the Calculation of Coldness in Iowa, a North Central Region, United States: A Summary on XML Based Scheme . . . . . . . . . . . . . . . . . . . Sugam Sharma, Shashi Gadia, and S.B. Goyal Development of a Three Layer Laminate for Better Electromagnetic Compatibility Performance at X-Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Dharma Raj, G. Sasibhushana Rao, P.V.Y. Jayasree, B. Srinu, and P. Lakshman

400

406

Pre-Conﬁrmation Neural Network for Reducing the Region of Interest in an Image for Face Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Femina Abdulkader and Ajit Joseph

411

Low Voltage Low Power Op Amp with Gain Boosting and Frequency Compensation Technique for Battery Powered Applications . . . . . . . . . . . K. Sarangam and Hameed Zohaib Samad

417

Performance of Clustering in Mobile Domain . . . . . . . . . . . . . . . . . . . . . . . . Soumen Kanrar and Aroop Mukherjee

423

Morphological Analyzer for Telugu Using Support Vector Machine . . . . . G. Sai Kiranmai, K. Mallika, M. Anand Kumar, V. Dhanalakshmi, and K.P. Soman

430

Visualization of State Transition Systems in a Parallel Environment . . . . Subbu Ramanathan, Haresh Suresh, Amog Rajenderan, and Susan Elias

434

Facilitating Eﬃcient Integrated Semantic Web Search with Visualization and Data Mining Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S.K. Jayanthi and S. Prema Image Object Classiﬁcation Using Scale Invariant Feature Transform Descriptor with Support Vector Machine Classiﬁer with Histogram Intersection Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biplab Banerjee, Tanusree Bhattacharjee, and Nirmalya Chowdhury

437

443

Palmprint Recognition System Using Zernike Moments Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Esther Rani and R. Shanmuga Lakshmi

449

A QOS Framework for Mobile Ad-Hoc in Large Scale Networks . . . . . . . . A. Boomaranimalany and R.M. Chandrasekaran

455

An Interactive Content Based Image Retrieval Method Integrating Intersection Kernel Based Support Vector Machine and Histogram Intersection Based Similarity Measure for Nearest Neighbor Ranking . . . Tanusree Bhattacharjee, Biplab Banerjee, and Nirmalya Chowdhury

458

Table of Contents

XVII

ICT 2010 – Poster Paper Analysis and Prediction of Blocking Probability in a Banyan Based ATM Switch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Pugazendi and K. Duraiswamy

463

Performance Evaluation of QoS Aware Routing in OLSR (Optimized Link State Routing Protocol) Using Genetic Algorithm . . . . . . . . . . . . . . . M. Pushpavalli and A.M. Natarajan

469

Handloom Silk Fabric Defect Detection Using First Order Statistical Features on a NIOS II Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M.E. Paramasivam and R.S. Sabeenian

475

Performance Modeling of MANET Routing Protocols with Multiple Mode Wormhole Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yogesh Chaba, Yudhvir Singh, Kanwar Preet Singh, and Prabha Rani

478

Mining a Ubiquitous Time and Attendance Application Schema Using Oracle Data Miner: A Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binu Jacob and K.V. Promod

484

4T Carry Look Ahead Adder Design Using MIFG . . . . . . . . . . . . . . . . . . . . P.H.ST. Murthy, L. Madan Mohan, V. Sreenivasa Rao, and V. Malleswara Rao

490

Microcontroller Based Monitoring and Control of Greenhouse Enivironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gaytri Gupta

495

Hand Written Text to Digital Text Conversion Using Radon Transform and Back Propagation Network (RTBPN) . . . . . . . . . . . . . . . . . . . . . . . . . . . R.S. Sabeenian and M. Vidhya

498

Resource Allocation and Multicast Routing Protocol for Optical WDM Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N. Kaliammal and G. Gurusamy

501

An Approximate Algorithm for Solving Dynamic Facility Layout Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Surya Prakash Singh

504

Homology Modeling and Protein Ligand Interaction to Identify Potential Inhibitor for E1 Protein of Chikungunya . . . . . . . . . . . . . . . . . . . C.S. Vasavi, Saptharshi, R. Radhika Devi, Lakshmi Anand, Megha. P. Varma, and P.K. Krishnan Namboori Tied Mixture Modeling in Hindi Speech Recognition System . . . . . . . . . . . R.K. Aggarwal and M. Dave

510

514

XVIII

Table of Contents

Propagation Delay Variation due to Process Induced Threshold Voltage Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Krishan Gopal Verma, Brajesh Kumar Kaushik, and Raghuvir Singh

520

Biomedical Image Coding Using Dual Tree Discrete Wavelet Transform and Iterative Projection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanjay N. Talbar and Anil K. Deshmane

525

Privacy-Preserving Na¨ıve Bayes Classiﬁcation Using Trusted Third Party and Oﬀset Computation over Distributed Databases . . . . . . . . . . . . B.N. Keshavamurthy, Mitesh Sharma, and Durga Toshniwal

529

Extraction of Pose Invariant Facial Features . . . . . . . . . . . . . . . . . . . . . . . . . Singh R. Kavita, Zaveri A. Mukeshl, and Raghuwanshi M. Mukesh

535

On the Segmentation of Multiple Touched Cursive Characters: A Heuristic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tanzila Saba, Ghazali Sulong, Shafry Rahim, and Amjad Rehman

540

Context Representation and Management in a Pervasive Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Vanathi and V. Rhymend Uthariaraj

543

A Comparative Study and Choice of an Appropriate Kernel for Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Sangeetha and B. Kalpana

549

Color Image Restoration Method for Gaussian Noise Removal . . . . . . . . . J. Harikiran and R. Usha Rani

554

A Decision Tree Approach for Design Patterns Detection by Subgraph Isomorphism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshara Pande, Manjari Gupta, and A.K. Tripathi

561

Realisation of Various EBG Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Bhuvaneswari and K. Malathi

565

Identiﬁcation of Melanoma (Skin Cancer) Proteins through Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Babita Rathore, Sandeep K. Kushwaha, and Madhvi Shakya

571

BPNN and Lifting Wavelet Based Image Compression . . . . . . . . . . . . . . . . Renu Singh, Swanirbhar Majumder, U. Bhattacharjee, and A. Dinamani Singh

576

Combined Oﬀ-Line Signature Veriﬁcation Using Neural Networks . . . . . . D.R. Shashi Kumar, R. Ravi Kumar, K.B. Raja, R.K. Chhotaray, and Sabyasachi Pattanaik

580

Table of Contents

Distribution of Continuous Queries over Data Aggregators in Dynamic Data Dissemination Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mahesh Gadiraju and V. Valli Kumari Formal Veriﬁcation of IEEE802.16m PKMv3 Protocol Using CasperFDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K.V. Krishnam Raju, V. Valli Kumari, N. Sandeep Varma, and K.V.S.V.N. Raju Digital Image Steganography Based on Combination of DCT and DWT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vijay Kumar and Dinesh Kumar Mobile Robot Pose Estimation Based on Particle Filters for Multi-dimensional State Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Divya Udayan, T. Gireesh Kumar, Roshy M. John, K.J. Poornaselvan, and S.A. Lakshmanan

XIX

584

590

596

602

Neural Networks Based Detection of Purpose Data in Text . . . . . . . . . . . . P. Kiran Mayee, Rajeev Sangal, and Soma Paul

606

Comparative Performance Analysis of QoS-Aware Routing on DSDV, AODV and DSR Protocols in MANETs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajneesh Gujral and Anil Kapil

610

Steering Control of an Automated Vehicle Using Touch Screen with Simulation Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Apeksha V. Sakhare, V.M. Thakare, and R.V. Dharaskar

616

A Low Cost GPS Based Vehicle Collision Avoidance System . . . . . . . . . . . Samarth Borker and R.B. Lohani

619

Human Skin Region Identiﬁcation Using Fusion Technique . . . . . . . . . . . . Vijayanandh Rajamanickam and Balakrishnan Ganesan

622

Avoidance of Electromagnetic Interference in Modem with Minimal Bit Error Rate Using Tele Typewriter Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Annadurai, D. MuthuKumaran, C. Charithartha Reddy, and M. Kanagasabapathy

626

A State Variables Analysis for Emerging Nanoelectronic Devices . . . . . . . K. Vaitheki and R. Tamijetchelvy

632

Unconditional Steganalysis of JPEG and BMP Images and Its Performance Analysis Using Support Vector Machine . . . . . . . . . . . . . . . . . P.P. Amritha, Anoj Madathil, and T. Gireesh Kumar

638

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ASSR Fair Load Distribution Using Eﬃciency Division Factor with Greedy Booster Approach for MANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmad Anzaar, Husain Shahnawaz, Chand Mukesh, S.C. Gupta, and R. Gowri

641

Secret Sharing Scheme for Image Encryption Using New Transformation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Kalai Selvi and M. Mohamed Sathik

647

Measuring the Reusability Level of Software Packages Using Reusability Testing Scripts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Kamalraj, A. Rajiv Kannan, P. Ranjani, and R. Hemarani

651

A New Threshold Calculation Approach in the Performance Enhancement of Spread Spectrum System Using Double Density Discrete Wavelet Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Arunarasi Jayaraman and Indumathy Pushpam

654

Recent Trends in Superscalar Architecture to Exploit More Instruction Level Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ritu Baniwal and Kumar Sambhav Pandey

660

A Neural Network Based Solution to Color Image Restoration Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Satyadhyan Chickerur and M. Aswatha Kumar

666

Spline Biorthogonal Wavelet Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . T. Arathi, K.P. Soman, and Latha Parameshwaran

672

Securing Password File Using Bezier Curves . . . . . . . . . . . . . . . . . . . . . . . . . Sunil Khurana and Sunil Kumar Khatri

675

Reading a 4-Bit SLC NAND Flash Memory in 180nm Technology . . . . . . Nilesh Shah, Rasika Dhavse, and Anand Darji

681

Keystroke Dynamics Authentication Using Neural Network Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Venkateswaran Shanmugapriya and Ganapathi Padmavathi

686

Moving Object Tracking Using Object Segmentation . . . . . . . . . . . . . . . . . Sanjay Singh, Srinivasa Murali Dunga, A.S. Mandal, Chandra Shekhar, and Anil Vohra

691

Mobile Health Care System for Patient Monitoring . . . . . . . . . . . . . . . . . . . Titir Santra

695

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

701

Design and Simulation of Security Sub-layer of WMAN IEEE 802.16 Standard (Wi-Max Compliant) Rajat Sheel Jain, Neeraj Kumar, and Brijesh Kumar Information Technology, Lingaya’s University, Faridabad, India [email protected] [email protected] [email protected]

Abstract. The Data could be provided from the MAC Layer to the Security Sub Layer that is going to be designed & simulated. In this paper the security sublayer will provide subscribers with total privacy across the fixed broadband wireless network. This is done by encrypting connections between Subscriber Station (SS) and Base Station (BS). Also, strong protection from theft of service is provided through the BS protection to data transport services, by enforcing encryption of the associated service flows across the network. The data when entered into the security sub layer first will be certified. After the certification of the data, it could be encrypted through techniques like Data Encryption Standard (DES), Advanced Encryption Standard (AES) that we will call as the Data Encryption. After the encryption, the data is again encrypted through Secure Hash Algorithm (SHA-1) which is used for the message digest of that encrypted data. This operation will take place in the Integrity Unit (part). Keywords: Advanced Encryption Standards, Data Encryption Algorithm, RSA Standards, Wireless Security.

1 Introduction The paper is about to design a security Sub-Layer, defined in Data Link Layer of IEEE 802.16-2004 [1] standard. This standard is defined for Wireless Metropolitan Area Networks (WMAN). It is an alternative technology to provide the broadband technology to the end users. IEEE 802.16 comprises a set of standards, the principal ones are: (a) IEEE 802.16, for the fixed users, working in frequency bands between 10 and 66 GHz. (b) IEEE 802.16e, for mobile users, working in frequency bands between 2 and 6 GHZ. The difference between nomadic and mobile users is: nomadic users do not require to be connected to the network while they are moving. While mobile users usually working with hand-held devices like mobile phones, pocket PC that remain always connected to the network, even when they are moving. Therefore, mobile users require the network to be able to perform hand-over (or hand-off) operation i.e. connections must be ‘kept on’ while users move from an area covered by a certain base station to another area covered by another base station. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 1–7, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. Comparison between IEEE 802.16 Families of Standards

2 Various Encryption Techniques There are different cryptographic algorithms that are used in our framework to analyse them. In our crypto processor implementation, the dedicated crypto block of the crypto processor permits fast execution of encryption, decryption, and key scheduling operations for RSA and IDEA private Key. The 64-bit plain text data streams entered into the 64-bit input register are encrypted with a proper key and control signals based on the RSA and IDEA Algorithms After plain text data streams are encrypted, the 64-bit cipher texts are output to the 64-bit output register. The decryption process is the same as the encryption process except for the control signals. We use the crypto algorithms like DES, AES, SHA-1 and RSA that are going to be explained below one by one. 2.1 Data Encryption Standards (DES) The Data Encryption Standard (DES) is the most well-known symmetric-key block cipher. It is defined by the American standard FIPS 46–2. The design of DES is related to two general concepts: Product ciphers and Feistel ciphers. Each involves iterating a common sequence or round of operations. The basic idea of a product cipher is to build a complex encryption function by composing several simple operations which offer complementary, but individually insufficient protection. Basic operations include transpositions, translations (e.g., XOR) and linear transformations, arithmetic operations, modular multiplication, and simple substitutions. DES is a Feistel cipher which processes plaintext blocks of n = 64 bits, producing 64-bit cipher text blocks. The effective size of the secret key K is k=56 bits; the input key K is specified as a 64-bit key, 8 bits of which (bits 8; 16; : : : ; 64) may be used as parity bits. A widely held belief is that the parity bits were introduced to reduce the effective key size from 64 to 56 bits, to intentionally reduce the cost of exhaustive key search by a factor of 256.

Design and Simulation of Security Sub-layer of WMAN IEEE 802.16 Standard

3

2.2 RSA Public-Key Encryption The RSA cryptosystem is used to provide both secrecy and digital signatures and its security is based on the intractability of the integer factorization problem. In public key cryptosystems, everyone has two related complementary keys, a publicly revealed key and a secret key. Each key unlocks the code that the other key makes. Knowing the public key does not help you deduce the corresponding secret key. The public key can be published and widely disseminated across a communications network. This protocol provides privacy without the need for the same kind of secure channels that a conventional cryptosystem requires. Anyone can use a recipient's public key to encrypt a message to that person, and that recipient uses her own corresponding secret key to decrypt that message. No one but the recipient can decrypt it, because no one else has access to that secret key. Not even the person who encrypted the message can decrypt it. Message authentication is also provided. The sender's own secret key can be used to encrypt a message, thereby ``signing'' it. This creates a digital signature of a message, which the recipient (or anyone else) can check by using the sender's public key to decrypt it. This proves that the sender was the true originator of the message, and that the message has not been subsequently altered by anyone else, because the sender alone possesses the secret key that made that signature. Forgery of a signed message is infeasible, and the sender cannot later disown his signature. Algorithm: Key Generation for RSA Public-Key Encryption Summary: Each entity creates an RSA public key and a corresponding private key. Each entity A should do the following: 1. Generate two large random (and distinct) primes p and q, each roughly the same size. 2. Compute n = pq and _ = (p − 1)(q − 1). 3. Select a random integer e, 1 < e < phi, such that gcd (phi (n), e) = 1. 4. Use the extended Euclidean algorithm to compute the unique integer d, 1 < d
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R.S. Jain, N. Kumar, and B. Kumar

bits. The AES specification uses the same key size alternatives but limits the block length to the 128 bits. A number of AES parameters depend on the key length. The key length of 128 bits which is likely to be the one most commonly implemented. The characteristics that AES performs are corresponding to system architecture as given in figure 2.

Fig. 2. System Architecture

3 System Architecture The above figure performs the following characteristics: a) Resistance against all known attacks. b) Speed and code compactness on a wide range of platforms. c) Design simplicity.

4 Results and Discussions By using above discussed strategies and models we found some good results in encryption and decryption of data. Some results of our work are displayed here:

Fig. 3. File to be encrypted

Design and Simulation of Security Sub-layer of WMAN IEEE 802.16 Standard

Fig. 4. Encryption of File

Fig. 5. Output of Encrypted data

Fig. 6. Decryption of Encrypted data

5

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R.S. Jain, N. Kumar, and B. Kumar

Fig. 7. Output of the Decrypted Data

5 Conclusions and Future SCOPE 5.1 Conclusions IEEE 802.16 has a potential market because of its NON-LOS transmission and other salient features like high data rates, good ranges. But the weak security implementation is making the corporate customers reluctant to adopt the technology. With the enhanced security features and mobility schemes in the forthcoming IEEE 802.16 is expected to fetch a good market. In this paper “we have implemented crypto algorithms in software, but such algorithms are typically too slow for real-time applications”, such as Conditional Access Module, storage devices, embedded systems, network routers, etc. For this reason, it becomes necessary to implement crypto algorithms in software with high speeds. In our crypto processor implementation, the dedicated crypto block of the crypto processor permits fast execution of encryption, decryption, and key scheduling operations for RSA and IDEA private Key. 5.2 Future Scope of the Work The paper “Design and Simulation of Security Sub–layer of WMAN IEEE 802.16 standard (WiMax Compliant)” may provide the future work on the given hypotheses: (a) Design the rest of the security sub-layer components. (b) Design of the other layers of WMAN IEEE 802.16 standard. (c) Integration of the layers of the WMAN IEEE 802.16 standard.

Acknowledgment The authors owe their sincere thanks to Prof. G.V.K Sinha, Chancellor, Prof. S. Mukhopadhayay, Pro- VC & Dr. Pratap Singh, Director Lingaya’s University, Faridabad, for their encouragement and keen interest in our work and also for providing technical facilities as was required in the development of this work. One of

Design and Simulation of Security Sub-layer of WMAN IEEE 802.16 Standard

7

the authors (Rajat Sheel Jain) expresses his thanks to Dr. B.L. Raina, Professor, Department of IT, Lingaya’s University for his valuable suggestions.

References 1. IEEE 802.16-2004 standard on WMAN, Air Interface for Fixed Broadband Wireless Access Systems (November 2004), http://www.ieee802.org/16 2. IEEE 802.16-2001, IEEE Standard for Local and Metropolitan Area Networks -Part 16: Air Interface for Fixed Broadband Wireless Access Systems (April 8, 2002) 3. IEEE P802.16a/D3-2001: Draft Amendment to IEEE Standard for Local and Metropolitan Area Networks — Part 16: Air Interface for Fixed Wireless Access Systems — Medium Access Control Modifications and Additional Physical Layers Specifications for 2–11 GHz (March 25, 2002) 4. IEEE Std 802.11-1999, IEEE Standard for Local and Metropolitan Area Networks, part 11, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specification. IEEE Press, Los Alamitos (1999) 5. RSA Cryptography Standard, RSA Public Key Cryptography Standard #1 v.2.0, RSA Laboratories (October 1998), http://www.rsasecurity.com/rsalabs/pkcs/pkcs-1/ 6. Wimax Forum Website, http://www.wimaxforum.org 7. Revest, Shamir, A., Adleman: A Method for obtaining Digital Signature and Public Key Cryptosystem. Communication of the ACM 21, 120–126 (2000) 8. WiMAX PICS wiMAX Forum, PICS for WirelessMAN-OFDM and Wireless HUMAN (OFDM) 9. IEEETM 802.11 Wireless LAN Medium Access Control (MAC) and Physical (PHY) Layer Specifications 10. The IEEE 802.16 Working Group on Broadband Wireless Access Standards 11. Johnston, D., Walker, J.: Overview of IEEE 802.16 Security. IEEE Security and Privacy 2(3), 40–48 (2004) 12. Intel White Paper, IEEE 802.16 and WiMAX, Broadband Access for Everyone (2003)

Speech Recognition of Assamese Numerals Using Combinations of LPC - Features and Heterogenous ANNs Manash Pratim Sarma and Kandarpa Kumar Sarma Department of Electronics And Communication Technology, Gauhati University, Assam, India [email protected], [email protected]

Abstract. A fully automated and effective method of detection of Assamese numerals captured under varied recording conditions and moods is presented here. The work also deals with gender variations taken as part of the speech recognition using a combination of Linear Predictive Code (LPC) and Vector Quantization applied to a combination of Self Organizing Map (SOM) and Multi Layer Perceptron (MLP) based system. The SOM and MLPs are used to constitute a Learning Vector Quantization (LVQ) block which is necessitated by the fact that the LPC-VQ methods fail to produce the expected outcome while dealing with numerals of Assamese- a language spoken by a sizable population in the North-Eastern part of India. The performance of the LPC feature set is further compared with that obtained PCA features applied to the same LVQ block. Keywords: Linear Predictive Code(LPC), Vector Quantization, Code-book, ANN, SOM, LVQ.

1 Introduction Speech recognition is a method that uses an audio input enabling an Automatic Speech Recognition (ASR) system to interpret the meaning out of it for further modification, processing or storage. Speech results after a gradual process involving years of practice [1]. The purpose of this work is to recognize digits uttered by different persons with gender, mood and recording time variations. The work initially uses Linear Predictive Code (LPC) and Vector Quantization to carry out speech recognition of Assamese numerals under the assumed conditions with variations. The results derived fall below the expected level. Therefore, a Learning Vector Quantization (LVQ) based technique is adopted using a combination of Self Organizing Map (SOM) and Multi Layer Perceptron (MLP) with inputs of Linear Predictive Code (LPC) features. The results are compared with that obtained using PCA (Principal Component Analysis) features and SOM - MLP combination. Some of the relevant literature in this connection is cited in [2], [3], [4]. Assamese spoken by a sizeable population in the north-eastern part of India is a segment of IndoEuropean family of languages [4] [5]. Assamese pronunciation has no retroflex sounds but has a series of alveolar sounds. It also extensively uses a velar nasal η. The V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 8–12, 2010. © Springer-Verlag Berlin Heidelberg 2010

Speech Recognition of Assamese Numerals

9

voiceless velar fricative /x/ is a distinct feature of Assamese language. These variations require an ASR system extensively developed to handle Assamese speech.

2 Conceptual Framework and Experimental Details The whole system approach can be subdivided into two major parts, first training and second testing. In training period features of the required patterns are extracted which involves pre-emphasis, normalization, frame blocking, windowing, LPC Parameters and Vector Quantization (VQ). The finally extracted data are stored in a code-book (a database) so that they can be compared at the time of recognition. The testing period is the recognition period where a real time voice is feature extracted as in the training phase and is compared with the code-book. The best matched data is processed and the required output is generated. During pre-emphasis, the speech signal (here, also refereed as word), is filtered with a first-order FIR filter to spectrally flatten the signal. After pre-emphasis, each word has its energy normalized. Based on the energy distribution along the temporal axis, it is computed the center of gravity, and this information is used as reference for temporal alignment of the words. The energy of each word was computed using 60 non overlapping windows. The pre-emphasized speech signal is blocked into frames of N samples, with adjacent frames being separated by M samples. Each individual frame is windowed to minimize the signal discontinuities at the borders of each frame. We used a Hamming window, a typical window used for the autocorrelation method of LPC. The next processing step is the LPC analysis [2], [3], [4] using the autocorrelation method of a specific order. For every sample a set 28 LPC coefficients is taken at each time and these frames are added repeatedly. Then average is taken from the final frame. The cepstral coefficients, which are the coefficients of the Fourier transform representation of the log magnitude of the spectrum, have been shown to be more robust for speech recognition than the LPC coefficients. Next, vector quantization is performed which is a lossy data compression technique. It is a fixed-to-fixed length algorithm which is an approximation method. For each 28 LPC coefficients vector lengths of quantized value are varied. Almost all speech recognizers use cepstral analysis techniques because it operates in a domain in which the excitation function and vocal tract filter function are separable [1], [2], [3]. 2.1 Training and Classification Next, a code-book of cepstral coefficients is generated. Each one of the ten classes of words (numbers zero to nine) is represented by 10 vectors, each one with coefficients. Each vector represents a frame of a class. The average vector for each frame based on sets of 30 words for each class is calculated. The code-book is stored and used in the classification routine. The classification procedure for arbitrary spectral vectors is basically a full search through the codebook to find the `best' match. A classification routine computes the cepstral coefficients of the unknown input word. After that, it computes the Euclidean distance between each vector of the input word and the corresponding vector in the codebook. The input vector is classified with the number associated with the class that

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M.P. Sarma and K.K. Sarma

gives the minimum total distance. Now following the same procedure we have performed testing section and then find the best match for the particular voice. If there is matching for a particular voice (word) then some process is fired. The above process is repeated by taking 14, 21 and 28 coefficients of the LPC samples.

3 Results and Discussion To know the differences between male and female voice spectrum we have analyzed the spectrum and PSD of male and female voices for all the Assamese numerals which is uttered five times with increasing loudness and at different moods. The results of this work include the VQ matrix that is stored in the code-book and the performance estimation tables.

Fig. 1. Corpus set of paas(Assamese five) to nau (Assamese nine) of girl mood1

The performance of the system for every mode viz. noiseless, noiseless, noise mixed, stressed and stress-free for both male and female voices were tested. Average performance results are shown in Table 2. It also shows the effect of using the LPC VQ features of different lengths. The feature set of size 28 gives the best results though it is below then what is required. Hence, the work is extended to the LVQ form generated using a Self Organizing Map (SOM) and Multi Layer Perceptron (MLP) combination. The architecture of the SOM- MLP based LVQ method is shown as in Figure 3. Table 1. Average performance of Male and Female Voices for sunya (zero in Assamese)

Codebook Size 7 14 21 28

Average Performance in % 69.48 70.42 72.31 73.63

Speech Recognition of Assamese Numerals

11

The LVQ based speech processing block consists of SOM and MLP blocks each tasked with a specific work to perform the speech recognition. The LVQ block is formed by a SOM and a MLP. The SOM is a unsupervised ANN that works with the principle of winners take all [6]. SOM is considered to be most suitable ANN type to simulate the learning process of the human brain. The MLP is a feed-forward network trained with back-propagation algorithm. The SOM and the MLPs take different times to train. Some of the average results for 50 samples of each of the 10 sets of noise-free, noise mixed, stress free and stressed samples applied to SOM and MLP blocks. The LPC features extracted from pre-processed samples are applied to the SOM which produces the code analogous to the VQ form. At the end of 1000 training epochs the code generated by the SOM is applied to the MLP which compares it with the desired output. The output is a class-code which is known to the trained MLP. The MLP is trained to recognize the class codes.

Fig. 2. LVQ block for speech recognition using SOM - MLP combination Table 2. Variation of the average training time with LPC-VQ length of the samples applied to the Composite LVQ block

The MLP block is trained with error back propagation with Levenberg - Marquardt optimization. At the end of 2000 epochs, on an average for 50 samples of each of the 10 sets of noise-free, noise mixed, stress free and stressed samples, the MSE reaches the 10-4 and recognition performance around 94% mark. Table 4 shows the training time and success rate variation with respect to the frame size of the samples. The 28frame LPC vector size produces the best results though it takes the longest time to train. The results are shown in Table 4. The results though show improvement compared to the LPC - VQ technique, it still requires improvement. The feature set next is completely changed and a Principal Component Analysis (PCA) set extracted for all the samples. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible [7]. The PCA- feature

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M.P. Sarma and K.K. Sarma

set produces comparable results with that generated by the LPC - feature set when used with the LVQ block designed using the SOM and MLPs Table 3. Average performance LVQ System using LPC - features & PCA features showing results of Male and Female Voices uttering sunya (zero in Assamese)

4 Conclusions The LVQ system used for speech recognition of ten Assamese numerals recorded with gender and mood variation show performance characteristics though quiet satisfactory yet has margins of errors which indicate that further improvement will be required. In case saari (Assamese four) accuracy is less, may be due to soft pronunciation style. Also there is a conflict between the pronunciation of saat (Assamese seven) and aath (Assamese eight) due to the unique velar fricative nature, so performance reduces. An extended part of the work can include hybrid ANN models for improvement of performance even further and incorporation of vowels and consonances which ultimately can be developed into an ASR to tackle Assamese scripts.

References 1. Atal, B.: Efficient coding of LPC parameters by temporal decomposition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ’83, vol. 8, pp. 81–84 (1983) 2. Lee, K.Y., Kondoz, A.M., Evans, B.G.: Speaker adaptive vector quantization of LPC parameters of speech. Electronics Letters 24(22), 1392–1393 (1988) 3. Gray, R. M., Toeplitz and Circulant Matrices: A review, http://ee.stanford.edu/~gray/toeplitz.pdf 4. Courtesy: Prof. Gautam Baruah, Dept. of CSE, IIT Guwahati, Guwahati-781039, Assam, India, http://tdil.mit.gov.inassamesecodechartoct02.pdf 5. The ‘X’ sound in Assamese language. The Assam Tribune Editorial on March 5 (2006), http://www.questia.com/library/encyclopedia/indo-iranian.jsp 6. Haykin, S.: Neural Networks A Comprehensive Foundation, 2nd edn. Pearson Education, New Delhi (2003) 7. http://www.fon.hum.uva.nl/praat/manual/ Principal-component-analysis.html

Design and Modeling of Power Efficient, High Performance 32-bit ALU through Advanced HDL Synthesis K. Dhanumjaya1, G. Kiran Kumar2, M.N. Giriprasad3, and M. Raja Reddy4 1

Research scholar, Jntu Ananthapur, AP [email protected] 2 M. Tech student, Jntu Ananthapur, AP [email protected] 3 Principal, Jntuce pulivendula, AP [email protected] 4 SUN Energy Pvt Ltd, Chennai [email protected]

Abstract. Arithmetic and Logic Unit (ALU) is a combination circuit that performs a number of arithmetic and logical operations within a microprocessor. We present guidelines for low- and ultra low-power & high performance 32- bit ALU [1] units and analyze structures for logical operations to determine the most suitable for these regions of operations for low power applications. An ALU design is then modeled in VHDL for further testing with pre-manufactured parts of the 32 bit ALU using XILINX ISE tools. A novel method that chooses better designs of flipflops and latches in different places of the data path, based on the data and clock activities. Our simulation results indicate that, for the 180nm-65nm CMOS technologies [6] it is possible to reduce the ALU total energy by 18%24%, with minimal delay degradation. In addition, there is up to 22%-32% reduction in leakage power in the standby mode. Keywords: 32 bit ALU, low power design methodology, power saving modes, leakage power demand, macro statistics, advanced scaling trends.

1 Introduction Arithmetic-logic units can be found in a heart of any Microprocessor. We find 6 integer execution units in Intel Pentium 4 Microprocessor has and each unit of them containing a 32-bit ALU. 1.1 High Level Structure The high-level block diagram of a high- performance ALU (as used in Pentium 4) is shown in Fig 1 ALU with four major parts: • Arithmetic block • Logic block • Multiplexers • Registers V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 13–21, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. ALU high level block diagram

1.2 Design Factors 1.2.1 Technology The design is implemented in a 65nm-180nm CMOS process with 6 metal layers. 1.2.2 Power Supply Supply voltage and logic swing up to 2.5 V. it should be assured that could be the appropriate model while performing hand analysis. 1.2.3 Performance Metric The propagation delays for static designs could be defined as the time interval between the 50% transition point of the inputs and the 50% point of the worst-case output signal. 1.2.4 Area The area is defined as the smallest rectangular box that can be drawn around the design. The area estimations based on the total transistor width and the wiring complexity. 1.2.5 Naming Conventions The input operands of the adder are named A<31:0> and B<31:0>. The output is SUM<32:0>, where SUM<32> is the carry out bit. 1.2.6 Registers Registers have no place in this phase of the design. The data flow from input to output should be combinational logic. 1.2.7 Clocks Global clocks are not used in combinational design. Precharge/evaluate clock is used for dynamic logic. But the result must become available after ONE evaluate phase (no pipelined logic). 1.2.8 VOH, VOL, Noise Margins It should be noted that the noise margins should be at least 10% of the voltage swing. The VTC between one of the inputs and the output signals for a static design. For a

Design and Modeling of Power Efficient, High Performance 32-bit ALU

15

dynamic circuit, apply an input signal with a 10% noise value added to the input and observe the outputs. 1.2.9 Rise and Fall Times All input signals have rise and fall times of 50 ps. The rise and fall times of the output signals (10% to 90%) Should not exceed 200 ps. 1.2.10 Load Capacitance Each output bit of the adder should have a load of 0.5pF, appropriately buffered. 1.2.11 Input Capacitance Each input of the adder should not load the previous stage with more than 50fF (less is OK). 1.3 Power Optimization The circuits turn on only the necessary paths for execution, this process shown to have faster execution time. Conventional methods for power reduction rely on the reduction of voltage and the reduction of frequency to allow for lower voltage operations. Using the (1) equation P=CV2F

(1)

The power consumption by relying on ‘α’. ‘α’ can also dependent on the width of the operation and the length of the clock. The following are the revised equations: 2

P =CV F'α’

(2)

α' = α 0 α width α clock F’= F0 ÷’α’clock The reduced width of operation decreases the critical path of computation, thus the ‘α’ clock represents the reduction of time when circuit spends in operation mode. The flip side of reduced clock period is the option to increase frequency, and because of α width, there is still overall power savings.

2 Design Methodology In showcasing the power reduction and performance boosting circuits for general application, it has been developed to a full suite of implementation techniques [3] to quickly convert any standard ALU design. In the discussion of power saving, the following modes of operation need to be defined: clock gating, sleep, and operation [5]. These modes shown in fig 2.1 define the power consumption of each data path depending on the word length of the input. Clock gating – When the circuit is the process of sensing data path the capacity to evaluate the current input is null. This particular data path will remain in precharge mode. The ability to do so reduces dynamic power dissipation since the clock charges and discharges the input capacitances of precharge PMOS transistors. Hence Power consumption is P=CV2 F α and α for a continuously active clock is 1. But α for a gated clock is dependent on the input it Receives and is likely to be much less than 1.

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Sleep mode – The data path remains unused for a prolonged period of time and the data path will be shut down and all pre charged nodes are allowed to leak away. Sleep mode can be achieved by turning off both the precharge PMOS and the evaluate NMOS, and there by introduces two large resistances and thus minimizes power consumption of unused circuitry. Operation mode – The data path that is determined to be necessary for processing data will receive a clock signal to precharge the path if it is in sleep node or it can directly evaluate if the path was in clock gating mode.

3 ALU Design through Low Power Methodology Techniques In this section, the main focus is laid on some of the circuit strategies to achieve low power ALU operation: 1. Reduced swing clocking scheme is utilized for the non-critical path latches and flip-flops, 2. CPL logic and C2MOS MUXes is utilized to design the logic/shifter units & to minimize overall switching capacitance and data buffer/driver sizes, 3. Shared clock footers and reducing buffer sizes are used in designing the 32-bit adder PG unit (Propagate-generate unit)[2]. 3.1 Designing of Latches for Dual Supply Clocking To save energy and maintain high performance the data path circuitry was kept at nominal supply voltage and lowering the clock swing of the latch/flip-flop clock transistors. This problem is further aggravated for high performance data path designs that normally operate at high junction temperatures (high switching frequency).The circuitry and energy-delay tradeoffs of a latch that uses only n-MOS clocked transistors allowing static power-free dual supply ALU operation [5]. 3.2 Clock Gating Clock gating [8] is a popular dynamic low-power technique. It saves power consumption by eliminating clock power of unused flip flops, latches, and dynamic blocks. It also eliminates spurious transitions in the logic after flip flops, latches, and dynamic blocks. Flip flops, latches, and dynamic blocks such as the ALU and adders got the most benefit from clock gating (28% reduction for flip flops, 47% for latches, 41% reduction for the ALU, and 30% for adders).

Design and Modeling of Power Efficient, High Performance 32-bit ALU

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3.3 Swing Restored CPL-Based Logic To design the non critical units of the ALU Complementary pass transistor logic allows us to eliminate the p-MOS network required to implement a logic function when using the static CMOS style. This result in lower switching capacitance, smaller data buffer sizes and area for the logic unit. CPL logic that uses n-MOS pass transistors result in “weak” 1 and also transfer output keepers to restore the CPL gate output signal to full swing. Fig 3.4 shows the usage of the CPL style to implement the logic unit gates for a single bit-slice of the 32-bit ALU. 3.4 PG Unit with Clock Footer Sharing The performance of the 32-bit adder is the critical core of the ALU design is implemented using compound domino logic (CDL) with clocked footer transistors [7]. when the width of the effective n-MOS clock transistor width is reduced by ~29%, the P and G signal worst case delays increase by ~4ps while allowing 16% energy savings for a data activity (α) of 0.1 (8% energy savings when α=1). 3.5 Low-Power 32- Bit Data Path Design A prototype low-power data path analyzes the energy consumption of the data path. The hindrance to low-power design of a data path [8] is its intrinsic complexity. Data paths are collections of numerous irregular blocks which perform various functions. They have complex interconnect structures, a wide variety of circuit types, and a rich set of activation patterns. A register file, an ALU, a shifter, a multiplier/divider, and aligners/sign-extenders for load and store instructions are the main blocks of the data path.

4 Energy Breakup for Low Power ALU Design The architecture of a 32-bit ALU demonstrates the impact of the circuit techniques discussed earlier in ensuring low power operation. It is also discussed the scaling trends and energy-delay tradeoffs associated with the circuit techniques for the 180nm-65nm CMOS technologies [4].The full-custom ALU design consists of approximately 11.5k transistors and has an operating frequency of 4.2GHz. For 65nm CMOS technology and has relaxed timings.

Fig. 2. ALU Design energy break-up for180nm technology[6]

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Energy break-up for the ALU [6], averaged over 10 cycles of operation (includes logic, arithmetic and shift operations).The 180nm technology simulations indicate that the entire clock network and contributes to 59.4% of the ALU total energy, while the arithmetic unit consumes 15.6% energy (worst case switching vector). In addition to this the instruction decoder, logic unit and shifter contribute up to 12.8% of energy while the input stage flip-flops and ALU output latch data energy contribute 12.2%. It should be noted that the results in Fig 3 pertain to the baseline ALU design; there is 18%-24% savings in total energy for the 180nm-65nm technologies.

Fig. 3. ALU energy savings and scaling trends[5]

5 ALU Leakage Power Demand The various circuit techniques discussed so far allow us to lower the ALU leakage power consumption[6] as indicated in Fig.4 When Design is scaled from 130nm to 65nm technology, there is a 27x increase in the standby mode leakage power (~30% gate leakage). With dual supply, the total leakage power reduces by 22% (32%) for the 130nm (65nm) generation. The gate leakage reduces significantly (~40%) when the power supply is lowered for Design, and contributes to ~18% of the total ALU leakage power for the 65nm generation.

Fig. 4. ALU standby mode leakage power plots

Design and Modeling of Power Efficient, High Performance 32-bit ALU

19

6 Results and Discussions Through the results that we have obtained through simulation conditions stated in Table 1 through which we have synthesized the design of 32 bit ALU.The design statistics and macro statistics are also tabulated in Table 2 & Table 3. By making use of these statistics we can modeled the design of ALU. Table 1. Simulation Conditions & Threshold Voltage S.No 1.

Parameter Process Technology

2. 3. 4. 5.

Process Supply voltage(v) Temperature( celcius) Input slew(ns)

6.

Offchip capacitance(pF)

7.

Test vector

Worst case vector for delay

Threshold voltage(V)

Random vector for power consumption 0.56(nmos), -0.56(pmos)

8.

Range 180nm-65nm CMOS single-poly triple-metal Typical(NM0S) - Typical(PM0S) 3.3 25 0.2 22

Fig. 5. RTL schematic of 32 bit ALU

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6.1 Advanced HDL Synthesis Statistics Table 2. Macro Statistics Macro statistics

Quantity

#Adders/Subtractor

6

8-bit adder

3

8-bit adder carry in

1

8-bit subtractor

8

# Multiplexers

2

8-bit8-to-1 multiplexer # Xors

2 1

8-bit xor2

1

Table 3. Design statistics Design statistics

Quantity

# Ios

29 Cell Usage

# BELS

590

# AND2

200

#AND3

34

# AND4

7

# INV

170

# OR2

90

#XOR2

59

# IO Buffers

29

#IBUF

21

#OBUF

8

Design and Modeling of Power Efficient, High Performance 32-bit ALU

21

7 Conclusion An approach for the design and modeling of 32-bit ALU for low- and ultra low-power applications through advanced scaling trends is quite represented in this work. The energy-delay space results that all demonstrated when designing for low power, a comparison of designs at a single voltage (or) a comparison based on gate count is insufficient for determining the optimal structures .The use of high performance structures combined with supply-voltage scaling, results in reduced energy compared to traditional designs for low power and ultra low-power operation.

References 1. Vratonjic, M., Zeydel, B.R., Oklobdzija, V.G.: Low- and Ultra Low-Power Arithmetic Units: Design and Comparison. Advanced Computer Systems Engineering Laboratory (ACSEL) University of California, Davis, CA 95616 2. VLSI and SPICE Modeling of ALU Saeid Moslehpour University of Hartford. Srikrishna Karatalapu University of Hartford 3. Low Power Design for ASIC Cores Alvar Deana, David Garrettb’t, Mircea R. Stanb’*and Sebastian Ventronealbm Microelectronics, Essex Junction; ECE Dept., University of Virginia, Charlottesville, VA 22903 4. A 1.5 ns 32 bit cmos alu in double pass pass transistorlogic byMakoto suzuki,norio ohkubo,toshiaki yamanaa,akihiro shimizu,katsurosasaki 5. Automated Design Techniques for Low-Power High-Speed Circuits On a Self-Configuring 64-bit Wallace Tree Multiplier EE241: Advanced Digital Integrated Circuits Midterm Report Zhujie Lin, Michael Liao 6. Chatterjee, B., Sachdev, M.: A CPL-based Dual Supply 32-bit ALU for Sub 180nm CMOS Technologies. Dept. of Electrical and Computer Engineering. University of Waterloo Waterloo, ON, [email protected] 7. Designing a 32-bit Arithmetic-logic unit by Borivoje Nikolić University Of California College of Engineering, Department of Electrical Engineering and Computer Sciences 8. A Thesis on Arithmetic logic unit (alu) designUsing Reconfigurable Cmos logic by ChandraSrinivasan. Department of Electrical and Computer Engineering, Mysore

A New Multi-language Encryption Technique for MANET Prasenjit Choudhury1, Rajasekhar Gaddam1, Rajesh Babu Parisi1, Manohar Babu Dasari1, and Satyanarayana Vuppala2 1

Department of Computer Applications Department of Information Techonlogy National Institute of Technology, Durgapur, India [email protected] 2

Abstract. As various applications of wireless ad hoc networks have been proposed, security has become one of the salient research challenges and is receiving increasing attention. Recently, several security schemes for wireless ad hoc networks have been proposed using encryption schemes. Our proposed scheme is more secure against adaptive chosen cipher text attacks because of randomness. In this paper we propose an algorithm that focuses on encryption of plain text over a range of languages supported by Unicode .Our assumption is to make sure that sender and receiver must have share Secret key and Mapping array before establishing communication. Keywords: Unicode, Encryption, Decryption, MANET, Multilanguage Encryption, Round Function, Sub key, Cryptography.

1 Introduction An ad hoc network is defined as an infrastructure less network, meaning a network without the usual routing infrastructure like fixed routers and routing backbones. Typically, the ad hoc nodes are mobile and the underlying communication medium is wireless. Each ad hoc node may be competent of acting as a router. The mobile ad hoc networks (MANET) [3] have several salient traits, such as Dynamic topologies, Bandwidth-constrained, variable capacity links, Energy-constrained operation, Limited physical security. The above novel characteristics of MANETs [4] present a new set of nontrivial challenges to security design. These challenges make up open network architecture, shared wireless medium, legitimate resource constraints, and highly dynamic network topology. The ultimate goal of the security solutions for MANETs is to provide security services, such as authentication, confidentiality, integrity, anonymity, and availability, to mobile users. To fulfill above security requirements cryptography is suitable. Cryptography is the art and science of achieving security by encoding messages to make them non-readable. More importantly, computers now perform these cryptographic functions/algorithms, thus making the process rapid and secure. But, cryptanalysis has simultaneously cracked these encryption techniques from time to V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 22–28, 2010. © Springer-Verlag Berlin Heidelberg 2010

A New Multi-language Encryption Technique for MANET

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time. Hence, the fundamental task of cryptography is not only to protect the secrecy of messages transmitted over MANET but also to resist such cryptanalytic attacks which tend to evolve with the passage of time. In symmetric key cryptography, the same key is used by the sender and the receiver for encryption and decryption respectively. The representative algorithms of this approach are AES, DES [1] and RC5. Asymmetric or public key cryptography uses two keys namely, private key which is kept by the receiver and public key which is announced to the public. Cryptosystems like RSA [2], PGP and ECC fall under this category. The above standard algorithms are not so feasible for MANET which requires low bandwidth and minimum utilization of peripheral devices. Motivated by this, here, we propose a novel algorithm that focuses on encryption of plain text over a range of languages supported by Unicode [6]. To minimize the complexity in proposed algorithm we introduced new type of round function which is based on modulo divisions. The use of mapping techniques makes our algorithm efficient and easier to implement. The rest of the paper is organized as follows. In Section 2, we present the background. Next, in Section 3 we discussed the proposed scheme. In Section 4, we exemplified about our proposed algorithm. In Section 5, we illustrated a example. In section 6 we disserted the security analysis Conclusion and future works are mentioned in Section 7.

2 Background Data encryption is utilized in various applications and environments. The specific utilization of encryption and the implementation of the DES and AES will be based on several factors particular to the computer system and its associated components. In general, cryptography is used to protect data while it is being communicated between two points or while it is stored in a medium vulnerable to physical theft. But the utilization of resources is high, so this is not suitable for MANET environment. In [1], DES processes as follows • •

The DES algorithm is designed to encipher and decipher blocks of data consisting of 64 bits under control of a 64-bit key. Deciphering must be accomplished by using the same key as for enciphering.

The above all operations in DES take more resources in MANET, so there is need of algorithm which consumes less resources.There are quite a few techniques have been employed for encryption and decryption but cryptanalyst has his/her own technique to crack the cipher text all the time. The use of a multilingual (MULET) approach in cryptography is not regnant. In MULET [5], proposed an algorithm that focuses on encryption of plain text over a range of languages supported by Unicode. Also, it will facilitate the localization of Cryptographic Software tools. MULET has its own cons, some are listed below: • • • •

A replacement strategy is incorporated and it checks for only successive repetitions of characters. If such repetitive patterns are found. The divisor value(K) is limited to the maximum value of Unicode There is no proper importance for secret key. Low Security because the size of the key is so small , brute force is very high.

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3 Proposed Scheme Our proposed algorithm enhances security through the following steps: Block-cipher. Block ciphers can be used to build cryptographic primitives such as Cryptographic hash functions, one way compression functions and block cipher modes of operation. So, we used block-ciphers to encrypt plaintext. Initial vector. We used initial vector to plain-text against chosen cipher text attacks. It is used like the salt to plain text to change the format of the message. Plain text: DURGAPUR Initial Vector (IV): FFFFFFFFFFFFFFFF Secret key. We used secret key of size 64-bit which is known to both the sender and receiver only. Consider a secret key : 00FF 00FF 00FF 00FF Round function. It performs a set of operations and the outcome of the previous round becomes the input of the successor round. Sub key generation. Sub key can be generated by secret key. Interchange a bit i of every byte corresponding to sub key ki, i=1, 2….n. Sub key 1: 80FF 80FF 80FF 80FF Sub key 2: 40FF 40FF 40FF 40FF Mapping array. A mapping array comprises of characters taken from different languages in Unicode. The use of mapping techniques makes the algorithm fast, efficient and easier to implement. Consider an example: Index Characters (Multilanguage)

0

卓

1

ఈ

2

含

3

ক

4

5

鵝辛

4 Proposed Algorithm 4.1 Jargon Plain text: The message to be encrypted. Cipher text: Final encrypted message. k: Secret key which is known to both sender and receiver only . IV: Initial vector which is generated based on the random generator. Partition (P): Based on P fragment the message into partition set Partition set (PS): Fragment the data based on partition value Divisor (D): Divides each partition set to get quotient and remainder values.

A New Multi-language Encryption Technique for MANET

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4.2 Our Proposed Scheme The entire message was fragmented into 64-bit blocks. Initially it applied to transformation using initial vector IV for randomization. The resultant 64 bit data sent to Successor round. In this round 64 bit data and corresponding sub key ki,i=1,2…n can be applied to XOR transformation . In each round the division operation performs on the basis of partition value (P). In this context we get a set of quotients and corresponding remainder values. Then concatenate both the quotient and remainder separately. This concatenated value can be sent to next round. The outcome of the previous round becomes the input of the successor round, finally substitute the characters with mapping array. Hence we obtained the encrypted text. Step 1: Select secret key (k) (64 bit). Step 2: Generate divisor, partition (P) based on secret key. Step 3: Generate initial vector (IV) from Random Generator. Step 4: Generate sub-keys (k1, k2 . . . kn rounds) from k. Step 5: Fragment the message into 64-bit blocks.

Plain text (64bit) (( ((((64bi IV Round 1

Round 2

Round n

Mapping

Cipher text

Step 6: Perform Round Function Step 7: Substitute the characters on the basis on mapping array. Step 8: Resultant string can be treated as cipher text.

K

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P. Choudhury et al.

Round Function K1

M

M1

Div (M1)

r1r2…..rn

q1q2…qn

q1q2...qn || r1r2......rn No. of partitions(s) = (total no. of digits in message /partition size) M1 = M ⊕ ki Div (M1) = M11 ||M12 ||M13 ||…|| M1s q = q1, q2 . . . qs r = r1, r2 . . . rs A mapping array comprises of characters taken from different languages in Unicode. In case of insertion of alien language characters in the array, the cryptanalyst of this encryption method require the aid of a language expert. For example, if we take this algorithm for encrypting Hebrews text, and if we consider the mapping array to be in Bengali (or any other non local language), the ciphered text is going to be undecipherable for any local sniffer. Let us define a mapping array as below:

A New Multi-language Encryption Technique for MANET

27

5 Examples and Discussions Plaintext: DURGAPUR (64 bit data) Generate IV as: FFFFFFFFFFFFFFFF (64 bit data) M1 = IV ⊕ Plain text M1 = BB A9 AC B7 BE AE A9 AC Consider secret key k: 00FF00FF00FF00FF (64 bit key) Sub key k1: 80FF80FF80FF80FF Round 1 M1 ⊕ k1: 3B562C483E512953 Let divisor =16, P=3 Partition set becomes (in Decimal): – 598 – 644 – 726 – 281 – 418 – 3 – Now, Perform Div operation on partition set Quotients are: | 25 | 28 | 2D | 11 | 1A | 0 | Remainders are: | 6 | 4 | 6 | 9 | 2 | 3 | Concatenate both quotient and remainder values, Hence we get output of first round and it becomes the input of successor round. So, | 25282D111A0 | ... | 646923 | Now generate sub key ki,i = 2…n. Perform next round function. Repeat these steps till n rounds we get final output in hexadecimal of variable size. Let final round output is: 25282D111A0646923 Replace the characters based on the mapping array. Finally we got cipher text, as follows:

含辛含鵝含そ鵝鵝鵝秩含哮昱そ担そ含 6 Security Analysis As we have demonstrated, the MULET algorithm is capable of encrypting messages from different languages. MULET algorithm provides security through mapping array which consists of characters taken from different languages in Unicode. In case of insertion of alien language characters in the array, the hackers of this encryption method require the aid of a language expert. Our proposed algorithm enhances security through randomization, round function, concept of sub key. We can generate initial vector, sub keys, partition (P), divisor (D) on basis of the randomization. In any context, hacker wants to decrypt the cipher text he/she needs to attempt for DP times to get the message for each round. Note that these are random numbers. Based on secret key, sub key can be generated in each round.Perform suitable

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transformation for both sub key ki,i=1,2,…n and message for each round. This will increase security level.

7 Conclusion Security has become a primary concern in order to provide protected communication between mobile nodes in a hostile environment. In this article, we propose an algorithm that focuses on encryption of plain text over a range of languages supported by Unicode .Our assumption is to make sure that sender and receiver must have share secret key and Mapping array before establishing communication.

References 1. Data Encryption Standard, Federal Information Processing, Standards Publication (FIPS PUB) 46, National Bureau of Standards, Washington, DC (1977) 2. Rivest, R., Shamir, A., Adleman, L.: A Method for Obtaining Digital Signatures and Public Key Cryptosystems. Communications of the Associataion of Computer Machinery 21(2), 120–126 (1978) 3. Corson, S., Macker, J.: Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations. RFC 2501 (January 1999) 4. Yang, H., Luo, H., Ye Songwulu, F., Zhang, L.: Security in Mobile ADHOC NETWORKS: Challenegs and Soltutions. In: IEEE Wireless Communications and Networking Conference (2004) 5. Kumar, P., Murmu, A.K., Parajuli, B., Choudhury, P.: MULET: A Multilanguage Encryption Technique. In: The 7th International Conference on Information Technology: New Generations (ITNG 10), Las Vegas, Nevada, USA, April 12-14 (2009) 6. Unicode Character, http://www.unicode.org 7. Ad hoc networks, http://www.adhocnets.org

Bi-variate Polynomial Approximation of Fuzzy Controller Using Genetic Algorithm for Trajectory Control of PUMA560 A. Mona Subramaniam, A. Manju, and Madhav J. Nigam Department of electronics and computer engineering Indian Institute of Technology Roorkee, Roorkee, India-247667 [email protected], [email protected], [email protected] Abstract. In this paper, fuzzy controller for the trajectory control of PUMA560 robot manipulator arm is approximated into a Bi-variate Polynomial using Genetic Algorithm. In the proposed method, initially Fuzzy PD+I controller is used for control of PUMA560. The input and output data points are collected from the Fuzzy PD controllers of individual joints. With these data points a bivariate polynomial approximation function is obtained through interpolation process using genetic algorithm. This Fuzzy PD controller approximate is then employed and evaluated. Keywords: Bi-variate polynomial approximation, Fuzzy controller, Genetic Algorithm, trajectory control, PUMA560, Robotics.

1 Introduction PUMA560 - a class of industrial robot has a wide variety of applications including material handling, welding, assembling, painting, grinding and other industrial applications [1]. The Fuzzy PD+I controllers are the most general use controller as it has the following advantages of being Simple , having Less overshoot, Removes steady state error, smoothens control signal [2]. The most popular technique in evolutionary computation research has been the genetic algorithm and they can be applied to any problem that can be formulated as function optimization problem [3]. By tuning the gains of the Fuzzy PID controller using genetic algorithm better results are obtained [4]. The interpolation and approximation theory are quite mature fields in by themselves which has received and continues receiving not deep but constant attention [5]. Various methods of polynomial interpolation give efficient results but are mathematically quite rigorous and difficult for a multi-variate case [6]. The main motivation for writing this paper is to use genetic algorithm, which is one of the stochastic optimization algorithm, to determine the coefficients of polynomial that approximates a system without getting into rigorous mathematical analysis. This paper is organized as follows. Section 2 introduces the PUMA560 robot and its dynamics. Section 3 provides the discussion of Fuzzy PD+I controller scheme. Section 4 provides a description of Genetic Algorithm. Section 5 briefly talks about the Bi-variate interpolation scheme. Section 6 explains how the Bi-variate interpolation is done using Genetic Algorithm to produce an approximate model of V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 29–35, 2010. © Springer-Verlag Berlin Heidelberg 2010

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A. Mona Subramaniam, A. Manju, and M.J. Nigam

Fuzzy controller. Section 7 provides experimental results and Section 8 gives conclusion and Future scope.

2 Dynamics of PUMA 560 Dynamics of a serial n-link rigid robot [7] can be written as: ..

.

M (q ) q + c (q, q ) + g (q ) = τ

(1) .

Where q is the n×1 vector of joint displacements, q is the n×1 vector of joint velocities, τ is the n×1 vector of actuators applied torques, M(q) is the n×n .

symmetric positive definite manipulator inertia matrix, c( q, q ) is the n×1 vector of centripetal and Coriolis torques and g(q) is the n×1 vector of gravitational torques due to gravity. We assume the robot joints are joined together with revolute joints. Let the desired joint position qd be a twice differentiable vector function. We define a control problem to determine the actuator torques in such a way that the following control aim be achieved: lim q(t ) = q d (t )

(2)

t − >inf

A six DOF PUMA-560 robot is considered for the simulation, the Kinematical and dynamical parameters of the arm and the torque limitations are adopted from the work of Srinivasan et all, and references therein [7].

3 Fuzzy PD+I Controller In this structure, the fuzzy system is applied only to the proportional and derivative signal of the linear PID controller [8]. The integral signal uses conventional linear method. The major roll of the integral signal is to eliminate the steady state error. The transient response is affected mostly by the proportional signal and the derivative signal. For the enhancement of the transient response, the varying gains are implemented on the proportional and derivative parts using two-input fuzzy system. The nonlinearities that make the varying gains possible are added by the fuzzy control rules and the membership functions. Gaussian membership function is employed, universe of discourse is [-1 1]. Figure 1(a) shows the structure of fuzzy PD+I controller and Figure 1(b) shows the rulebase used.

(a)

(b)

Fig. 1. (a) Structure of the Fuzzy PD+I controller, (b) Rulebase of the Fuzzy PD controller

Bi-variate Polynomial Approximation of Fuzzy Controller Using Genetic Algorithm

31

where GCE, GE, GIE and GU in Figure 1(a). are the gains of Fuzzy PD+I controller and which can be varied to tune the controller. Genetic Algorithm is used in this paper to tune these gains in order to produce reference system for approximation. The data points are collected from the inputs and the outputs of the individual Fuzzy PD controllers of the 6 Degree of Freedom PUMA560 robot arm. With these data points bi-variate polynomial interpolation is carried out using Genetic algorithm.

4 Genetic Algorithm Genetic algorithm (GA) is a powerful search algorithm that performs an exploration of the search space that evolves in analogy to the evolution in nature [9]. GAs use probabilistic transition rules instead of deterministic rules, and handle a population of potential solutions known as individuals or chromosomes that evolve iteratively. Each iteration of the algorithm is termed as a generation. Evolution of solutions is simulated through a fitness function and genetic operators such as reproduction, crossover and mutation. The fittest individual will survive generation after generation, while also reproducing and generating offsprings that might be stronger. At the same time, the weakest individuals disappear from each generation. A genetic algorithm is typically initialized with a random population ranging from 20 to 100 individuals. This population (mating pool) is usually represented by a realvalued number or a binary string called a chromosome. How well an individual performs a task is measured and assessed by the objective function, which assigns each individual a corresponding number called its fitness. The fitness of each chromosome is assessed and a survival of the fittest strategy is applied.

5 Bi-variate Polynomial Interpolation is the problem of constructing a function p belonging to a (simple) finite dimensional linear space from a given set of data [6]. Usually, the interpolation data are obtained by sampling another (more difficult) function f and, in that case, it is said that p interpolates f, in the sense that both functions coincide on that data set. Interpolation by polynomials of several variables is a very intricate problem and is a subject which is currently an active area of research. A bi-variate polynomial is a polynomial in two variables [10]. It has the form as given in Eqn. 3. f ( x, y ) = ∑ ai , j x i y j

(3)

i, j

A bi-variate polynomial when evaluated over a grid of input values for both x and y, traces a surface defined by f(x, y) of the Eqn. 3. This paper proposes to approximate the control surface of the fuzzy controller using bi-variate polynomial function. In this paper the bi-variate polynomial chosen for approximating the fuzzy control surface is (x+y+1)10 , with all the coefficients being replaced by variables that can get modified by genetic algorithm so that an optimal value of these coefficients can be found. The

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A. Mona Subramaniam, A. Manju, and M.J. Nigam

polynomial so found closely approximates fuzzy controller behavior in the region of operation for which the data points were collected.

6 Bi-variate Polynomial Approximation Using Genetic Algorithm Let us assume that the data points collected from the fuzzy controller are i1 and i2 for inputs and o1 for outputs where i1, i2 and o1 are vectors of same size. With these data points an estimate bi-variate polynomial model is created, bi-variate in terms of i1 and i2 variables. The mean square error between the estimate model output vector f(x,y) and the o1 vector is calculated. This mean square error is minimized by the Genetic Algorithm. This minimization is done by manipulating the coefficients of the polynomial by genetic algorithm to obtain an approximate model of fuzzy controller in terms of bi-variate polynomial function. Eqn. 4 shows the relation between the .

polynomial P(i1, i2) and Fuzzy PD function F (e, e) . Figure 2. shows the Structure of bi-variate polynomial approximate of Fuzzy PD+I controller. .

P (i1 , i 2 ) ≈ F (e, e)

(4)

Where P(i1, i2) is of the form specified in the Eqn. 3.

Fig. 2. Structure of the bi-variate polynomial approximate of Fuzzy PD+I controller

7 Experimental Results The simulation was carried out using MATLAB Version 7.0.1.24704 (R14), 2 GHz computer with 2 GB RAM. While giving the input to the robot, care needs to be taken that the trajectory used is continuous and smooth. The input joint trajectory used in this paper is 1-cos (pi*t) since it is a continuous and smooth function which can be double differentiated, t is time in seconds. This trajectory is used for all the joints. Figure 3(a). shows Input signal given to the individual joints and Figure 3(b). shows the joint error generated by the Fuzzy PD+I controller for given input.

Bi-variate Polynomial Approximation of Fuzzy Controller Using Genetic Algorithm

(a)

33

(b)

Fig. 3. (a) Input trajectory signal given to the individual joints, (b) the joint error generated by the robot arm with Fuzzy PD+I controller

From the data points used for plotting the Figure 3(b). Bi-variate Polynomial approximates are created using genetic algorithm. The plots for response surfaces of the polynomials hence created are shown in the Figure 4. The joint error generated by the input trajectory for this controller is shown in Figure 5. It is observed from the plot that the approximation error due to approximation has had both constructive and destructive effect.

Fig. 4. Response surfaces of the Bi-variate Polynomial approximate for the 6 controllers of PUMA560

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A. Mona Subramaniam, A. Manju, and M.J. Nigam

Fig. 5. The joint error generated by the robot arm with polynomial approximated controller

8 Conclusion and Future Scope It is seen that the Bi-variate polynomial of sufficiently high degree can approximate a fuzzy controller. The approximation error can act both constructively and destructively. The aim of the work is to create a method which will extend to approximating a multivariable and a complex system. With this proposed approximation technique any systems approximate model can be created. This particularly is useful when we need to make an approximate model of a real time system without going through rigorous mathematical procedures. The proposed Bi-variate polynomial approximation can be easily extended to n-variate polynomial approximation without making many changes to the objective function of the Genetic algorithm, thereby easing the approximation of even complex multivariable systems.

References 1. Mazhari, S.A., Kumar, S.: PUMA 560 Optimal Trajectory Control using Genetic Algorithm, Simulated Annealing and Generalized Pattern Search Techniques. International Journal of Electrical, Computer and Systems Engineering 2(1), 71–80 (winter 2008) 2. Jantzen, J.: Foundations of Fuzzy Control. John Wiley & Sons Ltd., Chichester (2007) ISBN 978-0-470-02963-3 3. Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithms. Springer, Heidelberg (2008) ISBN 978-3-540-73189-4 4. Mazhari, S.A., Kumar, S.: Heuristic Search Algorithms for Tuning PUMA 560 Fuzzy PID Controller. International Journal of Computer Science 3(4) (fall 2008) 5. Gasca, M., Sauer, T.: On the history of multivariate polynomial interpolation. Journal of Computational and Applied Mathematics 122, 23–35 (2000) 6. Gasca, M., Sauer, T.: Polynomial interpolation in several variables. Advances in Computational Mathematics 12(4), 377–410 (2000) 7. Alavandar, S., Nigam, M.J.: Fuzzy PD + I control of a six DOF robot manipulator. Industrial Robot: An International Journal 35(2), 125–132 (2008)

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8. Kim, B.J., Chung, C.C.: Design of Fuzzy PD + I Controller for Tracking Control. In: Proceedings of the American Control Conference Anchorage, AK, May 8-1, pp. 2124–2129. AACC (2002) 9. Giriraj Kumar, S.M., Jain, R., Anantharaman, N., Dharmalingam, V., Sheriffa Begum, K.M.M.: Genetic Algorithm Based PID Controller Tuning for a Model Bioreactor. Indian Chemical Engineer 50(3), 214–226 (2008) 10. Terr, D.: Bivariate Polynomial, From: MathWorld–A Wolfram Web resource, http://mathworld.wolfram.com/BivariatePolynomial.html

Protecting Digital Images Using DTCWT-DCT K. Ramani1, E.V. Prasad2, and S. Varadarajan3 1

Department of Information Technology, Sree Vidyanikethan Engg. College, Tirupati, India [email protected] 2 Department of Computer Science and Engineering, JNT University, Kakinada, India [email protected] 3 Department of Electrical and Electronics Engineering, SV University, Tirupati, India [email protected]

Abstract. This paper introduces a multi resolution cascaded transform domain method for copyright protection of digital images. An algorithm is developed that embeds the watermark information, with out much distortion to the cover image, while allows us to extract the watermark by use of correlation. The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is achieved by Dual Tree Complex Wavelet Transform (DTCWT), which is modeled to show directional selectivity and as a result consistent with the Human Visual System (HVS). The Discrete Cosine Transform (DCT) is resistant to several attacks. In this paper combined DTCWT-DCT technique is proposed, which uses a unique method for spreading, embedding and extracting the watermark. This paper compares various watermarking algorithms like DCT, Discrete Wavelet Transform (DWT), DTCWT, DCT-DWT with DTCWT-DCT method. This hybrid DTCWT-DCT shows superior performance in the presence of geometric and signal processing operations. Keywords: Digital Image watermarking, Dual Tree Complex Wavelet Transform, Discrete Cosine Transform, Discrete Wavelet Transform, Signal Processing operations and Geometrical Attacks.

1 Introduction The copyright protection of digital media has become an important issue in the Internet world. The digital watermark can be used to identify the rightful owner. Furthermore, it is an important issue to develop a robust watermarking scheme that is robust and has better imperceptibility [1]. There are number of information hiding techniques based on least significant bits (LSBs) for embedding message, however these are highly insecure, because it can be easily detectable that the data is hidden in cover object [2, 3]. This problem can be avoidable by hiding data using Bit-Plane Complexity Segmentation (BPCS) steganography, in which image is decomposed into bit planes of sub band wavelet V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 36–44, 2010. © Springer-Verlag Berlin Heidelberg 2010

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37

coefficients obtained by using Integer Wavelet Transform (IWT). At lower levels of wavelet decomposition changes made in the cover image can not be identified by human naked eye. BPCS takes the advantage of human naked eye. As the level of image transformation using IWT increases maximum capacity that can be embedded decreases, PSNR decreases and bit error rate increases [3]. The data extracted from cover image also depends on the complexity pattern of the cover image. Robustness is a key issue in many prospective applications of data hiding technologies. Spread spectrum watermarking suggests good robustness to many attacks and is very secure system. This method is comparatively simple, lacks the basic robustness that may be expected in any watermarking applications. It can survive simple operation such as cropping, any addition of noise. However lossy compression is going to defeat the watermark. Usually, the frequency domain offers higher capacity and better robustness against attacks [4]. DCT domain watermarking is comparatively much better than the spatial domain encoding since DCT domain watermarking can survive against the attacks such as noising, compression, sharpening, and filtering. A common problem with DCT watermarking is block based scaling of watermark image changes scaling factors block by block and results in visual discontinuity [5]. The most advanced choice from all the frequency domain method is DWT. Since, the magnitude of DWT coefficients are larger in the lowest band at each level of decomposition, it is possible to use a larger scaling factor for watermark embedding. The method in [6] shows high PSNR values between original and watermarked images even at level 3, but shows poor robustness for various geometric and signal processing attacks. Every watermark bit should be embedded in each detail (i.e horizontal, vertical and diagonal) coefficient. Hence, the secret sorting method for watermarking for watermarking proposed in [7] shows moderate robustness against various geometric attacks, but it is provided with the lower gain factors. DCT based watermarking is based on two facts. The first fact is that much of the signal energy lies at low frequencies sub band which contains the most important visual parts of the image. The second fact is that high frequency components of the image are removed through compression and noise attacks. The watermark is therefore embedded by modifying the coefficients of the middle frequency sub band that so the visibility of the image will not be affected and the watermark will not be removed by compression. [8]. Due to its excellent spatio-frequency localization properties, the DWT is very suitable to identify the areas in the host image where a watermark can be embedded effectively. In particular, this property allows exploitation of masking effect of human visual system such as if a DWT coefficient is modified, only the region corresponding to that coefficient will be modified [8]. Using the DWT prior to the DCT to provide better imperceptibility in harmony with the human visual system, and higher robustness against signal processing operations [9, 10]. The DWT has two drawbacks. First, it lacks shift invariance, which means small shifts in input signal can cause big changes in energy distribution of wavelet coefficients. Second, the DWT has poor directional selectivity for diagonal features, which is evident from the impulse responses of the filters individual sub bands. On other hand, the complex wavelet transform (CWT) is efficient, and has only a modest

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K. Ramani, E.V. Prasad, and S. Varadarajan

amount of redundancy; yet it provides approximate shift invariance and better directional selectivity [9,10]. A dual tree complex wavelet transform (DTCWT) [11] is an improved version of the wavelets transforms which has good results when applied image watermarking [12].In the DTCWT Gabor filters are used which are modeled to show directional selectivity and as a result are consistent with the HVS [12]. It has better results than combined DWT-DCT method. Hence, the proposed system combines DTCWT-DCT characteristics for watermarking and extraction of watermark.

2 Discrete Cosine Transform The discrete cosine transform (DCT) is one of the most common linear transformations in digital signal process technology. Two dimensional discrete cosine transform (2D DCT) is defined as g (u , v ) =

M −1 N −1 ( 2 m + 1 ) u ∏ co s ( 2 n + 1 ) v ∏ 2 α u α v ∑ ∑ I ( m , n ) co s N 2M 2N u =0 v=0

2 M

(1)

where αu=

1/√2 1

α v=

for u=1,2,….N-1

1/√2 1

for u=0

for v=0

for v=1,2,….N-1

The corresponding inverse transformation (2D IDCT) is defined as I ( m, n ) =

2 M

2 N

( ∑ ∑ α α g ( u , v ) cos

M −1 N −1 u =0 v =0

u

v

2 m + 1) u ∏ ( 2 n + 1) v ∏ cos 2M 2N

(2)

The 2D-DCT can not only concentrate the main information of original image into the smallest low frequency coefficient, but also it can cause the image blocking effect being the smallest, which can realize the good compromise between the information centralizing and the computing complication. So it obtains the wide spreading application in the compression coding.

3 Dual Tree Complex Wavelet Transform The DTCWT decomposition of an n×n image results in a decimated dyadic decomposition into i=1..m scales, where each scale is of dimension n/2i× n/2i. At each decimated location of each scale, we have a set S of 6 complex coefficients, denoted as S={ρ1eiθ1, ..., ρ6eiθ6}, corresponding to responses to the 6 subband orientations, namely: 15°, 45°, 75°, 105°, 135°, 165°. It turns out that, for some applications of the discrete wavelet transform, improvements can be obtained by using an expansive

Protecting Digital Images Using DTCWT-DCT

39

wavelet transform in place of a critically-sampled one. (An expansive transform is one that converts an N-point signal into M coefficients with M > N).

4 Combined DTCWT-DCT Algorithm In the proposed method 512X512 sized image is taken as cover image and 50X20 watermark image are taken as input. The watermark is converted into binary 1’s and 0’s and then converted into 1-D vector. 4.1 Embedding Process 1. 2. 3. 4. 5.

6.

Apply second level DTCWT to decompose the cover image, f(x,y) into six non–overlapping sub bands. Take one middle and high frequency sub bands and divide them into 4x4 blocks. Apply DCT to each block of sub bands. Transform the grey scale watermark, w(x,y) image into binary and a vector of zeros and ones. Generate two pseudorandom sequences. One sequence is used to embed the watermark bit 0(PN_0) and other sequence is used to embed the watermark bit 1(PN_1). Number of elements in each of the two pseudorandom sequences must be equal to the number of mid band elements of the DCT transformed DWT sub-bands. Embed the two pseudorandom sequences, PN_0 and PN_1, with a gain factor α, in the DCT transformed 4x4 blocks of selected DTCWT sub bands of cover image. Embedding is not applied to all coefficients of the DCT block, but only to the mid-band DCT coefficients. If SB represents, the matrix of the mid band coefficients of the DCT transformed block, then embedding process can be described as: If the watermark bit is zero then SB’=SB+ α * PN_0

(3)

SB’= SB+ α * PN_1

(4)

Else,

7. 8.

Apply IDCT (inverse DCT) to each block after its mid-band coefficients have been modified to embed the watermark bits as described in the previous step. Apply the inverse DTCWT (IDTCWT) on the DTCWT transformed image, including the modified sub band, to produce the watermarked host image.

4.2 Extraction Process The original cover image is not needed to extract the watermark, since it is a blind watermarking scheme.

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K. Ramani, E.V. Prasad, and S. Varadarajan

1. 2. 3. 4. 5.

6.

Apply second level DTCWT to decompose the watermarked image into six non-overlapping sub bands. Divide mid and high sub bands into 4x4 blocks. Apply DCT to each block in the chosen sub bands and extract the mid band coefficients of each DCT transformed block. Regenerate the two pseudorandom sequences (PN_0 and PN_1) using the same seed used in the watermark embedding procedure. For each block in the mid-band coefficients and the two pseudorandom sequences (PN_0 and PN_1). If the correlation with the PN_0 is higher than the correlation with PN_1, then the extracted watermark bit is considered 0, otherwise the extracted watermark is considered 1. Reconstruct the watermark using the extracted watermark bits, and compute the similarity between original and extracted watermarks.

5 Experimental Results The 512x512 sized cover image is used as carrier of the information. The proposed method and methods [7,9,10,13,14] are designed and implemented in Matlab7.0. The performance of these methods at embedding intensity,α=10 is studied and their results are shown in Table 1. Compared to all other methods the proposed method is having better PSNR, normalized cross correlation values and moderate embedding and extraction times. The cover image, original watermark, watermarked image and extracted watermarks for Gaussian noise density=0.01, Salt and pepper noise density=0.01, Compression factor=60, Scaling factor=0.75 and uncropped area [100 100 300 300] are shown in Fig. 11 to 15. Gaussian noise. Gaussian noise is added to the watermarked image with different noise densities and the performance of the proposed method is better compared to other methods. The Fig. 1 and 2 show, the computed PSNR and Cross correlation values for the proposed method along with methods [7,9,10,13,14]. Also, for nd=0.9 extraction of the watermark is quite possible only with proposed method. Salt and Pepper Noise. Salt and pepper noise is added to the watermarked image with different noise densities ranging from 0.01 to 0.1and the proposed method has proven record of better performance than the methods [7,9,10,13,14]. Even for nd=0.8 also extraction of the watermark is possible for the proposed method. Compression. The performance of the compressed watermarked images evaluated for different quality factors sequentially. All extracted watermarks are recognizable. Fig. 5 and 6 show the results of different qualities of compression rates. Even for 20% compression factor the watermark is perfectly extractable for the proposed method. For more than 80% compression rate the extracted watermark is almost similar to watermark extracted without any compression operation. The proposed method has reasonable good improvement in PSNR and normalised cross correlation values compared to methods [7,9,10,13,14].

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41

Scaling. The watermarked image is scaled by 50%, 75%, and 150%. Experimentally obtained PSNR and Normalized cross correlation values are given in Fig. 7 and 8. The proposed method is pursuing slightly better results than methods [7,9,10,13,14]. Cropping. The method [14] (DTCWT) is showing better results against cropping attack than proposed method and methods [7,9,10,13]. As per the results DWT and DTCWT based watermarking methods are more robust than individual DCT, combined DWT-DCT and combined DTCWT-DCT. The performance of the proposed method and methods [7,9,10,13,14] are graphically shown, in Figs.[110],against selected image processing operations and geometrical attacks.

Fig. 1. Noise density Vs PSNR for Gaussian Operation

Fig. 2. Noise density Vs normalized cross correlation for Gaussian Noise Operation

Fig 3. Noise density Vs PSNR for Salt and for Pepper Noise

Fig. 4. Noise density Vs normalized cross correlation Salt & Pepper Noise

Fig. 5. Quality factor Vs PSNR for JPEG Compression Operation

Fig. 6. Quality Vs normalized cross correlation for JPEG Compression operation

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Fig. 7. Scaling factor Vs PSNR for Scaling attack

Fig. 8. Scaling factor Vs normalised cross correlation for Scaling attack

Fig. 9. Uncropped area Vs PSNR for cropping attack

Fig. 10. Uncropped area Vs normalised cross correlation for cropping attack

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 11. Results of Gaussian noise operation on watermarked image with a noise density=0.01, GF(α)=10 (a) Cover image (b) Original watermark (c) DTCWT-DCT Watermarked image [Proposed method ] (d) Extracted watermark from Proposed method (DTCWT-DCT) (e) DWT watermarked image method[10] (f) Extracted watermark from method[10]

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 12. Results of Salt & Pepper Noise operation on watermarked image with a noise density=0.01, GF(α)=10 (a) Cover image (b) Original watermark (c) DTCWT-DCT Watermarked image [Proposed method] (d) Extracted watermark from Proposed method (DTCWT-DCT) (e) DWT watermarked image method[10] (f) Extracted watermark from method[10]

Protecting Digital Images Using DTCWT-DCT

(a)

(b)

(c)

(d)

(e)

43

(f)

Fig. 13. Results of compression operation on watermarked image with a compression factor q=60 and GF(α)=10 (a) Cover image (b) Original watermark (c) DTCWT-DCT Watermarked image [Proposed method ] (d) Extracted watermark from Proposed method (DTCWT-DCT) (e) DWT-DCT watermarked image method[10] (f) Extracted watermark from method[10]

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 14. Results of scaling attack on watermarked image with a scaling factor= 0.75 and GF (α)=10 (a) Cover image (b) Original watermark (c)DTCWT-DCT Watermarked image [Proposed method ] (d) Extracted watermark from Proposed method (DTCWT-DCT) (e) DWTDCT watermarked image method[10] (f) Extracted watermark from method[10]

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 15. Results of cropping on watermarked image with uncropped area =[100 100 300 300] and GF (α)=10 (a) Cover image (b) Original watermark (c)DTCWT-DCT Watermarked image [Proposed method ] (d) Extracted watermark from Proposed method (DTCWT-DCT) (e) DWTDCT watermarked image method[10] (f) Extracted watermark from method[10]

6 Conclusions The DTCWT has six bands and any band can be chosen for embedding watermark, so it provides additional security. The method [14] does not require the original image in the extraction phase. The algorithm for embedding and extraction is very easy. It is robust to JPEG compression, salt and pepper noise, Gaussian noise, scaling and cropping compared to the method [9] and other methods [7,10,13]. The combined DTCWT-DCT method is more robust against compression, scaling, and noise attacks compared to other methods and not very much robust against cropping attack and size of the watermark depends on the size of DCT block size.

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References 1. Cox, I.J., Miller, M.L.: The first 50 years of electronic watermarking. J. of Applied Signal Processing 2, 126–132 (2002) 2. Kingsbury, N.G.: Image Processing with Complex Wavelets. Phil. Trans. Royal Society London 357, 2543–2560 (1999) 3. Kingsbury, N.G.: Complex Wavelets for Shift Invariant Analysis and Filtering of Signals. J. of Applied and Computational Harmonic Analysis and Filtering of Signals (2001) 4. Kun-Yu, H., Mei-Yi, W., Jia-Hong, L.: An iterative method of palette-based image steganography. Pattern Recognition Letters 25, 301–309 (2004) 5. Ramani, K., Prasad, E.V., Varadarajan, S.: Steganography Using BPCS to the Integer Wavelet Transformed Image. Int. J. of Computer Science and Network Security 7(7), 293– 301 (2007) 6. Tao, B., Dickinson, B.: Adaptive watermarking in the DCT domain. In: IEEE Int. Conf. ASSP 1997 (1997) 7. Langelaar, G., Setyawan, I., Lagendijk, R.L.: Watermarking Digital Image and Video Data. IEEE Signal Processing Magazine 17, 20–46 (2000) 8. Ramani, K., Prasad, E.V., Varadarajan, S.: DWT Based Watermarking For Biometric Data. Indian J. of Information Science and Technology 3, 35–41 (2007) 9. Ramani, K., Prasad, E.V., Varadarajan, S., Subramanyam, A.: A Robust Watermarking Scheme for Information Hiding. In: The Sixteenth Int. Conf. on Advanced Computing and Communication. Anna University, Chennai, India (2008) 10. Al-Haj, A.: Combined DWT-DCT Digital Image Watermarking. J. of Computer Science 3, 740–746 (2007) 11. Loo, P., Kingsbury, N.G.: Digital watermarking with complex wavelets. In: Proc. IEE Colloquium on Secure Images and Image Authentication, IEE, London (2000) 12. Kingsbury, N.G.: The Dual Tree Complex Transforms: A efficient tool for Image Restoration and Enhancement. J. Applied and Computational Harmonic Analysis 10, 243– 253 (2001) 13. Kuraz, Y., Modar, A.H.: Improve Watermark Security Via Wavelet Transform and CDMA Techniques. Al-Rafidain Engineering 16(3), 39–49 (2008) 14. Ramani, K., Prasad, E.V., Varadarajan, S.: Improved Imperceptible Image Watermarking Using Dual Tree Complex Wavelet Transform. Submitted to Journal of Multimedia, Germany (2010)

A Novel High Speed Dynamic Comparator Using Positive Feedback with Low Power Dissipation and Low Offset Silpakesav Velagaleti, Pavankumar Gorpuni, and K.K. Mahapatra NIT Rourkela, India [email protected], [email protected], [email protected]

Abstract. A new differential CMOS dynamic comparator using positive feedback with less power dissipation, less offset has been proposed. For the performance verification, the design was simulated in CADENCE GPDK 90nm CMOS Technology at 1.8Vsupply voltage. Nearly 18mV Offset Voltage, power dissipation 2.564mW, speed 2.812GHZ, propagation delay 0.3556ns is easily achieved with the proposed structure. Inputs are reconfigured from typical differential pair comparator such that near equal current distribution in the input transistors can be achieved for a meta-stable point of the comparator. Restricted signal swing clock for the tail current is also used to ensure constant currents in the differential pairs. Keywords: Dynamic comparator, offset voltage, Post layout simulation, Positive feedback, CADENCE GPDK 90 nm.

1 Introduction In today’s world demand for portable operated devices is increasing, a major thrust is given towards low power methodologies for low offset applications. This reduction in power, less offset can be achieved by moving towards smaller feature size process and architectures [3]. The performance limiting blocks in such ADCs are inter-stage gain amplifiers and comparators. The performance of such comparators, which is defined by its speed, offset along with power dissipation is of keen interest in achieving higher performance of ADCs. The paper is organized as follows. Section 2 introduces the Conventional dynamic comparator [5] and we proposed a dynamic comparator using positive feedback in section 3. Simulation results and analysis on two dynamic comparators were given in Section 4. Section 5 concludes with the findings of the paper.

2 Conventional Dynamic Comparator A fully differential dynamic comparator [5] has shown in Fig1. The comparator consists of two cross coupled differential pairs with inverter latch at the top. When the clock is at 0V, then the comparator is inactive means the current source transistors NM6 and NM7 are switched off and no current path exists between the supply V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 45–49, 2010. © Springer-Verlag Berlin Heidelberg 2010

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voltages. The NMOS transistors NM0 and NM1 of the latch conduct and also force the drains of all the input transistors NM2-NM5 to Vdd potential. When the clock is raised to Vdd, the outputs are disconnected from the positive supply and the switching current sources NM6 and NM7 enter saturation and begin to conduct. To improve the offset [6], reduce the power dissipation a new architecture called “Dynamic comparator using positive feedback” has been proposed in the next section which is useful for pipeline and flash ADC.

Fig. 1. Circuit For Dynamic Comparator

3 Proposed Dynamic Comparator Using Positive Feedback In order to make a dynamic comparator more robust against power dissipation, offset, a fully differential dynamic comparator using positive feedback has been developed and shown in Fig 2. In this proposed new topology the current sources are switchable and feedback circuit is directly connected between source coupled differential pairs and supply voltage.

Fig. 2. Circuit for Proposed Dynamic Comparator Using Positive feedback

3.1 Operation of Conventional Dynamic Comparator Using Positive Feedback The operation of the dynamic comparator using positive feedback is same as before. In this comparator instead of latch, feedback circuit was incorporated. It has two paths of feedback. The first one is current series feedback through the common-source node of transistors NM2 and NM5.The second path is the voltage-shunt feedback through the gate-drain connections of transistors PM14 and PM16. Input and reference voltages are given to differential pairs. Assume that plus and minus supplies are used and that the gate of NM2 is tied to ground. So with the input of NM5 much less than

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zero, NM2 is on and NM5 is off, thus turning on PM17 and PM14 and turning off PM15 and PM16. All of i6 flows through NM2 and NM17, so Vout2 is high. At this time out2 will give logic1.So MOSFETS PM16 and PM15 will be off. As the input voltage increases toward the threshold point some of the tail current begins to flow throughNM5. This continues until the point where the current through NM5 equals the current in PM14. Just beyond this point the comparator switches state.

4 Experiments and Results The standard structure, Fig. 1 and proposed structure Fig.2 were designed in CADENCE GPDK 90 nm process. Key values used for both structures were listed in Table I. For reducing the offset voltage all the transistors channel lengths were set to 100 nm. The supply voltage is 1.8V, and reference, input voltages are Vref1=600mV, Vref2=100mV, Vin1= ref (as parameter) sweeping from -1V to 1.8V, Vin2=200mV. For finding DC response all the input and reference voltages were kept as mentioned above and found that tripping was occurred at the reference voltage 500 mV. Offset voltage can be calculated by keeping all the input and reference voltages were given to Zero DC voltage. For finding transient response one of the input voltage (in1) was given to 0 to 1.8V pulse from low to high, one of the reference voltage(ref1) was given to 0V , keeping other input and reference voltages as before. The offset and transient responses of this proposed comparator are presented in Fig.3 and Fig.4 respectively. The delay, offset, power dissipation are tabulated in table I and compared with that of the conventional dynamic comparator. The delay can be calculated by taking average of differences of input and output rise, fall times.

Fig. 3. Offset waveform for dynamic Comparator using positive feedback

Fig. 5. Layout Comparator

of

Proposed

Dynamic

Fig. 4. Transient waveform for dynamic Comparator using positive feedback

Fig. 6. Post Layout Simulation of Dynamic Comparator

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Delay= [(rise time of input-rise time of output) + (fall time of output-fall time of input)]/2 Power dissipation was calculated in the analog environment using suitable tools at 1.8V. The outputs were connected to the buffer for finding single ended output. After post layout simulation the delay, speed, power dissipation was tabulated in Table 1. The transient response of proposed comparator after post layout simulation was presented in Fig. 4.The layout and post layout of proposed comparator was presented in Fig. 5, Fig.6 respectively. Table 1. Comparison Results of Comparators Key Values

Before Post Layout Simulation

After Post Layout Simulation

Offset Voltage

Dynamic Comparator 48.32mV

Dynamic With Positive Comparator Feedback ------------

With Positive Feedback 18.07mV

Delay

0.383nS

0.428nS

0.46nS

0.435nS

Speed Power Dissipation

1.83GHz 4.14mW

2.33GHz 2.47mW

2.16GHz 3.543mW

2.28GHz 2.29mW

Table 2. Key values used for Dynamic Comparator and with feedback Key Values

For Dynamic Comparator

For Dynamic Comparator using Positive feedback Vdd=1.8V,Vss=-0.9V

Power supply

Vdd=1.8V,Vss=-0.9V

Input differential transistor sizing Current sources

(W/L)NM2,3,4,5=(2.38µm/100nm) (W/L)NM2,3,4,5=(3µm/100nm) (W/L)NM6,7=(7.14µm/100nm)

(W/L)NM6,7=(4.05µm/100nm)

Inverter transistor sizing

(W/L) PM2,3=(810nm/100nm) (W/L) NM0,1=(190nm/100nm)

(W/L)PM17=(6.78µm/100nm), (W/L)PM14,18=(2µm/100nm), (W/L)PM15= (120nm/100nm).

Clocks

Rise, fall time=0.1nS. Pulse width=5nS, Period=10nS. CLK high=0.9V, low=-0.9V. CLKB high=-0.3V, low=-0.9V.

Rise, fall time=0.1nS. Pulse width = 5nS, Period=10nS. CLK high=0.9V, low=-0.9V. CLKB high=-0.3V, low=0.9V.

Switches(PMOS)

(W/L )PM0,5=(3.9µm/100nm), (W/L)PM1, 4= (810nm/100nm).

(W/L) PM0, 5= (3.5µm/100nm).

Reference voltage

Vref1=600mV, Vref2=100mV

Vref1=600mV, Vref2=100mV

Input voltage Buffer sizing

Vin1=ref, Vin2=200mV (W/L)PM10,11,12=30µm/100nm, (W/L)NM12,13,14=150nm/100nm

Vin1=ref, Vin2=200mV (W/L)PM10,11,12=30µm/100nm, (W/L)NM12,13,14=150nm/10nm

Output inverter sizing

(W/L)PM13,NM15=150nm/100nm (W/L)PM13,NM15=150nm/10nm

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5 Conclusions In this paper a new dynamic comparator using positive feedback was proposed. The proposed topology gives low power consumption, high speed, less offset voltage. The modifications made to the typical dynamic comparator can easily reduce the overall offset voltage to only few milli-volts as compared to more than 40 mV. After post layout simulation it was found that delay, power dissipation were less in the proposed dynamic comparator with positive feedback when compared to the conventional dynamic comparator.

References 1. Xiaofeng, G., Xinquan, L., Yushan, L., Jie, Z.: Design and Application of the Novel LowThreshold Comparator Using Hysteresis. In: 6th International Conference on ASIC, ASICON 2005, pp. 549–553 (2005) 2. Philip, P., Allen, E., Holberg, D.R.: CMOS Analog Circuit Design, 2nd edn., pp. 259–397. Oxford University Press, Inc., USA (2002) 3. Sumanen, L., Waltari, M., Hakkarainen, V., Halonen, K.: CMOS Dynamic Comparators for Pipeline A/D Converters. IEEE ISCAS 5, 157–160 (2002) 4. Sumanen, L., Waltari, M., Halonen, K.: A Mismatch Insensitive CMOS Dynamic Comparator for Pipeline A/D Converters. IEEE ICECS 1, 32–35 (2000) 5. Katyal, V., Geiger, R.L., Chen, D.J.: A New Low Offset Dynamic Comparator for High Resolution High Speed ADC Application. In: Semiconductor Research Corporation’s Student Symposium (2006) 6. Le, H.P., Zayegh, A., Singh, J.: Performance analysis of optimised CMOS comparator. Electronics Letters 39, 833–835 (2003)

VLSI Design of Four Quadrant Analog Voltage-Mode Multiplier and Its Application Ankita Tijare and Pravin Dakhole Yeshwantrao Chavan College of Engineering, Nagpur, India [email protected], [email protected]

Abstract. A new CMOS voltage-mode Four-quadrant analog Multiplier is proposed and analyzed. By applying inputs signals to set of complementary diode pair connection & to that of voltage difference circuit. The circuit is formed by cascading the complementary diode pair connection with the voltage difference circuit. Based on the proposed multiplier circuit, a low voltage high performance CMOS four quadrant analog multiplier is designed and fabricated by using 0.35micron technology. The measured 3dB bandwidth is 15 MHz Simple structure, low-voltage, low power, and high performance makes the proposed multiplier quite feasible in many applications. Keywords: Voltage Mode, Multiplier, Low power.

1 Introduction Analog multipliers are important circuit blocks for many applications such as frequency mixers, variable frequency oscillators, adaptive filters, neural networks automatic gain control, amplitude modulation, etc. In order to improve the overall power efficiency of such application which is now regularly required for modern analog and mixed signal design dedicated for portable equipments, the analog multiplier to be used must be able to operate under a reduced supply voltage and consume low current. Four quadrant analog multipliers are widely used in contemporary VLSI chips for non-linear operations, modulation/demodulation as well as frequency conversion. The basic functionality of the multiplier is to deliver output signal v0 proportional to the product of two input voltages Vx and Vy: V0=km . Vx. Vy The multiplier proposed in this paper also uses the MOS square law characteristic, but the circuit structure is applied from the complementary pairs of diode connection circuit.

2 Circuit Description 2.1 Complementary Pairs of Diode-Connection Circuit Fig 1 shows the n-type and p-type diode connected MOS transistors biased with constant current source IDD. In the following analysis, it is assumed that the drain V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 50–54, 2010. © Springer-Verlag Berlin Heidelberg 2010

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current of the MOS device in saturation is given by the following expression. Now by applying the input voltages V1 and V2, the current flowing through the drain terminal of the MOS transistor is given as,

(a)

(b) Fig. 1. Diode pair connection

The drain current of the transistors in the saturation region may be given as

Id1=K1(V01-V1-VTn)² Id2=K1(V01-V2-VTn)²

(1) (2)

Where Id1 is the drain current flowing through M1& Id2 is the current flowing through M2,V01 is the output voltage, K1=µ nCoxW/2L & VTn is threshold voltage of the transistor. From the circuit we can conclude that,

Id1+Id2=IDD

(3)

By adding equation 1 & 2, we can find the output voltage V01 as V01= (V1+ V2)/2 + VTn+√IDD/2K1 [1+K1/4IDD (2V1V2- V12-V22)]

(4)

For the P-type diode-connection shown in fig1.b, the output voltage V02 is also given by the same way as, V02= (V1+ V2)/2 -⃓VTp⃓- √IDD/2K3 [1+K3/4IDD (2V1V2- V12-V22)]

(5)

where K3 is the transconductance parameter of M5 & M6, VTp is the threshold voltage of the PMOS transistor .Now V1 of fig1-a is connected to V1 of Fig 1-b ,similarly V2 of Fig1-a is connected to V2 of fig 1-b, to form the circuit diagram of complementary diode -connection. Fig 2-a.shows the complementary diode pair connection and its output voltage waveform is shown in fig 2-b.In fig 2-b) the 3rd & 4th waveform shows the input signal of amplitude 0.3V. With this amplitude the fig.2-a is satisfying the equation 4 & 5 giving the output voltage V01 as 750mv & V02 as -0.45V shown in the 2nd & 1st waveform of fig2-b. The output voltages are in the form of multiplication of the input voltages V1 and V2, the sum and the square of the input voltages. Also the commonmode input voltage ( V1+V2)/2.

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(a)

(b) Fig. 2. Complementary diode pair connection

2.2 Voltage Difference Circuit For removing the common mode input voltage from the output equation of V01 and V02 of the complementary diode-connection, the output is driven by the voltage difference circuit shown in fig-3.

Fig. 3. Voltage Difference Circuit

Fig. 4. Circuit for generating the biased voltage vg1 & vg2

The circuit includes matched transistors M3,M4& M7,M8. For having the unity gain current mirror two identical PMOS devices M9& M10 are also used.From the circuit diagram of voltage difference circuit, the output voltage Vod is given as

Vod =Vgs2 + Vgs4 + Vss

(6)

Where, Vgs2 is the gate to source voltage of transistor M4 & Vgs4 is the gate to source voltage of M8 again made to operate in saturation region. By considering the drain current equations of the transistors the output voltage is expressed as Vod =Vi1 - Vi2 + Vss

(7)

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Equation 4 shows that the output voltage Vod is in the form of difference voltage between the input voltage Vi1 and Vi2. 2.3 Two Quadrant Multiplier By cascading the complementary diode-connection circuit of fig-2 and the voltage difference circuit shown in the fig-3, i.e., the output of complementary diodeconnection is applied to the voltage difference circuit, the two quadrant voltage-mode multiplier can be realized. The fig-4 shows the circuit for generating the biased voltage vg1 & vg2. It is assumed that the transconductance parameter K1 & K5 of all NMOS & PMOS are matched.The circuit for voltage-mode analog multiplier is shown in fig5. From equation (4), (5) and (7), the output voltage Vom of the two quadrant multiplier can be given as

Vom= VDC + ( (√K1 + √ K5 )/√32IDD) (2V1V2-V12-V22)

(8)

Where, the dc term VDC is given as,

VDC= VTn + ⃓VTp ⃓+ √ ( IDD /2)(1/√K1 +1/√K5 ) + Vss To diminish the effect of the second order term of the input voltages, the complete circuit diagram of CMOS voltage-mode multiplier can be shown in fig-6. Equation (8) indicates that the multiplication of V1 and V2 is achieved. As the output voltage also includes the components that are proportional to the square of the input voltages V1 and V2 . Thus the output voltage of each multiplier can obtained as

Vom1= VDC + ( (√K1 + √ K5 )/√32IDD) (2V1V2-V12-V22) Vom2= VDC + ((√K1 + √ K5 )/√32IDD) (-2V1V2-V12-V22) Finally subtracting is given by

Vom1 & Vom2 ,the output voltage of the four quadrant multiplier

Vout = Vom1 - Vom2 = ((√K1 + √ K5) / √2 IDD) V1V2

Fig. 5. Two Quadrant voltage-mode

Fig. 6. Four quadrant voltage- multiplier mode analog multiplier

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3 Simulation Result The proposed four quadrant multiplier shown in fig .6 & its blocks are designed using tanner S-Edit& are simulated with TSPICE using 0.35micron level 3 process. The power supply voltage is ±5V and the bias current IDD=50µA. Fig.8 shows the waveform for amplitude modulation as one of the application of a multiplier. The modulation is performed when the input voltage v1 & v2 are 100 KHz & 1 MHz with the peak amplitude of 400mV.

Fig. 7. Frequency response of multiplier

Fig. 8. Amplitude modulation of sinusoidal signals

4 Conclusions A four quadrant CMOS voltage-mode analog multiplier designed using complementary diode-pair connection & voltage difference circuit that achieves an output signal in voltage domain has been presented. The error voltage is measured within the range ±400mV for V1, while the other input V2 varies from ±400mV. The linearity error is 0.8%. The power consumption avg_power = 1.0468e-003.The proposed circuit achieves the output without using the passive component i.e. resistors.

References 1. Gilbert, B.: A precise four-quadrant multiplier with sub-nanosecond response. IEEE J SolidState Circuits SC-3(4), 373 (1998) 2. Badanezhad, J.N., Temes, G.C.: A 20 V four quadrant CMOS analog multiplier. IEEE J. Solid-State Circuits SC-20, 1158–1168 (1985) 3. Wang, Z.: A Four-Quadrant analog multiplier Using MOS Transistors Operating in the saturation Region. IEEE Trans. Instrum. Meas. 42(1), 75–77 (1993) 4. Franciotta, M., Colli, G., Castello, R.: A 100-MHz 4mW Four-quadrant biCMOS Analog Multiplier. IEEE J. Solid-State Circuits 32(10), 1568–1572 (1997) 5. Hsiao, S.-Y., Wu, C.-Y.: A 1.2 V CMOS Four-Quadrant analog multiplier. In: IEEE ISCAS 2005 Proceedings, pp. 241–244 (June 1997) 6. Boonchu, B., Surakampontorn, W.: A New NMOS Four-Quadrant analog Multiplier. In: IEEE ISCAS 2005 Proceedings, pp. 1004–1007 (May 2005)

An Optimizing Technique for MDGT Using DRSA Algorithm Association with IP Traceback Strategies S. Karthik1, V.P. Arunachalam 2, T. Ravichandran3, and M.L. Valarmathi4 1 Research Scholar, Professor and Head, Department of Computer Science Engineering SNS College of Technology, Sathy Main Road, Coimbatore-641035, Tamil Nadu, India Tel.: +91-9842720118 [email protected] 2 Principal & Research Supervisor, SNS College of Technology, Coimbatore 3 Principal & Joint Research Supervisor, Hindustan Institute of Technology, Coimbatore 4 Assistant Professor/CSE, Government College of Technology, Coimbatore

Abstract. DoS / DDoS(Distributed Denial of Service) attacks deny regular, internet services accessed by legitimate users, either by blocking the services completely, or by disturbing it completely, so as to cause customer baulking. Several traceback schemes are available to mitigate these attacks. The simulation approach also can be used to test the performing effects of different marking schemes in large-scale DDoS attacks. Based on the simulation and evaluation results, more efficient and effective algorithms, techniques and procedures to combat these attacks may be developed. DGT8, directional geographical trackback scheme, with 8 directions is one of them. Having a limited set of 8 directions, DGT8 may not work for routers with more than 8 interfaces. In this paper, we propose M-DGT i.e DGT 16, a 16 directional geographical traceback scheme having all the advantages of DGT. The 16 directions, though not having exactly equal interface, have nearly equal measures, and are identified using a novel scheme of Segment Direction Ratios (SDR). The SDR concept and the associated marking scheme allow the victim to defend against DDoS attacks independent of its ISP and also the generalization to DGT2n, having 2n directions (n>4). Keywords: DoS, DDoS, DGT (Directed Geographical traceback), IP traceback, SDR (Segment Direction Ratio).

1 Introduction A denial of services attack (DoS) is an attempt to prevent legitimate users of a service, from using that service. DoS attacks are essentially, resource overloading attacks and either crash the communication system of the host with the rest of the Network or degrade the host’s service rendering it unavailable for legitimate users. IP traceback is the process of identifying the actual sources of attack packets. This has the benefit of holding attacker accountable for abusing the internet. It helps in mitigating DoS attacks by isolating identified attack sources. To abort these attacks, many IP traceback schemes [15] have been advocated. Broadly they can be V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 55–61, 2010. © Springer-Verlag Berlin Heidelberg 2010

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categorized into 3 groups: those which reconstruct the entire attack path the attack packets have traversed[1],[8],[9] such as Probability Packet Marking (PPM); those which focus only on the sources of attack packets, irrespective of the path taken[3] such as Deterministic Packet Marking (DPM);and the third is the Directed Geographical Traceback (DGT) and geographical mapping techniques. The DGT Scheme of possesses many desirable features such as fast convergence, light weight, good scalability and attack mitigation capability. The DGT Scheme of considers only 8 directions and may not work well for Routers that have more than 8 interfaces. In this paper, we are generalizing the DGT scheme to 16 interfaces of nearly equal measures. By the novel scheme of Segment Direction Ratios (SDR), the 16 directions are identified by their SDR and every Router need know only the SDR of its immediate neighbors.

2 The Concept of SDR We assume a two dimensional square grid with Routers at selected grid points. The edge between 2 routers is thus a line in two dimensions whose directions are specified by its direction cosines (Cosα, Cosβ), where α,β are the angles made by the edge with positive E and N directions (refer fig.2). Direction cosines satisfy Cos2 α + Cos2 β = 1, always.Since most Cosθ values are cumbersome rationals and irrationals in [-1, 1], the concept of direction ratios (d.r) was introduced[10],[11]. Direction ratios (d.r) are proportional quantities to Direction cosines (d.c); are integers, denoted by (a,b) where in general a2 + b2 ≠ 1. From direction ratio (a, b) we can get the directional cosine (cosα, cosβ) as (a/r, b/r) where r = .

Fig. 1. For edge AB between routers at A, B with SDR (x2-x1,y2-y1)=(2,1)

In fig1, the direction ratios of the line are (2, 1), from which we can recover the dc as (2/√5, 1/√5). By segment, we mean the edge between 2 adjacent routers, with coordinates (x1, y1),(x2,y2) with suitable origin O, and OE, ON as axes of reference. The coordinates are in units of the grid size. If AB is the edge joining 2 routers A, B with coordinates of A (x1, y1) and B(x2, y2) then SDR (Segment Direction Ratio) of AB are defined as (x2 – x1, y2-y1) where |x2-x1|, |y2-y1| ≤ 2 and co primes (refer fig.2). In general for DGT of 2n directions we handle SDR with |x2-x1|,|y2-y1| ≤ (n2), and co primes for n ≥ 3. It is easy to see that (x2-x1),(y2 – y1) are only the grid steps to be taken in ± OE, ± ON directions (depending on the sign of SDR), to reach B from A.

An Optimizing Technique for MDGT Using DRSA Algorithm Association

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Fig. 2. Square grid where an edge line has d.c (Cos α,Cos β)

Fig. 3. DGT 16 SDR

They are the projections of the edge AB on OE, ON with appropriate sign attached. Fig 3, gives the 16 directions D1, to D16 (where D1 = OE, D5:= ON directions) with their SDR in bits. The SDR of DGT 16 are given as ordered 2 bits with appropriate sign. It is easily verified that for such SDR (a,b); (a,-b), (-a, b), (-a,-b) are also SDR. The assumptions of DGT2n for n≥4 are the same as in DGT8. The following basic assumptions are standard. i.e Any number of packets can be generated by an attacker, Attackers are aware of trace attempts on them, The routing behavior may be unstable, Circuits routing is not there, A router knows the SDR of its neighboring routers in one of the 2n directions (n ≥ 4). Specifically for n=4, in the 16 directions D1 to D16.Most of these assumptions are common to traceback schemes of one type or the other.

3 DGT16 Procedure with Encoding Requirements When a packet arrives at router Ri and is destined for router Rj where the direction Dij, is one of D1 to D16 the only task that Ri, has to perform is to add the ordered SDR values of Dij, to the corresponding ordered subfields in the IP header and subtract 1 from the TTL value. Thus for the implementation of DGT16, we require 2

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subfields in the IP header, to keep track of the cumulative grid step movements, from router to router, through their SDR. In this way, when a packet arrives at the victim, the geographical location of the attack router can be obtained from the data in the SDR subfields, regardless of the source IP address which may be incorrect or compromised [14]. Assuming that the lengths of internet paths seldom exceed 32 hops, the cumulative SDR value cannot exceed in magnitude, the integer 64, for DGT16. Hence 2 (1+7) = 16 bits are needed in the IP header for the CSDR totals. To calculate the total number of hops between the attack router and the victim router, as the difference of initial TTL value and the final TTL value, we need to store the initial TTL value in the IP header. Assuming that the IP header has (16+8 +1) 25 bits, for DGT 16, we use the 8 bit segment for storage of initial TTL value. Location of the attacker and the hop count enables the victim to process the traceback.

4 Comparison of DGT16 with Other Traceback Schemes DGT16 and DGT8 being like schemes, offer equivalent advantages with respect to computational burden, scalability and mitigation capability of the attack, except for the fact that 16 directions are available now, with nil or negligible additional computations. DGT, PPM and SPIE being different types of trackback schemes only qualitative comparison is possible The inferences are same as those reported in with respect to computational, scalability and capability parameters. A limitation of DGT16 is the inequality (though marginal) among the interfaces. This is the cost we have to pay to satisfy the integer requirements of the SDR and generalization to DGT2n.

5 DRSA (Direction Ratio Sampling Algorithm) The marking procedure at a router Ri of every packet w from the attacker is as Let x be a random number in (0, 1) and p is a chosen probability level. If x < p, then if the packet is unmarked, then write Ri mod (1000) in RF, Dj in DRF, 0 in SF. Otherwise (if the packet is already marked) or (x ≥ p) then only increment the distance field SF. After sufficient number of samples are dream, then using the property Ri (Dj) = Rj and the distance field count, the attack path can be reconstructed. The victim uses the DR (along with R) sampled in these packets to create a graph leading back to the source (s) of attack.

6 Results and Discussion If we constrain p to be identical at each router, then the probability of receiving a marked packet from a router d hops array is p (1-p) d-1 and this function is monotonic in the distance from the victim. Because the probability of receiving a sample is geometrically smaller, the further away it is from the victim, the time for this algorithm to converge is dominated by the time to receive a sample from the further router. We conservatively assume that samples from all of the d routers (in the path from A to V) appear with the same likelihood as the furthest router. Since these

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probabilities are disjoint, the probability that a given packet will deliver a sample from some router is at least dp (1-p)d-1 by addition law for disjoint events. As per the well known Coupon Collector problem [3], the number of trials required to select one of each of d equiprobable items.

Fig. 4. DGT32 SDR in the directions D1 to D9 in quadrant I

We can show that E(X) is optimal if p = 1/d (ie dE / dp = 0, d2E / d2p > 0 for p = 1/d). For example, if p=1/d, where d= attack path length, then the victim can typically reconstruct the path after receiving. E(x) = dd lnd / (d-1)d-1 packets for d=10; E(x)≤75 and hence a victim can typically reconstruct the path after receiving 75 packets from the attacker. The concept of SDR allows us to extend the DGT 16 to DGT2n for n>4, without any restriction, in an elegant manner. The only additional requirement that arises is the increased CSDR upper limits and consequently more bits in the IP header, for the 2 subfields, are needed. Specifically DGT2n restricts SDR of segment joining grid points A (x1,y1) and B (x2,y2) to the constraint of |x2-x1|,|y2-y1| being co primes and satisfying. |x2-x1|,|y2 – y1 | ≤ n -2, (n ≥ 3), and imparts a corresponding increased requirement for the two CSDR maximum totals for an optimal 32 hop situation. The SDR of the DGT32 scheme are given below IN Fig 4. These SDR with first or second or both components changed in sign give the SDR of the remaining directions, in Quadrants II, IV and III respectively. Ultimately the number n of scheme DGT2n, depends solely on the IP header bit capacity as is evident from the following table. Table 1. DGT 2n Specifications

n

2n

3 4 5 6

8 16 32 64

SDR bit Max step length moves 1 2 2 4

1 2 3 4

Max CSDR value 32 64 96 128

IP Header CSDR Length 2 (1+6) 2 (1+7) 2 (1+7) 2 (1+8)

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The Internet has transformed from an information repository to a vital channel for conducting business. Unfortunately, with this positive change has come an increased frequency in malicious attacks (http://portal.acm.org/citation.cfm?id=1092845)[6]. All the proposed traceback schemes have their own specific advantages and isadvantages. Currently, no single solution could fulfill all the requirements outlined for an effective trace-back method [4]. For any of these IP traceback solutions to be effective, they would need to be deployed across corporate and administrative boundaries in a substantial portion of the Internet infrastructure. One conclusion we can draw from this is that unless IP traceback measures are deployed all over the Internet, they are only effective for controlled networks than for the Internet. This same algorithm can efficiently discern multiple attacks. When attackers from different sources produce disjoint edges in the tree structure of reconstruction. The number of packets needed to reconstruct each path is independent of other paths. The limitations imposed by restricting the number of DR to /DR (2)/=49 at every stage and using R (mod 1000) instead of the full serial number of router R are marginal in nature . We need more space in the packet header to use elements of DR (3) and the full representation of the R serial number. In conclusion DRSA is a robust scheme of 3 dimensional, multi-directional, geographical IP trace back. The authors are working towards to extend this multidirectional geometrical two dimensional traceback scheme to three dimensions.

References [1] Yaar, et al.: FTT: Fast Internet Trackback. In: IEEE INFOCOM 2005, Miami, Florida (March 2005) [2] Al–Duwairi, B., et al.: Topology Based Packet Marking. In: IEEE Int. Conf. Computer Comm. and Networks, ICCN 2004 (October 2004) [3] Al-Duwairi, B., et al.: Novel Hybrid Schemes Employing Packet Marking and bagging for IP Traceback. IEEE Transactions on Parallel and Distribution Systems 17(5), 403–418 (2006) [4] Meadows: A formal framework and evaluation method for network denial of service. In: Proceedings of the 12th IEEE Computer Security Foundations Workshop (June 8-10, 1999) [5] Cisco. Strategies to Protect Against Distributed Denial of Service Attacks, http://www.cisco.com/en/US/tech/tk59/ technologies_white_paper09186a0080174a5b.shtml [6] Cisco. Strategies to Protect Against Distributed Denial of Service Attacks, http://www.cisco.com/warp/public/707/newsflash.html [7] Computer Incident Advisory Capability. Network Intrusion Detector Overview, http://ciac.llnl.gov/cstc/nid/intro.html [8] Song, D.X., Perrig, A.: Advanced and Authenticated Marking Schemes for IP Traceback. In: IEEE INFOCOM 2001 Anchorage AK, AP 2001, pp. 878–886 (2001) [9] Karthik, S., Arunachalam, V.P., Ravichandran, T.: Multi directional geographical trace back within directions generalization. J. Compute. Sci. 4(8), 646–651 (2008) ISSN: 15493636 [10] Karthik, S., Arunachalam, V.P., Bhavdharini, R.M.: Analyzing interaction between denial of service (dos) attacks and threats. In: Proceeding of the IEEE Int. Conf. on Computing, Communication and Networking, ICCCN 2008 (2008), 978-1-4244-3595-1/08/2008, doi:10.1109/ICCCNET.2008. 4787663

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[11] Karthik, S., Arunachalam, V.P., Ravichandran, T.: Analyzing interaction between denial of service (dos) attacks and threats. Int. J. Soft Computing, 68–75 (2009) ISSN: 18169503 Karthik.S is presently a PhD candidate at Faculty of Computer Science & Engineering, Anna University- Coimbatore,Tamilnadu, India. He received the B.Sc degree from Bharathiar University in 1998 , M.Sc degree from the Periyar University in 2000 and M.E degree from the Anna University in 2005. Before he came to Anna University in 2007, he was a Assistant Professor in SNS College of Technology, Coimbatore, India. His research interests include network security, web services and wireless systems. In particular, he is currently working in a research group developing new Internet security architectures and active defense systems against DDoS attacks. Mr. Karthik.S published more than 20 papers international journal and conference papers and has been involved many international conferences as Technical Chair and tutorial presenter. He is a member of IEEE, ISTE and Indian Computer Society. Professor Dr.V.P.Arunachalam received the B.E and M.E degrees from Madras University, Madras, India in 1971 and 1972, respectively, and PhD degree from the Bharathiar University, Coimbatore, India, in 1988. He is currently the Principal of SNS College of Technology, Coimbatore, Tamilnadu, India. Before joining SNS College of Technology, Professor Arunachalam has been a Professor and Vice Principal in Government College of Technology, Coimbatore, Tamilnadu, India. His research interests include theory and practical issues of building distributed systems, Internet computing and security, mobile computing, performance evaluation, and fault tolerant computing, CAD,CAM,CAE. Professor Arunachalam is a member of the IE and ISTE. Professor Arunachalam has published more than 50 papers in refereed international journals and refereed international conferences proceedings. Since 1990 Professor Arunachalam has been involved in research and produces more than 15 PhD Candidates. Professor Dr.T.Ravichandran received the B.E degrees from Bharathiar University, Tamilnadu, India and M.E degrees from Madurai Kamaraj University, Tamilnadu, India in 1994 and 1997, respectively, and PhD degree from the Periyar University, Salem, India, in 1988. He is currently the Principal of Hindustan Institute of Technology, Coimbatore Tamilnadu, India. Before joining Hindustan Institute of Technology, Professor Ravichandran has been a Professor and Vice Principal in Vellalar College of Engineering & Technology, Erode, Tamilnadu, India. His research interests include theory and practical issues of building distributed systems, Internet computing and security, mobile computing, performance evaluation, and fault tolerant computing. Professor Ravichandran is a member of the IEEE, CSI and ISTE. Professor Ravichandran has published more than 30 papers in refereed international journals and refereed international conferences proceedings. Professor Dr.M.L.Valarmathi received the B.E degree from Madurai Kamraj University and M.E degree from Bharathiar University, Tamilnadu, India in 1983 and 1990, respectively, and PhD degree from the Bharathiar University, Coimbatore, India, in 2007. She is currently the Assistant Professor, in the Department of computer Science & Engineering, Government College of Technology, Coimbatore, Tamilnadu, India. Before joining Government College of Technology, Professor Valarmathi has been a Lecturer in Allagappa Chettiyar College of Technology, Karaikudi, Tamilnadu, India. Her research interests include theory and practical issues of building distributed systems, Internet computing and security, mobile computing, performance evaluation, and fault tolerant computing, Image Processing, Optimization Techniques. She is a member of the ISTE. Professor Valarmathi has published more than 20 papers in refereed international journals and refereed international conferences proceedings

Designing a Promotor for a Novel Target Site Identified in Caspases for Initiating Apoptosis in Cancer Cells Mala S. Kumar, K.L. Lainu, V. Aghila, Dhanya Purushothaman, K. Varun Gopal, P.K. Krishnan Namboori, and Vrinda Harishankar Computational Chemistry Group, Computational Engineering and Networking, Amrita Vishwa Vidyapeetham University, Ettimadai, Coimbatore-641 105, India [email protected], [email protected], [email protected]

Abstract. Caspases are enzymes that can cleave other proteins and control normal and abnormal cell death. Cancer cells generally lack apoptosis. In this research work, a computational approach has been adopted to design a promotor that targets the inactivated caspases particularly Pro-caspase-3 or caspase-7, which are the effector caspases that cleave the downstream substrates like lamin-A, ICAD and PARP. Out of the 38 anti-carcinomic compounds selected for the analysis, some of them are found to have positive charged substituents similar to the known drug; PAC1, which cleaves the safety catch mode that blocks the IETD active site. Site specific interactions of the proteins with these ligands were performed. From the interaction analysis, it was found that 3 compounds; Choline, Glaziovine, Dasatinib can effectively target caspases and activate them. It has been suggested that these compounds favor the activation of the effector caspase proteins, thereby giving a better option in cancer therapy. Keywords: Apoptosis, caspases, docking, anti-cancer drugs, cancer.

1 Introduction The normal cell death process in human body is the deletion of old and damaged cells and replacement of these with healthy new ones. When cells are prone to damage, the body responds and inflames the area by rushing blood cells over there to clean up the mess. The damaged cells are trapped and are made ready for quick and clean death. When given a signal, the cell will rearrange its structure and fragments itself into small tiny pieces that are readily consumed by neighboring cells. This process of programmed, controlled death is called apoptosis or programmed cell death [1]. Caspases play a major role in the regulation and execution of apoptotic cell death. Caspases, a family of cysteinyl aspartate–a specific protease, are synthesized as zymogens with a prodomain of variable length followed by a large subunit (p20) and a small subunit (p10) that are separated by an intersubunit linker and an N-terminal prodomain. Depending on the structure of the prodomain and its function, caspases are typically divided into 3 major groups -inflammatory caspases (group I-Caspase-1, V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 62–67, 2010. © Springer-Verlag Berlin Heidelberg 2010

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4, 5), initiator caspases (group II-Caspase-2, 8, 9, 10), and effector caspases (group III-Caspase-3, 6, 7) [2]. The active site contains a reactive cysteine and three basic amino acids (2 arginines and a glutamate) that recognize the aspartate in the protein that is cleaved. These zymogens are floppy and these four aminoacids are not assembled into a tight active site. When the caspase is activated by making a few strategic cuts in the protein chain, the active site can form the proper conformation. The cysteine is bound to the target protein chain and the aspartate is nestled inside the 3 basic aminoacids. Cleavage of intersubunit linker at ASP175 alone is sufficient for autocatalytic activity. Also, to ensure that this zymogen is not prematurely activated, pro-caspase 3 has a tri-aspartic acid (DDD) safety catch that blocks access to the IETD (ile-glu-thr-asp) site of proteolysis. This results in the generation of mature active caspases that consist of the heterotetramer p202–p102. Subsequently, active caspases specifically process various substrates that are implicated in apoptosis and inflammation [3]. Apoptosis is one of the major defenses against cancer and deadly cancer cells often have mutations that disable their own apoptosis machinery. To prevent this inactivation, direct initiation of Pro-Caspase proteins are sufficient because apoptosis starts only when these zymogen proteins are cleaved. PAC-1 plays a role to activate procaspase-3 indiscriminately. When administered to cancer cells, it signals the cells to autodestruct themselves, by initiating the effector markable protein, Procaspase-3. A small molecule containing substituents that are positively charged at physiological pH (such as the piperazine nitrogen in PAC-1) may directly interact with the triaspartic acid safety catch, thereby inducing the auto activation of procaspase-3 [4]. Currently, PAC-1 is the only known chemical compound that has been reported to activate procaspase-3 and thereby promotes apoptosis. Studies suggest that various phytochemicals and compounds show anti-carcinomic activity. This work aims at investigating the possibility of using a suitable phytochemical as promoter for activating the caspase protein. This was done by checking the possibility of the phytochemicals to have a behavior similar to that of PAC-1. Curcumin, a pharmacologically safe compound, shows anti-carcinomic properties and current clinical trials have shown that curcumin can also be treated as a chemotherapeutic and chemopreventive agent [5]. Here in this work, 38 different compounds have been taken for the interactional studies.

2 Materials and Methods Proteins of human caspase-3, Caspase-6 and Caspase-7 were collected from PDB (Protein Data Bank) [6]. Then these molecules were subjected to sequence analysis, structural analysis and computational modeling, in order to characterize them. Initially, multiple sequence alignment for the proteins caspase-3, Caspase-6 and Caspase-7 was performed using ClustalW tool [7]. It calculates the best match for the selected sequences and helps in identifying the similarities and differences between the proteins. The physio-chemical properties of the proteins including instability index, aliphatic index, grand average hydropathy (GRAVY), molecular weight, theoretical pI, amino acid composition, atomic composition, extinction coefficient, and the half life of these proteins were found out using ProtParam [8]. From the half

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life period of the proteins, the rate of the reaction has been calculated based on first order kinetics and predicted the fastest activation pathway for apoptosis. Active sites of these proteins include families, functional sites, amino acid pattern, signatures and motifs and these were determined using PROSITE [9]. RADAR tool [10] has been used for finding the repeated regions in the protein sequences. The percentage of secondary structures such as alpha chains, beta sheets and random coils, evolutionary relationship and details about the class, folds, domains and super families were found using ‘Self-Optimized Prediction Method’, SOPMA [11] and SCOP [12] database. Sub-cellular location of the proteins was identified using Psort tool [13]. All the modeling and simulations were performed using Accelrys Discovery studio [14] with ‘smart minimiser’ algorithm and CHARMm force field. The input for the modeling was the PDB file collected from the repository Protein Data Bank. The collected protein structures were subjected to geometry optimization and the corresponding minimum interactional potential energy, RMS gradient, van der Waals energy; electrostatic energy and kinetic energy values were computed. Most of the structures attained convergence at about 2000 steps of iteration. To identify a potential promoter, 38 compounds with anti-carcinomic property have been collected. The anti-cancer reported drugs used in this analysis include Hesperetin, Paclitaxel, Choline, Dasatinib, etc and the reported phytochemicals used for the analysis include solasodine, Scilliglaucosidin, Cymarin, Quercetin, Homoharringtonine, Hellebrin, Oleandrigenin, Strophanthidin, Scillarenin, Gitoxigenin, Scilliroside, Betulin, Lapachol, GeranylGeraniol, Bullatacin, Curcumin, Daphnorectin, Glaziovine, Taxol, Rhein, Harringtonine, Eupatorin, Tretitonin, Genestin. To identify the interactions between the ligands and the proteins, docking was carried out. Initially, docking analysis of the protein procaspase-3 with the known ligand PAC-1 was performed using CDOCKER tool [15] of Discovery Studio. CDOCKER is a grid-based molecular docking method that employs CHARMm forcefield. The receptor is held rigid while the ligands are allowed to flex during the refinement. Each protein has numerous active sites and these are docked with the ligand. For each final pose, the CDOCKER energy (interaction energy plus ligand strain) and the interaction energy are noted. The poses are sorted by CHARMm energy and the top scoring one which is found to be exothermic (thus favorable for binding) is retained. The procedure was repeated with all the 38 compounds which were taken for the analysis. ADMET [16] tool in Discovery Studio was used for the prediction of pharmacokinetic properties of the given compounds. The results for solubility, absorption, blood brain barrier penetration, and hepatoxicity have been analyzed.

3 Results and Discussions The instability index values of Caspase-3, 6, 7 have been calculated and it has been found that they are stable in nature. Hydropathicity of these proteins were also calculated. From the results, it has been observed that all the proteins are hydrophilic in nature. The estimated half life of caspase-3 protein was found to be 2.24 hours and for caspase-7 it was found to be 3.00 hours. The rate of reaction for the 3 proteins was

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also obtained. It has been concluded that the activation of caspase-3 (value of K=0.3) is better than the other two proteins caspase-6 and caspase-7 (value of K=0.2287). The sub-cellular location of all 3 proteins has been identified as cytoplasm. Generally, Caspase-3 proteins are highly expressed in the lung, spleen, heart, liver and kidney, moderate levels in the brain and skeletal muscles and low levels in the testis. Expression of Caspase-7 proteins is absent in the brain. From the SCOP database, it was found that caspase-3, 6 and 7 belong to alpha and beta proteins and are in the ‘Caspase catalytic domain family’. Results from ClustalW tool showed the presence of conserved regions in these proteins. The active sites of these proteins have been identified using Prosite tool and can be used as the targets for ligand binding. The interactional potential energy of these proteins has been found to be negative. The van der Waals energy was found to be negative. The RMS gradient value was very close to zero indicating them to be in a stable state. Table 1. Interaction Energy of Pro-Caspase 3 with PAC-1 Protein ID 2C1E 2C2O 2C2K 2C2M 2J30 2J33

Site 2

Site 1 -42.82 -47.35 25.68 -36.95 -37.74 -40.55

-40.55 0 -44.16 0 0 0

Site 3

Site 4

Site 5

Site 6

Site 7

0 0 33.78 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

0 0 0 0 0 0

The site specific interaction energies of Procaspase-3 with the known ligand PAC1 has been calculated and is given in Table 1. Sites 1 and 2 were found to be the best functional sites for the known drug. The same procedure was carried out for the 38 compounds also. While analyzing the results, interaction energy of 7 ligands were in the same range as that of the known ligand. These 7 ligands were characterized using Pre-ADMET. The known drug possesses positive charged groups which are involved in the cleavage of procaspase protein. Table 2. Result for Ligand Characterization Ligand

Pac-1 Choline Glaziovine Dasatinib

No. of Positive Charged Groups 1 1 1 1

No. of Amine Groups 2 1 1 2

Polarizability

45.02 42.18 42.32 46.66

Charge on acceptor Atoms -0.003 -0.003 -0.003 -0.005

Ligand

Pac-1 Choline Glaziovine Dasatinib

Based on these results, 3 compounds which show positive charged groups were screened and listed in Table 2 and from these, the Dasitinib compound contains the same positive charged substituents as that of PAC-1, called piperazine nitrogen that

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cleaves the specific site ‘DDD’ which blocks the IETD active site. The screened ligands were then subjected to pharmacokinetic analysis (ADMET) and the results have been tabulated in Table 3. Table 3. Results of ADMET Analysis on Screened Compounds Ligands Choline Dasatinib Glaziovine

ADMET BBB Level 2 1 2

ADMET Absorption Level 0 1 0

ADMET Solubility Level 3 3 3

An ADMET absorption level zero indicates good absorption, 1 indicates medium absorption and 3 shows very poor absorption level. Solubility values 2, 3, 4 indicate low, good and optimal solubility levels respectively and solubility level 5 indicates insolubility. BBB levels of 0, 1, 2 and 3 indicate very high, high, medium and low BBB penetration respectively. Of the three compounds, Choline and Glaziovine were found to have good absorption properties, while Dasatinib showed optimal absorption. Considering solubility, all three compounds were found to exhibit good drug action. Choline and Glaziovine were found to exhibit medium BBB penetration while Dasatinib showed a relatively high BBB penetration.

4 Conclusion Death receptors activate initiator procaspases-8 when the ligand is bound to them. Auto-catalytically cleaved procaspase-8 then initiates executioner caspses-3 or 7 or 9 and leads to apoptosis. Inactivation of upstream pathway disrupts the activation of these downstream caspases and leads to uncontrolled growth of cells. By calculating the estimated half life period and using law of rate of reaction, activation of caspase-3 is found to be good. Current studies state that these have a safety catch mode with 3 aspartate aminoacids (DDD) that blocks the active site Isoleucine-GlutamineThreonine-Aspartate (IETD). These proteins are seen as zymogens, and this safety catch enables procaspase-3 to resist autocatalytic activation. Known drug PAC-1 cleaves this DDD aminoacids using a small molecule containing substituents that are positively charged (Piperazine Nitrogen) and activates mature caspase-3, eventually leading to apoptotic cell death. The main objective of this work was to find out a new promotor like the known drug PAC-1, for activating this premature protein, Procaspase-3. 38 anti-carcinomic compounds were screened and some of them possessed positive charged groups. Results showed that various compounds have interaction with the human caspase proteins. After the screening process, 3 compounds; drugs like Choline and Dasatinib and phytochemicals like Glaziovine which were selected based on the similar characteristics and thermodynamic interactions as same as the existing cancer drug with similar action; PAC-1. Finally from the ADMETox analysis, it was found that these ligands showed good solubility level and optimal Blood brain barrier penetration level. These selected ligands can be used as active effectors in the activation of Procaspase-3, in order to promote apoptosis in cancer cells.

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References 1. Kerr, J.F.R., Winterford, C.M., Harmon, B.V.: Apoptosis Its Significance In Cancer And Cancer Therapy. Cancer 73(8) (April 15, 1994) 2. Kaufmann, H., Fusseneger, M.: Caspase Regulation At Molecular Level. Cell Engineering 4, 1–24 (2002) ISBN 978-1-4020 3. Khasibhatla, S., Tseng, B.: Why Target Apoptosis In Cancer Treatment? Molecular Cancer Therapeutics 2, 573–580 (2003) 4. Putt, K.S., Chen, G.W., Pearson, J.M., Sandhorst, J.S., Hoagland, M.S., Kwon, J.-T., Hwang, S.-K., Jin, H., Churchwell, M.I., Cho, M.-H., Doerge, D.R., Helferich, W.G., Hergenrother, P.J.: Small-molecule activation of procaspase-3 to caspase-3 as a personalized anticancer strategy. Nature Chemical Biology 2(10) (October 2006) 5. LóPez-Lázaro, M.: Anticancer And Carcinogenic Properties Of Curcumin: Considerations For Its Clinical Development As A Cancer Chemopreventive And Chemotherapeutic Agent. Mol. Nutr. Food Res. 52, 103–127 (2008) 6. Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Research 28(1), 235–242 (2000) 7. Thompson, J., Gibson, T., Higgins, D.: Multiple Sequence Alignment Using Clustalw And Clustalx. Curr. Protoc. Bioinformatics, ch. 2 Unit 2.3 (2002) 8. Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R.D., Bairoch, A.: Expasy: The Proteomics Server For In-Depth Protein Knowledge And Analysis. Nucleic. Acids Res. 31, 3784–3788 (2003) 9. Hulo, N., Bairoch, A., Bulliard, V., et al.: The Prosite Database. Nucleic Acids Res. 34(Database Issue), D227–230 (2006) 10. Mackey, A.J., Haystead, T.A., Pearson, W.R.: Getting More from Less: Algorithms For Rapid Protein Identification With Multiple Short Peptide Sequences. Molecular And Cellular Proteomics 1(2), 139–147 (2002) 11. Geourjon, C., Deléage, G.: Sopma: A Self-Optimized Method for Protein Secondary Structure Prediction. Protein Eng. 7(2), 157–164 (1994) 12. Murzin, A., Brenner, S., Hubbard, T., Chothia, C.: Scop: A Structural Classification Of Proteins Database For The Investigation Of Sequences And Structures. J. Mol. Biol. 247(4), 536–540 (1995) 13. Jain, E., Bairoch, A., Duvaud, S., Phan, I., Redaschi, N., Suzek, B.E., Martin, M.J., Mcgarvey, P., Gasteiger, E.: Infrastructure For The Life Sciences: Design And Implementation Of The Uniprot Website. Bmc Bioinformatics 10, 136 (2009) 14. Accelrys Software Inc., Discovery Studio Modeling Environment, Release 2.1, Accelrys Software Inc., San Diego (2007) 15. Wu, G., Robertson, D.H., III Brooks, C.L.: Detailed Analysis of Grid-Based Molecular Docking: A Case Study of CDOCKER - A CHARMm-Based MD Docking Algorithm. J. Comp. Chem. 24, 1549 (2003) 16. Kerns, D., Di, L.: Drug-like Properties: Concepts, Structure Design and Methods: from ADME to Toxicity Optimization. Academic Press, London (2008)

Learning Classifier Systems Approach for Automated Discovery of Hierarchical Censored Production Rules Suraiya Jabin Department of Computer Science, Jamia Millia Islamia, New Delhi, India [email protected]

Abstract. This article presents Learning Classifier Systems (LCS) approach for automated discovery of Hierarchical Censored Production Rules (HCPR). A LCS is an adaptive system that learns to perform the best action given its input. By best is generally meant the action that will receive the most reward or reinforcement from the system’s environment. A classifier system consists of three main components: rule and message system, apportionment of credit system, genetic algorithm (GA). In the proposed LCS, concatenate of the Hierarchical Censored Production Rule-trees form the genotype, and therefore the GA operates on a population of HCPR-trees. More recently, LCSs have proved efficient at solving automatic classification tasks. Hierarchical Censored Production Rules is a system of knowledge representation that exhibited variable certainty as well as variable specificity and offered mechanisms for handling the trade off between the two. An appropriate chromosome representation scheme, suitable genetic operators, appropriate fitness function and also appropriate credit assignment scheme is proposed to evolve the best HCPR-trees. Experimental results are presented to demonstrate the performance of the proposed system. Keywords: Data Mining, Degree of Subsumption, Hierarchical Censored Production Rules, Learning Classifier System, Machine Learning.

1 Introduction Knowledge Discovery in databases (KDD) has become a very attractive discipline both for research and industry within the last few years. Its goal is to extract pieces of knowledge or patterns from usually very large databases. It portrays a robust sequence of procedures or steps that have to be carried out so as to derive reasonable and understandable results. Knowledge Discovery is the area in which new LCS models have been applied most [1]. The most predominant representation of the discovered knowledge is the standard production rules (PR) in the form IF P<premise> THEN D<decision>. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. A Censored Production Rule (CPR), proposed by Michalski and Winston, is an augmented production rule of the form: IF P <premise> THEN D <decision> UNLESS C , where Censor is an exception condition to the rule. From a control viewpoint CPRs are intended for situations in which the condition If P Then V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 68–77, 2010. © Springer-Verlag Berlin Heidelberg 2010

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D part of CPR expresses important information useful for any rule-based system while UNLESS C part acts as the switch that changes the polarity of D to ~D when C holds. However, A CPR is unable to capture the structure inherent in the knowledge about real world which is best expressed in the form of hierarchies. Integrating hierarchy generation process into the data mining algorithm can dramatically reduce the execution time [4]. Hierarchical structures summarizing large dataset are important because it provide comprehensible results that capture real-life inheritance of objects. Several efforts ([3], [4], [5], [8]) have been made in the recent past towards automated discovery of hierarchical structures in large databases. As an extension of CPR, Bharadwaj and Jain (1992) proposed Hierarchical Censored Production Rules (HCPR) as a system of knowledge representation. The general form of the HCPR ([6], [8]) is given as: < Decision (if precondition) (UNLESS censor_conditions) (GENERALITY general_information) (SPECIFICITY specific_information)> Systems using HCPRs are free to ignore the exception conditions when resources are tight. Given more time, the exception conditions are examined leading credibility to high speed answer or changing them. The specificity information in a HCPR is a clue about the next set of more specific concepts (goals, decisions, consequent or actions) in a knowledge base which are the most relevant and which are the most likely to be satisfied after successful execution of that HCPR under forward-chaining of reasoning system [6]. Learning Classifier System is an adaptive system that learns to perform the best action given its input. By best is generally meant the action that will receive the most reward or reinforcement from the system’s environment. By input is meant the environment as sensed by the system, usually a vector of numerical values. In a classification context, the available actions may be yes, no, or benign, malignant, etc. In general, an LCS is a simple model of an intelligent agent interacting with an environment. Invented in 1975 by John Holland, these systems are paradoxically less famous than GAs though GAs were originally a sub-part of LCSs. However, during the past several years LCS research has gained more visibility, giving rise to the opportunity of publishing a general presentation for a wide scientific audience. The goal of this paper is to present Learning Classifier Systems approach (XCSstyle) for automated discovery of Hierarchical Censored Production Rules. The two mechanisms on which LCS rely, namely GAs and Reinforcement Learning (RL) are modified according to proposed system’s requirement. Appropriate subsumption relation, encoding scheme, suitable genetic operators and effective fitness function is suggested for the proposed system. Also an apportionment of credit assignment scheme is proposed to guide the search process to evolve the best hierarchy.

2 Subsumption Matrix A class Di (Decision attribute from the data set e.g. dead/alive, Play/Don’t Play Golf etc.) can be defined by a set of properties (values of distinct attributes),

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class_prop(Di). Let Di and Dj be any two classes with the set of properties class_prop(Di) and class_prop(Dj), respectively. Degree of subsumption deg_sub(Di, Dj) is defined as follows [8]: deg_ sub ( Di , D j ) =

where

class _ prop ( Di ) ∩ class _ prop ( D j )

(1)

class _ prop ( D j )

deg_ sub( Di , D j ) ∈ [0,1] .

A Subsumption Matrix (SM) that summarizes the relationship between classes, D1, D2, D3……………..Dn is n x n matrix defined as under[13]: ⎧1 if deg_ sub( Di , D j ) = 1 i.e.Di ⊆ D j SM [ Di , D j ] = ⎨ ⎩0 otherwise

(2)

e.g.) deg_sub(D1, D2)= 10 / 10 = 1 (it indicates D1 completely subsumes D2). Total no. of records from data set where D1 and D2 are both true = 10, Total no. of records from data set where only D2 is true = 10. deg_sub(D2, D1)= 10 / 20 = 0.5 (it indicates D2 doesn’t subsume D1) deg_sub(D4, D3) = 20 / 30 = 0.66.

3 Proposed System: LCS Approach for Automated Discovery of HCPR-Trees XCS [1] has been the most studied and applied LCS for several years. The important research efforts concerned with this system have resulted in much improvement in both its performance and in understanding the reasons for this performance. The central idea in XCS consists in decoupling the Reinforcement Learning process and the population evolution process by introducing a fitness function that is not proportional to the expected reward, but to the accuracy of the prediction of this reward. Environment

Learning Classifier System

Information Payoff

Detectors 1 0

Message List 101 000 111

1

Effectors 1 1 1

Classifier Store 1 0 #: 1 1 1 00#:000 Apportionment of Credit Genetic Algorithm

Fig. 1. A General Learning Classifier System [1]

Action

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The classifiers that survive in the population are no longer necessarily those predicting a large reward, but those accurately predicting the reward they receive, be it large or small. XCS keeps in its population classifiers firing far from a source of positive reward, thus predicting a small reward, given that they do so accurately. As a result, XCS covers the state space more efficiently than any other LCS model. A schematic depicting the rule and message system, the apportionment of credit system, and the genetic algorithm is shown in figure 1. Information flows from the environment through the detectors-the classifier system’s eyes and ears-where it is decoded to one or finite length messages. These environmental messages are posted to a finite-length message list where the messages may then activate string rules called classifiers. When activated, a classifier posts a message to the message list. These messages may then invoke other classifiers or they may cause an action to be taken through the system’s action triggers called effectors [2]. 3.1 Genetic Algorithm Component Genetic algorithms ([1], [8], [9]) manipulate a population of individuals representing possible solutions to a given problem. GAs rely on four analogies with their biological counterpart: they use a code, the genotype or genome, simple transformations operating on that code, the genetic operators, the expression of a solution from the code, the genotype-to-phenotype mapping, and a solution selection process, the survival of the fittest. The genetic operators are used to introduce some variations in the genotypes. There are two classes of operators: crossover operators, which create new genotypes by recombining sub-parts of the genotypes of two or more individuals, and mutation operators, which randomly modify the genotype of an individual. The selection process extracts the genotypes that deserve to be reproduced, upon which genetic operators will be applied. 3.1.1 Chromosome Encoding Scheme and Population of Classifiers Input to the proposed system is large dataset whose every record typically has some property attributes and one classifying attribute that can have more than one decision values. One chromosome basically denotes one possible solution to the problem; so in the proposed system chromosomes are represented by HCPR-trees. An HCPR-tree is collection of various related HCPRs/genes (nodes of tree) and each node has the following form: Table 1. Individual Node of HCPR-tree If part The set of properties [P1,….,Pi]

Unless part The set of properties [C1,… ,Ci]

Generality part parent class G (formed out of Decision attributes)

Specificity part Set of specific classes S (formed out of Decision attributes)

THEN part Decision attribute

For random generation of one such chromosome: 1. A completely random tree of some height is generated by randomly choosing nodes from decision attributes i.e. first forming THEN part of every node in the HCPR-tree; suppose there are 5 decision values then randomly one decision attribute is chosen as

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root node of tree; Suppose maximum no. of children of a node is set as 4 (hash defined in the proposed system), any number between 0 and 4 is generated say 2, then for the next level of hierarchy formation, randomly two decision attributes leaving the decision attribute set as root node is chosen and so on for further levels till all the decision attributes are utilized, Generality part of root node is set as Null, generality part of node at level one is set as root node and so on. Something of this form: (Root (sub-tree 1) (sub-tree 2)… (sub-tree i)…(sub-tree k)) e.g.1) (D1 (D2 (D4)) (D7) (D3 (D5) (D6))) e.g.2) (D1 (D2) (D3 (D4) (D5))) 2. Then for each node If part and Unless part are set by choosing property attributes randomly from all property attributes by maintaining only these restrictions that: (i) For each node the two parts (IF and UNLESS) are mutually exclusive and (ii) None of the property or censor attribute will hold repeated attributes i.e. once P1 attribute is set as property of class D1 it can not be set as property attribute of any other Decision attribute. (iii) Any attribute that is chosen as censor attribute at a parent node in the hierarchy, will be automatically censor condition for the sub-hierarchy. (iv) Also these basic restrictions are always checked anytime after any genetic operator is applied to form new hierarchy by the proposed system. A sample and randomly generated chromosome is shown in Fig. 2. D1[P1] L P6

D2[P2] L (P7 V P8)

D3 [P3] L(P9 V P10)

D4[P4] L (P11 V P12)

D5[P5] L P13

Fig. 2. Chromosome Encoding of HCPR-tree

Individual node/genes of this chromosome/ HCPR-tree can also be summarized by Table 2. Table 2. Linearized Representation of HCPR-tree of Fig. 2 Level of hierarchy Level 0:

IF part

Level 1:

P2, P1 P3, P1 P4, P3, P1

Level 2:

P1

P5, P3, P1

UNLESS part P6 P6, P7, P8 P6, P9, P10 P6, P9, P10, P11, P12 P6, P9, P10, P13

GENERALITY part NULL (root node) D1 D1 D3

SPECIFICITY part D2, D3

THEN part D1

NULL (leaf node) D4, D5 NULL (leaf node)

D2 D3 D4

D3

NULL (leaf node)

D5

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3.1.2 Accuracy and Coverage Based Fitness Function Once the genetic representation has been defined, the next step is to associate to each solution (chromosome) a value corresponding to the fitness function. The most critical and most important concept of evolutionary algorithm is the fitness function. It varies greatly from one type of problem to another. In the proposed system fitness is evaluated at the following 2 levels: (a) node level fitness is calculated as summation of fitness’s of each individual HCPR forming the tree. Each individual HCPR is evaluated on the basis of its accuracy and coverage against the data set. (b) tree level fitness is calculated as summation of degree of subsumption (given by equation 1) attached with each arc in the tree showing the strength of relationship between a parent and a child. Fa = Fitness of individual node/gene forming the HCPR–tree (i.e. ignore the generality and specificity part of every node in tree, it becomes a simple CPR). Suppose h is the height of the tree, then fitness Fa is calculated as: Fitness of root node CPR + Sum of Fitness of all nodes/CPRs at level 1 + Sum of Fitness of all nodes/CPRs at level 2 ...+ Sum of Fitness of all nodes at level h-1 Fitness of a particular node (CPR) is defined as [Accuracy (A) + Coverage (C)]/2 A CPR of the form IF P <premise> THEN D <decision> UNLESS C is equivalent to two types of production rules: Type I. Rule with censor part present can be factorized as: i. P Λ ¬ C → D ii. P Λ C → ¬D Type II. Rule without censor iii. P → D iv. P → ¬D Here Accuracy and Coverage are defined as: Accuracy =

P∧D P

Coverage =

P∧D

D , e.g.) For rule type I (i), Accuracy is calculated by: P ∧ ¬C ∧ D

P ∧ ¬C

Similarly Accuracy and Coverage is calculated for other rule types also. Fb = Fitness of the entire hierarchy based on subsumption relation; This is defined as summation of degree of subsumption given by equation (1) corresponding to every arch in the HCPR–tree. Fitness (F) of our proposed system is given by: F = Fa + Fb 3.1.3 Genetic Operators A number of genetic operators suitable for HCPR-trees system are suggested. I used conventional genetic operators of selection, crossover, and mutation. More precisely, I used fitness proportional selection, one-point crossover, with probability 0.7, and mutation operator namely (insert, dropping) with probability 0.2. Crossover: Crossover is done at 2 levels: (i) Node level (CPRs are exchanged) (ii) tree level (sub trees are fused to generate compact hierarchy)

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i. Node level crossover: Consider the following two nodes of an HCPR–tree without considering generality and specificity part Di [P1, P2, P3, …..PK] L [C1, C2, C3……Cm] Dj [P1´, P2´, P3´, …..Pk´] L [C1´, C2´, C3´……Cn´] For crossover any two nodes in the tree will be chosen randomly and any one of these can happen randomly: Decision attributes are exchanged (exchange Di with Dj) or Set of properties are crossovered by exchanging the tails or Set of censors are exchanged by exchanging the tails ii. Tree level crossover: Fusion algorithm [6] enables the merging of two related HCPRs trees in the HCPRs system. In tree level crossover operation, any two eligible trees amongst the population (old + new) are fused by applying Fusion operation so that new and more compact hierarchies will result. The central idea behind fusion operation for merging two HCPR-trees is: first the roots of two trees are compared, if there is no common property in the IF sets of the roots of two HCPRs trees, no fusion is possible. If there is one or more property common in the root node, then the two trees can be fused, and in the new tree formed root node will contain those common properties. Mutation: For mutation any one of the following can be done at randomly: Dropping/ Introducing any censor/property condition from a randomly chosen node. Also new offspring are validated by checking its censor condition in each of the above operations using the following: (1) For a new HCPR-tree or offspring, if a property attribute is set as censor condition at the root node then it must also be censor condition for all other nodes in the hierarchy. Similarly, if a property attribute is set as censor condition at level h of a HCPR-tree then it must also be censor condition for all other nodes in the level h+1 of hierarchy. (2) P belongs to both classes D (covering large portion of data) and ¬D (covering small portion of data). To ensure that P in the class ¬D occurs rarely, we can check the constraint: |P Λ D| >>> |P Λ¬D| i.e. count of the cases where both premise part and decision part are true is much larger than count of cases where premise part is true and decision part is not true. 3.2 Apportionment of Credit Component An LCS is adaptive in the sense that its ability to choose the best action improves with experience. The source of the improvement is reinforcement technically, payoff; provided by the environment. Payoff received for a given action is used by the LCS to alter the likelihood of taking that action, in those circumstances, in the future. Inside the LCS is a set technically, a population of “condition-action rules” called classifiers. For the proposed system HCPR-trees form classifier population. Suppose there are N (N is hash defined in proposed system; say N=50) HCPR-trees in the population. When a particular input occurs, the LCS forms a so-called match set of classifiers whose conditions in the premise part of the root node of a particular HCPR-tree are

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satisfied by that input. In general, a classifier’s condition will refer to more than one of the input components, usually all of them. If a classifier’s condition is satisfied, then that classifier joins the match set and influences the system’s action decision. In a sense, the match set consists of classifiers in the population that recognize the current input. In supervised learning, the true classification of a training case is known to the system, and this information is used by the reinforcement component in adjusting the strengths of all classifiers in the system. In the proposed system Credit assignment component is implemented as: For all nodes within each HCPR-trees forming match set, a 10% tax is applied by reducing its fitness to avoid over fitting and premature convergence and also awarded a reward proportional to generality i.e. the more general the rule is, the more reward is awarded to it. Now the fitness function becomes: Fitness = Fitness * TAX + 1 / generality, where TAX = 0.90 for 10 % tax and generality is count of no. of conditions present in IF part; for rules where censor part is also present generality is proportionally increased depending on number of attributes present in UNLESS part [9].

4 Experimental Results The performance of the proposed system was tested over a no. of examples from which two are demonstrated below. In experiments the parameters used are as under: Each iteration of the proposed system consisted of a population of 30 individuals evolving over 500 generations. The proposed algorithm was terminated when the best Fitness did not change continually throughout 10 generations. The probability of crossover and mutation was set to 0.7 and 0.2 respectively. For time efficiency purpose and for avoiding oversized HCPR-trees the maximum tree depth and the maximum no. of offspring were fixed and hash defined so that one can change these settings to see the effect over output. Example 1: For input of real–life Shoe data set the best HCPR-tree evolved is shown in the figure 3. X wears_large_shoes [height=tall] L [feet=small] 1 X wears_soft_leather_shoes [likes = leather_items] L [quality=poor] 1

1 1 X wears_expensive_X wears_mod_ leather_shoes Priced_leather_shoe [rich=yes] L [rich=moderate] L [ ] [money_short=yes]

1 X wears_fine_fabric_shoes [likes=fabric_items] L [ ] 1

1

Xwears_natural_fiber_ Xwears_synthetic_ Xwears_in_exp_ _shoes[likes= shoes[likes=synthetic_items] leather_shoes nat_fib_items] L [ ] L [Allergic_synthetic=yes] [rich=no]L [gifted_exp_shoes = yes]

Fig. 3. Highly fit HCPR-tree generated for a real life shoe data set using the proposed system

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Example 2: Along with other data sets Zoo data set [7] was also given as input to proposed system and the best HCPR-tree generated is shown in figure 4. Eggs_Animal [Eggs] 1 Birds [Feathers]

1 1

1

1 Fishes Aquatic_ani [Fins] mal [Aquatic] 1

Insects [Airbone Λ Legs=6] Reptiles[Breaths Λ legs=0 Λ tail]

1

Mollucs [Predators] Amphibians [Toothed Λ Legs = 4] └ Backbone

Fig. 4. A highly fit HCPR-tree automatically generated for Zoo dataset using the proposed system

It is worth noting that LCSs are usually applied to reinforcement learning problems, and as such are involved in unsupervised learning. Experimental results demonstrate that LCS approach is significantly better approach for automated discovery of HCPRtrees using supervised learning.

5 Conclusions As an attempt towards integrating hierarchy generation processes with the data mining algorithm, an evolutionary Learning Classifier System scheme is presented that discovers production rules with exceptions and hierarchy. Experimental results have demonstrated the effectiveness of the proposed scheme in providing the user with compact and comprehensible rules in the form of HCPR-trees. One of the most important future research directions would be the discovery of Fuzzy Hierarchical Censored Production Rules from large datasets using LCS approach. Acknowledgments. Professor K. K. Bharadwaj and Deepa Anand are names which require special mention as without their suggestions this work must not have been completed.

References 1. Wilson, S.W.: Classifier Fitness Based on Accuracy. In: Evolutionary Computation (1995) 2. Olivier, S., Wilson, S.W.: Learning Classifier Systems: A Survey. Journal of Soft Computing-A Fusion of Foundations, Methodologies and Applications (Print) 1433-7479 (Online) 11(11), 1065–1078 (2007) ISSN 1432-7643 3. Liu, B., Hu, M., Hsu, W.: Multi-level organization and summarization of the discovered rules. In: SIGKDD 2000, Boston, USA, August 20-23, pp. 208–217 (2000) 4. Angryk, R.A., Petry, F.E.: Mining multi-level associations with fuzzy hierarchies. In: The 2005 IEEE International Conference on Fuzzy Systems, pp. 785–790 (2005)

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5. Han, J., Fu, Y.: Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. In: AAAI’94 (KDD 1994), Seattle, WA, pp. 157–168 (1994) 6. Jain, N.K., Bharadwaj, K.K.: Some learning techniques in hierarchical censored production rules (HCPRs) systems. International Journal of Intelligent Systems 13, 319–344 (1998) 7. Website address of an online data repository; from where Zoo, Mushroom training dataset and other training data sets are taken, http://www.sgi.com/tech/mlc/db/ 8. Bharadwaj, K.K., Hewahi, N.M., Brando, M.A.: Adaptive hierarchical censored production rule based system: A genetic algorithm approach. In: Advances in Artificial Intelligence. LNCS (LNAI), pp. 81–90. Springer, Heidelberg (1996) 9. Jabin, S., Bharadwaj, K.K.: Learning Classifier Systems Approach For Automated Discovery of Censored Production Rules. Transactions on Engineering, Computing and Technology 14 (2006) ISSN 1305-5313

Feature Based Watermarking Algorithm by Adopting Arnold Transform S.S. Sujatha1 and M. Mohamed Sathik2 1

Assistant Professor in Computer Science, S.T. Hindu College, Nagercoil, Tamilnadu, India 2 Associate Professor in Computer Science, Sadakathullah Appa College, Tirunelveli, Tamilnadu, India [email protected], [email protected]

Abstract. The central idea of this paper is to develop an algorithm that embeds the watermark information in host image to authenticate it. The host image is divided into non-overlapping blocks of size 2x2. Minimum value is obtained from each block and the resultant matrix is scrambled for three times with Arnold transform to enhance security. From the transformed matrix a binary watermark is constructed and is embedded within the host image. The operation of embedding and extraction of watermark is done in high frequency domain of Discrete Wavelet Transform since small modifications in this domain are not perceived by human eyes. Furthermore, the proposed algorithm is checked against various common image processing attacks. This watermarking scheme deals with the extraction of the watermark information in the absence of original image, so the blind scheme was obtained. Keywords: Digital watermarking, Discrete Wavelet Transform, Arnold transform, Image Authentication, Content based watermarking.

1 Introduction Internet plays a very important role in accessing information from any place. The development of web technology makes it possible for us to transfer digital content in a cost-efficient way. However it causes serious problems such as unauthorized use and manipulation of digital content. Digital watermarking is a technique which embeds additional information called digital signature or watermark into the digital content in order to secure it. Several methods have proposed in literature. A survey is in [1]. The proposed methods can be classified according to the domain in which the embedding is performed. They are spatial-domain techniques and frequency-domain techniques. Least Significant Bit (LSB) is the simplest technique in the spatial domain [2] which directly modifies the intensities of some selected pixels. The frequency domain technique transforms an image into a set of frequency domain coefficients [3]. The transformation adopted may be discrete cosine transform (DCT), discrete Fourier transforms (DFT) and discrete wavelet transforms (DWT) etc. After applying transformation, watermark is embedded in the transformed coefficients of the image such that watermark is not visible. Finally, the watermarked image is obtained by acquiring inverse transformation of the coefficients. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 78–82, 2010. © Springer-Verlag Berlin Heidelberg 2010

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In the proposed content based watermarking scheme, watermark is generated from the feature of image. Discrete wavelet transform (DWT) is used for embedding watermarks, since it is an excellent time-frequency analysis method, which can be well adapted for extracting the information content of the image [4]. Watermark has been embedded in the high frequency bands. In this paper we propose a novel DWT transform based watermarking scheme which is robust against many common image attacks and experimental results verify this.

2 Research Preliminaries This section briefly describes the techniques and methods that have been adopted by the proposed scheme, including DWT and watermark scrambling using Arnold transform. 2.1 Discrete Wavelet Transform The proposed algorithm is a frequency domain watermarking scheme and works by modifying the DWT coefficients. The wavelet transform decomposes input image into four components namely LL, HL, LH and HH. The lowest resolution level LL consists of the approximation part of the original image. The remaining three resolution levels consist of the detail parts and give the vertical high (LH), horizontal high (HL) and high (HH) frequencies. In the proposed technique, embedding and extraction of watermark takes place in the high frequency component. For a one level decomposition, the discrete two-dimensional wavelet transform of the image function f(x, y) is found in [7]. 2.2 Arnold Transform A digital image can be considered as a two unit function f(x, y) in the plane Z. It can be represented as Z = f(x, y) where x, y ∈ {0,1,2,3....N − 1} and N represents order of digital image. The image matrix can be changed into a new matrix by the Arnold transform which results in a scrambled version to offer security. It is a mapping function which changes a point (x, y) to another point (x1, y1) by the equation (1).

⎡x1 ⎤ ⎡1 ⎢ 1⎥ = ⎢ ⎢⎣ y ⎥⎦ ⎣1

1⎤ ⎡ x ⎤ ⎥ ⎢ ⎥ mod N 2⎦ ⎣ y⎦

(1)

3 Proposed Method The proposed watermarking scheme consists of three phases: Watermark generation, Watermark embedding and Watermark detection. A 1-level Discrete Wavelet Transform is performed for embedding watermark. Watermark information is embedded in the high frequency bands (HH1) since it is robust against various normal image processing and malicious attacks. The watermark is generated from the pixel value of original image and so there is no need of external image or logo. Hence a method is necessary to generate watermark. The procedure for generating watermark data is summarized below.

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3.1 Watermark Generation

The watermark is generated from the spatial domain information. It includes the following steps: • • • • •

The original image X of size M x N is partitioned into non-overlapping blocks of size 2 x 2. Compute minimum value from each block and construct a matrix Mb (p, q), where p ∈ {1,2,3....M / 2} and q ∈ {1,2,3....N / 2} . Find median value Md for the elements in Mb (p, q). Perform Arnold transform for three times on Mb (p, q) to scramble the elements and obtain matrix Ms (p, q). Form the watermark pattern to be embedded into original image as

⎧⎪0 if M s ( p, q) > M d W ( p, q ) = ⎨ ⎪⎩1 otherwise •

For a M x N image, a watermark pattern of size M/2 x N/2 is generated.

In this method, watermark is generated by performing some operations on image pixels rather than taking from external source, hence the name content based watermark. 3.2 Watermark Embedding

The watermark is embedded in the high frequency subband of DWT as follows: • • •

Apply 1-level DWT to original image. The watermark is embedded in the high frequency component HH1 of DWT. Perform inverse wavelet transform (IDWT) to obtain the watermarked image.

3.3 Watermark Detection

Proposed watermarking scheme deals with the extraction of the watermark information in the absence of the original image and so it can be referred as blind watermarking. The authentication process includes the following steps: • • •

Calculate watermark from watermarked image using the steps described under watermark generation in section 3.1. Apply 1-level DWT to watermarked image and extract watermark from HH1 subband. Compare the two watermarks (calculated and extracted) and decide the authenticity.

4 Experimental Results In this paper, we consider the images with number of rows and columns are exact multiples of 2 since the image is divided into blocks of size 2x2. For testing, the size

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of the original image is taken as 512x512. Fig.1 (a) shows original image. A 256x256 watermark signal is constructed from original image and is embedded within itself. The proposed method is demonstrated with several images using MATLAB. After embedding the watermark, the watermarked image that is produced and the original image cannot be distinguished by naked eye. Fig.1 (b) shows watermarked image. The absolute difference of the pixel intensities of the watermarked image and the original image is shown in Fig.1(c). The visual quality of original and watermarked images is measured using the Peak Signal to Noise Ratio and a comparison between extracted and original watermark can be done by computing Similarity Ratio (SR) between these two patterns as defined in [10].

(a)

(b)

(c)

Fig. 1. (a) Original image, (b) Watermarked image, (c) Difference image

The proposed algorithm was tested with several attacks. Table 1 gives the performance of proposed watermarking scheme under various attacks. Table 1. Quality evaluation of proposed scheme

Attacks No 0.001, 0 Adding 0.005, 0 Gaussian 0.01,0 noise 0.05,0 (mean, 0, 0.001 variance) 0,0.002 0.001 Adding 0.002 Salt & 0.005 Pepper noise 0.01 Median 3x3 filtering Linear 3x3 filtering Image adjustment Histogram Equalization

PSNR(dB) 54.1047 54.1047 45.6804 37.8092

SR 1 1 1 1

25.6831 30.0730 27.1791 35.1050 32.0153 28.1179 25.0121

0.9992 0.5188 0.5123 0.9898 0.9861 0.9751 0.9496

29.5819

0.5218

27.7792

0.5359

18.7003

0.8433

19.0192

0.7573

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The experimental results show that the qualities of watermarked images are satisfactory in the case of filtering and little additive noises since the PSNR values are greater than 25. The SR values are all greater than 0.76 (except filtering and some cases of Gaussian noise) indicates that the watermark is robust to common image processing attacks.

5 Conclusion This study has proposed a robust watermarking technique based on the frequency domain. In the designed method watermark is constructed from the image by applying Arnold transform to enhance security of it. It also makes use of the Discrete Wavelet Transform which provides a frequency spread of the watermark within the host image. Moreover the authentication process is simple, fast and allows easy extraction. The performance of the watermarking scheme is evaluated with some possible image processing attacks. Experimental results revealed a good resilience against such attacks. However the proposed method is not maintaining quality of watermarked images against attacks such as Histogram Equalization and Image adjustment. Future work may be concentrated on improving quality and resistance against those attacks.

References 1. Rey, C., Dugelay, J.: A Survey of Watermarking Algorithm for Image Authentication. Journal on Applied Signal Processing 6, 613–621 (2002) 2. Podilchuk, C.I., Delp, E.J.: Digital Watermarking: Algorithms and Applications. IEEE Signal Processing Magazine, 33–46 (2001) 3. Parthasarathy, A.K., Kak, S.: An Improved Method of Content Based Image Watermarking. IEEE Transaction on Broadcasting 53(2), 468–479 (2007) 4. Reddy, R., Prasad, M.V.N., Sreenivasa Rao, D.: Roubst Digital Watermarking of Color Images under Noise Attacks. International Journal of Recent Trends in Engineering 1(1) (2009) 5. Xia, X.-G., Boncelet, C.G., Gonzalo: Wavelet Transform based Watermark for Digital images. Optics Express 3(12), 497–511 (1998) 6. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, India (2008) 7. Kumar, S., Raman, B., Thakur, M.: Real Coded Genetic Algorithm based Stereo image Watermarking. IJSDIA 1(1), 23–33 (2009) 8. Liu, H., Rao, J., Yao, X.: Feature Based Watermarking Scheme for Image Authentication. IEEE, 229–232 (2008) 9. Dittmann, J.: Content-fragile Watermarking for Image Authentication. In: Proc. of SPIE, Security and Watermarking of Multimedia Contents III, vol. 4314, pp. 175–184 (2001) 10. Yuan, Y., Huang, D., Liu, D.: An Integer Wavelet Based Multiple Logo-watermarking Scheme. IEEE 2, 175–179 (2006)

Inference of Gene Networks from Microarray Data through a Phenomic Approach Rio G.L. D’Souza1, K. Chandra Sekaran2, and A. Kandasamy2 2

1 St Joseph Engineering College, Mangalore, India National Institute of Technology Karnataka, Surathkal, Mangalore, India [email protected], [email protected], [email protected]

Abstract. The reconstruction of gene networks is crucial to the understanding of cellular processes which are studied in Systems Biology. The success of computational methods of drug discovery and disease diagnosis is dependent upon our understanding of the biological basis of the interaction networks between the genes. Better modelling of biological processes and powerful evolutionary methods are proving to be a key factor in the solution of such problems. However, most of these methods are based on processing of genotypic information. We present an evolutionary algorithm for inferring gene networks from expression data using phenotypic interactions. The benefit of this is that we avoid the need for an explicit objective function in the optimization process. In order to realize this, we have implemented a method called as the Phenomic algorithm and validated it for stability and accuracy in the reconstruction of gene networks. Keywords: Gene networks, Microarray data analysis, Genetic algorithms, Fitness function, Phenomic algorithms.

1 Introduction The advent of high throughput methods such as microarray technology has made it possible for biologists to study hundreds of genes at a time, and to elucidate the relationships between them. The datasets that result from such studies have high dimensionality. Hence several researchers have developed methods of analysis which can determine useful patterns from the datasets without compromising the dimensionality [1]. Gene networks represent relationships between genes, based on observations of how the expression level of each gene affects the expression levels of the others [2]. The determination of these relationships from gene expression measurements is a reverse engineering or reconstruction activity. Evolutionary methods have been used by others [3] to analyze and capture the relationships between hundreds of genes with varying degrees of success. The Phenomic Algorithm, introduced in [4], presents an approach based on population dynamics. It is based on phenotypic interactions rather than genotypic mechanisms which are used in traditional genetic algorithms [5]. Here the aim is to model gene expression record of each gene as an individual and then to let these individuals V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 83–89, 2010. © Springer-Verlag Berlin Heidelberg 2010

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interact in an environment that simulates the survival of the fittest. Thus the need for an explicit objective function is avoided. We apply the phenomic algorithm to the yeast sporulation dataset [6] and results show a marked improvement in the quality of networks discovered. Segmentation of data into multiple segments

Microarray dataset

Add next segment

After processing of all segments, exit from loop and read links between genes Gene networks

Replication of segments

Consolidation of population

Interaction of population Fig. 1. Sequence of processing in the basic phenomic algorithm

The rest of this paper is organized as follows: In section 2, we review the related work done by others. We devote section 3 to a discussion about the methodology adopted by the basic phenomic algorithm and its implementation. Finally, section 4 presents the results and validation, followed by section 5 which concludes the paper.

2 Related Work For just about a decade now reconstruction of gene networks has acquired importance due to the dawn of systems biology. One of the first attempts is a simple method that was introduced by Somogyi et al. [7]. Liang et al. [8] developed a general algorithm using mutual information to identify a minimal set of inputs that uniquely define the

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output for each gene at the next time step. Akutsu et al. [9], [10] and D’haeseleer et al. [11] have also proposed several reverse engineering algorithms. The S-system proposed by Savageau [12] has been used by some researchers [5], [13] in order to formulate an objective function for the evolutionary algorithm that they use to reverse engineer gene networks. Lubovac and Olsson [14] have suggested bringing in additional information resources into the evolutionary algorithm, so that more relevant relationships between genes can be derived. It is possible to develop better evolutionary algorithms by finding better objective functions since the critical dependency between the genotype and phenotype is characterized by them [15], [16].

3 The Phenomic Approach The phenomic algorithm is an algorithm which utilizes phenotypic information to simulate an environment that allows the survival of the fittest individual. It was introduced in [4] and we present a brief description of the algorithm here so that our work, which is an extension of the basic algorithm, is better understood. Like most evolutionary algorithms, the phenomic algorithm begins with of a population of individuals. When constructing gene networks, we study the relationship between genes. If gi and gj are objects representing two such genes, their expression patterns across m samples may be written as

{

}

g i = {wik 1 ≤ k ≤ m} and g j = w jk 1 ≤ k ≤ m .

The similarity (or proximity) between gene expression patterns can be expressed in terms of a correlation coefficient, where wij is the expression level of the ith gene in the jth sample and μgi is the average of expression levels of the ith gene over all the samples. When the microarray dataset contains records which represent the expression of each gene at m time-steps (instead of m samples) of an experiment, it is possible to verify whether the expression pattern of a gene gi at a time-step (t-1) has any correlation with the expression pattern of a gene gj at time t. For this, we define the Pearson correlation coefficient [17] across time-steps (from gene gi at time-step t = (k-1), to gene gj at time-step t = k), as given in Eqn. (1).

∑ (w m

Pear ( g i , g j ) =

k=2

∑ (w

i ( k − 1)

m

k =2

i ( k −1)

)(

− μ g i w jk − μ g j

− μ gi

) ∑ (w 2

.

)

2

m

k=2

)

jk

− μgj

(1)

In the algorithm that follows, random pairs of genes are considered at a time and the proximity measure for each pair of genes is calculated. Only those gene pairs that have a proximity measure less than a preset threshold distance D are assumed to have an interaction between them. This condition is shown in Eqn. (2).

Pear ( g i , g j ) < D .

(2)

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In the next step of processing, these closely related pairs are analyzed further to verify whether causal relationships exist between them. We show the sequence of processing in the basic phenomic algorithm in Fig. 1. We give a brief description of the main functions of the basic phenomic algorithm here: 1. Modelling genes as individuals: While modelling the genes as individuals, we embed the expression profile of the gene within the object itself. Also we store the relationships with other genes, which are discovered during the interaction phase, within the individual itself. We ensure sufficient density of individuals by replicating them as required. 2. Simulating gene interaction: We set the stage for the survival-of-the-fittest by letting individuals to meet randomly. Eqns. (1) and (2) define the nature of these interactions between partners that meet. If a pair of genes has a close proximity as defined by the time-shifted Pearson correlation coefficient, a link is stored between them. 3. Enforcing natural processes: From time to time we consolidate the population by eliminating individuals which are replicates and have not acquired any links with other individuals. Thereafter we bring in the remaining segments of the data, one by one, till all segments have been considered. At the end of the process, the links between the genes, which are stored in the individuals, are used to construct the gene networks. We present the phenomic algorithm below. The structure is very similar to a genetic algorithm since phenotypic processing is encountered in every generation, just like in a genetic algorithm. The basic phenomic algorithm and its main functions.

basic_phenomic_algorithm( ) { divide gene expression data into segments; initialize population with first segment replicated; set segment count to 0; while population has not reduced to size of single segment and there are more segments to process { interact_population; consolidate_population; replicate and add next segment; increment segment count; } read gene-links stored in the final population; display gene networks constructed from links; } interact_population( ) { for a preset number of iterations; { select two individuals from current population;

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apply interaction criteria in Eqn. (2) update gene-links of both individuals; } } consolidate_population( ) { for a preset number of iterations; { select two individuals from current population; if the indices of both individuals are same eliminate one of them after copying its links; } } As seen from experimental results in Fig. 2, the algorithm is able to discover links between genes when applied to gene expression data.

4 Results and Discussion In this study, we used expression data from a study by Chu et al. [6]. Expression profiles of about 6100 genes are included in this dataset. Using them, we followed the same method as Chu et al. [6] to extract the genes that showed significant increase of mRNA levels during sporulation. Among them, we finally selected 45 genes, whose functions are biologically characterized by Kupiec et al. [18]. A typical gene network obtained from this yeast dataset is shown in Fig. 2, when applying the basic phenomic algorithm. We validated the results by performing 10fold leave-one-out-crossover validation (LOOCV). We made ten runs of the algorithm and compared the gene networks from of each run taken separately against the consensus gene networks of the other nine runs. The average number of correctly-identified edges resulting from all the ten comparisons indicates the stability of the algorithm. The complement of the average number of incorrectly-identified edges resulting from all the ten comparisons indicates the accuracy of the algorithm. We formally define these metrics in Eqn. (3) and Eqn. (4). SF =

1 n

AF = 1 −

∑ CE E n

i =1

1 n

i

.

(3)

i

.

(4)

i

∑ IE E n

i =1

i

Where CEi is the number of correctly identified edges in the ith comparison, IEi is the number of incorrectly identified edges in the ith comparison, n is the total number of comparisons, which is ten in our case, and Ei is the total number of edges in the ith consensus network.

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Fig. 2. A typical gene network generated by the basic phenomic algorithm, when D = 0.2

We compared our algorithm to two other algorithms that use evolutionary methods to infer gene networks from microarray data. We provide brief descriptions of the two algorithms here. Interested readers are referred to the original papers. In the first algorithm by Akutsu et al. [10], linear programming (LP) is used to solve the set of differential equations derived from a S-system [12] model of the gene network. In the second algorithm by Noman and Iba [13], trigonometric differential evolution (TDE) is used to derive the set of S-system parameters that best explain the observed microarray data. Table 1. Validation results obtained by 10-fold LOOCV using Yeast sporulation dataset Validation Metric Stability Factor, SF Accuracy Factor, AF

LP based approach [10] 0.90 0.82

TDE based approach [13] 0.91 0.86

Phenomic approach 0.94 0.89

The results of the validation tests are given in Table 1. As seen, the algorithm performs well in terms of stability, as well as accuracy. Hence this algorithm could be a viable alternative method for determination of gene networks.

5 Conclusion We have presented the reconstruction of gene networks using the basic phenomic algorithm and also validated it for stability and accuracy. The phenomic nature of the algorithm is manifested in its focus on the phenotypic, rather than genetic, information of an individual. Due to the implicit survival-of-the-fittest mechanisms the need for an explicit objective function was avoided. Currently we are working on applying these algorithms to other datasets in order to study their effectiveness as optimization tools.

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References 1. Schulze, A., Downward, J.: Navigating gene expression using microarrays - a technology review. Nature Cell Biology 3, E190–E195 (2001) 2. Soinov, L.A., Krestyaninova, M.A., Brazma, A.: Towards reconstruction of gene networks from expression data by supervised learning. Genome Biology 4(1), R6 (2003) 3. D’haeseleer, P., Liang, S., Somogyi, R.: Gene expression analysis and genetic network modelling: Tutorial. In: Pacific Symposium on Biocomputing, PSB 1999 (1999) 4. D’Souza, R.G.L., Chandra Sekaran, K., Kandasamy, A.: A phenomic algorithm for reconstruction of gene networks. In: CICI 2007. Venice, WASET, pp. 53–58 (2007) 5. Spieth, C., Streichert, F., Speer, N., Zell, A.: Optimizing topology and parameters of gene regulatory network models from time series experiments. In: Kurumatani, K., Chen, S.-H., Ohuchi, A. (eds.) IJCAI-WS 2003 and MAMUS 2003. LNCS (LNAI), vol. 3012, pp. 461– 470. Springer, Heidelberg (2004) 6. Chu, S., DeRisi, J., Eisen, M., et al.: The transcriptional program of sporulation in budding yeast. Science 282, 699–705 (1998) 7. Somogyi, R., Fuhrman, S., Askenazi, M., Wuensche, A.: The gene expression matrix: towards the extraction of genetic network architectures. In: Proc. of Second World Cong. of Nonlinear Analysts (WCNA 1996), vol. 30(3), pp. 1815–1824 (1997) 8. Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: PSB 1998, vol. 3, pp. 18–29 (1998) 9. Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pacific Symp. on Biocomputing, vol. 4, pp. 17–28 (1999) 10. Akutsu, T., Miyano, S., Kuhara, S.: Algorithms for inferring qualitative models of biological networks. In: Pacific Symp. on Biocomputing (2000) 11. D’haeseleer, P., Liang, S., Somogyi, R.: Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16(8), 707–726 (2000) 12. Savageau, M.A.: Power-law formalism: a canonical nonlinear approach to modelling and analysis. In: Proc. of the World Congress of Nonlinear Analysts 1992, pp. 3323–3334 (1995) 13. Noman, N., Iba, H.: Reverse engineering genetic networks using evolutionary computation. Genome Informatics 16(2), 205–214 (2005) 14. Lubovac, Z., Olsson, B.: Towards reverse engineering of genetic regulatory networks, Technical Report No. HS-IDA-TR-03-003, University of Skovde, Sweden (2003) 15. Kampis, G.: A Causal Model of Evolution. In: Proc. of 4th Asia-Pacific Conf. on Simulated Evolution and Learning (SEAL 2002), pp. 836–840 (2002) 16. Dawkins, R.: The blind watchmaker. Penguin Books (1988) 17. Stekel, D.: Microarray bioinformatics. Cambridge University Press, Cambridge (2003) 18. Kupiec, M., Ayers, B., Esposito, R.E., Mitchell, A.P.: The molecular and cellular biology of the yeast Saccharomyces. Cold Spring Harbour, 889–1036 (1997)

A Novel Lattice Based Research Frame Work for Identifying Web User’s Behavior with Web Usage Mining V.V.R. Maheswara Rao1 and V. Valli Kumari2 1 Department of Computer Applications, Shri Vishnu Engineering College for Women, Bhimavaram, AP, India and Research scholar from Computer Science Dept., Acharya Nagarjuna University [email protected] 2 V. Valli Kumari, Professor, Dept. of Computer Science & Systems Engineering, College of Engineering, Andhra University, Visakhapatnam, AP, India [email protected]

Abstract. Web mining is one of the mining technologies, which applies data mining techniques in large amount of web log data. Web navigational mining discovers users’ access patterns from web logs. This information can be used to identify the behavior of the web user. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behavior may be changed when the new web data is inserted into and the old web data is deleted from web logs. Therefore, the user behavior must be rediscovered from the updated web logs. However, it is very time-consuming to re-find the users’ access patterns. Hence, many researchers pay attention to the incremental mining, which utilizes the previous mining results and finds new patterns just from the inserted or deleted part of the web logs such that the mining time can be reduced. The present paper proposes an efficient incremental web navigational mining algorithm for discovering web navigational patterns when the user sequences are inserted into and deleted from original database. It avoids refinding the original web navigational patterns and re-counting the original candidate sequences. It uses lattice structure to keep the previous mining results such that just new candidate sequences need to be computed. Hence, the web navigational patterns can be obtained rapidly when the navigational sequence database is updated. Besides, maximal web navigational patterns can also be obtained easily by traversing the lattice structure. The experimental results show that the present algorithm is more efficient than the other approaches. Keywords: Incremental Mining, web navigational patterns, lattice storage structure.

1 Introduction With the trend of the information technology, huge amount of data would be easily produced and collected from WWW. It causes the web log data to grow up at amazing speed. Hence, how to obtain the useful information and knowledge efficiently from V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 90–99, 2010. © Springer-Verlag Berlin Heidelberg 2010

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web log data is becoming a typical task for web miners. Mining web navigational patterns [5, 6, 11, and 12] is to discover most of users’ access patterns from web logs. These patterns are able to lead to better customer classification, behavior analysis. In the following, to describe the definitions about web navigational patterns: Let I = {x1, x2, …, xn} be a set of all web pages in a website. A navigational sequence S = <w1, w2, …, wm> (wi Є I, 1 ≤ i ≤ m) is a list of web pages which is ordered by navigational time, and each web page can repeatedly appear in a navigational sequence. The length of a navigational sequence S is the total number of web pages in S. A navigational sequence with length l is called an l-navigational sequence. A navigational sequence database D, as shown in Table 1, contains a set of records. Each record includes navigational identifier (NID) and a user sequence. A user sequence is a navigational sequence, which stands for a complete browsing behavior by a user. The support of a navigational sequence α is the ratio of user sequences which contains α to the total number of user sequences in D. It is usually denoted as Support (α). The support count of α is the number of user sequences which contain α. For a navigational sequence <x1, x2, …, xl>, if there is a link from xi to xi+1 (for all i, 1 ≤ i ≤ l-1) in the web site structure, then the navigational sequence is a qualified navigational sequence. A navigational sequence α is a web navigational pattern if α is a qualified navigational sequence and Support (α) ≥ min_sup, in which the min_sup is the user specified minimum support threshold. For instance, in Table 1, if we set min_sup to 80%, then Support () = 4/5 = 80% ≥ min_sup = 80%, and there is a link from “A” to “B” in the web site structure shown in Fig. 1. Hence, is a web navigational pattern. If the length of a web navigational pattern is l, then it can be called an l-web navigational pattern. Table 1. Navigational sequence database

Fig. 1. Web site structure

However, the user sequences will grow rapidly and some of the user sequences may be antiquated. The web navigational patterns may be changed when the new user sequences are inserted into and the old user sequences are deleted from navigational sequence database. Therefore, one must re-discover the web navigational patterns from the updated database. The present paper proposes an Incremental Algorithm for Web Usage Mining IAWUM, to re-discover all the web navigational patterns when the database is updated. IAWUM utilizes lattice storage structure to store previous mining results and finds new web navigational patterns from the inserted and deleted part of the database such that the mining time can be reduced. The rest of this paper is organized as follows. Section 2 introduces recent researchers’ related work. Section3 describes proposed work. In next section 4, the experimental analysis of proposed work is shown. Finally in section 5 the conclusions are mentioned.

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2 Related Work Many of researchers in the literature [1, 3, 5, 6, 9] focused on techniques like FS-Full Scan algorithm, SS-Selective Scan algorithm and MAFTP-Maintenance of Frequent Traversal Patterns) algorithm and so on. But, these algorithms have limitations like simple path patterns, considering only forward references in navigational sequences. Hence the above algorithms proved to be unfit in the web environment. The present paper introduces, a web navigational algorithm considers not only the forward references but also backward references. Hence this algorithm is very much suitable in the web environment.

3 Research Frame Work To identify the web user behavior, it is essential to study the relationship among the pages browsed by the same user. In order to study the relationship among the pages one has to mine the web usage or navigational patterns incrementally. To perform incremental mining on web navigational patterns, one can use the previous mining results to discover new patterns such that the mining time can be reduced. Therefore, it is very important to choose a well storage structure to store previous mining results. In this research Frame work, lattice storage structure is selected to store the previous mining results. A novel Incremental Algorithm for Web Usage Mining – IAWUM is also proposed, which is effectively mines the web navigational patterns from the first level to last level of the lattice structure. 3.1 Concept of Lattice Storage Structure The lattice structure is a well storage structure to store previous mining results. It can quickly find the relationships between patterns. Fig. 2 shows the fundamental lattice structure for the database described in Table 1, when min_sup is set to 50%. In the fundamental lattice structure, only web navigational patterns are stored. To incrementally mine the web navigational patterns and speed up the mining processes, one can extend the fundamental lattice structure to store more information. The extended lattice structure is shown in Fig. 3. In Fig. 3, each node contains a navigational sequence whose support count is not less than 1. One can append the support information into the upper part of each node. This information helps to calculate and accumulate the support when the incremental mining is proceeding.

Fig. 2. Fundamental lattice structure

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Fig. 3. Extended lattice structure

Moreover, one can also append the NID information, in which the navigational sequence occurs, into the lower part of each node. This information helps to reduce the unnecessary database scans. The lattice structure is saved in hard disk level-bylevel. The total size of the lattice structure, including NID information is about 5 times larger than the original database in average. Because the present method is to discover the patterns level-by-level, it will not cause the memory be broken when just load one level of lattice structure into memory. The lattice structure can quickly find the relationships between patterns. For example, if one wants to search for the patterns related to web page “P”, one can just navigate the lattice structure from the node “P”. Moreover, if one wants to find the maximal web navigational patterns which are not sub-sequences of the other web navigational patterns, if any one just needs to navigate the lattice structure once and output the patterns in top nodes, whose supports are greater than or equal to min_sup. For example, in Fig. 3, the web navigational patterns , , and are the maximal navigational patterns. One can utilize the web site structure which is shown in Fig. 1 to mine the web navigational patterns from the navigational sequence database shown in Table 1. The final results are shown in Fig. 3 when the min_sup set to 50%. The reason for using the web site structure is that one wants to avoid the unqualified web navigational sequences to be generated in the mining process. For example, assume that a web site has 300 web pages and all of them are all 1-navigational patterns. If one does not refer to the web site structure, then 299*300=89,700 candidate 2-sequences can be generated. However, in most situations, most of them are unqualified. Assume that the average out-degree for a node is 10. If one refers to the web site structure, then just 300*10=3,000 candidate 2-sequences are generated. The candidate generation method is like the join method proposed in [3]. For example, candidate sequence can be generated by joining the two web navigational patterns and . For a candidate l-navigational sequence α, if a qualified length (l-1) sub-sequence of α is not a web navigational pattern, then α must not be web traversal pattern and α can be pruned. Hence, all of the qualified web navigational sub-sequences with length l-1 are to be checked to reduce some unnecessary combinations. In this example, one needs to check if and are the web navigational patterns. If one of them is not a web navigational pattern, is also not a web navigational pattern. One does not need to check , because is an unqualified web navigational sequence.

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3.2 Design and Development of IAWUM To mine the web navigational patterns incrementally, one can uses the previous mining results to discover new patterns such that the mining time can be reduced. The IAWUM algorithm mines the web navigational patterns from the first level to the last level of the lattice structure. For each level k (k ≥ 1), the k-web navigational patterns are generated. There are three main steps in each level k: In the first step, the deleted user sequences’ NIDs are deleted from each node of the kth level and the support count of the node is decreased if the node contains the NID of the deleted user sequence. The second step deals with the inserted user sequences. For each inserted user sequence u, one decomposes u into several navigational sequences with length k, that is, all the length k sub-sequences of the user sequence u are generated. According to the web site structure, the unqualified navigational sequences can be pruned. For each qualified k-navigational sequence s, if s has been contained in a node of the lattice structure, then just increase the support count of this node and add NID of user sequence u to the node. Otherwise, if all the qualified length (k-1) sub-sequences of s are web navigational patterns, then new node ns which contains navigational sequence s and the NID of user sequence u is generated in the kth level. The links between the nodes which contain the qualified length (k-1) sub-sequences of s in the (k-1)th level and the new node ns are created in the lattice structure. After processing inserted and deleted user sequences, all the k-web navigational patterns can be generated. If the support count of a node is equal to 0, then the node and all the links related to the node can be deleted from the lattice structure. If the support of a node is less than min_sup, then all the links between the node and the nodes N in the (k+1)th level are deleted, and the nodes in N are marked. Hence, in the kth level, if a node has been marked, then this node and the links between this node and the nodes in the (k+1)th level are also deleted. In the last step, the candidate (k+1)-navigational sequences will be generated. The new web navigational patterns in level k can be joined by themselves to generate new candidate (k+1)-navigational sequences. Besides, the original web navigational patterns in level k are also joined with the new web navigational patterns to generate new candidate (k+1)-navigational sequences. The original k-web navigational patterns need not be joined each other, because they are joined before. After generating the new candidate (k+1)-navigational sequences, the original database needs to be scanned to obtain the original support count and the NID information for each new candidate (k+1)-navigational sequence c. The new node nc which contains c is created and inserted into the lattice structure. The links between the nodes which contain the qualified length k sub-sequences of c in the kth level and the new node nc are created in the lattice structure. If there are no web navigational patterns generated, then the mining process terminates. Candidate generation algorithm & IAWUM algorithm are given below Algorithm : CandidateGen(NBPk, OBPk) Step1: for each x Є NBPk For each y Є NBPk If (x and y can be joined)

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Generate new candidate k+1 browsing sequence and store in set C end for Step 2: end for Step 3: for each z Є OBPk If (x and z can be joined) Generate new candidate k+1 browsing sequence and store in set C end for Step 4: for each c in C Count support and record user sequences TID’s which contains c from D Create a new node nc which contains c For each node ns in the kth level which contains qualified k sub sequence of c Create a link between ns and nc end for Step 5: end for Incremental Algorithm for Web Usage Mining – IAWUM Step 1: Let p be the user browsing pattern. Step 2: Initialize the level k=1 Step 3: while (k ≤ m) Step 4: for each p Є D Decompose p into qualified pattern sequences s with length k Step 5: end for Step 6: for each s Є p If(s is contained in a node n of the level k) Add p’s TID and CN++ in the node n. Else Create new node ns in the level k. Add p’s TID and CN++ in the node n. Step 7: End for Step 8: for each browsing sequence bs in level k If (CN of bs >= min_sup) BPk = BPK U {bs} /* BPK is the set of all browsing patterns */ NBPk = BPk – OBPk /* NBPK is the set of all new patterns*/ /*OBPK is the set of all original web browsing patterns*/

Step 9: Step 10: Step 11:

end for call candidateGen (NBPk, OBPk) for each node nm in level k If( CN of node nm < min_sup) The node n, all the links related to the node can be deleted and decrease CN from n.

Step 12: end for Step 13: k++ Step 14: end while

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3.3 Example of IAWUM For example, in Table 1, To insert one user sequence (7, PQRTP) and delete two user sequences (1, PQRTS) and (2, PQRS) as shown in Table 2. The min_sup also sets to 50%. At the first level of the lattice structure in Fig. 3, NID 1 and NID 2 are deleted and the support count is decreased from each node which contains NID 1 or NID 2 for level 1. Then, the inserted user sequence NID 7 is decomposed into length 1 navigational sequences. The NID 7 is added and support count is increased to each node which contains one of the decomposed 1-navigational sequences. Because there are no new web navigational patterns generated in level 1, level 2 is processed in the lattice structure. The updated lattice structure is shown in Fig. 4 when the level 1 is processed. Because there is no new 1-web navigational pattern generated and no node deleted, the number of the nodes and the links between the first level and the second level are not changed. According to the deleted sequences’ NIDs, the NID 1 and NID 2 are deleted and the support count is decreased in each node of level 2. Then, the inserted user sequences NID 7 are decomposed into length 2 navigational sequences. The NID 7 is added and the support count is increased to each node which contains one of the decomposed 2navigational sequences. Finally, it can find the navigational sequence terns out to be 2-web navigational pattern and the original web navigational patterns , , and are not web navigational patterns after updating the database and , , , and <SPS> are marked. Fig. 5 shows the lattice structure after processing the inserted and deleted navigational sequences in level 2. The sequence with double line is the new web navigational pattern. After generating the new 2-web navigational patterns, the two new candidate navigational sequences and are generated. Similarly, the last level is processed and the lattice structure about level 3 is updated. Fig. 6 shows the final result in the present example. Table 2. Navigational sequence database after inserting and deleting user sequences from Table 1

Fig. 4. Updated lattice structure after processing level 1

Fig. 5. Updated lattice structure after processing level 2

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Fig. 6. Updated lattice structure after processing level 3

4 Experimental Analysis Because there is no incremental mining algorithm on finding web navigational patterns currently, the algorithm MFTP [12] has been used which is also used to find the web navigational patterns to compare with the present algorithm IAWUM. The number of web pages is 300. Four synthetic data sets are generated in which the numbers of user sequences are set to 30K, 50K, 70K and 100K, respectively. The four original data sets are increased by inserting 2K, 4K, 6K, 8K, 10K, 12K, 14K, 16K, 18K and 20K user sequences. In the first and second experiments, the min_sup is set to 5%. Fig. 7a and 7b shows the relative execution times for MFTP and IAWUM on the four synthetic data sets. The graphs clearly show IAWUM out performs than MFTP algorithm, since IAWUM uses the lattice structure and web site structure to prune a lot of candidate sequences. The performance gap increases when the size of original database increases. The performance gap decreases as the number of deleted user sequences increases. Relative execution times for MFTP and IAWUM (min_sup = 5%)

Fig. 7a. Increased size

Fig. 7b. Decreased size

In the third and fourth experiments, a synthetic data set is used in which the numbers of user sequences is 100K, and the min_sup is set to 10%, 8%, 5%, 3% and 1%, respectively. Fig. 8a and Fig. 8b shows the relative execution times for MFTP and IAWUM, in which one can observe that IAWUM performs well than MFTP algorithm significantly. The lower the min_sup, the more the candidate sequences generated for MFTP algorithm. MFTP needs to spend a lot of time to count a large number of candidate sequences. For IAWUM, just few new candidate sequences generated, especially, when the number of inserted user sequences is small. Hence, if the number of inserted user sequences becomes smaller, IAWUM algorithm would find new web navigational patterns from the inserted user sequences. The performance gap increases as the number of deleted user sequences decreases.

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Relative execution times for MFTP and IAWUM (Dataset = 100K)

Fig. 8a. Increased size

Fig. 8b. Decreased size

5 Conclusion and Future Work The present paper proposes an Incremental Mining algorithm for discovering web navigational patterns when the user sequences are inserted into and deleted from Web Log. In order to avoid re-finding the original web navigational patterns and recounting the original candidate sequences, IAWUM uses lattice structure to keep the previous mining results such that, just new candidate sequences need to be computed. Hence, the web navigational patterns can be obtained rapidly when the navigational sequence database is updated. Besides, the web navigational patterns related to certain pages or maximal web navigational patterns can also be obtained easily by navigating the lattice structure. Besides, the number of web pages and the user sequences will grow up all the time. The lattice structure may become too large to fit into memory. Hence, investigation may be continued on how to reduce the storage space and partition the lattice structure such that all the information can fit into memory for each partition.

References 1. Chen, M.S., Huang, X.M., Lin, I.Y.: Capturing User Access Patterns in the Web for Data Mining. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, pp. 345–348 (1999) 2. Cooley, R., Mobasher, B., Srivastava, J.: Web Mining: Information and Pattern Discovery on the World Wide Web. In: Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (1997) 3. Chen, M.S., Park, J.S., Yu, P.S.: Efficient Data Mining for Path Traversal Patterns in a Web Environment. IEEE Transaction on Knowledge and Data Engineering 10(2), 209–221 (1998) 4. Cheng, H., Yan, X., Han, J.: IncSpan: Incremental Mining of Sequential Patterns in Large Database. In: Proceedings of 2004 International Conference on Knowledge Discovery and Data Mining (KDD’04), Seattle, WA (August 2004) 5. Lee, Y.-S., Yen, S.-J., Tu, G.-H., Hsieh, M.-C.: Web Usage Mining: Integrating Path Traversal Patterns and Association Rules. In: Proceedings of International Conference on Informatics, Cybernetics, and Systems (ICICS 2003), pp. 1464–1469 (2003)

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6. Lee, Y.-S., Yen, S.-J., Tu, G.-H., Hsieh, M.-C.: Mining Traveling and Purchasing Behaviors of Customers in Electronic Commerce Environment. In: Proceedings of IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2004), pp. 227–230 (2004) 7. Brin, S., Motwani, R., Ullman Jeffrey, D., Shalom, T.: Dynamic itemset counting and implication rules for market basket data. In: SIGMOD 1997 (1997) 8. Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: SIGMOD 2000, pp. 1–12 (2000) 9. Pei, J., Han, J., Nishio, S., Tang, S., Yang, D.: H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. In: Proc. 2001 Int. Conf. on Data Mining (2001) 10. Brown, C.M., Danzig, B.B., Hardy, D., Manber, U., Schwartz, M.F.: The harvest information discovery and access system. In: Proc. 2nd International World Wide Web Conference (1994) 11. Frakes, W.B., Baeza-Yates, R.: Infomation Retrieval Data Structures and Algorithms. Prentice Hall, Englewood Cliffs (1992)

Design and Analysis of Specification Based Ids for Wireless Networks Using Soft Computing Vydeki Dharmar1 and K. Jayanthy2 1 Dept of ECE ME-Communication Systems Easwari Engineering College, Chennai-89 Tel.: 919600005340, 919962444894 [email protected], [email protected] 2

Abstract. A Mobile ad-hoc network (MANET) is an autonomous system of routers (and associated hosts) connected by wireless links. The Ad hoc OnDemand Distance Vector (AODV) routing protocol meant for MANETs is an improvement over Destination-Sequenced Distance Vector (DSDV) because it typically minimizes the number of required broadcasts by creating routes on demand basis, as opposed to maintaining a complete list of routes as in the DSDV algorithm. AODV is vulnerable to both external and internal security attacks. In Specification Based Intrusion Detection, the correct behavior of critical objects are manually abstracted and crafted as security specifications, which are compared with the actual behavior of the objects. We propose a technique to analyze the vulnerabilities of AODV protocol, specifically to monitor the network layer attacks such as Black Hole attack and to develop a Specification Based Intrusion Detection System (IDS) using soft computing technique. The proposed system is based on fuzzy logic which analyzes the performance of the wireless nodes in a MANET and provides relevant information about the various attacks. The Fuzzy logic control (FLC) system specifies a set of Fuzzy rules based on the essential features of the AODV routing protocol such as RREQ forwarding rate, Packet forwarding rate and so on. The performance of the MANET is analyzed based on the FLC system results. Keywords: Ad-hoc On Demand Vector (AODV), Specification Based Intrusion Detection, Black Hole Attack, Fuzzy Logic Control.

1 Introduction A mobile ad hoc network (MANET) is a collection of mobile computers or devices that cooperatively communicate with each other without any pre-established infrastructures such as a centralized access point. Computing nodes (usually wireless) in an ad hoc network act as routers to deliver messages between nodes that are not within their wireless communication range. Because of this unique capability, mobile ad hoc networks are envisioned in many critical applications (e.g., in battlefields). Therefore, these critical ad hoc networks should be sufficiently protected to achieve confidentiality, integrity, and availability. The dynamic and cooperative nature of V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 100–107, 2010. © Springer-Verlag Berlin Heidelberg 2010

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MANETs presents substantial challenges in securing these networks. Unlike wired networks which have a higher level of security for gateways and routers, ad hoc networks have the characteristics such as dynamically changing topology, weak physical protection of nodes, the absence of centralized administration, and highly dependence on inherent node cooperation. As the topology keeping changing, these networks do not have a well-defined boundary, and thus, network-based access control mechanisms such as firewalls are not directly applicable. In addition, there is no centralized administration, making bootstrapping of crypto systems very difficult. It is extremely easy for a malicious node to bring down the whole network. As a result, ad hoc networks are vulnerable to various attacks including eavesdropping, spoofing, modification of packets and distributed denial-of-service (DDoS) attacks. Security services, such as authentication services and access controls, can enhance the security of ad hoc networks. Nevertheless, these preventive mechanisms alone cannot deter all possible attacks (e.g., insider attackers possessing the key). Therefore, it is necessary to have other security mechanisms to deal with misbehaving insider nodes that possess the valid key and access rights. Intrusion detection, which has been successfully used in wired networks to identify attacks, can provide a second line of defense. In particular, intrusion detection and response capability is very important as many of the real ad hoc networks will be deployed in hostile environments in which legitimate nodes could be captured and used by adversaries. Intrusion detection involves the runtime gathering of data from system operation, and the subsequent analysis of the data; the data can be audit logs generated by an operating system or packets “sniffed” from a network. Intrusion detection techniques can be mapped into three concepts: signature-based detection, anomaly detection, and specification-based detection. In signature-based intrusion detection, the data is matched against known attack characteristics, thus limiting the technique largely to known attacks, even excluding variants of known attacks. In anomaly detection, profiles of normal behavior of systems, usually established through automated training, are compared with the actual activity of the system to flag any significant deviation. Training phase in anomaly-based intrusion detection determines characteristics of normal activity; in operation, unknown activity, which is usually statistically significantly different from what was determined to be normal, is flagged as suspicious. Anomaly detection can detect unknown attacks, but often at the price of a high false alarm rate. In specification-based detection, the correct behaviors of critical objects are manually abstracted and crafted as security specifications, which are compared with the actual behavior of the objects. Intrusions, which usually cause object to behavior in an incorrect manner, can be detected without exact knowledge about them. So far, specification-based detection has been applied to privileged programs, applications, and several network protocols. This paper describes the on-going research on intrusion detection for mobile ad hoc networks. In particular, specification-based techniques were employed to monitor the ad hoc on-demand distance vector (AODV) routing protocol, a widely adopted ad hoc routing protocol. AODV is a reactive and stateless routing protocol that establishes routes only as desired by the source node. AODV is vulnerable to various kinds of attacks. This paper analyzes some of the vulnerabilities, specifically discussing attacks against AODV that manipulate the routing messages. A solution is proposed

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based on the specification-based intrusion detection technique to detect attacks on AODV.

2 Vulnerabilities in Aodv AODV is vulnerable to many different types of attacks. In this section, specific vulnerabilities in AODV that allow subversion of routes were examined. In addition, several attack scenarios that exploit the vulnerabilities were provided to motivate the research. 2.1 Overview of Aodv The Ad hoc On-demand Distance Vector (AODV) routing protocol is a reactive and stateless protocol that establishes routes only as desired by a source node using route request (RREQ) and route reply (RREP) messages. When a node wants to find a route to a

destination node, it broadcasts a Route Request (RREQ) message with a unique RREQ ID (RID) to all its neighbors. When a node receives a RREQ message, it updates the sequence number of source node and sets up reverse routes to the source node in the routing tables. If the node is the destination or the node has a route to the destination that meet the freshness requirements1, it unicasts a route reply (RREP) back to the source node. The source node or the intermediate nodes that receives RREP will update its forward route to destination in the routing tables. Otherwise, it continues broadcasting the RREQ. If a node receives a RREQ message that has already processed, it discards the RREQ and does not forward it. In AODV, sequence number (SN) plays a role to indicate the freshness of the routing information and guarantee loop-free routes. Sequence number is increased under only two conditions: when the source node initiates RREQ and when the destination node replies with RREP. Sequence number can be updated only by the source or destination. Hop count (HC) is used to determine the shortest path and it is increased by 1 if RREQ or RREP is forwarded each hop. When a link is broken, route error packets (RERR) are propagated to the source node along the reverse route and all intermediate nodes will erase the entry in their routing tables. AODV maintains the connectivity of neighbor nodes by sending hello message periodically.

Fig. 1. AODV- Route Discovery Process

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Fig. 1 illustrates the flow of the RREQ and RREP messages in a scenario wherein a node A wants to find a route to a node D. (Initially, nodes A, B, C and D do not have routes to each other). Node A broadcasts a RREQ message (a1), which reaches node B. Node B then rebroadcast the request (b1). Node C receives the messages and broadcasts the message (c1), which arrives at the destination node D. Finally, node D unicasts back the RREP message to node A. When node A receives the RREP, a route is established. In case where node A receives multiple RREP messages, it will select a RREP message with the largest destination sequence number value. 2.2 Vulnerable Fields in Aodv Control Messages In general, AODV is efficient and scalable in terms of network performance, but it allows attackers to easily advertise falsified route information to redirect routes and to launch various kinds of attacks. In each AODV routing packet, some critical fields such as hop count, sequence numbers of source and destination, IP headers as well as IP addresses of AODV source and destination, and RREQ ID, are essential to the correct protocol execution. Any misuse of these fields can cause AODV to malfunction. Table denotes several vulnerable fields in AODV routing messages and the possible effects when they are tampered. Vulnerable Fields in AODV Packets

3 Black Hole Attack in Aodv The route discovery process as described earlier is susceptible to a black hole attack. The attacker advertises the hop count to be of the least value and forges its destination sequence number, thus pretending to have the short and fresh enough route information to the destination. More precisely, upon receiving the broadcasted RREQ message, the attacker creates a RREP message with a spoofed destination sequence

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number; a relatively high destination sequence number in order to be favored against others. Once the source node receives the reply from the attacker, it routes the data traffic through the attacker. Upon receiving the data packets, the attacker normally drops them and creates a ‘black hole’, as the attack name implies. Alternatively, this attack can be used as the first step in the man-in-middle attack, where the malicious node may monitor, delay, delete or manipulate the data packets. The flow diagram shown in Fig.3 describes the algorithm that is used to find out whether the particular node is black hole or genuine node.

4 Soft Computing Approach 4.1 Overview Fuzzy logic is a set of concepts and approaches designed to handle vagueness and imprecision. A set of rules can be created to describe a relationship between the input variables and the output variables, which may indicate whether an intrusion has occurred. Fuzzy logic uses membership functions to evaluate the degree of truthfulness. Fuzzy Logic (FL) is a multi-valued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Fuzzy system is an alternative to traditional notions of set membership and logic.

Fig. 2. Fuzzy Logic Process

4.2 Proposed Specification Based IDS To identify a black hole in a large wireless network, we propose a Fuzzy Logic Control (FLC) algorithm which uses the following three parameters: i) ii) iii)

Number of Packets Dropped (PD) Packet Delivery Ratio (PDR) and RREQ Forwarding Rate (RFR)

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Start

Get inputs PD, PDR, RFR

If PD<=std

It is a genuine node

If (PD>std) It is a black hole

It is not a black hole

Stop

Fig. 3. Flow Diagram of Proposed Technique

4.3 Analysis of Proposed Technique The performance of the proposed Specification Based IDS was analyzed by simulating the required scenario in ns2 and Fuzzy Logic Control algorithm in MATLAB. The proposed technique is as follows: Using ns2, wireless networks with 10, 15 and 20 nodes were simulated. These networks use AODV as their routing protocol. Black hole nodes were introduced in the above networks by making appropriate changes in the AODV protocol of ns2. The performance of the networks in terms of the different parameters such as number of packets being received forwarded and dropped by each node were stored in an MS-Excel workbook. From the tabulated data, the parameters PD, PDR, RFR for a node are computed. Fuzzy membership functions were specified in Matlab for each of the three parameters as shown in Fig.4. Different Fuzzy rules combining the different parameters to detect the black hole node were specified. By applying FLC, the node was identified as whether a black hole or a genuine node. The performance of the algorithm was plotted for networks with and without black hole.

5 Results The samples of various results obtained for the proposed system are shown in Fig.4 and Fig.5.

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Fig. 4. Performance of the 10-node network with and without black hole

Fig. 5. Performance of the 15-node network with and without black hole

From figures 4 and 5 it is evident that the network performance is degraded with the introduction of black hole. Also seen that the algorithm identifies the black hole node with its number.

6 Conclusion and Future Work In this paper, security issues and the vulnerabilities of AODV protocol, has been monitored with respect to the network layer attacks such as Black Hole attack. A Specification Based Intrusion Detection System (IDS) is designed, developed using fuzzy logic and tested with the simulated networks of 10, 15 and 20 nodes. This design will be tested with more nodes and the performance will be compared in terms of speed and accuracy of the algorithm as the number of node increases. We can extend this work to develop a technique to isolate the detected black hole. As an alternate approach the details of the malicious node can be publicized so that all the nodes in the neighborhood may avoid selecting the black hole in the Route Discovery Process.

Acknowledgement The authors greatly thank Dr.RS.BHUVANESWARAN, Asst.Professor, Anna University, Chennai for his constant encouragement and support towards the progress of our work.

References [1] Karlof, C., Wagner, D.: Summary of Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures (April 27, 2005), [email protected] [2] Ramanujan, R., Kudige, S., Nguyen, T., Takkella, S., Adelstein, F.: Intrusion-Resistant Ad Hoc Wireless Networks. In: Proceedings of MILCOM 2002 (October 2002) [3] Karlof, C., Wagner, D.: Secure routing in wireless sensor networks: Attacks and Counter measures. Elsevier’s AdHoc Networks Journal, Special Issue on Sensor Network Applications and Protocols 1(2-3), 293–315 (2003)

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[4] Sun, H., Liu, L.: A linear output structure for fuzzy logic controllers. Fuzzy Sets and System 131(2), 265–270 (2002) [5] Hsiao, F.H., Hwang, J.D.: Stability analysis of fuzzy large-scale systems. IEEE Trans. System, Man, Cybern. B, Cybern. 32(1), 122–126 (2002) [6] Du, W., Deng, J., Han, Y.S., Varshney, P.K.: A Pairwise Key Predistribution Scheme for Wireless Sensor Networks. In: 10th ACM Conference on Computer and Communications Security (CCS), Washington DC, October 27-31 (2003)

A New Image Content-Based Authenticity Verification Procedure for Wireless Image Authentication Scheme V. Lokanadham Naidu, K. Ramani, D. Ganesh, Sk. Munwar, and P. Basha Department of Information Technology, Sree Vidyanikethan Engineering College, A. Rangampet, Near Tirupati, Chittoor Dist.-517 102, A.P., India [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. In this paper, we proposed a new image content-based authenticity verification procedure for wireless image authentication scheme. The existing image content-based authentication procedure for wireless image authentication scheme has the following shortcomings: 1) High computational cost and 2) Low performance. To overcome above drawbacks, a new image content authenticity verification procedure has been proposed. The proposed method enhances the authentication results with low computational cost and high performance by comparing with existing content-based authentication procedure. The proposed scheme implemented the existing methods like secret wavelet filter parameterization, wireless image authentication and structural digital signature. Keywords: Image Content-Based Authenticity Verification, Wireless Image Authentication, Secret Wavelet Filter Parameterization, Structural Digital Signature (SDS).

1 Introduction The introduction of wireless communication systems and digital multimedia technologies has created a large number of multimedia communication applications. Most of those applications are deployed in an insecure distributed network environment that makes multimedia content (i.e., images, audio, video, etc.,) vulnerable to privacy and malicious attacks. In insecure environment there is a chance for an adversary to tamper or modify multimedia content during transmission. To conform multimedia content integrity and to prevent forgery, different content-based authentication techniques have come out. These techniques are required to be robust against transmission errors, normal image processing and they are able to detect malicious tampering on the multimedia data [1]. Such authentication techniques will be very useful for applications like medical, e-commerce and defense. The multimedia content authentication methods are broadly categorized into two types. They are 1) Watermark based content authentication and 2) Digital signature based content authentication. Watermark based methods embeds a text or image into an image data and the hidden data will be extracted for verification of authenticity of V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 108–115, 2010. © Springer-Verlag Berlin Heidelberg 2010

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the image content [2] [3]. Watermark based approaches only useful to protect the integrity of the image. A digital signature or also called as crypto-hash is a set of extracted multimedia content features (like textures, edges, etc.,) which stores the important content in compact representation [1] [4]. It can be stored separately and used later for content authentication. Signature-based authentication methods are useful for both to protect the integrity of the image and repudiation prevention of the sender. 1.1 Content-Based Image Authentication The main concept behind image authentication is to extract the image features or contents and use them during authentication process. During transmission the information carried by the image is retained even when the image has undergone reasonable levels of filtering, distortion or noise corruption. Based on these modifications and due to transmission errors, bit-by-bit verification is not a suitable method to authenticate an image [5]. Content-based image authentication is an efficient approach, which uses the verification process and makes an image as authentic when there is no change in the content. It is desired that the verification method for image authentication is in a position to resist content-preserving modifications while being sensitive to content-changing modifications. Table 1. Content Preserving and Content Changing Manipulations Content Preserving Manipulations 1. Scaling 2. Rotation 3. Noise (Gaussian) 4. Compression 5. Transmission errors 6. Cropping 7. Blur 8. Brightness adjustment 9. Median filtering 10.Color conversions

Content Changing Manipulations 1. Modifying image objects 2. Moving of image objects 3. Deleting image objects 4. Adding new objects 5. Changing of the image background location 6. Changing image characteristics like textures, structure, impression, etc.

Normally, image content manipulation methods are classified into two types [6]. They are 1) Content-preserving manipulations and 2) Content-changing manipulations. Content-preserving manipulations only change the pixel values, it results in different levels of visual distortion in image but the content of the image is still preserved which carries the same visual information to the recipient. Content-changing manipulations change the image to a new one which will carry different visual information to the recipient. The different types of content preserving and changing manipulations on images are summarized in the Table 1. Earlier content-based image authentication methods were developed under the ideal assumption of reliable noise-free transport [7] [8]. These methods are insecure and do not work well when used to transmit images over the error-prone insecure wireless channels i.e., there is no robustness [9].

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Recently, S. M. Saad developed a secure digital signature scheme for image authentication over wireless channels [10]. This scheme is very simple and enhances the existing methods in terms of security and robustness. The authenticity verification process used in this method has the following short comings. They are 1) High computational cost and 2) Low performance. The contribution of this paper is to propose a new content authenticity verification procedure to solve above problems. This paper is organized as follows. Section 2. discusses the wireless image authentication scheme and Section 3. describes the proposed authenticity verification procedure. The results and conclusions are given in Sections 4 and 5 respectively.

2 Wireless Image Authentication The proposed image content authenticity verification procedure implements the same wireless image authentication scheme [10]. This scheme is based on the secure digital signature (SDS) using secret wavelet transform. 2.1 Image Signing Procedure The block diagram of image signing procedure and its internal behavior is shown in Fig. 1.

Fig. 1. Image Signing Procedure

The system generates a digital signature by performing a signing process on the image in the following order. 1) Decompose the image using parameterized wavelet filter 2) Extract the SDS and 3) Generate crypto signature (hash). For consideration of robustness this method has not used compression methods because they will cause error propagation [11].

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2.2 Structural Digital Sig gnature The wireless image authen ntication scheme implemented the same Structural Diggital Signature (SDS) procedurre used in [7] with the employment of wavelet fiilter parameterization to increease the security. Implementing secret wavelet fiilter parameterization in image authentication a methods has the following advantages. T They are 1) Improved security 2) Filter coefficients are constructed in such a wayy to improve the robustness ag gainst malicious attacks. For more information regardding wavelet filter parameterizattion refer [7] [10]. In the wavelet domain of an image, jooint (inter scale) parent-child (p p, c) pairs exist. Each parent–child pair maps to a sett of spatial pixels, which is of a non-fixed size and possesses certain contexttual dependencies [12]. This deependency arises from the perceptually important featuures. For example, edges and teextures are shown in Fig. 2. The structural contents are preserved.

(aa)

(b)

Fig. 2. Structural Digital Sign nature a) Illustrates the parent-child pairs of SDS in the wavvelet domain (Two Level) b) Shows the significant pairs are mapped back into spatial domain

The basic concept of the SDS algorithm relies on the fact that the parent-child ppairs with large magnitudes aree not vulnerable to attacks whereas those with smaaller magnitudes tend to be eassily attacked. Therefore one can use the larger pairss to indicate robustness (conten nt-changing manipulations) and use smaller pairs to refflect fragility (content-preserving g manipulations). The construction of SDS is given beloow: Construction of Structura al Digital Signature (SDS) 1. Apply DWT on o image. 2. Select parent--child pairs with their magnitude difference larger than a pre-determined p threshold value δ 3. For each seleccted parent-child pair, perform any one of the folllowing four cases Caase I Caase II Caase III Caase IV

: : : :

p>0 and |p|>|c| p<0 and |p|>|c| c>0 and |p|<|c| c<0 and |p|<|c|

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4. Create SDS array according to step 3 i.e., SDS [i, j] = I or II or III or IV where i, j denotes the child coordinates in wavelet domain. 2.3 Image Authentication Procedure The image authentication procedure is shown in Fig. 3. This process is used to authenticate images by using their associated digital signatures.

Fig. 3. Image Authentication Procedure

The authentication process is in the following order. 1) Extract the SDS of the received image using the same method used in image signing 2) Decrypt the received crypto signature by using the sender’s public key 3) Perform a content authenticity verification procedure using both the decrypted signature and the extracted one at receiver side, to check the authenticity (DU) and un-authenticity (DA) of the image and 4) Consider the image is authentic if 0 ≤ IAU ≤ 2L otherwise the image is unauthentic.

3 Content Authenticity Verification Procedure For content authenticity verification, the following new procedure is proposed. The basic idea of this procedure is to check the authenticity of an image from content preserving attacks. This procedure calculates the image authenticity and unauthenticity then, finally obtains the authentication results. The results are shown in the Table 2. Consider two arrays P and Q. The array P holds the extracted SDS of the received image through noise channel. The array Q holds the decrypted signature (i.e., SDS

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which is extracted and encrypted by the private key of the sender at sender side) associated with that particular image. P[i, j] = SDS (Extracted at receiver side) Q[i, j] = DPU

{ Received Signature }

Received Signature = E PR { SDS (Extracted at sender side } Where E, D, PU and PR denote encryption, decryption, public key (sender) and private key (sender) respectively. Calculate the magnitude difference of individual values of the above two arrays is greater than or equal to the predefined threshold value δ . i.e., IAU = Count ( |P[i, j] – Q[i, j]| ≥ δ )

(1)

The authenticity and un-authenticity of an image is defined as DA

0< IAU ≤ 2L

DU

IAU> 2L

(2)

Where DA and DU states that, the image is authentic and un-authentic respectively. Here the term L is a constant value varies from 1 to 5. Based on the value L, the level of authenticity will be achieved. The experimental results shown in section 4 are conducted by considering the L value 4.

4 Experimental Results All experiments were conducted with a number of classic benchmark images including the traditional Lena, Cameraman, Baboon, Peppers, Fishing Boat, Pentagon, Gold Hill, etc. To test the authenticity of an image using proposed scheme against several acceptable manipulations, experiments are conducted by applying a variety of attacks. For this the digital signature is generated using a secret wavelet filter a0 = 1.5755, a1 = 1.0555. Table 2. Shows the average of the Degree of Authentication (DoA) results of proposed method and existing method [10]. The Degree of Authentication is defined as follows: DoA =

(( D

A

+ DU ) − DA )

( DA +

DU )

(3)

The DoA calculated for many different images by applying several content-preserving attacks like rotation, scaling, adding noise, deletion of lines from the image etc,. In all the cases, DoA without non-malicious attacks is always higher than 89.4%, and all content-preserving attacked images are authentic. The results of the proposed method shown enhanced results compared with existing method [10]. The results shown that, the proposed method is simple, reduces the computational cost as well as it increases the performance. Fig. 4 demonstrates the experimental results (various contentpreserving attacks on Lena image) based on proposed method.

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Re 80.1 95.7 85.9 87.3 85.8 92.2 79.6 95.2 93.3 --

Rp 89.4 96.1 90.9 94.3 91.4 97.3 92.1 95.8 93.5 95.5

where, Re= Average of the DoA (%) based on method [10], Rp= Average of the DoA (%) based on proposed method.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig. 4. Lena image with various attacks: a) Original Image of Lena b) Median Filtering c) Rotation d) Noise (Gaussian) e) Blur f) Cropping g) Brightness Adjustment and h) Deletion of Lines

The proposed scheme is more practical and easy method for content authenticity verification based on structural digital signature for image authentication over wireless channels.

5 Conclusion In this paper, a new image content-based authenticity verification procedure has been proposed for wireless image authentication scheme based on secure digital signature.

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The experimental results proven that, the proposed method enhances the authentication results with low computational cost and increased performance by comparing with the existing content authenticity verification procedure. The proposed method is robust against transmission errors as well as content preserving manipulations. Contentdependent structural image features and wavelet filter parameterization methods are implemented in this proposed method to enhance the system robustness and security. In particular, this method is suitable for wireless image authentication applications and other real-time multimedia applications.

References 1. Lou, D.C., Liu, J.L., Li, C.T.: Digital Signature-Based Image Authentication: Multimedia security: Steganography and digital watermarking techniques for protection of intellectual property. In: Lu, C.S. (ed.), Idea Group Inc. (2003) 2. Rey, C., Dugelay, J.L.: A survey of watermarking algorithms for image authentication. EURASIP J. Applied Signal Processing 6(3), 613–621 (2002) 3. Fridrich, J., Baldoza, A.C., Simard, R.J.: Robust digital watermarking based on key dependent basis functions. In: Aucsmith, D. (ed.) IH 1998. LNCS, vol. 1525, pp. 143–157. Springer, Heidelberg (1998) 4. Swaminathan, A., Mao, Y., Wu, M.: Robust and secure image hashing. IEEE Trans. Inf. Forensics Sec., 215–229 (2006) 5. Schneider, M., Chang, S.F.: A Content based digital signature for image authentication. In: Proc. IEEE Int. Conf. Image Processing (ICIP 1996), pp. 227–230 (1996) 6. Han, S., Chu, C.H., Yang, S.: Content-based Image Authentication: Current Status, Issues and Challenges. In: Proc. IEEE Int. Conf. Semantic Computing (ICSC 2007), pp. 630–636 (2007) 7. Lu, C.S.: On the security of structural information extraction/embedding for image authentication. In: Proc. IEEE ISCAS 2004, pp. 169–172 (2004) 8. Sun, Q., He, D., Ye, S.: Feature selection for semi fragile signature based authentication systems. In: Proc. IEEE Workshop on Image Signal Processing, pp. 99–103 (2003) 9. Ye, S., Lin, X., Sun, Q.: Content-based error detection and concealment for image transmission over wireless channel. In: Proc. IEEE Int. Symp. Circuits and Systems, Thailand (2003) 10. Saad, S.M.: Design of a robust and secure digital signature scheme for image authentication over wireless channels. IET Inf. Secur. 3(1), 1–8 (2009) 11. Kunder, D., Hatzinakos, D.: Digital watermarking using multiresolution wavelet decomposition. In: Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Seattle, Washington (1998) 12. Peter, M., Uhl, M.: Watermark security via wavelet filter parameterization. In: Proc. Int. Conf. ICASSP 2000, USA (2000)

Enhanced Substitution-Diffusion Based Image Cipher Using Improved Chaotic Map I. Shatheesh Sam1, P. Devaraj2, and R.S. Bhuvaneswaran1 1

Ramanujan Computing Centre, College of Engineering, Guindy, Anna University, Chennai, India 2 Department of Mathematics, College of Engineering, Guindy, Anna University, Chennai, India [email protected]

Abstract. This paper proposes an enhanced substitution-diffusion based image cipher using improved chaotic map. The first step consists of permutation which uses the odd key values. Byte substitution is applied in the second step to improve the security against the known/chosen-plaintext attack. Finally, confusion and diffusion are obtained using the sub diagonal diffusion of adjacent pixels and XORing with the chaotic key. The numbers of rounds in the steps are controlled by combination of pseudo random sequence and original image. The security and performance of the proposed image encryption technique have been analyzed thoroughly using statistical analysis, key sensitivity analysis, differential analysis, key space analysis and entropy analysis. Results of the various types of analyzes are showing that the proposed image encryption technique is more secure and fast. Keywords: Permutation, Sub diagonal diffusion, Byte substitution.

1 Introduction Multimedia content security is one of the important issues in the present information age. With the rapid development of multimedia and network technologies, images are being transmitted over networks more and more frequently. Consequently, reliable security in storage and transmission of digital images is needed in many applications, including both public and private services such as medical imaging systems, confidential video conferencing, military image databases, online personal photograph album, satellite information systems etc. The development of various conventional encryption techniques such as RSA, DES, AES,IDEA, etc. [2] are not much reliable for the image encryption due to some intrinsic features of images such as bulk storage capacity, high redundancy, strong correlation among adjacent pixels, etc. In order to provide a better solution to image security problems, a number of image encryption techniques have been suggested in the last two decades. Chaos-based cryptographic algorithm [1] is an efficient encryption algorithm, first proposed in 1989. It has many unique characteristics different from other algorithms such as the sensitivity dependence on initial conditions [3], non V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 116–123, 2010. © Springer-Verlag Berlin Heidelberg 2010

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periodicity, non-convergen nce and control parameters. The one dimensional chhaos system has the advantagess of simplicity and high security [4]. Many studies w were proposed to adapt and im mprove it. Some of the cryptanalysis techniques [6] are suggested to break the scheme and reduce the flaws in the algorithm design. In this paper, an enhanced substitution-diffusion based image cipher ussing improved chaotic map is su uggested to overcome the weakness of [5, 7] security level. The algorithm uses significant features such as sensitivity to initial conditiion, permutation of keys, imprroved chaotic maps, byte substitution and sub diagoonal diffusion. The rest of this paper p is organized as follows. Section 2 introduces logiistic map. In section 3, the imag ge encryption based on improved chaotic map is propoosed including a new algorithm. In section 4, the security of new algorithm is analyzzed. Finally, the conclusions aree discussed in section 5.

2 Logistic Map Logistic map is a simple bu ut broadly researched dynamic system, also called as inssect population model. It is desccribed as follows: Xn+1 = µxn(1 - xn) where µ is system parameteer, 0 < µ ≤ 4 and xn is a floating number in (0,1), n = 0, 1, 2, 3… When µ > 3.57 78964263, this system becomes chaotic in behavior. Its bifurcation diagram is show wn in Figure 1.

Fig. 1. Bifurcation of Logistic Map

Fig. 2. Distribution of Sequence for the Improved Chaotic Map

Among the special featu ures of logistic map, its high sensitivity to initial value and parameter makes it suitablee for image encryption. Though, the logistic map is beetter for image encryption whicch has some common problems such as stable windoows, blank windows, uneven diistribution of sequences and weak key [8]. New typess of improved chaotic logistic maps have been proposed in the paper to alleviate the problems in the logistic map. The maps are mixed together so as to achieve larger key space and to attain chaoticc behavior. We have attempted to improve it by chaootic transformation. The propossed improved chaotic logistic maps are defined and kkeys are generated in the next section. Thus, the proposed improved chaotic logistic m map does not have security issu ues which are present in the logistic map. Moreover, the

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resulting chaotic sequences are uniformly distributed (see the Figure 2) and the key size has been increased greatly.

3 Proposed Scheme The plain image is stored in a two dimensional array of M × N pixels. 3.1 Initial Permutation The chaos based image encryption schemes are mainly consisting of image pixel permutation stage otherwise called as confusion stage and pixel value diffusion stage. Generally, the confusion effect is considered by permutation step. The method is defined by: C[i, j] = P[1 + (23 × i + 3) mod 256, 1 + (17 × j + 3) mod 256] where P[i, j] represents the (i, j)th pixel of the original image and C[i, j] denotes the (i, j)th pixel of the cipher image. This method uses two constant values to improve the pixel scrambling of the image. 3.2 Key Generation The improved chaotic map and the keys have been generated in the following way: for i = 1 to 256 for j = 1 to 256 xi,j+1 = (3.653429 × k1 × (1- xi,j) + yi,j) mod 1 yi,j+1 = (3.999283 × k2 × yi,j × (1/1+(xi,j+1)2)) mod 1 zi,j+1 = (3.669943 × k3 × xi,j+1× yi,j+1×sin(zi,j)) mod 1 Xi,j = ⎣xi,j+1 × 256⎦ Yi,j = ⎣yi,j+1 × 256⎦ Zi,j = ⎣zi,j+1 × 256⎦ end xi+1,1 = xi,j+1 yi+1,1 = yi,j+1 zi+1,1 = zi,j+1 end where ⎪k1⎪ > 27.7, ⎪k2⎪ > 29.7, ⎪k3⎪ > 27.2 respectively. To increase the key size we can use k1, k2, k3 as another set of keys. Along with the key ki the distribution of the sequences becomes better. 3.3 Byte Substitution Each individual pixel byte of the state is replaced with a new byte by using the S-box of the AES (Advanced Encryption Standard) algorithm. It improves the security against the known/chosen plaintext attacks.

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3.4 Sub Diagonal Diffusion The procedure is as follows for i = 1:max out = in(i,max-i+1) end for j = 1:max-1 for i = 1:max-j out = in(i,max-(j-1)-i) end for i = 1:max-j out = in(i+j,(max-i+1)) end end where max is the maximum size of the image, in and out are the input and output of the image. The pixel is modified by XORing the first and second pixels with chaotic key, the third pixel is modified by XORing the modified second and third pixels with key and the process continues till the end of the image. for i = 1 to max step 1 for j = 1 to max - 1 step 1 outi,j+1 = outi,j+1 ⊕ outi,j ⊕ Zi,j end if (i < max) outi+1,1 = outi+1,1 ⊕ outi,j+1 ⊕ Zi,j end end where Zi,j is the chaotic key. Finally, the diffusion is obtained with the help of sub diagonal XORing and XORing with chaotic key. The security of the scheme is improved by iterating 3 rounds. 3.5 Decryption The decryption algorithm is just the reverse of encryption one. In order to get the original image, encrypted image pixel values XORing with the same set of secret key which we used in the encryption process. First, the reverse sub-diagonal diffusion is applied and the inverse S-box is used for byte substitution. 3.5.1 Inverse Permutation The permutation is replaced by inverse permutation. The inverse method is described by: P[i, j] = C[1+ (167 × i - 4) mod 256, 1 + (241 × j - 4) mod 256] The original image can be recovered once the above decryption process is completed.

4 Security and Performance Analysis A good encryption scheme should be robust against all kinds of cryptanalytic, statistical and brute force attacks. Some experimental results are given in this section

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to demonstrate the efficiency of our scheme. All the experiments are performed on a PC with Intel Core 3.0 GHz CPU, 4 GB RAM with Windows Vista Business Edition. The compiling environment is MATLAB 7.4. 4.1 Statistical Analysis In order to resist the statistical attacks, which are quite common now-a-days, the encrypted images should possess certain random properties. A detailed study has been undertaken and the results are summarized. Different images have been tested, and we found that the intensity values are similar. 4.2 Histogram Analysis Histograms may reflect the distribution information of the pixel values of an image. An attacker can analyze the histograms of an encrypted image by using some attacking algorithms to get some useful information of the original image.

Fig. 3. Histogram Analysis of Plain Image and Cipher Image

Thus, the histograms of an encrypted image should be as smooth and evenly distributed as possible, and should be very different from that of the plaintexts. Figure 3 shows a comparison of the histograms between plaintext and encrypted images. 4.3 Correlation Coefficient Analysis We use the 256 grey levels image Lena (256 × 256 pixels) as the original image. Experiment shows that image scrambling effect is inverse to the correlation coefficient function of the adjacent pixels. Correlation coefficient function is used as follows. 1

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where x is the grey value of pixel point; k is the number of pixel point; E(x) is mathematical expectation of x and D(x) is variance of x. 1

,

where x is grey value of the former pixel point; y is grey value of the latter pixel point; cov(x,y) is the covariance of x,y. ,

where rxy is the related coefficients. Carrying out the experimental analysis on the adjacent pixel points of the primitive image, the final result is presented in table 1. Table 1. Correlation coefficients of two adjacent pixels Direction Horizontal Vertical Diagonal

Plain image 0.9386 0.9689 0.9138

Cipher image 0.0015 0.0019 0.0012

4.4 Differential Analysis Deferential attack would become ineffective even if a single pixel change in the plainimage, causes a significant deference in the cipher-image. In order to measure this capability quantitatively, the following measures are usually used: number of pixels change rate (NPCR) and unified average changing intensity (UACI). They are defined as follows: 1, 0,

The NPCR is defined as ∑,

100%

The UACI is defined as 1

100% ,

where Cij and C′ij are the two cipher-images at position (i, j) whose corresponding plain-images have only one-pixel difference and M and N are the number of rows and columns of images. The results of NPCR and UACI are listed in Table 2.

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UACI%

Lena

99.6291

33.4992

Baboon

99.6343

33.4821

House

99.6352

33.4811

Tree

99.6327

33.4812

In order to assess the influence of changing a single pixel in the original image on the encrypted image, the NPCR and the UACR are computed in the proposed scheme. The results show that a small change in the original image will result in a significant difference in the cipherimage. So the scheme proposed has a high capability to resist anti differential attack. 4.5 Keyspace Analysis There are series of improved chaotic-maps parameters, initial values, and ki values that can also be used as keys in our scheme. The key space is in the range between 192 to 380 bits. 4.6 Information Entropy Analysis Information entropy is one of the criteria to measure the strength of the cryptosystem in symmetric cryptosystem. The entropy H(m) of a message m can be calculated as P m log

1 p m

where p(mi) represents the probability of occurrence of symbol mi and log denotes the base 2 logarithm. If there are 256 possible outcomes of the message m with equal probability, it is considered as random. In this case, H (m) = 8 is an ideal value. In the final round of proposed scheme, it is found that the value is 7.9969. This means that information leakage in the encryption process is negligible and the encryption system is secure upon entropy attack.

5 Conclusion In this paper, an enhanced substitution-diffusion based image cipher using improved chaotic map has been proposed. The proposed cipher provides good confusion and diffusion properties that ensures extremely high security. Confusion and diffusion have been achieved using permutation, byte substitution and sub diagonal diffusion. We have carried out statistical analysis, key sensitivity analysis, differential analysis, key space analysis and entropy analysis to demonstrate the security of the new image encryption procedure. Based on the various analyzes, it has been shown that the

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proposed scheme is more secure and fast and may be found suitable for real time image encryption for transmission applications.

Acknowledgement The first author is partly funded by All India Council for Technical Education (AICTE), New Delhi, India.

References 1. Matthew, R.: On the derivation of a chaotic encryption algorithm. Cryptologia 8, 29–42 (1989) 2. Schneier, B.: Applied cryptography: protocols algorithms and source code in C. Wiley, New York (1996) 3. Baptista, M.S.: Cryptography with chaos. Phys. Lett. A 240, 50–54 (1998) 4. Chen, G.R., Mao, Y.B., Charles, K.C.: A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos, Solitons & Fractals 21, 749–761 (2004) 5. Patidar, V., Pareek, N.K., Sud, K.K.: A new substitution diffusion based image cipher using chaotic standard and logistic maps. Commun. Nonlinear Sci. Numer. Simulat. 14, 3056–3075 (2009) 6. Alvarez, G., Shujun, L.: Cryptanalyzing a nonlinear chaotic algorithm (NCA) for image encryption. Commun. Nonlinear Sci. Numer. Simulat. 14, 3743–3749 (2009) 7. Rhouma, R., Solak, E., Belghith, S.: Cryptanalysis of a new substitution-diffusion based image cipher. Commun. Nonlinear Sci. Numer. Simulat. 15, 1887–1892 (2010) 8. Jianquan, X., Chunhua, Y., Qing, X., Lijun, T.: An Encryption Algorithm Based on Transformed Logistic Map. In: IEEE International Conference on Network Security, Wireless Communications and Truested Computing, pp. 111–114 (2009)

Network Forensic Analysis by Correlation of Attacks with Network Attributes Atul Kant Kaushik, Emmanuel S. Pilli, and R.C. Joshi Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, India {akk22pec,emshudec,rcjosfec}@iitr.ernet.in, [email protected]

Abstract. Network forensics involves the capture, recording, and analysis of network events in order to discover the source of security attacks and other problem incidents. We extend our previously proposed model for collecting network data, identifying suspicious packets, examining protocol features misused and correlating attack attributes. This model is capable of handling attacks on the TCP/IP suite. The results obtained by this model are validated. Keywords: Network forensics, pcap, Perl, TCP/IP, correlation, investigation.

1 Introduction Network forensics is a dedicated investigation technology that enables capture, recording and analysis of network packets and events for investigative purposes. It involves monitoring network traffic and determining if there is an anomaly in the traffic and ascertaining whether it indicates an attack. If the attack is found, forensic techniques enable investigators to identify and prosecute the attackers. Google [1] has revealed that the Gmail accounts of Chinese human rights activists were targeted in December 2009. A review [2] of the major attacks in 2009 proves an increase in the security breaches. The large number of intrusions and the increasing sophistication of these cyber attacks is the driving force behind network forensics. We extend our proposed network forensic system [3] for ICMP based network attacks to handle TCP/IP attacks. This model enables forensic experts to analyze the marked suspicious network traffic, thus facilitating cost effective storage and faster analysis of high bandwidth traffic. We identify the significant features which enable security attacks on TCP/IP protocol. Rule sets for various TCP/IP attacks have been designed and are queried on the database to calculate various statistical parameters and thresholds. This information is used for validating the presence of attacks. The paper is organized as follows: Section 2 provides a literature survey of related work and the background on our proposed ‘Network Forensic System’. In section 3, significant parameters for various TCP/IP based network attacks are correlated. Rule sets are designed to identify and generate the statistics for some of the TCP attacks. Section 4 describes the details of the experiments performed and results obtained. Conclusions and future work are presented in section 5.

2 Background Mukkamala and Sung [4] addressed the issue of identification of significant features by ranking the importance of input features. Almulhem and Traore [5] proposed the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 124–128, 2010. © Springer-Verlag Berlin Heidelberg 2010

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architecture of a network forensics system that records data at the host-level and network-level. The main idea was to mark the ‘malicious’ packet using a list of suspicious IP addresses maintained by a group of sensors. It is still an open challenge to identify such a list of IP addresses. Staniford et al. [6] proposed a data reduction approach to infer the event likelihood and only consider the anomalous packets for further analysis. However, the work was only concentrated towards detecting stealthy portscans. Bailey et al. [7] focused on scalable monitoring of darknets and reducing the amount of data for the forensic honeypots by using source-distribution based methods. Maier et al. [8] suggested storing the network traffic up to a cutoff limit of bytes per connection. Our approach, however, focuses on data reduction for forensic analysis of network attacks by correlating the attacks and corresponding identified significant network features. 2.1 Proposed Model for Network Forensics We have proposed a model [3] for network forensic which includes five phases as shown in Figure 1. The phases are – (1) Collection: Collects packets in pcap format using various tools and extracts packet attributes. (2) Identification and Marking – correlates various network attacks and corresponding affected network parameters. (3) Conversion into database – creates the database of packet attributes while considering only the features which are useful for analysis. (4) Analysis – generates various statistical data by analyzing the database on the basis of designed rule sets. (5) Validation and Investigation – validates the network attack events and report the attacker information based on the statistics generated.

Fig. 1. Network forensic system for TCP/IP attacks

3 Network Forensic System for TCP/IP Attacks Our proposed framework [3] is extended to handle attacks on various protocols of the TCP/IP. The correlation between attacks on TCP and the significant parameters manipulated are shown below in Table 1. In the analysis phase the rule sets (SQL queries) for various TCP attacks are added.

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Significant Parameters

SYN Flood

S & A Flag (flag = 18)

SYN Scan

S Flag (flag =2)

XMAS Scan

URG, FIN & PUSH flags (flag = 41)

NULL Scan

flag = 0

Land Attack

S Flag, Source & Destination IP Address

SYN/FIN Attack

S & F Flag (flag = 3)

S/D Port Attack

Source & Destination port

The sample SQL queries for some of the attacks on TCP are shown below. Similarly the SQL queries for all the attacks are written and fired on the database to generate the statistics. The validation phase also comprises of SQL queries which uses the database updated by the analysis phase. Sample SQL queries to generate the statistics and reports SYN FLOOD Attack Analysis INSERT INTO sfcount (src_ip,xmascount) (SELECT dest_ip, COUNT( dest_ip ) AS sfcount FROM tcp WHERE flag =18 AND src_ip = host_ip AND dest_ip != host_ip AND (seqnum+1) NOT IN(SELECT acknum FROM tcp WHERE flag=16) GROUP BY dest_ip) Reporting SELECT src_ip, sfcount FROM synflood WHERE sfcount >= 5 XMAS Scan Analysis INSERT INTO xmas (src_ip,xmascount) (SELECT src_ip, COUNT(src_ip) as xmascount FROM tcp WHERE flag = 41 AND src_ip != host_ip GROUP BY src_ip) Reporting SELECT src_ip, xmascount FROM xmas WHERE xmascount >= 1 SYN/FIN Attack Analysis INSERT INTO sinfin (src_ip,sfcount) (SELECT src_ip, COUNT(src_ip) as sfcount FROM tcp WHERE flag = 3 AND src_ip != host_ip GROUP BY src_ip) Reporting SELECT src_ip, sfcount FROM sinfin WHERE xmascount >= 1

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4 Experiments and Results The implementation of the framework is executed and tested with the dataset ‘isrgdt.pcap’ generated in the Lab. Normal internet activity was carried out by the user and two systems were used to launch attack traffic. The entire traffic was logged using Wireshark [9]. The size of the dataset was 86.2 MB, having Sweep, SYN flood, SYN scan, Xmas scan and NULL scan attacks launched in it, using nmap, hping and free port scanner. Our proposed model is applied for reduction of the dataset. The total number of packets was 614300 and marked packets were 276185 as shown in Figure 2. The amount of reduction achieved is 55.04% which significantly improves the analysis complexity.

Fig. 2. Reduction on dataset ‘isrgdt.pcap’

The marked packets are ported to the mysql database named ‘packet_attributes’. The analysis phase is executed which updates the database and creates the statistics for thresholds. Now attack validation phase is executed which reported results as shown in Table 2. It also reported the date of attack as Wed, 24 Feb 2010 (not Table 2. Result of reporting phase

Name of the Attack SYN Flood SYN Scan XMAS Scan NULL Scan

IP Address 192.168.111.203 192.168.111.5

No of attack packets

Time of Attack

232

17:54:55 GMT

72

17:54:52 GMT

192.168.111.203

75539

17:54:55 GMT

192.168.111.5

199341

17:54:52 GMT

192.168.111.203

29

17:54:56 GMT

192.168.111.5

11

17:54:56 GMT

192.168.111.203

28

17:54:56 GMT

192.168.111.5

116

17:54:56 GMT

Land Attack

No host has launched Land Attack

SYN/FIN Attack

No host has launched SYN/FIN Attack

S/D Port Attack

No host has launched S/D Port Attack

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included in the Table) for each of the launched attacks. The thresholds chosen for the attacks in our experimentation are 5, 5, 1, 1, 1, 1 and 1 respectively. Since the last five attacks are used to launch with the packets which are not generally used for any legitimate work, low thresholds values were chosen. The threshold values are chosen for a low traffic network environment similar to our institute’s network. In order to validate our framework we run the same dataset with the popular IDS, Snort. Snort gave false negative for null scan. The remaining attacks were alerted by Snort in a similar manner to our model.

5 Conclusion and Future Work The major challenge in network forensics is handling the massive size of network packet capture. We address this problem by reducing the packet capture file size by marking the attack packets using the packet header information. Our model shows a significant reduction in the number of packets to be analyzed. The results validate the correctness of the framework and are better in some cases. The information reported will be useful for investigation process. The size of the number of packets marked will increase when more attacks will be added but at least all the legitimate packets will be removed which guarantees the reduction in the number of packets to be investigated. The framework is scalable to the increasing number of attacks on any kind of protocol. We would like to extend the proposed framework by including the investigation module, which will trace the actual attacker even if the IP address discovered by the proposed framework is a spoofed one.

References 1. The official google blog, http://googleblog.blogspot.com/2010/01/ new-approach-to-china.html 2. DDOS attackers continue hitting Twitter, Facebook, Google, http://www.computerworld.com/s/article/9136402/ DDOS_attackers_continue_hitting_Twitter_Facebook_Google 3. Kaushik, A.K., Joshi, R.C.: Network Forensic System for ICMP Attacks. Int’l J. of Comp. App. 2(3), 14–21 (2010) 4. Mukkamala, S., Sung, A.H.: Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligent Techniques. Int’l J. of Dig. Evidence 1(4), 1–17 (2003) 5. Almulhem, A., Traore, I.: Experience with engineering a network forensics system. In: Kim, C. (ed.) ICOIN 2005. LNCS, vol. 3391, pp. 62–71. Springer, Heidelberg (2005) 6. Staniford, S., Hoagland, J.A., McAlerney, J.M.: Practical automated detection of stealthy portscans. J. of Comp. Security 10(1/2), 105–136 (2002) 7. Bailey, M., Cooke, E., Jahanian, F., Provos, N., Rosaen, K., Watson, D.: Data reduction for the scalable automated analysis of distributed darknet traffic. In: 5th USENIX/ACM Internet Measurement Conference, pp. 239–252 (2005) 8. Maier, G., Sommer, R., Dreger, H., Feldmann, A., Paxson, V., Schneider, F.: Enriching network security analysis with time travel. In: ACM SIGCOMM 2008, pp. 183–194 (2008) 9. Wireshark’s Users Guide, http://www.wireshark.org

Robust and Real Time Data Delivery in Wireless Sensor Networks Deepali Virmani and Satbir Jain B5/107 Mayur Apartment, Sector 9, Rohini, Delhi, India [email protected]

Abstract. Providing real-time data delivery in wireless sensor networks is a challenging research problem. In this paper we propose a centralized control plane incorporating the timed token protocol in the MAC layer for providing real-time data delivery in wireless sensor networks. In this approach hard real time guarantee is provided by a dynamic ring structure, where high priority stations have more chance of admittance and stations with low priority can be removed from the ring. Soft real time guarantee is provided by using proactive wireless routing protocol (DSDV) for path finding and maintenance, timely delivery of data is done through a prior bandwidth reservation. Simulation results show that the proposed control plane and bandwidth reservation ensures higher priority traffic more bandwidth than lower priority traffic and guarantees real-time data delivery. Keywords: Real time, robust, communication, Control plane, Bandwidth.

1 Introduction Over the last years, sensor networks are widely used in many smart applications, including military applications and earthquake response systems. While these applications remain diverse, one common point they all share is the need of an efficient and reliable real-time communication mechanism. How ever, the potential contention in MAC protocols (e.g., IEEE 802.11 and 802.15.4), the node mobility nature of the sensor networks, and the interference between the transmitting nodes, all make it difficult to achieve good quality real-time communication (data delivery) [1][2]. In this paper we are proposing a Robust and Real Time Data Delivery in Wireless Sensor Networks (RRTD) mechanism which uses centralized control plane incorporating the timed token protocol in the MAC layer for wireless token ring architectures for providing hard real time guarantee and in advance bandwidth reservation method to provide soft real time guarantee. The reason to adapt the timed token protocol to wireless networks is that it has special timing properties and solid mathematical foundations [3][4][5][6] While a task is executing, RRTD reserves enough bandwidth between the source and destination nodes. In addition, to deal with network failures, RRTD simultaneously transmits data in multiple paths. The remainder of this paper is structured as follows. In Section 2 we present the system architecture. The proposed centralized control plane will be given in Section 3. Bandwidth reservation and multiple path delivery methods will be described in section 4 V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 129–135, 2010. © Springer-Verlag Berlin Heidelberg 2010

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The results obtained through simulation studies will be presented in Section 5 and finally Section 6 concludes the paper.

2 System Architecture With the timed token protocol, a synchronous bandwidth allocation (SBA) scheme must also be used to allocate synchronous bandwidth to the stations properly for guaranteeing the deadlines of real-time messages. Various SBA schemes have been proposed in the literature and also the non-optimality of the most famous schemes have been shown [7][8][9] the proposed control plane, EMCA (Enhanced Minimum Capacity Allocation) [10] is used as the SBA scheme due to its good performance and simplicity. To achieve hard real-time communication three important functions are implemented in the control plane, namely the Request Status procedure, the Priority management procedure and a traffic differentiation mechanism. The request status procedure determines whether a connection request should be accepted and as a result the requesting station could be admitted into the ring network. This decision is actually based on the current network state such as the current load, the number of connections established and the class of traffic carried over these connections. If the expected QoS of the connection request can be satisfied, the requesting station is accepted to join to the ring. If the connection cannot be accepted, the management station executes the priority management procedure. To achieve soft real-time communication RRTD executes bandwidth reservation (or BR) before the real data delivery (or RD) begins. In this way, when a real-time task completes and data is ready for delivery, it can immediately transmit data with desired sending rate. RRTD uses an existing wireless routing protocol (DSDV in this paper) to provide immediate data delivery path.

3 The Network and the Message Model for the Control Plane In the proposed architecture the timed token protocol [10] is adapted into the MAC layer and EMCA [10] SBA scheme is used for synchronous bandwidth allocation. The dynamic ring network is assumed to consist of n nodes at an instant. Message transmission is controlled by the timed token protocol. Token walk time τ includes the ring latency, the token transmission time and other network dependent overheads and thus represents the portion of TTRT (Target Token Rotation Time) that is not available for message transmission. 3.1 Target Token Rotation Time (TTRT) The messages generated in the network are classified as synchronous and asynchronous messages. These are n streams of synchronous messages at a certain moment

Sm = {Sm1 , Sm2 ,K , Smn }

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where stream Sm i originates at node i .Also each synchronous message stream Sm can be characterized as

Smi = { Ti , I i , Di , Pi

i

}

where T i is the maximum amount of the time required to transmit a message in the stream, I i is the interarrival period between messages in the stream., Di is the relative deadline of messages in the stream, that is the maximum amount of time that can elapse between a message arrival and completion of its transmission, Pi is the priority of the stream.Each synchronous message stream places a certain load on each system. We define the effective utilization, EU i of the stream Smi as follows: EU i =

Ti min (I i , Di )

(1)

The total utilization of the synchronous message set Sm is the fraction of time used to transmit the synchronous messages and is denoted as TU(Sm) n

TU ( Sm) = ∑ EU i

(2)

i =1

Each station can transmit its synchronous messages as much as the synchronous TM i . bandwidth allocated to it namely

TM i ≤ TTRT − τ

(3)

3.2 Delivery Bandwidth Computation In order to know how much bandwidth is available for a node to use, we must take into consideration all transmissions that directly affect its opportunities to transmit. To avoid the “hidden terminal” problem, before data transmission, the source node sends “Request to Send" (or RTS), and the destination node replies “Clear to Send" (or CTS). Every other node receiving RTS/CTS should remain in silence during the transmission period. With RTS/CTS, a node is not allowed to transmit whenever [5]: 1) It is receiving data; 2) One of its neighbors is receiving data (due to the reception of a CTS); 3) One of its neighbors is transmitting data to a node that is neither another neighbor nor the node itself (due to the reception of a RTS). The available bandwidth for a node x to transmit Bavl x is calculated as follows:

⎛ Bavl x = Beft x − ⎜ Brec x + ⎜ ⎝

∑B

y∈Nx

rec

y+

∑B

yk

y∈Nx , k ∉Nx+

⎞ ⎟ ⎟ ⎠

(4)

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where Brec x and

Brec y are the receiving bandwidths used by node x and node y ,

B yk is the traffic from node y to k, and N x / N x + is the set of neighbors of node x excluding/including itself. Algorithm 1 is used to compute required bandwidth. Required bandwidth for real-time data delivery is denoted by Brq . In delivery processes, nodes not only need to reserve Brq bandwidth, but also need to consider the extra bandwidth which they use to remain in silence due to the reception of RTS/CTS.

if x = source / destination then if destination / source in neighbors then

≤ Bavl x Brq else if

; else

≤ Bavl x / 2

50

Miss Ratio(%)

Brq

60

;

x ∈ N source / destination

40

30

20

10

0

then

Brq

≤ Bavl x / 3

0

10

20

30

40

50

60

70

80

90

100

Rate (P/sec)

;

Algorithm 1. Bandwidth Required Calculation

DSR

AODV

GF

RRTD

Fig. 1. Miss Ratio

4 RRTD Bandwidth Reservation In Equation 4, we observe that a node's available bandwidth is affected by its receiving bandwidth ( Brec x ), neighbor nodes' receiving bandwidth ( Brec y ) and sending bandwidth ( B yk bjk). In order to reserve enough bandwidth, a node (the source, destination or intermediate) should collaborate with its neighbors. Except current available bandwidth delivery,

Bavl x , the extra required bandwidth for real-time data

Bex x , is given by: Bex x = Brq − Bavl x ,

If

(5)

Bex x ≤ 0 , there is no need to reserve extra bandwidth.

5 Simulation Studies We conducted a set of simulations of the RTRD mechanism using J-sim, and its performance is compared with several other mechanisms, e.g.AODV, DSR, GF which is available in the literature that also provides real-time services in ad-hoc networks.

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5.1 Simulation Environment We consider a single broadcast region with an available link capacity of 2 Mb/sec under the IEEE 802.11 protocol with an effective data rate of approximately 1.43 Mb/sec. Each node generates variable-rate traffic (randomly uniform distribution) according to the exponential on-off traffic. Real-time data chunk sizes are randomly generated subject to uniform distribution with the minimum value of 50 Bytes. Other parameters, e.g., 802.11 physical layer parameters, were set to default values as recommended in J-Sim. 5.2 Simulation Results 5.2.1 End to End Miss Ratio The miss ratio is the most important metric in soft real-time systems. We set the desired delivery speed SSP to 1km/s, which leads to an end-to-end deadline of 200 milliseconds. In the simulation, some packets are lost due to congestion or forceddrops. The results shown in Figure 1 are the summary of 16 randomized runs. AODV and DSR didn’t perform well in the face of congestion because both algorithms flood the network in order to discover a new path when congestion leads to link failure. This flooding just serves to increase the congestion GF only switches the route when there are link failures caused by heavy congestion. The routing decision is based solely on distance and does not consider delay. Only RRTD tries to maintain a desired delivery speed through MAC and network layer adaptations, and therefore has a much less miss ratio than other algorithms. Due to its transient behavior, RRTD still has about a 20% miss ratio when the network is heavily congested. 5.2.2 Congestion Avoidance To achieve reliable real-time data delivery, RRTD adopts multi-path delivery mechanism (the number of paths is application-specific). To test the congestion avoidance capabilities, we use a base station scenario, where 6 nodes, randomly chosen from the left side of the terrain, send periodic data to the base station at the middle of the right side of the terrain. The average hop count between the node and base station is about 8~9 hops. Each node generates 1 CBR flow with a rate of 1 packet/second. To create congestion, at time 80 seconds, we create a flow between two randomly chosen nodes in the middle of the terrain. This flow then disappears at time 150 seconds into the run. In order to evaluate the congestion avoidance capability under different congestion levels, we increase the rate of this flow step by step from 0 to 100 packets/second over several simulations. Figure 2 plot the end-toend (E2E) delay for the four different routing algorithms. At each point, we average the E2E delays of all the packets from the 96 flows (16 runs with 6 flows each). The 90% confidence interval is within 2~15% of the mean, which is not plotted for the sake of legibility. Under the no or light congested situations, Figure 2 shows that all geographic based routing algorithms have short average end-to-end delay in compare to AODV and DSR. The reason for even higher delay in AODV than DSR is that DSR implementation intensively uses a route cache to reduce route discovery and maintenance cost.

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45

300

40 E nergy Cosum ption (m Whr)

250

Delay(m s)

200

150

100

35 30 25 20 15 10

50

5 0

0 0

10

20

30

40

50

60

70

80

90

100

0

10

20

30

DSR

AODV

GF

40

50

60

70

80

90

100

Rate(P/S)

Rate(P/S) RRTD

Fig. 2. Delay

DSR

AODV

GF

RRTD

Fig. 3. Energy Consumption

5.2.3 Energy Consumption Under energy constraints, it is vital for sensor nodes to minimize energy consumption in radio communication to extend the lifetime of the sensor networks. From the results shown in Figure 3, we argue that geographic based routing tends to reduce the number of hops in the route, thus reducing the energy consumed for transmission. AODV performs the worst as a consequence of sending out many control packets during congestion. DSR has larger average hop counts and more control packets than other geographic base routing algorithms. RRTD only takes delay into account, which leads to longer routes. Figure 3 shows that RRTD has nearly the same power consumption as GF and because under such situations, RRTD tends to choose the shortest route and does not sent out any on demand beacons.

6 Conclusion In this paper we proposed a Robust and Real Time Data Delivery in Wireless Sensor Networks (RRTD) mechanism to provide robust real time data delivery. This uses centralized control plane incorporating the timed token protocol in the MAC layer for wireless token ring architectures for providing hard real time guarantee and in advance bandwidth reservation method to provide soft real time guarantee. The primary goal of the control plane is to manage the dynamic wireless ring network and provide sufficient bandwidth to higher priority traffic in order to satisfy their hard-real time constraints. For soft real time communication RRTD executes bandwidth reservation (or BR) before the real data delivery (or RD) begins. In this way, when a real-time task completes and data is ready for delivery, it can immediately transmit data with desired sending rate. The simulation results justify that the mechanism ensures higher priority traffic more bandwidth than lower priority traffic and guarantees that deadline constraints are satisfied. As a result of the simulations it is also seen that the new protocol decreases the miss ratio, delay and congestion. As well minimizes the energy consumption. All of these results show that by adapting the proposed mechanism in the wireless sensor network robust and real time communication is achieved.

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References 1. Czajkowski, K., Fitzgerald: Real-time corba 2.0: Dynamic scheduling specification, Tech. Rep., OMG, 123–132 (2007) 2. Giusto, P., et al.: Reliable estimation of execution time of embedded software. In: DATE 2007, pp. 26–40 (2007) 3. Johnson, M.: Proof That Timing Requirements of the FDDI Token Ring Protocol Are Satisfied. IEEE Transactions on Communications 35, 620–625 4. Johnson, M., Sevcik, K.: Cycle Time Properties of the FDDI Token Ring Protocol. In: Joint International Conference on Measurement and Modeling of Computer Systems, pp. 234–241 (2006) 5. Chen, B., Zhao, W.: Properties of the Timed Token Protocol, Real-time Systems Group Technical Report, pp. 92–118 (2006) 6. Zhang, S., Burns, A.: 1995 Timing Properties of the Timed Token MAC Protocol. In: Proceedings of Computer Communications and Networks, pp. 67–74 (2007) 7. Buzluca, F.: 1997 Design of an Efficient Real-time Communication Structure for FDDI Based Network System, PhD Thesis, İTÜ The Institute of Science and Technology, Istanbul (2005) 8. Zhang, S., Lee, E.: The Nonoptimality of Synchronous Bandwidth Allocation Schemes for the Timed Token Protocol. IEEE Communications Letters, 101–103 (2008) 9. Buzluca, F., Harmanci, E.: Dynamic Synchronous Bandwidth Allocation Scheme for Hard Real-Time Communication in FDDI Networks. In: IEE Proceedings of Computers and Digital Techniques, pp. 15–22 (2008) 10. Zhang, S., Burns, A.: An Optimal Synchronous Bandwidth Allocation Scheme for Guaranteeing Synchronous Message Deadlines With the Timed-token MAC Protocol. IEEE/ACM Transactions on Networking, 729–741 (1995)

Multiple QoS Guided Heuristic for Independent Task Scheduling in Grid Sameer Singh Chauhan1 and R.C. Joshi2 1

Information Technology Department, Sardar Vallabhbhai Patel Institute of Technology, Vasad, Gujarat, India - 388306 2 Electronics & Computer Engineering Department, Indian Institute of Technology Roorkee, Roorkee, India - 247667 [email protected], [email protected]

Abstract. Scheduling a task with multiple QoS needs such as deadline, reliability, cost, trust, etc., is called QoS based scheduling. Several heuristics have been proposed for QoS based scheduling and it has been proved that it is a NP-hard problem. In this paper, we have proposed a heuristic for multiple QoS based scheduling for independent tasks. The heuristic considers multiple QoS needs of a task and finds the total utility of the task. It groups the tasks on the basis of their utility values. It schedules tasks in the descendent order from higher to lower utility values. The results show that the proposed heuristic is better in makespan and load balancing than other heuristics such as QoS Guided Min-Min and Weighted Mean Time Min-Min Max-Min Selective. Keywords: QoS, Makespan, Load Balancing, Grid Computing.

1 Introduction Grid enables sharing, selection and access of geographically distributed heterogeneous resources. The management of grid resources is very complex because of organizations’ access policies and cost models. Resource owners and users have different objectives, aims, and demands. The users compete for the available resources to execute their tasks. The users pose many QoS demands. QoS is very extensive concept and it may vary from user to user and system to system. For a user a QoS may be network bandwidth, CPU speed, execution cost, availability, reliability, etc. To provide the desired QoS is one of the crucial goals of grid computing. It has been proved that QoS based task scheduling in Heterogeneous Computing environment(HC) is NP-hard problem[1]. The classical heuristic algorithms such as Min-Min, Max-Min, Sufferage[2], do not consider the QoS demands of tasks for scheduling. QoS needs of a task is one of the important factors in scheduling. QoS guided Min-Min[3] algorithm considers network bandwidth as QoS parameter in scheduling. It divides the tasks in two groups: high and low QoS. It first schedules the tasks from the high and afterword from the low group. The priority grouping algorithm[4], groups the tasks in n groups. These groups are formed on the basis the QoS services provided by the resources. This algorithm also considers network V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 136–141, 2010. © Springer-Verlag Berlin Heidelberg 2010

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bandwidth as QoS parameter. The QoS based algorithms given in [3, 4] shows that the results given by them are better than the classical scheduling algorithms such as given in [2]. In this paper, we have proposed a heuristic that considers multiple QoS needs of a task. We have included multiple QoS parameters in Weighted Mean Time Min-Min Max-Min Selective[5], our previous work, heuristic. We have used various utility functions to model the QoS parameters. The user supplies its tasks with utility functions and weight vectors of each QoS parameter. The paper is organized as follows. In section 2 the task scheduling and QoS requirement model is presented. In section 3 the Multiple QoS Guided Weighted Mean Time Min-Min Max-Min heuristic is discussed. In section 4 results are discussed. Conclusion and future work is discussed in section 5.

2 Task Scheduling and QoS Requirement Model 2.1 Task Scheduling Model Independent task scheduling in grid is the process of mapping a set of task (a meta task) to a set of resources. Here, we are assuming that there are n{t1, t2, …., tn} tasks and m {r1, r2,….rm} resources in grid system at the time of scheduling. We have taken the following assumption (i)

(ii) (iii)

The tasks are independent (a meta-task), means there is no inter-task dependencies and no communication between them is required. One task can only be executed on one resource at one time. The resource once start execution of task can’t start another task until the first one is finished. The expected execution time of all tasks is known on all resources using predication model.

2.2 QoS Requirement Model The QoS requirements differ from application to application. The QoS requirements may be for network bandwidth, cost, availability, reliability, priority, etc. A user requests desired QoS from the grid and executes his task in the light of their QoS preferences. QoS preference means, a user can give more weight to one QoS than other one. Assume that for each task from the meta-task, there are total di QoS requirements. A utility function is associated with each QoS. The utility function defines the benefit perceived by the user. The benefit depends on the QoS parameter. If the QoS parameter is cost based like execution cost then, the user will be benefited by the minimum value and if the QoS parameter is efficiency based then the user will be benefited by the maximum value. We have used the equation (2.1), given in [6], to compute the total utility of a task. ∑

(2.1)

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Here wj represents the weight assigned to the utility uj. The user can assign weight to the utility and ∑wj=1. Indirectly, we can say user gives the preferences to the various QoS needs by assigning different weight values to QoS parameters. Pi is the priority of the task and Pmax is the maximum priority assigned to the task. For the testing purpose we have chosen response time, execution cost and priority as QoS needs of a task. We have modeled the cost parameter by following method. First we have computed the cost value of task on each resource using equation (2.2) ,

,

(2.2)

Here cj is the cost of execution per unit time. Then, we calculate the average execution cost of task ti using equation (2.3) ∑

,

(2.3)

Here, m is the total resources which can satisfy the QoS requirements. We find execution cost of task on each resource and chose the minimum from it. ,

,

(2.4)

For the response time QoS parameter, we have used the following method. We have computed the response time of task ti on every resource using equation (2.5). ,

,

,

(2.5)

Here, rt(i,j) is the response time of task ti on resource rj. ft(i,j) and st(i,j) is the finish and start time of task ti on resource rj, respectively. We have computed the average execution time of task ti using equation (2.6). ∑

,

(2.6)

We choose the minimum value of avgET(ti). For the priority QoS parameter, we have generated the priority value in the range from 1 to 4. Now, we are going to give the terminology [3] used in this paper. The expected execution time, ETij of task ti on resource rj is defined as the amount of time taken by rj to execute ti given that rj has no load when ti is assigned. The expected completion time CTij of task ti on resource rj is defined as the wall-clock time at which rj completes ti after having finished previously assigned work. The makespan of the schedule is defined as max tiϵ{t1, t2,…,tn}CTij, where task ti is assigned to resource rj. Hence, ,

,

(2.7)

Here rtj is the ready time of resource rj. Ready time is the time after which the resource will be free to execute a new task.

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3 Multiple QoS Guided Weighted Mean Time Min-Min Max-Min Selective (MQWMTS) Heuristic The MQWMTS heuristic is shown in table 1. Table 1. MQWMTS Heuristic (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)

(15) (16) (17) (18)

Find out the total utility of each task. Divide the tasks in n groups While (i < n) For each QoS group For all resources rj compute

∑

avg For all resources ri compute the weight w ∑

For all tasks ti in group For all resources rj CTij = ETij + rtj For all tasks ti, compute the weighted mean time ∑ w ET wmt Compute the standard deviation (SD) using equation(3.1) Calculate relative standard deviation SD’ If SD’ < ST then Find task ti having minimum weighted mean execution time and assign it to the resource, from the QoS qualified set, that is giving minimum completion time Else Find task ti having maximum weighted mean execution time and assign it to the resource, from the QoS qualified set, that is giving minimum completion time Delete task ti from the MT Update ready time of resource rj End while

The working of the heuristic is as follows. First the algorithm computes the total utility of the task. It divides the tasks in number of the groups based on their utility values. We have created 2-4 groups for testing purpose. It schedules the group having the task with high utility. For each group it performs the following steps. It calculates the weight of the resources in that group. It calculates the weighted mean time of each task in the group. It calculates the standard deviation of the completion time of unassigned tasks of MT. The standard deviation[7] can be calculated using equation(3.1). SD

∑

(3.1)

Here avgCT is average of completion time of all unassigned tasks. It can be defined as

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avgCT

(3.2)

Which task, having maximum or minimum weighted mean time, will be chosen for the mapping that depends on the critical value of the relative standard deviation(SD’). The relative standard deviation can be computed using equation (3.3) SD’ = SD/avgCT

(3.3)

The relative standard deviation shows the degree of dispersion of a set of values, here the set of values are CTij. If the value of the relative standard deviation is less than the critical value of relative standard deviation(ST), then task with minimum weighted mean time is chosen for mapping otherwise task with maximum weighted mean time is chosen for mapping. The critical value of relative standard deviation can be found by experiments, which come out to be 0.64.

4 Results We have used GridSim[8] toolkit for simulation purpose. We have used the following three task scenarios for creating different types of tasks. Scenario 1: - A few short tasks along with many long tasks. Scenario 2: - A few long tasks along with many short tasks. Scenario 3: - Length of tasks is randomly determined.

In Thousand Seconds

MQWMTS

QMinMin

MQWMTS

WMTS

50 45 40 35 30 25 20 15 10 5 0

QMinMin

WMTS

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

1

2

Task Scenario Fig. 1. Makespan

3

1

2

3

Task Scenario Fig. 2. Load Balancing Degree

For each experiment total 1000 tasks and 20 resources are taken. The arrival of tasks is modeled with poison process. We have used makespan and load balancing degree performance matrices to test the heuristics. We have compared the MQWMTS heuristic results with the results of QoS Guided Min-Min(QMinMin) and Weighted Mean Time Min-Min Max-Min Selective (WMTS) heuristics. Figure 1 is showing the comparison of makespan results. We can see from the figure that the MQWMTS heuristic gives shorter makespan in all three task scenarios. Figure 2 is showing the

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comparison of load balancing degree results. The results show that the MQWMTS heuristic gives load balancing better than the other heuristics.

5 Conclusions and Future Work In this paper, we have proposed a heuristic for QoS based task scheduling. The heuristic considers multiple QoS needs of a task and computes the total utility of a task. It groups the tasks and schedules the task with maximum utility. From the figure 1, we can see that the MQWMTS heuristic significantly gives improvements in makespan. The load balancing results are also better than other heuristics. Many issues like dynamic pricing, systematic resources sharing etc are for further investigation. How to confirm the attribute weight is also one of the investigative problems. Verification of the heuristic under actual Grid environment can be considered as future problem.

References 1. Fernández-Baca, D.: Allocating modules to processors in a distributed system. IEEE Transactions on Software Engineering, 1427–1436 (November 1989) 2. Maheswaran, M., Ali, S., Siegel, H.J., et al.: Dynamic Mapping of a Class of Independent tasks onto Heterogeneous Computing Systems. In: 8th IEEE Heterogeneous Computing Workshop (HCW 1999), pp. 30–44 (April 1999) 3. Maheswaran, M., Ali, S., Siegel, H.J., et al.: Dynamic Mapping of a Class of Independent tasks onto Heterogeneous Computing Systems. In: 8th IEEE Heterogeneous Computing Workshop (HCW 1999), pp. 30–44 (April 1999) 4. Dong, F., Luo, J., Gao, L., Ge, L.: A Grid Task Scheduling Algorithm based on QoS Priority Grouping. In: Proceedings of the 5th International Conference on Grid and Cooperative Computing, pp. 58–61 (2006) 5. Chauhan, S.S., Joshi, R.C.: Weighted Mean Time Min-Min Max-Min Selective Scheduling Strategy for Independent Tasks on Grid. In: Proceedings of IEEE 2nd International Advance Computing Conference 2010, pp. 4–9 (February 2010) 6. Hong-cui, G., Jiong, Y., Yong, H., Hong-wei, L.: User QoS and System Index Guided Task Scheduling in Grid Computing. In: The Third China Annual Conference (2008) 7. Etminani, K., Naghibzadeh, M.: A Min-Min Max-Min Selective Algorihtm for Grid Task Scheduling. In: 3rd IEEE/IFIP International Conference in Central Asia on Internet (September 2007) 8. Buyya, R., Murshed, M.: GridSim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. Journal of Concurrency and Computation: Practice and Experience, 1175–1220 (2002)

A Framework for Network Forensic Analysis Emmanuel S. Pilli, Ramesh C. Joshi, and Rajdeep Niyogi Department of Electronics and Computer Engineering, Indian Institute of Techology Roorkee, Roorkee, India {emshudec,rcjosfec,rajdpfec}@iitr.ernet.in, [email protected]

Abstract. Network security approach addresses attacks from perspective of prevention, detection and mitigation. The alternative approach of network forensics involves investigation and prosecution which act as deterrence. Our paper presents a generic process model and reviews various implementations for network forensics. We propose a novel framework to address the research gaps and discuss the work-in-progress. Keywords: network forensics, data fusion, reconstruction, traceback and incident response.

1 Introduction Sophisticated cyber attacks on Google and at least 20 other large finance, media and chemical companies were launched in January 2010. New York Times [1] reported that the theft began when an instant message was used to gain access to the personal computer of a Google employee. Intruders then used this compromised computer to get access to the computers of a critical group of software developers. They eventually gained control of the software repository at Google. Google quickly learned of the theft and its security specialists have sealed the alarming possibility of the attackers inserting a secret back door in dozens of global data centers. Cybercrime investigators could trace the attacks to six Internet addresses, linked to servers in Taiwan, as reported by Washington Post [2]. This was possible as the investigators analyzed the captured network traffic and invoked the incident response procedure immediately. Network forensics deals with the capture and analysis of the network traffic, providing information about the intrusions. Network forensics is defined in [3] as “the use of scientifically proven techniques to collect, fuse, identify, examine, correlate, analyze, and document digital evidence from multiple, actively processing and transmitting digital sources for the purpose of uncovering facts related to the planned intent, or measured success of unauthorized activities meant to disrupt, corrupt, and or compromise system components as well as providing information to assist in response to or recovery from these activities” The paper is organized as follows: Section 2 explains a general purpose model for network forensics and reviews related work in framework implementations. Section 3 illustrates our proposed model and explains the novel framework in detail. Conclusion and future work are given in section 4. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 142–147, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Related Work Many established practices exist for the traditional discipline of computer forensics. However there is a need to expand the forensic view from memory and disk level to network level as attacks also occur in the network and cyberspace. Many digital forensic models [4-7] were proposed for the networked environments. Later framework implementations incorporating many of the phases in these models were also proposed. 2.1 Generic Process Model We proposed a generic process model [8] as shown in Figure 1. It has nine phases – preparation, detection, incidence response, collection, preservation, examination, analysis, investigation and presentation. The first five phases handle real-time network forensics. The preparation phase ensures the monitoring sensors are in place. Detection phase helps in attack identification and collection phase captures network packets ensuring integrity of data. A suitable incident response is generated based on the nature of attacks. A hash of the data is created and a copy is made in the preservation phase. The post attack investigation begins at the examination phase, where a copy of the packet capture file is given for investigation. The examination phase fuses inputs from various sources and identifies attack indicators. The analysis phase classifies attack patterns using data mining, soft computing or statistical approaches and reconstructs the attack events. The investigation phase involves traceback and attribution. The final presentation phase results in the prosecution of the attacker.

Fig. 1. Generic Process Model for Network Forensics

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2.2 Network Forensic Frameworks Researchers have proposed many network forensic frameworks based on the various models discussed above. Honeytraps [9] were proposed as a deception tool to collect information about blackhat activities so that defense mechanisms can be formulated. Once an attacker penetrates a honeytrap, data are captured to detect and record the actions and the forensic alert system is activated. The investigation process begins after the attack is contained. Shanmugasundaram et al. [10] propose ForNet, a distributed network logging mechanism. It has two functional components – Syn-App, designed to summarize and remember network events for a period of time and a Forensic Server, which is a centralized authority managing the set of SynApps in that domain. A forensic server receives queries, processes them in co-operation with SynApps and returns query results back to the senders after authentication and certification. Ren [11] proposed a reference model of distributed cooperative network forensic system. The server captures, filters and dumps network traffic and transforms it into database values, mines forensic database, and replays network behavior. It also does network surveying and attack statistic analysis. The distributed agent clients integrate data from firewall, IDS, Honeynet and remote traffic. The goal of this model is to discover the potential misbehavior and replay the misbehavior for forensic analysis. Kim et al. [12] develop a fuzzy logic based expert system for network forensics to aid the decision making for non statistical sources of imprecision. It provides analyzed information for an expert, reducing the time and cost of forensic analysis. Traffic analyzer captures network traffic and analyses it using sessionizing. Knowledge base stores rules which are used by the fuzzy inference engine to determine membership. The forensic analyzer decides whether the captured packets indicate an attack. Almulhem and Traore [13] propose a Network Forensics System (NFS) consisting of three main modules – marking, capture and logging. Marking decides whether a packet is malicious, capture module waits for the marked packets and logging module is a system repository where the attack data are being stored. The modules were implemented using open source tools. Nikkel [14] proposed a Portable Network Forensic Evidence Collector (PNFEC), designed for traffic collection between a network and a single node, having specific modes of operation, rapid deployment and stealthy inline operation. The traffic is promiscuously captured using pcap based capture tools and stored on a hard disk.

3 Proposed Framework for Network Forensic Analysis Network forensics is a very young science and though it is being researched since 2001, there are many aspects which are still not very clear. We propose a framework for network forensic analysis which will capture network traffic data, perform fusion of alerts and attack information, classify, correlate, and analyze this data in order to investigate the source of attack, while generating an incident response. Many phases like preparation, detection, preservation, and presentation in the generic process model discussed have been extensively researched. Standard techniques have been developed which are well tested by time. Few of these phases can be trivially

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addressed. The collection, examination, analysis, investigation and incidence response phases need to be addressed. Our proposed framework addresses these challenges as shown in Figure 2.

Fig. 2. Proposed Framework for Network Forensic Analysis

3.1 Multi-sensor Data Fusion Privacy protection of individuals’ data, chain of custody of captured data, date and time synchronization of the information captured can now be handled by established and standard techniques. The major challenge is to collect the logs from all network security products, deployed in the entire network and perform data fusion. This involves complete live response data collection from various sensors like packet analyzers (wireshark / tcpdump), intrusion detection systems (snort), routers, firewalls, log servers, etc. The alerts generated by IDS, statistics obtained from protocol analyzers and attack information by observing various threshold values need to be fused before analysis is done. Dempster-Shafer theory [15] for information fusion is used to determine the validity of the attack. 3.2 Identification of Attack Events The full data capture logged to record various network events results in a very large amount of storage. The network events useful for investigative requirements need to be identified and an effective mechanism is to be in place to identify attack features from the traces. Identification of the useful parameters (network events like connection establishment, DNS queries, fragmentation of IP packets, etc) for various attacks so that the data logged for analysis may be reduced. A minimum representative set chosen can potentially become evidence in a variety of cybercrimes.

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The focus of the attacks has shifted from manipulating attributes in TCP and IP protocols to exploiting application layer protocols [16] in the TCP/IP suite. SQL injection, cross site scripting (XSS), cookie poisoning are some examples. 3.3 Attack Reconstruction The obvious events of digital evidence are recognized and the skill level of the suspect(s) is assessed by employing data extraction techniques (e.g. keyword searches, extraction of unallocated space and file slack, file timeline/mapping, hidden data discovery/extraction). Important events involving intruders’ interaction with the compromised system are reconstructed [17] and the methodology of the attackers is analyzed. The communication between attacker and the compromised hosts is visible to the network. 3.4 Traceback and Attribution The results of the data analysis will lead to identifying a host or a network by its IP address. IP Traceback, a major challenge, is a method for reliably determining the origin of a packet on the Internet. Techniques based on packet marking, packet logging or hybrid approaches can be used. The attack attribution [18] can be done by analyzing the data packets transmitted, applications being run, traffic patterns observed and protocols violated. The investigative process must be adaptable to include the latest protocols being introduced regularly. 3.5 Incident Response The response processes are to be launched immediately when the alerts begin. The key issue to be maintained is that the attack must not be aware of the response. Incident response involves detection of unauthorized activity and validating the incident by reviewing pertinent logs, network topology, etc. It determines the vulnerability exploited in the compromise of a system and enforces protection against exploitation of the same on other systems. It develops a strategy regarding containment, eradication, recovery, and investigation.

4 Conclusion and Future Work We have proposed a framework for network forensic analysis whose objective is to overcome specific research gaps in existing models and implementations. The implementation focused on the following phases: collection, examination, analysis, investigation and incident response. The various modules of the framework are being implemented. Multi sensor data fusion ensures that the entire attack information is captured for reconnaissance of the attack. Identification of useful network events will facilitate data reduction as redundant data is removed. Attack reconstruction and traceback enable prosecution of the attacker. Incident response facilitates recovery and minimizes the damage. These modules will be integrated and validated so that the framework can facilitate comprehensive network forensic analysis.

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References 1. New York Times, Cyber attack on Google Said to Hit Password System, http://www.nytimes.com/2010/04/20/technology/20google.html 2. Washington Post, Google threatens to leave China after attacks on activists’ e-mail, http://www.washingtonpost.com/wp-dyn/content/article/2010/ 01/12/AR2010011203024.html 3. Palmer, G.: A Road Map for Digital Forensic Research. In: 1st Digital Forensic Research Workshop, pp. 27–30 (2001) 4. Casey, E., Palmer, G.: The investigative process. Digital evidence and computer crime. Elsevier Academic Press, Amsterdam (2004) 5. Carrier, B., Spafford, E.H.: Getting physical with the digital investigation process. Int’l J. of Dig. Evidence. 2(2), 1–20 (2003) 6. Ciardhuain, S.O.: An extended Model of Cybercrime Investigations. Int’l J. of Dig. Evidence 3(1) (2004) 7. Ren, W., Jin, H.: Modeling the network forensics behaviors. In: 1st Int’l Conf. Security and Privacy for Emerging Areas in Comm. Networks. pp. 1–8 (2005) 8. Pilli, E.S., Joshi, R.C., Niyogi, R.: Network forensic frameworks: Survey and research challenges. Dig. Investigation (Int’l. J. Dig. Investigation 2010) (in Press) 9. Yasinsac, A., Manzano, Y.: Honeytraps, a network forensic tool. In: 6th Multi-Conf. on Systemics, Cybernetics and Informatics, Florida, USA (2002) 10. Shanmugasundaram, K., et al.: ForNet: A distributed forensics network. In: Gorodetsky, V., Popyack, L.J., Skormin, V.A. (eds.) MMM-ACNS 2003. LNCS, vol. 2776, pp. 1–16. Springer, Heidelberg (2003) 11. Ren, W.: On the Reference Model of Distributed Cooperative Network Forensics System. In: 6th Int’l Conf. Information Integration and Web-based Application & Services, Jakarta, Indonesia, pp. 771–775 (2004) 12. Kim, J., Kim, M., Noh, B.N.: A Fuzzy Expert System for Network Forensics. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3043, pp. 175–182. Springer, Heidelberg (2004) 13. Almulhem, A., Traore, I.: Experience with Engineering a Network Forensics System. In: Kim, C. (ed.) ICOIN 2005. LNCS, vol. 3391, pp. 62–71. Springer, Heidelberg (2005) 14. Nikkel, B.J.: A portable network forensic evidence collector. Dig. Investigation (Int’l. J. Dig. Investigation) 3(3), 127–135 (2006) 15. Tian, J., Zhao, W., Du, R.: D-S Evidence Theory and Its Data Fusion Application in Intrusion Detection. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3802, pp. 244–251. Springer, Heidelberg (2005) 16. Fong, E., Okun, V.: Web Application Scanners: Definitions and Functions. In: 40th Ann. Hawaii Int’l Conf. on Sys. Sciences, Hawaii, p. 280b (2007) 17. Sekar, V., et al.: Toward a Framework for Internet Forensic Analysis. In: ACM SIGCOMM Third Workshop on Hot Topics in Networks, HotNets (2004) 18. Ponec, M., et al.: New payload attribution methods for network forensic investigations. ACM Trans. Info. Syst. Security 13(2), 32 (2010) Article 15

A New Trust Model Based on Time Series Prediction and Markov Model Sarangthem Ibotombi Singh and Smriti Kumar Sinha Department of Computer Science & Engineering, Tezpur University, Napaam, Tezpur -784 028, Assam, India {sis,smriti}@tezu.ernet.in

Abstract. In this paper, we propose a new statistical predictive model of Trust based on the well-known methodologies of the Markov model and Local Learning technique. Repeatedly appearing similar subsequences in the trust time series constructed from history of direct interactions or recommended trust values collected from intermediaries over a sequence of time slots are clustered into regime. Each regime is learnt by a local model called as local expert. The time series is then modeled as a coarse-grain transition network of regimes by using a Markov process and value of the trust at any future time is predicted by selecting the local expert with the help of the Markov matrix. Keywords: trust, reputation, Markov model, local expert, regime, time series, clustering, dynamical system.

1 Introduction In the environment, where it is extremely hard to obtain perfect information about the potential interacting partners, trust and reputation mechanism may reduce the risk involved in an interaction[2]. Using proper trust management for evaluating the trustworthiness of interacting partners, those with malicious intensions can be prevented from causing any harm or unwanted incident. Trust and reputation are assumed to have the attributes - context-sensitive, transferable, dynamic and history-based which should be synthetically dealt with when handling any interacting partners [3]. Trust and reputation of an agent may either increase or decrease over a period of time[4][5][7]. History-based property emphasizes that current trust and reputation of an entity can be predicted based on their previous values [4]. According to trust dynamics, discussed in [6], trust and reputation are built up slowly and torn down quickly. Given the above nature of trust and reputation, in the present work, we propose a time-dependent, context-specific and history-based trust model using Markov process.

2 Related Works A number of trust and reputation models have been published in the literature [1][2]. Here we survey only those trust models that are based on Markov model. In [8] a trust model for autonomous agents in multi-agent environments based on Hidden Markov models(HMM) and reinforcement learning is proposed. An HMM per agent V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 148–156, 2010. © Springer-Verlag Berlin Heidelberg 2010

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is used to decide and predict whether or not the agent is malicious. The HMM is updated from observations which come in the form of ratings after direct experiences or recommendations requested from other intermediaries. The authors, in [9], provided an idea of a trust model based on HMM to cope with the inability of probabilistic trust models to capture the dynamic aspect of trust making over time. A HMM based approach to measuring an agent’s reputation as a recommender is proposed in [11]. They model the chained recommendation events as an HMM. The model in [12] uses a Markov chain constructed from a reputation time series to model the dynamic nature of trustee’s trustworthiness. The current state vector show the repute value of the trustee at a time slot. The Markov matrix shows the probability of transition from one trustworthiness level to another trustworthiness level. The future state vector determined by multiplying the current state vector with the Markov matrix decides the predicted trustworthiness level. Our proposed model is similar to[12]. Their model does not consider the trust values in the past except for deciding the Markov Matrix. However, in our model the Markov chain is constructed by analyzing the repeating patterns in the direct /recommended trust values collected from the intermediaries over a time horizon. The prediction of future trust value is to be done by honoring all similar patterns in the past grouped into a regime using a local prediction model.

3 Proposed Model 3.1 Main Social Actors In social trust relations [10], intermediaries may be either an advisor or a guarantor. Always there is an element of risk involved while taking the recommendation from advisors. The trustor, however places trust on a guarantor’s performance and integrity just as the later does in that of the trustee. So we claim the following : • •

A guarantor intermediary is one with whom the trustor has already established a recommendation trust relationship and it holds an opinion on the trustee’s trust behavior. An advisor intermediary is one with whom the trustor has not established any relationship earlier, yet it can provide an opinion on the trustee’s trust behavior.

3.2 Information Sources According to [10], the placement of trust on a trustee is essentially based on the information from three sources • Trustor’s assessment of trustee’s performance. • Recommendations from other intermediaries who have a position similar to the trustor’s and similar interest on the placement of trust. • Recommendations from other intermediaries who do not have a position similar to the trustor’s and do not have the similar interest on the placement of trust. First source will be most likely to lead to a correct assessment. Second source often leads to the decision about trust as made by other intermediaries whose judgment was

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trusted. Finally third source, provides the independent evidence of the decision. Position and interest similarity among the agents is decided based on the context of their interactions. 3.3 Categorization of Intermediaries With the help of a context similarity operator CS, we determine the similarity of an intermediary’s position and interest on the placement of trust to that of a trustor. Let , be the contexts of interactions. The trustor wants to make an analysis of turstee’s behavior in the context . An intermediary, at some point in time, had a position and interest on the placement of trust similar to that of the trustor if it had interacted with such that , 1. We can directly ask for the trustee in a context recommendation from such types of intermediaries. On the other hand, if , 1, we say that the intermediary’s position and interest was not similar to that of the trustor. In this case, we use transferability property while seeking the recommendation from the intermediary. Again an intermediary may be a known or an unknown one. We maintain the similar opinion with [6] in respect of unknown agents, that is, recommendations from unknown agents cannot simply be discarded by treating them as untrustworthy. An intermediary is within the reputation trust range of the trustor if its trustworthiness in providing the recommended trust opinion is above a threshold value. We now formulate the intermediaries used in our model. • Related guarantor : A guarantor who is within the reputation trust range of the trustee and who, at some point of time, had a position and interest on the placement of trust similar to that of the trustor. • Independent guarantor : A guarantor who is within the reputation trust range of the trustee and who, however, did not have a position and interest on the placement of trust similar to that of the trustor. • Related advisor : An advisor who had a position and interest on the placement of trust similar to that of the trustor. • Independent advisor : An advisor who did not have a position and interest on the placement of trust similar to that of the trustor.

Fig. 1. Here situations (a), (b), (c), and (d) can happen at different time points during a time slot. In (a) and (b), intermediary B is known to trustor A from earlier recommendation trust relationship. B can play the role of related guarantor or independent guarantor depending on , 1 or , 1. In (c) and (d), it plays the roles of related advisor or independent advisor depending on , 1 or , 1. In all cases, B knows C from a previous direct (interaction) trust relationship.

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3.4 Trust Equation The model we propose in this paper, calculates the trustworthiness of the trustee based on the history of direct interactions and/or recommended trust obtained from intermediaries. So the trust of an agent A on another agent B over a context in time point t is defined as : , ,

, ,

, ,

, , is the direct trust based on direct experience, where recommended trust and , are weighting factors such that 3.5 Estimation of Direct Trust :

(1) , ,

is the

1.

, ,

If the trustor have already interacted directly with the trustee in the current context or in a similar context during time spots falling in the current time slots, the value , , c , is calculated by using the equation (2). of , ,c

1 1 2 ∑

, ,c , ,

, ,c ,

(2)

age of the interaction; time point of interaction; total number of interactions with the trustee in the current context c during the current time slot; total number of interactions with the trustee in a similar , ,c , , , ,c , context context c during the current time slot; and specific trust values of the trustee B as perceived by trustor A in the time point . In case the trustor does not have any prior direct interactions with the trustee during the current time slot, it then uses the history of their interactions prior to the current time slot to predict trustworthiness value for the current time slot. Prediction mechanism is explained in section 3.7. Sometimes, it may be the case that the above mentioned history is also not available because the trustee is a complete stranger to the trustor. In this scenario, the trustor takes trust based decision based only on , , component of equation (1). Now we provide in the form of a procedure to calculate the direct trust component: here,

Direct Trust Procedure: 1. Trustor A decides the time point t of interaction with trustee 2. Decide the time slots in which t falls. Lt it be ts. 3. If there are records of direct interactions in the current context ci and similar context during the time slot ts, use these trust values to calculate the direct trust value using the formula (2). otherwise use the direct trust value using prediction mechanism explained afterward in section 3.7. 4. Based on the outcome of the interaction, trustor A updates its direct trust record about the trustee.

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3.6 Estimation of Recommended Trust :

, ,

The trustor first seeks the recommendations from the intermediaries mentioned in section 3.3 by submitting a reputation query specifying the following : • • •

The identity of the trutee. The current context . The starting time spot of the current time slot and the time spot at which the trustee wanted the trust.

On receipt of the reputation query, all the intermediaries will communicate their opinion about the trutee based on their experience with it during the time period specified in the query. This opinion is quantified as a recommended trust value using equation (2). Each recommended trust value is additionally qualified by a time point that is the latest time point used in (2). All the communicated trust values are aggregated into a single value according to equation (3). , ,

∑

, ,

v, , ,

, , ,

∑ 3

here, latest time point; total numbers of guarantors; total numbers of current context; context in which the intermediary interacted with advisors; reputation trust of a guarantor ‘u’ as perceived by the trustor trustee, B; from its earlier recommendation trust relationship with it; general reputation trust of an advisor ‘v’ as perceived by other members of the agent society; the weight controlling the importance given to the recommendations from advisors, may be anything from 0 to 1, = context similarity measure in the , rnage [0.1]; and , , , , v, , , context specific recommended trust values of the trustee B as passed by guarantor u and advisor v respectively. It may so happen that the trustor is unable to obtain the recommended trust values from the intermediaries during the current time slot. In such scenario, the trustor needs to determine the recommended trust values from previous time slots and makes use of the prediction mechanism discussed in the section 3.7 for deciding the trust value in the current time slot. We now formulate the recommendation trust calculation in the form of a procedure. Recommendation Trust Procedure : 1: Trustor A request the intermediaries in A’s reputation trust range for recommended trust about the trustee B by submitting a reputation query. 2: All the received recommendation trust values are collected in a buffer and A calculate the recommended trust based on equation(3). If such recommendation trust values could not be obtained, then use the prediction method explained in the next section 4.7 to calculate recommended trust. 3 Update the reputation trust of all the intermediaries who provided the recommendation trust after the outcome of A’s interaction with B.

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3.7 Prediction Mechanism When the trustor A wants to interact with the trustee, B in a time point t and direct trust and/or recommended could not be calculated as mentioned above sections, it uses the mechanism explained below for prediction of trust value. Generation of direct/recommended Trust Time Series 1. 2. 3.

4.

A first decides the time horizon by specifying the starting and ending time points The time horizon is divided into a number of T time slots. Case a: Direct trust : A determines if it has the context specific trust information of trustee B generated from its direct interactions in the time horizon. If such information is available, these trust values are classified into different time slots according to the time point of interaction. Using the formula (2), a single aggregated trust value for each time slot is generated. Case b: Recommended trust: Through a recommendation query, recommendation trust values over the time horizon are collected from all intermediaries. Trustor A then classified these values into different time slots and an aggregate for each slot is generated using equation (3). Using the aggregated trust values in all time slots, a time series of direct/recommended trusts on the trustee B of length T can be generated. Let us called it .

Regimes Extraction and Expert Training From the field of time series analysis, the time series a state space model:

can be converted into

and

(4)

where, ,.. , is the state vector formed by embedding d previous values of the times series and is the evolutionary age of the delay vector . The value of d controls the degree of coarsening in our model. By taking d = 1, we can generate the model of [12] from our model. The sequence of delay vectors then form the set | , 1, . . , . We consider as the course grain structure of the recommended trust time series Y. We now define the dynamical situation of the series as follows : ,

,..

(5)

Using k-means partitioning algorithm, all the delay vectors are clustered into similar NC numbers of clusters (regimes), ; 1, . . , , according to their characteristics given by equation (5). The vectors in a cluster Rj are then used to train a local expert. The rational behind our approach is that vectors belonging to a cluster represents a regime in the time series and must be extracted to train a local prediction model called expert over the regime. The experts can be anything from a regression model to structures such as MLP. Each expert responds to a kind of regime in the time series.

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Course Grain Markov Chain Model As vector corresponding to will indicate to which regime the point belongs, the whole sequence can be analyzed as a regime transition network using a Markov Chain and we refer it as course-grain Markov Chain. Current State Vector Construction : The current state vector V is a 1 matrix. It shows the regime of the times series at the time slot t. First the regime containing the delay vector identified and this membership information is recorded by entering a value of ‘1’ in the ith column of the state vectors and a value of ‘0’ in the remaining columns. Construction of Transition Matrix : The transition matrix A is a matrix that represents the probability of changing from one regime to another regime between any two consecutive time slots. In order to determine the matrix A, we defined the following : ─ Inter-regime Transition, occurred if there exist a vector ─ Intra-regime Transition, occurred if there exists a vector

: A transition such that : A transition such that

is said to have is said to have .

Now we define the regime transition probability as : (6)

∑

(7)

∑

where ∑ is total transitions from regime Ri. to other regimes. Illustrative Example: Let . Using d = 2, we can extract the delay vectors to form the course-grain time series. , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , Here the superscript denotes the evolutionary age of each delay vector. Further let the clusters be as follows : ,

, , ,

, , ,

,

, , ,

,

, ,

,

, , ,

, , ,

,

, , ,

,

, , ,

,

,

,

, , ,

, ,

, ,

, ,

,

The Markov Matrix constructed by using the equations (6) and (7) and the corresponding state transition diagram are shown Figure 2

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Fig. 2. Markov matrix and transition regime transition diagram. For the regime vector , and the current state vector is 0,0,1,0 and the futre state vector is 0, , , .

In the future state vector, regime D has the highest transtion probability, so trust prediction is done by our local expert trained using only the relevant delay vectors in D. In other words, the trustee agent has similar behavior at time t = 12, 14, 16, 17, 19 and 20, so prediction using similar behavioral patterns will yield more generalized value. Prediction of Trust Once the Transition matrix M and current state vector V for the current time slot t have been derived from the coarse-grained Markov Chain, prediction of the trustworthiness of the Trustee B in the following steps : ─ Construction of future state vector : The future state vector, F is a 1 matrix formed by multiplying the Transition matrix, A by the current state vector, V i.e. . ─ Prediction of the future regime : We select that regime which has the highest transition probability in V as the future regime. ─ Selection of query state vector: Keeping in view of the history-based property of trust, the local model is centered in the youngest vector in the future regime. The is called as our query state vector. ─ Prediction of future trust value: Then the prediction of the trustworthiness of the trustee agent B is done by using the local expert about the query state vector Based on the value of the predicted trust value, trusting agent A will decide whether to interact or not to interact with the trustee agent B in the future time point.

4 Conclusion and Future Work We have proposed a new trust model based on coarse-grained Markov model where the goal of the trusting agent is to make optimal trust decision in future time. Istead of predicting the future trust value based on single step change in the trustworthiness level, our model bases the prediction on the similar patterns in the whole time space

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over which analysis of the dynamic nature of trustworthiness of the trustee is performed. Implementation in a simulation framework to test the effectiveness of the proposed model in an ongoing work. Our future work is to see if the model can be improved by mixing the opinions of more than one experts.

References 1. Sabater, J., Siera, C.: Review on Computational Trust and Reputation Models. Artificial Intelligence Review (24), 33–60 (2005) 2. Josang, A., Ismail, R., Boyd, C.: A Survey of Trust and Reputation Systems for Online Service Provision. Decision Support Systems 43(2), 618–644 (2007) 3. Ibotombi Singh, S., Sinha, S.K.: A New Trust Model Based on Social Characteristics and Reputation Mechanisms using Best Local Prediction Selection Approach. In: International Conference on New Trends in Information and Service Science, pp. 329–335 (2009) 4. Hussain, F.K., Chang, E., Hussain, O.: A Robust Methodology for prediction of Trust and Reputation Values. In: ACM workshop on Secure Web Services, Alexandria, Virginia, USA, pp. 97–108 (2008) 5. Chang, E., Dillon, T., Hussain, F.K.: Trust and Reputation for Service-Oriented Environments: Technologies for Building Business Intelligent and Consumer Confidence, p. 350. John Wiley and Sons, U.K. (2005) 6. Walter, F.E., Battison, S., Schweitzer, F.: A model of trust-based recommendation system on a social network. In: Autonomous Agents and Multi-Agent Systems, pp. 57–74. Springer, Netheralnds (2007) 7. Hussain, F.K., Chang, E., Dilon, T.S.: Markov Model for Modelling and Managing Dynamic Trust. In: 3rd IEEE International Conference on Industrial Informatics (INDIN), pp. 725–773 (2005) 8. Moe, M.E.G., Tavakolifard, M., Knapskog, S.J.: Learning Trust in Dynamic Multiagent Environments using HMMs. In: 13th Nordic Workshop on Secure IT Systems (NordSec 2008). Copenhagen, Denmark (2008) 9. Sassone, V., Krukow, K., Nielsen, M.: Towards a Formal Framework for Computational Trust. In: de Boer, F.S., Bonsangue, M.M., Graf, S., de Roever, W.-P. (eds.) FMCO 2006. LNCS, vol. 4709, pp. 175–184. Springer, Heidelberg (2007) 10. Coleman, J.: Foundations of Social Theory. Havard University Press, London (1994) 11. Song, W., Phoda, V.V., Xu, X.: The HMM-based Model for evaluating Recommender’s Reputation. In: IEEE International Conference on E-Commerce Technology for Dynamic E-Business (CEC-East’04), pp. 209–215. IEEE, Los Alamitos (2004) 12. Hussain, F.K., Chang, E., Dillon, T.S.: Markov model for modeling and managing dynamic trust. In: 3rd IEEE International Conference on Industrial Informatics, INDIN’05, pp. 725–733. IEEE, Los Alamitos (2005)

A Novel J2ME Service for Mining Incremental Patterns in Mobile Computing Ashutosh K. Dubey1 and Shishir K. Shandilya2 1

M.Tech Scholar, PG Dept. of Computer Science & Engineering NRI Institute of Information Science and Technology Bhopal, India [email protected] 2 Head, PG Dept. of Computer Science & Engineering NRI Institute of Information Science and Technology Bhopal, India [email protected]

Abstract. Data mining services play an important role in the telecommunications industry. Considering the importance of data mining services to provide intelligence locally on devices on mobile environments, we propose a data mining service that adopts the embedded data mining algorithm according to situation. In this paper, we propose a novel data mining algorithm named J2ME-based Mobile Progressive Pattern Mine (J2MPP-Mine) for effective mobile computing. In J2MPP-Mine, we first propose a subset finder strategy named Subset-Finder (S-Finder) to find the possible subsets for prune. Then, we propose a Subset pruner algorithm (SB-Pruner) for determining the frequent pattern. Furthermore, we proposed the novel prediction strategy to determine the superset and remove the subset which generates a less number of sets due to different filtering pruning strategy. Finally, through the simulation our proposed methods were shown to deliver excellent performance in terms of efficiency, accuracy and applicability under various system conditions. Keywords: J2ME, DMS, S-Finder, SB-Pruner, J2MPP-Mine.

1 Introduction With the advancements of the new technology with wireless communication techniques and increasing popularity of mobile devices, i.e., mobile phone, PDA, contribute to a new business model. In any time and any place, user can get the service through mobile devices from Information Service and Application Provider. In 1999 J. Veijalainene et al.[1] and U. Varshney et al.[2] proposed business model which is called Mobile Commerce. The new mobile model provide better communication using 3G services, provide digital payment system, multimedia information interchange etc. In the future, M-Commerce (Mobile Commerce) is expected to be as popular as e-commerce [3]. The regular enhancement in service invocation may come from the dependencies between services or the proximity of service providers. It is potentially beneficial to discover such mobility services and V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 157–164, 2010. © Springer-Verlag Berlin Heidelberg 2010

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service patterns to facilitate the user behavior. Many mobility learning schemes and motion prediction algorithms have been proposed to explore the benefit of mobility patterns [4], [5]. Data mining techniques are used in the discovery of user behavior patterns [6], [7], [8] using several algorithms. Data mining can find interesting valuable patterns or relationships describing the data and predictive or classify the behavior of the model based on available data. In other words, it is an interdisciplinary field with a goal of predicting outcomes and uncovering relationships in data. It uses automated tools that employ several methodologies and algorithms to discover mainly hidden patterns, associations, anomalies, and/frequent structure from large amounts of data stored in data warehouses or other information repositories and filter necessary information from this big dataset. Telecommunications industry is a typical data intensive industry, with the deepening of telecom reform, competition is also becoming fierce increasingly. Compared with other industries, the telecommunications industry have more crucial personal user’s data, which can help people analyze the data accurately and obtain useful knowledge, in order to maintain and win the competition , people should find more interactive business opportunities and provide users with better service with short time duration. As a result, data warehouse and data mining has important value in the telecommunications industry today. Many previous studies contributed to the efficient mining of sequential patterns or other frequent patterns in time-related data [9], [10], [11], [12], [13], [14], [15], [16], [17], and [18]. Agrawal et al.[19] proposed the definition of sequential patterns in [20] to include time constraints, sliding time window, and user-defined taxonomy, and presented a priori-based, improved algorithm GSP (i.e., generalized sequential patterns). Miller et al. [21] proposed a problem mining frequent episodes in a sequence of events, where episodes are essentially acyclic graphs of events whose edges specify the temporal precedent-subsequent relationship without restriction on interval. In this paper, we proposed a novel data mining algorithm named J2MPP-Mine for efficiently mining the J2MPPs of users in mobile environments. Furthermore, we propose a novel subset prediction strategy that utilizes the J2MPPPs to effectively predict subset (progressive pattern) which follow the minimum support count which is used in pruning set. In the J2MPP-Mine algorithm, we proposed a pruning algorithm to prune the data set and find the frequent pattern. Finally remove the subsets and include there supersets. The remaining of this paper is organized as follows. We briefly review the related work in Section 2. In Section 3, we describe the proposed data mining algorithm, namely J2MPP-MineThe empirical evaluation and result study is made in Section 4. The conclusions and future work are given in Section 5.

2 Related Works In recent years, a number of studies have been made about using data mining techniques to discover useful patterns from World Wide Web [22], transaction databases [23],[24] , [25] and data mining services in mobile from [26],[27],[28],[29], [30].In 2000 J. Pei et al. [22], proposed an algorithm named WAP-Mine for efficiently discovering the web access patterns from web logs by using a tree-based data structure without candidate generation.

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In 1993 R. Agrawal et al. [23] proposed mining association rules to discover important items in a transaction database. In 1994 R. Agrawal et al. [24] proposed Apriori algorithm to improve the performance of association rule mining. In 1995 R. Agrawal et al. [25] proposed Sequential pattern mining which was introduced for discovering the time ordered patterns named sequential patterns from transaction databases. In 2005 I. Foster [27] proposed the Weka4WS software prototype which has been developed by using the Java WSRF library which is provided by Globus Toolkit (GT4). It involve Grid nodes in Weka4WS applications use the GT4 services for standard Grid functionality, such as security, data management, and so on. In 2006 D. Talia et al. [28] proposed performance evaluation of the execution mechanism on different platform. In 2003 D. Talia et al. [26] proposed the Knowledge Grid which is a Grid services-based environment providing knowledge discovery services for a wide range of high performance distributed applications. It offers users high-level abstractions and a set of services by which they can integrate Grid resources to support all the phases of the knowledge discovery process. In 2007 D. Talia et al. [29] proposed that the DMS (Data Mining Service) can perform several data mining tasks from a subset of the algorithms provided by the Weka4WS systems. When a data mining task is submitted to the DMS, the appropriate algorithm of the Weka library is invoked on a Grid node to analyze the local data set specified by the mobile client. The mobile client is composed by three components: the MIDlet, the DMS Stub, and the Record Management System (RMS). The MIDlet is a J2ME application designed to operate on an MIDP (Mobile Information Device Profile) small computing device allowing the user to perform data mining operations and show their results. The DMS Stub is a WSRF Service which allowing the MIDlet to invoke the operations of a remote DMS. The stub is generated from the DMS interface to conform with the JSR172 specifications, even if the DMS stub and the MIDLET are two logically separated components, they are distributed and installed as a single J2ME application. RMS is simply a record oriented database for persistently store the data. In 2001 F. Berman [30] proposed that knowledge discovery applications are a major goal. To reach this goal, the Grid needs to evolve towards an open decentralized platform based on interoperable high-level services that make use of knowledge both in providing resources and in giving results to end users.

3 Proposed Method: J2MPP-Mine In this section, we describe the proposed method. The entire system architecture consists of three phases: 1) Find possible subset for prune based on the minimum support count by S-Finder. 2) Start Pruning by SB-Pruner 3) Add the superset in the list and remove the related subset from the list. Finally we find the frequent patterns or knowledge from huge amount of data. Consider the database of employee and their Item-Sets. Employee Arrival patterns are from E1, E2 and E3.E1 visit the Item-Sets 10,20 E2 visit the Item-Sets 10,20,30 and E3 visit the Item-Sets 10,20,30,40. Here Space and “,” are different in terms of recognizing the frequent pattern. We apply differentiation from Item-Sets by “,” and differentiate employee Item-Sets by space.

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A. Item set for Mobile Let us consider a progressive customer transaction database which is shown in table 1. AssumptionsMin-support-Minimum Support value defined by user. AS- All possible set of Items PS- All possible set of Items with support >Min-support count LI- Final List CD- Count of sequences P- A sequence of length P. PCD- All P-sequence in AS with support ≥ Min-support S-Finder- to find the possible subsets for prune. SB-Pruner- for determining the frequent pattern Table 1. Database for Employee and their Item-Sets Emp-ID E1 E2 E3

Item-Sets 10,20 10,20,30 10,20,30,40

Algorithm J2MPP-Mine (AS, Min-support) Algorithm SB-Pruner (PCD) 1. Input Item-Sets in mobile database. 1. Input PCD 2. for ( ; AS!=NULL ; ) 2. for ( ; PCD! =NULL ; ) 3. { 3. P= first element of PCD 4. Find the count of all sequences by S-Finder. 4. If P is not frequent in the database 5. if (CD>Min-sup) 5. { 6. { 6. Remove P and its supersets from PCD 7. PCD=PS 7. } 8. if (PCD!=NULL) 8. else 9. SB-Pruner (PCD) 9. { 10. } 10. Add supersets to PCD 11. else 11. Remove P from PCD 12. { 12. } 13. exit(0) 13. Add PCD to LI 14. } 14. Print LI 15. } 15. Finish. B. Work of S-Finder A sequence of transactions of an employee, ordered by transaction times T1, T2 and T3. Given a database of Table 1 for employee transactions, the problem of mining Incremental Sequential patterns is to find the maximal Incremental Sequences among all sequences that have a certain user-specified minimum support. We use the SFinder technique by which we can assign a minimum support value and according to that value we can compare the transactions of an employee. If count of sequences (CD) is greater than the minimum support we include those sequences in the pruning

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list, otherwise we skip those transactions from the list .Each such maximal Incremental Sequence represents an Incremental sequence pattern which is included for SB-Pruner. C. Work of SB-Pruner We apply SB-Pruner on P-sequence in AS with support greater than or equal to Min-sup in the database of employee transaction. First we input the data set of PCD. The database is short in comparison of the original database, we only consider those item set which having a support greater than or equal to minimum support. We apply the pruner algorithm on the database until all the transactions are compared. Let the first element in the PCD is P. If P is not frequent in the current database, remove its subset and superset from the current database which is PCD. We apply the process for each Transactions of employee in the current database. If it is frequent in the current database then add its superset in the PCD and removes its subset from the PCD. It means we include the superset of P but delete the transaction P from the current database. We can apply the J2MPP-Mine method to determine the pattern and then the pruning strategy to find the Increment sequential frequent pattern in the current database.

4 Evaluations and Result In this section, we describe the evaluation and result of proposed method. The entire evaluation and result consists of three phases: 1) Read the data from the database 2) Apply S-Finder to find the frequent sequences which is greater than or equal to minsupport 3) Apply SB-Pruner 4) Add the superset in the list and remove the related subset from the list. Finally we find the frequent pattern or knowledge from huge amount of data. 1) Read the data from the database Fig 1 shows read data from the database where we want to apply the S-Finder method. Item-Sets 10,20 10,20,30 10,20,30,40 Fig. 1. Read Data

2) Apply S-Finder We apply SB-Pruner on P-sequence in AS with support greater than or equal to Minsup in the database of employee. Fig 2 shows that individual Item sets are counted and check with minimum support and find the final list to prune.

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Count

10,20 10,20,30 10,20,30,40

3 2 1 Fig 2. S-Finder

3) Apply SB-Pruner We apply SB-Pruner on P-sequence in AS with support greater than or equal to Minsup in the database of employee transaction. First we input the data set of PCD. According to the count values item sets are arranged and find the final result after pruning which is shown in fig 3. Sequential Pattern 10,20 10,20,30 10,20,30,40

Final Prune No

10,20,30 10,20,30,40 10,20,30,40 10,20,30,40 10,20,30,40

No No Yes

Fig. 3. Final Result after Pruning

4) Delete Subset First checks the subset if it is presents in the superset then delete the subset. Add the superset in the final list and remove the related subset from the list. This eliminates the redundancy occurs in the frequent list. The final result is 10, 20, 30.The final result shown in the simulator is the superset value which is frequent. Our experiments show that the system performance depends almost entirely on the computing power of the server on which the data mining task is executed. On the contrary, the overhead due to the communication between MIDlet and Data Mining Service does not affect the execution time in a significantly way, since the amount of data exchanged between client and server is very small. In general, when the data mining task is relatively time consuming, the communication overhead is a negligible percentage of the overall execution time.

5 Conclusions and Future Work In this paper, we have proposed a novel method, namely J2ME-based Mobile Progressive Pattern Mine (J2MPP-Mine) for effective mobile computing. We use different strategies and method subsequently like S-Finder for finding the frequent pattern based on minimum support. Then apply the SB-Pruner method to apply the pruning strategy on the S-Finder database. Finally include the superset belonging to the frequent subset and delete all the subset. To evaluate the performance of the proposed method, we conducted a series of experiments.

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The small size of the screen is one of the main limitations of mobile device applications. In data mining tasks, in particular, a limited screen size can affect the appropriate visualization of complex results representing the discovered model. In future we can overcome this limitation by splitting the result in different parts and allowing a user to select which part to visualize at one time. There is a need of such mobile system

that would be able to manage the data and functional complexity overheads and could provide the synchronization between MIDLET and data mining services. So we also concern on small PDA devices which include the best features of MIDP and CLDC.

References 1. Veijalainene, J.: Transaction in Mobile Electronic Commerce, Dagstuhl Castle, Germany (September 1999) 2. Varshney, U., Vetter, R.J., Kalakota, R.: Mobile Commerce: A New Frontier. IEEE Computer 33 (October 2000) 3. Ben-Dor, A., Yakhini, Z.: Clustering gene expression Patterns. Journal of Computational Biology 6, 281–297 (1999) 4. Akyildiz, I.F., Wang, W.: The Predictive User Mobility Profile Framework for Wireless Multimedia Networks. IEEE/ACM (December 2004) 5. Soh, W.-S., Kim, H.: QoS Provisioning in Cellular Networks Based on Mobility Prediction Techniques. IEEE Comm. (January 2003) 6. Akyildiz, I.F., Mcnair, J., Ho, J.S.M., Uzunalioglu, H., Wang, W.: Mobility Management in Next-Generation Wireless System. IEEE, Los Alamitos (1999) 7. Peng, W.-C., Chen, M.-S.: Mining User Moving Patterns for Personal Data Allocation in a Mobile Computing System. In: 29th Int’l Conf. Parallel Processing, pp. 573–580 (August 2000) 8. Yun, C.-H., Chen, M.-S.: Mining Mobile Sequential Patterns In a Mobile Commerce Environment. IEEE Man, and Cybernetics (2007) 9. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Int’l Conf. Data Eng. (ICDE 1995), pp. 3–14 (March 1995) 10. Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, Springer, Heidelberg (1996) 11. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery (1997) 12. Wang, J., Chirn, G., Marr, T., Shapiro, B., Shasha, D., Zhang, K.: Combinatiorial Pattern Discovery for Scientific Data: Some Preliminary Results. In: Proc. 1994 ACM-SIGMOD Int’l Conf. Management of Data, SIGMOD 1994 (May 1994) 13. Zaki, M.J.: Efficient Enumeration of Frequent Sequences. In: Seventh Int’l Conf. Information and Knowledge Management, CIKM 1998 (1998) 14. Masseglia, F., Cathala, F., Poncelet, P.: The PSP Approach For Mining Sequential Patterns. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, Springer, Heidelberg (1998) 15. Lu, H., Han, J., Feng, L.: Stock Movement and n-Dimensional Inter-Transaction Association Rules. In: DMKD (June 1998) 16. Bettini, C., Wang, X.S., Jajodia, S.: Mining Temporal Relationships with Multiple Granularities in Time Sequences (1998)

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17. Ózden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: ICDE 1998 (Feburary 1998) 18. Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the Discovery of Interesting Patterns in Association Rules. In: VLDB 1998 (August 1998) 19. Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: EDBT (1996) 20. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: ICDE 1995 (March 1995) 21. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology (1999) 22. Pei, J., Han, J., Mortazavi-Asl, B., Zhu, H.: Mining Access Patterns Efficiently from Web Logs. In: Proceedings of 4th Pacific Asia Conference on Knowledge Discovery and Data Mining, Kyoto, Japan, pp. 396–407 (April 2000) 23. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rule between Sets of Items in Large Databases. ACM SIGMOD (May 1993) 24. Agrawal, R., Srikant, R.: Fast algorithm for mining Association rules in large databases (September 1994) 25. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14 (March 1995) 26. Cannataro, M., Talia, D.: The Knowledge Grid. Communications of the ACM (2003) 27. Foster, I.: Globus Toolkit Version 4: Software for Service-Oriented Systems. In: Jin, H., Reed, D., Jiang, W. (eds.) NPC 2005. LNCS, vol. 3779, pp. 2–13. Springer, Heidelberg (2005) 28. Talia, D., Trunfio, P., Verta, O.: WSRF Services for Composing Distributed Data Mining Applications on Grids: Functionality and Performance. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 1080–1089. Springer, Heidelberg (2006) 29. Talia, D., Trunfio, P.: Mobile Data Mining on Small Devices through Web Services. John Wiley & Sons, Chichester (2007) 30. Berman, F.: From TeraGrid to Knowledge Grid. Communications of the ACM 44(11), 27–28 (2001)

CDPN: Communicating Dynamic Petri Net for Adaptive Multimedia Presentation Sarath Chandar A.P, Arun Balaji S, Venkatesh G, and Susan Elias Department of Computer Science and Engineering Sri Venkateswara College of Engineering Tamil Nadu, India [email protected], [email protected] [email protected], [email protected]

Abstract. The programmable Dynamic Petri Nets(DPN) can efficiently model interactive and iterative distributed multimedia presentations. However, the dynamic adaption of the presentation is not possible using isolated DPNs. This paper proposes the concept of communicating Dynamic Petri Nets (CDPN). The declaration and utilization of global variables and functions has been used in the paper to augment the existing DPNs with the communicating feature. There are several distributed systems that can be modeled using the proposed CDPN. In this paper a domain specific application illustrating the potential of CDPN to model adaptive e-learning system is presented. Keywords: Communicating, dynamic, petri nets, e-learning, distributed applications, multimedia.

1 Introduction and Related Work Petri Nets have been used widely by researchers for modeling concurrency. The feature of communicating petri nets has not yet been explored and is the focus of this proposed work. The motivation behind this work was the need to develop a distributed multimedia presentation system that would ensure the synchronized playout, while simultaneously handling adaptation and user interactions effciently. A labelled Petri net [1] is a petri net in which transitions are labeled by actions. It is extended to Communicating Interface Process(CIP) in [2] for the design of asynchronous modules. Here communication is done by means of low level signaling scheme. Communication between Finite state machines has been presented in [3]. This system was purely a broadcast communication and not suitable for adaptation. Hence this paper proposes the integration of this communicating feature into an existing dynamic petri net model. The basic Petri Net [1] model has been extended in several research work including DPN [4]. The CDPN is primarily an extension of the DPN model. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 165–170, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Proposed – Communicating Dynamic Petri Net 2.1 Definitions A Communicating Dynamic Petri Net structure, S, is a 13-tuple. S=(P,T,I,O,τ,Pd{F},NL,FL,NG,FG,P{F},Oc{F},Ip). 1. P={p1,p2,....px},where x≥0, is a finite set of places. 2. T={t1,t2,....ty}, where y≥0,is a finite set of transitions, where P ∩ T ≠ φ i.e.., the set of the places and transitions are disjoint. 3. I : T→P∞ is the Input Arc, a mapping from places to bags of transitions. 4. O : T→P∞ is the Output Arc , a mapping from transitions to bags of places. 5. τ={ τ1, τ2,.... τα}, where α≥0 is a finite set of time intervals representing playback time intervals. This is derived from OCPN [5]. 6. NL={n1,n2,....nβ}, where β≥0 is a finite set of persistent control variables. These variables are persistent through every marking of a single DPN. 7. FL={f1,f2,....f γ}, where γ≥0 is a finite set of control functions that perform functions based on any control variable NL. 8. NG={ng1,ng2,....ngδ}, where δ≥0 is a finite set of global control variables. These variables are persistent through every marking of a group of DPN. 9. FG={fg1,fg2,....fgη}, where η≥0 is a finite set of global control functions that perform functions based on any global control variable NG. 10. P{F}:P{F} ⊆ P is a finite set of (static) control places (a subset of P)that executes any control function F. 11. Oc{F}:Oc⊆O is a finite set of (static) control arcs that may be disabled or enabled according to any control function F. 12. Pd{F}:Pd⊆P is a finite set of dynamic places (a subset of P) that takes their value from some control function F. 13. Ip : T→P∞ is the priority Input Function, a mapping from transitions to bags of places. Token={token1,token2,....tokenz},z≥0,is a finite set of dynamic markings on places. The CDPN model adds some new elements to the existing DPN, as well as inheriting some from the extensions of original Petri Net. 1. Local control variables NL are a set of predefined integer variables that have certain relationship to the system being modeled. They hold their values throughout every marking of a single DPN. They may only be modified by local control functions. They are typically initialized by an 'init' function at the start place. 2. Local control functions FL are a set of pre-defined functions which act on control variables in a way determined by some pseudo-code statements. To avoid ambiguity, local control functions follow this rule. For any marking, any control function with conditional statements will be executed after control functions without conditional statements are executed. Further more, the control functions of a control output arc is executed after all other control functions have been executed.

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3. Global control variables NG are a set of predefined integer variables that are persistent through every DPN in the given system. They may be modified by the local control function to indicate the status of the system. They are initialized by the 'init' function at the start place of the main DPN. 4. Global control functions FG are a set of pre-defined functions which act on the global control variable in a way determined by some pseudo-code state- ments. The local control variables of a particular DPN cannot be accessed by Global Control function. But it is to be noted that the local control functions can access global control variable.For any marking, control functions which access the global control variables or make a call to global control functions are executed first. The firing rules of the CDPN have been modified and presented here. 1. A transition is enabled when all input places that are connected to it via an input arc have at least one token. 2. A transition is enabled if there is at least one token in an input place that is connected to the transition via a priority input arc regardless of whether there are tokens in any input place that are connected to the transition via a non priority input arc. 3. A firing of a transition removes a token from its input place and places a token in its output place. The priority rules for the execution of the control functions for each marking are as follows. 1. Control functions which access the Global control functions and Global control variables have the highest priority and are executed first. 2. Control functions without conditional statements have the next higher priority and are executed next. 3. Control functions with conditional statements are executed next. 4. Control functions of control output arcs are of lowest priority and are executed last. 2.2 Design of CDPN and Discussions The power of programmability introduced by DPN in Petri Net is enhanced in CDPN by parameter passing and global functions. CDPN inherits the iterative ability and asynchronous ability of DPN and at the same time reduces the time overhead caused by DPN when adaption is required. In the design of the CDPN global functions are used to control the token. Any bug in the global control function may cause the entire system to perform erratically. It is to be noted that priority arcs are inherited from Pnet [6]. Its importance will be explained in the example application in next section. 2.3 CDPN Applications CDPN can be used to model any distributed systems. The communicating features of the CDPN helps to model them effectively. One such application is explained in the next section.

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3 Dynamic Adaption n of E-Learning Content Using CDPN Consider a scenario where the entire presentation is prepared in English and DPN NA (Figure 1) is used to model this presentation. Audio voice-over in regional languuage could be integrated when reequired. An interactive application could be developed tthat captures the need for add ditional explanation in regional languages based on tthis. When such a need is detected DPN B (Figure 2) is used to run the required regioonal NA audio of the same presentation. Whenever the user requests for more clarity, DPN can communicate and pass this message to B which can play the regional auddio. Meanwhile DPN A will disable its audio.

Fig. 1. DNP-A model

3.1 Design of DPN-A Consider a media playback system of an indeterminate number, n , of meedia resources. Upon starting a token is created at Pstart. Npb, Nui and G are set to 1 via function init. Arc is disabled d and and are enabled. On the nnext transition, the dynamic plaaces for playing audio and video plays segment 1 of eeach resource. A token is now in n Pinc. The inc control function increments the value of Npb by 1. Assuming that numbeer of media segments is greater than 1, remains enabbled

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and remains disabled. Thus T a token will be created in Pcount in the next markiing. Now dynamic place for vid deo will play segment 2 and the dynamic place for auudio will play segment 2 if Nui=1. = This is checked by play function. When the numberr of media segments increases to o N, is enabled and is disabled and the media endds. 3.2 Design of DPN-B DPN B is similar to DPN A except that it plays only audio segments. Wheneveer a token is created at ´ , ´ is initialized and on next transition, audio segm ment ´ corresponding to is played at ´ . Next transition is enabled and ´ is incremented at ´ by inr function. f If ´ N, will be disabled and willl be enabled which continues neext iteration. 3.3 Modeling ADAPT Fu unction To model the required aud dio adaptation, a global control variable G and a gloobal control function adapt arre used. Whenever the learner cannot understand the language, he/she can requ uest for adaptation. A token is created at Padapt w when adaptation is requested. On n next transition, ad_ audio function at Paa will set Nuii=2. When the token comes to Pcont in DPN A, user interrupt is checked by the functionn ui and if Nui=2, Npb is decrem mented to go back few steps. Npb value is stored in a gloobal variable ‘start’ and adapt fu unction is called . This adapt function will create a tokenn at ´ of DPN B. Now ´ of DPN B is initialized to ‘start’ value and regional auudio is played. At the same time play function disables the audio playback of DPN A.

Fig. 2. DNP-B model

Here, no token will bee created at Pinc, if is disabled. Hence priority arc is used after to assure that the playback continues even when the audio is muted.

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3.3 Modeling RESUME Function A token is created at the place Pres whenever the learner thinks that the presentation can be continued in English. On next transition , reg_audio function will set Nui=1 and calls the global function ‘resume’ .‘resume’ will set G=1 and hence will be disabled. Also it will remove the token at ´ hence regional audio playback is stopped. As Nui is set to 1, will play the audio segment Npb.

4 Conclusion and Future Work The CDPN is a model that enhances the programmability and flexibility of DPN to the next level. It can be applied in any distributed system environment. The major issue of CDPN is the synchronization among the Petri Net when they run concurrently. Future work may be done on improvising the synchronization characteristics among the communicating Petri Net. In addition , some CDPN-based author ware, toolkits or middleware can be developed.

Acknowledgement We would like to acknowledge and thank the Defence Research Development Organisation (DRDO), New Delhi, India for granting us Extramural Research Funds for carrying out this research work. This work is part of the research project titled “The design and development of a multimedia presentation system that streams MPEG-21 compatible media-on-demand”.

References 1. Peterson, J.L.: Petri net theory and the modeling of systems. Prentice-Hall, Englewood Cliffs (1981) 2. Lin, B., de Jong, G., Kolks, T.: A communicating petri net model for the design of concurrent asynchronous modules. In: DAC 1994: Proceedings of the 31st annual Design Automation Conference, pp. 49–55. ACM, New York (1994) 3. Brand, D., Zafiropulo, P.: On communicating finite state machines. J. ACM 30(2), 323–342 (1983) 4. Tan, R., Sheng-Uei: A dynamic petri net model for iterative and interactive dis- tributed multimedia presentation. IEEE Transactions on MultiMedia 7(5), 869–879 (2005) 5. Little, T.G.C., Ghafoor, A.: Synchronisation and storage models for multimedia objects. IEEE J. Select. Areas Commun. 8, 413–427 (1990) 6. Guan, S.U., Yu, H.Y., Yang, J.S.: A prioritized petri net model and its application in distributed multimedia systems. IEEE Trans. Comput. 47(4), 477–481 (1998)

Developing a Web Recommendation System Based on Closed Sequential Patterns Utpala Niranjan1, R.B.V. Subramanyam2, and V.Khanaa3 1

Department of Information Technology, Rao & Naidu Engineering College, Ongole, Andhra Pradesh, 523 001, India [email protected] 2 Department of Computer Science and Engineering, National Institute of Technology, Warangal, Andhra Pradesh 506 004, India [email protected] 3 Department of Information Technology, Bharath University, Chennai, Tamilnadu 600 020, India [email protected]

Abstract. The proposed system is mainly based on mining closed sequential web access patterns. Initially, the PrefixSpan algorithm is employed on the preprocessed web server log data for mining sequential web access patterns. Subsequently, with the aid of post-pruning strategy, the closed sequential web access patterns are discovered from the complete set of sequential web access patterns. Then, a pattern tree, a compact representation of closed sequential patterns, is constructed from the discovered closed sequential web access patterns. The Patricia trie based data structure is used in the construction of the pattern tree. For a given user’s web access sequence, the proposed system provides recommendations on the basis of the constructed pattern tree. The experimentation of the proposed system is performed using synthetic dataset and the performance of the proposed recommendation system is evaluated with precision, applicability and hit ratio Keywords: Sequential pattern mining, Web Personalization, Web recommendation, Web server log data, Prefixspan, Sequential database, Web access pattern, Pattern tree.

1 Introduction The development of data mining techniques has been centralized on discovering hidden data in an efficient way that is beneficial for corporate decision-makers [1, 2]. Sequential pattern mining is an important subject of data mining which is extensively applied in several areas [3]. In general, sequential pattern mining is defined as determining the complete set of frequent subsequences in a set of sequences [4, 8]. Each event is considered as a collection of items/itemset occurring at the same time [5]. Generally, all the transactions of a customer are collectively considered as a sequence, known as customer-sequence, where each transaction is denoted as an item set in that sequence and all the transactions are listed in a particular order in connection with the transaction-time [6]. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 171–179, 2010. © Springer-Verlag Berlin Heidelberg 2010

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The rest of the paper is organized as follows: Section 2 presents a brief review of the researches related to the proposed system. Section 3 details the proposed web recommendation system. The experimental results and performance evaluation of the proposed system are given in Section 4 and finally the conclusions are summed up in Section 5.

2 Review of Related Research Numerous researches are available in the literature for web recommendation system using sequential pattern mining. Here, we present some of the researches related with closed sequential pattern mining along with web recommendation system based on sequential pattern mining. Zhou. B et al. [9] have proposed an intelligent Web recommender system identified as SWARS (sequential Web access-based recommender system) that employs sequential access pattern mining. In the proposed system, CS-mine, an efficient sequential pattern mining algorithm was made use of to recognize frequent sequential Web access patterns. The access patterns were then stored in a compact tree structure (Pattern-tree) which was then employed for matching and generating Web links for recommendations. The performance of the proposed system was assessed on the basis of precision, satisfaction and applicability. An efficient sequential access pattern mining algorithm, called CSB-mine (Conditional Sequence Base mining algorithm) was presented by Baoyao Zhou et al..[10] The presented CSB-mine algorithm was on the basis of conditional sequence bases of each frequent event which removes the need for constructing WAP-trees. This enhanced the efficiency of the mining process considerably in comparison to WAP-tree based mining algorithms, particularly when the value of support threshold becomes smaller and the database size gets larger. Cui Wei et al. [11] have presented a hybrid web personalization system which was on the basis of clustering and contiguous sequential patterns. Their system clustered log files to find out the basic architecture of websites, and for each cluster, they employed contiguous sequential pattern mining to optimize the topologies of websites further. They have presented two evaluating parameters to test the performance of our system. Zhenglu Yang et al. [8] have presented an efficient sequential mining algorithm (LAPIN_WEB: LAst Position INduction for WEB log), which is an extension of previous LAPIN algorithm to extract user access patterns from traversal path in Web logs. Web log mining system comprises of data preprocessing, sequential pattern mining and visualization. The experimental results and performance studies established that LAPIN_WEB was efficient and outplayed familiar PrefixSpan by up to an order of magnitude on real Web log datasets.

3 A Proposed Web Recommendation System Based on Sequential Patterns Web Personalization is an application of data mining and machine learning techniques to build models of user behavior. It can be useful to the task of predicting user needs and adapting future interactions with the main aim of improved user satisfaction. A

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unique and important class of personalized Web applications is represented by Web recommendation systems. It highlights on the user-dependent filtering and selection of relevant information. Several approaches like Content-Based Filtering, Clustering Based Approaches, Graph Theoretic Approaches, Association and Sequence Rule Based Approaches are available in the literature for designing web recommendation system. Here, the proposed web recommendation system is designed based on the closed sequential patterns. The proposed system is used for generating a personalized Web experience for a user. The block diagram of the proposed web recommendation system based on closed sequential patterns is given in Fig 1.

Fig 1. Block diagram of the proposed web recommendation system

3.1 Preprocessing In data preprocessing phase, raw Web logs need to be cleaned, analyzed and converted for further step. A Web log is a file to which the Web server writes information each time a user requests a resource from that particular site. Most logs use the format of the common log format. Each entry in the log file consists of a sequence of fields relating to a single HTTP transaction with the various fields. The input for the proposed web recommendation system is a web server log data and it comprises IP address, access time, HTTP request method used, URL of the referring page and browser name (for an instance, Web server log file: 192.162.26.12 [12/Oct/2009:11:17:55] "GET / HTTP/1.1" “http://en.wikipedia.org/wiki/ Association_rule_learning" Mozilla/5.0 Windows xp). It is difficult for these web server log data to be directly used to mine the desired sequential pattern mining process. So, due to that phenomenon, the following preprocessing techniques need to be used in the raw web server log data. (1) User Identification: User identification means identifying each user accessing web page, whose goal is to mine every user’s access characteristic. Users may be tracked based on the IP address of the computer requesting the page and user sessions. A new IP address is used to identifying the new user and at the same time, the user session must be within certain time limits.

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(2) Mapping: For every identified user, the visiting web pages are arranged into row-wise in such a way that it forms the sequential database D. 3.2 Mining of Sequential Web Access Pattern The Sequential web access patterns are mined from the sequential database D , which is a set of 2-tuples ( sid , α ) , where sid is a user-id and α is a sequence of web pages accessed by users. The problem of mining sequential pattern is defined as: Let set of web access events. A sequence

α = Z1 K Z l

W = {z1 , K , z n

} be a

is an ordered list of web access

Z i (1 ≤ i ≤ l ) in a sequence may have a special attribute like, Z i . t , which registers the time when the web access event Z i was

events. A web access event timestamp denoted as executed.

As

a

notational

convention,

α = Z 1 L Z l , Z i . t < Z j . t for 1 ≤ i < j ≤ l. .

α

with length

len(α ) = l . A sequence α = Z 1 K Z n

another sequence β

= Y1 LYm

α⊆β,

there

,

( n ≤ m)

and

β

a

sequence

The number of web access event in a

sequence is called the length of the sequence. A sequence sequence, denoted as

for

l

is called an

l-

is called a subsequence of

a super-sequence of

α , denoted as

1 ≤ i1 < L < in ≤ m such that Z 1 ⊆ Yi1 , L , Z n ⊆ Yin . A tuple ( sid , α ) in a sequence database S is said to contain a sequence γ , if γ is a subsequence of α . The number of tuples in a sequence database S containing sequence γ is called the support of γ , denoted as sup(γ ) . Given a positive integer min_ sup as the support threshold, a sequence γ is a sequential pattern in sequence database S if sup(γ ) ≥ min_ sup . if

exist

integers

3.2.1 PrefixSpan Algorithm The proposed recommendation system utilizes Prefixspan, a well-known patterngrowth algorithm, for mining the complete set of sequential web access pattern from the sequential database D . The main advantage of PrefixSpan is the use of projected databases. An a-projected sequence database is the set of subsequences in the sequence database that are suffixes of the sequences that have the prefix a. In every step, the algorithm checks for the frequent sequences with prefix a, in the correspondent projected database. The algorithmic description of PrefixSpan algorithm is shown in Fig 2. 3.3 Mining of Closed Sequential Web Access Patterns Definition: The set of closed frequent sequential pattern is defined as follows The recommendation CS = {α | α ∈ FSand ∃/β ∈ FSsuch thatα ⊆ β andsupport(α ) = support(β )} system used with the closed sequential pattern mining has the same expressive power as compared with the regular sequential pattern mining. Additionally, the closed

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Fig 2. Pseudo Code of the PrefixSpan algorithm

sequential patterns have the capability to provide the compact result set (reducing the generation of the number of sequential patterns). First, each sequential web access pattern in the pattern set FS i is compared with the other patterns in the set, for instance FS j . The patterns

FS i

is removed from the

pattern set FS , if and only if, (1) the support of both web access patterns FS i and FS j should be same and (2) FS i must be a subset of FS j ( FS i ⊆ FS j ). At the end, we obtain the closed pattern set CS ( CS ⊆ FS ) from the pattern set FS . Example: Consider a sequential database of web access sequence D in Table 1. The complete set of sequential patterns with min_ sup = 2 are FS = {a:4, aa:2, ab:4, abb:2, abc:4, ac:4, b:4, bb:2, bc:4, c:4, ca:3, cab:2, cabc:2, cac:2, cb:3, cbc:2, cc:2}. The set FS consists of 17 sequences. From these 17 frequent sequential patterns, the closed sequential patterns discovered are: CS = {aa:2, abb:2, abc:4, ca:3, cabc:2, cb:3}. From the above example, it is clear that closed sequential pattern set CS is a compact list of frequent sequential pattern set FS . Table 1. Sample sequential database of web Sequences

User id 1 2 3 4

Web access sequence caabc abcb cabc abbca

3.4 Construction of Pattern Tree The procedure for constructing the pattern tree for an efficient system based on closed sequential patterns is 1.Create an empty root node. 2. Insert the most sub pattern in the closed pattern set CS into a node next to the root node 3. Insert the postfixes of pattern into child node if the current pattern to be inserted is a super pattern of

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inserted patterns. 4. Otherwise, current pattern is inserted into the node next to the root node. 5. Step 3 and step 4 is repeated for every pattern in the closed pattern set CS. Example: The closed web access sequential pattern CS ={aa: 2, abb: 2, abc: 4, ca: 3, cabc: 2, cb: 3}. Initially, we create an empty root node. Then, web access patterns , , and are inserted next into the root node based on the procedure depicted above. Web access pattern is a super pattern of so that the postfix of i.e., is inserted into a child node of . Moreover, is also inserted next to the root node. The pattern tree for the chosen example is shown in Fig 3.

Fig. 3. The Pattern-tree 3.5 Generation of Recommendations for User Access Sequences The constructed pattern tree is used for matching and generating web links for recommendations for user access sequences. Once the pattern tree is constructed, instead of using the sequential database of web access sequence D , we can use the constructed pattern tree for the generation of recommendations. The recommendations are retrieved for a given user’s web access sequence S , length of the user web access sequence S must satisfy the thresholds (minlen and maxlen). For a user’s web access sequence, initially, the sequences with the presence of the user access sequence are identified from the constructed pattern tree. Then, the postfixes of those patterns are provided as recommendation to the user’s web access sequence. For a pattern without postfixes, it’s child node are provided as recommendations.For instance, the proposed system provides recommendations R for user web access sequences and as follows: Case 1: The constructed pattern tree is searched for sequential patterns with the presence of User’s web access sequence . In the pattern tree (shown in figure 3), next to the root node has the sequences and that are subset of given user web access sequence . So the postfixes of these sequential patterns i.e., and are given as recommendations to the user ( R = b: 2 and c: 4). Case 2: For user web access sequence , the pattern tree (shown in figure 3) is searched for pattern with the presence of . As the pattern does not have any postfixes, it’s child node i.e., is provided as recommendations for the user ( R = b: 2).

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4. Experimental Results and Analysis The proposed recommendation system is implemented in Java (jdk 1.6). The synthetic dataset is used for evaluating the performance of the web recommendation system. The used synthetic dataset has a collection of web access sequence and it is spitted into two parts: (1) Training dataset: It is used for designing the web recommendation system based on mined closed sequential patterns on it and (2) Test dataset: It is used to test the designed web recommendation system. At first, the pattern tree is constructed by using the training dataset and then, the proposed web recommendation system is evaluated with the test dataset by using the evaluation measures given below. 4.1 Evaluation Measures By evaluating the proposed system, we have used three measures such as precision, applicability and hit ratio. The formal definition of these three measures is given as, P recision

Where,

=

R+ R

+

(1)

+ R−

R + Æ Number of correct recommendations. R − Æ Number of incorrect recommendations.

Definition: Let S = s1 s 2 L s j s j+1 L s n be a web access sequence of test dataset. The recommendation R = {r1 , r2 ,L, rk } is generated by using the constructed pattern

subsequence S sub = s1 s 2 L s j ( minlen ≤ j ≤ maxlen) . The is recommendation is said to be correct, if it contains s j +1 ( s j +1 ∈ R ). Otherwise, said to be incorrect recommendation.

tree

for

the

Applicability =

Where,

R+ + R− |R|

(2)

| R | Æ Total number of given requests Hit ratio = Precision × Applicability =

R+ R

(3)

4.2 Experimental Results The experimental results of the proposed recommendation system are presented in this section. We measure the precision, applicability and the hit ratio for different support thresholds and the results are plotted in graph as shown in figure 4. The comparison of sequential pattern mining (SPM) with closed sequential pattern mining (Closed SPM) in terms of generated web access patterns is given in Table 2 and the corresponding graph is shown in fig 5.

U. Niranjan, R.B.V. Subramanyam, and V. Khanaa EvaluationMeasures(%)

178

120 100 80

Precision

60

Applicablity

40

HitRatio

20 0 10

20

30

40

50

Support (%)

Fig 4. Evaluation measures vs. Support threshold

Support

SPM

10 20 30 40 50

6203 1125 559 218 92

Closed SPM

4760 1027 470 201 85

7000 6000 patterns (%)

Number of web access

Table 2. Number of patterns generated for SPM and closed SPM

5000 4000

SPM

3000

Closed SPM

2000 1000 0 10

20

30

40

50

Support (%)

Fig 5. Comparison graph of SPM and Closed SPM

5 Conclusion The proposed Developing a web recommendation system is validated with the synthetic dataset and the experimental results showed that the proposed system outperformed with good precision, applicability and hit ratio.

References 1. Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons Inc., New York (2002) 2. Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)

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3. Hou, S., Zhang, X.: Alarms Association Rules Based on Sequential Pattern Mining Algorithm. In: Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Shandong, vol. 2, pp. 556–560 (2008) 4. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, pp. 3–14 (1995) 5. Orlando, S., Perego, R., Silvestri, C.: A new algorithm for gap constrained sequence mining. In: Proceedings of the ACM Symposium on Applied Computing, Nicosia, Cyprus, pp. 540–547 (2004) 6. Zhao, Q., Bhowmick, S.S.: Sequential Pattern Mining: A Survey, Technical Report, CAIS, Nanyang Technological University, Singapore, no.118 (2003) 7. Yang, Z., Wang, Y., Kitsuregawa, M.: An Effective System for Mining Web Log. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 40–52. Springer, Heidelberg (2006) 8. Eirinaki, M., Vazirgiannis, M.: Web Mining for Web Personalization. ACM Transactions on Internet Technology (TOIT) 3(1), 1–27 (2003) 9. Zhou, B., Hui, S.C., Chang, K.: An intelligent recommender system using sequential Web access patterns. In: IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 393–398 (December 2004) 10. Zhou, B., Hui, S.C., Fong, A.C.M.: Efficient sequential access pattern mining for web recommendations. International Journal of Knowledge-based and Intelligent Engineering Systems 10(2), 155–168 (2006) 11. Wei, C., Sen, W., Yuan, Z., Lian-Chang, C.: Algorithm of mining sequential patterns for web personalization services. ACM SIGMIS Database 40(2), 57–66 (2009) First UTPALA NIRANJAN received M.Sc., and M.Tech., degrees from Department of Computer Science and Information Technology from Sri Venkateswara University, Tirupati and Bharath University, Chennai in 1999 and 2005 respectively. He is working in Rao & Naidu Engineering College, Ongole. He is currently pursuing the Ph.D degree from Bharath University, Chennai, working closely with Dr.R.B.V Subramanyam, NIT Warangal and Dr.V.Khanaa, Bharath University Chennai. Second Dr.R.B.V..SUBRAMANYAM received the M.Tech., and Ph.D degrees from IIT Kharagpur. He is working in NIT Warangal, Andhra Pradesh. He works in the field of Data mining. He is one of the reviewer for IEEE Trans. On Fuzzy Systems and also for Journal of Information Knowledge Management. Membership in Professional bodies:1.Member, IEEE; 2. Associated Member, The Institution of Engineers (INDIA). Third Dr.V.KHANAA is working as Dean – Information, Bharath University, Chennai.

Nearest Neighbour Classification for Trajectory Data Lokesh K. Sharma1, Om Prakash Vyas2, Simon Schieder3, and Ajaya K. Akasapu1 1

Rungta College of Engineering and Technology, Bhilai, India [email protected], [email protected] 2 Indian Institute of Information Technology, Allahabad, India [email protected] 3 Westfälische Wilhelms-Universität Münster, Germany [email protected]

Abstract. Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes a nearest neighbour based trajectory data as two-step process. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy). In our method first, we build a classifier from the pre-processed 03 days training trajectory data and then we classify 04 days test trajectory data using class label. The resultant figure shows the our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics. Keywords: Trajectory Data, Classification, Trajectory Data Mining.

1 Introduction Spatiotemporal data mining represents the confluence of several fields including spatiotemporal databases, machine learning, statistics, geographic visualization, and information theory. Exploration of spatial data mining [4][7] and temporal data mining has received much attention independently in KD (Knowledge Discovery) and DM (Data Mining) research communities [3]. Nevertheless, the need to investigate both “spatial” and “temporal” relations at the same time complicates the data mining tasks. A crucial challenge in spatiotemporal data mining is the challenge the exploration of efficient methods due to the large amount of spatiotemporal data and the complexity of spatiotemporal data types, data representation, and spatial data structure. Advances in wireless technologies gave rise to various wireless services such as mobile communication, vehicle telematics and satellite navigation. Today Personal Digital Assistants (PDA), mobile phones and various other devices equipped with Global Positioning System (GPS), Global System for Mobile Communications (GSM), Bluetooth and finally Radio Frequency Identification (RFID) are a part of our daily life [7]. Huge amounts of time-stamped location data, regarded as spatiotemporal data due to its time and space attributes, are being collected by wireless service providers Databases [8]. Examples include human movement, animal movement data, vehicle position data and hurricane track data. There is an increasing V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 180–185, 2010. © Springer-Verlag Berlin Heidelberg 2010

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interest to perform data analysis over this trajectory data or mobility data and such data contains valuable information that needs to be discovered. Mining the trajectory data or mobility data is an emerging area of research [4][6]. It aims at the analysis of mobility data by means of appropriate patterns and models extracted by efficient algorithms. Classification is one of the most important research areas of knowledge discovery in trajectory data. It aims at explaining the behavior of current moving objects and predicting that of future ones. Urban traffic simulations are a straightforward example of application for this kind of knowledge, since a classification model can represent a sophisticated alternative to the simple ad hoc behavior rules, provided by domain experts, on which actual simulators are based [1]. Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. In this paper we focus on building a Nearest Neighbour (NN) classifier for trajectory data. This technique works well in traditional data mining applications and is supported by a strong intuitive appeal. The main issue of a Nearest Neighbour classifier is measuring the distance between two items, and this becomes more complicated for Trajectory Data. In this work, a trajectory similarity technique to measure the distance is used. The support of efficient trajectory similarity techniques is indisputably very important for the quality of data analysis tasks in Trajectory Databases [7].

2 Trajectory Data and Models Trajectory data are normally obtained from location-aware devices that capture the position of an object at a specific time interval. The collection of these kinds of data is becoming more common, and as a result large amounts of trajectory data are available in the format of sample points. In many application domains, such as transportation management, animal migration, and tourism, useful knowledge about moving behaviour or patterns can only be extracted from trajectories, if the background geographic information where trajectories are located is considered. Therefore, there is a necessity for a special processing on trajectory data before applying data mining techniques. Let ℜ denote the set of real numbers and ℜ 2 is restricted to the real plane (although all definitions and results can be generalized to higher dimensions). Then, trajectory, sample trajectory and speed of trajectory can be defined as follows [5]. Definition 1 (trajectory). A trajectory T is the graph of mapping I ⊆ ℜ → ℜ2: t → ∝(t) = (∝x (t), ∝y (t)), i.e. T = {(t, ∝x (t), ∝y (t)) ∈ ℜ × ℜ2 | t ∈ I}. The image of the trajectory ‘T’ is the image of the mapping ‘∝’ that describes ‘T’. The set ‘I’ is called the time domain of ‘T’. Definition 2 (trajectory sample). A trajectory sample is a list {(t0, x0, y0), (t1, x1, y1)… (tN, xN, yN)}, with ti, xi, yi ∈ ℜ for i = 0,…, N and t0 < t1 <…< tN. For the sake of finite representability, the time space points (ti, xi, yi) are assumed rational coordinates.

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3 Related Works on Trajectory Data Mining Trajectory data mining is emerging as a novel area of research and it offers wide application areas. Though , data miners’ are having many constraint to mining the trajectory data, They analyze the trajectory data by means of appropriate patterns and models extracted by efficient algorithms and develop novel knowledge discovery processes explicitly modified to the analysis of mobility with reference to geography, at appropriate scales and granularity. Giannotti et al [2] extended a temporal sequence mining method for trajectory data and introduced trajectory patterns as concise descriptions of frequent behaviours, in terms of both space and time. This algorithm is not efficient for large data sets. Lee et al [6] have proposed partition and group frame based trajectory clustering technique. The advantage of this framework is to discover common sub-trajectories from a trajectory database. This algorithm consists of two phases: partitioning and grouping. The first phase presents a formal trajectory partitioning algorithm using the Minimum Description Length (MDL) principle. The second phase presents a density-based line-segment clustering algorithm. Further this technique is used by Lee et al [8] and they proposed a trajectory classification technique. A number of trajectory classification methods have been proposed mainly in the fields of pattern recognition, bioengineering and video surveillance. Besides, similar problems exist in the field of time-series classification [7]. A common characteristic of earlier methods is that they use the shapes of trajectories to do classification, e.g., by modeling a whole trajectory with a single mathematical function such as the Hidden Markov Model (HMM) [4][7].

4 Nearest Neighbour Trajectory Classification 4.1 Problem Description Classification is one of the fundamental problems in machine learning theory. Suppose we are given n classes of trajectories, and when we are faced with a new, previously unseen trajectory, we have to assign it to one of the classes. The problem can be formalized as follows: (T1, c1),...,(Tm, cm) ∈ (TD × C)

(1)

where TD is a non empty set of the trajectories samples list {(t0, x0, y0), (t1, x1, y1)… (tN, xN, yN)}, with ti, xi, yi ∈ ℜ for i = 0,…, N and t0 < t1 <…< tN and in the present context C = {1,...,n}; the ci ∈ C are called labels and contain information about which class a particular trajectory belongs to. Classification means generalization to unseen trajectory data (T, c), i.e we want to predict the c ∈ C given some new trajectory T ∈ TD. Formally, this amounts to the estimation of a function f: T → C using the inputoutput training data, generated independently and identically distributed according to an unknown probability distribution. 4.2 Algorithm In this study, a methodology which classifies trajectories is proposed. A Nearest Neighbour Trajectory Classification (NNTC) is explored for such purpose. Distance

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similarity is an important issue for Nearest Neighbour Classification therefore an efficient trajectory similarity technique is used [4]. A Nearest Neighbour classifier is a ‘lazy learner’ that does not process patterns during training. When a request to classify a query vector is made the closest training vector(s), according to a distance metric are located. The classes of these training vectors are used to assign a class to the query vector. The nearest-neighbor method predicts the class of a test example. The training phase is trivial: simply store every training example, with its label. To make a prediction for a test example, first compute its distance to every training example. Then, keep the k closest training examples, where k ≥ 1 is a fixed integer; look for the label that is most common among these examples. This label is the prediction for this test example. To predict the c ∈ C given some new trajectory T ∈ TD, NNTC starts with train the trajectories and build a model. Algorithm 1 represents nearest neighbor trajectory classification. Algorithm 1: NN Trajectory Classification Input: Train Trajectories, Test Trajectories Output: Classify Test Trajectories Methods: Compute number of training trajectories NTRAIN Compute number of test trajectories NTEST For i = 1:NTRAIN ; For j = 1:NTEST Sim[i,j] = Route_Similarity(xtrain(,i), xtest(:,j)) End;End Find train-trajectory xtrain which is closest to xtest Assign the class label c(xtest) = c(xtrain) 4.3 Experimental Investigation 4.3.1 Data Preprocessing For the task of NN Trajectory Classification, we used a dataset of moving vehicles in Milan City, which is provided by Milan Metropolitan Authority for research purpose. Data consists of positions of the vehicles, which has been GPS-tracked between April 1, 2007 and April 7, 2007, and are stored in a relational database. The data have been recorded only while the vehicles moved. Each record includes the vehicle-id, date and time, the latitude, longitude, and altitude of the position. To facilitate analysis of movement data, initial preprocessing in the database is performed, which enriches the data with additional fields: the time of the next position in the sequence, the time interval and the distance in space to the next position, speed, direction, acceleration (change of the speed), and turn (change of the direction) [4]. The enriched preprocessed data set is used to derive starting point and ending point of vehicle and Trajectories are separated. The training trajectories and testing trajectories are filtered to use trajectory sampling methods. The training data set contains 8805 points and 221 trajectories. It is classified in 16 classes. The test data set contains 12802 points and 277 trajectories. Training trajectory data and summarized training trajectories are visualized in Fig. 1(a) and Fig. 1(b) test trajectory data.

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(a)

(b)

Fig. 1. (a) Sample training trajectory data set. (b) Sample test trajectory data set.

4.3.2 Result Analysis Our experiment is a two-step process. First, we build a classifier from the preprocessed 3 days training trajectory data. Second, we classify 4 days test trajectory data using class labels after building the classifier. Fig. 2 shows the classified test trajectories. Our experimental investigation yields a significant output in terms of the correctly classified success rate being 98.2%. The summary of accuracy is given in Table 1. To measure the agreement between predicted and observed categorization of a dataset, while correcting for agreement that occurs by chance, is carried out by Kappa statistic. Some people are interested in trajectories in some specific range of area. Therefore we modified NNTC algorithm with introducing some distance by threshold value. The summary this experiment is shown on Table 2. This experiment produces low success rate (91.6%) compared to the same experiment as without distance threshold and also that some trajectories are unclassified in experiment with distance threshold. These limitations can be overcome by using an adaptive technique, and also that our experiment however gives reasonably good results. Table 1. Summary of accuracy (Without distance threshold) Correctly Classified Trajectories Incorrectly Classified Trajectories Kappa statistic Mean absolute error

272 05 85.19 0.018

98.1% 1.8%

Root mean squared error Relative absolute error Root relative squared error Total Number of Trajectories

0.16 0.153 0.023 277

Table 2. Summary of accuracy (With distance threshold= 500 m) Correctly Classified Trajectories Incorrectly Classified Trajectories Unclassified Trajectories Kappa statistic Mean absolute error

254 05 18 83.19 0.018

91.6% 1.8% 6.4%

Root mean squared error Relative absolute error Root relative squared error Total Number of Trajectories

0.16 0.18 0.04 277

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Fig. 2. Test trajectory data set after Classification

5 Conclusion A Nearest Neighbour classification method for trajectory data has been proposed in this paper. Its primary advantage is the high classification accuracy. Extensive experiments have been conducted using real data sets of moving vehicle in Milan city (Italy). The classification results have demonstrated performing classification accuracy as well as classification efficiency. Overall, we have provided a paradigm in trajectory classification. Various real-world applications can benefit from our proposed framework. Though there are many challenging issues such as integration with other feature generation frameworks, and currently being investigated into detailed issues by us as a further and future study.

References 1. Giannotti, F., Pedreschi, D.: Mobility, Data Mining and Privacy: Geographic Knowledge Discovery. Springer, Heidelberg (2008) 2. Ginnotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Trajectory Pattern Mining. In: Proceedings of the 13th ACM SIGKDD, pp. 330–339 (2007) 3. Andrienko, G., Malerba, D., May, M., Teisseire, M.: Mining spatio-temporal data. J. of Intelligent Information Systems 27(3), 187–190 (2006) 4. Andrienko, G., Andrienko, N., Wrobel, S.: Visual Analytics Tools for Analysis of Movement Data. ACM SIGKDD, 38–46 (2007) ISSN: 1931-0145 5. Kuijpers, G., Othman, W.: Trajectory Databases-Data Models, Uncertainty and Complete Query Languages. In: Schwentick, T., Suciu, D. (eds.) ICDT 2007. LNCS, vol. 4353, pp. 224–238. Springer, Heidelberg (2006) 6. Lee, J., Han, J., Whang, K.: Trajectory clustering: a partition-and-group framework. In: Proceedings ACM SIGMOD Int. Conf. on Management of Data, pp. 593–604 (2007) 7. Lee, J., Han, J., Li, X., Gonzalez, H.: TraClass- Trajectory classification Using Hierarchical Region Based and Trajectory based Clustering. In: ACM, VLDB, New Zealand, pp. 1081– 1094 (2008) 8. Koperski K., Adhikary J., Han J.: Spatial Data Mining: Progress and Challenges. In: SIGMOD Workshop on data Mining and Knowledge Discovery (DMKD), pp. 1–10, (1996).

Security-Aware Efficient Route Discovery for DSR in MANET Sanjeev Rana1 and Anil Kapil2 1

Computer Engineering Department, M. M. Engineering College, M. M. University, Ambala, India [email protected] 2 M.M. Institute of Computer Technology and Business Management, M. M. University, Ambala, India [email protected]

Abstract. Most recent Mobile ad hoc network (MANET) research has focused on providing routing services without considering security. The malicious node can easily disturb the functioning of routing protocols. Route discovery phase of Dynamic Source Routing (DSR) requires the ability to verify that no node has been deleted from the path, and no node can be inserted in the path without a valid authentication. In this paper, we present a security mechanism that provides message integrity, mutual authentication and two-hop authentication mechanism without the assistance of online certification authority. Our mechanism not only prevents identity impersonation, replay attacks, but also detect and drop inconsistent RREQ to save network bandwidth. Keywords: DSR, Authentication, MANET, Two-hop Neighbor.

1 Introduction MANET [1] is a group of autonomous mobile nodes connected through wireless links without the support of communication infrastructure. The topology of the network changes dynamically as nodes moves and nodes reorganize themselves to enable communications with nodes beyond their immediate wireless range by relaying messages for one another [2]. In MANET, one of the major concerns is how to increase the routing security in the presence of malicious nodes. Here, our main focus is on the security of DSR [3] protocol. Secure DSR protocols [4],[5] employ cryptographic authentication to facilitate verification of the integrity of the established route. However the nature of the protocol and the specific cryptographic primitives used for authentication will play a large role in determining when and by whom inconsistencies can be detected. In most secure DSR protocol, malicious modification to RREQ message cannot be detected by intermediate nodes that forward the RREQ. In some protocols the destination node can detect inconsistencies and drop such RREQ. In some only the source node, at the end of reverse path, can detect inconsistencies after the route reply (RREP) reaches the source. Obviously, it would be very desirable to facilitate intermediate nodes to be V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 186–194, 2010. © Springer-Verlag Berlin Heidelberg 2010

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able to detect inconsistencies in RREQ to avoid onwards relay of defective RREQs, which after wasting network bandwidth will ultimately fail. In this paper, we proposed a security mechanism for DSR which facilitate intermediate node to detect inconsistencies and thus to avoid defective RREQs and save network bandwidth.

2 Security Vulnerabilities in DSR In MANETs, One of the simplest attacks in DSR protocol is node deletion attack in route discovery phase and described below: Node Deletion Attacks. During route discovery phase, the route list attribute will be updated by every intermediate node. A malicious node can delete the previous node information from the route list and forward it to the next hop. Because route list has been altered maliciously and does not reflect the actual route, this defective RREQ must be dropped as fast as possible. But this does not happened in DSR and it will propagate toward the destination node. For example, consider a scenario, suppose in a MANET topology, there is route exist between node S and node D. It follows a (S → A → B → C → E → D) path. If node C removes all fields inserted by node B, such as the identity of node B, in effect, node C claims to have received the RREQ directly from node A as shown in figure 1. But this defected RREQ will be received and accepted by the destination node D. This type of attack can be solved if we add some kind of authentication which satisfies following requirement, where each node, say, E is able to verify: 1. 2. 3.

the authentication appended by node B and node C that node C is a neighbor of node E, and that node B is a neighbor of node C to prevent node deletion attacks. Table 1. Notation used in the Proposed Security Mechanism

A C D S

B E

Destinatio n N d

Source Node

Actual Route for RREQ

X

Tempered Route for

Symbol K i+

K i−

+ K ca

K −ca

Description Public and Private key of node i

A → B = [M ] {X}K || TA

Public and Private key of Certification Authority Message M from Node A to Node B Message X encrypted with key K Concatenating different part of message Timestamp added by node A

LA

Lifetime of message originated by node A

Cert A RREQ A

Certificate of Node A

Sig A

Route Request created by Node A means that address of Node A is added to the route list Signature created by Node A by encrypting information

RREP A

Route reply created by Node A

THNA A → B

Ticket which prove the claim of node A that is node B its one-hop neighbor to other network nodes

Fig. 1. Node detection attack in RREQ of DSR

3 Literature Review Many security-aware solution [6], [7], [4], [5] has been introduced for DSR. In [7], a security extension to the on demand ad hoc routing protocol is proposed which deals with the lifetime or the validity of the control messages. In this, intermediate nodes do

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not introduce any authentication. Thus, even external nodes can take part and disrupt the routing process. The approach proposed in [6] to secure ad hoc on demand routing protocol used a challenge-response mechanism. A three-way communication occurs between every pair of intermediate nodes increasing overhead considerably and assuming bi-directionally. In Ariadne [4] every intermediate node appends a HMAC based on a TESLA a key that will not be disclosed at least until the destination receives the RREQ. The TESLA keys used for authentication during the forward path are released during the reverse path. Thus, at the end of RREP, the destination can discover node deletion attacks. But this approach [4] assumes prior distribution of secret between every pair. In [5] the authors present many different forms of authentication strategies for securing route discovery. The main focus of the protocol in [5] is to reduce the overhead for carrying over authentication by employing authentication strategies that can be aggregated to save bandwidth. However, the schemes proposed in [5] do not consider node deletion attacks. In this paper, we proposed a Security Mechanism for DSR which facilitate intermediate node to detect inconsistencies and thus to avoid defective RREQs and save network bandwidth.

4 The Proposed Security Mechanism The proposed Security Mechanism is consisting of two modules mutual authentication and two-hop neighbor authentication module. The purpose of mutual authentication module is to identify and authenticate its one hop neighbor. Where, two-hop neighbor authentication module enables a node to regulate the behavior of other node. We are motivated with security enhancement mechanism [8], enables network entities to identify each other in a quick manner. Security services are implemented by extending existing control messages of the DSR protocol as shown in figure 2. There are no changes to the protocol operation itself but each node now performs additional, security related functions, when DSR messages are exchanged. For this, we make the few assumptions such as, the scheme is based on public key cryptography using offline certification authority (CA) and there is no two node colluding attack. Recently schemes that are well suited for this purpose have been identified [9] for efficient predistribution of keys which takes advantage of the fact that even mobile devices can have easy access to large amount of inexpensive storage with very low computational complexity. We use the notation shown in Table 1 to describe our security mechanism and cryptographic operations. 4.1 Mutual Authentication Module

Each node gets a pair of public/private key and its certificate from CA in a secure fashion before communication. We propose a simple and efficient algorithm to authenticate two nodes each other that also foil replay attack. All nodes must know + of CA. A certificate signed by the CA also includes a valid time the public key K ca and expiration time ( Cert i = {ID i , K + , T V , T e }K ca− ). i

Step1: Node A introduces itself to node B using its certificate.

A →

B : C

A

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Step2: Node B introduces itself to Node A and also sends a challenge (Nonce) signed using public key K +A of Node A. B → A : C B || { N B } K + . A

Step3: Node A receives the above message and gets NA , Node A sends its challenge N A to Node B and also add the reply of B challenge. A → B : { N A , N B } K +B

Step 4: Node B decrypt the message, N B shows that this message is the response of previous message and also verify that this message is originated by Node A. Node B prepares the response of Challenge play by Node A. B →

A : ( N A } K +A

4.2 Two-Hop Neighbor Authentication Module

The main purpose of two-hop neighbor authentication module (THNA) is to verify the claim of any node about its neighbors. Node must provide a piece of information which ensures that the relationship with its neighbor node cannot be reproduced by some other node. This requirement can be achieved using digital signature of link information between two nodes. First, two nodes verify each other identity using mutual authentication process, then, they generate a two-hop neighbor authentication ticket in the following format: THNA

Original Sender part

Intermediate Node part

A→B

= [ ID

A

|| ID

B

|| T I || C B || { ID

A

|| ID

B

|| T I } K

− B

] New Node B enters Network

Standard RREQ Message Length Signature Algorithm Lifetime of RREQ Message Timestamp Certificate Length Certificate of Source Signature of Source Standard RREQ Message Length Signature Algorithm Lifetime of RREQ Message Certificate Length Certificate of Intermediate node Signature of Intermediate node THNA ticket with a node from which RREQ received

THNAB→A

B E

SIGB→A

C

SIGA→B A

THNAA→B D

Fig. 2. Secure Extension RREQ DSR

Node A uses THNA during route discovery process

Fig. 3. Two-Hop Neighbor Authentication Mechanism

This is THNA ticket of Node A created for node B and can be read as “Node A ensures that Node B is its one hop neighbor and Node B approves the claim of node A”. Thus, a remote node is able to verify the claim of node A by evaluating the ticket of node A to node B. The significance of THNA is if all connection in a path from a node to its destination is verified by respective THNA, from some initial or start time TI to TI + system_defined_valid_time, one can believe that the route is secure and trustworthy. Each node collects THNA and uses it to build a trusted and secure routing path. Figure 3 shows the two-hop neighbor authentication mechanism in an example of how a new node joins the network. Step1: When new node B enters network. First, mutual authentication will take place, between node A and node B. SIG

A → B

= [ ID

A

|| T 1 ] K

− B

and

SIG

B → A

= [ ID B || T 2 ] K −A

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Step2: Then node A and node B creates their respective THNA tickets and use it during route discovery process as shown in figure 3. THNA

THNA

B

= [ ID

B → A

= [ ID

A →

A

|| ID

A

|| ID

B

|| T

B

|| T

I

2

|| Cert

|| Cert

B

A

|| { ID

|| { ID

A

A

|| ID

|| ID

B

B

|| T

I

|| T

}K 2

}K

− B

] − A

]

This module allows a remote node to build a trusted and secure relationship without the assistance of online trusted authority. The link status must be confirmed by both the nodes hence compromised nodes cannot forge link that don’t exist.

5 Implementation of Security Mechanism to DSR As the source node and destination node initiate the process of RREQ and RREP respectively, only intermediate nodes include respective THNA ticket when forward the control messages on the network. The route discovery process in DSR begins when the source node floods the network with RREQ message. Each node receiving RREQ message performs following actions: • • • • •

First, verify the identity of RREQ forwarding node using mutual authentication to ensure that RREQ received from a genuine one hop neighbor or not. Second, evaluate THNA tickets included in the message for two-hop authentication to prevent node deletion attacks. Third, validates the signature of signed element using the public key of sender by its certificate. Fourth, receiving node checks for replay attack using the RREQ ID and timestamp. Fifth, validates the life time of the message to determine whether it has expired or not.

If any of these tests fail then receiving node must discard this message otherwise, receiving node rebroadcast the route request. Assume that a source node S wants to discover a route with a destination node D and that such a route exist, with intermediate nodes A and B. The source node S must authenticate itself to other nodes when passing its request to locate a target destination. The source node S achieves this by broadcasting the RREQ with security extension as shown in figure 4 is described below in step1. Step1: S → * = [ RREQ

S

|| L S || T S || Cert

S

RREQ message contains a lifetime

|| Sig

S

] Where , Sig

S

= { RREQ

s

|| L S || T S } K

− S

LS that indicates how long this request is valid. If a target node receives the message and finds the period LS has expired, then discard it. A time stamp TS together with the RREQ ID in the original message format will indicate the freshness of the message and help to prevent replay attacks. This message content is signed using private key of the sender so that receiving nodes can verify the integrity of message. The sender certificate is included for the benefit of those nodes that do not already have that certificate. The sender identity is not separately included in the signed part as it is already the part of RREQ S . Node A

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performs all the actions described earlier and if successful then node A first authenticate node S as its one-hp neighbor using mutual authentication and then create THNA A → S with node S. Now, node A rebroadcast route request as in step2. Step2: A → * = [ RREQ Sig

= { RREQ

A

A

A

|| L S || T S || T A || Cert

|| L S || T S || T A || THNA

S

|| Sig A→S

S

|| Cert

A

|| Sig

A

|| THNA

A→S

]

} K −A

This message securely place node A in a possible route between node S and node D. Node A is including THNA A → S in the control message, any next hop node can evaluate and verify it for two-hop neighbor authentication which ensures that node A receives this route request from node S. The is because node A cannot create this ticket alone without the consent node S ( as node S private key is required to create ticket) that further need the successful completion of mutual authentication process between them. Thus, all intermediate nodes can verify the route list using THNA tickets. Only difference in RREQ S and RREQ A is that the latter has added node A’s address to the route list attribute in the RREQ message. So, Route request of any node can be reconstructed and can be verified using their respective signature. If a node receives the same RREQ messages from two different intermediate nodes but with the same ID, it discards the later arrivals, according to the existing DSR definition. When node B receives route request flooded by node A, it performs all the actions describe above. If any of these tests fail then it must discard this message otherwise, rebroadcast the route request in step3. Step3: B → * = [ RREQ Cert

THNA Sig

B

B

= [ THNA

= { RREQ

|| L

B

_ Sig _ List

B

B

S

|| T S || T A || T B || Cert

= [ Sig A → S

|| L

S

S

|| Cert

|| THNA

A

|| Sig

B→ A

A

S

|| Cert _ Sig _ List

|| Cert

B

|| Sig

B

B

|| THNA

B

]

]

]

|| T S || T A || T B || THNA

A → S

|| THNA

B→ A

}K

− B

The process of addition of node address to the route list, signature of message and THNA of intermediate nodes continues until reach to destination node D, if such a route exists. Here, destination node D analyzes and evaluates the message using different signatures and THNAs attached in the message for authentication, replay, validity or lifetime and identity impersonation. Furthermore, if node B tries to delete node A from route list, it need a THNA which cannot be created without the consent of node S. If all tests validates, node D creates a RREP message (unidirectional) and uses the route list attribute of RREQ as in step4. Step4: D → B = [ RREP D || RREQ

_ ID || L D || T D || Cert _ Sig _ List D _ RREP

|| THNA

_ List D _ RREP

]

Cert _ Sig _ List D _ RREP = [ Cert S || Sig S || Cert A || Sig A || Cert B || Sig B || Cert D || Sig D ] THNA _ List D _ RREP = [ THNA A → B || THNA B → D ] Sig D = { RREP D || RREQ

_ ID || D || S || L D || T D || THNA

_ List D _ RREP } K −D

This message allows node D to authenticate itself to all nodes in the route as well as, eventually, to the source S. Node D add its certificate and THNA with its previous node and signature to those of node S, and all intermediate nodes. This will enable Node S to learn the identity of all nodes along the route. The signature of node D and

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the inclusion of ROUTE_ID bind the reply to the initial request. The lifetime L D gives a validity period for the reply. As receiving nodes may use this reply to confirm the path back to the sender, the lifetime L D could be relatively long. THNA inclusion protect from node deletion attack. The purpose of the message in step5 is for node B to authenticate itself to its previous hop Node A and to correlate the request message forwarded by Node A with its response thereby preventing replay attacks from happenings. It also verifies if the reverse path taken by the message indeed matches with the original forward path. The signed element includes the original request and response identification number. Step5: B → A = [ RREP Cert _ Sig _ List THNA _ List Sig

B _ RREP

|| RREQ _ ID || L D || T D || T B || Cert _ Sig _ List

D

B _ RREP

B _ RREP

= { RREP

= [ Cert

= [ THNA D

S

|| Sig

A→S

S

|| Cert

|| THNA

A

|| Sig

B→ A

A

|| Cert

|| THNA

B

B→D

B _ RREP

|| Sig

B

|| THNA _ List

|| Cert

D

|| Sig

D

B _ RREP

|| Sig

]

B _ RREP

]

]

|| RREQ _ ID || D || S || L D || T D || THNA _ List

B _ RREP

} K −B

If, in our example, during packet transmission the node B is unable to reach node D, node B must send a RERR route error message back to the source node S as in step6. This message is unicast to source node S, either via node A or through some other route in the link between node B and node A if it was unidirectional earlier. Step6: B → S = [ RERR B || RREQ _ ID || T B || Cert _ Sig _ List B _ RERR

|| THNA _ List B _ RERR ]

Cert _ Sig _ List B _ RERR = [ Cert S || Sig S || Cert A || Sig A || Cert B || Sig B _ RERR ] THNA _ List B _ RERR = [ THNA A → B ] Sig B _ RERR

= { RERR B || RREQ _ ID || T B || THNA _ List B _ RERR } K −B

6 Performance Evaluation 6.1 Need of Fast Detection

One of the primary motivations of proposed security mechanism is to ensure that malicious modification to RREQ control messages are detected fast/early so that defective RREQs can be dropped as soon as possible. To see how dropping malicious RREQs soon enough can improve the performance of route discovery process. Consider the topology in figure 4, where node D receives two RREQs, through paths (A,B,C,E) and (J,K,L,M,N,P,Q,R). Assume that both path have a malicious node say node C in the first path and node N in the second path which perform illegal modifications to the RREQ which are recognized by the destination node D. The fact that no good path was discovered in this instance does not necessarily means that no good path exists. In this specific scenario, several good paths (without malicious nodes) such as (J,K,L,M,H,I,,E) and (A,B,L,M,H,I,E) exist. Unfortunately, as the RREQ relayed by node C reaches node E earlier than RREQs from other paths, the first three will not be discovered. Similarly, as node U receives the RREQ from node C first, the fourth and fifth good path will not be discovered. On the other hand, if defective RREQs can be detected immediately and stopped from further propagation, the chances of discovering good paths can increase significantly.

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Malicious Nodes: C & N N W

P

G

Q

M

L

K

H

R

I

J A

B

C

E

D

S X

Y

Fig. 4. Topology of a MANET to illustrate the advantages of fast detection defective RREQs

Fig. 5. Improvement in performance realized due to early detection of defective RREQs

6.2 Simulation Model and Result

We use NS2 simulator to simulate our proposed security mechanism to determine the percentage of route discovery attempts between randomly chosen node pairs that succeeds. For the simulations, we generated many random realizations of 250 nodes in a square with unit edges. The range of each node was assumed to be 0.1 units. Out of 250 nodes 50 nodes were assigned as malicious nodes. It is assumed that the malicious node will illegally modify the RREQ. In our simulations, each node had an average of 5 neighbors. Each node could receive as many as RREQ as many of its neighbors. We assume that route discovery attempt between two nodes fail if every such RREQ path includes a malicious node. We simulated RREQ propagation between every pair of good nodes. The simulation result are shown in figure 5 for two cases first, bad RREQs are detected only by the destination node that is late detection (Dashed line) and second, bad RREQs are detected within two hops that is fast/early detection (Solid line) and stopped for further propagation and substantially improve the performance of any demand routing solution by preventing preemption of good path by defective RREQs. The plots have the hop-count between the chosen source-destination pair as the Xaxis. And the Y-axis is the fraction of node pairs that succeed in the route discovery attempts.

7 Conclusion and Future Scope In this paper, we have proposed security mechanism for DSR protocol which enables intermediate nodes to identify and dropped the defective route request attempt during route discovery process. It not only reduces the congestion on the network and save network bandwidth but also prevent from node deletion and node insertion attacks. Although, the processing time for the control message at each node will increases because of security extension of DSR control message format. But, as the computing power is increasing, it is likely to become null and void in future.

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References [1] Corson, S., Macker, J.: Mobile Ad Hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Consideration. IETF RFC 2501 (January 1999) [2] Siva Ram Murthy, C., Manoj, B.S.: Adhoc wireless networks: Architectures and protocols, 1st edn., p. 880. Prentice Hall, USA (2004) ISBN: 10:013147023X [3] Johnson, D.B., Maltz, D.A., Hu, Y.-C.: The Dynamic Source Routing protocol for Mobile Ad hoc Networks (DSR), internet-Draft, draft-ietf-manet-dsr-08.txt, (February 24, 2003) [4] Hu, Y.-C., Perrig, A., Johnson, D.B.: Aridane: A secure on-demand routing protocol for ad-hoc networks. In: The Proceeding of 8th ACM International Conference on Mobile Computing and Networking (September 2002) [5] Kim, J., Tsudik, G.: Securing Route Discovery in DSR. In: IEEE Mobiquitous 2005 (July 2005) [6] Venkatraman, L., Agarwal, D.P.: An Optimised Inter-Router Authentication Scheme for Ad Hoc networks. In: Proceeding of the 13th International Conference on Wireless Communication, Calgary, Canada, July 9-11, p. 129 (2001) [7] Zapata, M.G.: Secure Adhoc On-Demand Distance Vector Routing (SAODV), Mobile Adhoc Networking Group Internet Draft (October 2001) draft-guerrero-manet-saodv-00.txt [8] Rana, S., Kapil, A.: Defending against Node Misbehavior to Discover Secure Route in OLSR. In: Proceeding of BAIP 2010, April 2010. CCIS, vol. 70, pp. 430–436. Springer, Heidelberg (2010) [9] Ramkumar, M.: I-HARPS: An efficient Key Predistribution Scheme for Mobile Computing Application. In: IEEE Globecom, San Francisco, CA (November 2006)

Regression Modeling Technique on Data Mining for Prediction of CRM Manisha Rathi Jaypee Institute of Information technology, Noida [email protected]

Abstract. In this paper Regression Modeling Technique is proposed for the retention of customer and maintains customer loyalty. Prediction attempts to predict the pattern of events on the basis of the input data Here the aim of the paper is to launch desktops and laptops of various configurations on the basis of age, Gender, Price and monthly income. This paper aims at how the concepts of data mining and regression analysis can be applied to achieve the response of the customer by analyzing the relationship among the various customer related attributes. Keywords: Data mining, Prediction, Regression Analysis.

1 Introduction CRM is utilization of the customer related information to enhance the growth of organization for retention of customer. It helps to identify the preference of customer related groups, products and services according to their liking to enhance the cohesion between the customer and organization. Data Mining plays an important role in the prediction of customer related information to help focus them on the most important decision regarding benefit of organization. It is an analytical process in searching out the consistent patterns or relationships among the variables from large data sets and then to validate the findings by applying the relationships to subset of the data. Prediction can be done with the help of existing information stored as historical data. This paper proposes the regression Model for CRM in order to retain customers of organization.

2 Literature Review For implementing best CRM different success factors are available. The data analytics approaches can be followed to predict the target customer which can be different types for example Statistical Analytics or Dynamic Analytics. Further for the data Analysis there can be two forms that can be used to extract models which describes important data classes or predict future data trends [5]. The Prediction can be done on the basis of historical data which predicts the uncertainty of data sets that whether the customer will be satisfied by the product or not. There are two different types of values discrete type values or continuous valued attributes. Here our proposed work will be to predict the continuous values using regression techniques. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 195–200, 2010. © Springer-Verlag Berlin Heidelberg 2010

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3 Problem The problem identified is that how can the predictive Model be used to estimate the probability value associated data attributes. The problem has to be solved if the number of attributes are more than the one independent and one dependent variable.

4 Proposed Work In this paper we propose Regression Modeling Technique which deals with the correlation and association between statistical variables, the variables here are treated in a symmetric way. The various steps of Regression Modeling can be firstly develop CRM Model that collects and analyzes data and targets the desired customer by finding out relationships between customer attributes. then generate Regression Model, next apply Regression Modeling for Data Mining. Finally generate Results. This approach is followed by discussing each and every step and analyzing the Results for predicting the future by examining relationships among the data sets. 4.1 Regression Model The solution Approach to the above problem can be applying Regression Modeling on data Mining Techniques which is based on statistical Methods for CRM that analyses continuous valued attributes. It can be used to estimate the probability value associated with data cube cells. The more number of attributes, more will be number of dimensions in the cuboid. The dimensions are mapped to the attributes of the data set collected. Further the dimensions can be reduced to the 3-D cuboids and 2-D cuboids for the particular set of attributes. Thus the high order cuboids can be built from the lower- order ones. This is the building block of the most of our Regression Model . Thus our Regression Model includes the following steps : 4.2 Regression Analysis The data can be analyzed with the help of statistical analytic technique. These techniques include Linear Regression, which is the simplest form of regression. It models a random variable, Y called a response variable, as a linear function of another variable X which is called as a Predictor Variable [7]. Thus the equation becomes according to linear Regression is: Y= a+bX

(1)

Where the variance of Y is assumed to be constant, a and b are regression coefficients which specifies the Y-intercept and slope of line. The coefficients can be solved with the method of Least Squares, which helps in minimization of the data between the actual data and the estimated line. where Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2), Intercept(a) = (ΣY - b(ΣX)) /N

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5 Results Case I: Here the data is collected about customer w.r.t Age and the corresponding Price of the Laptop can be calculated by Regression Analysis by putting value of X as 22 which comes out to be 40.499.96 Case II: Here the data is collected about customer w.r.t Annual Income and the corresponding Price of the Laptop can be calculated by substituting the values of X as 4.5L which gives the value of Price of Laptop as 47833.33 Case III: The Target Customer can be achieved by substituting the values of X For Example substituting the values of X as 15 which gives the value of Price of Laptop as 34111

Table 1.

Age in Yrs(X) 10 15 20 22 21 23 30 35

Price (Y) 15000 20000 40000 45000 52000 62000 50000 40000

Calculated Y 25777.77778 31912.03704 38046.2963 40500 39273.14815 41726.85185 50314.81481 56449.07407

Error 10777.77778 11912.03704 1953.703704 4500 12726.85185 20273.14815 314.8148148 16449.07407

Table 2.

Annual Income(X) 200000 250000 350000 400000 450000 600000 500000 450000

Price (Y) 15000 20000 40000 45000 52000 62000 50000 40000

Best Calculated Y 17.16666667 23 34.66666667 40.5 46.33333333 63.83333333 52.16666667 46.33333333

Error 2.166666667 3 5.333333333 4.5 5.666666667 1.833333333 2.166666667 6.333333333

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M. Rathi Table 3.

Per month expenses(X) 10 12 20 25 25 22 16 14

Price (Y)i 15 20 40 45 52 62 50 40

Best calculated Y 11.27077276 25.58924545 49.93092038 51.08003172 51.08003172 50.63019388 45.17328081 38.15272801

Best Function Error 3.72922724 5.589245452 9.930920378 6.080031722 . 9199682784 11.36980612 4.826719188 1.847271988

Table 4.

Total Numbers Slope(b) y-Intercept(a)

8 1226.85185 13509.2593

8 0.11667 -6166.66667

8 2.13025 2.15546

Regression Equation(y)

13509.26+1226.85x

-6166.67+0.12x

2.16+2.13x

Mutiple Regression This forms the X and Y dimensions of the 2-D Cuboid. This can be further transformed to 3-D cuboid, which can be further transformed 4-D Cuboid. Taking two attributes as independent variable(Predictor) X1 and X2 and Y one response (dependent variable). Table 5. Per month expenses(X1)

Annual Income (X2)in Lakhs

Price (Y)

Calculated Y

Error

10000

200000

15000

14696.64655

303.3534541

12000

250000

20000

21147.48491

1147.484909

20000

350000

40000

37641.91818

2358.081824

25000

400000

45000

46787.32394

1787.323944

25000

450000

52000

51441.78404

558.2159624

22000

600000

62000

62710.59691

710.5969148

16000

500000

50000

48012.54192

1987.458082

14000

450000

40000

41561.70355

1561.703555

Regression Modeling Technique on Data Mining for Prediction of CRM

199

Table 6. Per month expenses(X1)

Annual Income (X2)in Lakhs

Price (Y)

Calculated Y

Error

10000

200000

15000

14696.64655

303.3534541

12000

250000

20000

21147.48491

1147.484909

20000

350000

40000

37641.91818

2358.081824

25000

400000

45000

46787.32394

1787.323944

25000

450000

52000

51441.78404

558.2159624

22000

600000

62000

62710.59691

710.5969148

16000

500000

50000

48012.54192

1987.458082

14000

450000

40000

41561.70355

1561.703555

Table 7.

Per month expenses(X1) 10000 12000 20000 25000 25000 22000 16000 14000

Age in Yrs(X2) 10 15 20 22 21 23 30 35

Annual Income (X2)in Lakhs 200000 250000 350000 400000 450000 600000 500000 450000

Price (Y) 15000 20000 40000 45000 52000 62000 50000 40000

Best calculated Y 11637.02176 23641.19559 45057.9373 50750.16373 48158.89606 52817.82341 52828.85375 38810.96716

Table 8. Taking three attributes as independent /predictor variables and one response variable

Annual Income Per month Age in expenses(X1) Yrs(X2) (X3) 10000 10 200000 12000 15 250000 20000 20 350000 25000 22 400000 25000 21 450000 22000 23 600000 16000 30 500000 14000 35 450000

Price Best (Y) calculated Y 15000 11637.02176 20000 23641.19559 40000 45057.9373 45000 50750.16373 52000 48158.89606 62000 52817.82341 50000 52828.85375 40000 38810.96716

Best Function Error 3362.978244 3641.195586 5057.937303 5750.163727 3841.103944 9182.176592 2828.853747 1189.032837

7 Future Work and Conclusion Here Linear Regression techniques have been applied and it has been seen that how the target customer can be achieved by applying the Linear regression Analysis and

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further Multiple Regression Analysis can be applicable where it is based on more than two predictor variables. Then the further constants can be retrieved by applying Least Square Methods. Further the Non-Linear Models can be converted to linear Models. As seen in our approach the implementation of Log-Linear Model can be shown with the help of data sets collected where the attributed can be transformed from categorical labels to continuous valued attributes Thus an iterative techniques can be followed to build higher order cubes from lower order cubes.

References 1. Siddiqi, J., Akhgar, B., Wise, T., Hallam, S.: A Framework for the Implementation of a Customer Relationship Management Strategy in Retail Sector. Department of Applied Computer Science. Sheffield Hallam University, UK (July-September 2002) 2. Bueren, A., Schierholz, R., Kolbe, L., Brenner, W.: Customer Knowledge Management Improving Performance of Customer Relationship Management with Knowledge Management. St. Gallen, Switzerland 3. Weiss, S.M., Kulikowski, C.A.: Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Morgan Kaufmann, San Mateo, CA (1991) 4. Weiss, S.M., Indurkhya, N.: Predictive Data Mining. Morgan Kaufmann, San Francisco (1998) 5. Li, Y.-M.: A general linear-regression analysis applied to the 3-parameter Weibull distribution. IEEE Transactions on Reliability 43(2), 255–263 (1994) 6. Lan, Y., Guo, S.: Multiple Stepwise Regression Analysis on Knowledge Evaluation. In: International Conference on Management of e-Commerce and e-Government, ICMECG 2008, pp. 297–302 (2008)

The figure shows linear relationship among attributes (X,Y) and difference n expected and actual output.

Price

Case I 80000 60000

Actual

40000 20000 0

Expected

0

10

20 Age

Fig. 1.

30

40

Congestion Games in Wireless Channels with Multipacket Reception Capability Debarshi Kumar Sanyal1, Sandip Chakraborty2, Matangini Chattopadhyay1, and Samiran Chattopadhyay1 1

Jadavpur University, India [email protected] 2 Indian Institute of Technology, Guwahati, India [email protected]

Abstract. A wireless transmission channel with multipacket reception capability is expected to be a common feature of next generation telecommunication systems. Probability of correctly receiving simultaneously transmitted packets at the base station in a given time slot depends on the number of transmitted packets as well as the geographical proximity of the transmitter to the base station. This paper formulates a congestion game with player-specific costs to model the situation, characterizes its Nash equilibrium and analyses the slot allocation at the operating point. Keywords: Congestion games, Nash equilibrium, Wireless networks, Multipacket Reception, Performance.

1 Introduction A common premise in the analysis of wireless channels is that simultaneous transmissions from multiple stations result in collision that destroys all transmitted packets. While this holds good for most current designs, next generation radio technologies are expected to empower these channels with multipacket reception (MPR) capability, that is, more than one packet among all simultaneous transmissions can be successfully decoded at the receiver. In [2] Ghez et al. study the stability of an Aloha channel with MPR capability where the number of successes in a slot is solely a function of the number of attempted transmissions in the slot. Celik [1] considers a more mature model of wireless communications: spatially distributed nodes with MPR capable receiver. They analyze a situation involving distributed nodes transmitting at the same power level through a fading channel to a base station that can decode multiple packets received simultaneously. In this model, probability of correctly receiving more than one packet in a specific time slot, the decode probability, depends on the number of simultaneous transmissions as well as the spatial distribution of nodes, that is, their geographical proximity to the receiver station. Based on probability analysis we can formulate the problem of slot allocation in wireless MPR channel: find out the optimal distribution of nodes at the slots that maximizes the decode probability for all nodes, minimizing packet loss. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 201–205, 2010. © Springer-Verlag Berlin Heidelberg 2010

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In this paper we model this problem using congestion game [6] - a game theoretic concept that can be used for resource allocation problems. In these games, each player chooses a subset of resources from a set of available resources. Each resource has a cost that depends only on the number of players using it and the cost to a player is the sum of the costs of all resources it chooses. It has been shown in [6] that a congestion game always possess a pure strategy Nash equilibrium. Congestion games with player-specific payoffs and restriction of each player to a single resource were introduced by Milchtaich [5]. These games, too, admit of a Nash equilibrium in pure strategies and it is reachable in polynomial time from any given initial distribution of resources to players. Given that the packet decode probability of a node in a slot depends on the number of transmissions in the slot and the distance of the node from the receiver, we formulate the slot allocation problem for MPR channels as a congestion game with player-specific costs. We then analyze the slot distribution at Nash equilibrium. Solving the slot allocation problem optimally (using, say, Integer Linear Programming) is an involved exercise. Our approach gives a lightweight and fast technique to obtain a good solution (which may be sub-optimal) with the useful property of fairness that results from fair competition among the players. Note that, unlike our work, Celik [1] uses Markov chain analysis. Study of MPR channels using non-cooperative games has been done in [4] but it uses the channel model in [2] and does not use congestion games. To the best of our knowledge, ours is the first attempt at analyzing MPR channels using congestion games. Unlike common game theoretic frameworks that analyze selfish users, we use congestion games to model a resource allocation problem where congestion effects are felt.

2 The Channel Model We consider a network of n spatially distributed nodes that transmit to a common base station. Packets are one-slot long. The transmit power of all nodes are identical and equal to PT. The propagation model includes path loss and Rayleigh fading. The received power of a transmission from node i, located at distance ri from the base station, is given by:

PR (i ) = R 2 Kri− β PT

(1)

where R is a Rayleigh random variable with unit power, β is the power loss exponent and K is the attenuation constant. For systems without fading, we set R = 1. We use the well-known SINR capture model [3]: given k simultaneous transmissions, the packet of user i is captured at the base station if PR (i )

SINR(i ) = N+

k

∑ PR ( j)

>z

(2)

j =1, j ≠ i

where z is the capture threshold ratio and N is the background noise. The background noise is typically much lower than the power level of interference and hence, will be ignored. For single packet reception 1 ≤ z ≤ 10 while for multipacket reception, z < 1.

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The analysis of the MPR channel may be carried out for various node distribution scenarios. However, instead of analyzing special cases, we consider a very general network setting where nodes are randomly located on a disk with the base station at the center and there is multipath fading in the channel. Define pc, k(r0) as the probability that a packet transmitted from a station at distance r0 is successfully captured by the base station given that there are k-1 other transmissions where k ≤ n. For β = 4 and uniform spatial distribution [1], p c,k (r0 ) = (1 − z r02 tan −1 (

1 z r02

)) k −1

(3)

Therefore the probability of packet destruction pd,k(r0) given that there are k-1 other transmissions is p d .k (r0 ) = 1 − p c,k (r0 )

(4)

3 Packet Capture Games The foregoing discussion shows that probability of successfully capturing a packet in a given time slot at the base station decreases with the number of simultaneous transmissions in the slot and the distance of the node from the base station. In other words, chances of a node’s success in transmission in a slot depend on the congestion on the slot, the exact dependency related to the identity of the node. Formally, the situation can be modelled as a congestion game with player-specific costs. A congestion game with player-specific costs is a non-cooperative game G = {ℵ, E , {S i }i∈ℵ , {ci }i∈ℵ } where ℵ is the set of players, E is the set of resources, and S i ⊆ 2 E is the strategy set of player i. Let d e : E × ℵ → ℜ is the delay associated with resource e. de (j) is non-decreasing in j. Let s = ( s1 ,..., s N ) be a state of the game where

player

ci (s) = g i (

i

plays

strategy

si ∈ S i

and

let f s (e) = {i : e ∈ s i } .

Then

∑ d e ( f s (e))) is the cost incurred by player i in a player-specific way gi(.).

e∈si

A congestion game is symmetric if all players share the same set of strategies. A singleton congestion game is a congestion game in which the strategy of every player is a singleton set. Denote by s ⊕ s i' = ( s1 ,..., s i −1 , s i' , s i +1 ,..., s N ) , the state s except that player i plays strategy s i' . Now, given a state s, we call a strategy s i* ∈ S i , a best response of player i to state s if c i ( s ⊕ s i* ) ≤ c i ( s ⊕ s i' )∀s i ∈ S i . We call a state s a Nash equilibrium if no player can decrease its cost by changing its strategy unilaterally. Milchtaich [5] shows that symmetric singleton congestion games with player-specific payoffs possesses at least one Nash equilibrium in pure strategies and it is reachable in polynomial time by best response paths. We too consider symmetric singleton congestion games with player-specific costs. The players in our game are the nodes, the resource set is the set of slots to be allocated, the strategy of each node

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is a single slot, and the cost function of each node is its packet destruction probability. The game is symmetric since all nodes must choose from the same set of slots. It is singleton as each player may choose exactly one slot. From equation 4, the cost is clearly observed to be player-specific. We are now ready to furnish a formal definition. Consider a wireless collision channel with multipacket reception (MPR) capability. A Packet Capture Game (PCG) is a symmetric singleton congestion game G = {ℵ, E , {S i }i∈ℵ , {ci }i∈ℵ } where ℵ = {1,2,..., N } is the set of players, E is the set of slots in which a node may transmit, and s i ∈ S i = { j : j ∈ E} is the strategy of player i with |si| = 1. Player i incurs a cost 2 −1 1 k −1 )) where k is the number of players c i = p d ,k (ri ) = 1 − (1 − z r0 tan ( z r02 using the same slot and ri is the distance of player i from the base station. Each player is interested in minimizing its own cost of packet transmission, that is, maximizing the probability that the packet transmitted by it is correctly received at the base station. The stable operating point of a PCG is a Nash equilibrium since no player has an incentive to deviate from it unilaterally. This equilibrium is fair in the sense that it is a natural consequence of fair competition among the players. For a player to choose the slot that affords it the least probability of packet destruction, given the other players' choices, it must know the current strategies of all other players - a knowledge difficult to acquire in distributed random access protocols. However, consider the following model: a set of slots is available for a set of players; the base station knows which players intend to transmit (perhaps by polling them). Now the base station is entrusted to assign exactly one slot to each player so that no player can increase its success probability further by unilateral deviation from its current strategy. By definition, this stable assignment is a Nash equilibrium. The base station simulates a PCG with artificial players (corresponding to the transmitter nodes) playing best-responses to determine the slot allocation.

4 Equilibrium Analysis We developed a C++ simulator to study packet capture games and their Nash equilibria in pure strategies. We set a fixed number of players and slots with all nodes assigned the same slot initially. Given an allocation of slots, each player chooses its best response strategy to decrease its current cost. The game stops when a Nash equilibrium has been reached. In all simulations, the capture threshold z is set 0.25. Figure 1 shows that the congestion game reaches its Nash equilibrium very fast. Different nodes are at different distances from the base station. Note that the average cost is not necessarily optimal at Nash equilibrium. Sub-optimal performance is a common phenomenon in non-cooperative sharing of common-pool resources. Figure 2 shows the allocation of 6 slots to 20 players. Slot ID varies from 1 to 6. The distances of the nodes from the base station follows an arithmetic progression. Since z = 0.25, at most 1/z = 4 packets can be decoded simultaneously. Hence, the resource allocation at equilibrium allows full decode.

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Fig. 2. Allocation of 6 slots to 20 players

5 Conclusion We analyzed a slot allocation problem in wireless MPR channels with spatially distributed nodes. We modeled it using symmetric singleton congestion games with player-specific costs. Nash equilibrium of the system is computed using best response dynamics and the allocation of slots to the players at this stable operating point is studied. Since convergence of the dynamics occurs in polynomial time, this modeling allows a simple and efficient mechanism to solve the slot allocation problem for MPR systems. Future work would involve quantifying how worse the Nash equilibrium is with respect to a socially optimal state and computing the minimum number of slots and a corresponding allocation when every player specifies a tolerable packet destruction probability.

References 1. Celik, G.D.: Distributed MAC protocols for networks with multipacket reception capability and spatially distributed nodes. An M.S Thesis in the Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (May 2007) 2. Ghez, S., Verdu, S., Schwartz, S.C.: Stability properties of slotted Aloha with multipacket reception capability. IEEE Transactions on Automatic Control 33(7), 640–649 (1988) 3. Lau, W., Leung, C.: Capture models for mobile packet radio networks. IEEE Transactions on Communications 40(5), 917–925 (1992) 4. MacKenzie, B., Wicker, S.B.: Stability of multipacket slotted Aloha with selfish users and perfect information. In: Proceedings of IEEE INFOCOM 2003 (April 2003) 5. Milchtaich: Congestion games with player specific payoff functions. Games and Economic Behavior 13, 111–124 (1996) 6. Rosenthal, R.W.: A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory 2, 65–67 (1973)

Secure and Revocable Multibiometric Templates Using Fuzzy Vault for Fingerprint and Iris V.S. Meenakshi1 and G. Padmavathi2 1

Department of Computer Applications, SNR sons College(Autonomous) Coimbatore, India [email protected] 2 Department of Computer Science, Avinashilingam Deemed University for Women Coimbatore, India [email protected]

Abstract. Biometric systems are subjected to a variety of attacks. Stored biometric template attack is very severe compared to all other attacks. Providing security to biometric templates is an important issue in building a reliable personal identification system. Multi biometric systems are more resistive towards spoof attacks compared to unibiometric counterpart. This work provides security and revocability to iris and fingerprint templates using password hardened multimodal biometric fuzzy vault. Password hardening provides security and revocability to biometric templates. Security of the vault is measured in terms of min-entropy. Keywords: Biometric template security, min-entropy, fuzzy vault, fingerprint, iris, revocability.

1 Introduction Biometrics is superior to traditional password based authentication method. Iris biometrics has certain merits compared to fingerprint. Iris and fingerprint are combined as multimodal to overcome the limitations of unibiometrics. They are secured in fuzzy vault and are more resistive towards attacks. After hardening they become revocable and overcome cross-matching. Moreover hardening imparts diversibility to biometric templates. 1.1 Multi Biometric Fuzzy Vault Hardening Fuzzy vault is a biometric crypto system frame work used to protect biometric templates and secret key[2]. Security and revocability are two important aspects of a biometric templates Fuzzy vault provides security but fails to provide revocability. Anyhow, when hardened with password fuzzy vault becomes revocable. Hardened fuzzy vault avoids function creeping; facilitate diversity and introduces an additional layer of security by password. If the password is compromised the basic security and privacy provided by the fuzzy vault is not affected. However, a compromised password makes the security level same as that of a fuzzy vault. Security of the password is V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 206–214, 2010. © Springer-Verlag Berlin Heidelberg 2010

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crucial. It is very difficult for an attacker to compromise both the biometric templates and password at the same time. The proposed method constructs a multimodal biometric fuzzy vault using the feature points extracted from iris and fingerprint. The multimodal biometric fuzzy vault is then hardened using the password. Figure 1 depict the steps in password hardening of multimodal fuzzy vault. Steps in hardening scheme 1.

A random transformation function is derived from the user password.

2.

The password transformed function is applied to the iris template.

3.

The password transformed function is applied to the fingerprint template.

4.

Fuzzy vault frame work is constructed to secure the transformed templates by using the feature points from both the iris and fingerprint.

5.

The key derived from the same password is used to encrypt the vault.

Fig. 1. Hardening Multi Biometric Fuzzy Vault

2 Background Umut uludag et al [1] uses the concept of fuzzy vault to protect a secret S of 128 bits length. The x and y coordinates of the fingerprint minutiae and iris is used as the locking/unlocking unit ‘u’ (x|y) of the vault. The secret key S (128 bits) is added with its CRC code (16 bit) to obtain SC (144 bits). SC is divided into 16 bit segments to obtain the polynomial coefficients. Two sets namely, the Genuine set (G) and chaff set (C) are generated. Chaff set is generated in such a way that it does not overlap on the genuine set. Matching is done during decoding process. The security of the fuzzy vault depends on the infeasibility of the polynomial reconstruction and the number of chaff points. Vulnerabilities and attacks against biometric systems are shown in the work of [4, 9, 10]. Merits of multibiometrics are depicted in the work of [12, 13]. Karthick Nandakumar et al [5, 6] show the password hardened fingerprint fuzzy vault in which password acts an additional layer of security. Srinivasa Reddy [3] followed the same idea of [5, 6] to implement an iris based hardened fuzzy vault. The basic idea of hardening multimodal biometric fuzzy vault is derived from the work done by Karthick Nandakumar et al [5] and Srinivasa Reddy [3]. The feature level fusion [7] is done to combine the minutiae point set from both the biometric templates.

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Most vulnerable specific attacks against fuzzy vault [14] are record multiplicity attack, stolen key inversion attack and blended substitution attack. Password hardened fuzzy vault is robust towards these attacks [11]. Section 3 presents the proposed password hardened multimodal biometric fuzzy vault.

3 Proposed Method The proposed work constructs the password hardened multimodal fuzzy vault in three steps. In the first step, the iris and fingerprint template are subjected to random transformation using password separately. This process enhances the user privacy and facilitates the generation of revocable templates. This transformation reduces the similarity between the original and transformed template. In the second step, multimodal fuzzy vault is constructed to secure the transformed templates. The key used in fuzzy vault construction is randomly generated and transformed using the same password. In the third step, the vault is encrypted by the key derived from the password. 3.1 Extraction of Feature Point from Iris and Fingerprint The proposed work uses the algorithm of Sharat Chikkerur [8] for extracting the fingerprint minutiae. Figure 2(a) shows the original fingerprint template and Figure 2 (b) shows the highlighted fingerprint template with minutiae points. The following operations are applied to the iris images to extract lock/unlock data. Canny edge detection is applied on iris image to deduct iris. Hough transformation is applied first to iris/sclera boundary and then to iris/pupil boundary. Then threshold is done to isolate eyelashes. Histogram equalization is performed on iris to enhance the contrast. The following sequence of morphological operation is performed on the enhanced iris structure. (i) closing-by-tophat (ii) opening (iii) thresholding. Finally thinning is done to get structures as a collection of pixels. Now the (x, y) coordinates of the nodes. Figure 3(a) shows the localized iris image, Figure 3(b) exhibits the iris image with the minutiae patterns and Figure 3(c) shows the permuted and transformed points.

(a)Fingerprint Image

(b)Fingerprint Minutiae

(c) Red: Permuted Points and Blue Transformed Points

Fig. 2. Fingerprint Extraction and Transformation

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(a) Localized Iris Image

(b) Highlighted Iris Minutiae

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(c) Red: Permuted Points and Blue: Transformed Points

Fig. 3. Iris Minutiae Extraction and Transformation

3.2 Implementation of Password Hardened Multimodal Fuzzy Vault The proposed system is implemented in Matlab 7.0. Fingerprint samples are taken from FVC2002 DB2 fingerprint database. Fingerprint images are resized to 256 X 136. Iris Samples are taken from CUHK Iris Database and resized to 256 X 256. This implementation identifies the lock/unlock data by highlighting the fingerprint minutiae and iris minutiae. The (x, y) attributes, of the fingerprint minutiae and iris minutiae structure of the biometric images are found out. Permutation and Translation operations are applied on the fingerprint and iris minutiae points by using the password separately. The transformed feature points are protected in the combined fuzzy vault. In this implementation 128 bit random key is generated. This key can also be generated from the iris structure or fingerprint for added security. This key is transformed by the 64 bit user password and is used to encrypt the vault 3.3 Feature Point Transformation The fingerprint template and iris template containing the highlighted bifurcation feature points are subjected to simple permutation and translation. Figure 2(b) shows the minutiae before transformation and Figure 2(c) shows the minutiae after transformation for fingerprint. Figure 3(b) shows the feature point before transformation and Fig. 3(c) shows the feature point after transformation for iris. This results in the original feature points being transformed into new points. The user password is restricted to the size of 8 characters. Therefore, the length of the password is 64 bits. These 64 bits are divided into 4 blocks of each 16 bits in length. The feature point highlighted in fingerprint template and iris template is divided into 4 quadrants. One password block is assigned to each quadrant Permutation is applied in such a way that the relative position of the feature point does not change. Each 16 bit password block is split into two components Tu of 7 bits and Tv of 9 bits in length. Tu and Tv represent the amount of translation in the horizontal and vertical directions, respectively.

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The new feature points are obtained by the following transformation.[3, 5]

Xu and X’u are the horizontal distance before and after transformation. Similarly Yv and Y’v are the vertical distance before and after transformation respectively. This transformation is applied for both fingerprint and iris template. 3.4 Encoding The transformed features are encoded in the multi biometric fuzzy vault. The minutiae points from fingerprint and iris are combined together. Secret message is generated as a 128 bit random stream. This secret message is transformed with the password.The 16 bit CRC is appended to transformed key S to get 144 bit SC. The primitive polynomial considered for CRC generation is

In the combined set, the minutiae points whose Euclidian distance is less than D are removed. 16 bit lock/unlock unit ‘u’ is obtained by concatenating x and y (each 8 bits) coordinates. The ‘u’ values are sorted and first N of them are selected. The Secret (SC) is divided into 9 non overlapping segments of 16 bits each. Each segment is converted to its decimal equivalent to account for the polynomial coefficients (C8, C7 …C0). All operations takes place in Galois Field GF(216). The projection of ‘u’ on polynomial ‘p’ is found. Now the Genuine points set G is (ui, P(ui)). Random chaff points are generated which are 10 times in number that of the genuine points. Both the genuine and chaff point sets are combined to construct the vault. The vault is List scrambled. 3.5 Decoding In the authentication phase, the encrypted vault and minutiae feature point are decrypted by the user password. Password based transformation is applied to the query feature points and the vault is unlocked. From the query templates of the fingerprint and iris, unlocking points (N in number) are extracted. The unlocking set is found as in encoding. This set is compared with the vault to separate the genuine point set for polynomial reconstruction. From this set, all combinations are tried to decode the polynomial. Lagrangian interpolation is used for polynomial reconstruction. For a specific combination of feature points the polynomial gets decoded. In order to decode the polynomial of degree 8, a minimum of at least 9 points are required. If the combination set contains less then 9 points, polynomial cannot be reconstructed. Now the coefficients and CRC are appended to arrive at SC*. Then SC* is divided by the CRC primitive polynomial. If the remainder is zero, query image does not match template image and the secret data cannot be extracted. If the remainder is not zero, query image matches with the template image and the correct secret data can be extracted. In this case SC* is divided into two parts as the 128 bit secret data and 16 bit CRC code.

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3.6 Parameters Used in Implementation The parameters used in this implementation are shown in Table 1. Chaff points hide the genuine points from the attacker. More chaff points makes the attacker to take much time to compromise the vault but consumes additional computation time. Table 1. Parameters for Vault Implementation Parameter

Fingerprint

Iris

Multimodal

No.of. Genuine points(r)

30

28

58

No. of Chaff points(c) Total no. of points (t = r+c)

300

280

580

330

308

638

4 Experimental Results and Security Analysis The vertical and horizontal distances of the iris and fingerprint minutiae are used for the polynomial projections. The iris and finger print template is transformed for three different user passwords to check for revocability namely ‘security’, ‘template’ and ‘quadrant’ respectively. Consider a 8 character user password ‘security’, the ASCII value of which is given by (115, 111, 99, 117, 114, 105,116,121) or 64 bits. These 64 bits are divided into four blocks of 16 bits each and these are further divided into 7 bits and 9 bits for transformation in horizontal and vertical directions respectively. Figure 4 and Figure 5 illustrate the password transformations.

(a) Password 'security'

(b) Password 'template'

(c) Password 'quadrant'

Fig. 4. Transformed Fingerprint minutiae

The feature point transformation is done with other two user passwords ‘template’ and ‘quadrant’. For the same original template different transformed templates are obtained when password is changed. This property of hardened fuzzy vault facilitates

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(a) Password 'security'

(b) Password 'template'

(c) Password 'quadrant'

Fig. 5. Transformed Iris Features

revocability. Different passwords can be utilized for different applications to avoid cross matching. In the proposed method the security of the fuzzy vault is measured by min-entropy which is expressed in terms of security bits. According to NandaKumar [7] the min-entropy of the feature template MT given the vault V can be calculated as r n+1 H∞ (MT | V) = -log

(1)

2

r+c n+1 r = number of genuine points in the vault; c = number of chaff points in the vault t = the total number of points in the vault (r + c); n = degree of the polynomial The security of the single modal fingerprint, iris and multi modal vault is tabulated in Table. 2 Table 2. Security Analysis of the Password Hardened Multi biometric Fuzzy Vault

Vault Type

Degree of polynomial

Minentropy of the vault (in security bits

Iris

8

33

6.1088 X 10 16

6.9069 X 10 6

Minentropy No: of + Evaluations guessing To be entropy of performed the to decode password the vault (in security bit ) 8.8445 X 109 51 to 63

8

33

1.1457 X 10

17

1.4307 X 10 7

8.0079 X 10 9

51 to 63

13

51

1.8395 x 1028

1.0143 x 1013

1.8136 x 1015

68 to 81

Fingerprint Combined Fingerprint and Iris

Total no: of combinations tried

Combinations Required to decode the vault

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In order to decode a polynomial of degree n, (n+1) points are required. The security of the fuzzy vault can be increased by increasing the degree of the vault but requires lot of computation and memory. This makes the system slow. Polynomial with lesser degree can be easily reconstructed by the attacker. In the case of the vault, with polynomial degree n. and If the adversary uses brute force attack, the attacker has to try total of (t, n+ 1) combinations of n+ 1 element each. Only (r, n+1) combinations are required to decode the vault. Hence, for an attacker to decode the vault it takes C(t, n+1) / C(r, n+1) evaluations. The guessing entropy for an 8 ASCII character password falls in the range of 18 – 30 bits. Therefore, this entropy is added with the vault entropy.

5 Conclusion Iris is highly secure and uses a stable physiological trait. Iris is less prone to either intentional or unintentional modification when compared to fingerprint. Password hardening of the multi biometric fuzzy vault introduces two layers of security namely password and biometrics. Even if the attacker gains the password, he/she may not be able to access the genuine feature points. When the attacker compromises both the biometrics and password simultaneously then he can capture the vault. This is not possible in real life situation. Hence the proposed password hardened multi biometric fuzzy vault is robust against stored biometric template attacks like record multiplicity attack, stolen key inversion attack and blended substitution attack. Better hybrid schemes can be implemented to resist more attacks. Iris and fingerprint combination can be used in applications like issue of passport and access to computers and networks.

Acknowledgments A public version of the FVC2002 fingerprint database is available from http://bias.csr.unibo.it/fvc2002/ A public version of the CUHK Iris Database is available from http://www2.acae.cuhk.edu.hk.

References 1. Uludag, U., Pankanti, S., Jain, A.K.: Fuzzy vault for fingerprints. In: Proceedings of International Conference on Audio Video Based Person Authentication (2005) 2. Juels, Sudan, M.: A fuzzy vault scheme. In: Proceedings of IEEE International symposium Information Theory (2002) 3. Srinivasa Reddy, E., Ramesh Babu, I.: Performance of Iris Based Hard Fuzzy Vault. In: Proceedings of IEEE 8th International conference on computers and Information technology workshops (2008) 4. Uludag, U., Pankanti, S., Prabhakar, S., Jain, A.K.: “Biometric Cryptosystems: issues and challenges. Proceedings of the IEEE (June 2004)

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5. Nandakumar, K., Nagar, A., Jain, A.K.: Hardening Fingerprint Fuzzy Vault using Password. In: International Conference on Biometrics (2007) 6. Nandakumar, K., Pankanti, S., Jain, A.K.: Fingerprint-based Fuzzy Vault Implementation and Performance. IEEE Transacations on Information Forensics and Security (December 2007) 7. NandaKumar, K.: Multibiometric Systems: Fusion Strategies and Template Security, PhD Thesis, Department of Computer Science and Engineering, Michigan State University (January 2008) 8. Chikkarur, S., Wu, C., Govindaraju, V.: A systematic Approach for feature Extraction in Fingerprint images, Center for Unified Biometrics and Sensors(CUBS), university at Buffalo, NY,USA 9. Jain, K., Ross, A., Pankanti, S.: Biometrics: A Tool for Information Security. IEEE Transactions on Information Forensics and Security 1(2), 125–143 (2006) 10. Jain, K., Ross, A., Uludag, U.: Biometric Template Security: Challenges and Solutions. In: Proceedings of European Signal Processing Conference (EUSIPCO), Antalya, Turkey (September 2005) 11. Jain, A.K., Nanda Kumar, K., Nagar, A.: Biometric Template Security. EURASIP Journal on Advance in Signal Processing, special issue on Biometrics (January 2008) 12. Jain, A.K., Ross, A.: Multibiometric systems. Communications of the ACM 47(1) (January 2004) 13. Jain, A.K., Ross, A.: Learning User-specific parameters in a Multibiometric System. In: Proc. IEEE International Conference on Image Processing (ICIP), Rochester, New York, September 22-25, pp. 57–60 (2002) 14. Scheirer, W.J., Boult, T.E.: Cracking fuzzy vaults and biometric encryption. In: Proceedings of the Biometrics, Baltimore, Md, USA (September 2007)

High Speed Cache Design Using Multi-diameter CNFET at 32nm Technology Aminul Islam1 and Mohd. Hasan2 1

Dept. of ECE, Birla Institute of Technology (deemed university), Mesra, Ranchi, Jharkhand, India 2 Dept. of Electronics Engg., Aligarh Muslim University, U.P., India [email protected]

Abstract. This paper proposes a high-speed multi-diameter CNFET-based 7T (seven transistor) SRAM (static random access memory) cell. It investigates the impact of process, voltage and temperature (PVT) variations on its design metrics and compares the results with its counterpart − CMOS-based 7T SRAM cell. The proposed design offers 77.4× improvement in write access time along with 88.1× reduction in write access time variation and 117.8× saving in write power along with substantial reduction in write EDP/write EDP variation. The proposed memory cell shows 40% improvement in SNM (static noise margin) and better robustness against PVT variations. Keywords: Carbon nanotube field effect transistor (CNFET), chirality vector, noise margin, process variation.

1 Introduction In traditional VLSI design, D2D (die-to-die) fluctuations are the major concern, and the WID (within-die or intra-die) fluctuations are bypassed [1, 2] by allowing some design margins. As technology scales down to sub-100nm regime, such conservative design approaches cannot be continued because the area-speed-power budget is shrinking fast, and process-induced variation and time dependent shifts in device parameters are increasing rapidly. Thus, WID fluctuations are a growing threat to the performance and functionality of future gigascale integration (GSI). These fluctuations are more prominent in minimum-geometry devices commonly used in area-constraint circuits such as SRAM (static random access memory) cells [3]. SRAM constitutes more than half of chip area and more than half of the number of transistors in modern designs [4]. It is expected that µPs will have 90% area embedded with cache by 2011 [5]. Hence, the design and reinvestigation of SRAM cell in terms of its design criteria is not only important but also its robustness against PVT variations is necessary in Deep Submicron Technology such as 32nm technology node. As CMOS is reaching the scaling limits, the need for alternative technologies is necessary. Nanotechnology-based fabrication is expected to offer the extra density and potential performance to take electronic circuits the next step. Several nanoscale electronic devices are demonstrated in the recent past by researchers, some of the most promising being carbon nanotube (CNT) based field effect transistor (CNFET). V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 215–222, 2010. © Springer-Verlag Berlin Heidelberg 2010

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The remainder of this paper is organized as follows. Section 2 briefly introduces CNFET. Section 3 briefly explains CMOS-based 7T SRAM cell (hereafter called 7T) and proposed CNFET-based 7T SRAM cell (hereafter called CNFET-7T). Section 4 presents the analysis with simulation results. Section 5 concludes the paper.

2 Characteristics of CNFET CNFET is the most promising technology to extend or complement traditional CMOS technology. With the use of CNFET technology, the historic trend (Moore’s law) of device scaling can be continued for another 2 to 3 technology generation and the CMOS technology roadmap can be extended up to 10nm device length as it can be scaled down to 10nm channel length and 4nm channel width, thereby enhancing throughput in terms of speed and power compared to the MOSFET [6]. An SWCNT (single-walled CNT) can work differently depending on its chirality (n1, n2) − the direction in which the single atomic layer of graphite is rolled up to form a seamless cylinder. The CNT acts as metal if n1 = n2 or n1 – n2 = 3i, where i is an integer. Otherwise, CNT works as semiconductor. The DCNT (diameter of CNT) and Vt (threshold voltage) of CNT are calculated using chirality vector (n1, n2) and Vπ respectively as [6]

D CNT =

a π

n 12 + n 22 + n 1 n 2

(1)

aV

π (2) 3 × qD CNT where Eg is energy gap, q = electronic charge, a = 2.49Å is the CNT atomic distance and Vπ = 3.033eV is the carbon π to π bond energy. In this paper, multi-Vt and multi-diameter CNFETs are used using chrial vector values (11, 0), (13, 0) and (19, 0). The DCNT of the CNFET with chiral vector value (11, 0), (13, 0) and (19, 0) are computed using (1) to be 0.8719nm, 1.03nm and 1.5nm respectively. The Vt of the CNFET with chiral vector value of (11, 0), (13, 0) and (19, 0) are computed using (2) to be 0.5018V, 0.4246V and 0.29V respectively. Compared to silicon technology, the CNFET shows better device performance, even with device nonidealities [7, 8]. Other important device and technology parameters related to CNFET are tabulated in Table 1.

V

t

=

Table 1. Device and Technology parameters for CNFET Parameter Lch Wg tox Kox (n1, n2) n_CNT

Description Physical channel length The width of metal gate (sub_pitch) The thickness of high-k top gate dielectric material (planer gate). Dielectric constant of high-K gate oxide Chirality of the tube Number of tube/device

Value 32nm 6.4nm 4nm 16 (11,0), (13,0), (19, 0) 1

The threshold voltages of N-CNFET are computed using (1) and (2) with chirality vector ranging from (7, 0) to (36, 0). The computed values of threshold voltage are

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plotted in Fig. 1, which shows two end points with Vt = 0.78857V at n1 = 7 and Vt = 0.153V at n1 = 36. Other three important points in this plot are (11, 0.5018), (13, 0.4246) and (19, 0.29) which indicate Vt = 0.5018V at n1 = 11, Vt = 0.4246V at n1 = 13 and Vt = 0.29V at n1 = 19. These are the threshold voltages of CNFETs used in the proposed design.

Fig. 1. Threshold voltage (Vt) versus Chirality Vector (n1)

3 CMOS-Based 7T SRAM Cell and Proposed CNFET-Based 7T SRAM Cell 3.1 CMOS-Based 7T SRAM Cell Authors in [9] proposed a CMOS-based 7T SRAM cell similar to the cell shown in Fig. 2 to reduce the activity factor αBL for reduction of dynamic power while writing to a cell given by PWRITE = αBL×CBL×V2×FWRITE. Important device and technology parameters used in this analysis for 7T are tabulated in Table 2. As MN1 ≥3 and MN2 ≥2 ensure stable read operation, the transistors in 7T cell are sized as shown in the Table 2. The 7T also requires low-Vt MN5. To avoid extra masking cost and to fulfill this requirement, the diameter of MN5 is increased to reduce its Vt in proposed design. Other devices used for 7T are square transistor with minimum channel length. Table 2. Device and Technology parameters of 7T Parameter/device Vtn0 Vtp0 MP1, MP2, MN3, MN4, MN5 MN1 MN2

CMOS-based 7T 0.63V -0.5808V W=32nm, L=32nm W=96nm, L=32nm W=64nm, L=32nm

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3.2 Proposed CNFET-Based 7T SRAM Cell This paper proposes a multi-diameter CNFET-based 7T SRAM cell as shown in Fig. 2. To investigate the performance in terms of various design metrics, extensive simulations are run on HSPICE and finally CNFETs with three different diameters are selected to achieve optimum results. The values of device diameters, threshold voltages and the corresponding chirality vectors are tabulated in Table 3. As mentioned in Section 2, single-tube CNFETs are used for the proposed design. Therefore, default gate width (Wg) of 6.4nm is used.

Fig. 2. Proposed CNFET-based 7T SRAM cell Table 3. Device Parameters of Proposed Design Device MP1 MP2 MN1 MN2 MN3 MN4 MN5

Chirality Vector (11, 0) (11, 0) (13, 0) (13, 0) (19, 0) (11, 0) (19, 0)

Threshold Voltage (V) -0.5018 -0.5018 0.4246 0.4246 0.29 0.5018 0.29

Diameter (nm) 0.8719 0.8719 1.03 1.03 1.5 0.8719 1.5

Hence, the device size of the proposed design is 6.4nm×32nm for all the CNFETs. Thus, the cell area in proposed design is 7× 6.4nm×32nm ≈ 1434nm×nm resulting in 86% improvement in cell area compared to 7T. For writing a bit @ Q, complement of the bit is applied on BLB and WL is raised. MN4 and BL do not take part in writing. Read operation of 7T/CNFET-7T is similar to that of standard 6T SRAM cell.

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4 Results and Discussion This Section presents measurements of various design metrics which were measured during Monte Carlo simulation on HSPICE using 32nm BPTM [10]. Monte Carlo simulation is a method for iteratively evaluating a design. The goal is to determine how random variation on process parameters, voltage and temperature affects the performance and reliability of a design. As Std. Dev. (standard deviation) is a measure of dispersion (or variability) that states numerically the extent to which individual observations vary on the average, it is used to assess variation in parameter. 4.1 Static Noise Margin Measurements The Static Noise Margin (SNM) of SRAM cell is defined as the minimum DC noise voltage necessary to flip the state of the cell. SNM of an SRAM is a widely-used design metric that measures the cell stability. The measured values for SNM when plotted are called "butterfly curve". Fig. 3 shows “butterfly curve” of 7T and CNFET7T in a single plot to illustrate their characteristics and SNM values. The side length of the largest square that can be embedded within the smaller wing of the butterfly curve represents the SNM of the cell. This definition holds good because, when the value of applied noise voltage increases from 0, the VTC (voltage transfer characteristic) for INV1 formed with MP1 and MN1 moves to the right and the VTC−1 (inverse VTC) for INV2 formed with MP2 and MN2 moves downward. Once they both move by the SNM value, the curves meet at only two points and any further noise flips the cell [11]. Fig. 3 shows that the proposed CNFET-7T outperforms the 7T by 40% improvement in SNM. To understand why this happens, remember that the MN1 and MN2 of CNFET-7T have lower Vt which implies that switching threshold of both the inverters are lower than that of 7T. This moves VTC of INV1 to the left and VTC-1 down thereby widening both the lobes of the butterfly curve of CNFET-7T.

Fig. 3. Static Noise Margin (SNM) of 7T and CNFET-7T SRAM

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4.2 Write Access Time Measurements The average Write Access Time (TWA) is estimated during simulation and results are tabulated in Table 4. The values of TWA are normalized to the values of TWA of CNFET-7T and the normalized values are reported in bracket. The Table 4 shows that the 7T takes 7.8× and 77.4× longer TWA, while writing “1” and “0” @ Q respectively. These differences in TWA occur due to the difference in capacitance of storage node (say, CQB). For example, CQB mainly depends on drain diffusion capacitance of MN1, MP1 and MN5. The diffusion capacitance has two components per transistor – bottom-plate junction capacitance and side-wall junction capacitance as given by [12]: C diffusion = C j WL + C sw X j (W + 2 L) (3)

where Cj is the junction capacitance per unit area, Xj is the junction depth, Csw is the total (of three sides) side-wall junction capacitance per unit area, W and L are width and length of transistors. Equation (3) signifies the W and L (area and perimeter) dependency of CQB. The MP1, MN1 and MN5 of 7T has total area of 5120nm×nm and that of CNFET-7T has only 614.4 nm×nm, giving rise to ~8.3× difference only in bottom area. Moreover, CNFETs have cylindrical geometry. Table 4. Write access time and standard deviation of write access time SRAM

TWA while writing “1” @Q (ps)

TWA while writing “0” @Q (ps)

7T CNFET-7T

67.15(7.8) 8.638(1)

2244(77.4) 28.98(1)

Std. Dev. of TWA While writing “1” @ Q (Ps) 1.37(2.4) 0.5610(1)

Std. Dev. of TWA While writing “0” @Q (Ps) 87.86(88.1) 0.9972(1)

VDD (V)

1 1

Thus, if bottom area as well as three sidewalls per transistor is taken into account, there will be large difference in diffusion capacitance, and hence, CQB between 7T and CNFET-7T, giving rise to longer difference in TWA. Due to the asymmetry of the cell, capacitance of storage node Q and QB differs, giving rise to difference in write delay. Std. Dev. of TWA is also measured and reported in Table 4. The normalized values are reported in bracket. Table 4 shows that the spread of TWA of 7T is 2.4× and 88.1× wider than that of CNFET-7T while writing “1” and “0” @ Q respectively. This tighter spread of Std. Dev. of TWA of proposed design implies its robustness against PVT variation. 4.3 Write Power Measurements

The authors in [9], proposed CMOS-based 7T SRAM cell with the intention to reduce write power (WPWR). This paper has further attempted to reduce WPWR by using a new nanodevice in the SRAM architecture. This attempt is successful, which is visible from the result presented in Table 5. Power consumptions during write operation are measured at nominal voltage of 1V while writing “0” as well as “1” at Q. The values

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of WPWR are normalized with respect to the values of that of CNFET-7T and reported in bracket. The Table 5 shows that the CNFET-7T consumes 117.8× and 1.2× lesser WPWR than that of 7T while writing “0” and “1” @ Q respectively. This difference in WPWR occurs due to the difference in capacitance of storage node (say, CQB), because the Storage Power (PSTORE) depends on CQB as PSTORE = αBL×CQB×V2×FWRITE. As mentioned in Section 4.2, the difference in CQB between 7T and CNFET-7T exists. This gives rise to difference in write power consumption. Table 5. Average write power SRAM

7T CNFET-7T

Write Power while writing “1” @ Q (pW) 2851(1.2) 2358(1)

Write Power while writing “0” @ Q (pW) 17100(117.8) 145.2(1)

VDD (V) 1 1

4.4 Write Energy Delay Product Measurements

The Write Energy Delay Product (WEDP) of 7T and CNFET-7T are measured at VDD = 1V and 0.9V (−10% of nominal). The WEDP is estimated as WEDP = WPWR×T2WA. The results are presented in Table 6. Table 6. Write energy delay product and standard deviation of write energy delay product SRAM

WEDP while writing “1” @ Q (Js)

WEDP while writing “0” @ Q ((Js)

7T CNFET-7T 7T CNFET-7T

1.323e-29 1.766e-31 1.929e-27 2.123e-31

8.624e-26 1.163e-31 2.634e-25 1.412e-31

Std. Dev. of while WEDP writing “1” @ Q (Js) 1.107e-29 2.260e-32 1.604e-27 2.752e-32

Std. Dev. of while WEDP writing “0” @ Q (Js) 7.875e-27 8.290e-32 1.604e-26 9.706e-32

VDD (V)

1 1 0.9 0.9

Table 6 shows that the WEDP of CNFET-7T is lesser than that of 7T implying its superiority. The Std. Dev. of Write EDP (WEDP) is also presented in Table 6, which shows that the spread in WEDP is tighter in CNFET-7T implying its robustness against PVT variation.

5 Conclusion This paper proposes a CNFET-based, robust 7T SRAM cell suitable for high-speed cache and successfully analyses the impact of PVT variations on write access time and write EDP. It exhibits PVT variation tolerance in terms of tighter spread in write access time and write EDP. This is mainly due to the proper selection of diameter of the appropriate transistors and the cylindrical geometry of CNFET. The proposed design also achieves higher SNM (40%) compared to CMOS-based 7T SRAM cell.

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References 1. Duvall, S.G.: Statistical circuit modeling and optimization. In: 5th Intl. Workshop on Statistical Metrology, pp. 56–63 (2000) 2. Eisele, M., Berthold, J., Schmitt-Landsiedel, D., Mahnkopf, R.: The impact of intra-die device parameter variations on path delays and on the design for yield of low voltage digital circuits. In: Proc. ISLPED 1996, pp. 237–242 (1996) 3. Burnett, D., Erington, K., Subramanian, C., Baker, K.: Implications of fundamental threshold voltage variations for high-density SRAM and logic circuits. In: Proc. Symp. VLSI Tech., pp. 15–16 (1994) 4. Rusu, S., Tam, S., Muljono, H., Ayers, D., Chang, J., Cherkauer, B., Stinson, J., Benoit, J., Varada, R., Leung, J.: A 65-nm Dual-Core Multithreaded Xeon Processor With 16-MB L3 Cache. IEEE Journal of Solid-State Circuits (2007) 5. Alam, M., Kang, K., Paul, B.C., Roy, K.: Reliability and Process –Variation Aware Design of VLSI Circuits. In: Proceedings of 14th IPFA 2007, Bangalore, India (2007) 6. Stanford University CNFET Model Web site (2008), http://nano.stanford.edu/model.php?id=23 7. Deng, J., Wong, H.-S.P.: A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application - Part I: Model of the Intrinsic Channel Region. IEEE Trans. Electron Devices 54, 3186–3194 (2007) 8. Deng, J., Wong, H.-S.P.: A Compact SPICE Model for Carbon-Nanotube Field-Effect Transistors Including Nonidealities and Its Application - Part II: Full Device Model and Circuit Performance Benchmarking. IEEE Trans. Electron Devices 54, 3195–3205 (2007) 9. Aly, R., Faisal, M., Bayoumi, A.: Novel 7T SRAM cell for low power cache design. In: Proc. IEEE SOC Conf., pp. 171–174 (2005) 10. Berkeley Predictive Technology Model, UC Berkeley Device Group, http://wwwdevice.eecs.berkeley.edu/~ptm/ 11. Calhoun, B.H., Chandrakasan, A.P.: Static Noise Margin Variation for Sub-threshold SRAM in 65-nm CMOS. IEEE Journal of Solid State Circuits 42(7) (2006) 12. Rabaey, J.M.: Digital Integrated Circuits: A Design Perspective. Prentice-Hall, Englewood Cliffs (1996)

Dynamic Load Balancer Algorithm for the Computational Grid Environment Rajkumar Rajavel1, Thamarai Selvi Somasundaram2, and Kannan Govindarajan3 1 Lecturer, Department of IT, Adhiyamaan College of Engineering, Hosur, India 2 Professor and Head, Department of CT, MIT Campus, Anna University, Chennai, India 3 Research Associate, CARE Lab, MIT Campus, Anna University, Chennai, India

Abstract. One of the challenging issues in computational grid is load balancing. In many approaches load balancing is done only at the local scheduler level, which is applicable to small application and leads to more communication overhead between the resources. For the large scale application load balancing at the local scheduler level will not provide the feasible solution. So the novel Load Balancer algorithm is proposed, which provides the load balancing at the meta-scheduler level. To initiate the load balancing triggering policy is used, which determines the appropriate time period to start the load balancing operation by using Boundary value approach. This approach increases the performance by reducing the waiting time of jobs and by maximizing the utilization resource which is least loaded. Keywords: Load Balancing, Load information, Triggering policy, Boundary value approach.

1 Introduction The important issues in grid environment, is performance degradation due to load imbalance [2]. In the grid environment, each cluster is considered as a resource. So far the load balancing algorithm is implemented by exploiting the static resource information [1]. If the local users fire the job to the resource without the knowledge of meta-scheduler, then the resource is forced to handle more number of jobs from metascheduler and local scheduler which leads to overloading. So the load balancing is a preferable solution to provide application level load balancing for individual jobs. In the Dynamic Load Balancer algorithm, it is unacceptable to frequently exchange state information between neighboring processors because of high communication overhead [2,7]. In our approach the problem of frequent exchange of information is alleviated by estimating the load information on demand by using the Event Notification approach. The impact of using Load Balancer algorithms in the metascheduler will reduce waiting time of the job and maximize the utilization of the resource which is idle or least loaded. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 223–227, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Dynamic Load Balancer

Fig. 1. Structure of Dynamic Load Balancer

In this paper, Dynamic Load Balancer (DLB) model is proposed using Load Balancer (LB) and Job Migration (JM) algorithms as shown in Fig 1. The Request Handler component provides user interface through which client can submit the job described using Job Submission Description Language (JSDL) specification. The users will submit their jobs to the meta-scheduler which falls into the queue of request handler. The Dispatch Manager, which is present in the CARE Resource Broker (CRB) obtains the submitted job information periodically from the queue and sends the jobs to Load Balancer (LB) component for discovering suitable resource [3]. By exploiting the information gathered from Load Monitor and Information Manager, the LB will perform load balancing and JM. The load monitor contains the information about all the Load Agents. The Load Agent acts as a server and a copy of load agent has to run on all the resource where the users want to run their applications. Load agent provides system load information such as queue length of the resource. By using event notification approach if any change occurs in load information, it will automatically update to Load Monitor which in turn gives to LB component for load balancing. The Information Manager will query the Monitoring and Discovery Service (MDS) and sends the host information to the LB. The Transfer Manager is invoked by the Dispatch Manager with the job-id and the matched resource-id as input. Once it is invoked, the Transfer Manager creates a remote directory for the given path name as specified in user input. Transfer manager gives the permission rights for the execution of given job in the remote directory. Once this process is over, it informs the Dispatch Manager through messages. The Execution Manager is invoked by the Dispatch Manager when the Transfer Manager completed the creation of directory in the remote host. The Dispatch Manager will dispatch the job for execution. Execution Manager will keep updating the job status to the LB. Finally it reports the completion or failure of job to the LB. 2.1 Estimation of Load Cost The impact of considering this performance metrics will reduces the waiting time of the job. First we calculate the number of job unit JUi present in the job Ji as follows, JUi = NC (Ji) * ET (Ji)

(1)

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Where NC denotes number of node count required by job Ji. Then compute estimated arrival rate λi and estimated service rate µ i of the resource Ri at the time T as follows, λi (T) = ∑ (JU1,JU2,JU3,...,JUN)

(2)

µ i (T) = ∑ (C1,C2,C3,...,CK) * CPU SPEEDi

(3)

Where JU1, JU2, JU3, ..., JUN denotes number of job units in the queue of the resource Ri and C1, C2, C3, ..., Ck indicates the number of computing node present in the resource Ri. Finally the load cost Li (T) of the resource Ri is estimated by Little’s Formula using equation (2) and (3) [6], [7]. Li (T) = λi (T) / µ i (T)

(4)

2.2 Load Balancer and Job Migration Algorithm In the job pool, the job information such as job id, required free memory, required CPU speed, required node count and execution time of job is present. Similarly in the resource pool, the resource information such as resource id, available free memory, CPU speed and the number of task unit remaining in resource queue. In the proposed model triggering policy is considered which determines the appropriate period to start a load balancing operation by using Boundary value approach. This approach is used when no jobs arrived to the meta-scheduler for the specified time interval. The LB algorithm using Boundary value approach will works as shown in Algorithm 1. First this LB algorithm calculates the total load of the resource TLR (T) by adding all the resource load information at time T as follows, TLR (T) = ∑ L1 (T), L2 (T),…, LM (T)

(5)

Where L1, L2, L3 ,..., LM represents the load of the resource R1, R2, R3 ,..., RM respectively.

Then it compute the average of load resource ALR (T) at the time T, by taking the ratio of the TLR (T) to the total number of resource ’M’. ALR (T) = TLR (T) / M

(6)

Then set the Load Upper Boundary and Load Lower Boundary of the resource R as, LUBR = ALR (T) + α

(7)

LLBR = ALR (T) – α

(8)

Where α is the parameter denotes the boundary value estimation factor and its value is based on the multi-processor system [4]. If the resource have load greater than LUBR is said to be overloaded and the resource having the load lesser then LLBR is said to least loaded. A resource is said to be in moderately load, if it’s between LUBR and LLBR [5]. If the load exceeds the boundary value, then LB will migrate the job to other resources which are below the boundary value. If the resource is overloaded means JM algorithm is used which works as shown in Algorithm 2.

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R. Rajavel, T.S. Somasundaram, and K. Govindarajan Algorithm 1. Load Balancer (LB) Algorithm using Boundary Value approach Begin Get load information of all resource using load monitor service Calculate load cost for all resource Calculate the total load cost TLR(T) Compute average load cost ALR(T)= Total Load Cost / N Set the upper boundary as LUBR = ALR(T) + & Set the lower boundary as LLBR = ALR(T) - & for ( all the resource ) if ( resource Load Cost > LUBR) Resource is overloaded Use Algorithm 3 else if ( resource Load Cost < LLBR ) Resource is least loaded else Resource is moderately load End

Algorithm 2. Job Migration (JM) Algorithm Begin Get the list of overloaded resource Get the list of least loaded resource for ( all the overloaded resource) While (overloaded resource load cost > LUBR) Take the one job from resource queue Compute the completion time of job CTJK in each resource for ( all least loaded resource ) Choose the best resource BR(JK) having minimum CTJ Then migrate the job to the resource End

In-order to provide better solution, it is necessary to estimate the Completion Time of Job (CTJ) in remote resource before the job is migrated to remote location as follows, CTJK (Ri) = ET (Jk) + DTT (Jk) + CTWJ (Ri)

(9)

Where CTJK (Ri) represents the completion time of job Jk at resource Ri, DTT (Jk) denotes the Data Transfer Time of the job Jk to the resource Ri and CTWJ (Ri) denotes the Completion Time of Waiting Job at resource Ri. In the JM algorithm, it calculates CTJ in the remote resource and chooses the resource having minimum CTJ as, BR (JK)= min ( CTJK (R1), ....., CTJK (Rm) ) Where BR(JK) represent the best resource for migrating job JK.

(10)

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3 Experimental Results and Performance Evaluation In the result phase we have simulated the result by exploiting ten resources and hundreds of jobs. The performance of the proposed Load Balancer algorithm using Boundary value approach works better than the Normal Load Balancer with respect to the load of the resource and waiting time of job as shown in the Fig 2. L o a d V s W a i t i n g T i me o f j o b

Wai t i ng T i me

N o r ma l Loa d B a l a nc e r A l go r i t h m

10 5

of j ob 0 0.16

0. 16 Load

0. 243

Loa d B a l a nc e r A l go r i t h m usi ng B o unda r y Va l ue

Fig. 2. Performance Evaluation of Normal Vs Proposed Load Balancer Algorithm

4 Conclusion Our proposed LB algorithm is evaluated with simulation and traces from real time grid environment in CARE laboratory. The proposed LB algorithm using Boundary value approach works better than Normal LB algorithm. The result obtained with performance evaluation can balance the load and increase the utilization of the resource, which are idle or least loaded.

References 1. Foster, I., Kesselman, C.: The Grid: Blueprint for a future Computing Infrastructure. Morgan Kaufmann, San Francisco (1999) 2. Coulouris, G., Dolimore, J., Kindberg, T.: Distributed Systems: Concepts and Design. Addison-Wesley Longman, Amsterdam (1994) 3. Somasundaram, T.S., Amarnath, B.R., Kumar, R., Balakrishnan, P., Rajandar, K., Rajiv, R., Kannan, G., Rajesh Britto, G., Mahendran, E., Madusudhanan, B.: Care Resource Broker: A Framework for scheduling and supporting Virtual Resource Management. Journal: Future Generation Computer System (2010) 4. Xiangchun, H., Duanjun, C., Jing, C.: One centralized Scheduling pattern for Dynamic Load Balance in Grid. In: IEEE International Forum on Information Technology and Applications (2009) 5. Lu, B., Zhang, H.: Grid Load Balancing Scheduling Algorithm based on Statistics Thinking. In: 9th IEEE International Conference for Young Computer Scientists (2008) 6. Gondhi, N.K., Durgesh Pant: An Evolutionary Approach for Scalable Load Balancing in Cluster Computing. In: IEEE International Advance Computing Conference (2009) 7. Shah, R., Veeravalli, B., Mistra, M.: On the Design of Adaptive and Decentralized LoadBalancing Algorithms with Load Estimation for Computational Grid Environments. IEEE Transactions on Parallel and Distributed Systems (December 2007)

Instance-Based Classification of Streaming Data Using Emerging Patterns Mohd. Amir and Durga Toshniwal Department of Electronics and Computer Engineering IIT Roorkee, Roorkee, India [email protected], [email protected]

Abstract. Classification of Streaming Data has been recently recognized as an important research area. It is different from conventional techniques of classification because we prefer to have a single pass over each data item. Moreover, unlike conventional classification, the true labels of the data are not obtained immediately during the training process. This paper proposes ILEP, a novel instance-based technique for classification of streaming data with a modifiable reference set based on the concept of Emerging Patterns. Emerging Patterns (EPs) have been successfully used to catch important data items for addition to the reference set, hence resulting in an increase in classification accuracy as well as restricting the size of the reference set. Keywords: Instance based, classification, emerging patterns, k nearest neighbour, streaming data, single pass classification, streaming data, ILEP.

1 Introduction Data streams are applications in which the data is modelled best not as persistent relations but rather as continuous flow of data sets. This class of data-intensive application has been of wide usage and recognition. In this paper, we address the problem of classification of streaming data. In conventional data sets, we assume the availability of a large labelled training set and the training procedure is error driven because the correct label can be obtained as soon as the classification is done. But in case of streaming data classification problem, we assume that in the beginning, we do not have any training set or we have a very small training set. Moreover, the test data set comes in the form of data streams to the system and the true labels are obtained only after certain time lag. We derive our new classification algorithm from Instance-based (IB) classifiers [1]. The main step in designing an IB classifier is the maintenance of the reference set, which stores only some of the data items seen so far. The idea is to store only those data items in the reference set, that seem to be key to accurate classification of newer data items. Moreover, the size of the reference set is also an important factor, because it determines the speed of classification. The smaller the size of reference set, the faster is the classification. We have implemented the classical IB classifiers with modifiable reference set and used an EP miner, developed in [2], to extract emerging V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 228–236, 2010. © Springer-Verlag Berlin Heidelberg 2010

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patterns from the reference set. We then use the obtained emerging patterns to selectively update the reference set with the streaming data, hence we name it ILEP – Instance-based Learning using Emerging Patterns. This updated reference set is in turn used for classifying future streaming data. The paper proceeds with a brief discussion of the background work that has been used by us. In section 2, we discuss the classical algorithms of Instance-based classifiers, namely IB1, IB2 and IB3. In section 3, we present the concept of emerging patterns and how they can be used for mining streaming data. This is followed by section 4, where we present the design of ILEP. Section 5 gives details about the experiments performed with our model and also presents a comparative study with the previous technique. The paper ends with concluding remarks in section 6.

2 Instance Based Classifiers For our purpose, we mainly focus on online nearest neighbour classification strategies (Instance-based learning). Instance based classifiers are used because they are the most suitable types of online classifiers for streaming data classification. There are three popular algorithms for instance based classification, namely IB1, IB2 and IB3. For our purpose, we employ IB2 and IB3 algorithms. This is because IB1 algorithm adds all data items seen till the instant to the reference set, which will not be possible in streaming data classification, due to huge storage requirements in this case. IB2 starts with an empty reference set S. Upon receiving a new object, it classifies it using the objects currently held in memory. If the classification is correct, the object is discarded. Conversely, if the classification is incorrect, the object is added into S. IB3 is an extension of IB2 that employs a "wait and see" evidence gathering method to determine which of the saved instances are expected to perform well during classification. IB3 maintains a classification record (i.e., the number of correct and incorrect classification attempts) with each saved instance. A classification record summarizes an instance's classification performance on subsequently presented training instances and suggests how it will perform in the future. IB3 employs a significance test to determine which instances are good classifiers and which ones are believed to be noisy. IB3 accepts an instance if its classification accuracy is significantly greater than its class's observed frequency and removes the instance from the concept description if its accuracy is significantly less. Kuncheva et al. in [3] have proposed a classification strategy for streaming data by making modifications in IB2 and IB3 algorithms. Due to unavailability of true labels at the time of classification, the additions to the reference set have been made based on a simple heuristic i.e. whether the two nearest neighbours of the data item in the reference set have the same label. If not, the classification is assumed to be wrong and the data item is added to the reference set with the label of the nearest neighbour. This technique resulted in good accuracy of classification, but still led to increase in the size of reference set, which was seen to be marginal in large data sets. Although no name was given to this scheme, for our scheme, we will refer to it as the Simple IB Scheme. We will perform comparisons of this technique with ours in section 5.

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3 Emerging Patterns EPs (Emerging Patterns) are a new kind of patterns introduced recently [4]. They have been proved to have a great impact in many applications. EPs can capture significant changes between datasets. They are defined as item sets whose supports increase significantly from one class to another. The discriminating power of EPs can be measured by their growth rates. The growth rate of an EP is the ratio of its support in a certain class over that in another class. Usually the discriminating power of an EP is proportional to its growth rate. The process of finding out the emerging patterns is based on mining the strongest EPs from strongest instances in the reference set. The set of EPs is updated according to the strength of EPs and data instances. If gr(e) is the growth rate of a pattern from one class to another and s(e) is the support of that patter, then the strength of an EP e, strg(e), is defined as follows. (1) The strength of an EP is proportional to both its growth rate (discriminating power) and support. Notice that if an EP has a high growth rate and a low support its strength might be low. In addition, if it has a low growth rate and a high support its strength might also be low. The strength of an instance, I, is defined by a fitness function as follows. ∑

(2)

The fitness function of a data instance can be measured by the average support of the attribute values in this instance. Suppose that we have an instance i {a1, a2, a3, ... an}. We first find the supports of all the attribute values (from a1 to an). We then average these supports to obtain a measure that tells how good the instance is. For our purpose, we use the Emerging Patterns Discovery application developed by Roman Podraza and Mariusz Kalinowski [2].

4 Design of the ILEP Scheme The basic design of the ILEP has been shown in Fig. 1. The design includes an implementation of the instance based classifier – the IB classifier, two sets of data namely emerging patterns set (contained in the EP Filter) and the reference set and a decision block that decides whether to add a data to the reference set or not. The input to the system includes a stream. At step t in the delayed labeling scenario, we have a reference set St and a new unlabelled object xt+1. After predicting the label for xt+1, we receive the label of the object that arrived τ steps earlier, xt−τ+1. Objects from xt−τ+2 to xt+1 are still without labels.

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To output

Streaming Data

Delayed Labels

IB Classifier

Reference Set

EP Filter

Incorrect?

Predictor Discard

Correct?

Add Data to reference set

Fig. 1. Design of the ILEP scheme

Using the techniques of EP mining discussed before, we first mine the EPs from the initial reference set and maintain an Emerging Patterns Set in the EP Filter. At any instant, as a data point is received as input, it is instantly classified by the IB classifier and the result is sent to the output. At the same time, EP Filter matches this data point with the mined EP Set. If the data point satisfied a strong EP i.e. an EP with large value of growth and high support of target class, then this data item is passed to the decision making system (else it is filtered out), which decides whether to add that item to the reference set or not. The versions of ILEP that uses IB2 is called ILEP2 while one that uses IB3 is called ILEP3 There are essentially two areas of concern where we have to focus for accurate classification – addition of data items to the reference set and EP set extraction. 4.1 Maintenance of Reference Set In ILEP, the reference set forms the backbone of the IB classifier. At the same time, it is also used to mine EPs to be used by the EP Filter. Then, it is used by the predictor component to decide whether to add the newer data items or not. The reference set is non-static, meaning that it will be continuously modified during the course of classification. While sometimes, it will be modified by the IB classifier’s error driven correction mechanism, sometimes, it will also be modified by the output data items from the Predictor. Here modification in the former case implies addition or deletion of the data item, while in the latter case, it implies only addition to the reference set.

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Once we have a data item that satisfies a strong EP, we may need to add that item to the reference set. For addition of a data item to the reference set, we need to know its true label. But in the problem statement, it is made clear that there is no way of knowing the true label of data item before a certain time lag. First we make sure if the data item is important enough to be added to the reference set. In the simple IB scheme, an intuitive heuristic method was proposed for deciding whether to add the data item to the reference set. This heuristic checked the first two nearest neighbours of the item in the reference set and if they were dissimilar, the item was added to the reference set. We improve upon this heuristic and add more conditions to the decision maker, emphasizing the fact that one of our main focus is to keep the reference set under limit. The two conditions that we add to the decision maker are: firstly, whether the target class predicted by the EP miner and the target class predicted by the IB classifier match and secondly, whether label of the nearest neighbour and the target class of the EP miner are same. If so, we conclude that the data item is a strong distinguishing point and its addition to the reference set will improve the classification accuracy of the IB classifier. Hence we add such a data item to the reference set, with its label same as that predicted by the IB classifier. It must be noted at this point that such an addition may lead to addition of a data item with wrong label. 4.2 Mining of Emerging Patterns The procedure for mining of the EPs has already been discussed before. The EP set consists of only few top EPs from the total mined EPs, with maximum growth rates. But the main issue is that we cannot classify the complete set of streaming data using one single EP set that was mined in the beginning as in that case, any recent changes in the concept description will not be captured by the EP set and hence the decision maker will rely upon the old EP set even for newer data items. Hence we need to have a progressive EP set. For this, we need continuous EP mining of streaming data. But mining EPs continuously is neither feasible (as it is a very intensive process and cannot be used in case of streaming data) nor required as small addition in the reference set will not make much difference in the EP set. A simple yet effective strategy would be to time the mining of EPs at regular intervals, so that the decision making system is constantly updated. A more active strategy would be based upon the number of modifications in the reference set. As part of this strategy, we maintain a modification counter initialized to zero. Every time a data item is added or deleted from the reference set, we increment this counter. Every time this counter reaches a certain overflow limit, we update the EP set by mining the EPs again and reset the counter. The overflow limit will be called EP update counter limit. This strategy is much better than the fixed update frequency method because in that case, we have to initialize the frequency in advance, which will depend upon the type of data set. Hence we need to have some knowledge about the data set we are working on. But in this approach, the strategy decides the value of the update frequency by itself. We just need to initialize the counter limit, which can be same for all type of data sets.

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The complete classification procedure can be summarized in following steps: 1. 2.

3. 4. 5. 6.

As a new data stream block is received, it is classified using the IB classifier and the result is outputted. Proceed to step 2. An EP Mining system, that extracts the emerging patterns from the reference set of the IB classifier, is used to filter out data items matching the emerging patterns with high growth rate. A decision making system, which uses various heuristics proposed previously, adds some of the data items from step 2 to the reference set. If modification counter reaches the EP update counter limit, mine EPs again and reset the counter. On receipt of the delayed class labels of already received data items, the corresponding items of the reference set are updated accordingly On arrival of newer streaming data, continue again from step 1.

5 Experiments Due to unavailability of real time streaming data, we carried out our experiments on streaming data classification techniques by using data sets chiefly from UCI repository [6]. We simulated the streaming process of data items for classification using data sets given in table I. Table 1. Data sets used Data Sets

Features

Classes

Objects

Iris Wine Sonar Glass Ionosphere Liver Page Block Vehicle Letters

4 13 60 10 34 6 10 18 16

3 3 2 7 2 2 5 3 26

120 148 178 171 316 293 5409 752 17619

The determining factors for effective strategy will be accuracy of the classification as well as the final percentage change in the size of the reference set. We mainly conducted three experiments to compare our results. First we obtained accuracy results for the simple IB scheme (using IB3 algorithm, as according to [3], IB2 does not perform well with the ‘Do Nothing’ approach that we have used in our work). Then we obtain accuracy results for ILEP2 and then ILEP3 algorithm. The results have been compared as bar charts in figure 2. Similarly, we obtain results for change in size of reference set in these three cases. These results have been compared in Figure 3. The value of K for the IB classification (K – nearest neighbour) is 20% of

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the initial size of reference set. The EP update counter limit is set to 15 and delay in arrival of true labels is 10. The percentage of top EPs considered by the decision maker is set to 30.

Accuracies

100 80 60 40 20 0

Data Sets Accuracy using Simple IB technique

Accuracy using ILEP2

Accuracy using ILEP3 Fig. 2. Comparing classification accuracies using the simple IB scheme, ILEP2 and ILEP3

The first and most important result is the effectiveness of using emerging patterns technique in classification of streaming data. Figure 2 compares the accuracies obtained from the classification experiments using the simple IB scheme (IB3), ILEP2 and ILEP3. We observe that using ILEP, we could not gain much increase in the accuracy level. In fact, in many cases, the classifier of simple IB scheme was found to be more accurate. Also, while comparing IB2 and IB3, IB2 is found to have an edge over IB3, but the difference is not very high. But, from the point of view of reference set size, our technique definitely stands ahead, as is clear from figure 3, which shows the percentage increase in the reference set size of various data sets after the final classification in the three experiments that we did. It should be noted that in smaller data sets as those of Iris, wine, sonar etc., the increase in reference set size is almost zero. This, along with the fact that there classification accuracy was good (using any technique) resonates well with the fact that reference set size increases on addition of data items when there classification is predicted incorrectly. The advantage of using our technique becomes clear from the results of large data sets like Letters, where the increase in reference set size using EP miner is almost zero whereas that using technique of [3] is around 1500%. The simple IB scheme ends up with good accuracy but with a very huge final reference set size due to large number of testing objects. It can be easily predicted that this technique will keep on increasing its reference set size as more and more objects are tested by it and hence is very unsuitable for large data sets, unlike ILEP.

% Increase in reference set sizes

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1600 1400 1200 1000 800 600 400 200 0 -200

Data Sets % increase in reference set size using Simple IB technique % increase in reference set size using ILEP2 % increase in reference set size using ILEP3 Fig. 3. Comparing increase in reference set sizes using simple IB scheme, ILEP2 and ILEP3

Figure 3 also shows comparisons between classifications obtained from ILEP2 and ILEP3 algorithms. As is clear, while the reference set size increases in case of ILEP2 algorithm for all data sets, ILEP3 is able to do an almost equally good classification with a shorter reference set. This result is of great importance, because in case of streaming data sets, the amount of data input is huge, and if we do not limit the size of reference set, the classification efficiency will be very low, resulting in slow down of the process, which may be disastrous. Therefore, we would say that if the accuracy of the system can be slightly compromised, we will always choose the EP miner with IB3 method for classification. But if the accuracy of the system is important, then we will have to compare the two algorithms upon the data set that is going to be used.

6 Conclusion This paper implemented a novel technique that integrates instance-based classifiers with EP miner for classification of streaming data. The ILEP2 and ILEP3 classifiers were applied on various data sets of the UCI repository. The accuracy results of the implemented classifiers were found to be similar to those of the simple IB scheme, but a significant gain was obtained in terms of the final reference set size. While in the simple IB scheme, the size of reference set drastically increases and goes to values as high as 1000% to 1500%, in ILEP, the reference set remains almost of the same size in case of ILEP2 algorithm and decreases in size in case of ILEP3 algorithm. It was found that both ILEP2 and ILEP3 can be of good use depending upon the type of

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data sets used. While ILEP2 is ahead of ILEP3 in terms of accuracy, ILEP3 is better than ILEP2 when it comes to restricting the size of reference set. Although the implementation for the proposed work has been done on a centralized system, the scalability of the design becomes more apparent, when a distributed system is used for the purpose of classification of data streams. Distributed systems are getting more and more importance in this age of overwhelming data storage. Since streaming data is normally associated with huge amount of data, which is very difficult to be handled with a centralized system, the use of distributed systems for classification purpose becomes all the more obvious. The most important thing about our proposed scheme that makes it suitable for distributed systems is that our scheme is easily decomposable into distributed and self dependent components that can be easily implemented on any platform. There are many aspects that have been left untouched for future works. We have not considered the time taken by the online classification for each data item. Moreover, the EP mining system that we used in our work is meant for conventional data. More work needs to be done so as to develop a system for mining EPs from streaming data systems.

References [1] Podraza, R., Walkiewicz, M., Dominik, A.: Credibility Coefficients in ARES Rough Set Exploration System. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 29–38. Springer, Heidelberg (2005) [2] Kuncheva, L.I., S´anchez, J.S.: Nearest Neighbour Classifiers for Streaming Data with Delayed Labelling. In: 8th IEEE International Conference on Data Mining, Pisa, Italy, pp. 869–874 (2008) [3] Dong, G., Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In: International Conference on Knowledge Discovery and Data Mining (KDD 1999), San Diego, CA, USA, pp. 43-52 (1999) [4] Nakayama, H., Yoshii, K.: Active forgetting in machine learning and its application to financial problems. In: International Joint Conference on Neural Networks, Como, Italy, pp. 123–128 (2000) [5] UCI Machine learning repository, http://archive.ics.uci.edu/ml/ [6] Siebert, J.P.: Turing Institute Research Memorandum TIRM-87-018: Vehicle Recognition Using Rule Based Methods (March 1987) [7] Seeger, M.: Learning with labeled and unlabeled data. Technical Report, University of Edinburgh, UK (2002) [8] Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38(3), 257–286 (2000) [9] Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: 6th International Conference on Knowledge Discovery and Data Mining, pp. 71–80. Association for Computing Machinery, Boston (2000)

Modified Go-Left Balls and Bins Algorithm for Server Load Balancing Prasun Banerjee, Stephan D’Costa, and Sukriti Bhattacharya Department of Computer Science & Engineering University of Calcutta , West Bengal, India {banerjee.prasun,dcosta.stephan,bhattacharya.sukriti}@gmail.com http://www.caluniv.ac.in

Abstract. This paper proposes a modified version of Go-left Balls & Bins algorithm for server load balancing using K-Partite property of a graph. The previous algorithms all had to keep knowledge of the past load distribution information while distributing new load -an issue that is virtually impossible in real life as it itself congests the server with load. But ours algorithm is random in nature and hence sheds this overhead; this is also quite realistic in nature and close to the implementation domain. Keywords: Server Load balancing, Go-left Balls & bins, K-partite graph.

1 Introduction Internet server programs supporting mission-critical applications such as financial transactions, database access, corporate intranets, and other key functions must run 24 hours a day, seven days a week. Network servers are now frequently used to host ERP, e-commerce and a myriad of other applications. The foundation of these sites the e-business infrastructure - is expected to provide high performance, high availability, and secure and scalable solutions to support all applications at all times. However, the availability of these applications is often threatened by network overloads as well as server and application failures. Resource utilization is often out of balance, resulting in the low-performance resources being overloaded with requests while the high-performance resources remain idle. Server load balancing [1][2][4] is a widely adopted solution to performance and availability problems. Load balancing is a technique used for distributing service requests evenly among servers that offer the same service. Load refers to a number assigned to a service request based on the amount of time required to execute that service. Loads are assigned to services so that the system can understand the relationship between requests. To keep track of the amount of work, or total load, being performed by each server in a configuration, the administrator assigns a load factor to every service and service request. A load factor is a number indicating the amount of time needed to execute a service or a request. On the basis of these numbers, statistics are generated for each server and maintained on the bulletin board on each machine. Each bulletin board keeps track of the cumulative load associated with each server, so that when all servers are busy, the system can V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 237–242, 2010. © Springer-Verlag Berlin Heidelberg 2010

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select the one with the lightest load. The two major categories for load-balancing algorithms are static, and dynamic. Static load balancing algorithms allocate the tasks of a parallel program to workstations based on either the load at the time nodes are allocated to some task, or based on an average load of workstation cluster. Dynamic load balancing algorithms make changes to the distribution of work among workstations at run-time; they use current or recent load information when making distribution decisions. As a result, dynamic load balancing algorithms can provide a significant improvement in performance over static algorithms. There are three major parameters which usually define the strategy a specific load balancing algorithm will employ. These three parameters answer three important questions: Who makes the load balancing decision? What information is used to make the load balancing decision? and Where the load balancing decision is made? Based on these three questions can categorize the load balancing strategies as – Sender-Initiated vs. Receiver-Initiated Strategies: The first question is answered based on whether a sender-initiated or receiver-initiated policy is employed. In sender initiated policies, congested nodes attempt to move work to lightly-loaded nodes. In receiver-initiated policies, lightly-loaded nodes look for heavily-loaded nodes from which work may be received. – Global vs. Local Strategies: Global or local policies answer the second question. In global policies, the load balancer uses the performance profiles of all available workstations. In local policies workstations are partitioned into different groups. The benefit in a local scheme is that performance profile information is only exchanged within the group. – Centralized vs. Distributed Strategies: A load balancer is categorized as either centralized or distributed, both of which define where load balancing decisions are made. In a centralized scheme, the load balancer is located on one master workstation node and all decisions are made there. In a distributed scheme, the load balancer is replicated on all workstations. In this paper we state and improve the limitations of traditional go-left balls and bins algorithm and propose a centralized, global and dynamic version of it, using K-partite property of the graph. The paper is structured as follows. Section 2 illustrates traditional ’Balls & Bins’ go-left algorithm and its limitations. In section 3, we propose our algorithm, the modified version of ’Balls & Bins’ go-left using K-partite graph property. In section 4, we show the results. In section 5, we conclude by discussing the main advantages of our algorithm.

2 Go-Left 'Balls and Bins' Suppose n balls have to be assigned to n bins, where each ball has to be placed without knowledge about the distribution of previously placed balls [3][4]. The goal is to achieve an allocation that is as even as possible so that no bin gets much more balls than the average. In [1] it is shown that a non-uniform and possibly dependent choice

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of the locations for a ball can improve the load balancing. The algorithm can be stated as follows, For each new incoming unit load (call it a ball) perform the following: Step 1: Divide the graph (of nodes) into 2 partitions. Step 2: Select a node (call it a bin) randomly from the first partition; Step 3: And choose a 2nd bin from the second partition depending on the 1st choice. Step 4: Then compare the load (number of balls) of both the bins and places the ball in the bin with lesser load. In case of a tie it goes left (quite in a biased way) and places the ball into the 1st bin, i.e., go-left. This algorithm is called dependent, because every node from the 2nd partition is chosen depending on the choice of node of the 1st partition. This algorithm is called nonuniform for a biased tie-breaker mechanism. The above algorithm lacks the following points: • • •

We need a uniform node numbering; based on which the nodes of the 2nd partition can be chosen as a function of the nodes chosen from the 1st partition. The algorithm mentioned above is silent about how to make partitions; it somehow makes that partitions randomly or assume that is already done. Though it makes partitions it completely ignores the concept of clustering network; an idea that can be incorporated depending on node’s connectivity, topology and geographical locations.

3 K-partite Go-Left 'Balls and Bins' Algorithm Here we look forward to use the centralized-global-dynamic algorithm by making use of previous Go-left balls and bins algorithm. K-partite Go-left Algorithm has been proposed to distribute the jobs among several nodes for processing. Thus increasing the system performance. Asymmetry often helps to distribute load and still brings good performance that we don’t usually expect it shows that performance gets a hike if we distribute it rather nonuniformly and in a dependent fashion. Therefore, we tackle the limitations stated in the last section as follows: 1. We are using a uniform numbering system of the form (Partition Id:node Id). This form of numbering easily finds its’ slimily in the network address of the form (Host Id:net Id) 2. Here we make partitioning explicitly by using K-Partite property of a graph. 3. The K-partite nature of the graph account for its connectivity and topologyan issue that is considered when clustering a network. With these modifications our algorithm is stated in the following. The algorithm starts with a connected random graph G with n nodes, each node has a unique node index associated with it. The load associated to each node index is set to zero in the

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initialization phase. Then G is divided into K parts depending on its k-partite property [2][3][5]. Each partition has n unique partition index associated. The incoming loads are distributed in the following way. Modified K-partite Go-left Balls & Bins Algorithm: 1: 2: 3:

4:

Generate random graph (We don’t consider the graph with disconnected components). Initialize it with Load 0 for all the nodes. Partition the graph into K parts depending on its k-partite property: 3.1: Take the first node randomly and assign it the partition index = 0 and the node index = 0. 3.2: For every other node that are still not member of any partition 3.2.1: For every other partition 3.2.1.1: Check whether the new node is connected to any nodes of this partition. 3.2.1.2: If it is not, let it be a member of this partition 3.2.2: Otherwise assign it to a new partition(with new partition index). Distribute the load between the partition using a modified version of the goleft algorithm: 4.1: For each unit load 4.1.1: Take a random node from the first partition. 4.1.2: For each remaining partition. 4.1.2.1: Choose a node Newnodeindex = f(Oldnodeindex) Function f can be defined as f(Oldnodeindex) = counttotal_node (partion index) + (Oldnodeindex)2% countnumber_of_partitions

(

counttotal_node countnumber_of_partitions

)

4.1.2.2: If this node has a load less than the previous node assign it a unit load. 4.1.2.3: In case of a tie; always go left.

4 Results L

The optimum efficiency of each node is calculated by n %, where L is the total load to be distributed and n is the total number of nodes. The result of our algorithm (the actual efficiency) is plotted across X and Y-axis along with the optimum efficiency. The X-axis denotes total number of nodes considered each execution (from 10 to 100), while the corresponding Y-axis shows the maximum amount of load a node can support (the actual efficiency). The black curve denotes efficiency of our algorithm where the gray curve denotes optimal efficiency. Here we run our algorithm for 1024 loads.

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Fig. 1. Performance graph for 1024 loads

5 Discussion This proposed K-partite-go-left algorithm finds its use in the load balancer software used to do server side load balancing. In actual world there can be some CPU overhead for partitioning the network, re-partitioning the network once a new node is added or removed, balancing the load, re-balancing the load when new bulk of loads come. The complexity of load distribution algorithm is O(n2) and the complexity of partitioning is O(n3); and this is an overhead of this algorithm. But this algorithm has the modularity to support the dynamic load and dynamic node availability. In case a new node is added or deleted, we only need to run the partition routine on the modified adjacency matrix. In case a new load comes, we need to run the distribute routine. Additionally for managing a new bulk of loads together, we can actually run the load balancer in multiple synchronized threads. Our algorithm is actually not an optimal choice algorithm as it is nonuniform and dependent in nature in choosing the right bin. But it pays much lesser cost in building this near optimal algorithm. Because this algorithm uses the biased go left method, it removes the load oscillation. Acknowledgements. The authors would like to thank Mr. Sanjit Setua for his continuous support and helpful suggestions.

References 1. Vocking, B.: How Asymmetry Helps Load Balancing. In: Proceedings of the 40th Annual Symposium on Foundations of Computer Science. IEEE, Los Alamitos (1999)

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2. Long, B., Wu, X., Zhang, Z.M., Yu, P.S.: Unsupervised learning on k-partite graphs. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York (2006) 3. Harary, E.: Graph Theory. Addison-Wesley, Reading 4. Mitzenmacher, M.: The Power of Two Choices in Randomized Load Balancing. PhD thesis, University of California at Berkeley (1996) 5. Knuth, D.E.: The Art of Computer Programming, vol. 3. Addison Wesley, Reading (1998)

Three Layered Adaptation Model for Context Aware E-Learning Minu M. Das, Manju Bhaskar, and T. Chithralekha Pondicherry Central University, Pondicherry, India [email protected], [email protected], [email protected]

Abstract. Current context aware e-learning system lacks in providing highly customized information to the learner, which considers context parameters discretely and there is no complete standardized set of learner contexts. The proposed three layered adaptation model gives all most all learner contexts in a standardized way, which improves the efficiency of learning process. The design solution provides a three layered architecture based on learner’s characteristics, which is divided into three layers such as conceptual layer, logical layer and physical layer which helps to improve the efficiency of learning process. Keywords: e-learning, context aware e-learning, layered architecture, adaptation model, learning path, intention, preference.

1 Introduction E-learning is aimed at supporting learners in fulfilling a specific learning need with in a specific situation through the use of information and communication technology. Unfortunately, current e-learning systems do generally not consider some important characteristics capable of providing a complete context aware e-learning system to the learner. Existing e-learning systems do not pay attention to the learner’s complete characteristics, thus all students are given the same materials and activities. Nor do didactic materials offer, due to the access possibilities to different devices such as PDA, mobile, PC, Laptops and so on, in an efficient way. Context aware e-learning systems select or filter the learning resources in order to make the e-learning content more relevant and suitable for the learner in his/ her situation. However, looking for a stronger learning personalization, the design of courses to ensure the right knowledge acquisition by the student, in his own way, taking in to account of learners characteristics, preferences, intentions and device used. Thus a standardization of contexts of learner needed for efficient e-learning process. The proposed three layered adaptation model for Context aware e-learning gives the standardized contexts of learner in all aspects. The whole paper is organized as follows: Section 2 describes the existing works in context aware e-learning. The Need for standardized Context aware e-learning is described in section 3. Section 4 illustrates the three layered adaptation model for context aware e-learning. The case study and system snapshots are described in section 5. Finally section 6 concludes the paper. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 243–248, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Existing Works in Context Aware E-Learning In the existing works contextualization are achieved by delivering appropriate material, or by considering the preference or intentions of the learner, or by giving the learning material in suitable media. The details of each category of contextualization are described in the following section. 2.1 Learning Paths Learning path defines the sequence of learning activities that is carried out by learner going through the learning units in the e-learning system. Learning unit is an abstract representation of a course, a lesson, a workshop, or any other formal or in-formal learning or teaching event [17]. Context aware learning path generation is about creating a learner experience that purposely adjusts to various conditions such as personal characteristics, pedagogical knowledge and the learner interactions over the period of time with the intentions to increase success criteria. 2.2 Learning Object Model The design of the standardized context model requires a flexible learning object model. That is, the learning object structure should not be static. The structure of the learning object will change according the learner’s preferences and intentions. This requires that the learning object is structured in terms of different levels of abstractions as given below in Table 1. That is, the same learning object is available in the form of a concept, detailed concept, example, case study, simulation and demonstration. Each of these corresponds to the various abstraction of the same learning object. Every abstraction would be available in different media types such as text, audio, video, animation etc. When a learner whose learning preference is learning by case study approaches the E-Learning system with the intention of preparing for interview, the different learning object abstraction chunks chosen to constitute the learning object structure and the sequencing order of these abstractions while presenting to the learner is as shown below Simple Concept Æ Case Study Æ Example. Thus, for the above mentioned scenario, the learning object is structured with three abstractions – simple concept, case study and example. This structure is dynamically determined based on the learning preference and intention of the learner. Formalized way of representing the learning object model for different intentions of the learner is given in Table 2. This shows the different learner’s intention and the corresponding sequencing of the learning object abstractions for each of these intentions.

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Table 1. Learner’s Preferences and Intentions in Existing System ontext Parameters Considered Learning Preference

Learning Intention

Sub Context Parameters Conceptual Example-Oriented Case Study Simulation Demonstration Research Survey/ Overview Quick Reference Basic Introduction Project Assignment Seminar

Existing Systems [1],[2],[3],[4],[5],[6],[7] [9],[10],[11],[12],[13], [15],[16],[18],[19]

[6],[7],[14],[18],[19]

Table 2. Sequencing of contents based on learner’s preferences and Intentions Learner’s Intentions Research Survey

Learning chunk abstraction constituting the learning object Simple Concept, Detailed Concept, Example, Case Study, Demonstration, Simulation Detailed Concept, Example, Case Study

Quick Reference Basic Introduction Project

Simple Concept, Example, Case Study Simple Concept, Example Detailed Concept, Example, Case Study, Simulation, Demonstration

Seminar Assignment

Detailed Concept, Example, Case Study, Demonstration Detailed Concept, Example, Case Study

3 Need for the Proposed Context Model Existing context aware e-learning systems are using few parameters which are described in section 2. There is no such context aware e-learning system which in cooperates all of the parameters which are described in section 2. So in order to include all the parameters and to give a standardized form for learner characteristics, a three layered adaptation model is proposed. Proposed system is using a three layered adaptation model, which helps to include all the learning characteristics. The following section 4 describes about the three layered adaptation model for context aware e-learning.

4 Three Layered Adaptation Model for Context Aware E-Learning Three layered adaptation model gives a standardized model which completely capture the learner’s characteristics. The adaptation model divides all learner contexts in three

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layers so that it helps efficient personalization in the learning process. Fig. 1. shows the three layered model for context aware e-learning system, in which each layer taken care of specific characteristics of learner. Three layers in the model are given below.

Physical Layer

Logical Layer

Adaptivity Increases

4.1 Conceptual Layer 4.2 Logical Layer 4.3 Physical Layer

Conceptual Layer

Fig. 1. Three Layered Adaptation Model for Context Aware E-learning

4.1 Layer 1: Conceptual Layer Conceptual layer deals with learning path generation. The learning path generation is important because different learners may have different characteristics, prior knowledge or motivation or needs [8]. This diversity commonly requires the presentation of different information to different learners in different formats [19]. That is why conceptual layer is very important which consider various aspects of individual students when presenting information and or practice opportunities in order to make the learning process as effective, efficient and motivating as possible. The parameters helps for providing adaptivity in conceptual layer given [8] are prior knowledge and prior skills, Learning capabilities, Learning preferences, Performance level and knowledge state, Interest, Personal circumstances, Motivation.

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4.2 Layer 2: Logical Layer The second layer, logical layer mainly concentrates the learner’s preferences and learner’s intentions. Each learner has his/her specific preference and intentions for learning. The values for learner’s preferences which are considered for adaptation are Concept, Detailed Concept, Example, Case study, Simulation and Demonstration of a particular learning material. The intention of the learner can be research, survey work, interview purpose, assignment work, project or just to learn the concept. Based on the intentions and preferences the sequencing of the contents will differ. 4.3 Layer 3: Physical Layer The third layer, physical layer provides information about the device used by the learner for learning. The device can be mobile, PDA, laptop, PC and so on. According to the intention, preferences and media preferred by the learner, physical layer delivers appropriate learning material in specified media. Media can be audio, video , text or animation.

5 Case Study: Gurudev To explain how to perform the three layered adaptation process in e-learning system, the following section briefly explains the procedure. The experimental system is named as Gurudev and computer network subject is taken as example.. Based on the conceptual layer, logical layer and physical layer, context adaptive learning scheme is generated. Fig. 3. shows the generated learning scheme which is given to the server to get the personalized learning materials. The server is acted as learning content management system which dynamically composes the learning objects and is given to the learner which is given in Fig 4.. The snapshots of implemented system are given below.

Fig. 3. The generated Adaptive Learning Scheme Fig. 4. Dynamically composed learning object

6 Conclusion Three layered adaptation model for context aware e-learning system provides three layers of adaptation which helps for achieving personalization. The three layered adaptation model considers all most all learner characteristics in a standardized way. So the model helps to improve the e-learning process in an efficient manner.

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References 1. Chen, P., Meng, A., Zhao, C.: Constructing Adaptive Individual Learning Environment Based on Multi- Agent System. In: IEEE International Conference on Computational Intelligence and Security Workshop, pp. 374–377 (2007) 2. Dietze, S., Gugliotta, A., Domingue, J.: Addressing Context-Awareness and Standards Interoperability in E-Learning: A Service-oriented Framework based on IRS III 3. Jeongwoo, K., Fumihiko, M., Teruko, M., Eric, N., Masahiko, T., Ichiro, A.: ContextAware Dialog Strategies for Multimodal Mobile Dialog Systems. J. AAAI (2006) 4. Jose, M.M., Juan, A.O., Luis, G., Francisco, V.: Creating adaptive learning paths using Ant Colony Optimization and Bayesian Networks. In: IEEE International Joint Conference on Neural Networks, pp. 3834–3839 (2008) 5. Jovanovic, J., Gasevic, D., Knight, C., Richards, G.: Ontologies for Effective Use of Contextin e-Learning Settings. Educational Technology & Society 10(3), 47–59 (2007) 6. Howe, D.: Free online dictionary of computing, Imperial College Department of Computing London, UK (2006) 7. Kareal, F., Klema, J.: Adaptivity in e-learning. Current Developments in TechnologyAssisted Education, 260–265 (2006) 8. Kawanishi, N., Jin, K.S., Si, H., Kawahara, Y., Morikawa, H.: Building Context-Aware Applications and Probe Space Infrastructure. In: IEEE International Symposium on Intelligent Signal Processing and Communications, pp. 103–106 (2006) 9. KounTem, S., HsinTe, C.: The Study of Using Sure Stream to Construct Ubiquitous Learning Environment. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp. 534–548 (2008) 10. Lanzilotti, R., Ardito, C., Costabile, M.F., De, A.A.: eLSE Methodology: a Systematic Approach to the eLearning Systems Evaluation. Educational Technology & Society 9(4), 42–53 (2006) 11. Limongelli, C., Sciarrone, F., Temperini, M., Vaste, G.: Adaptive Learning with the LSPlan System: a Field Evaluation. IEEE Transaction on Learning Technologies (2008) 12. Srimathi, H., Srivatsa, S.K.: Identification of ontology based learning object using instructional design (2008) 13. Stockley, D.: E-learning Definition and Explanation (Elearning, Online Training, Online Learning) (2003) 14. Sun Microsystems E-learning Framework 15. Thyagharajan, K.K., Ratnamanjari, N.: Adaptive Content Creation for Personalized e-Learning Using Web Services. J. Applied Sciences Research 3(9), 828–836 (2007) 16. Tuparova, D., Tuparo, G.: Learning paths in open source e-learning environments. Current Development in Technologies-Assisted Education, 1565–1569 (2006) 17. Wang, M., Ci, L., Zhan, P., Xu, Y.: Applying Wireless Sensor Networks to ContextAwareness in Ubiquitous Learning. In: IEEE Third International Conference on Natural Computation, vol. 5, pp. 791–795 (2007) 18. Yang, S.J.H.: Context Aware Ubiquitous Learning Enviornments For Peer to Peer Collaborative Learning. Educational Technology & society 9(1), 188–201 (2006) 19. Zajac, M.: Using Learning Styles to Personalize Online Learning. J. Campus- Wide Information System 26(3), 256–265 (2009)

AFDEP: Agreement Based CH Failure Detection and Election Protocol for a WSN Amarjeet Kaur and T.P. Sharma Computer Science And Engineering Department, National Institute of Technology, Hamirpur – 177005 India [email protected], [email protected]

Abstract. In this paper, we propose an agreement-based fault detection and recovery protocol for cluster head (CH) in wireless sensor networks (WSNs). The aim of protocol is to accurately detect CH failure to avoid unnecessary energy consumption caused by a mistaken detection process. For this, it allows each cluster member to detect its CH failure independently. Cluster members employ distributed agreement protocol to reach an agreement on failure of the CH among multiple cluster members. The detection process runs concurrently with normal network operation by periodically performing a distributed detection process at each cluster member. To reduce energy consumption, it makes use of heartbeat messages sent periodically by a CH for fault detection. Our algorithm would provide high detection accuracy because of agreement protocol.

Keywords: Wireless Sensor Network, Clustering, Fault detection, Agreement protocol, Detection accuracy.

1 Introduction Wireless sensor networks (WSNs) consist of hundreds and even thousands of small tiny devices called sensor nodes distributed autonomously to monitor physical /environmental conditions, infrastructure protection, battlefield awareness etc. Each sensor node has sensing, computation, and wireless communication capabilities [1]. Sensor nodes sense the data and send it to base station (BS). Sensor nodes have energy constraint. Sensor nodes are often left unattended which makes it difficult or impossible to re-charge or replace their batteries. The cost of transmitting information is much higher than computation and hence it is necessary to reduce the number of transmissions. Clustering is an effective way to reduce number of transmission and prolongs network lifetime. There are number of clustering-based routing protocols proposed in literature for WSNs [2-11]. These protocols improve energy consumption and performance when compared to flat large-scale WSNs. Sensor nodes are prone to failure due to harsh environment. The failure of a sensor node affects the normal operation of a WSN [12].The failure of a CH makes situation even worse. In literature, number of authors have proposed fault tolerant protocols [13-16]. In this paper, we propose a fault tolerant protocol for WSN, which is based on agreement protocol. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 249–257, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Related Work Clustering is an effective way for improving the energy efficiency and prolonging the network lifetime of WSNs. The CH failure causes the connectivity and data loss within cluster. It also disconnects cluster members from rest of the network. Hence, it is crucial to detect and recover the CH failure to maintain normal operation of cluster and network as a whole. In [17], an agreement-based fault detection mechanism is proposed for detecting CH failures in clustered Underwater Sensor Networks (UWSNs). In this, each cluster member is allowed to independently detect the fault status of its CH and at the same time a distributed agreement protocol is employed to reach an agreement on the fault status of the CH among multiple cluster members. It runs parallel with normal network operation by periodically performing a distributed detection process at each cluster member. It provides high detection accuracy under high packet loss rates in the harsh underwater environment. FTEP [18] is a dynamic and distributed CH election algorithm with fault tolerance capabilities based upon two-level clustering scheme. If energy level of current CH falls below a threshold value or any CH fails to communicate with cluster members then election process is started which is based on residual energy of sensor nodes. This election process appoints a CH and a back up node to handle CH failure. It has a single point (back up node) to detect failure which may itself be disastrous. In cellular approach to fault detection and recovery [19], network is partitioned into a virtual grid of cells, where each cell consists of a group of sensor nodes. A cell manager and a secondary manager are chosen in each cell to perform fault management tasks. Secondary manager works as back up node which will take control of the cell when cell manager fails to operate. This protocol handles only those failures which are caused due to energy depletion. In [20], a method to recover from a gateway fault is proposed, which is based on agreement protocol. A periodic status updates are sent through inter-gateway communication. The status updates can be missed due to link failures between two sensor nodes, hence a consensus has to be reached by all gateways before recovery commences. When a gateway is identified as completely failed all the sensor nodes in its cluster are recovered. Venkataraman algorithm [21], proposed a failure detection and recovery mechanism which is also based on energy exhaustion. It focused on sensor node notifying its neighboring sensor nodes before it completely shuts down due to energy exhaustion.

3 System Model 3.1 Assumptions Here we make some assumptions as following: • Nodes failure due to energy depletion or any hardware or software problem. • All nodes are homogenous, immobile and have limited energy [22]. And initially have same amount of energy. • Each node has fixed number of transmission power level.

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• Transmission power is uniform across network. • Every node knows its current energy level [18]. • A message sent by a node is received correctly with in a finite time by all nodes in the cluster. • The clusters are static i.e. are formed at the start of the network. After that CH rotates. • Nodes know about their location [18]. • The BS is fixed and not located between sensor nodes [22]. 3.2 Network Model Fig. 1(a) shows the network model used. Various symbols and terms used are shown in Table 1. All sensor nodes are homogeneous, which have two transmission modes i.e. high power transmission mode for communication between CHs and BS and low power transmission mode for communication between cluster members and CH. The distribution of sensor nodes is uniform throughout the environment. Communication medium is radio links. Links between two sensor nodes is considered bidirectional. There is only single channel for communication between sensor nodes.

Sensor node Cluster Head

Fig. 1. Network Model Table 1. Notions used to explain protocol Symbol

′ ′′

Meaning of symbol Distance that message travels Number of bits in the message Energy dissipated in transmitter electronics per bit (taken to be 50nJ/bit) Energy dissipated in transmitter amplifier (taken to be 50nJ/bit) Energy dissipated in receiver electronics per bit (taken to be 50nJ/bit) Energy consumed in transmission Energy consumed in receiving Location of node Current energy of node Energy level at which sensor node can participant in election of Energy level at which current starts election process Energy level up to which election process must be completed Candidate set

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During the network deployment, all the sensor nodes are assigned same initial energy value. All sensor nodes are assumed to know their geographical [23]. We assume that clusters may overlap during election procedure so that every sensor node comes under at least one cluster. Initially, some sensor nodes are randomly selected as CHs and they announce their energy levels and location information. These CHs start working in high power transmission mode while other regular sensor nodes work in low power transmission mode. 3.3 Sensor Node’s Energy Model A sensor node consists of sensors, analog signal conditioning, data conversion circuitry, digital signal processing and a radio link [3]. Each component of sensor node consumes energy for sending and receiving data. The following energy consumption model shows the energy consumed by components of sensor node as shown in Fig 1 (b) (redrawn from [3]). Assuming 1/ path loss, the energy consumption on each sensor node is: (1) (2) According to eq. 1, the transmitter unit consumes energy to send bits; where is the energy consumed by transmitter electronics per bit and is the energy used by amplifier per bit. According to eq. 2, the receiving unit consumes energy to receive bits, where is the energy used by receiver electronics per bit. Table 1 summarizes the meaning of each term and its typical value. The values for ′ , and are updated during each election process. Typically, value of for next election round is set to the average value of the energy levels of all ′ is set according to candidate nodes during current election round. The values of ′′ ′ . The values of is set according to as follows: ′′

- (energy consumption during election process + energy consumption in data transmission during that period).

4 AFDEP Protocol 4.1 Setup Phase Clusters are formed only once during the setup phase before the network starts to run. Initially, some sensor nodes are randomly selected as a CH, because energy of each sensor node is equal in amount. CHs send advertisement messages that contain energy and location information of CHs to neighboring sensor nodes. Each sensor node that listen to this advertisement message responds with a return message comprising its residual energy and location. However, a sensor node may be in the range of multiple CHs, but finally it must be associated with a single CH. If any sensor node falls within the overlapping region of more than one CHs, it decides its association to a CH by calculating the value of e/d (energy/distance). CH, has maximum e/d value is selected as final CH for that sensor node. If more than one CHs yields same

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maximum e/d value, then any of them is randomly selected. If a sensor node does not fall within the range of any CH, it declares itself as a CH and gets activated in high power transmission mode. When clusters are established, the CHs collect the data from cluster members, perform local data aggregation and communicate with the BS. 4.2 Steady State Phase Once cluster is formed, CH creates a TDMA schedule for cluster members and sends it to them. Sensor nodes sense data and send it to CH according to TDMA schedule. This process continues for all clusters until CH’s current energy level ( ) equals ′

. Then CH starts election process of new CH for next round. CH to or less than for next round in low power transmission mode, which is average broadcasts energy of those cluster members who participated in last election process. All sensor ). nodes within cluster listen message and compare with their current energy level ( Sensor node which have greater than or equal to , marks itself as a participant for election process. All participant sensor nodes broadcast their and location ( ) in low transmission mode. All participant sensor nodes can listen to each other because all sensor nodes are within low power transmission range of each and of each other. Because of this, all participant sensor nodes know about other. Hence, each participant sensor node is aware about higher energy participant upgrades itself as sensor node. The participant sensor node with highest value of CH and gets activated in high power mode; where as sensor node with second highest and of all energy upgrades itself as back up CH. New CH receives participant sensor nodes during election process, it calculates average of all and , which is used for next round. Both new CH and back up node know gets value of the value of . All participant sensor nodes mark themselves as non-participant sensor nodes again. The previous CH also starts working in low power mode. Failure Detection. The detection process runs parallel with normal network operation by periodically performing a distributed detection process at each cluster member. For failure detection mechanism each cluster member maintains a status vector. In status vector each bit corresponds to a cluster member. Initially all bits of are set to zero of status vector on each sensor node. A bit in the vector is set once its corresponding cluster member detects that CH has failed. CH of each cluster periodically sends a hello message (i.e. notification that CH is alive) to cluster members. Cluster member, who does not listen hello message, sets its corresponding bit as one in status vector and locally decides that CH has failed and broadcasts data plus status vector. Other cluster members also listen this message. They extract status vector from message and merge it with own status vector and this process continuous up to the end of the TDMA schedule. At the end of the TDMA frame, cluster members reach on an agreement about failure of CH. If all bits of status vector are set then it is decided that CH has failed. Failure Recovery. By using agreement protocol when cluster members confirm about CH failure then cluster member who has last slot in TDMA schedule informs to back up node about failure. Back up node elects itself as a CH and sends an advertisement message in high power transmission mode. It keeps on working as CH till its residual

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energy level reaches a critical limit or it fails. New back up node is required for new CH, so CH start election process for new back up node with sending in low power transmission mode. Back up node election process is similar to election process of CH.

5 Performance Evaluation 5.1 Simulation Environment In this section, we evaluate the performance of our proposed AFDEP protocol. We used OMNET-4.0 [24] as simulator and same radio model as discussed in. The basic simulation parameters are given in Table 2.

In order to check the performance of AFDEP protocol, we take following metrics/ clustering attributes: Network lifetime. This metric gives the time up to which a network remains alive. It shows number of rounds (including fault tolerance) up to which network remains alive for different number of nodes in network. One round consists of an operation of network from sensing the phenomenon to receiving data at sink node including election process and fault handling if any. Detection Accuracy. It shows how accurately nodes can detect faults. The detection accuracy is defined by the probability of false alarm, which is the probability that an operational CH is mistakenly detected as a faulty one. Detection accuracy performance is measured under different packets loss rates and cluster sizes. Table 2. Experiment Parameter Parameter Area of sensor field Sink position Initial energy per node Path loss exponent

Value 100×100 m2 At origin (0,0) 1J 2 50 nJ/bit 100 pJ/bit/m2 50 nJ/bit Size of data packet 500 bits Size of control packet 20 bits Sensing Interval 0.5 s High transmission range 60 m Low transmission range 20 m No of Nodes 300 Cluster Size 10, 20, 30

5.2 Simulation Results and Discussion To find out more reliable and accurate results, we executed AFDEP protocol with different number of nodes, number of times and failure frequency.

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Network Lifetime. It can be observed form Fig. 2 that as the number of nodes increases, network lifetime increases. But after certain number of nodes, the network life time starts decreasing due to more overhead of cluster maintenance. When number of nodes are 40, network is alive up to 900 rounds. AFDEP FTEP

1600 1400

Number of Rounds

1200 1000 800 600 400 200 0 0

50

100

150

200

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Number of Nodes

Fig. 2. Network Lifetime

Detection Accuracy. From Fig. 3, we can observe the effects of the packet loss rate on detection accuracy for different cluster size. For simulation, we consider the packet loss rate range from 0.2 to 0.4. It can be observed that with the increase of the packet loss rate the probability of false alarm positive increases, which leads to lower detection accuracy. A larger number of sensor nodes lead to a smaller probability of false alarm positive, i.e., higher detection accuracy. As expected, AFDEP can achieve high detection accuracy. 0.1

Probability of False Alarm Positive

0.01

n=20 n=15 n=10

1E-3

1E-4

1E-5

1E-6

1E-7

1E-8 0.20

0.25

0.30

0.35

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Packet Loss Rate

Fig. 3. Detection Accuracy

5 Conclusion AFDEP is agreement-based fault detection and recovery protocol for faulty CH in WSNs. AFDEP periodically checks for CH failure. This detection process runs parallel

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with network operation. It provides high accuracy, because it allows each cluster member to detect its faulty CH independently. It employs a distributed agreement protocol to reach an agreement on the failure of CH among multiple cluster members. In order to recover from faulty CH, back up node is elected as new CH and new back up node is elected locally. Election of CH and back up node is based on residual energy of sensor nodes. Simulation results show, AFDEP achieves high detection accuracy in harsh environment.

References 1. Akyildiz, I.F., et al.: Wireless sensor networks: a survey. Computer Networks 38, 393–422 (2002) 2. Akkaya, K., Younis, M.: A survey of routing protocols in wireless sensor networks. Elsevier Ad Hoc Network 3/3, 325–349 (2005) 3. Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless sensor networks. In: Proceeding of the Hawaii International Conference System Sciences, Hawaii (2000) 4. Manjeshwar, A., Agrawal, D.P.: TEEN: a protocol for enhanced efficiency in wireless sensor networks. In: Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, San Francisco, CA (2001) 5. Manjeshwar, A., Agrawal, D.P.: APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In: Proceedings of the 2nd International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile computing, Ft. Lauderdale, FL (2002) 6. Younis, O., Fahmy, S.: HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks. IEEE Transaction on Mobile Computing 3(4) (2004) 7. Hussain, S., Matin, A.W.: Hierarchical Cluster-based Routing in Wireless Sensor Networks. In: Proceeding of 5th Intl. Conf. on Information Processing in Sensor Network (IPSN 2006), USA (2006) 8. Banerjee, S., Khuller, S.: A clustering scheme for hierarchical control in multi-hop wireless networks. In: Proceeding of IEEE INFOCOM, Anchorage, Alaska, USA, vol. 2, pp. 1028–1037 (2001) 9. Sajjanhar, U., Mitra, P.: Distributive energy efficient adaptive clustering protocol for wireless sensor networks. In: Proceeding of International Conference on Mobile Data Management (MDM 2007), Mannheim, Germany (2007) 10. Moussaoui, O., Naimi, M.: A distributed energy aware routing protocol for wireless sensor networks. In: Proceeding of ACM PE-WASUN 2005, Montreal, Quebec, Canada, pp. 34– 40 (2005) 11. Shah, R.C., Rabaey, J.M.: Energy aware routing for low energy ad-hoc sensor networks. In: Proceeding of IEEE Wireless Communication and Networking Conf (WCNC), Orlando, pp. 1–5 (2002) 12. Jiang, P.: A New Method for Node Fault Detection in Wireless Sensor Networks 12821294 (2009), http://www.mdpi.com/journal/sensors 13. Souza, L.M., Vogt, H., Beigl, M.: A survey on Fault Tolerance in Wireless Sensor Networks, http://www.cobis-online.de/

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14. Yu, M., Mokhtar, H., Merabti, M.: A Survey on Fault Management in Wireless Sensor Networks. Computer Networks (2007) 15. Lee, M.H., Choi, Y.H.: Fault detection of wireless sensor networks. Elsevier Computer Communications 31, 3469–3475 (2008) 16. Jiang, P.: A New Method for Node Fault Detection in Wireless Sensor Networks, pp. 1282–1294 (Feburary 2009), http://www.mdpi.com/journal/sensors 17. Wang, P., Zheng, J., Li, C.: An Agreement-Based Fault Detection Mechanism for Under Water Sensor Networks. In: Proceeding Global Telecommunications Conference, GLOBECOM 2007. IEEE, Los Alamitos (2007) 18. Bansal, N., Sharma, T.P., Misra, M., Joshi, R.C.: FTEP: A Fault Tolerant Election Protocol for Multi-level Clustering in Homogeneous Wireless Sensor Networks. In: Proceeding 16th IEEE International Conference on Networks, ICON 2008 (2008) 19. Asim, M., Mokhtar, H., Merabti, M.: A cellular approach to fault detection and recovery in wireless sensor networks. In: Third International Conference on Sensor Technologies and Applications, SENSORCOMM 2009, 18-23, pp. 352–357 (2009) 20. Venkataraman, G., Emmanuel, S., Thambipillai, S.: Energy-efficient cluster-based scheme for failure management in sensor networks. IET Commun. 2(4), 528–537 (2008) 21. Venkataraman, G., Emmanuel, S., Thambipillai, S.: A Cluster-Based Approach to Fault Detection and Recovery in WSNs. In: IEEE ISWCS 2007 (2007) 22. Khadivi, A., Shiva, M.: FTPASC: A Fault Tolerant Power Aware Protocol with Static Clustering for Wireless Sensor Networks (2006) 23. Fan, K.W., Liu, S., Sinha, P.: On the Potential of Structure-free Data Aggregation in Sensor Networks. In: Proceedings IEEE Infocom (2006) 24. http://www.omnetpp.org/

Problem Area Identification with Secure Data Aggregation in Wireless Sensor Networks Paresh Solanki, Gaurang Raval, and Srikant Pradhan Institute of Technology, Nirma University, Ahmedabad, Gujarat, India {08mce018,gaurang.raval,snpradhan}@nirmauni.ac.in http://nirmauni.ac.in/it/

Abstract. The primary use of wireless sensor networks (WSNs) is to collect and process data. Most of the energy consumption is due to data transmission. Because of the unique properties of WSNs all raw data samples are not directly sent to the sink node instead data aggregation is preferred. Since sensor nodes are deployed in an open environment such as a battlefield or similar applications, data confidentiality and integrity are vital issues in such conditions, hence secure aggregation is required. End to end secure aggregation is less demanding compared to hop by hop secure aggregation so former is superior. When aggregation is performed on data, crucial information is lost which may be indicating alarming situation. This paper presents an idea to reduce the amount of information transmitted with retention of critical data so that the problem area could be identified. Privacy Homomorphism(PH) preserves the data characteristics even in the encrypted form. This paper is based on the PH technique which provides secure data aggregation without significant loss of individuality of data. Keywords: Data aggregation, convergecast, security, wireless sensor networks.

1 Introduction In wireless sensor networks, sensor nodes collect data from hostile environment and send it to sink node where it is processed, analyzed and used by the application. In these resource constrained networks, the general approach is to send the data jointly which is generated by different sensor nodes. While being forwarded towards the base station such in-network processing of data is known as data aggregation. When base station queries to the network, all nodes do not send their data to sink node directly but aggregator node collects data and responds to sink node. Data aggregation reduces the number of data transmissions thereby improving the bandwidth and energy utilization in the network but results in loss of individuality of reading which could be of important use. Because of peculiar characteristics of sensor network, security on data aggregation [2] is most crucial. There is a strong conflict between security and data aggregation protocols. Security protocols require sensor nodes to encrypt and authenticate any sensed data prior to its transmission and prefer data to be decrypted by the base station. On the other hand, data aggregation protocols prefer plain data to V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 258–266, 2010. © Springer-Verlag Berlin Heidelberg 2010

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implement data aggregation at every intermediate node so that energy efficiency is maximized. Moreover, a data aggregation result in alterations in sensor data and therefore it is a challenging task to provide source and data authentication along with data aggregation. Due to these conflicting goals, data aggregation and security protocols must be designed together so that data aggregation can be performed without sacrificing security and individuality of data. This paper is based on secure data aggregation using cluster based approach for problem area identification. In the implementation Jist/SWANS simulator [10][11] was used. The basic code of heartbeat application was modified to implement the clustering strategy with dynamic selection of clusters. The energy model [13] was hooked in to the simulator. Separate application was developed for both plain aggregation and secure aggregation with PH method integration. Specific reason of selecting PH method is its ability to preserve individuality of data after encryption. It was assumed that nodes are aware of their location. Jist/SWANS significantly outperform ns2 and GloMoSim, both in time and space [12].

2 Related Works In wireless sensor network, there are so many challenges like how to improve lifetime of network, how to provide robustness to network and security issues. WSNs collect the data from sensor nodes, process it and send it to the base station. 70% [2] of energy consumption is due to data transmission. It is widely accepted that the energy consumed in one bit of data transfer can be used to perform a large number of arithmetic operations in the sensor processor. Moreover in a densely deployed sensor network the physical environment would produce very similar data in close-by sensor nodes and transmitting such data is more or less redundant. Therefore, all these facts trigger the concept of grouping of nodes such that data from a group can be combined together in an intelligent way and transmit only compact data. This process of grouping of sensor nodes in a densely deployed large-scale sensor network is known as clustering. One major goal of clustering is to allow in network pre-processing, assuming that cluster heads collect multiple data packets and relay only one aggregated packet [1]. To reduce the latency present in the tree-based aggregation, recent trend is to group sensor nodes into clusters so that data is aggregated with improved efficiency and low latency. Attackers may capture secret data as sensor network deployments are vulnerable, so secure aggregation is required. By using traditional symmetric key cryptography algorithms, it is not possible to achieve end-to-end confidentiality and in-network data aggregation together. If the application of symmetric key based cryptography algorithms is combined with data aggregation, then the messages must be encrypted hop-by-hop. Clearly, this is not an energy efficient way of performing secure data aggregation and it may result in considerable delay. Secondly due to resource constraints of sensor nodes, symmetric key cryptography is preferable over asymmetric key cryptography [4]. In addition, this process requires neighboring data aggregators to share secret keys for decryption and encryption. Hop by hop secure data aggregation is highly resource consuming because data aggregator nodes first decrypt the data then aggregate it and again encrypt it. So end to end secure encrypted

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data aggregation is preferred because aggregation process is done on encrypted data[3][5]. In order to achieve end-to-end data confidentiality and data aggregation together without requiring secret key sharing among data aggregators, PH based cryptography becomes obvious choice. Sensor nodes share a common symmetric key with the base station that is kept hidden from intermediate aggregators. Currently different schemes are available for end to end secure encrypted data aggregation but needs some more attention. Few enhancements cannot be ruled out in existing secure data aggregation methods to solve the issue of problem area identification.

3 Secure Data Aggregation Aggregated WSNs provide better power conservation and efficient use of communication channels but also introduce additional security concerns. Most existing schemes for data aggregation are subject to attack. Because of this, the need for secure data aggregation is raised and its importance needs to be highlighted [6]. Hop-by-hop secure data aggregation increases the computational demand at the inner nodes (aggregator) a lot though they are the most important ones and should save on energy as much as possible. Thus it would be desirable to process data without having to decrypt it while preserving the content. The aggregating node does not necessarily need to interpret the data; it only has to be able to work with it. A concept which meets the above requirements is called Privacy Homomorphism and has been introduced by Rivest, Adelman and Dertouzos. This PH method is used in end-to-end secure data aggregation. 3.1 Privacy Homomorphism PH is an encryption function which allows operations like additions or multiplications on the encrypted data. The result will yield an encrypted codeword which is similar to the codeword that would be obtained by applying the operation on the cleartext first and encrypting the result afterwards. Additions or multiplications are of particular interest in this context. An instance of such method was suggested by Domingo-Ferrer in a provably secure additive and multiplicative privacy homomorphism. PH is an encryption transformation that allows direct computation on encrypted data [7][8][9]. It is a symmetric encryption scheme which uses the same key for encryption and decryption. Let Q and R denote two rings, + denote addition and x denote multiplication on both. Let K be the keyspace. Following is the encryption transformation E: K x Q -> R and the corresponding decryption transformation is D : K x R -> Q. Given a, b є Q and k є K we term additively homomorphic and multiplicatively homomorphic. a + b = Dk (Ek (a) + Ek (b))

(1)

a * b = Dk (Ek (a) * Ek (b))

(2)

RSA is a multiplicative PH, while Domingo-Ferrer presented an additive and multiplicative PH which is a symmetric scheme and secures against chosen cipher text attacks. Asymmetric PH is not acceptable in the context of WSNs due to execution times.

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4 Implementation of Secure Data Aggregation The algorithm requires the same secret key for encryption and decryption. The aggregation is performed with a key that can be publicly known, i.e., the aggregator nodes do not need to decrypt the encrypted messages. However, it is required that the same secret key is applied on every node in the network that needs to encrypt data. For very secure parameter combinations (d > 100), the messages become very big. However, with reasonable parameters it also fits the needs of constrained devices. Parameter settings, encryption and decryption algorithm process is shown below: Parameter Settings: 1. A large integer p which holds the following two properties: (a) It should consist of a large number of divisors. p simply is a product of integers with repeatedly multiplying prime numbers. (b) The large p should be chosen such that many integers c can be found for which an cinv exists so that c x cinv mod p = 1 where c
∑

b i =1

ai mod p '

2. Compute, E k (a ) = ( a1c mod p , a 2 c 2 mod p ,......, ab c b mod p ) 3. So (e1 (a), e2 (a),.....eb (a)) are derived. Aggregation :( at aggregator) 1. Perform Scalar addition modulo p 2. Suppose we have (e1 (a), e2 (a),.....eb (a)) and (n1 (a), n2 (a),.....nb (a)) so scalar addition is, e1 (a) + n1 (a) mod p and so on. Decryption :( at base station) 2 b 1. Dk ( Ek (a)) = (e1 (a)cinv mod p, e2 (a)cinv mod p,....., eb (a)cinv mod p)

2. To retrieve cleartext value perform, Dk ( Ek (a)) = ∑i =1 ai mod p ' b

4.1 Encrypted Data Aggregation Process

Suppose sensor nodes S1 to S n encrypt their data S1 to S n resulting in encrypted form e1' to en' before transmitting data to the AGGR (aggregator node). Then, AGGR perform aggregation on the encrypted data and ' ' ' computes z = f (e1 ,....., en ) . The aggregator AGGR transmits z’ to the BS (Base Station) which perform decryption operation on the z’ and derives the accumulated data z = D( c, p ') ( z ' ) Fig.1 illustrates the approach.

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Fig. 1. Secure Data Aggregation in WSNs

4.2 Implementation Steps of Secure Data Aggregation

End to end secure data aggregation was implemented in JIST/SWANS simulator. Following steps show the implementation of secure data aggregation. − Randomly place the n number of sensor nodes. Sensor node ID zero works as cluster head and its position is at the center of field area of simulation − Randomly select some nodes as a Cluster Head. Leaf nodes join with cluster heads. − Leaf node first encrypts the data and sends the data packet to the cluster head. − Each cluster head performs secure data aggregation process on received encrypted values from their leaf nodes. Each cluster head also finds the mini mum and maximum values from encrypted values. − Resultant data of secure data aggregation is now sent to the base station. − Base station now performs decryption operation on received encrypted data. 4.3 Simulation of Secure Data Aggregation Simulation Setup: A square fileld of 1000m X 1000m is taken where 50 nodes and 100 nodes are randomly deployed in different simulation environment. Three nodes are designated as cluster-head(CH) for 50 nodes, five nodes are designated as clusterhead(CH) for 100 nodes. Node zero is designated as Base Station. Energy Model measures the energy usage of each node during the Receive, Transmit, Sleep and Idle Mode. Parameter of Energy Model is defined in the the energy model package of JIST/SWANS simulator. Simulation results: Two different simulations were performed. First cluster heads are selected statically for both data aggregation and secure data aggregation process and then dynamic selection of cluster heads for both data aggregation and secure data aggregation process. Privacy homomorphism security algorithm was implemented. Each sensor data is divided into two parts. Each part of the value is encrypted and sent to the data aggregator. Now data aggregators perform the pair wise aggregation operation (SUM) on these decrypted values and send it to the base station. Next, base station performs decryption operation and receives clear text data. Minimum and maximum values from the encrypted values of each cluster was also found to augment the secure aggregation method with the capability of problem area identification. Fig. 2 shows the energy usage with static selection of the cluster head and Fig. 3 shows the

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Fig. 2. Remaining Energy - 100 sensor nodes, 1000m2 area, 5 CHs statically selected Table 1. Simulation Results No. of Nodes 50 100 30 50 100

CH selection Policy Static Static Dynamic Dynamic Dynamic

Number of CH 3 5 2 3 5

Average Energy Usage % DA SDA 7.2 12.21 11.95 18.05 6.98 7.66 8.51 11.94 11.55 21.99

Average Energy w/o min/max % DA SDA 2 6.4 5.62 15.79

Average Energy with min/max % DA SDA 3 8 6.99 17.58

Fig. 3. Remaining Energy - 100 sensor nodes, 1000m2 area, 5 CHs Dynamically selected

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energy usage with dynamic selection of the cluster head. The graph suggests that secure data aggregation consumes more energy than data aggregation at each cluster head. It can be observed that the dynamic allocation of cluster head strategy selects any node as cluster head as per the given measures. Table 1 shows the energy consumptions for different settings of network and aggregation options. As the number of sensor nodes are increased the residual energy of the cluster heads starts dropping accordingly as it has to do more work compared to the plain aggregation. Fig. 4 shows the Remaining Energy of sensor nodes with 50 and 100 sensor nodes configured to collect and propagate minimum and maximum values from the cluster to the base station. The readings were taken for the same set of nodes and clusters but without collecting and passing minimum and maximum values. The minimum and maximum values are collected from the cluster so as to identify the key points where some action may be required. The energy consumption is higher of this strategy as the number of bits to be transmitted by the sensor nodes and cluster head is higher.

Fig. 4. Remaining Energy - With and Without Minimum/Maximum value

5 Conclusion The major contribution of this work is exploring the applicability of end to end encryption for reverse multicast traffic between the sensors and the base station while retaining the individuality of data. Aggregation of data is very crucial in sensor networks because of the scarcity of energy. But when aggregation is performed on multiple sensor readings the important information which may be very critical to take certain actions may be lost so it is required to retain such information while reducing the energy requirement in data transmission. Privacy homomorphism is capable of keeping the data intact as it was even in the encrypted form. Same truth has been

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exploited in this work. Existing method was modified with inclusion of new set of messages to identify the problem areas in the field so the necessary action can be taken at sink node. The energy consumption increases in the secure aggregation method compared to the plain aggregation. As the number of nodes increases the energy level drops further of cluster heads. The proposed method was tested with 30 sensor nodes to 100 sensor nodes in the area of 1000 square meters. The method is more secure and less energy consuming compared to hop by hop secure aggregation. When the secure aggregation was added with minimum and maximum data transmission it incurred negligible energy consumption but at the same time it reports the crucial information to the base station where some important decision can be taken based upon the data arriving from cluster heads. Around 5% more energy consumption was observed with secure aggregation compared to plain aggregation. Energy consumption increased by around 2% for the problem area identification.

6 Future Work Although method provides the robust security cover to the data moving in the network the energy consumption is still an issue as the network grows. The protocol can be made more scalable and fine tuned using multi level clustering where the cluster can have 2-3 level tree so it can cover more number of nodes with lower energy consumption. The clusters are presently chosen without considering the location of the other nodes. So the energy consumption can be further controlled with location aware clustering which may cover almost entire network in uniform way.

References 1. Ozdemir, S., Yang, X.: Secure data aggregation in wireless sensor networks: A comprehensive overview. Elsevier B.V., Amsterdam (2009) doi:10.1016/j.comnet.2009.02.023 2. Alzaid, H., Ernest, F., Juan, G.N.: Secure Data Aggregation in Wireless Sensor Network: A Survey. In: ACSC 2008, Australia (January 2008) 3. Pranay, T.: Data Aggregation in Cluster-based Wireless Sensor Networks. Thesis, IIIT Allhabad (July 2008) 4. Yingpeng, S., Hong, S.: Secure data aggregation in wireless sensor networks: a survey. In: Seventh International Conference PDCAT 2006 (December 2006) 5. Ramesh, R., Pramod, K.V.: Data aggregation techniques in sensor networks: A survey. IEEE Communications Surveys & Tutorials 8(4), 48–63 (2006) 6. Ozdemir, S.: Concealed Data Aggregation in Heterogeneous sensor networks using privacy homomorphism. In: ICPS 2007: IEEE International Conference on Pervasive Services, Istanbul, Turkey, pp. 165–168 (2007) 7. Joao, G., Dirk, W., Markus, S.: CDA:concealed data aggregation for reverse multicast traffic in wireless sensor networks. In: 40th International Conference on Communications, IEEE ICC 2005 (May 2005) 8. Levent, E., Johan, H.Y.: Implementation of Domingo Ferrers a new privacy homomorphism in securing wireless sensor networks, WSN (2007)

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9. Castelluccia, Aldar, C., Gene, T.: Efficient and Provably Secure Aggregation of Encrypted Data in Wireless Sensor Networks. ACM Transactions on Sensor Networks 5(3) (May 2009) Article 20 10. Barr, R., Hass, Z.J., Van Renesse, R.: JiST: Embedding Simulation Time into a Virtual Machine. In: 5th EUROSIM Congress on Modeling and Simulation, Paris, France (September 2004) 11. Barr, R., Zygmunt, J.H., Van Renesse, R.: JiST: An efficient approach to simulation using virtual machines. Software Practice and experience, 1–7 (2004) 12. Barr, R.: SWANSScalable Wireless Ad hoc Network Simulator. User Guide (March 19, 2004) 13. Trishla, S., Imad, M., Ali, H., Ahmed, B.: Implementation of an Energy Model for JiST/SWANS Wireless Network Simulator. In: Sixth International Conference, ICN 2007 (April 2007)

Operational Transconductance Amplifier Based Two-Stage Differential Charge Amplifiers Dinesh.B. Bhoyar and Bharati Y. Masram Dept. of ET,Y.C. College of Engg. Nagpur, India [email protected], [email protected]

Abstract: A novel approach to the design of high-performance operationalamplifier-based differential charge amplifiers is proposed. It is based on a twostage topology: The first stage performs a differential measurement to single ended signal conversion, providing a common mode rejection that only depends on the matching between two resistors; the second stage filters the signal. These novel topologies that are based on this technique are presented, analyzed, and measured, and design criteria are finally given. Their performance is compared with there topology that is used as the benchmark, and it results in a better common- mode rejection ratio (CMRR). Keywords: Accelerometer, charge amplifier, charge-to-voltage converter, chargetype sensor, current-to-voltage converter, current-type sensor, photodiode, piezoelectric sensor, Transconductance amplifier.

1 Introduction Charge amplifiers are typically charge measuring instrument the charge is transfer to the reference capacitor and the resulting voltage across the capacitor is measured .[1].defined as Tran capacitance circuits—they transform electrical charge into volt age by integration [1]–[3]. In data acquisition systems, they are the first natural conditioning stage to process the information carried by a charge signal coming from a sensor and to deliver it in a suitable form for further analog processing or digitalization. Desirable properties of charge amplifiers are negligible input and output impedance for optimum coupling with the generating element and the subsequent electronics, high sensitivity, and low noise to increase the signal-to-noise ratio (SNR)According to its physical nature, the typical charge amplifier input signal can be represented by either charge or current sources. It can be single ended when only one terminal of the sensor at the input is available and the other is grounded, or it can be differential when both terminals are available. In any case, the information is encoded in the differential mode (QD, ID), and it is always mixed with unwanted signals like, e.g., dc offset, interference, or thermal noise. Since charge generating sensors are usually operated under virtual ground conditions. The technical paper is scarce on differential circuits for charge- and current-type sensors. Either the counterpart of the well-known three-operational-amplifier (op-amp) instrumentation V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 267–273, 2010. © Springer-Verlag Berlin Heidelberg 2010

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amplifier [Fig. 1(a)] or the simpler topology [4] [Fig. 1(b)] is almost the only available solution; however, it requires at least two matching conditions between resistances and between capacitors that can be practically hard to fulfill. To validate the proposed technique, novel topologies are designed, analyzed considering dc analysis, ac analysis, transient analysis ,phase margin, dc gain margin offset analysis done with respect to the known topologies. To achieve a better common-mode rejection ratio (CMRR). A preliminary short version of this paper can be found in [1]. Last, it is noted that since current is defined as the derivative of charge, the proposed topologies are of application with charge and current sources (e.g., piezoelectric accelerometers and photodiodes [5]). Only some minor redesign is needed to cope with specific issues such as signal levels, noise shaping, and dc errors .When a voltage input is applied ,the circuit is named as transconductance or voltage to current converter so that in case of hardware circuit design charge measurement should be done.

2 Theoretical Analysis Differential circuits are routinely described by two transfer functions [6]: GD is the ratio between the output and the differential input, with a null common-mode input; GC is the ratio between the output and the common-mode input, with a null differential-mode input. Both parameters can refer to current or voltage output and to charge or current. The first way to obtain a differential charge amplifier is given by the counterpart of the popular three-op-amp differential amplifier [Fig. 1(a)] that has been proposed for photodiode [7] and charge sensors [8] and used with minor modifications in a number of applications. Its first stage is a fully differential charge amplifier obtained by two ideally identical integrator topologies (or, alternatively, by an integrator based on a fully differential op-amp. The second stage is the well-known difference amplifier, which provides differential to single ended voltage conversion. The CMRR of the circuit in Fig. 1(a) relies on the matching between resistors, capacitors, and even op-amps. A much more compact solution [Fig. 1(b)] has been Proposed in a recent patent [4]. This topology is obtained by adding to the common single-ended integrator impedance equal to the feedback impedance and placed from the no inverting op-amp input to ground. To properly reject the common-mode input (GC= 0), the circuit needs two matching conditions on resistors and capacitors.

Fig. 1. Conventional differential charge amplifier topologies

A close look at the two above-presented circuits shows that input, depending on the case. The CMRR is defined as the rate GD/GC. Ideally, it is desirable to obtain GC= 0 (i.e., CMRR = ∞); however, component mismatch makes it unreachable. Hence, the

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aim of the design is to obtain circuits that, for the desired GD, yield the highest CMRR. Therefore, dependence on a minimum set of matching conditions and critical components is a design goal. their limitations mainly depend on the fact that common-mode rejection and integration are simultaneously accomplished by a pair of matched branches, which includes capacitance in addition to resistance. However, the basic functions that are required in these kinds of circuits (i.e., common-mode rejection, differential measurement to single-ended signal conversion charge-tovoltage signal conversion or integration, and amplification) can be also implemented in a different way. Thus, the design technique here proposed (Fig. 2) allows the design of differential charge amplifiers, splitting the basic functions into two independent stages. The first one converts the differential- mode input into a singleended signal and rejects the common mode input, whereas the second one performs low-pass filtering and provides a single-ended output voltage. The first stage provides common-mode rejection, performing a differential to single ended signal conversion. The second one integrates the resulting signal to be independent, the output impedance of the first one and the input impedance of the second one have to be properly chosen. The most important way is to design first stage by using folded cascade OTA(operational Transconductance amplifier ) because the folded cascade does not required perfect balance of current in the differential amplifier excess current can flow in to or out of current mirror but the bias current as flow in the OTA should be design so that dc current in the flows in the current mirror never goes to zero model Fig.3. Shown. It is now clear that the proposed circuits in Fig 2.required the designing of folded cascade OTA..

INPUT VOLTAGE

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OUTPUT VOLTAGE

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Fig. 2. General approach that is proposed to implement two stage differential charge amplifier The first stage provide common mode rejection performing differential to single ended signal conversion. The second one integrates the resulting signal.

2.1 Sensor Model Before going over the analysis, it is necessary to take into account the nature of the typical sensors that are applied to the charge amplifiers. For most practical purposes, a suitable model is shown in Fig. 5, where CS represents the capacitive nature, RS is the leakage resistance, ID is the generated differential-mode current that carries the information provided by the sensor according to the measured physical magnitude, and IC is the common-mode current typically due to interference from the environment (the authors found this latter problem, e.g., in sensors that are attached to metallic materials that are susceptible to strong electrostatic charge or discharge processes). The model applies, for instance, to piezoelectric sensors, where typically, CS is in the order of hundreds of Pico farads, and RS is in the order of tens of mega ohms, so that, in practice, it is often considered infinite. Moreover, the sensor also

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exhibits some kind of resonance, a typical characteristic of piezoelectric materials. Most of the time, it is avoided when the sensor is used as an accelerometer or pressure sensor, or exploited, such as, for instance, in ultrasonic receivers and transmitters.

Fig. 3. Two-stage differential charge and Tran resistance amplifiers with (a) active and (b) passive second stage. Proposed as first implementation of the general approach of Fig. 2.

3 Simulation Results and Discussion 3.1 Offset Analysis The Vin, off defined as differential input voltage needed to restore Vo=0 v in the real device here ,the dc sweep analysis of input offset voltage has been calculated as VO| dc = 0.598mV as shown in fig 6(a )

Fig. 4. Two-stage differential charge and Tran resistance amplifiers, proposed as second implementation of the general approach of Fig. 2

Fig. 5. (a) Typical sensor used in conjunction with charge and Tran resistance amplifiers can be represented by (b) its charge or (c) its current equivalent model

3.2 Transient Analysis This analysis has been done input w.r.t output showing high Gain response as shown in fig.6 (b). 3.3 A.C. Analysis Simulation for Unity Gain Bandwidth at 14 MHz, Phase Margin and dc gain are presented as shown in Fig 6(c).

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Two-stage differential charge amplifier, proposed as second implementation of the general approach of Fig. 2. Now as per required results of OTA ,by using it ,it very much simple to design integrator (as shown in Fig 7.) as well as CMR( common mode rejection circuit) as shown in Fig 9.and therefore applying square wave pulse to the input side results in proper output as shown in fig 8. Interconnecting both the stages two stage charge amplifier is finally design(as shown in Fig 10) where at the output end we can measure the charges in terms of voltage as shown in Fig.11. The sensor common-mode voltage rejection, the output impedance, and the complexity. The circuits in Figs. 3(a) and 4, in comparison with that in Fig. 1(a), can feature the same sensitivity with lower component count, and their main drawback is the signaldependent common- mode voltage of the sensor. The presented circuits can be used as charge amplifiers with only minor redesign.

Fig. 6. Schematic diagram of OTA

Fig. 6(a). Transfer characteristics of OTA

Fig. 6(b). Transient Analysis of OTA

Fig. 6(c ). AC analysis at 14 MHz GBW

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Fig. 7. Schematic diagram of integrator

Fig. 9. Schematic design for CMMR circuit

Fig. 8. Simulation result for Integrator

Fig. 10. .Schematic design for two stage Diff. Charge Amplifier

Fig. 11. Simulation result for two stage differential Charge Amplifier

4 Conclusion and Future Scope We cannot think an analog circuit without Op-amp. The design procedure for OTA and synthesis tool has been developed which will be going to be used in the proposed two stage Differential charge amplifier that are widely used to interface differential charge generating sensors because of high gain amplification of OTA it is used to generate the single ended conversion also to calculate the high gain CMRR which is practically limited because it is relies on at least two matching condition between resistor and capacitor .this is because the common mode rejection and the charge to voltage conversion are both implement designed by a single stage . This paper has proposed a design technique to obtain two stage differential charge amplifier.

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References [1] Massarotto, M., Carlosena García, A., López-Martín, A.J.: Two-stage differential charge and transresistance amplifiers. In: Proc. 23rd IEEE IMTC 2006, pp. 1920–1925 (2006) [2] Buchman, S., Mester, J., Sumner, T.J.: Charge measurement. In: Webster, J.G. (ed.) The Measurement, Instrumentation and Sensors Handbook, CRC, Boca Raton (1999) [3] Pallas-Areny, R., Webster, J.G.: Sensors and Signal Conditioning, 2nd edn. Wiley, Hoboken (2000) [4] Yamashita, M.: Amplification circuit for electric charge type sensor. U.S.Patent 2002/0 [5] Pallas-Areny, R., Webster, J.G.: Analog Signal Processing. Wiley, New York (1999); Baker, B.C.: Photodiode Monitoring With Op Amps. Tucson, AZ:rr-Brown Corp., 1994 Application Bulletin AB-075. shock sensors. Murata Manuf. Co., Ltd., Kyoto (August 1994) Hi-Tech Rep. [6] Philips, A.E., Douglas, H.R.: CMOS Analog Circuit Design, 2nd edn. Oxford University Press, Oxford (2002) [7] Jacob Baker, R., Li, H.W., Boyee, D.E.: CMOS Circuit Design,Layout and Simulation. In: IEEE Press Series on Microelectronics System, PHI (2002) [8] Gandelli, A., Ottoboni, R.: Charge amplifiers for piezoelectric sensors. In: Proc. IEEE Instrum. Meas. Technol. Conf., pp. 465–468 (1993)

Graceful Degradation in Performance of WaveScalar Architecture Neha Sharma and Kumar Sambhav Pandey National Institute of Technology, Computer Science &Engineering Hamirpur (H.P.), India-177005 [email protected], [email protected]

Abstract. With the advancement in technology in the field of transistors it has become easy to have millions of transistors on one dice. It is still a challenge to translate the available resources into convenient application. Many conventional processors has failed to achieve that level of performance. A new alternative to the conventional processors is the scalable WaveScalar. WaveScalar is a dataflow instruction set based execution model with low complexity and high performance features. It can run real world programs, non-real world programs without changing the language and still having the same parallelism. It is designed as a intelligent memory system where each instruction executes in its place and then communicates with its dependent. If a high-performance processor is to realize its full potential, complexity should be least. Here is this paper, we have proposed solution to reduce the complexity of the wavescalar processor without affecting its performance Keywords: Wavescalar, dataflow, ISA, parallel, performance.

1 Introduction Wavescalar is a decentralized, scalable data-flow execution based processor. This model can execute the instructions whenever the values are available locally. However, it solves many problems of dataflow model [1, 2]. One of them is providing support for conventional and imperative languages which helps in providing seamless integration between memory interfaces and multiple threads. It also helps in defining a scalable micro architecture that can execute programs that can be implemented using current process technology. Such combination of features creates a processor that can not only work as a conventional processor for programs, but which can also provide parallelism with easier way of designing. Wavescalar is based on intelligent cache-only computing systems. There is no central processing unit or centralized control in cache only computing environment. Instead of that, it consists of pool of processing elements that works as a central processor and cache of the processor. Wavescalar constitute of a distributed instruction cache named as WaveCache which takes the responsibility of intelligently executing and caching the instructions. In dataflow model there is no program counter to guide the flow of instructions [3, 4]. As operations on instructions are based on data driven fashion, it executes out of linear order which introduces the need V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 274–280, 2010. © Springer-Verlag Berlin Heidelberg 2010

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of wave ordering and memory ordering to solve the problem of sequencing in decentralized environment. It does not follow total ordering, instead it implements partial ordering. Each of these partial order instructions are not data dependent, which make it possible to execute them in parallel [5, 6, 7]. For every operation data value is to be sent to the instruction that requires it.

2 Wavescalar ISA Wavescalar represents programs as dataflow graphs. Each node in the graph is an instruction and the arc between nodes represents static data dependences between them [8]. Wavescalar gets the input from the output of the previous node. It has no PC to guide as is in case of other processors which make instruction fetch and decode more complex. However, it takes instruction as nodes in a dataflow fashion that executes only when all the operands are available [9]. Even memory operations are based on the availability of data which makes the result to be executed in out-of order fashion. It enforces partial ordering i.e. some of the instruction which are data independent can be executed in parallel, which provides more parallelism. As there is no program counter, so to guide the flow of data, processor uses steer function which steers the values into one part of the dataflow graph and prevents them from flowing into another [10]. It can also use predication to perform computation on both the part and later on based on the condition discard the wrong path. In case of loop, STEER instruction is sufficient to provide a basic branching facility. However, it must also distinguish dynamic instances of values from different iterations of a loop. 2.1 Waves and Wave Number Waves are the small pieces of the application broken down by the compiler [11]. The key properties of the waves are: Every time the wave is executed, each instruction of wave is executed at least once. Instructions are partially ordered in wave. A wave is a connected, directed acyclic control flow graph with a single entrance. In wavescalar, the same processing elements are used to handle the instruction for all iterations. So to distinguish the values uniquely, wavescalar carries a tag. These tags are called as wave numbers which are aggregated across the wave and is used to differentiate between the dynamic values. WAVE-ADVANCE is a special kind of instruction that manages wave numbers. The WAVE-ADVANCE instruction takes a data value as input, increments the wave number and outputs the original data value with the updated wave number [12, 13]. As they leave the WAVE-ADVANCE instructions, all values have the same wave number, since they all came from the same previous wave. In the case of a loop, the value propagates through the loop body, and the back-edges to the top of the loop. Each time the control enters the loop wave number is incremented and each time new wave number is assigned to each value.

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2.2 Memory Ordering As dataflow ISA enforces static data dependencies in a program, but have no mechanism to ensure memory ordering. WaveScalar implements load-store ordering using wave- ordered memory. In this kind of memory ordering, each memory operation is annotated with its memory location in the wave and its relationship of ordering with the other memory operation in the same wave. With each execution the annotation helps the memory system to identify the memory requests and apply the memory operation in the correct order. To annotate memory operation in correct order each memory operation is assigned with a unique sequence number and it is traversed in the breadth-first order. By using such sequence numbers, the compiler ensures that it increases along the path in the wave. Compiler levels each memory operation with a unique sequence number, predecessor and successor to form a link [14, 15]. This helps in enforcing load and store ordering from the programmer’s aspects. Whenever memory instruction executes it sends its link, wave number address and data to the memory. It helps to arrange load and store in correct order. It has to be ensured that there is no gap in the sequence. Memory system uses predecessor and successor information to identify the gap. If there is any gap MEMORY-NOP instruction is used to remove the ambiguity 2.3 Indirect Jump Different constructs are required for additional instruction, INDIRECT-SEND which has three inputs: a data value, an address and an offset value. Each argument of the function is passed through INDRECT-SEND instruction and these values are received by set of instructions and then execution of the function starts. The caller sends the return address to provide the target address for an INDRECT-SEND that returns the function’s result. The addresses of the called function need not to be known at compile time.

3 Limitation of WaveScalar As discussed above wavescalar efforts are made to make processor to achieve more parallelism In case of looping, to achieve maximum level of parallelism WAVEADVANCE instruction is used. Every time the iteration is performed wave-number is incremented. So each loop will generate multiple values. Adding a wave-number to each wave and its multiple copies of values make the looping more complex As from the above discussion it is clear that it will enhance the parallelism but with that it will make the computation and system more complex. So even if there is any small present of increase in performance at the cost of complexity then that make no benefit in making such complex processor computations.

4 Proposed Solution To avoid generation of multiple values in a loop a new scheme has been proposed here. According to this scheme data flow graph consist of nodes and directed edges.

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These directed edges represent the flow of data in graph. The nodes represent the behavior of the operation or the calculation which has to be done on the node and the edges of the graph represent the values between these two operations. Hence the edge indicates that the result of one operation is passed as input to other operation. Each instance of data is called as a token. The order of the execution is defined as partial order as dependent operations are included in same graph and the data independent operations can run in parallel to that operation. Every argument value of the operation is taken as input port and resultant operation is seen as the output port of the operation. It is necessary to have one incoming edge to be allowed to arrive on any input port and several outgoing edges can leave from the output port. U

trigger

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Fig. 1. Loops in wavescalar and proposed solution: WaveScalar loop implementation (b). Proposed correction in the loop (c).

To allow a proper execution of the loops the token passing method is applied for which special initialization. When the execution of a graph is started, all entry nodes must obtain a token at their control input. Token selects input port through which throughput or external data should enter in loop. As from the fig.1. (c), the input can be from the external instruction or it may be the value generated by the loop. It passes on the value only when it has value on both the ports i.e. on control port and input port. Exit nodes do not obtain any such kind of initialization token [16]. On exit node based on the control generated on the control port the data value is sent back in the loop or the data value exits the loop. As soon as the value comes out of the loop, control tokens are automatically left.

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As in all these processes the exit and entry nodes are controlled by control signal. This restricts the value to be generated on both side so that only the value of the current iteration is available on the output port. It gives maximum flexibility in synchronization and timing issues. Of course clock cycle is needed to the loop, but there is a free choice for the placement of this clock boundary. It does not have to be at the entry nor at the exit nodes. It is even perfectly allowed to desynchronize the loop cycling of different variables, i.e. it takes different cycles for different variable to execute which gives flexibility in the execution and synchronization.

5 Performance Evaluation In this section of the paper, to evaluate the performance of the proposed scheme we have used WaveScalar ToolSet [17] as a simulator. To evaluate the performance we assumed few parameters as listed in fig.2 (b). To achieve better performance it is required to place dependable instruction in same cluster and in the closest processing element. To fulfill this requirement greedy approach is used. For studies initially we have assumed that is queue used is of fixed size. For optimal results address is stored immediately in the memory as soon as the data is available. This helps load instruction to work more quickly and get the result value to right address. Here we have used four benchmarks equake, twolf, art, fft [18, 19] to evaluate the performance of the processor. The reason of choosing these benchmarks is because they provide a variety of applications and it is easy to execute these applications by the binary translator present in the simulator we are using. 6

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Cache Capacity PE per domain PE Input Queue PE Output Queue L1 Cache L2 Cache Network Switch

2K 8 16 Entries 4 Entries, 2 Ports 32KB, 4 accesses per cycle 1MB, 10 Cycle access 2 port, bidirectional

(b)

Fig. 2. Result analysis: Results in form of graph (a). List of parameters used for analysis (b)

The results are being evaluated in terms of Alpha-Instruction per Cycle (AIPC). We have used AIPC for comparison as this term fairly helps in comparing application level performance of proposed scheme with the wavescalar. This graph 1 shows the comparison between the wavescalar and the improvement made in the design of the wavescalar. The evaluation is being reported in terms of alpha-equivalent instruction per cycle (AIPC). As it is visible in the fig 2 (a) above that in most of application either the performance is almost same or it is marginally degraded in few places. Even if the performance of the wavescalar is better in few cases because in our scheme the

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flow of data is controlled opposite to the case of wavescalar. But due to the presence of WAVE-ADVANCE number in the wave the computation has increases. It takes lot of resources to keep track of WAVE-ADVANCE number for each of the value and doing operations on it each time. It takes lot of storage space too. All this increases the complexity of the system. In proposed scheme the flow of data is very simple and it do not require much of computation. In spite of all this overhead of computation still the result of performance in wavescalar is almost the same. So it makes no sense to increase the complexity for very such minimal increment in performance.

6 Conclusion Even though there is a marginal increase in the performance of the wavescalar as compared to the proposed scheme but still the complexity of processor is much higher. Although there is degradation in the performance but still increase the utility of the resources and reduces the computational requirement of the processor for every instruction. it is a good idea to have a system with marginally low performance but with less complexity of computation and resources. This difference exits only if we use large number of processors for computation. If we run small programs the performance is same.

References 1. Wall, D.W.: Limits of instruction-level parallelism. In: International Conference on Architectural Support for Programming Languages and Operating System (1991) 2. Nagarajan, R.N., Sankaralingam, K., Burger, D., Leckler, S.W.: A design space evaluation of grid processor architectures. In: International Symposium on Microarchitecture (2001) 3. Dennis, J.B.: A preliminary architecture for a basic dataflow processor. In: International Symposium on Computer Architecture (1975) 4. Allan, S.J., Oldehoeft, A.E.: A flow analysis procedure for the translation of high-level languages to a data flow language. IEEE Transactions on Computers 29(9) (1980) 5. Kim, S., An, J.E.: instruction set and micro architecture for instruction level distributed processors. In: International Symposium on Computer Architecture (2002) 6. Palacharla, S., Jouppi, N.P., Smith, J.E.: Complexity-effective superscalar processors. In: International Symposium on Computer Architecture. ACM Press, New York (1997) 7. Lam, M.S., Wilson, R.P.: Limits of control flow on parallelism. In: International Symposium on Computer Architecture (1992) 8. Sakai, S., Yamaguchi, Y., Hiraki, K., Kodama, Y., Yuba, T.: An architecture of a dataflow single chip processor. In: International Symposium on Computer Architecture (1989) 9. Culler, D.E., von Eicken, T.: Two fundamental limits on dataflow multiprocessing. In: Conference on Architectures and Compilation Techniques for Fine and Medium Grain Parallelism (1993) 10. Dennis, J.B.: Dataflow supercomputers. IEEE Computer 13 (1980) 11. Swanson, S., Michelson, K., Scherwin, A., Oskin, M.: Wavescalar. In: 36th Annual IEEE/ACM International Sym (MICRO), IEEE Computer Society, Los Alamitos (2003) 12. Oskin, M., Petersen, A., Putnam, A., Mercaldi, M., Schwerin, A., Swanson, S.: Reducing Control Overhead in dataflow Architecture. submission to ACM Transactions (2006)

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13. Oskin, M., Petersen, A., Putnam, A., Mercaldi, M.: The WaveScalar architecture. submission to ACM Transactions on Computer Systems, TOCS (2006) 14. Songwen, P., Baifeng, W., Min, D., Gang, C.: SpMT WaveCache: Exploiting ThreadLevel Parallelism in WaveScalar. In: World Congress on Computer Science and Information Engineering (2009) 15. Leandro, J., Marzulo, F., Franca, V.: Santos Costa.: Transactional WaveCache: Towards Speculative and Out-of-Order DataFlow Execution of Memory Operations. In: 20th International Symposium on Computer Architecture and High Performance Computing (2008) 16. Arvind.: Dataflow: Passing the token. In: Keynote at the International Symposium on Computer Architecture, ISCA (2005) 17. http://wavescalar.cs.washington.edu/ 18. SPEC.: Spec CPU 2000 benchmark specifications. SPEC2000 Benchmark Release (2000) 19. C. Lee, M. P., W. H.: Mediabench: A tool for evaluating and synthesizing multimedia and communications systems. In International Symposium on Microarchitecture. (1997).

Comments from the Reviewers • More related works and discussions are required to substantiate the proposed system. • Include more references to substantiate the related works. • Paper doesn’t carry recent literature review. • Paper should strictly format according to the single column Springer format. • References are not according to the Springer standard guidelines.

Dual Tree Complex Wavelet Transform Based Video Object Tracking Manish Khare1, Tushar Patnaik2, and Ashish Khare1 1

Department of Electronics & Communication, University of Allahabad, Allahabad, India 2 School of IT, Centre for Development of Advanced Computing, Noida, India [email protected], [email protected], [email protected]

Abstract. This paper presents a new method for tracking of an object in video sequence which is based on dual tree complex wavelet transforms. Real valued wavelet transform, mostly used in tracking applications, suffers from lack of shift invariance and have poor directional selectivity. We have used dual tree complex wavelet transform in tracking because it avoids shortcomings of real wavelet transform. In the proposed method, object is tracked in next frames by computing the energy of dual-tree complex wavelet coefficients corresponding to the object area and matching this energy to that of in the neighborhood area. The proposed method is simple and does not require any other parameter except complex wavelet coefficients. Experimental results demonstrate performance of the proposed method. Keywords: Object tracking, Shift-sensitivity, Complex wavelet transform.

1 Introduction Object tracking is an important task within the field of computer vision [1, 5]. There are three key steps in video analysis: detection of moving objects of interest, tracking of such objects from frame to frame, and analysis of object tracked to recognize their behavior. The use of object tracking is pertinent in the tasks of: motion-based recognition, automated surveillance, video indexing, human-computer interaction, etc. Object tracking requires the segmentation of the object from scene followed by tracking [5]. In its simplest form, tracking can be defined as a problem of estimating the trajectory of an object in the image plane as it moves around a scene. The popular methods for tracking are based on a moving object region tracking [5]. These methods identify and track a bounding box, which is calculated for connected components of moving object in 2D space. Major shortcoming of these methods is that they rely on many properties of object such as size, color, shape, velocity, etc. For avoiding these shortcoming, feature based tracking is used [2]. We have used complex wavelet coefficients of the object as a feature of object. Although real-valued wavelet transform can be a used as feature, but it suffers from shift-invariance and poor directional selectivity [3.4]. Use of complex wavelet transform reduces this shortcoming. Several complex wavelet transforms like dual tree complex wavelet transform (DTCWT) [3], V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 281–286, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Daubechies complex wavelet transform [2], projection-based complex wavelet transform [7], steerable pyramid complex wavelet transform [9], etc. have been proposed and widely used in many applications. These transforms are approximate shift-invariant and directional selectivity in nature. In the present work we have used Dual tree complex wavelet transform (DTCWT), which is shift invariant and have good directional selectivity. Literature for tracking using complex wavelet transform is few and far between. Magary and Kingsbury [10] described a motion estimation algorithm, using separate 2-D Discrete Wavelet Transform. One more efficient motion estimation algorithm using complex wavelet transform is given by Yilmaz and Severcan [8]. Rest of the paper is organized as follows: Section 2 describes dual tree complex wavelet transform. Section 3 describes proposed video tracking algorithm followed by the experimental results in section 4. Finally conclusions of the paper are given in section 5.

2 Dual Tree Complex Wavelet Transform Complex wavelets are not been used widely in Image Processing applications due to the difficulty in designing complex filters, which need to satisfy a perfect reconstruction property [6]. To overcome this, Kingsbury [3, 4] proposed a dual tree implementation of the complex wavelet transform, called DTCWT, which uses two trees of real filters to generate the real and imaginary parts of wavelet coefficient separately [3]. This dual tree complex wavelet transform comprises of two parallel wavelet filter bank trees that contain carefully designed filters of different delays that minimizes the aliasing effects due to downsampling. The structure is shown in figure 1. It should be noted that there are no links between the two trees, which makes it easy to implement them in parallel.

Fig. 1. Iterated Filter bank of Dual Tree Complex Wavelet Transform

The dual-tree transform was developed by noting that approximate shift invariance can be achieved with a real DWT by doubling the sampling rate at each level of the tree. For this to work, the samples must be evenly spaced. We can double all the sampling rates by eliminating the down sampling by 2 after the level 1 filters [3].

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The inverse DTCWT is calculated as 2 normal inverse wavelet transform, one corresponding to each tree and the result of each of the 2 inverse function are then averaged to give the reconstructive signal. Object tracking is a problem where moving object may be present in translated as well as rotated form among different frames [2]. Therefore any object feature which remains invariant by translation and rotation of the object will be helpful for tracking. The dual tree complex wavelet transform is invariant with respect to translation and approximately invariant with respect to rotation as well.

3 The Proposed Tracking Algorithm The proposed tracking algorithm exploits the shift invariance property of dual tree complex wavelet transform. The tracking algorithm searches the object in next frame according to its predicted centroid value, which is computed from the previous four frames. Similar to the tracking method as in [2], we have computed centroid of the object. For this, first we have computed distance between previous four frames with the help of coordinates of centroids of these frames and using equation of motion followed by velocity calculation after first three frames by dividing their distances by time. Then we have calculated acceleration in fourth frame by using distance of fourth frame and initial velocity and acceleration. Finally we predicted distance of centroid of next frame with the help of velocity and acceleration. At last we used again equation of motion and predicted the centroid of next frame. The searching is done using this centroid value. In all the computations, it has been assumed that the frame rate is adequate and the size of object should not change between adjacent frames. However our algorithm is capable of tracking an object whose size changes within a range in various frames. This computation makes each object correspond to a single point. Calculation of velocity of the moving object is based on its position coordinates. Also, we assume that the movement in a few adjacent frames is close to straight line. The tracking algorithm does not require any other parameter except complex wavelet coefficient. Complete tracking algorithm is as follows – Step 1: Segment the first frame, make a square bounding box to cover the object with centroid at (C1, C2) and compute the energy of wavelet coefficients of the square box, say E, as

E

|wave_coeff|

where wave_coeff(i,j) is wavelet coefficients at (i,j)th point. Step 2: for frame_no = 2 to last do compute the wavelet coefficients of the frame, say wave_coeff(i,j) search_length = 3 if frame_no > 4

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predict the centroid (C1,C2) of the current frame with help of centroids of previous four frames and basic euqations of straight line motion. end if for i = - search_length to + search_length do for j = - search length to + search_length do Cnew1 = C1 + i; Cnew2 = C2 + j; Make a bounding box with centroid (Cnew1, Cnew2) Compute the difference of energy of wavelet coefficient of bounding box, with E, say di,j. end end find minimum of {di,j} and its index, say (m,n) C1 = C1 + m; C2 = C2 + n. Mark the object in current frame with bounding box with centroid (C1, C2) and energy of bounding box E, as

E

|wave_coeff|

end

4 Experiments and Results In this section, we show the experimental results of the proposed algorithm. We implemented tracking method described in section 3 and tested on several video clips. One of them, tracking of basketball, is shown in the paper.

Fig. 2. Centroids of cropped balls in the order of the frames

First we segment the video in first frame. We applied it to video clips of basketball match, for tracking basketball. The frame size is 256 by 256. The results are shown in figure 3 and figure 2 shows path of object according to centroids of the cropped ball

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in the order of frames forr video clips. It illustrates the result of proposed methhod applied on the sequence. Here H one level dual tree complex wavelet transform is ussed. From figure 2 and 3, it is clear that the proposed tracking algorithm method perforrms well. In every frame centro oid values are also given. We have traced the positionn of centroid in complex waveelet domain and this value gives information about the locality of object.

Frame 1 (Centroid (136, 80))

Frame 2 (C Centroid (140,78))

Frame 3 (Centroid (144,76))

Frame 4 (Centroid(146,72))

Frame 5 (Centroid (152,68))

Frame 6 (Centroid(156,64))

Frame 7 (Centroid(162,60))

Frame 8 Frame 9 (Centroid (162,56)) (Centroid(168,54))

Frame 10 (Centroid(170,52))

Fig. 3. Tracking of ball in 10 consecutive frames

5 Conclusion Wavelet transform is kno own to providing position localized information. T This information can be obtained d at various levels depending on the resolution we requuire. However for moving objects the use of real valued wavelet transform is not appropriate because of its shift-sensitivity. In the present work we have shown tthat the reduced shift sensitivity y of complex wavelet transform can be used for trackking the moving object in video o clips. From the results, it can be observed that theree is

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small deviation in bounding box of the frames, but this is due to the change in the gray values of pixels between the frames as well as background. The reduction of search space has been done intelligently based on the two properties – 1. The object boundaries and hence the centroid of the object can be computed with the help of dual tree complex wavelet coefficients of the object only (not the whole image). 2. The total energy computed with the help of modulus of complex wavelet coefficient remains approximately constant in various frames. In addition, the rigid body assumption and smooth velocity change assumption provide extra reduction in search space. The method is simple, efficient and robust. The tracking algorithm does not require any human intervention. However for asymmetrical shapes of the object the method may require some change.

Acknowledgement The authors are thankful to University Grants Commission (UGC), New Delhi, India for providing research grant vide its grant no. 36-246/2008(SR) for major research project.

References [1] [2]

[3] [4]

[5] [6]

[7] [8]

[9] [10]

Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Computing Surveys 38(4) Article 13 (2006) Khare, A., Tiwary, U.S.: Daubechies Complex Wavelet Transform based Moving Object Tracking. In: Proceeding. of IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, Hawaii, USA, April 1-5, pp. 36–40 (2005) Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The Dual –Tree Complex Wavelet Transform. IEEE Signal Processing Magazine, 123–151 (2005) Kingsbury, N.G.: A Dual-Tree Complex Wavelet Transform: A new Technique for shift invariance and Directional filters. In: Proceeding of 8th IEEE DSP Workshop, Utah (August 9-12, 1998) Sonka, M., Hlavac, V., Boyle, R.: Image Processing Analysis and Machine Vision, 3rd edn. Thomson Asia Pvt. Ltd, Singapore (2007) Kingsbury, N.G.: ‘Image processing with complex Wavelets. Phil. Trans. Royal Society London A, Special issue for the discussion meeting on Wavelets: the key to intermittent information?, February 24-25, 2543–2560 (September 1999) Fernandes, F.C.A., Spaendonck, R.L.C., Burrus, C.S.: A new framework for complex wavelet transform. IEEE Transactions on Signal Processing 51(7), 1825–1837 (2003) Yilmaz, S., Severcan, M.: Complex discrete wavelet transform based motion estimation for vision-based tracking of targets. In: Proc. of 13th European Signal Processing Conference, Antalya, Turkey, September 4-8 (2005) Bharath, A.A., Ng, J.: A Steerable complex wavelet construction and its application to image denoising. IEEE Transactions on Image Processing 14(7), 948–959 (2005) Magarey, J.F.A., Kingsbury, N.G.: Motion Estimation using a complex valued wavelet transform. IEEE Transactions on Signal Processing 46(4), 1069–1084 (1998)

Design of New Indexing Techniques Based on Ontology for Information Retrieval Systems K. Saruladha1, G. Aghila2, and Sathish Kumar Penchala3 1

Pondicherry Engineering College, Pudhucherry, 605014, India [email protected] 2 Pondicherry University, Pudhucherry, 605014, India, 605014 [email protected] 3 Sathish Kumar Penchala, Pondicherry Engineering college, India [email protected]

Abstract: Information Retrieval [IR] is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web. This paper describes a document representation method instead of keywords ontological descriptors. The purpose of this paper is to propose a system for content-based querying of texts based on the availability of ontology for the concepts in the text domain and to develop new Indexing methods to improve RSV (Retrieval status value). There is a need for querying ontologies at various granularities to retrieve information from various sources to suit the requirements of Semantic web, to eradicate the mismatch between user request and response from the Information Retrieval system. Most of the search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. The indexes do not contain synonyms, cannot differentiate between homonyms and users receive different search results when they use different conjugation forms of the same word. Keywords: Ontology, indexing methods, content-based querying, information retrieval.

1 Introduction The semantic web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Information retrieval (IR) systems use a simpler data model than database systems, in this Information organized as a collection of documents and this documents are unstructured, no schema. An IR model uses a matching procedure which matches the document representation with the query and reports to the user with the best set of documents which matches the user needs. The retrieval process is based on how the document is represented and how well the IR model matches. At present several tools are developed to semantically annotate web pages manually or automatically based on pre-existing structured data sources such as XML V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 287–291, 2010. © Springer-Verlag Berlin Heidelberg 2010

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or relational databases by using RDF [5] and language ontologies such as DAML [6]. In this paper an approach for a search engine that links free texts to ontological concepts (descriptors) is discussed. An important technique for such a concept based search engine is the use of word sense disambiguation techniques. According to [2], these can be classified into several categories like supervised disambiguation, unsupervised disambiguation, dictionary based, other machine learning approaches (e.g. corpus statistics), combined methods. Different approaches on word sense disambiguation are reviewed in [3]. Word sense disambiguation is a very active field of research, with potential applications in language translation, information extraction and in search engines. The notions of controlled vocabularies and ontologies, their formal notations, and how they should be implemented is controversial. However, the ontology definition is very similar to the prevalent RDF recommendation, and to Stumme’s definition of a core ontology [4]. Definition: Controlled vocabulary CV Definition: Ontology O O: = G(CV,E) With E = CV × CV and a totally defined function t: E → T,

(1)

which defines the types of edges. T is the set of possible edge types, i.e. the semantics of an edge in natural language.

2 Mapping Ontologies 2.1 Overview of Mapping Ontologies The aim is to develop a search engine that uses several mapped RDF ontologies for concept based text indexing. The system consists of several components that together fulfill the required functionality (see Fig. 1). The main idea is to store the results of the different processing steps in a relational database. In the following, an overview of the different steps is given Importing and mapping ontologies: First the different ontologies have to be imported to the system by using an RDF-parser. Then the equivalent concepts of the different ontologies are mapped. Spidering: Webspiders (or crawlers) search and download web pages from the Internet, as a prerequisite for the indexing process. Displayed Spidering and indexing denotes for downloading web pages from the Internet and mirror it in the local file system (see Fig. 1). Apart from the ontological indexing, also a simple keyword based index is generated [1]. Indexing: Now the previously downloaded files can be indexed word by word. In this steps, “normal” keyword based indexing is used, in order to be able to compare the ontology based indexing method to the “normal” indexing method. Ontological indexing: In this step, the words in the indexed text are linked to ontological concepts. In order to support word sense disambiguation (mouse as a

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pointing device vs. mouse as a animal), the context in which a word appears in a text is compared to the context of homonymous concepts (subconcept vs superconcept). Indexing Stopwords

Tag Base Rank

Badwords

URL 2 Concepts

Keywords

Spidering

Ontological indexing

IMPORTED URLS

URL_2 Concepts

Fig. 1. Ontological Indexing

The context wcont of a word w is defined as the set of all stems for all the words that occur in the same document as w. The context also does not contain stopwords. The ontological indexing process maps words in the text to concepts of the ontologies. For each individual word, we calculated a mapping score ms(w, c), that indicates how good the word w is mapped to the ontological concept c by comparing wcont and ccont. Front-end: The frontend serves users to use the index in order to search web pages using keywords and ontological concepts. 2.2 Mapping Algorithm This mapping algorithm is simplified in order to keep it easy to understand. The implemented algorithm takes care of situations where for a given word several concepts have the same score (see Fig 2). If this is the case, the word is mapped to both concepts. When equivalent concepts of different ontologies are mapped, the concept is only mapped to the concept of the biggest ontology. However, this concept has no relations to any other concepts. 2.3 Ontology Indexing and Mapping of Documents The ontological indexing process maps words in the text to concepts of the ontologies. For each individual word, calculate a mapping score ms(w, c), that indicates how good the word w is mapped to the ontological concept c by comparing wcont and ccont. This number is divided by the context size, in order to make up for different context sizes. In addition part-of-speech information is taken into account, and for the comparison of words and concepts word stemming is used. Definition : Mapping score ms ms(w,c) =

(2)

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// Mapping documents into set of ontologies Input: XML documents Output: Conceptual documents // Construct a word list W from a set of documents D. W : {w | w in document} // loop through all words in the document that is indexed for i = 1 to |w| // Creation of concept list from multiple ontologies for k = 1 to |O| L={cЄO} for m = 1 to |L| // concepts c, where the stem of the concept name or the stem of a synonym of the concept equals the stem of the word, and where the if (w = name(c) or w = synonyms(c) // then then C = C U {c} next m next k next i Fig. 2. Mapping Algorithm

3 Zone Indexing Zones are similar to fields, except the contents of a zone can be arbitrary free text. Whereas a field may take on a relatively small set of values, a zone can be thought of as an arbitrary, unbounded amount of text. Consider a set of documents, each of which has zones. Let g1,… g ∈ [0, 1] such that

∑

1 for 1
position of the document depending on the space between each word. Finally weighted zone indexing is termed as:

∑

(3)

5 Conclusion This paper has discussed the matching of documents through keywords and ontological descriptors. A simple ontological keyword indexing method is shown and mapped. The purpose of this paper is to show the mapping of ontologies and to improvise the retrieval status value through the indexing methods. We have tested small set of documents with ontological descriptors. We are working on ontological descriptors to enhance the effectiveness of semantic web. We are also heading towards the zone indexing methods through ontological descriptors.

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References 1. Kohler, J., Philippi, S., Specht, M., Ruégg, A.: Ontology based text indexing and querying for the semantic web. In: Bioinformatics, BAB, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK University of Koblenz, Germany, RG Statistical Genetics, Max Planck Institute of Psychiatry, Munchen, Germany, Technical Faculty, University of Bielefeld, Germany (2006) 2. Mihalcea, R., Moldovan, D.: An iterative approach to word sense disambiguation. In: Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society (FLAIRS), Florida, USA, AAAI Press, Orlando (2000) 3. Voorhees, E.: Natural language processing and information retrieval. In: Pazienza, M.T. (ed.) Information Extraction: Towards Scalable, Adaptable Systems, pp. 32–48. Springer, New York (1999) 4. Stumme, G., Maedche, A.: FCA-Merge: A Bottom-Up Approach for Merging Ontologies. In: Proceedings of the International Joint Conference on Artificial Intelligence, Seattle, Washington, USA, pp. 225–234 (2001) 5. Jena, M.B.: Implementing the RDF Model and Syntax Specification. In: Decker, S., Fensel, D., Sheth, A.D., Staab, S. (eds.) Proceedings of the Second International Workshop on the Semantic Web – SemWeb 2001, Hong Kong, China (2001) 6. Hendler, J., McGuinness, D.L.: The DARPA agent markup language. IEEE Intelligent Systems 15(6), 67–73 (2000)

Identifying the Attack Source by IP Traceback K.C. Nalavade and B.B. Meshram Computer Engineering Department, V.J.T.I., Matunga, Mumbai [email protected], [email protected]

Abstract. The common attacks on the internet are denial of service and spoofing. Spoofing hides the identity of the attacker by modifying source IP address field and can cause the denial of service which makes the services unavailable to the legitimate users. Tracing the source of the attacking packet is very difficult because of stateless and destination based routing infrastructure of Internet. In this paper we propose a system which uses packet marking mechanisms along with Intrusion Prevention Systems for efficient IP traceback. The data mining techniques can be applied to the data collected from the packet marking scheme for detecting attack. The resultant database of knowledge can be further used by network Intrusion prevention systems for decision making. The data mining techniques are providing very efficient way for discovering useful knowledge from the available information. The combination of packet marking scheme, Intrusion prevention system and data mining can give us very effective results. Keywords: detection.

DOS, IP traceback, Packet marking, Data mining, Intrusion

1 Introduction With the global Internet connection, network security has gained significant attention in the research and industrial communities. The internet made it easy to access information from anywhere but this accessibility makes it extremely vulnerable. The tools for disruption are readily available to the internet attackers. Any part of computing system can be target of a crime. When we refer to a computing system, we mean to a collection of hardware, software, storage media, data and people that an organization uses to perform computing tasks. Sometimes we assume that parts of a computing system are not valuable to an outsider, but often we are mistaken. Any system is most vulnerable at its weakest points. Vulnerabilities and bugs of information systems are often exploited by malicious users to intrude into information systems and compromise security (e.g., availability, integrity and confidentiality) of information systems. In order to protect information systems, it is highly desirable to detect intrusive activities while they are occurring in information systems. The common attacks on the internet are Denial of Service and spoofing. The Denial of service makes the services unavailable to the legitimate users and spoofing hides the identity of the attacker by modifying source IP address field. In this paper we propose V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 292–296, 2010. © Springer-Verlag Berlin Heidelberg 2010

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the architecture for detecting attacker’s packet from legitimate user’s packet. In section 2 the literature survey about the attacks, packet marking scheme and Intrusion Prevention System is given. In section 3 we propose system architecture to detect spoofed IP addresses. In section 4 we conclude the paper with observations about the model.

2 Literature Survey An intruder must be expected to use any available means of penetration. The penetration may not necessarily be by the most obvious means, nor is it necessarily the one against which the most solid defense has been installed. A human who exploits vulnerability perpetrates an attack on the system. An attack can also be launched by another system as when one system send an overwhelming set of messages to another, virtually shutting down system’s ability to function. There are different types of attacks observed but here we will discuss spoofing and DoS attacks in detail. A. Spoofing: - The TCP/IP protocol suite is currently inadequate in the way it handles security. In the TCP/IP protocol suite, most of the security responsibilities are addressed inside of the Application Layer. This abstracts away most security responsibilities from the protocol suite itself and moves it onto the code developed by application programmers. In the past, this approach has worked but the problems of IP spoofing attacks may require some of the security responsibilities be addressed within other layers of the TCP/IP protocol suite in the future. IP Spoofing is the forging the source addresses in IP packets. By masquerading as a different host, an attacker can hide his true identity and location. [2] It has been shown that large part of Internet is vulnerable to IP spoofing. [3] The key to the IP spoofing attack is the ability of the attack program to predict the initial sequence numbers which need to be acknowledged for the target host. Once the initial sequence number inspection and IP spoofing attack programs were developed and debugged, an actual IP spoofing attack using the software tools could be attempted.[4] B. IP traceback methods provide the victim’s network administrators with the ability to identify the address of the true source of the packets causing a DoS. IP traceback is vital for restoring normal network functionality as quickly as possible, preventing reoccurrences, and, ultimately, holding the attackers accountable. Several efforts are under way to develop attacker-identification technologies on the Internet. C. Packet Marking methods are characterized by inserting traceback data into the IP packet to be traced, thus marking the packet on its way through the various routers on the network to the destination host. This approach lets the host machine use markings in the individual packets to deduce the path the traffic has taken. To be effective, packet marking should not increase the packet size. Furthermore, packet-marking methodologies must be secure enough to prevent attackers from generating false markings. Problems also arise when we try to work within the framework of existing IP specifications. The order and length of fields in an IP header are specified, so for

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the packet marking method to be effective, it must work with those settings and not alter them. Packet-marking algorithms and associated routers must be fast enough to allow real-time packet marking. As shown in figure1 every router marks his address in packet before forwarding it. The end router will create the log of routing information received in marked packets. This scheme is useful for finding source of incoming packets. IP spoofing hides the identity and location of attacker but when the source router forwards the packet with marking we can identify the spoofed packet by the difference between source IP address and source router address.

Fig. 1. Packet Marking Scheme Deployed in Routers

D. Intrusion Prevention Systems- The inadequacies inherent in current IDS defenses have driven the development of a new breed of security products known as Intrusion Prevention Systems (IPS). An IPS is any device that has the ability to detect attacks, both known and unknown, and prevent the attack from being successful. [1]. IPS sits online on the network and monitors it and when an event occurs it takes action based on prescribed rules. An IPS typically consists of four main components, Traffic normalizer, Service scanner, Detection engine, Traffic shaper. The traffic normalizer will interpret the network traffic and do packet analysis and packet reassembly as well as performing basic blocking functions. The traffic is then fed into the detection engine and the service scanner. The service scanner builds a reference table and the appropriate response is determined. [5] Attack detection involves placing a sensor outside the firewall to record attack attempts and is useful for tracking the number and types of attacks against your network. Intrusion detection involves a sensor placed inside the network [5].

3 Proposed System In our proposed system we wish to traceback source of packet in the case of IP address is spoofed. This is required because the denial of service attack in internet is increasing day by day. Alone Intrusion Prevention systems or firewalls are not sufficient to detect these attacks because looking only at source address and the type of packet or TCP flags will not provide information about the attacker. The attacker may be smart enough to spoof the IP address of some other host to hide his identity and location. Our system aims at finding attacks by looking at the first marked router in the packets and the source address in the packets. Even though IP is spoofed the forwarding will take place from the default router of the network. The difference in addresses can make it possible to detect attackers at the sending end itself. At receiving end, by finding patterns in the database between the different source addresses and sending router addresses determining spoofed traffic is possible. In our proposed system we assume that the every router will mark the packets that they are forwarding. The destination router will create a database of marked entries of

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received packets. We apply the data mining algorithm to find sequence pattern between the source address and the router entries in the packet. If the first router is always same and the source IP address is varying then second level security checks are applied. At second level, the source addresses with same initial forwarding router are scrutinized. The IP addresses with same network part as that of initial forwarding router are separated. We can consider them as genuine packets from legitimate users. Our stress in this model is to detect the spoofed IP packets from network traffic. The IP addresses with different network address part than the initial forwarding router can be considered as attack packets coming from same source with different spoofed address. If number of such packets are above some threshold value then traffic from such network can be blocked for some time avoiding denial of service attack. The Sensor for our network Intrusion prevention system can be placed at router. The router will create the log of incoming packets as shown in figure 2. Our NIPS will try to find out attacks from the generated database.

Fig. 2. Proposed System Architecture for Data Storage and Retrieval

In our proposed system we wish to separate attack packets from the legitimate user’s data. This can be done by applying mining on the stored information. Applying mining will also reduce the number of false alarms from the system.

Fig. 3. Proposed System Architecture

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In our proposed system we wish to separate attack packets from the legitimate user’s data. This can be done by applying mining on the stored information. Applying mining will also reduce the number of false alarms from the system. The general system flow diagram is shown in figure 3.

4 Conclusion Network Intrusion Prevention Systems are designed to be defensive measures that stop or at least limit the negative consequences of attacks on systems and networks. This paper presented a system that integrates intrusion detection prevention, packet marking mechanism and data mining techniques to achieve IP traceback in internet. In the process of prevention of attacks some legitimate users may get blocked. Our system will take decision of attack based on packets source address and first router entry. The continuous monitoring of incoming stream of packets may provide us details of attacker. By composing localized monitoring with global communication and control is able to support in-depth analysis and high-level reporting and configuration. Future work will focus on to improve our system model to better evaluate the impact that widespread attacks could have on a large-scale infrastructure. It is possible that these attacks could leverage complex, unforeseen interdependencies between subsystems that are difficult to represent and analyze. Secondly, we plan to use simulation-based analysis to evaluate the ability of the system to scale to hundreds of networks with thousands of sensors.

References 1. Nalavade, K.C., Meshram, B.B.: Intrusion Prevention System: Data Miniining Approach. In: International Conference and Workshop on Emerging Trends in Technology, Mumbai (2010) 2. Duan, Z., Yuan, X., Chandrashekhsr, J.: Controlling IP spoofing through Interdomain Packet Filters. IEEE Transactions on Dependable and secure computing 5(1) (JanuaryMarch 2008) 3. Beverly, R., Bauer, S.: The spoofer project: Inferring the Extent of Internet Source Address Filtering on the Internet. In: Proc. First Usenix Steps to reducing Unwanted Traffic on he Internet Workshop (July 2005) 4. Hastings, N.E., McLean, P.A.: TCP /IP Spoofing Fundamentals. IEEE 3255-5 (1996) 5. Endorf, C., Schultz, E., Mellander, J.: Intrusion Detection and Prevention, Tata Mc-Graw Hill edn., ISBN 0-07-061606-X 6. Kim, Y., Lau, W.C., Chuah, M.C., Jonathan Chao, H.: PacketScore: A Statistics-Based Packet Filtering Scheme against Distributed Denial-of-Service Attacks. IEEE Transactions on Dependable And Secure Computing 3(2) (April-June 2006) 7. Koller, R., Rangaswami, R., Marrero, J., Hernandez, I., Smith, G.: Anatomy of a Real-time Intrusion Prevention System. In: International Conference on Automonic Computing School of Computing and Information Sciences, Florida International University FL 33 (1996) 978-0-7695-3175-5

An Approach towards Secure and Multihop Time Synchronization in Wireless Sensor Network Arun Kumar Tripathi1, Ajay Agarwal1, and Yashpal Singh2 1

Department of Computer Application, Krishna Institute of Engg. and Tech., Ghaziabad 2 CSE Department, Bundelkhand Institute of Engineering and Technology, Jhansi [email protected], [email protected], [email protected]

Abstract. Wireless sensor networks (WSN) have been identified as being useful in a variety of domains such as environment monitoring, target tracking, etc. Time synchronization is an important component of sensor networks to provide a common clock time in sensor nodes. Time synchronization protocols provide a mechanism for synchronizing the local clocks of the nodes in a sensor network. Some of the sensor nodes may be malicious, which can disrupt the normal operation of a sensor network. In this paper, we find out malicious nodes out of existing nodes and propose multi-hop time synchronization based secure protocol for a group of non-malicious nodes. Keywords: Sensor Networks, Security, Time Synchronization, Malicious nodes, Multihop.

1 Introduction Wireless Sensor Network (WSN) consists of hundreds or thousands of micro sensor nodes that are joining together to form a network. Wireless sensor network [1] accurately monitors remote environment intelligently by combing the data from individual nodes. The special nature of wireless sensor network imposes challenging requirements on secure and multihop time synchronization design. All the attacks either in single hop or multihop time synchronization protocols have one main goal, to somehow convince some nodes that their neighbor’s clocks are at a different time [2] [3] than they actually are. There exist two types of attacks [4]: (i) External and (ii) Internal. External attacks are those in which an attacker manipulates the communication between pairs of trusted nodes and causes the nodes to desynchronize, or to remain unsynchronized even after a successful run of the synchronization protocol. Pulse delay attack is an example of external attack. Internal attacks are those in which internal attackers (group members) report false clock references to their neighboring nodes. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 297–302, 2010. © Springer-Verlag Berlin Heidelberg 2010

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The rest of the paper is organized as follows: In Section 2 we survey the existing time synchronization protocols [5]. An approach for the proposed protocol is given in Section 3. Concluding remarks and future work are made in Section 4 and 5 respectively.

2 Related Work Researchers have proposed many protocols for time synchronization based on receiver-receiver [5], [6] and sender-receiver [5], [7] classification. In receiver-receiver based synchronization (RRBS), sender sends message to more than one receiver and then exchange of messages take place between receivers to synchronize each other and compute their offsets based on the difference in reception time. Sender does not take part in the synchronization. Reference broadcast synchronization (RBS) [8] protocol and time synchronization protocol for sensor network (TPSN) [9] are example of RRBS. RRBS protocols are not secure during exchange of packets and not suited for highly mobile nodes. On the other hand, in sender-receiver based synchronization (SRBS) [6] protocol, the sender node periodically sends a message with its local time as a timestamp to the receiver. The receiver then synchronizes with the sender using the timestamp it receives from the sender. The message delay [7] between the sender and receiver is calculated by measuring the total round-trip time, from the time a receiver requests a timestamp until the time it actually receives a response. Secure pair-wise synchronization (SPS) [10] protocol is example of SRBS protocol. SRBS protocols are not able to handle the internal attackers.

3 Proposed Protocol We have proposed an approach to develop a multihop protocol. The protocol not only finds malicious node(s) but also counts them within the group. Further, it synchronizes all non-malicious nodes to a common clock i.e. fastest clock in the group. Multihop sensor networks are often organized in hierarchical topologies in the presence of a base station. When sensor nodes are deployed, their readings must be sent to the base station for processing. Sensor nodes employ form spanning trees rooted at the base station, in order to minimize the number of hops across which their readings must travel. This hierarchy lends itself to a sender to receiver synchronization protocol. Let us assume that group membership is known to all group nodes in the group and all group nodes reside in each other’s power ranges. Let us consider Gs is a sender node which is a non- malicious and not considered in a group. The sending time of the packet at node Gs is represented by Ts (time measured by node Gs) and receiving time of packet by node Gj is Tj (already sent by node Gs). These times are measured by two different clocks. Ts is measured in the local clock of node Gs (i.e. Cs) whereas Tj is

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measured by the local clock of node Gj (i.e. Cj). The offset (or the difference between the local clocks) between the two nodes is represented by δsj (calculated by node Gj with respect to node Gs). The delay for the packet transfer from Gs to Gj is represented by dsj. In proposed approach we consider given node as malicious if it does not report exact time at which it receives or sends the packet. 3.1 Steps of Proposed Protocol Step 1: Firstly find out minimum spanning tree from existing group nodes. The sender node can use one or more sensor node(s) as an intermediate node(s) for communication and synchronization of end nodes. Step 2: Sender node Gs broadcasts packets containing its node identifier (ID) and challenge nonce (Ns) to all group members in its power range. For nodes which do not reside in the power range of sender node, sender node uses one or more nodes as intermediate nodes. Let us assume that, node Gs wants to synchronize to node Gj which are not in power range of Gs and from spanning tree path between Gs and Gj is two hops and Gi intermediate node. In proposed protocol the initiator node is taken as sender node. Step 3: In this step of the protocol, every node Gj, which have received the challenge packet acknowledges back to sender node Gs, known as response packet. This packet contains triples {Tj, Ns, Gs}, where Tj is the receipt time of the challenge packet from node Gi, Ns is nonce by sender and Gs is node-id of sender respectively. It also contains Message Authentication Code (MAC), which enables Gs to authenticate the packet sent by Gj in this step. The response packet also includes the sending time (T′j) from node Gj. MAC is used to provide resiliency against external attacker. So in this step N MACs are calculated one for each Gs and Gj pair and then each Gj sends messages to Gs. A pair wise secret key (Ksj) which is shared between nodes Gs and Gj is also used in the response messages. Step 4: Now node Gs calculates the delay occurred (dsj), corresponding to challengeresponse and if all the calculated delays for each node are less than a maximal delay (d*) then node Gs calculates the offset for each node Gj. If any node’s calculated delay is more than maximal delay then Gs assumes that Gj is external attacker. Step 5: Node Gs will calculate for every other node, Gj, in the group Ssj (Ssj is sent time of packet from node Gs to Gj) and Rjs (Rjs is received time of packet from node Gj to Gs). If Gj is malicious then Ssj should not be equal to Rjs. This step also calculates number of internal attackers. Step 6: Sender forms a circular path, P, of all remaining non-malicious nodes and calculate sum of all offsets along the path P. If this sum is zero, it synchronizes every node of the path P to the fastest clock. The pseudo code for proposed protocol is given in the Table 1.

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Proposed Protocol for Secure Multihop Time Synchronization { Gj (1,……,N ) } 1. Find out minimum spanning tree among existing nodes. 2. Gs(Ts)ĺGi(Ti)ĺGj(Tj ) : Gs, Ns, sync. /* Node Gs broadcasts a challenge packet for synchronization, containing its node-id Gs and nonce Ns at time Ts to all nodes in the power range of Gs. If node Gj is not in power range of Gs then one or more nodes (e.g. Gi ) can be used as intermediate node */ 3. Gj(Tƍj)ĺGi(Tƍi)ĺGs(Tƍs) : Gj, Tƍj, m, M, ACK m = {Tj, Ns, Gs } M = {MAC{ Ksj}[Gj, Tƍj, Tj, Ns, Gs,,ACK]} /* Node Gj sends response packet (which may include intermediate nodes) to sender Gs at time Tƍj . The packet also contains receiving time of challenge packet at Gj i.e.Tj from node Gs with nonce Ns. */ 4. /* Find out external attacker */ Compute Ds={ dsj : dsj=[(Tj–Ts)+(Tƍs–Tƍj)]/2, j=1,..,N } /* Calculate end-to-end delay for each node from source node. */ if all dsj d* then Os={ įsj : įsj=[(Tj–Ts)–(Tƍs-Tƍj)]/2, j=1,...,N } /* Calculate offset set between each node from source node. */ else Gj is Malicious (external attacker) end if 5. /* Finding internal attackers */ counter=0 /* counter for internal attacker */ for each pair of Gs and node Gj in the group if (| Ssj || Rjs |) /* Ssj= sent time of packet from node Gs to Gj. Rjs= received time of packet from node Gj to Gs. */ then Gj is Malicious (internal attacker) counter= counter + 1 end if end for Print “Total number of malicious nodes”= counter 6. Calculate įsum= sum of all offsets along any circular path of non-malicious nodes if (įsum==0) then synchronize every node Gj to the fastest clock end if

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4 Conclusion Existing solutions for time synchronization in sensor networks are not resilient to malicious behavior from external attackers or internally compromised nodes. The feasibility of a pulse-delay attack (external attack), whereby an attacker can introduce arbitrarily long delays in the packet propagation time directly can affect the achieved synchronization precision. The external attacks can be resolved with the help of MAC message authentication codes and the use of private keys. Internal attacks which occur in case of group wise synchronization can’t be resolved completely and efficiently by the existing protocols till date. We have proposed a protocol to remove external as well as internal attacker problems in group synchronization. Further, proposed protocol ensures that it can cope up with both problems securely. This protocol uses MAC to cope up with the external attacker problem, and uses a consistency check mechanism to cope up with the problem of internal attacker. It firstly ensures that the group is secured against attackers. Consistency check mechanism is applied on every node and if this step is successful then each node synchronizes to the fastest clock in the group. Further, the proposed protocol also finds out whether a node(s) is(are) malicious or not and also counts number of malicious nodes in the group.

5 Future Work Synchronization on nodes depends on packet transfer among nodes which consumes energy. The proposed protocol can further be modified to reduce the communication overhead, so that energy consumption can further be reduced.

References 1. Mukherjee, B., Ghosal, D., Yick, J.: Wireless sensor network survey. Computer Network 52(12), 2292–2330 (2008) 2. Kopetz, H., Ochsenreiter, W.: Clock Synchronization in Distributed Real-Time Systems. IEEE Transactions on Computers 36(8), 933–940 (1987) 3. Kshemkalyani, A.D., Sundararaman, B., Buy, U.: Clock synchronization for wireless sensor networks. A Survey on Ad-hoc Networks, 281–323 (2005) 4. Capkunl, S., Ganeriwal, S., Han, S., Srivastava, M.: Proceedings of Securing Timing Synchronization in Sensor Networks, pp. 369–390. Springer, New York (2006) 5. Li, H., Chen, K., Wen, M., Zheng, Y.: A Secure Time Synchronization Protocol for Sensor Network. In: Washio, T., Zhou, Z.-H., Huang, J.Z., Hu, X., Li, J., Xie, C., He, J., Zou, D., Li, K.-C., Freire, M.M. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4819, pp. 515–526. Springer, Heidelberg (2007) 6. Wang, C., Ning, P., Sun, K.: Secure and resilient clock synchronization in wireless sensor networks. IEEE Journal on Selected Areas in Communications 24(2), 395–408 (2006) 7. Song, H., Zhu, G.C.S.: Attack-resilient time synchronization for wireless sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, p. 772 (2005)

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8. Estrin, D., Elson, J., Girod, L.: Fine-grained network time synchronization using reference broadcasts. In: Proceedings of the 5th symposium on Operating systems design and implementation Special Issue Boston, pp. 147–163 (2002) 9. Srivastava, M.B., Kumar, R., Ganeriwal, S.: Timing-sync protocol for sensor Networks. In: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems, Los Angeles, CA, pp. 138–149 (2003) 10. Ganeriwal, S., Popper, C., Capkun, S., Srivastava, M.B.: Secure Time Synchronization in Sensor Networks. ACM Transactions on Information and System Security 11(4) Article No. 23 (2008)

Improving Dynamic Difficulty Adjustment to Enhance Player Experience in Games A. Joy James Prabhu College of Engineering, Anna University, 600 025 Chennai, India [email protected]

Abstract. The player experience is a significant parameter in evaluating the overall success of a game. It is necessary to create a game that provides: (1) satisfaction and (2) challenge. Dynamic difficulty adjustment (DDA) helps in producing interesting games. This paper focuses on improving DDA systems by introducing: (a) dynamic weight clipping, (b) differential learning and (c) adrenalin rush. Experimental results indicate that these features can implement an ideal DDA system that can engage the human player by creating equally competent opponents.

1 Introduction Game difficulty is relatively static and the responsibility of choosing the appropriate level lies with the human player. As the players generally find it difficult to estimate their abilities, they end up playing a game which is either too difficult or too simple for them. The significant issues are: - Limited difficulty variations provided in a few static levels. - Large difficulty gaps between the static levels. - Unresponsiveness of agent to player learning. - Quantitative implementation of difficulty variation in agent. The design of the agent AI needs to be greatly improved in addition to other game characteristics [1]. This is the inspiration for this work.

2 Dynamic Scripting Dynamic scripting is a learning technique for creating agent AI characterized by stochastic optimization [2]. Rules are used to create scripts that control the agent. They are selected from the database based on the player’s difficulty level. Probability of selection of a rule for a script is proportional to rule’s weight. Dynamic scripting is the technique of adjusting these weights in such a manner that the fitness performance of the agent varies dynamically in response to player learning. The team fitness function is defined in equation (1). Here, g denotes the team, c denotes an agent, Ng is the total number of agents in team g, and ht(c) is the health of agent c at time t. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 303–306, 2010. © Springer-Verlag Berlin Heidelberg 2010

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F (g ) =

1

∑ 〈( 2 N c∈ g

)( 1 + g

ht ( c ) )〉 h0 (c )

(1)

3 Complexity Switching The weight adjustment mechanism performed is implemented in such a manner that the total weight is conserved. To implement this, the increment/decrement of specific rule-weights is evened out by decreasing/increasing all remaining rule-weights in a global manner. This can be achieved by using rule-classes, collections of rules of similar complexity. Table 1. Description of Rule-classes used in the experiment

Class(i) 1 2 3 4 5

Level Novice Amateur Intermediate Professional Expert

Class-Weight(µ i) 0.15 0.30 0.50 0.70 0.85

Fitness < 25 25 – 40 40 – 60 60 – 75 > 75

3.1 Learning Threshold Each rule class is assigned a class-weight in the range [0,1], which are denoted by µ i. The rule-classes in the experiment are listed in Table 1. The fitness values of different levels are given in the range of [0,100]. Beyond a limit, the agent AI is unable to further scale-up or scale-down its complexity in response to player learning. Then, the agent AI is defined to have reached its learning threshold, as illustrated in Figure 1. The aggregate complexity is denoted by ξ. To formalize, let η denote the number of rule classes, wi denote the weight assigned to the ith rule-class and µ i denote the class weight of the ith rule-class. The maximum pair wise difference between class-weights constrains the ability of the game to remain unobtrusive, which is denoted by κ. Using this notation, the equations (2, 3) are derived:

ξ =

η

∑ 〈(μ i =1

κ =

i

)( w i ) 〉

μ max − μ min μ min

Thus, κ = (0.85 - 0.15) /0.15 = 4.67. Higher makes the agent AI more obtrusive.

κ

(2) (3)

improves agent’s ability to learn, but

4 Dynamic Weight Clipping Complexity switching inherently leads to biased-scripting, wherein some rule-classes are more favored than others. To solve this, the weights of all rule-classes must be in

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[Wmin, Wmax], eliminating biased-scripting by limiting complexity-switching. Static weight clipping creates incompetent agent as the game progresses. In dynamic weight clipping, Wmax and Wmin vary dynamically as the agent AI learns. Windowsize of the script is defined as the ratio of the difference between the maximum and minimum values of the set of class-weights to the minimum value of the same set and is denoted by Ф. λmax and λmin are based on trade-off between exploration and exploitation by script. Maximum window-size is denoted by Фmax . The value of Ф is constrained by Фmax as defined in equation (4):

Φ max = 〈

λmax − λmin 〉 λmin

(4)

Fig. 1. (A) Differential learning (B) Learning Threshold

5 Differential Learning Algorithm Learning curves indicate that the agent has to learn faster initially and then fluctuate as per the player at a slower pace. This phenomenon is defined as differential learning depicted in Figure 1. The maximum difficulty variation possible by weight adjustment is restricted by a quantity defined as the learning rate, denoted by ξrate. The initial player fitness is denoted by ψ0. The difference between current and initial player fitness values is termed as fitness change and is denoted by Δψ. The ratio of the fitness change to the initial player fitness is defined as learning coefficient and is denoted by ψc. 5.1 Adrenalin Rush A plot of Δψ as the game progresses is helpful in measuring player learning. For a given ψ0 , a corresponding value of Δψ is defined as the adrenalin limit. The curve providing the fitness change needed to cross the adrenalin limit as a function of ψ0 is defined as the adrenalin curve. When the player’s complexity crosses the adrenalin limit, the learning rate must be reduced. Thus:

ψc =

Δψ

ψ0

(5)

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Id 0 1 2 3 4

Version Technique Dynamic scripting Static weight clipping Differential learning Dynamic weight clipping Adrenalin rush

Agent Win 65.41 79.51 57.12 58.37 53.87

Std. Deviation 300.22 84.24 107.51 91.32 62.79

Window Size 1.96 1.45

6 Results The MiniGate game [3] was the basis of currently implemented DDA modules [4] in Neverwinter Nights. The experiments involved analyzing data from 120 rounds of experiments, each comprising of 100 games. The results are presented in Table 2.

7 Conclusions It is observed from these experiments that DDA systems can be developed for a generic game, wherein the human player plays against an agent. The fitness functions must be formulated by the game developers depending on the desired difficulty scaling [5]. The agent created by these techniques exhibits the desired agent behavior.

References 1. Hunicke, R.: The case for dynamic difficulty adjustment in games. In: Proceedings of ACM SIGCHI Conference on Advances in computer entertainment technology, pp. 429–433 (2005) 2. Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., Postma, E.: Adaptive game AI with Dynamic scripting. Machine Learning (63), 217–248 (2006) 3. Spronck, P.: MiniGate Game Environment (2007), http://ticc.uvt.nl/~pspronck/minigate.html 4. Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Online adaptation of game opponent AI with dynamic scripting. Journal of Intelligent Games and Simulation (3), 45–53 (2004) 5. Yannakakis, G.N., Hallam, J.: Towards capturing and enhancing entertainment in computer games. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds.) SETN 2006. LNCS (LNAI), vol. 3955, pp. 432–442. Springer, Heidelberg (2006)

A Novel Method for Cross-Language Retrieval of Chunks Using Monolingual and Bilingual Corpora Tayebeh Mosavi Miangah1 and Amin Nezarat2 1

English Language Department, Payame Noor University, Yazd, Iran [email protected] 2 Information Technology Department, Shiraz University, Shiraz, Iran [email protected]

Abstract. Information retrieval (IR) is a crucial area of natural language processing (NLP). One of the fundamental issues in bilingual retrieving of information in search engines seems to be the way and the extent users call for phrases and chunks. The main problem arises when the existing bilingual dictionaries are not able to meet the users’ actual needs for translating such phrases and chunks into an alternative language and the results often are not reliable. In this project a heuristic method for extracting the correct equivalents of source language chunks using monolingual and bilingual linguistic corpora as well as text classification algorithms is to be introduced. Experimental results revealed that our method gained the accuracy rate of 86.13% which seems very encouraging. Keywords: chunk retrieval, cross-language information retrieval, linguistic corpora, text classification, Persian language.

1 Introduction Unlimited and public approachability to this bulk of information have become one of the greatest challenges the specialists in the field of computer sciences need to tackle with. Searching keywords in Internet using search engines and gaining the required outputs and resources in a language other than the search language is a growing need of most users. In this research we tried to use some statistical models based on monolingual and bilingual linguistic corpora and their combination to obtain a disambiguation method for various chunks as keywords entered by users in the search engines. The traditional method for cross-language retrieval was based on one or more bilingual dictionaries used in the time of searching and displaying the outputs. This method, though rapidly spread out, has its own various drawbacks including: limited number of words in a dictionary, incompatibility of the existing words in these dictionaries with the most recent and current words of a language, the various equivalents of a given word in the target language and the way of selecting the most appropriate one, and some others. In fact, the last one of the above mentioned drawbacks seems to be the most important one as so many words and phrase have V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 307–312, 2010. © Springer-Verlag Berlin Heidelberg 2010

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more than one translation depending on the subject area to which the word or phrase belongs. This problem can frequently be observed in studying the different languages of Middle East and East Asian due to the lexical richness of such languages. In the present research we are going to demonstrate the effectiveness of our novel heuristic method in automatic extraction of all possible and valid chunks in Persian language, and at the same time selecting the most appropriate translation of each chunk among those equivalents presented by the bilingual parallel corpus. We believe that the unstructured but complete information available in linguistic corpora can provide more precise and relevant responses in retrieval tasks compared to the structured but incomplete information from the existing monolingual and bilingual dictionaries.

2 Related Works In recent years many researchers have tried to develop high-efficiency systems of information retrieval using various methods, almost all of which were based on linguistic dictionaries. There are quite a number of non-Persian studies carried out in this respect, some of which are to be mentioned here. A research taking the dictionary approach and using retrieval precision demonstrate that word-to-word translating of the queries leads to a 40% - 60% decrease in retrieval efficiency comparing to phraseto-phrase translation of the same queries [3]. Chen has also examined effect of phrase translation in cross-language information retrieval between Chinese and English. Using the methodology of program evaluation, he found that translation by phrase is more successful than translation by single words. His findings showed that phrase translation with 53% of efficiency compared to word translation with 42% efficiency had a better performance in information retrieval task. He added that the rate of efficiency could be enhanced in case of exploiting some complementary resources. Doing that, he achieved 83% of efficiency for monolingual information retrieval [2]. Among the works which have been done in cross-language information retrieval for Persian is a study during which Alizade and his colleagues evaluate a system in which only a machine-readable bilingual dictionary was used. Their findings were specified as follows: higher efficiency when 1) using the first equivalent compared to using all equivalent of a given query; 2) morphological processing of all query words before their translation compared to the lack of any kind of processing; 3) adopting the phrase translation procedure compare to word translation of the queries [1]. Shams and her colleague tried a hybrid approach towards semantic retrieval of documents in English and Persian for queries proposed in Persian language. The main advantage of this approach is the integration of linguistic, conceptual, and statistical characteristics of queries and documents. In this approach, a bilingual weighted ontology as well as a bilingual dictionary were used in order to use extract concepts from documents and queries. As word for word translation may not be considered a precise method of translation, they tried to use a phrase translation pattern. This way they could improve the efficiency of their retrieval system to a high degree [7]. In another study on cross-language information retrieval, Mosavi Miangah made an attempt to use a bilingual parallel corpus to extract suitable equivalents for query words. There, she reported very encouraging findings regarding the use of a bilingual

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corpus instead of a bilingual dictionary [4]. The present research is, in fact, in the line of the mentioned study improving methodology in order to deal with phrases and chunks using a large monolingual corpus as well.

3 Methodology 3.1 Corpora Used in This Study For the purpose of this experiment we tried, as the very first stag, to revise and complete our already existing 110-million monolingual corpus of Persian to reach about 200 million words. The corpus is comprehensive in the sense that it has been divided into different sub-corpora of various text types. All the texts are to be processed before entering to the corpus. Moreover, the texts should be converted to an XML format to be suitable for use on Internet sites. In this stage the texts entered the pre-designed corpus distinguished by the text type and then all the texts are automatically segmented at the sentence level. While entering each text, its type or specialized field is determined and indexed in order for the type to be appeared next to the sentences when retrieving them. The English-Persian parallel corpus has been compiled as a bilingual textual database consisting of aligned original English texts and their translations into Persian, and of original Persian texts and their translations into English [5]. For the purpose of this experiment, the number of words in this corpus reached about 6,000,000 words resorting to various methods such as hiring translators to do the job and the like. All texts in this corpus have been manually aligned at the sentence level in order for the corpus to be highly reliable and without any noise. After complementing the two monolingual and bilingual corpora in the above mentioned manner, we set to exploit them as the main material of the present experiment. 3.2 Automatic Extraction of Chunks In order to disambiguate the search inputs, first the association score among all components of a sequence of words should be determined. Then, the gained association score will decide on the probability degree of the sequence as an acceptable chunk or phrase occurring in the given language. For calculating the association score, we made use of text classification methods. In these methods, the degree of association or disassociation of several words in a sentence is introduced as probability functions. In order to examine the association score between d and c, a formula for calculating association probability of X2 is to be used. In this formula, the association degree of c and d is calculated using probability functions of P(DC) = P(D) P(C) as follows:

N℮

d, c ℮

,

℮

,

℮

E℮

E℮

℮

℮

in which ed indicates the occurrence of the chunk d in the sentence, and ec indicates the occurrence of the chunk c in the sentence. The quantity of E is also calculated as follows:

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E℮

N℮

℮

N℮

N℮

℮

N

℮

℮

where N is the total number of sentences in the corpus, and Nt,c is the number of occurrence of the chunks t and c in the corpus, so that N11 equals the number of simultaneous occurrence of both chunks in the sentence, and N02 equals the number of occurrence of the second chunk without the first one. To calculate the association score between the two chunks c and t, we used the following formula is applied [6]:

N11 N10 N01 N00 N11 N00 – N10 N01 2 N11 N01 N11 N10 N10 N00 N01 N00 After computing the frequency and putting it in the above formula and calculating d, c , the highness or lowness of the association level of the chunk elements is determined using the table 1, and threshold of which equals 6.63. Table 1. Critical values of the P 0.1 0.05 0.01

2

X Value) 2.71 3.84 6.63

distribution with one degree of freedom (Critical

P

2

X Value) 0.005 0.001

(Critical 7.88 10.83

Therefore, if is smaller than the threshold of 6.63, it means there is some degree of association between two given elements and other quantities greater than the threshold would be rejected as implying no significant association in this respect.

4 Implementing the Method on Monolingual Corpora In order to determine the association score between different components of a chunk searched by a user, different relationships inside the chunk are to be valuated using formula. Consider, for instance, the chunk “‫( “ﻳﮏ روز در ﻣﻴﺎن‬every other day) is entered as a search input by a user. As the first stage, the association score between every two words of the given chunk are calculated one by one using their relative frequencies in the monolingual corpus of Persian. Then, using the formula the degree of association between all components of the chunk is calculated and the degree of probability of the chunk as an acceptable one in Persian language is gained referring to threshold table above. After calculating , using the software implemented on the monolingual corpus of Persian, the critical value of 4,94 was gained which is smaller than the threshold and thus can be accepted as a valid and acceptable chunk in Persian language.

5 Extracting the Correct Equivalents Using Bilingual Corpus When the valid chunks were extracted using information gained from the monolingual corpus of Persian, it’s time to find the most suitable equivalent of this Persian chunk

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in English using the English-Persian bilingual parallel corpus. Again, the association score is to be used. For this purpose, first all Persian sentences including such a chunk are retrieved from the bilingual corpus, and then for each single sentence the association probability of the chunk having different combinations of words inside the sentence is calculated. Take, for example, the following Persian sentence and its corresponding English sentence found in bilingual corpus: .‫ ﮐﻤﻴﺴﻴﻮن ﻳﮏ روز در ﻣﻴﺎن ﺗﺸﮑﻴﻞ ﺟﻠﺴﻪ ﻣﯽ دهﺪ‬.1 2. The committee convenes every other day. Now, using a rather complicated algorithm designed by the authors, we set to break the English and Persian sentences into all possible chunks one of which is shown roughly in Tables 2. Gaining the association scores of all possible chunks presented in table 2, the valid ones have been starred among which No. 6 in table 2 and No. 18 in table 3 are translations of each other. Then, is calculated for every corresponding English and Persian chunks separately. Afterwards, a software, designed for this purpose, begins to delete improbable cases (based on the threshold defined in table 1). Finally, among those remaining probable cases, the chunk with the greatest (association score) is selected as the most appropriate equivalent for the given Persian chunk. Table 2. All possible chunks for the Persian sentence 1 for calculation No 1

Chunk The committee

No 7

Chunk convenes every

2 3

The committee convenes The committee convenes every

8 9

convenes every other convenes every other day

4 5

committee convenes committee convenes every

10 11

every other

6

committee convenes every other

12

every other day other day

6 Experimental Results In order to demonstrate the effectiveness of the heuristic method on extracting all possible chunks in Persian language and finding the most appropriate equivalents for them in English, we carried out an experiment using a small fraction of the monolingual corpus and tried to extract a collection all Persian chunks out of these words to be passed through the subsequent stage of the experiment. In the second phase, all possible equivalents for every chunk extracted from the previous stage are generated. Every Persian chunk in the test corpus presents a set of possible translations and choosing the most likely one is the ultimate goal. Implementing the related algorithm, the average accuracy of the method reached 86.13%. The results of this experiment are very encouraging and support our initial claim that the unstructured but complete information available in linguistic corpora can provide more precise and relevant responses in retrieval tasks compared to the structured but incomplete information from the existing monolingual and bilingual dictionaries.

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7 Conclusion and Further Development One of the consequences of the present project is to enhance the precision of the information retrieval systems in search machines using two corpora, far richer than dictionaries. The Robot algorithm for browsing monolingual sites and extracting the indexed sentences can also be used for similar research on other languages provided the referable addresses change according to the given language. Considering the parameters of formula which are used in calculating the number of occurrence of the lexical items t and c in the corpora, we notice that the degree of computational complexity of the algorithm would be higher and higher as the corpora get bigger and bigger adding more records. As a result, the computation time gets longer. In order to optimize the algorithm of the formula, it is suggested that the corpora are divided formula in each part and into smaller components. After calculating the introducing the new formula, the association degree of the s are calculated and then the association between d and c is recalculated. As almost all users expect the retrieval systems in search machines to be able to quickly and accurately respond their needs, it seems more economical to use a sorting algorithm in order to sort out all possible chunks in the monolingual corpus of the given language.

References 1. Alizade, H., et al.: Studying the efficiency of the existing methods in cross-language information retrieval using a machine-readable bilingual dictionary. Iranian Information and Documentation Centre 25(1), 53–70 (2009) 2. Chen, H.: Chinese information extraction Techniques. Presented at the SSIMIP, Singapore (2002) 3. Hull, D., Grefenstette, G.: Querying Across Languages; A Dictionary – Based Approach to Multilingual Information Retrieval. In: Proceedings of the 19th Annual International ACM Sigir, Zurich, Switzerland, pp. 49–57 (1996) 4. Mosavi Miangah, T.: Automatic term extraction for cross-language information retrieval using a bilingual parallel corpus. In: Proceedings of the 6th International Conference on Informatics and Systems (INFOS 2008), Cairo, Egypt, pp. 81–84 (2008) 5. Mosavi Miangah, T.: Constructing a large-scale English-Persian Parallel Corpus. META 54(1), 181–188 (2009) 6. Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009) 7. Shams, M., Pourmahmoud, S.: A linguistic-conceptual approach for cross-language information retrieval. In: Proceedings of the 13th National Conference of Computer Society of Iran, pp. 1–8. Kish Island, Iran (2008)

Application of Kohonan SOM in Prediction Sathya Ramadass1 and Annamma Abhraham2 1

Dept. of MCA, Jyoti Nivas College (Autonomous), Bangalore [email protected] 2 Dept. of Mathematics, R.V. Engineering College, Bangalore [email protected]

Abstract. As neural network modeling of learning continues, further applications to education could become more apparent. Some implication of such model is to predict how the students will perform in the course, during the admission procedure. Many researches implemented mathematical models and concluded that they are not very effective to predict. However, advancement of artificial intelligence has proved many innovation and renovation in various fields. This paper discusses the Self Organizing Map (SOM), neural network architecture, to predict the student’s performance. Keywords: Kohonan map, Neural Network, Self Organizing map, SOM, Unsupervised learning.

1 Introduction There were number of educational concerns that were beyond the ability of theoretical model. For example, learning without a teacher, calculating success rate of a graduate student of an institution, finding the success rate of regular students and distance learning students etc. Many mathematical models like regression analysis, statistical tools have been used to find the success rate of a student. However, the advent of knowledge representation and machine learning, artificial neural network to be more precise, has been used to overcome some of the difficulties faced in the mathematical models. Artificial neural networks (ANN) find application in diverse fields as modeling, time series analysis, pattern recognition, signal processing etc., due to the ability to learn from input data with or with out a trainer. This paper discusses the ANN to predict the performance of a MBA student at the time of admission to the course.

2 Related Work During the past years, a number of researchers attempted to study student data in order to predict the success of graduate / postgraduate programmes in management and business. A number of statistical methodologies were used and each of these approaches produced various levels of success. Many researchers have identified the key point to success of a MBA graduate is GMAT score and undergraduate GPA [1], V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 313–318, 2010. © Springer-Verlag Berlin Heidelberg 2010

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[2], [3], [4], [5]. Wright et al., used nine variables and contradicted from the above that verbal and quantitative components should get the importance than GMAT score [6], [7]. Adams et al., in their research stated that amount of work experience prior to enrollment is a predictor of success and showed better association with success than GMAT and undergraduate GPA [8], [9]. Some researchers taken the gender as one factor and have shown that relative to male counterparts, women are underperforming in the GMAT but they perform equally well or even better than men with similar qualifications [10], [11]. Hardgrave et al., discussed the same problem and compared classification techniques of discriminant analysis, logistic regression, and ANN. They concluded that due to the non linearity, ANN performs as well as the other mathematical model or even more [12]. Arnold et al., used linear discriminant analysis and neural networks to classify applicants into the executive MBA program. It was stated that the ANN model appeared to perform better in capturing the complex interactions among the predictor variables in their research [13]. Ibrahim et al., in their comparative study, used ANN, Decision Tree and Linear Regression to predict under graduate students performance. The input variables included family financial status, previous school studied, IT application knowledge, final CGPA etc. This study has taken 206 students data set and concluded that ANN with 5 hidden neurons is better than the other two models [14]. Karamouzis et al., have developed ANN model with a threelayered perceptron with 11 input parameters, a hidden layer and an output layer. The network is trained with back-propagation algorithm. Among 1407 training data set, the model could predict 86.04% successful graduates and 68.21% of unsuccessful graduates. They recommended including more parameters in the input set to get higher predictability rate [15]. Another research has been carried out by Oladokun et., al. for Engineering graduates. They used multilayer perceptron with 10 input parameters, 2 hidden layers of 5 neurons each and an output layer with 3 neurons to predict Good, Average and Poor. The network was trained with 112 data and achieved 74% of accuracy [16]. Naik et., al., used feedforward ANN to classify applicants into “marginal” and “successful” student pools [17]. They compared the success rate of ANN model with statistical models and reported that the classification accuracy of the ANN is 89.13%, which is higher than the statistical model. They modeled the ANN with 10 input variables, two hidden layers and one output layer declares the status either success or marginal. Though the prediction was through, they encountered some limitations like network topology design, expensive training, initial value of learning rate parameter etc. Our present study focuses on the design of Self Organizing Map (SOM), an unsupervised learning classification algorithm for the prediction of MBA students’ success.

3 Artificial Neural Network Artificial neural networks are computational networks, which attempt to simulate the networks of human nervous system. The dynamic nature and the computational advantage offered by ANN makes it popular and lead to applications in interpretation, prediction, planning, monitoring, control, pattern recognition etc [18]. The architecture

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of ANN has an associated learning algorithm that allows it to adapt its internal parameters to improve its response to a desired input-output behaviour. Learning in ANN can be of two kinds: Supervised and Unsupervised. In supervised learning, a global error signal governs the adaptation of weights of the network using an error correction method. Whereas in unsupervised learning, the network creates internal representations of the impinging vector stream using information that is locally available to a connection. Self-organization is an unsupervised ANN model, which learns and organizes information without being given correct answers for input patterns would be suitable for the specified problem. 3.1 Self Organizing Map The goal of SOM is to transform an incoming signal pattern of arbitrary dimension into a one or two-dimensional discrete map, and to perform this transform adaptively in a topologically ordered fashion. Professor Kohonen developed the Self-Organizing Map. This popular neural network model belongs to the category of competitive learning networks called “winner take all” with an input layer of linear units and an output layer of units with nonlinear output function. At equilibrium, they produce larger activation on a single output unit and small activation on all other units. If the weights of the network are suitably, adjusted different units in the output layer can be made to win for different input patterns. Now when a test input pattern is given it will generate an output, which is the neighborhood of the outputs for similar pattern. This type model is used for clustering pattern without knowing the class memberships of the input pattern and to detect features inherent to the problem and thus called as SelfOrganizing Feature Map (SOFM) [19], [20]. The SOM starts first by initializing the synaptic weights in the network randomly. Followed by, (i) Sampling, (ii) Similarity Matching, (iii) Updating, and (iv) Continuation.

4 Implementation Like inputs clustered together is the operation of SOM. To improve decision making on the MBA students’ performance during admission, SOM is taken as ANN architecture. This has two layers of units, 10 input units (length of training) and two output units (number of categories).

Fig. 1. SOM model for MBA student’s prediction

Input units are fully connected with weights to output units as in Fig 1. Student’s information is analyzed under three categories. They are, Educational qualification includes, major in undergraduate (UG), UG percentage and Higher secondary percentage, Eligibility includes, KMAT/PGCET score, personal interview and prior

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work experience and personal information includes, gender, status (BC/MBC/SC/ ST/OC), location and family background (business). x = [x1,x2,…xm] .

where m = 0,1, …, 9

(1)

The weight vector is initialized with small random values between 0-1 has the same dimension as the input space. wj = [wj1, wj1,… wjm] .

where j = 0,1,.., 9

(2)

The neighbourhood hj,i(x) distance is set to positive value. This is symmetric about the maximum point defined by di,j = 0. hj,i decreases monotonically with increasing lateral distance di,j, decaying to 0 for di,j → ∞. The process begins with presenting the training vectors to the network. During this ordering phase the topological ordering of the weight vectors takes place. The unit (best matching neuron) closest to the original input vector (lowest Euclidean distance) is chosen as the winning neuron. i(x) = Σ (x(t)l,k – wj,k(t))2 . where k = 1 to n

(3)

The learning rate parameter η is usually set to a relatively high value, always closer to unity and is decreased exponentially with increasing time or iteration but remain above 0.01. η(t) = η0 (exp(-( t / time))) .

where t = 0,1,2….

(4)

The weight for the winning neuron is updated at each iteration so that it moves closer to the input vector. wj (t+1)= wj(t)+ η(t) hj,i(x) (xl- wj(t)) .

(5)

This process continues until no noticeable changes in the weight vector are observed. To fine-tune the feature map, the convergence phase will start its process. Here, the learning rate parameter is maintained at a small value, on the order or 0.01. Now the neurons in the feature map are labeled. Each pattern of input finds the best matching neuron in the map. This neuron is the one with the minimum distance between the weight vector and the input. The class label corresponding to the pattern is assigned to the neuron. In this case, it is Success and Average. Now when a new pattern is present to the network, by stimulus nature, it is assigned to either success or marginal. Since the neurons are labeled, it is possible to group the pattern and classify.

5 Results and Discussions SOM architecture is trained and tested using C programming with 200 different input patterns and tested with 50 different data set. This unsupervised model achieves 80% of overall accuracy. It is then compared with the original result and found out the rate of difference (error) is 20%. Table 1 shows the success and average rate for specific cases. The network could predict 20 out of 25 successes and 18 out of 25 averages.

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This might be due the range of values of that particular pattern. Since the scale of individual variables plays an important role in the determining the final result. Observation have been made that score of Undergraduate, KMAT and Commerce background have important role in their success. In addition, the higher graduate percentage with no commerce major, good score in KMAT with working experience improves the success of the students. Table 1. Result table

Output pattern

Success

Average

Success

20

5

Average

7

18

6 Conclusion In this paper, we explained the SOM model to predict the performance of a MBA graduate in their course during the admission. The training of SOM proceeds in an unsupervised manner. The model required no target output vectors, and simply organized itself into the best representation for the data used in training. A 10 × 2 planar array of neurons was taken and trained with 200 input variables and achieved 80% of overall accuracy. This proves that SOM efficacy is equivalent to the feedforward network architecture and other mathematical tools for prediction. In this work, we can only predict if the student could finish the MBA course successful or average. Since scoring 60% and over also referred as success, future work can try to bring more specific feature about success and also to improve the accuracy of SOM by careful selection of input pattern. Also, this can be implemented to other postgraduate programmes in order to improve the standards of the candidates admitted into the institution.

References 1. Gayle, J.B., Jones, T.H.: Admission Standards for Graduate Study in Management. Decision Sciences 4, 421–425 (1973) 2. Baird, L.L.: Comparative Prediction of First Year Graduate and Professional School Grades in Six Fields. In: Educational and Psychological Measurements, vol. 35, pp. 941– 946 (1975) 3. Paolillo, J.G.P.: The predictive validity of selected admissions variables relative to grade point average earned in a Master of Business Administration program. Educational and Psychological Measurement 42, 1163–1167 (1982) 4. Peiperl, M.A., Trevelyan, R.: Predictors of performance at business school and beyond demographic factors and the contrast between individual and group outcomes. J. Management Development 15(5), 354–367 (1997)

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5. Ahmadi, M., Raiszadeh, F., Helms, M.: An examination of admission criteria for the MBA program: a case study. Education 177, 540–546 (1997) 6. Wright, R.E., Palmer, J.C.: GMAT scores and undergraduate GPAs as predictors of performance in graduate Business programs. J. Education for Business. 70, 344–348 (1994) 7. Wright, R.E., Palmer, J.C.: Examining performance predictors for differently successful MBA students. J. College Student 31, 276–281 (1997) 8. Adams, A.J., Hancock, T.: Work Experience as Predictor of MBA Performance. J. College Student 34, 211–216 (2000) 9. Braunstein, A.W.: Factors Determining Success in a Graduate Business Program. J. College Student 36, 471–477 (2002) 10. Gropper, D.M.: Does the GMAT Matter for Executive MBA Students? Some Empirical Evidence. In: The Academy of Management Learning and Education (AMLE), vol. 6(2), pp. 206–216 (2007) 11. Drecko, R.F., Wounderberg, H.W.: MBA Admission Criteria and Academic Success. Decision Sciences 8, 765–769 (1977) 12. Hardgrave, B.C., Wilson, R.L., Kent, K.A.: Predicting Graduate Student Success: A Comparison of Neural Networks and Traditional Techniques. Computers & Operations Research 21, 249–263 (1997) 13. Arnold, L.R., Chakravarty, A.K.: Applicant Evaluation in an Executive MBA Program. J. Education for Business 71, 277–283 (1996) 14. Ibrahim, Z., Rusli, D.: Predicting Students’ Academic Performance: Comparing Artificial Neural Network, Decision Tree and Linear Regression. In: 21st Annual SAS Malaysia Forum, Shangri-La Hotel, Kuala Lumpur (September 5, 2007) 15. Karamouzis, S.T., Vrettos, A.: An Artificial Neural Network for Predicting Student Graduation Outcomes. In: Proceedings of the World Congress on Engineering and Computer Science, WCECS 2008, San Francisco, USA, October 22-24 (2008) 16. Oladokun, V.O., Adebanjo, A.T., Charles-Owaba, O.E.: Predicting Students’ Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course. The Pacific Journal of Science and Technology 9(1) (May-June 2008) (Spring) 17. Ragothaman, S., Naik, B.: Using Rule Induction for Expert System Development–The Case of Asset Writedowns. International Journal of Intelligent Systems in Accounting, Finance and Management 3(3), 187–203 (1994) 18. Fu, L.: Neural Networks in Computer Intelligence. Tata McGraw-Hill edn., New York (2003) 19. Haykin, S.: Neural Networks: A comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999) 20. Kumar, S.: Neural Networks, A classroom approach. Tata McGraw-Hill publishing company Ltd., New York

Evaluation of the Role of Low Level and High Level Features in Content Based Medical Image Retrieval K.S. Arun1 and K.S. Sarath2 1

Department of Computer Science, St. Joseph’s College of Engineering, Pala, Kerala, India [email protected] 2 Department of Electronics & Communication, College of Engineering, Kidangoor, Kerala, India [email protected]

Abstract. The Content based medical image retrieval, which aims at searching the image database using invariant features, is an important research area for manipulating large amount of medical images. So designing and modeling methods for medical image search is a challenging task. This paper proposes an approach by combining DICOM header information (high level features) and content features (low level features) to perform the retrieval task. A novel approach of rotation invariant contourlet transform (CT) is proposed for texture feature extraction and fixed resolution format is used to derive the shape features. Initially the DICOM header information is extracted which is used to perform a pre-filtering on the original image database. Content based search is performed only on these pre-filtered images which speed up the retrieval process. The retrieval performance of this method is tested using a large medical image database and measured using commonly used performance measurement. Keywords: CBIR, Rotation Invariant Contourlet Transform (CT), Fixed Resolution Format, High level Features, Low level Features.

1 Introduction Image based medical diagnosis is one of the important service area in medical and healthcare sector. Medical images are playing an important role to detect anatomical and functional information of the body part for diagnosis, medical research and education. Content-based image retrieval (CBIR) makes use of image features, such as color, shape and texture, to index images with minimal human intervention. It retrieves relevant images from the image data base for the given query image, by comparing the features of the query image and images in the database [1]. CBIR has been proposed by the medical community for inclusion into picture archiving and communication systems (PACS) to provide an efficient search function to access the desired images. In medical research, researchers can use CBIR to find images with similar pathological areas and investigate their association [2]. Case-based reasoning, a clinical decision-making technique, which searches already solved problems similar V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 319–325, 2010. © Springer-Verlag Berlin Heidelberg 2010

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to the current one and tries to apply those solutions to the current problem can incorporate medical image retrieval system. Texture contains important information about the structural arrangement of surfaces and their relationship to the surrounding environment [3].Various texture representations have been investigated in pattern recognition and computer vision. In the proposed method, an image is represented in the contourlet transform domain, which overcomes the limitations of wavelet transform such as directionality and anisotropy [4]. Contourlet transform express information in few coefficients for images having more directional information with smooth contours than compared to wavelet transform [5]. Rotation invariant contourlet transform can handle databases consisting of images with different rotations. The use of shape as a feature is less developed than the use of color or texture, mainly because of the inherent complexity of representing it [6]. Yet, retrieval by shape has the potential of being the most effective search technique in many application fields [4].In the literature various shape descriptors have been proposed which mainly falls into two categories such as: contour-based and region-based descriptors [3]. Only the boundary information is used in Contour-based shape descriptors while region based shape descriptors uses interior pixels of the shape also.

2 High Level Feature Extraction The DICOM File Format is described by the American College of Radiology (ACR) and National Electrical Manufacturers Association (NEMA) in PS3.10 specification, of the DICOM Standard to aid the distribution and viewing of medical images, such as CT scans, MRIs, and ultrasound. Imaging equipment used in hospitals generates images which are in DICOM format. It is a standard format used to obtain, store and distribute medical images. A single DICOM file contains both a header as well as all of the image data. The DICOM header size varies depending on how much header information is stored. The header describes the image dimensions and retains other text information about the scan such as descriptions of study, patient, body region examined and modality. This information’s are termed as high level features. Usually tags, which are textual or numerical sequences of pairs, are used to describe the image. This information’s are considered as the semantic or high level features. For all the DICOM files the image and the relevant tags are extracted and are stored in the database. The image is stored in jpeg file format. The extracted semantic information is stored in the database which is used later during the retrieval process.

3 Low Level Feature Extraction A feature vector is generally used to represent each image in the CBIR system. To determine the similarity between images we measures the distance between their corresponding feature vectors. For medical images shape and texture are the two important low level features which describe the content of the image. So in the

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proposed system shape and texture features are extracted and stored in the database as feature vectors. 3.1 Texture Feature Extraction Using Rotation Invariant Contourlet Transform Contourlet transform (CT) extracts edge and texture orientations as image features for retrieval. The contourlet transform is an extension of the wavelet transform which uses multiscale and directional filter banks [3].In practice wavelets are not effective in representing the images with smooth contours in different directions. Image representation in the contourlet transform domain addresses these problems by having the properties such as anisotropy and directionality. Contourlet filter bank is constructed by combining the Laplacian pyramid [4] with the directional filter bank (DFB) as proposed in [5]. Laplacian Pyramid (LP) is used to capture the point discontinuities and Directional Filter Bank (DFB) is used to link these point discontinuities into linear structures. 3.1.1 Laplacian Pyramid Decomposition Laplacian Pyramid is used to obtain multiscale decomposition of the original image [3, 4]. During this decomposition process the original image is convolved with a Gaussian kernel. The resulting image is a low pass filtered version of the original image. The Laplacian is then computed as the difference between the original image and the low pass filtered image. This process is continued to obtain a set of band-pass filtered images (since each one is the difference between two levels of the Gaussian pyramid). Thus the Laplacian pyramid is a set of band pass filters. As shown in Fig. 1 LP decomposition at each level generates a down sampled low pass version of the original and the difference between the original and the prediction, resulting in a band pass image. Here M is the sampling matrix. H and G are called analysis and synthesis filters. A drawback of the LP is the implicit oversampling. However, in contrast to the critically sampled wavelet scheme, the LP has the distinguishing feature that each pyramid level generates only one band pass image (even for multi-dimensional cases),which does not have scrambled frequencies. This frequency scrambling happens in the wavelet filter bank when a high pass channel, after down sampling, is folded back into the low frequency band, and thus its spectrum is reflected. In the LP, this effect is avoided by down sampling the low pass channel only. In the LP decomposition the prediction error or the band pass image fb ( i , j) is obtained through the following equation

f b (i, j ) = f (i, j ) − fˆ (i, j )

(1)

where fˆ (i, j ) is the prediction which is obtained as the output of the synthesis filter. The Directional decomposition is performed on fb (i , j) and the further decomposition can be carried by applying equation (1) on fl (i , j) to get fl1(i , j), fl2(i, j).... fln(i , j) where n represents the number of pyramidal levels.

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Fig. 1. LP Decomposition of the Original Image

3.1.2 Directional Filter Bank Decomposition The high frequency contents like smooth contours and directional edges were captured by directional filter bank [4, 6]. The DFB used in this work consists of two parts. The first part is a two-channel quincunx filter bank with fan filters. It divides a 2-D spectrum into two directions, horizontal and vertical. The second part is a shearing operator which amounts to the reordering of image pixels. This DFB is implemented by using a k-level binary tree decomposition that leads to 2k directional sub-bands with wedge shaped frequency partitioning as shown in Fig. 2. Due to these two parts, directional information is preserved. This Combination of a LP and DFB is termed as contourlet filter bank.

Fig. 2. Directional Filter-Bank Frequency Partitioning

3.1.3 Proposed Algorithm 1. Apply the contourlet filter bank, which decomposes the given image into directional sub bands at multiple scales.

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2.

323

Compute the mean and standard deviation of the directional sub-bands as: Mean ,

Mk =

1 W ∗H

W −1 H −1

∑ ∑ (I i=0 j =0

k

( i , j ))

(2)

Standard deviation

σk = 3.

1 W −1 H −1 ( I k (i, j ) − M k ) 2 ∑ ∑ W ∗ H i =0 j =0

The feature vector is then obtained by combining the mean and standard deviation of each directional sub-bands as follows:

f texture = [ M s1b1 , σ s1b1 , M s1b2 , σ s1b2 , ....M snbn , σ snbn ] 4.

(3)

(4)

To obtain the rotation invariant feature vector rearrange the above obtained feature vectors based on their dominant orientation which is calculated as: W

H

Ek = ∑∑ | I (i, j ) 2 |

(5)

i =1 j =1

3.2 Texture Feature Extraction The fixed block resolution format [2] is used for shape representation and extraction of shape features. The details of the extraction step are described as below. Step 1: Divide the image into isometric blocks that contain N x N pixels. Step 2: Judge whether there are over p % (p is between 1~100) pixels greater than a certain critical value in each block; if it is true, the index of this block will be set to 1, if it is not, it will be set to 0. Step 3: Judge whether the shapes of the two objects are similar; comparing the block index produced in step 2, if they are different, then add 1 to the result; the smaller result represents that the shapes are more similar. In short, the decision rule is shown as below, where OI is original image and F is formatted image 1.

if

OI m,n (i, j ) > threshold , Fm,n (i, j ) = 1 Fm ,n (i, j ) = 0

else N

2.

if

N

∑∑ F i =1 j =1

m,n

Index(m, n) = 1; else

(i, j ) > N 2 ∗ P% Index(m, n) = 0

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K.S. Arun and K.S. Sarath

dist =

H /NH /N

∑ ∑ | Index(m, n) − QIndex(m, n) | m =1 n =1

4 Experimental Result Experiments were conducted with and without the semantic information obtained from DICOM header. The retrieval efficiency of the proposed rotation invariant contourlet transform is also estimated. Retrieval performance in terms of precision of the proposed retrieval system was tested by conducting experiments on a database consists of 3,000 medical images of different modalities like CT’s, MRI’s and radiographs. These images include various parts of the body like lung, heart, liver, bones etc .Most images are grey level images converted from the DICOM format into JPEG. Fifteen images from the database collection representing various anatomical regions were chosen as the query images for evaluation of the system. The most common evaluation measures used in CBIR such as Precision and Recall are used here to represent the retrieval accuracy. Precision (P) is the percentage of similar images retrieved with respect to the number of retrieved images. Recall (R) is the percentage of similar images retrieved with respect to the number of relevant images in the collection.

P= R=

r number of relevant images = n1 number of retrieved images

(6)

number of relevant images r = n 2 number of relevant images in database

(7)

To show the role of high level features in the retrieval process, experiments were conducted with and without combining the semantic information. The precision rates are then calculated as the average of 20 queries. The obtained results showed that the retrieval accuracy has been increased when high level features are combined with low level features. Table 1 shows the average precision values obtained. Table 1. Average Precision Values

No. of Retrieved Images Precision

1

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.88

.84

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Recall

Precision Very Hard

0.8

0.8

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Very Easy

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Easy Very Easy

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0.4

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0.2

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0

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10

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20

25

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35

Number of Images 0

0

500

1000

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2000

Fig. 3. Precision vs. No. of Images and Recall vs. No. of Images Graph

5 Conclusion To reduce the gap between low level features and high level features, this paper describes a content based medical image retrieval system by combining the DICOM header information and the content information’s. A pre filtering of the image database is done using the DICOM features which produces a set of relevant images given to the query image. Retrieval is performed on this pre filtered image database using low level features (shape and texture). Thus the proposed method reduces the time taken to search the entire medical image database and also it improves the accuracy of the search. To conclude we can say that low level as well as high level features are equally important during the retrieval process.

References 1. Tagare, H.D., Jaffe, C., Duncan, J.: Medical image databases: a content-based retrieval approach. Journal of Am. Med. Informatics Association 4(3), 184–198 (1997) 2. Misna, P.A., Sirakov, N.M.: Intelligent Shape Feature Extraction and Indexing for Efficient Content-Based Medical Image Retrieval. In: Proc. of IEEE Computer Based Medical Systems, Houston, TX, June 23-24 (2004) 3. Gopalan, C., Manjula, D.: Contourlet Based Approach for Text Identification and Extraction from heterogeneous Textual Images. International Journal of computer Science and Engineering, 208–211 (April 2008) 4. Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983) 5. Bamberger, R.H., Smith, M.J.T.: A filter bank for the directional decomposition of images: Theory and design. IEEE Transactions on Signal Processing 40(4), 882–893 (1992) 6. Satini, S., Jain, R.: Similarity Measures. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9), 871–883 (1999)

A Reinforcement Learning Approach for Price Offer in Supplier Selection Process Vali Derhami1, Mohammad Ali Saadatjoo2, and Fatemeh Saadatjoo3 1 Yazd University, Electrical and Computer Engineering Department, Iran Islamic Azad University of Qazvin, Information Technology Department, Iran 3 Yazd-ACECR Higher Education Institute, Computer Department, Iran {vderhami,fsaadatjoo}@yazduni.ac.ir, [email protected] 2

Abstract. Supplier selection negotiation is a challenged, complex, and nondeterministic problem. To solve the problem well, it is necessary to develop an intelligent system for negotiation support in supplier selection process. Reinforcement Learning (RL) is a powerful algorithm which can be used for the price offer in supplier selection negotiation with the aim of maximizing the demander’s profits. In this paper, we formulate the supplier selection as a RL problem. States, actions, and reinforcement function are defined in this problem. In the next step, we compare the proposed RL method with traditional method. Keywords: Supplier selection process, Reinforcement learning, Price-offer.

1 Introduction One of the developments in E-markets is using agents in supplier selection so that agent maximizes the demander’s profits in supply chain management. Most of supplier selection processes are based on negotiation mechanism [1, 2, 3, and 4]. Intelligent systems are used in negotiation support with the aim of enhancing the negotiation abilities to understand their counterparts, their needs and limitations and to maximize the resultant profits of the negotiation [5]. In [6], with using neural network methods a supplier is selected. From studying above items, it comes that most of researches [5, 6] do not consider price offer in choosing the supplier. Here, we propose a RL approach to maximize the profits from dealing and then describe the supplier selection negotiation process and interactive bidding strategies for both-side parties. The first round of the negotiation will start with the offer made by supplier ( P (t ) ) [7] then based on the resultant price of suppliers, demander makes a counter-offer ( P (t ) ). Thus, suppliers make an offer in the first and the demander makes a counter-offer at each round. This sequence will be repeated until the negotiation finishes or fails in a given finite rounds. The negotiation will be over if any of the following conditions occurs: s

d

1. 2.

Any of both-side offers a price that neglects its own reservation price. If the time of negotiation exceeded the t max , the negotiation will be end without any certain result.

V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 326–329, 2010. © Springer-Verlag Berlin Heidelberg 2010

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3.

327

If the suppliers bid price in round t become lower than the demander's bid price in round t −1.

The rest of this paper is organized as follows: Section 2 introduces Reinforcement learning approach for supplier selection. Simulate results are given in Section 3. The final section addresses the conclusions.

2 Reinforcement Learning Approach for Supplier Selection One of the most well known RL algorithms is Q-learning. In every time step t , the agent observes the current state st and selects action at from a set of actions available under the decided policy and applies it to the environment. Environment will go to next state st +1 and the agent will receive a reinforcement signal rt +1 . The agent tries to select actions to reach to the states with greater values. The update formula to estimate the action value function in Q-Learning is as follows: Q(st , at ) ←Q(st , at ) +αt , [rt+1 + γ maxQ(st+1 , b) − Q(st , at )] b∈A

(1)

where α is the learning rate, st is state. To apply reinforcement learning algorithm in a problem, it is necessary to determine the following factors: states, actions and reinforcement function. We consider states in two dimensions as follows: S

x1 (t) = min pis (t) − pd (t) , x2 (t) = x1 (t) − x1 (t − 1) i =1

(2)

x1(t) is the difference of the demander and supplier bid prices. Demander’s bid price is computed in round t using following equation: pd (t ) = pd (t − 1) + a (t ) * Δ

(3)

where a (t ) is the demander selected action in round t and Δ is a basis price. Here we define following formula for reinforcement function: ⎧1 negotiatio n win ⎪ ⎪(max(x1) − ps (t) − pd (t)) duringthenegotiatio n r(t) = ⎨ max(x1) ⎪ ⎪−1 negotiatio n failure ⎩

(4)

3 Simulation Here we compare our method and the traditional method in a simulated environment. This environment is adapted from paper [8]. To train the demander, demander bidding learning process is repeated 1000 times. The amount of action value pairs is initially set to zeros.

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For comparison between results of our method and traditional method, we run both methods 50 times. The obtained results are taken average on these runs. As seen in Table 1, average of agreement price in our approach is better than traditional method and it can also be observed that number of failures in proposed method is less than traditional method, significantly. In Fig.1 and Fig.2, we illustrate the price offer in traditional and proposed methods for a sample run. 30

30 25

20

bid price

bid price

25

demander’s bid

15

supplier’s bid

10 5

20 Demander’s bid

15

supplier’s bids

10 5

0

0 1

2

3

4

5

6

1

2 3

4

5

time

6

7 8

9 10 11 12 13 14

time

Fig. 1. Traditional negotiation

Fig. 2. Proposed negotiation

Table 1. Comparison between traditional and recommended method Method Traditional Ours

Average of agreement price 15.62 11.99

Number of failures 6 2

4 Conclusions This paper proposed a RL method to supplier selection. Elements of RL in this problem are defined. The simulation result showed that the proposed method in using RL algorithm for solving the bidding price challenges is so efficient. Briefly, advantages of the proposed method are recounted as: online learning that facilitate its usage in real situation, increase of benefit and low computation overheads. As a future work to accelerate the process, we can consider knowledge embedding in bidding agent, initially.

References 1. Cakravastia, A., Nakamura, N.: Model for negotiating the price and due date for a single order with multiple Suppliers in a make-to order environment. International Journal of Production Research 40, 3425–3440 (2002) 2. Cakravastia, A., Takahashi, K.: Integrated model for supplier selection and negotiation in a make-to-order environment. International Journal of Production Research 42, 4457–4474 (2004) 3. Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human– Computer Studies 48, 125–141 (1998) 4. Carbonneau, R., Kersten: Predicting opponent’s moves in electronic negotiations using neural networks. Expert Systems with Applications 34, 1266–1273 (2008)

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5. Rau, H., Tsai, M.H., Chen, C.W.: Learning based automated negotiation between shipper and forwarder. Computers & Industrial Engineering 51, 464–481 (2006) 6. Wei, S., Zhang, J., Li, Z.: A supplier-selection using a neural network. In: IEEE International Conference on Intelligent Proceeding Systems, pp. 468–471 (1997) 7. Lee, C.C., Ou-Yang, C.: Neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process. Expert Systems with Applications 36, 2961–2970 (2009)

Generation of k-ary and (k,m)-ary Trees in A-order Using z-Sequences N.A. Ashrafi Payaman Engineering Department of Teacher Training University, Tehran, Iran [email protected]

Abstract. An algorithm is presented for generating trees in A-order using zsequences series. This algorithm generates both k-ary and (k,m)-ary trees and it is the first algorithm which generates these trees independently from their structures. This algorithm has implemented in java with two classes completely and works successfully. I have used an array to store all information which are necessary about nodes. Both space and time complexities of this algorithm are optimal. Keywords: Trees, Encoding, Z-Sequences, A-order, Algorithm, Generation.

1 Introduction Tree is used widely in computer science and many algorithms have been presented for generating and working with it[2,5,6,7,8,9,10,11,12,13,14]. In many situations it needs to transfer generated trees. Tree transferring is not reasonable because the amount of space they need for storing is remarkable. One reasonable solution is to encode them and use this encoded data for transferring which its space is less and increases communication speed. Several encodings have been proposed for trees such as z-sequences, A-sequences[1], RD sequences and Gray sequences[2]. Each of these encodings is a sequence of numbers which shows a tree. With having these encodings it is sufficient to generate these encodings instead of generating trees themselves. For the trees generated, each of them has a specific position in this list and an algorithm which determines this position is called Ranking algorithm. In fact Ranking algorithm get a tree and returns a number as its position. On the contrary there is another algorithm which returns the tree if it will be given the rank of that tree. For trees generation, ranking and unranking algorithms are very important and for every encoding these algorithms must be discussed. The order in which trees are generated is important and they are named A_order and B_order[3]. A_order use overall information to generate trees whereas B_order use local information . In a zsequence each tree is represented with an integer sequence such that any number in this sequence is corresponding with a node in the tree and shows the number of internal nodes which are located before this node in preorder traversal. In A-sequence any number in the sequence shows the number of nodes in its left subtree. In RD distance every number in the sequence shows a distance. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 330–335, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Definitions We assume that reader to be familiar with basic definitions of tree. There are some related features about tree which although they are preliminary but most important to understand what are presented in this paper. We review some of these material following. A k-ary tree is a tree which every node of it has degree of at most k. In a kary tree, nodes of degree 0 are called leaves or external nodes and other nodes are called internal nodes. A k-ary tree with n internal nodes is represented by T(n). A (k,m)-ary tree[4] is a tree which all nodes on even levels has degree of at most k and all nodes on odd levels have degree of at most m. For example some (3,2)-ary trees with 3 internal nodes are as Fig 1. For generating trees an ordering must be imposed on them. There are two orders for the generation of trees. One order is A-order[10]. The list of trees in A-order are in non decreasing lexicographic order. To make clear this matter we can consider all (3,2)-ary trees with 3 nodes in A-order which are as Fig. 1.

1,4,6

1,4,5

1,3,6

1,3,5

1,3,4

1,2,6

1,2,5

1,2,4

1,2,3

Fig. 1. All (3,2)-ary trees with 3 internal nodes which are listed in A-order

3 Generation Algorithm I have designed a class to show every node. In fact in this class we have considered required fields to show whatever is necessary to have about nodes to generate other trees. I have named this class Node and it is as below.

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public class Node { public int numOfChilds,nodeNo,level; public Node parent; public Node children[]; public Node(int numOfChilds,int nodeNo,int level,Node parent) {this(numOfChilds);this.nodeNo=nodeNo; this.level=level; this.parent=parent; } public Node(int n){numOfChilds=n; children=new Node[numOfChilds]; setChildrenNull();} public Node(){this(2); } //other methods } This class has 5 fields which we have brought their explanations below. numOfChilds : a number field for keeping the number of children. nodeNo : a number field for showing node’s number. level : a field of number type for showing the level which node is located parent : a field of Node type to point to the parent of current node . children : a field of array type to point to the children of this node. Since all nodes don’t have the same number of children, we have to consider a field to keep the number of its children and in addition an array to store the addresses of its children. Keeping the parent of a node is required to move toward the root of the tree. The level field is required to determine the number of children of the node. Considering this structure for a node is necessary to understand the following method for generating trees.

4 Generate the First Sequence I consider the first tree in A-order and store the information of its nodes in an array which it is named nodes. I have done this work with the makeInitTree method. The first node is created as the root of the tree and then other nodes are created and linked to the tree. With makeInitTree method the first tree is created which is a right chain tree and all fields for every node of it are set to their correct values. For every node its parent, level, number and children is important and must be set. In fact we have an array that contains all nodes of the tree and also have necessary information about these nodes. With having these information other trees can be created. It must be mentioned that we also have a class for tree which is as below. We only have brought its fields and constructor and avoid from bringing its methods for not being confused. I think the fields of this class are obvious and don’t need any explanation. public class Tree { private int k,m,n; Node nodes[]; public Tree(int n,int k,int m){ this.n=n;nodes=new Node[n]; this.k=k;this.m=m; } //other methods of this class }

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After creating an object of the class Tree, I called the below method which is one of Tree’s methods to generate the first tree. After calling makeInitTree method the nodes field of the created object of Tree class has all information about the nodes of the right chain tree which is the first tree in A-order. public void makeInitTree(){ Node node=new Node(k,1,1,null); nodes[0]=node; for (int i = 1; i <nodes.length ; i++){ int nodeNo=nodes[i-1].nodeNo; //num=the number of children of the new node int num=k+m-nodes[i-1].numOfChilds; nodeNo=nodeNo+nodes[i-1].numOfChilds; node=new Node(num,nodeNo,nodes[i-1].level+1,nodes[i-1]); nodes[i]=node; nodes[i-1].children[nodes[i-1].numOfChilds-1]=nodes[i]; }/*for*/ }//makeInitTreees method As we can see, above method create some nodes which their number is equal to the length of the nodes array. The length of nodes array has been set in the constructor of the Tree class which in fact is equal to the number of internal nodes in the tree. In fact for constructing an object of the Tree class it is necessary to determine the number of the internal nodes, the maximum number of the children for nodes on even levels and also the maximum number of the children for nodes which are on odd levels. For example to generate trees in Fig. 3 we can use below code segment: Tree t=new Tree(3,3,2); t. makeInitTree(); t. makeSubsequentTrees(); Above code segment print z sequences of all trees with 3 internal nodes according to A-order exactly as we can see in Fig 1.

5 Generation of the Subsequent Trees I create the rest of the trees with the below method. This method is a method of the Tree class and print encodings of the trees other than the first tree. public void makeSubsequentTrees() { while(until all trees haven’t been generated){//while 1 while(node number difference between current and previous node is greater than 1) {//while 1-1 if(left sibling is empty) { locate this node in place of its left sibling update related fields}//if else if(left sibling is used currently) {//if 1-2 from the left sibling move toward low along the tree to reach a null node and then set the current node as the last child of the last node which is seen along the path

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}//if 1-2 print generated sequence }//while1-1 while(node number difference between current and previous node is equal to 1) move toward to first node if(the current node is the first node) exit; if(the child before the last child of its parent is null /*left sibling*/){ locate the current node in place of its left sibling and update related fields }//if else if(the left sibling is used) { from the left sibling move toward low along the tree to reach a null node and then set the current node as the last child of the last node }//else for (every node which is located after this node) {update the related fields}//for print generated sequence }//while 1 };//makeSubsequenTrees

6 Explanation of the Above Mentioned Method I create subsequent trees by working on nodes array from the last of the array toward its front as we can see in makeSubsequentTrees method. As it is clear from the algorithm it starts with the last node of the tree and try to move it toward left or initial part of the tree to make other trees. In this situation depending on the left sibling is empty or not, we face with two cases and for each of them do related changes. If the left sibling is not empty, searching for a empty node or a null node is started from that node towards the leaves of the tree and whenever it finds such a node locate considered node in that position and update the rest of the array. By working in this way, other trees are created. This trend is terminated when we reached to the first node of the tree for moving it. This algorithm have written generally and generates both k-ary trees and (k,m)-ary trees using z-sequences. For listing all trees with a determined number of the nodes, at first we must create an object of the Tree class by sending to it 3 parameters which are the number of the internal nodes, the maximum number of the children for nodes on even levels and also for nodes located on odd levels. It is clear that for a symmetrical tree we must send a same number for the maximum number of the children for both nodes on even and odd levels. As I mentioned this algorithm works for both symmetrical and asymmetrical trees. In this algorithm I used an array to implement a tree instead of using linked list. In this specific kind of application using array is more better than linked list. It must be noticed that there isn't any null node in the middle of array used for showing trees and this fact reveals advantage of using array for this specific application.

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7 Conclusion An algorithm for generating trees in A-order has been presented which generates both symmetrical and asymmetrical trees based on z-sequences. This algorithm has been implemented in java with two classes named Node and Tree. This algorithm has tested with large amount of n for several times. The result of test has shown that the algorithm works correctly and it is also very fast. Being implemented by java has the advantage of modifying it easily.

References 1. Ahrabian, H., Nowzari-Dalini, A.: Parallel generation of trees in A-order. Parallel Computing 31, 955–984 (2005) 2. Ahrabian, H., Nowzari-Dalini, A.: Gray code algorithm for listing k-ary trees. Studies in Informatics and Control 13, 243–251 (2004) 3. Zaks, S.: Lexicographic generation of ordered tree. Theor. Comput. Sci. 10, 63–82 (1980) 4. Rosena, R., Du, x., Liu, F. (k,m)-Catalan numbers and hook length polynomials for plane trees, January 24, pp. 1312–1321 (2007) 5. Ahrabian, H., Nowzari-Dalini, A.: On the generation of the P-sequences. Adv. Modeling Optim. 5, 27–38 (2003) 6. Er, M.C.: Efficient generation of k-ary trees in natural order. Comput. J. 35, 306–308 (1992) 7. Korsh, J.F.: A-order generation of k-ary trees with 4k-4 letter alphabet. J. Inform Optim. Sci. 16, 557–567 (1995) 8. Pallo, J.: Generating trees with n nodes and m leaves. Intern J. Comput. Math. 21, 133–144 (1987) 9. Vajnovszki, V., Philips, C.: Optimal algorithm for generating k-ary trees. In: Woodfill, M.C. (ed.) Proc. 12th International conference on computer and applications, pp. 201–204. ISCA, Raleight (1997) 10. Ahrabian, H., Nowzari-Dalini, A.: Parallel generation of trees in A-order. The Computer Journal (2007) 11. Aigner, M.: Enumeration via ballot numbers. Discrete Mathematics (2008) 12. Bergeron, N., Descounens, F., Zabrockia, M.: A filtration of (q,t)-catalan numbers. Advances in Applied Mathematics 44, 16–36 (2010) 13. Huglund, J.: Conjectured statistics for the q; t-Catalan numbers. Advances in Mathematics 175, 319–334 (2003) 14. Zaks, S.: Lexicographic generation of ordered tree. Theor. Comput. Sci. 10, 63–82 (1980)

Wavelet and Hadamard Transforms for Image Retrieval Using Color Models Sanjay N. Talbar1 and Satishkumar L. Varma2 1

Professor and Dean, Department of Electronics & Telecommunication, SSGGS Institute of Engineering &Technology, Nanded 431606, Maharashtra, India 2 Assistant Professor, Department of Information Technology, Don Bosco Institute of Technology, Mumbai 400070, Maharashtra, India [email protected], [email protected]

Abstract. The discrete image transforms are used for energy compaction primarily and so used in image data compression. The level of energy in the image depends on level of colors used. In this paper we use two discrete image transforms namely Discrete Hadamard Transform (DHT) and Discrete Wavelet Transform (DWT). These transforms are applied on two different color models namely HSV and YCbCr separately in a given large standard database with 1000 images formed from 10 different classes taken from the Corel collection. The proposed features are effective and useful for image indexing and retrieval. Keywords: HSV, YCbCr, DHT, DWT, Image Indexing, Image Retrieval.

1 Introduction As more and more digital images are acquired into the internet worked multimediacomputing environment, searching and retrieving of image data based on their information content are essential prior to the development and utilization of an effective image database. So the problem of storage and retrieval needs comparatively more attention. Various systems such as iMATCH [1], IRMOMENT [2] are available today for offline content based image retrieval (CBIR). The image database was represented using a set of image attribute, such as color [3] [4], shape [4] [5] [6], texture [7] and layout [8] also. Image indexing using compressed transforms was dealt by J. Berens, G. D. Finlayson and G. Qiu [9]. It uses the standard transform encoding methods (the Karhunen-Loeve transform, the discrete cosine transform [10] [11]. In this paper, two discrete transforms namely DHT and DWT are applied on HSV and YCbCr color models separately. The image indexing and retrieval system is overviewed in section 2. In section 3, the retrieval performances are shown. Finally, the conclusion and future work are given in section 4.

2 Image Indexing and Retrieval System The system architecture is given in Fig. 1. The YCbCr and HSV color components separately are divided into block sizes of 8*8. The DWT and DHT are applied on V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 336–338, 2010. © Springer-Verlag Berlin Heidelberg 2010

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each of these blocks. The top 2x2 coefficients form the feature vector. The chi-square [5] was used for distance measure and image retrieval.

Fig. 1. Image Indexing and Retrieval System Architecture

3 Experimental Results The 100 images each of size 256*384 and 384*256 are taken from 10 semantic groups: tribesmen, elephants, horses, flowers, foods, greek architecture, buses, dinosaurs, snow clad mountains, and beaches. The system results of retrieved images of Dinosaur and Elephant are shown in Fig. 2. The average precision comparison based on the three sample images using feature vectors are plotted in Fig. 3. The average precision using DWT was 0.63 and it was 0.59 in case of DHT. The DWT performed better than DHT.

(a)

(b)

(c)

(d)

Fig. 2. Retrieval of Dinosaur (a and b) and Elephant (c and d) as query image using DWT (a and c) and DHT (b and d)

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Average Precision

Performance Comparison between DWT & DHT

DWT, average=0.63 DHT, average=0.59

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 0

1

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Fig. 3. Performance Comparison between DWT and DHT

4 Conclusion and Future Work It was observed that the DWT performs better than DHT. The retrieval efficiency of 100% was achieved in one of the 10 semantic groups. The results can be further improved by deploying hybrid transforms and using hybrid colors.

References 1. Talbar, S.N., Varma, S.L.: iMATCH: Image Matching and Retrieval for Digital Image Libraries. In: ICETET, pp. 196–201 (2009) ISBN: 978-0-7695-3884-6 2. Talbar, S.N., Varma, S.L.: IRMOMENT: image indexing and retrieval by combining moments. In: IET Digest 2009, vol. 38 (2009) doi:10.1049/ic.2009.0148 3. Nezamabadi-Pour, H., Saryazdi, S.: Object-Based Image Indexing and Retrieval in DCT Domain using Clustering Techniques, vol. 3 (January 2005) 4. Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7(1), 11–32 (1991) 5. Jain, A.K., Vailaya, A.: Image retrieval using color and shape. Pattern Recognition 29(8), 1233–1244 (1996) 6. Mokhtarian, F., Abbasi, S.: Shape similarity retrieval under affine transforms. Pattern Recognition 35, 31–41 (2002) 7. Manjunath, B.S., Ma, W.Y.: Texture feature for browsing and retrieval of image data. IEEE PAMI 8(18), 837–842 (1996) 8. Smith, J.R., Li, C.S.: Image classification and querying using composite region templates. In: Computer Vision and Understanding, vol. 75, pp. 165–174. Academic Press, London (1999) 9. Berens, J., Finlayson, G.D., Qiu, G.: Image indexing using compressed color Histograms. In: IEEE Proc. Vision Image Signal Processing, vol. 147(4) (August 2000) 10. Lay, J.A., Guan, L.: Image Retrieval Based on Energy Histograms of the Low Frequency DCT Coefficients. IEEE (1999) 0-7803-5041-3/99 11. Obdrzalek, S., Matas, J.: Image Retrieval Using Local Compact DCT-based Representation. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 490–497. Springer, Heidelberg (2003)

A Rough Set Integrated Fuzzy C-Means Algorithm for Color Image Segmentation Byomkesh Mandal and Balaram Bhattacharyya Department of Computer & System Sciences, Visva-Bharati University, Santiniketan-731 235, West Bengal [email protected]

Abstract. A rough set incorporated fuzzy C-means (FCM) algorithm for color image segmentation is introduced. It aims construction of more appropriate clusters in the domain. Dominant peaks in hue (H), saturation (S) and intensity (I) histograms are captured from the input image and all possible combinations of them are taken as initial set of points for processing. Reduction theory of rough set is applied for refinement to the set. The centers thus obtained represent overall pixel colors and hence generate improved clusters when given as input to FCM algorithm. Experiments on several images exhibit effectiveness of the proposed approach. Keywords: Color image segmentation, fuzzy C-means, rough set applications, reduction theory, cluster center formation.

1 Introduction Fuzzy C-means algorithm (FCM) [1], [2], unlike crisp clustering algorithms, allows each data point to belong to several clusters at the same time. This additional flexibility has made it a very useful tool in numerous applications such as statistical data analysis, machine learning, optimization, medical imaging etc. However, similar to other clustering algorithms, it is also heavily influenced by the number of clusters and initial values of their centers. If the initial values of the cluster centers are chosen unsuitably, it is prone to falling into local minima and may not be able to generate acceptable segmentation results [3]. Deciding the number of initial clusters is another challenging task [4]. Starting with a large number of clusters may result in over segmentation, whereas too few clusters can be a cause of under segmentation. Several approaches [5], [6] have been proposed to overcome these limitations. With a fixed number of clusters ab initio or finding an appropriate set by utilizing image information is among those. But the problem remains challenging. The approach proposed in this paper estimates not only the number of clusters to start with, but the positions of their initial centers as well in an effective manner. For these purposes, reduction theory of rough set is applied over the knowledge extracted from hue, saturation and intensity histograms. The knowledge is then conveyed to FCM algorithm to generate final clusters of pixels in the input image. Experiments on several color images establish effectiveness of the proposed approach. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 339–343, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Fuzzy C-Means Algorithm FCM algorithm, one of the most widely used methods in fuzzy clustering, can be summarized as follows. Let, X = {x1, x2, …, xn} is a set of n data points, where each data point xi (i = 1, 2, …, n) is a vector in a real d-dimensional space Rd, Ucn is a set of real c × n matrices and c (2 ≤ c < n) is an integer. Then the classical FCM algorithm aims to minimize the following objective function: n

J FCM (U, V) =

c

∑ ∑ (u

c

ij

) m ( d ij ) 2 , subject to

i=1 j =1

∑u j =1

ij

= 1 and

n

∑u i =1

ij

>0

(1)

Where U (= [uij]) ∈ Ucn, uij is the degree of membership of xi in the jth cluster, V = {v1, v2, …, vc} is a cluster center set, vi ∈ R d and m ∈ [1,∞) , known as fuzzy exponent, is a weighting exponent on each fuzzy membership which indicates the amount of fuzziness in the entire classification process. For simplicity, it is assumed for the rest of the paper that m = 2. dij is the distant norm which indexes the vector distance between the data point xi and the center of the jth cluster, vj. Usually, the Euclidean distance is taken, i.e. d ij = x i − v

j

(2)

Minimization of the cost function JFCM is a nonlinear optimization problem which can be implemented by using the following iterative process: Step 1: Choose appropriate value for c and initialize the cluster center set V randomly. Also select a very small positive number ε and set the step variable t to 0. Step 2: Calculate (at t = 0) or update (at t > 0) the membership matrix U = [uij] by u

( t + 1) ij

1

= c

∑

k=1

⎛ ⎜ ⎜ ⎝

x i− v xi − v

(t ) j (t) k

2 ⎞ ( m − 1) ⎟ ⎟ ⎠

for

j = 1 , 2, ..., c

(3)

Step 3: Update the cluster center set V by n

v

( t + 1) j

∑ =

( u ij( t +1) ) m . x i

for j = 1, 2, ..., c

i= 1 n

∑

(4)

( u ij( t +1) ) m

i= 1

Step 4: Repeat steps 2 and 3 until

|U

( t + 1)

−U

(t )

|≤ ε

(5)

3 Proposed Methodology Rough set theory, proposed by Pawlak [7], is a mathematical tool to analyze vagueness and uncertainty inherent in making decision. It is a formal approximation

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of a set in terms of a pair of sets which give the lower and upper approximation of the original set. Reduction theory, the core of rough sets, is utilized here in developing initial clusters for FCM algorithm. 3.1 Reduction of Initial Cluster Center Set The preliminary set of cluster centers as obtained from H, S and I histograms are inspected through some decision rules from image characteristics and finally reduced by approaches proposed by Shi et al. [8]. Obtaining Preliminary Set of Cluster Centers. First of all, hue (H ∈ [0°, 360°]), saturation (S ∈ [0, 100]) and intensity (I ∈ [0, 255]) histograms for an input image are constructed. Each of the histograms is then inspected and high frequency values (peaks) are chosen for construction of initial values for cluster centers, as those values are promising representatives of overall pixel colors available in the image under study. Suppose m, n and p number of dominant peaks are found in H, S and I channels, respectively. A set K consisting of m × n × p preliminary cluster centers is then constructed taking exactly one value from each. That is, if ωH i (i = 1, 2, ..., m),

ωS j

(j = 1, 2, ..., n) and

ωI k

(k = 1, 2, …, p) are the positions of such peaks in H, S

and I histograms, respectively, K is defined as

K = {(ωH i , ωS j , ωI k )} ; i = 1, 2, ..., m ; j = 1, 2, ..., n; k = 1, 2, ..., p

(6)

Each cluster center is characterized by three features, namely, FH, FS and FI. Now each feature Fx (x = H, S or I) is described in terms of its fuzzy membership values characterized by a π-membership function given by 2 ⎧ ⎛ | Fx − c x | ⎞ ⎟ ⎪ 2 ⎜⎜ 1 − ⎟ λx ⎪ ⎝ ⎠ 2 ⎪ ⎛ | Fx − c x | ⎞ ⎪ ⎟⎟ μ ( F x ) = ⎨ 1 − 2 ⎜⎜ λx ⎠ ⎝ ⎪ 0 ⎪ ⎪ ⎩⎪

λx

for

≤ Fx − c x ≤ λ x

2

for 0 ≤ F x − c x ≤

(7)

λx 2

otherwise

λx is called the radius of the π-membership function with cx as the central point. These values are defined as,

λH =

H max − H min S − Smin I − I min , λS = max and λI = max . 2 2 2 N

N

And

cH =

∑H i =1

N

i

, cS =

∑S i =1

N

i

(8)

N

and c I =

∑I i =1

i

.

(9)

N

Where Hi, Si and Ii (i = 1, 2, …, N) represent the hue, saturation and intensity value of the ith pixel, respectively, N is the total number of pixels and Hmax and Hmin, Smax and Smin, and Imax and Imin are maximum and minimum hue, saturation and intensity values available in the image, respectively.

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Construction of a Decision Table. Definition 1: The degree of similarity, ξ, is defined as ξ =

μ ( F H ) + μ ( FS ) + μ ( F I )

.

(10)

3

Lesser the difference in degree of similarity, the closer the two cluster centers is. Definition 2: If two clusters have similarity in cluster center values they are treated as redundant to each other. They are merged into one. Assertion 1: If two cluster centers A and B are redundant to each other and B and C are redundant to each other, they belongs to the same cluster center, i.e.,

A ↔ B, B ↔ C ⇒ A ↔ B ↔ C

.

(11)

Now considering the set of initial cluster centers (K) as object, features (Fx), central points (cx) and radii (λx) as conditional attributes and similarity metric (ξ) as decision attribute, a decision table for initial cluster center set can be constituted as

T = < K, P ∪ R, C, D >

(12)

Here P is a set of condition attributes, R is a set of decision attributes, C = {pH, pS, pI} (where px is a domain of the initial cluster center category attribute) and D : K × P ∪ R → C is the redundant information mapping function, which defines an indiscernability relation on K. Elimination of Redundant Cluster Centers from the Preliminary Set. Assuming D(x) denotes a decision rule, D(x)|P(condition) and D(x)|R(decision) denote the restriction D(x) to P and R, respectively, i and j denote two different cluster centers and keeping other assumptions as the same as mentioned earlier, reduction of the initial cluster center set can be achieved through the following steps: Step 1: If D(i)|P(condition) = D(j)|P(condition) and D(i)|R(decision) = D(j)|R(decision), then the decision rule is compatible. If D(i)|P(condition) = D(j)|P(condition), but D(i)|R(decision) ≠ D(j)|R(decision), then the decision rule is incompatible. Step 2: Let p ∈ P. Now, if the decision rule is compatible and IND(P) = IND(P-p), then p is a redundant attribute and can be omitted. If the decision rule is not compatible p can be left out only when POS(P, R) = POS(P-p, D). Step 3: For each condition attribute p, the above process is repeated until the set of condition attribute remains unchanged. The reduced set of cluster centers is taken as initial input to FCM algorithm. After convergence of FCM clustering, each pixel will be associated to each cluster with a membership value. Segmentation of the image can be achieved by assigning a pixel to the cluster with the highest degree of membership.

4 Experimental Results The proposed method of segmentation is applied to a set of color images widely used in segmentation. Results presented in Fig. 1 exhibit better quality of segmentation.

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Fig. 1. Results of segmentation using FCM algorithm. 1st row: original color images; 2nd row: before applying reduction theory; 3rd row: after applying reduction theory.

5 Conclusions A rough set integrated FCM algorithm for color image segmentation is proposed. The result of FCM, to a large extent, is dependent upon the number of clusters to start with and initial positions of their centers. These pitfalls are resolved in a convenient and efficient manner. First of all, a preliminary set of cluster centers is obtained with the help of the peaks in H, S and I histograms. Refinement to the set is done using reduction theory of rough set. The updated set is finally taken as input to FCM algorithm. Experimental results demonstrate the efficiency of the proposed method. In addition, it makes FCM to converge faster. The method is thus effective both in computation as well as in generating appropriate clusters for images under study.

References 1. Bezdek, J.C.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic, Boston (1999) 2. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999) 3. Alsultan, K.S., Selim, S.: A Global Algorithm for the Fuzzy Clustering Problem. Pattern Recognition 26(9), 1357–1361 (1993) 4. Xu, R., Wunsch, D.: Survey on Clustering Algorithms. IEEE Transactions on Neural Networks 16, 645–678 (2005) 5. Dutta, S., Chaudhuri, B.B.: Homogenous Region Based Color Image Segmentation. In: WCECS 2009, vol. II (2009) 6. Sowmya, B., Sheelarani, B.: Colour Image Segmentation Using Soft Computing Techniques. International Journal of Soft Computing Applications 4, 69–80 (2009) 7. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1992) 8. Shi, Z., et al.: Rough Set Based FCM Algorithm for Image Segmentation. International Journal of Computational Science 1(1), 58–68 (2007)

Local Monitoring based Reputation System with Alert to Mitigate the Misbehaving Nodes in Mobile Ad Hoc Networks K. Gopalakrishnan and V. Rhymend Uthariaraj Ramanujan Computing Centre, College of Engineering, Guindy, Anna University Chennai, Chennai – 600 025, Tamil Nadu, India [email protected], [email protected]

Abstract. The researchers have proposed several local monitoring based reputation mechanisms to identify and isolate the misbehaving nodes in Mobile Ad hoc Networks. The simulation results of these mechanisms shows a considerable amount of improvement in overall network throughput but at the expense of false detection of a good node as a misbehaving one by the monitoring node due to lack of alerting the source of the packet about misbehaving link. So there exists a need for designing a new mechanism to minimize such false detections. This paper proposes a Local Monitoring based Reputation System with Alert [LMRSA] mechanism to minimize the effect of false detections without compromising the overall network performance. The simulation results were compared with the existing model that lacks the forwarding traffic rejection and alert mechanism. Keywords: Routing Security, Ad hoc Networks, Local Monitoring, Reputation System, Misbehaving Nodes.

1 Introduction Mobile Ad hoc Networks (MANETs) is a collection of mobile nodes which forms a self-organized network in order to communicate with each other without the need of a predefined infrastructure. The lack of infrastructure makes each mobile node in the network to function as a router for performing network functions. All the mobile nodes in the ad hoc network should cooperate with each other in order to discover and maintain routes between the nodes. The transmission range of each mobile node is limited so that distant node needs a multi-hop communication for their transmissions. The network performance is highly dependent on the collaboration of all participating nodes in the ad hoc network and the misbehaving nodes came into existence in a network due to scarcely available resources of mobile nodes such as battery power and computational resources [4], [11]. The presence of misbehaving nodes in the network makes the routing task difficult by not cooperating with the nodes [11]. The rest of the paper is organized as follows. In section 2 the related works are described. The section 3 describes about the proposed work. The simulation study and the results are discussed in section 4 and section 5 respectively. Finally, section 6 concludes the work and discusses about the future work. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 344–349, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Related Works Marti et al. [4] proposed a scheme which contains two components namely watchdog and path rater in each and every node to detect and mitigate the routing misbehavior in ad hoc network. The watchdog is used to identify misbehaving nodes and Path rater helps the routing protocol to avoid these nodes. Buchegger et al. [6] proposed a protocol called CONFIDANT (Cooperation Of Nodes: Fairness In Dynamic Ad-hoc NeTwork) to detect and isolate misbehaving nodes. Every node in this scheme have four components a monitor for observation, a trust manager to control trust, a reputation system for maintaining reputation records and a path manager to maintain routes according to reputation. These components interact with each other to provide and process protocol information. The reputation value of a node is calculated based on direct observation and trusted second-hand reputation messages. Michiardi et al. [7] proposed a mechanism called CORE (COllaborative REputation mechanism) to enforce node cooperation in MANETs. This scheme uses a collaborative monitoring technique and a reputation mechanism. The reputation is a measure of a nodes contribution to network operations. Bansal et al. [8] proposed a reputation mechanism termed as OCEAN (Observation-based Cooperation Enforcement in Ad hoc Networks) based on direct observation experienced by a node from its neighbors. All the traffic from a misbehaving node is rejected and considered it to be useful again after some time out period. Pirzada et al. [9] proposed a novel scheme for establishing and sustaining trustworthy routes in the network. Each node maintains trust levels for its immediate neighbors based upon their current actions. The nodes also share these trust levels (reputations) to get ancillary information about other nodes in the network. Zouridaki et al. [10] proposed a scheme called E-Hermes in which each node determines the trustworthiness of the other nodes with respect to reliable packet forwarding by combining first-hand trust information obtained independently of other nodes and second-hand trust information obtained via recommendations from other nodes.

3 Local Monitoring based Reputation System with Alert The LMRSA consists of three components namely a monitor responsible for detecting the misbehaving nodes, reputation system for maintaining the reputation value of the next hop nodes and a path manager for maintaining routes without containing misbehaving nodes in it. The source and the intermediate nodes except the previous hop of the destination have to monitor its next hop transmissions to detect the anomaly and the trust value of the next hop nodes are incremented or decremented based on its behavior. The initial trust value of all the nodes in the network is set to 0. Once a nodes reputation value reaches the Negative Threshold limit then it has to be added into the faulty list and all the traffic to and from the misbehaving node has been rejected. If an intermediate node detects the misbehaving node then an explicit route

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error packet has been generated and sent to the source of the packet by reversing the path to inform about the misbehaving link. Once a node receives an explicit route error packet then it will prune all the routes from both the primary and secondary cache which have the misbehaving link in it. The nodes in the faulty list are reintroduced after 100s; its trust value has been reduced by half for considering it as a useful node. The reason for reducing the trust value of the reintroduced node by half rather than reset to 0 is to make the detection fast if it continues to misbehave. The procedure for packet monitoring and evaluation of trust carried out by each node is shown in Fig.1. When a node receives a packet it checks the transmitting node trust value, if it is not equal to Negative Threshold the packet is considered for further processing else the procedure ends. Receive a packet Abbreviations Tx – Transmitting Node Rx – Receiving Hop RREQ – Route Request RERR – Route Error NT – Negative Threshold

Is Tx Trust Value!=NT?

Y Y

Y

N

Overhearing the RERR from receiving hop?

Y

N

N

Am I the Destination?

N

Rx Trust--

Y

Accept the Packet

Rx Trust++

Do Nothing

N

Is the packet Modified/ Spoofed?

Y

Overhearing the same packet from receiving hop?

Y Y

N

N Is the packet sent for me ?

Y

Is Rx Trust Value!=NT?

Y

Sent the packet to the next hop

Is the packet a RREQ?

N

Is receiving hop the Destination?

N

Fig. 1. Packet Monitoring and Trust Evaluation of LMRSA

Further it checks, whether it is a destination or the forwarding node of the packet, if so it will process the packet else the procedure ends. The packet has been accepted, if it is a destination node else it checks the trust value of the receiving hop. If trust value of the receiving hop is not equal to Negative Threshold then the packet is forwarded to the next hop else the procedure ends. It also further checks, whether the forwarded packet is a RREQ packet, if so the procedure ends else it checks the receiving hop is a destination or not. If it is a destination then the procedure ends else it promiscuously listens to overhear the packet transmission of the receiving hop. If it overhears the packet then it checks whether the packet is modified or spoofed, if so the trust value of receiving hop is decremented by 4 or else incremented by 1. On the other hand, if it does not overhear the packet then it waits for the route error packet. If the route error packet is not overheard within the packet timeout period then the trust value of the receiving node is decremented by 2 else the procedure ends. The RREQ packet is not monitored since it can be modified due to network functions and might also be dropped to avoid congestion in the network [9].

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4 Simulation Study The proposed system was implemented in ns2.34 as add on to the Dynamic Source Routing (DSR) [2] protocol and used two different mobility models to mimic the real world movement of the mobile nodes. The first one is a Random Way Point (RWP) mobility model based on Entity (E) mobility model in which the mobile nodes movements are independent of each other. The other one is a Reference Point Group Mobility (RPGM) Model based on Group (G) mobility model in which the mobile nodes move as a group. The RWP is based on CMU Monarch v2 implementation and RPGM based on [5]. There exists multiple group of mobile nodes and each group work towards different goal but there exists communication between groups [1], [3] so the group mobility model utilizes both inter and intra group CBR traffic patterns. The simulation parameters are shown in Table 1. Each node is assigned an initial value of energy (E) by using an uniform distribution function (Ei – 3J,Ei + 3J) where the energy is expressed in Joules (J) and the initial energy Ei = 500J. The transmission range of each node is 250 meters with the data rate of 8.0 kbps and the group mobility scenario was simulated using 5 groups and each consists of 10 nodes. Table 1. Simulation Parameters

Parameter Simulation Area Simulation Time Number of Nodes Speed Pause Time Positive Threshold (Reputation Value) Negative Threshold (Reputation Value)

Value 900 m x 900 m 900 s 50 10 m/s 30 s 40 -40

The simulation introduced five different kinds of misbehaving nodes to evaluate the LMRSA which includes three different kinds of packet droppers [11] along with the packet modification and identity spoofing. The LMRSA performance was measured by introducing 15 traffic sources with respect to packet delivery fraction, false detection of a good node as a misbehaving one and malicious drop which includes packet dropping, modifying and spoofing. This paper calculates a 95% confidence interval for the unknown mean and plots the confidence intervals on the figures.

5 Results and Discussions The packet delivery fraction of LMRSA has been improved by 17-40% and 2-11%, malicious drop was reduced by 44-68% and 31-48% and the false detection has been reduced by 38-50% and 18-22% with respect to entity and group mobility scenario when compared to WithOut Alert mechanism (WOA) as shown in Fig. 2, Fig. 3 and Fig. 4 respectively. The Defense Less (DL) scenario results were also shown in graph.

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Fig. 2. Packet Delivery Fraction in %

Fig. 3. Malicious Drop in Packets

Fig. 4. False Detection in %

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6 Conclusion and Future Work The proposed system shows that the false detections and malicious drop were reduced by alerting the source about the misbehaving link in a timely manner. It increases the overall network throughput when compared to existing mechanism that lacks in alerting the source about the misbehaving link. In future work, the neighborhood monitoring mechanism will be used to mitigate the colluding misbehaving nodes.

Acknowledgement The first author would like to acknowledge the support received from CSIR-HRDG (Council of Scientific and Industrial Research-Human Resource Development Group), India, through Senior Research Fellowship.

References 1. Hong, X., Gerla, M., Pei, G., Chiang, C.: A Group Mobility Model for Ad Hoc Wireless Networks. In: 2nd ACM International Workshop on Modeling and Simulation of Wireless and Mobile Systems, pp. 53–60. ACM, Seattle (1999) 2. Johnson, D.B., Maltz, D.A., Broch, J.: The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks. Internet Draft, The Internet Engineering Task Force (1999) 3. Liang, B., Haas, Z.: Predictive Distance-Based Mobility Management for PCS Networks. In: 18th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1377–1384. IEEE Computer Society, New York (1999) 4. Marti, S., Giuli, T.J., Lai, K., Baker, M.: Mitigating Routing Misbehavior in Mobile Ad Hoc Networks. In: 6th International Conference on Mobile Computing and Networking, pp. 255–265. ACM, Boston (2000) 5. Camp, T., Boleng, J., Davies, V.: A Survey of Mobility Models for Ad Hoc Network Research. J. Wireless Communication and Mobile Computing 2, 483–502 (2002) 6. Buchegger, S., Boudec, J.Y.L.: Performance Analysis of the CONFIDANT Protocol (Cooperation Of Nodes: Fairness In Dynamic Ad-hoc NeTworks). In: IEEE/ACM Symposium on Mobile Ad Hoc Networking and Computing, pp. 226–236. ACM, Lausanne (2002) 7. Michiardi, P., Molva, R.: CORE: A COllaborative Reputation Mechanism to enforce node cooperation in Mobile Ad hoc Networks. In: 6th Joint Working Conference on Communications and Multimedia Security, vol. 228, pp. 107–121. Kluwer, Portoroz (2002) 8. Bansal, S., Baker, M.: Observation-based Cooperation Enforcement in Ad hoc Networks. Technical Report, Stanford University (2003) 9. Pirzada, A.A., Datta, A., McDonald, C.: Incorporating trust and reputation in the DSR protocol for dependable routing. J. Computer Communications 29, 2806–2821 (2006) 10. Zouridaki, C., Mark, B.L., Hejmo, M., Thomas, R.K.: E-Hermes: A robust cooperative trust establishment scheme for mobile ad hoc networks. J. Ad Hoc Networks 7, 1156–1168 (2009) 11. Gopalakrishnan, K., Rhymend Uthariaraj, V.: Scenario based Evaluation of the Impact of Misbehaving Nodes in Mobile Ad Hoc Networks. In: 1st IEEE International Conference on Advanced Computing, pp. 45–50. IEEE Computer Society, Chennai (2009)

An Energy Efficient Cluster Based Broadcast Protocol for Mobile Ad Hoc Networks G. Kalpana1 and M. Punithavalli2 1 Lecturer, Department of Computer Science Sri Ramakrishna College of Arts and Science for women Coimbatore-44, Bharathiar University [email protected] 2 Director & HOD, Department of Computer Science Sri Ramakrishna College of Arts and Science for women Coimbatore-44, Bharathiar University

Abstract. Due to various reasons, the cluster based architecture has been rarely used for efficient broadcasting. In this paper, we propose to develop an Energy Efficient Broadcasting protocol using cluster based approach for the MANETs. Our protocol uses a target radius and a locally defined connected graph, to preserve the connectivity. In our algorithm, the broadcasting nodes select a subset of their neighbors using an efficient clustering technique, to forward the message using an efficient forward node selection mechanism. By simulation results, we show that our proposed protocol attains good delivery ratio with reduced delay, energy and overhead. Keywords: Mobile Adhoc Networks, Broadcasting, Energy Efficient Broadcast, Cluster Based Broadcast, Forward Node Selection.

1 Introduction 1.1 Mobile Adhoc Networks Mobile Adhoc Networks (MANETs) includes a collection of mobile nodes. Without employing any infrastructure or administrative support, the mobile nodes dynamically establish a wireless networks among them. Ad hoc wireless networks are selfcreating, self-organizing, and self-administering. In previous days, military, police, and rescue agencies particularly under disorganized or hostile environments, as well as isolated scenes of natural disaster and armed conflict, use adhoc networks due to the apparent advantages of the adhoc networks. [1]. 1.2 Broadcasting Issues in MANET In wireless adhoc network, broadcasting is a vital operation where a message from a given source must reach all other nodes. Each node has local broadcast capability. With a particular amount of transmitted energy, each node can transmit a message to V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 350–355, 2010. © Springer-Verlag Berlin Heidelberg 2010

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reach all nodes [2]. Even during the broadcast process, the network topology changes over time. Based on “Hello” intervals, the local 1-hop information is constructed. It is difficult to ensure consistent local or global views among nodes, since the nodes start their intervals asynchronously [3]. When there are mobile nodes, the k-hop collection process information acquires delay even for small k in localized solutions which may not reflect the current network topology. .The problems such as exposed terminal problem and hidden terminal problem are created in broadcasting [4]. The existing energy efficient protocols assume that the nodes have a fixed transmission range. A node can adjust its range to transmit a message to reach one or more nodes with a power control. To increase the life of power limited wireless adhoc networks, energy efficient communication is necessary. Therefore, a significant interest in minimum energy broadcast is considered [2]. Delivering messages from a source node to all nodes of network is guaranteed by the cluster broadcasting algorithm for mobile ad hoc networks. The existing cluster approaches are proposed to reduce the difficulty of the broadcasting storm problem [5]. However to the best of our knowledge, for various reasons, the cluster based architecture has been rarely used for efficient broadcasting [6]. In this paper, we propose to develop an Energy Efficient Cluster Based Broadcasting Protocol for the MANETs.

2 Related Work Jie Wu et al [7] have proposed a clustered network model in which each node is a clusterhead in the clustered architecture. Clusterheads are connected by selecting nonclusterhead nodes locally at each clusterhead to connect clusterheads within 2.5 hops. Information of neighbor clusterheads is piggybacked with the broadcast packet to further reduce each forward node set. Petra Berenbrink et al [8] have presented a broadcasting algorithm for random networks, where every node transmits at most once.. For general networks with known diameter D , they have presented a randomized broadcasting algorithm with optimal broadcasting time O( D log(n / D ) + log 2 n) that uses an expected number of O(log 2 n / log(n / D ))

transmissions per node. Subhas Kumar Ghosh et al [9] have presented algorithms for Minimum Energy Consumption Broadcast Subgraph (MECBS) problem. First, he focused on designing distributed algorithms for MECBS. Second, he presented an improved approximation algorithm for the MECBS problem with arbitrary asymmetric power requirement having performance ratio 1:5 (ln(n − 1) + 1) .

3 System Model and Protocol Overview Our protocol uses a target radius and a locally defined connected graph, to preserve the connectivity. In our algorithm, the broadcasting nodes select a subset of their neighbors using an efficient clustering technique, to forward the message using an efficient forward node selection mechanism.

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3.1 Topology Adaptation

Let G = (V , E ) be a connected graph. To preserve the network connectivity while modifying the transmission range of nodes, we compute a subgraph G1 = (V , E1) which has to be connected, sparse, bidirectional, and computed locally. From G , G1 , and T , we compute a range assignment r (x) for a node x defined by: ∀ x ∈ V, r(x) = max{d(x, y) | y ∈ V such that (d ( x, y ) ≤ T ∀( x, y ) ∈ E1)} In order to preserve connectivity, each node chooses a range that covers all its neighbors which belongs to G1 and also the nodes which are closer than T which belongs to G . This graph is denoted by Ga = (V , Ea ) . The radius which is required for preserving the connectivity can be further reduced by the removal of directional links. We denote this graph by Gb = (V , Eb ) and it is defined by: Eb = Ea I REa

Where REa = {( x, y ) ∈ V 2 | ( x, y ) ∈ Ea . Edges that belong to the set E a are directed, RE a contains the same set of reversed edges. After this step, each node has a radius as close as possible to T with a connectivity preserved, of course as long as the original graph was itself connected.

4 Cluster Formation Phase The source and a subset of nodes form a flood tree in a broadcast process. So any other node in the network is adjacent to a node in the tree. The nodes on the tree are called as forward nodes and form a connected dominating set (CDS).The 2-hop coverage method is used to define a clusterhead v’s neighbor clusterhead set NCH (v) which forms a CDS of the network. In 2-hop coverage, each cluster head covers all clusterheads in 2 hops and some clusterheads that are 3 hops away. That is, NCH (v) includes all cluster heads in N 2 (v) and the clusterheads that have nonclusterhead members in N 2 (v) . To gather neighbor cluster information, each node exchanges information with its neighbors. Each node needs only two sends after the formation of the cluster. Under the 2-hop coverage method, since each 2-hop neighbor belongs to only one cluster and it knows its clusterhead after the cluster formation process, these two sends are equivalent to the 2-hop neighborhood information gathering process in terms of the size of each packet (one with O(Δ ) and the other with O(Δ2 ) ). Cluster head v’s forward node set is a subset of gateways by which v can connect to the cluster heads in NCH (v) . The forward node set of v is computed on-demand to cover all the cluster heads in NCH (v) . Notice that since the 2-hop coverage generate

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different NCH (v) s , the corresponding forward node sets are also different. We use a greedy algorithm to determine the forward node set at each node.

5 Forward Node Selection (FNS) 5.1 Forwarding Node Set Selection Algorithm

In this algorithm, the node v selects its forwarding node set F from all candidate neighbors X to cover its uncovered 2-hop neighbors Y with a simple greedy algorithm. This Forwarding Node Set Selection (FNSS) algorithm is described below: Algorithm: 1

1.

2.

Initially, we have X = NH (v) = N (v) − {v} Y = NH 2 (v) = N 2 (v) − N (V ) F = NULL For each n in X , find Wn = BLn + D n

3.

Let Z = Sort ( X )

4.

where Sort ( X ) is the sorted set X in decreasing order of Wn . Find n in Z with the maximum effective neighbor degree such that deg e (n) = | N (n) I Y | |

5.

6.

F = F U {n} , Y = Y − N (n) and X = X − {n} . Repeat steps 2 and 3 until Y = NULL

Here N (v) and N 2 (v) are the one-hop and 2-hop neighbors of v . Y can be

6 Broadcasting Process For a node u, the sets of dominant neighbors N D (u ) and non-dominant neighbors associated with it N D1(u ) are defined by: ⎧ N D ( x) = { y | y ∈ D ∧ ( x, y ) ∈ Eb } ∀x ∈ ∨ ⎨ ⎩ N D1( x) = { y | y ∈ D1 ∧ x = p ( y ) ∧ ( x, y ) ∈ Eb Each node should send a message to its neighbors about its dominating set status in order to determine neighbors in the graph and properly select the target radius. The broadcasting algorithm proceeds as follows:

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• • •

•

A dominant node u that wishes to launch the broadcasting emits its message with the minimal range that covers N D ( x) and N D1( x) . A non-dominant node that wishes to launch a broadcasting emits its message to its nearest (associated) dominant neighbor. A dominant node u that receives the message and rebroadcasts it with the range which allows covering non-covered nodes in N D1( x) and N D1( x) .It does not take into account neighbors that have been covered (according to the knowledge of the node, extracted from messages previously received) when it received the message. A non-dominant node that receives the message never relays it.

7 Simulation Results We use NS2 [10] to simulate our proposed algorithm. In our experiment we compare our proposed EECBB protocol with the MECBS protocol [9] Nodes Vs Energy

Energy(J)

4 3 EECBB

2

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1 0 25

50

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Fig. 1. Nodes Vs Energy

Nodes Vs Delivery Ratio

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Fig. 2. Nodes Vs Delivery Ratio

We vary the number of nodes as 25, 50, 75 and 100. From the above figures we can observe that our proposed EECBB is better than the MECBS protocol.

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8 Conclusion In this paper, we propose to develop an Energy Efficient Broadcasting protocol using cluster based approach for the MANETs. Our protocol uses a target radius and a locally defined connected graph, to preserve the connectivity. In this protocol, using an efficient clustering technique, the broadcasting nodes select a subset of neighbors. Then the forward nodes are selected to relay the message using an efficient forward node selection (FNS) algorithm. The forward node selection algorithm first sorts the nodes by their weights in decreasing order. Now the broadcasting is performed over this new topology. Nodes in selected forward nodes remain active. If all nodes remain active then nodes not selected in dominating set do not retransmit, but impact the decisions of nodes from selected forward node. By simulation results, we have shown that our proposed protocol attains good delivery ratio with reduced delay, energy and overhead.

References 1. Ramesh, B., Manjula, D.: An Adaptive Congestion Control Mechanism for Streaming Multimedia in Mobile Ad-hoc Networks. IJCSNS International Journal of Computer Science and Network Security (2007) 2. Bian, F., Goel, A., Raghavendra, C.S., Li, X.: Energy-Efficient Broadcasting In Wireless Ad Hoc Networks: Lower Bounds and Algorithms. Journal of Interconnection Networks (2002) 3. Wu, J., Dai, F.: Mobility Management and Its Applications in Efficient Broadcasting in Mobile Ad Hoc Networks. In: INFOCOM (2004) 4. Lou, W., Wu, J.: Toward Broadcast Reliability in Mobile Ad Hoc Networks with Double Coverage. IEEE Transactions on Mobile Computing (2007) 5. Karthikeyan, N., Palanisamy, V., Duraiswamy, K.: Reducing Broadcast Overhead Using Clustering Based Broadcast Mechanism in Mobile Ad Hoc Network. Journal of Computer Science (2009) 6. Yi, Y., Gerla, M., Kwon, T.-J.: Efficient Flooding in Ad Hoc Networks Using on-Demand (Passive) Cluster Formation. Med-hoc-Net (2003) 7. Wu, J., Lou, W.: Forward-Node-Set-Based Broadcast in Clustered Mobile Ad Hoc Networks. In: Proceedings of the InterScience in Wireless Communications and Mobile Computing (2003) 8. Berenbrink, P., Cooper, C., Hu, Z.: Energy Efficient Randomized Communication in Unknown AdHoc Networks. Theoretical Computer Science (2009) 9. Ghosh, S.K.: Energy Efficient Broadcast in Distributed Ad-Hoc Wireless Networks. In: IEEE International Conference on Computational Science and Engineering (2008) 10. Network Simulator, http://www.isi.edu/nsnam/ns

A Hybridized Graph Mining Approach Sadhana Priyadarshini and Debahuti Mishra Institute of Technical Education and Research, Siksha O Anusandhan University, Bhubaneswar, Odisha, India [email protected], [email protected]

Abstract. Data mining analysis methods are increasingly being applied to data sets derived from science and engineering domains which represent various physical phenomena and objects. In many of data sets, a key requirement of effective analysis is the ability to capture the relational and geometric characteristics of the underlying entities and their relationships with vertices and edges, which provide a natural method to represent such data sets.In Apriori-based graph mining, to determine candidate sub graphs from a huge number of generated adjacency matrices, where the dominating factor is, the overall graph mining performance because it requires to perform many graph isomorphism test . The pattern-growth approach is more flexible for the expansion of an existing graph. Keywords: Graph mining, Pattern discovery, frequent subgraph.

1 Introduction Informally, a graph is a set of nodes, and a set of edges connecting some node pairs. In database terminology, the nodes represent individual entities, while the edges represent relationships between these entities. In Apriori-based frequent substructure mining algorithms shares similar characteristics with Apriori-based frequent item set mining algorithm. Searching is more flexible in pattern-growth approach. Breadthfirst search (BFS) as well as Depth-first search (DFS) can be used in this case.

2 Related Work For frequent patterns in this new graph model which we call taxonomy-superimposed graphs, there may be many patterns that are implied by the generalization/specialization hierarchy of the associated node label taxonomy[1].The gSpan, a computationally efficient algorithm is used for finding frequent patterns corresponding to geometric sub graphs in a large collection of geometric graphs[2].Graph Databases are able to represent as graphs of any kind of information, where naturally accommodate changes in data can be possible [3].We present an approach to implement a graph transformation engine based on standard relational database management systems V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 356–361, 2010. © Springer-Verlag Berlin Heidelberg 2010

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(RDBMS)[4,5].Within a graph model, one way of formulating the frequent pattern discovery problem is that of discovering sub graphs that occur frequently over the entire set of graphs. The gIndex, makes use of frequent substructure as the basic indexing feature. To reduce the size of index structure, two techniques, size-increasing support constraint and discriminative fragments, are introduced[6]. In Apriori-based graph mining, to determine candidate sub graphs from a huge number of generated adjacency matrices is usually the dominating factor for the overall graph mining performance as it requires to perform many graph isomorphism tests [7,8]. First understands what patterns are common in real-world graphs and then considered as a mark of normality/realism. The main contributions is the integration of points, view from physics, mathematics, sociology, and computer science.[9,10]

3 Goal of the Paper In this paper our intension is for faster data retrieval by transformation of Relational Database to Graph Database. As data, present in graphical format, we need graph mining technique to retrieve it efficiently. After getting data set in Graph Database, either Apriori-based approach or gSpan approach can be used to retrieve data.

4 Graph Database We developed the equivalent of a database system for graphs .In order to define the GDB we need to specify: Data Definition Language (DDL), Query Language (generally, Data Manipulation Language - DML), Informal Semantics of the DDL and DML languages.[3,8].

5 Apriori-Based Approach The search for frequent graph starts with graphs of small “size” and proceeds in a bottom-up manner by generating candidates having an extra vertex, edge, or path.[5]. BFS strategy can be used in Apriori-based approach because of its level-wise candidate generation. Two size-k frequent graphs are joined only if they have the same size-(k-1) subgraph[5,7]. Algorithm (AGM) Input : Output : Methode:

D, a graph data set ;min_sup, the minimum support threshold. GKthe frequent substructure set. Intialize G1 by single-element frequent in data set; Call AprioriGraph(G,min_sup,G1); Procedure Apriori graph (G, min_sup, Gk) 1. Initilalize Gk+1 by φ ; 2. for each frequent gi is element of Gk do 3. for each frequent gj is element of Gk do 4. for each size ( k+1) graph g formed by merge of gi and gj, do

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if g is frequent in D and g is not element of Gk+1 then insert g into GK+1 if G k+1 ia not equal to φ then AprioriGraph(G,min_sup,Gk+1); return;

6 gSpan Approach Searching is more flexible in pattern-growth approach. It can use BFS as well as (DFS), the latter consumes less memory [2]. Pattern Growth Graph gets rid of duplicate graphs, which reduce the workload [5].It need not search previously discovered frequent graphs for duplicate detection or not extend any duplicate graph, yet still guarantee the discovery of the complete set of frequent graphs[11].The gSpan algorithm introduces a more sophisticated extension method, which restricts the extension as follows: Given a graph G and a DFS tree T in G, a new edge e can be added between right-most vertex and another vertex on the right-most path (backward extension) ; or it can introduce a new vertex on right-most path (forward extension).We call them right-most extension ,denoted by G ◊ re[9-11]. Algorithm Input:s, a DFS code;D, a graph data set;min_sup,the minimum support threshold; Output: The frequent graph set, S. Method: Initialize S to φ ; Call gspan(s,D,min_sup,S); Procedure: Pattern Growth Graph(s, D,min_sup, S) 1. if s is not element of DFS code then 2. return; 3. insert s into S; 4. set C to φ; 5. scan D once,find all edges e such that s can be right-most extended to s ◊ re; 6. insert s ◊ re into C and count its frequency; 7. sort C in DFS lexicacographic order; 8. for each frequence to s ◊ re in C do 9. sSpan( s ◊ re, D, min_sup, S); 10. return;

7 Experimental Evaluation and Result Initially an original database consists of three relations as shown in figure 1, then converted into equivalent GDB shown in figure-2[4.8]. Then to get required graphs .in figure 3, figure 4 and figure 5 we used Apriori and gSpan algorithm.

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Step 1 : Conversion of RDBMS into GDB EMP ENo

Name

Address

Dept_ no

123 234 345

John James Jennifier

123,Houston,TX 453,Voss,TX 3321,Berry,TX

5 4 3

DEPT D_No 5 3 4

Supe r_mgr 101 122 143

Salary 30000 43560 50000

DEPT_LOCATION D_Name Research Administration Headquarter

Mgr_ Emp_No 166 234 678

Mgr_Start _Date 10-12-2008 23-04-2009 10-01-2010

D_No

D_Location

2 1

Houston Sugarland Stafford

Fig. 1. The original database of three tables (EMP, DEPT, DEPT_LOCATION)

Fig. 2. Mapping of the above database into graph database[3,4,7]

Step-2: Finding substructure satisfying minimum support in large graph database.

(subgraph-1)

(subgraph-2) (Newly generated graphs)

Fig. 3. Two size-5 frequent graphs are joined produce size-6 sub graph

Step 3: Merging of two sub graphs into one graph Then using the AprioriGraph algorithm, the newly generated graph of 6-vertices and the generated adjacency matrices, from above adjacency matrices of two sub graphs is given below[7].

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Fig. 4. Adjacency matrices for above graph

Step 4(a): Extending sub graph into one large graph by backward extension Then using the gSpan algorithm, the newly generated forward extended graph and the corresponding adjacency matrices from above graphs is given below

. (Original Graph)

(These are two backward extended graphs)

Fig. 5. (a) Extending graph by backward extension

The corresponding adjacency matrix is

Step 4(b): Extending sub graph into one large graph by forward extension.

(Original Graph) (These are four forward extended graphs) Fig. 5. (b) Extending graph by forward extension

Similarly, we can get the adjacent matrix for above forward extended graph.

8 Conclusion In this paper, we proposed to use the graph database, Apriori & gSpan graph mining method for large graph database or database of graph. By using Oracle 9i we stored the initial database in relational model. Then using JDBC connectivity, we mapped into Graph database .All the Apriori and gSpan algorithms are implemented in C++.

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References 1. Cakmak, A., Ozsoyoglu, G.: Taxonomy-superimposed graph mining. In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology. ACM International Conference Proceeding Series, vol. 261, pp. 217–228 (2008) 2. Kuramochi, M., Karypis, G.: Discovering frequent geometric subgraphs in Science Direct. Information System 32, 1101–1120 (2007) 3. Silvescu, A., Caragea, D.: Anna Atramentov Graph Databases Artificial Intelligence Research Laboratory, Department of Computer Science Iowa State University Ames, Iowa 50011 (2007) 4. Varró, G., Friedl, K., Varr, D.: Graph Transformation in Relational Databases. Electronic Notes in Theoretical Computer Science 127, 167–180 (2005) 5. Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent sub graphs. IEEE Trans. Knowl. Data Eng. 16(9), 1038–1051 (2004) 6. Yan, X., Yu, P.S., Han, J.: Graph Indexing: A Frequent Structure -based Approach. In: SIGMOD 2004, Paris, France, June 13-18 (2004) (Copyright 2004 ACM 1-58113-8598/04/0(2006)) 7. Nguyen, P.C., Washio, T., Ohara, K., Motoda, H.: Using a Hash-Based Method for Apriori-Based Graph Mining. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 349–361. Springer, Heidelberg (2004) 8. Cook, D.J., Holder, L.B.: Graph-Based Data Mining. IEEE Intelligent Systems 15(2) (2000) 9. Wang, Y., Chakrabarti, D., Wang, C., Faloutson, C.: Epidemic spreading in real networks: An eigenvalue viewpoint. In: Symposium on Reliable Distributed Systems, pp. 25–34. IEEE Computer Society Press, Los Alamitos (2003) 10. Van Dongen, S.M.: Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht (2008) 11. Tyler, Wilkinson, J.R., Hubernan, D.M.: Email as Spectroscopy: Automated Discovery of Community Structure Within Organizations. Kluwer, The Netherlands (2003)

Dynamic Contract Generation and Monitoring for B2B Applications with Composite Services Kanchana Rajaram1 and S. Usha Kiruthika2 1

Assistant Professor, 2 M. E. Student Department of Computer Science and Engineering, SSN College of Engineering, Anna University, Kalavakkam, Chennai – 603 110, India [email protected], [email protected] 1,2

Abstract. The Service Level Agreements (SLA) are e-Contracts that need to be established among business partners and monitored to ensure that web services comply with the agreed Quality of Service (QoS) values. Existing approaches deal with automated contract generation for simple Web Services. Many business enterprises implement their core business service, while outsourcing other application services. When a single service cannot satisfy the user requirements, multiple Web Services must be composed which can together fulfill the request. Therefore, establishment of SLA among the component services of a composite service and the users becomes important. Hence, we have designed and implemented a framework for generating and monitoring eContracts for business applications involving composite Web Services. We have demonstrated our work using the scenarios of an Insurance application. A template based approach is used for composing the Web Services dynamically. Keywords: Web Services, Composition, Service Level Agreement, e-Contract, Contract monitoring.

1 Introduction Business organizations offer their services as Web Services through the Internet which can be accessed by end users. But it is not always possible for a single service to satisfy all the needs of a user. Many times it is required to compose many services which together fulfill a single requirement of the user. Existing approaches [1, 2, 3, 4, 5] deal with automated contract generation for simple Web Services. Hence, a framework for generating and monitoring e-Contracts for business applications involving composite Web Services has been proposed. When services from different providers have to interact in a business-to-business (B2B) scenario, SLA must be established among them so as to improve the trustworthiness. There are many standards [6] for defining the SLA - WSLA (Web Service Level Agreement), WS-Agreement, WSOL (Web Service Offering Language), etc. Once the agreement is in place, the values of parameters established in the agreement are monitored at runtime by comparing with the real values. While monitoring, the violations are detected and violation messages are recorded. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 362–364, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Dynamic Composition of Web Services A template based approach [7] is used for composing the services dynamically. As the number of patterns for compositions is limited in business applications, a Business Process Integration Language (BPEL) workflow is created for the provided patterns and stored as a template library. Depending on the user request, the constituent services are determined based on the library. A business scenario where the user wants to buy an insurance policy is considered. A composition of services namely BDR (Birth and Death Registration) service, insurance policy issue service and credit card payment service is required to issue an insurance policy. A portal called ‘InsureServ’ is designed through which insurance services are offered to the consumer. After selecting the template, all the providers that offer the required constituent services are discovered through UDDI.

Fig. 1. System Architecture

Fig. 2. Snapshot of the monitoring log

3 SLA Establishment The SLA is established between each provider and InsureServ and also between InsureServ and the customer. The establishment of an SLA involves four phases: Contract Definition, Negotiation, Contract Establishment and Contract Enactment. The architecture of our framework is depicted in Fig. 1. In the first phase, the contract template corresponding to the BPEL workflow which has already been selected, is defined. The second phase deals with negotiation among the participants. The negotiation is performed over QoS parameters such as execution time and price. The negotiation process is automated as the manual

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negotiation is time consuming when many participants are involved. A negotiation service has been deployed for each of the functional service that is used to negotiate between one or more providers of the same service to derive the optimal values for the QoS parameters. Once the providers and their respective offers are finalized, WSLA [8] is generated in the Contract Establishment phase. Finally, in the contract enactment phase, the contract is monitored. SLA Monitoring [9] is done with the help of a log that stores the actual execution time of all the services. These values are then compared with the response time guarantees established in the contract. If there is a contradiction, a violation is logged. Too many violations of parameters by a particular service affect the reputation of that service. The snapshot of the monitoring log is shown in Fig. 2.

4 Conclusion A framework for automated contract generation has been designed and implemented using WSLA. The negotiation is based on execution time and price. SLA monitoring has been effectively done and violations are logged. An attempt has been made to generate WSLA contracts automatically, in the context of composite Web Services. More research needs to be done in automated negotiation dealing with QoS parameters other than time and cost.

References 1. Sahai, A., Durante, A., Machiraju, V.: Towards Automated SLA Management for Web Services, HP Laboratories, Palo Alto, California, HPL-2001-310R1 (2002) 2. Comuzzi, M., Kotsokalis, C., Spanoudakis, G., Yahyapour, R.: Establishing and Monitoring SLAs in complex Service Based Systems. In: IEEE International Conference on Web Services, pp. 783–790 (2009) 3. Comuzzi, M., Pernici, B.: A framework for QoS-based Web service contracting. ACM Transactions on the Web 3(3), 1–52 (2009) 4. Truong, H.L., Gangadharan, G.R., Treiber, M., Dustdar, S., D’Andrea, V.: On Reconciliation of Contractual Concerns of Web Services. In: NFPSLASOC 2008 (2nd Non Functional Properties and Service Level Agreements in SOC Workshop), Dublin, Ireland (2008) 5. Keller, A., Ludwig, H.: Defining and Monitoring Service Level Agreements for dynamic eBusiness. In: Proceedings of the 16th System Administration Conference, LISA (2002) 6. Tang, Y., Lutfiyya, H., Tosic, V.: An analysis of web service SLA management infrastructures based on the C-MAPE model. International Journal on Business Process Integration and Management 4(3) (2009) 7. Agarwal, V., Chafle, G., Mittal, S., Srivastava, B.: Understanding approaches for web service composition and execution. In: Proceedings of the 1st Bangalore Annual Compute Conference, COMPUTE (2008) 8. Ludwig, H., Keller, A., Dan, A., Franck, R., King, R.P.: Web Service Level Agreement (WSLA) Language Specification, IBM Corporation (2002) 9. Keller, A., Ludwig, H.: The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services. Journal of Network and Systems Management 11(1), 57–81 (2003)

Synchronization of Authorization Flow with Work Object Flow in a Document Production Workflow Using XACML and BPEL Subrata Sinha1, Smriti Kumar Sinha2, and Bipul Syam Purkayastha1 1

Department of Computer Science, Assam University, Silchar, Assam, India [email protected], [email protected] 2 Department of Computer Science & Engineering, Tezpur University, Napaam, Assam, India [email protected]

Abstract. The issue of synchronization of authorization flow with work object flow in a document production workflow environment is presented and discussed in this paper. We have shown how a work object flow is synchronized with the authorization flow using a central arbiter in Web service paradigms. The co-ordination of Web services is done using WS-BPEL which supports orchestration and XACML provides authorization for Web services. The synchronization is achieved by exploiting the obligation provisions in XACML. Keywords: DPW, MPMSD, XACML, WS-BPEL, Obligation.

1 Introduction Synchronization of authorization flow with the workflow is a fundamental security requirement in a workflow environment. In a general e-Business scenario, authorization flow is based on events not on temporal constraints. Authorizations for certain privileges are granted to a subject on some objects based on the occurrence of an event and revoked automatically on occurrence of the other conjugate event. The most common conjugate pair of events in an e-Business scenario is send and receive. As soon as the work object is sent to the next task, the current role and hence the task loses full or partial privileges on the work object, whereas next role gains certain privileges on the work object to act upon by the task assigned to the role. In a MultiPart Multi-Signature Document (MPMSD) [5], the first part is the request document, the last part is the response document and the other parts in between are the reactions of the reviewers. A reviewer can read the previous parts but cannot modify, delete or reorder the content of any of the previous parts. The work object of such a workflow is a multi-part object. Each part is the output of a task. Previous user looses the write privileges as soon as the MPMSD is sent to the next user. The present user gains the privileges to read previous parts and add a new part as soon as she receives it. A comprehensive study to the MPMSD protocol with a central arbiter was done in [5]. In this paper, our main aim is to study the protocol in Web service domain, specially V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 365–370, 2010. © Springer-Verlag Berlin Heidelberg 2010

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the issue of synchronization of authorization flow with the work object flow, both in architecture level and protocol level, in document production workflow environment. The remaining sections in this paper are organized as follows – in section 1.1, the travel plan scenario and in section 1.2 the security issues are discussed. The MPMSD production protocol is discussed in section 2. The WS-BPEL for DPW is discussed in section 3. In section 4, we have discussed XACML for authorization flow. In section 5, we have discussed about synchronization in architecture level and protocol level. The section 6 is the conclusion with some limitations and future scopes. 1.1 A Scenario This is a Document Production Workflow (DPW) scenario common to many offices. An office worker, A submits an application; mA regarding his leave sanction for approval to the Head, B of the department, where he is working. B verifies the leave rules and gives his comment, mB and forwards it to the Registrar, C. C verifies all the leave status and gives his comment, mC on the application and forwards it to the Director, D. D approves the leave application with the addition of his approval note, mD. The multi-part document finally may go back to the originator A. This is a case of the travel plan workflow. Details are available in [6]. 1.2 Security Issues The Security issues of DPW are like part integrity of a MPMSD, reuse of parts and authorization flow. Details of these issues are discussed in [5]. However, the issue of synchronized authorization flow is not clearly discussed. In this paper we focused mainly on this issue particularly in the parlance of Web Services. The challenge in the Web Service domain is how to implement the protocol using the current industrial standards, like BPEL and XACML and ensure the synchronization of authorization flow with the MPMSD flow.

2 MPMSD Production Protocol The responsibilities of generation, storage and flow control of MPMSD lies with a central, neutral, trusted and strong arbiter. First, an employee Ai submits a travel plan request di to the arbiter N. The arbiter N will verify the identity of the employee Ai whether she is a valid user or not. As soon as the employee Ai submits an application di to the arbiter N, she loses modify or delete privileges and gains read privilege depending on arbiter policy. The arbiter N will present a multi-part document di to a reviewer Ai+1. The reviewer Ai+1 will submit her comment mi on the document di to the arbiter N along with the user-id Id of the next reviewer Ai+2. The next reviewer Ai+2 can gain read privileges of previous comments as well as add her comment mi+1 on the document but loses modify or delete privileges on the document. The arbiter N will verify the comment mi+1 and add it to the document as a part and then present the document to the next reviewer and so on. The protocol steps of above discussion for MPMSD production are as follows -

Synchronization of Authorization Flow with Work Object Flow 1. 3. 5.

Ai → N : { Ai, mi, Id, Ai + 1, di} N → Ai +1 : {N, di, mi} N → Ai+1 : { N, di}

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2. N → Ai : { N, di } 4. Ai+1 → N : { Ai+1, di} 6. Ai+1 → N : { Ai+1, mi+1, Id, Ai+2}

3 WS-BPEL for DPW The co-ordination of Web services is done in two ways: either by choreography or by orchestration. Business Process Execution Language (WS-BPEL) is the standard which supports orchestration [1]. The central arbitration mechanism discussed in section 2 is a form of orchestration. Therefore, an ideal technology to implement DPW with central arbitration mechanism is WS-BPEL. WS-BPEL process coordinates all the Web services and also controls the flow of the work object. WSBPEL specification is discussed in detail in [7]. We can workout the WS-BPEL implementation for DPW. The <partnerLinks> element indicates the participators or reviewers in the travel plan application process. The element defines the variable name as ‘travelRequest’ for travel plan application. In <sequence> element, all the steps involved in travel plan request will be invoked in an ordered sequence. An employee submits a travel plan request to the arbiter. The element receives a travel plan request from the employee. The element will manipulate the data variables and the element will copy data between and element by assigning the variable ‘travelRequest’ to any other variable. The reviewerA review the travel plan request invoked by the element on the employee’s document. The and element will concatenate the variables ‘travelRequest’ and ‘travelAReview’ using ‘concat’ function. The reviewerB review the travel plan request which is invoked by the element. The and element will concatenate the variables ‘travelAReview’ and ‘travelBReview’ using the ‘concat’ function. The approver makes a decision for the travel plan request which is further invoked by the element. The and element will concatenate variables ‘travelBReview’ and ‘travelApprove’ using ‘concat’ function. Finally, the travel plan response is sent back to the employee by the element. The implementation part of WS-BPEL for travel plan DPW is not shown in the appendix due to space restriction.

4 XACML for Authorization Flow The eXtensible Access Control Markup Language (XACML) is an XML based language which is required to make authorization decisions. The XACML architecture [2] specifies the implementation of a Policy Enforcement Point (PEP), which is an entity that performs access control by enforcing authorization decision (adec) to an access requestor (Ai). A Policy Access Point (PAP) is an entity that creates policies or policySets (psi). A Policy Decision Point (PDP) is an entity that evaluates applicable policy or policySets to renders an authorization decision as an access response (ares). A Policy Information Point (PIP) is an entity which is having information about the attributes of subject (sattr), resource (rattr) and action (aattr). The Context Handler (CH) receives the access request (areq) from PEP and converts the

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access request context to the XACML context (xacmlreq). The authorization flow using XACML in DPW play an important role while grant and revoke privileges on a work object (di). The data flow model of XACML architecture is discussed in detail in [2][3]. The XACML protocol steps are discussed as follows 1. 3. 5. 7. 9.

PAP → PDP : {PAP, psi} PEP → CH : { PEP, areq, di} PDP → CH : {PDP, sattr, rattr, aattr } PIP → CH : { PIP, sattr, rattr, aattr } PDP → CH : {PDP, di, adec}

2. Ai → PEP : {Ai, areq, di} 4. CH → PDP : { CH, areq , xacmlreq} 6. CH → PIP : { CH, sattr, rattr, aattr } 8. CH → PDP : { CH, sattr, rattr, aattr} 10. CH → PEP : {CH, di, ares}

5 Synchronization In this section we are going to discuss the synchronization of authorization flow with a work object flow in two levels – the architecture and the protocol. In the conceptual architecture level, we are going to propose a three tier architecture and in the protocol level, we are going to merge the protocol for work object flow and the authorization flow. 5.1 Architecture A three tier architecture depicted in Figure 1 consists of three layers namely presentation layer, logic layer and data layer. The presentation layer consists of view which contains GUIs (Graphical User Interfaces) [4], the logic layer consists of processes to manipulate data and data layer stores data. The logic layer is again split into two – client logic and arbiter logic because the data manipulation will be done by the client and the arbiter co-operatively. The components of the architecture are described below – View comprises of various interfaces through which a reviewer can interacts with the client logic module to review a document and adds own comment and forwards it to the next reviewer. Through this tier a user interacts with the workflow agent of the client logic. The client logic module is populated by client side objects with attributes and methods. The main agent found in this module is MPMSD client. This agent is responsible for client activities contain an object which submits an access request to the MPMSD arbiter for authorization decision, get the response from the arbiter as an authorization decision and display the document in the GUIs module. In the arbiter logic, the arbiter is the central hub of the architecture. The services of the arbiter are provided by the MPMSD arbiter, which includes, forwarding a MPMSD to the next reviewer (client). In our scenario, since MPMSD arbiter is acting as a TTP (Trusted Third Party), so without any loss of generality, in addition, it can also function as PEP and obligation handler. This is how synchronization of authorization flow with work object flow can be achieved. The Storage Manager stores and retrieves the document in and from the data storage. The components like CH, PDP, PAP and PIP are as in the standard XACML architecture which is already discussed in section 4. The data storage layer is basically a repository of office objects [5]. MPMSD storage stores multi-part documents which is managed by storage manager. The Target storage stores the attributes of subjects, resources and actions along with their values as per

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the XACML architecture which is managed by PIP. Policy storage stores authorization policies managed by PAP.

Fig. 1. The Three Tier Architecture

5.2 Synchronization Protocol The synchronized protocol steps of both XACML and DPW are as follows 1. 3. 5. 7. 9. 11. 13.

Ai → N : {Ai, add, di, mi} 2. N → CH : {N, Ai, add, di, mi} CH → PDP : {CH, Ai, add, di, mi} 4. PDP → CH : { PDP, Ai, di, add} 6. PIP → CH : {PIP, Ai, di, add } CH → PIP : {CH, Ai, di, add } 8. PDP → PAP : { PDP, Ai, di, add} CH → PDP : {CH, Ai, di, add } 10. PDP → CH : { PDP, permit/ deny, obl} PAP → PDP : { PAP, psi} CH → N : { CH, permit/ deny, obl} 12. N → Sm : { N, add, di, mi} 14. N → PDP : {N, (Ai, revoke, add), Sm → N : {Sm, OK} (Aj, grant, add)}

First, the MPMSD client Ai submits the document di to the MPMSD arbiter N, the comment mi to be added to the document di. The MPMSD arbiter N then requests the Context Handler for an authorization decision. The Context Handler sends the XACML request adds to the PDP for authorization decision. The PDP then sends a request to the Context Handler for collecting the information about the attributes of subject, resource and action (Ai, di, add). The Context Handler then sends the request to the PIP for information about the attributes. The PIP provides all the information about the attributes to the Context Handler. The Context Handler then gives the information about the attributes to the PDP. The PDP requests for policySets psi to the PAP. The PAP then writes policySets psi to the PDP which will be matched with the attributes for an authorization decision. After matching the attributes with the policySets psi, the PDP sends an authorization decision either permit or deny with some obligations to the Context Handler. The Context Handler then sends the

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authorization decision with some obligations to the arbiter N. The MPMSD arbiter N then submits the comment mi to be added to the document di to the storage manager Sm . The storage manager Sm then gives an OK report to the arbiter N. Finally, the arbiter N requests the PDP to revoke from Ai add comment privilege and grant the same privilege to Aj, the next reviewer already predefined in the arbiter. PDP revokes and grants accordingly.

6 Conclusion In this paper, we discussed the issue of synchronization of authorization flow with the work object flow in a workflow problem in general and DPW in particular. This issue is important but hitherto not focused in workflow literature. The synchronization is shown in this paper in architecture level and protocol level exploiting obligation mechanism available in XACML standard. It is evident from the above discussion that a central arbiter is needed to give the solution of synchronization of authorization flow with a work object flow. The discussion is limited to synchronization of orchestrated web services with XACML only. However, synchronization of choreographed Web services with XACML is another interesting area to be explored. This remains to be our future endevour.

References 1. Wang, X., Zhang, Y., Shi, H., Yang, J.: BPEL4RBAC: An Authorization Specification for WS-BPEL. LNCS, pp. 381–395. Springer, Heidelberg (2008) 2. Chadwick, D., Otenko, S., Nguyen, T.A.: Adding Support to XACML for Dynamic Delegation of Authority in Multiple Domains. In: Leitold, H., Markatos, E. (eds.) IFIP International Federation for Information Processing 2006. LNCS, vol. 4237, pp. 67–86. Springer, Heidelberg (2006) 3. Sanchez, M., Lopez, G., Gomez, A.F., Canovas, O.: Using Microsoft Office Infopath to Generate XACML Policies. In: ICETE 2006. CCIS, vol. 9, pp. 134–145. Springer, Heidelberg (2006) 4. Sinha, S.K., Barua, G.: An Architecture for Document Production Workflow in an Office. In: International Conference on Information Technology (CIT 1999), Bhubaneswar, India, pp. 45–52. Tata McGraw Hill Publishers, New York (1999) 5. Sinha, S.K., Barua, G.: A Protocol for Secure Flow of Persistent Multi-Part Documents in an Office. In: International Forum cum Conference on Information Technology and Communication at the Dawn of the New Millennium, Bangkok, Thailand, vol. 3, pp. 157– 172 (2000) 6. Sinha, S.K., Sinha, S.: Limitations of Web Service Security on SOAP Messages in a Document Production Workflow Environment. In: 16th International Conference on Advance Computing and Communications, pp. 342–346. IEEE Press, Los Alamitos (2008) 7. Juric, M.B., Mathew, B., Sarang, P.: Business Process Execution Language for Web Services. Packt Pub. Ltd., Birmingham (2006) ISBN 1-904811-81-7

Virtual Nodes for Self Stabilization in Wireless Sensor Networks Deepali Virmani and Satbir Jain B5/107 Mayur Apartment, Sector 9, Rohini, Delhi, India [email protected]

Abstract. Networking in Wireless Sensor networks is a challenging task due to the lack of resources in the network as well as the frequent changes in network topology. Although lots of research has been done on supporting QoS in the Internet and other networks, but they are not suitable for wireless sensor networks and still QoS support for such networks remains an open problem. In this paper, a new scheme has been proposed for achieving QoS in terms of packet delivery, multiple connections, better power management and stable routes in case of failure. It offers quick adaptation to distributed processing, dynamic linking, low processing overhead and loop freedom at all times. The proposed scheme has been incorporated using QDPRA protocol and by extensive simulation the performance has been studied, and it is clearly shown that the proposed scheme performs very well for different network scenarios. Keywords: Wireless Sensor Networks, Virtual Nodes, Self Stabilization, Power aware.

1 Introduction and Motivation A wireless sensor network is a collection of sensor nodes equipped with interfaces and networking capability [1]. In this paper a new scheme the stable routing by using virtual nodes for self stabilization with power factor (SRVNP) has been suggested which would allow sensor nodes to maintain routes to destinations with more stable route selection. This scheme responds to link breakages and changes in network topology in a timely manner. Quality of service [2] means providing a set of service requirements to the flows while routing them through the network. A new scheme has been suggested which combines two basic features to achieve QoS; these are stable routing and concept of battery power as the battery is main concern with WSN’s. The scheme uses virtual nodes for stable routes and uses power factor to determine active nodes to participate in routing. The rest of the paper is organized as follows: Section 2 analyzes new proposed scheme (SRVNP), Section 3 describes the simulation environment and results and Section 4 summarizes the study and the status of the work.

2 Proposed Scheme: SRVNP The proposed scheme takes care of on demand routing along with a new concept of virtual nodes with power factor. Many protocols have been discussed using concept of V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 371–375, 2010. © Springer-Verlag Berlin Heidelberg 2010

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power in many existing schemes [3-9]. In all the schemes discussed under concept of power routing, no concern has been taken for stable routing or better packet delivery. All emphasis is on concept of battery power or energy requirement for routing process. In this paper two different concepts have been joined together to make an efficient protocol. In the proposed scheme, the virtual nodes help in reconstruction phase in fast selection of new routes. Selection of virtual nodes is made upon availability of nodes and battery status. Each route table has an entry for number of virtual nodes attached to it and their battery status. The protocol has been divided into three phases. Route Request (RReq), Route Repair (RRpr) and Error Phase (Err). 2.1 Route Construction (RReq) Phase This scheme can be incorporated with reactive routing protocols that build routes on demand via a query and reply procedure. The scheme does not require any modification to the QDPRA’s[10] RReq (route request) propagation process. In this scheme when a source needs to initiate a data session to a destination but does not have any route information, it searches a route by flooding a ROUTE REQUEST (RReq) packet. Each RReq packet has a unique identifier so that nodes can detect and drop duplicate packets. An Intermediate node with an active route (in terms of power and Vitual Nodes), upon receiving a no duplicate RReq, records the previous hop and the source node information in its route table i.e. backward learning. It then broadcasts the packet or sends back a ROUTE REPLY (RRep) packet to the source if it has an active route to the destination. The destination node sends a RRep via the selected route when it receives the first RReq or subsequent RReq’s that traversed a better active route. Nodes monitor the link status of next hops in active routes. When a link break in an active route is detected, an Err message is used to notify that the loss of link has occurred to its one hop neighbor. 2.2 Local Route Repair (Err Phase) When a link break in an active route occurs, the node upstream of that break may choose to repair the link locally if the destination was no farther and there exists VNs that are active. The Time to live (TTL) of the RReq should initially be set to the following value:

TTL = max(Min _ Rpr _ TTL + VN , 0.5 ∗ # hops ) + power status

(1)

Where Min_Rpr_TTL is the last known hop count to destination, #hops is the number of hops to the sender of the currently undeliverable packets. VN is the virtual nodes attached to the said node and the power status is power state of the node at that time. As 0.5* #hops is always less than Min_Rpr_TTL + VN , so the whole process becomes invisible to the originating node.

3 Simulation and Results Simulation study has been carried out to study the Performance study of existing different protocols. Simulation Environment used is J-Sim(Java simulator) to carry

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out the process. Simulation results have been compared with QDPRA, AODV, DSR and TORA. Simulation study has been performed for packet delivery ratio, Throughput and End to End delay evaluations. 3.1 Packet Delivery Ratio In simulation study 100 nodes were taken in a random scenario of size 1000 × 1000. Two parameters have been takes as Pause time and speed. Figure 1 represents the results. DSR is not delivering more than 84% in denser mediums. It is unable to perform better in higher congestion zones. AODV outperforms DSR in congested medium. AODV is delivering more packets to DSR in most of the cases and has an edge over it. QDPRA performance is better than AODV and DSR .New scheme (SRVNP) is overall best for 100 nodes. It starts with 86% and with increasing pause time gets stable and delivers more than 95% packets.

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QDPRA is still more than DSR. The delay for SRVNP is more and the reason is that it spends more time in calculation of stable route. SRVNP does deliver even those packets, which may have been dropped in AODV as it has better recovery mechanism and local repair system for faster recovery. Throughput in bytes per second has been calculated and speed has been varied from 0 to 3.5 meter per second. Figure 3 shows the graphical representation. DSR, AODV and QDPRA and SRVNP have an increase in throughput. The throughput increase can be further explained by TCP behavior, such that, when ACK is not received back, TCP source retransmits data. With increased mobility, source may distribute several identical packets to different intermediate nodes, because of route changes At 1.5 m/s speed, AODV protocol also shows a decreasing behavior with the increased network speed. But SRVNP shows increase in throughput even if speed is increased.

4 Summary A new scheme has been presented that utilizes a mesh structure and alternate paths in case of failure. The scheme can be incorporated into any on-demand unicast routing protocol to improve reliable packet delivery in the face of node movements and route breaks. Alternate routes are utilized only when data packets cannot be delivered through the primary route. Simulation results indicated that the technique provides robustness to mobility and enhances protocol performance. It was found that overhead in this protocol was slightly higher than others, which is due to the reason that it requires more calculation initially for checking virtual nodes. This also caused a bit more end to end delay. The process of checking the protocol scheme is on for more sparse mediums and real life scenarios and also for other metrics like Path optimality, Link layer overhead.

References 1. National Science foundation, Research priorities in Wireless and mobile networking, http://www.cise.nsf.gov 2. Crawley, E., Nair, R., Rajagopalan, B., Sandick, H.: A framework for QoS based routing in the internet, RFC 2386 (2008) 3. Ettus, M.: System Capacity, Latency, and Power Consumption in Multihop-routed SSCDMA Wireless Networks. In: Radio and Wireless Conference (RAWCON 2008), pp. 55– 58 (2008) 4. Lin, X., Stojmenovic, I.: Power-Aware Routing in Ad Hoc Wireless Networks. In: SITE, University of Ottawa, TR-98-11 (2008) 5. Chockalingam, A., Zorzi, M.: Energy Consumption Performance of a Class of Access Protocols for Mobile Data Networks. In: Proc. IEEE VT, pp. 820–824 (2009) 6. Michail, A., Ephremides, A.: Energy Efficient Routing for Connection Oriented Traffic in Ad-hoc Wireless Networks. In: Proc. IEEE PIMRC 2009, pp. 762–766 (2009) 7. Zussman, G., Segall, A.: Energy efficient routing in ad hoc disaster recovery networks. In: Proceedings of IEEE INFOCOM (2006)

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8. Schurgers, C., Srivastava, M.B.: Energy efficient routing in wireless sensor networks. In: Proceedings of IEEE MILCOM, pp. 28–31 (October 2008) 9. Toh, C.K.: Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc Networks. IEEE Comm. Mag., 138–147 (June 2009) 10. Virmani, D., Jain, S.: Quality of service on-demand power aware routing protocol for Wireless Sensor Networks. LNCS, ch. 46 CCIS, pp. 272–283 (2010)

Small Square Microstrip Antenna L. Lolit Kumar Singh1, Bhaskar Gupta2, and Partha P. Sarkar3 1

Department of Electronics and Communication Engineering, NERIST, Nirjuli, Itanagar-791109, India 2 Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata-700032, India 3 USIC, University of Kalyani, Dist. Nadia, pin- 741235, West Bengal, India [email protected], [email protected], [email protected]

Abstract. A small shorted square microstrip antenna with air dielectric (εr = 1.001) substrate is presented. The probe fed square microstrip antenna incorporates a single shorting post of radius 0.6mm which significantly reduces the overall size or area by 82% from a conventional square microstrip antenna of same substrate and height. After connecting a shorting post 55% reduction in resonant frequency is achieved for the same square patch. The maximum gain and directivity of the shorted square microstrip antenna are 3.72 dBi and 4.68 dBi respectively at resonant frequency 3.29 GHz. Simulated return loss, gain, directivity and radiation patterns are shown. Keywords: Square microstrip antenna, shorting post.

1 Introduction Microstrip patch antenna has many advantages like low cost, compact size, simple structure and compatibility with integrated circuitry. One of the many advantages of microstrip patches over conventional antennas is their small size. However, there are many present day applications where even these small radiators are too large. A circular microstrip antenna incorporated with a single shorting post [1] with reduction in overall size or area by about 66% with conventional circular patch. By changing the number of shorting posts and the relative position of these posts, the resonance frequency of the short-circuited microstrip patch can be adjusted [2]. In this paper, the probe-fed single layer square patch with air dielectric substrate incorporating a single shorting post is presented. The performance of shorted patch is summarized and compared with conventional probe-fed square microstrip patch of same substrate and height. The shorted square patch is found to be reduced by 82% size wise and 55% in terms of resonant frequency in comparison with conventional square patch antenna with the same substrate and same height. This comparison is probably the most appropriate for applications where size is of primary concern. Finally, the performance of the shorted probe-fed square patch is presented, illustrating the effects of shorting. The IE3D simulation software based on Method of Moments (MoM) is used for simulation and analysis. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 376–379, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Antenna Design Analysis is performed for single layer single feed square patch antenna (Let us say Antenna 1) with air dielectric (εr = 1.001) substrate is used. After optimization the proposed microstrip antenna has patch dimensions of L=W=17.7 mm , its resonant frequency is 3.29 GHz as shown and height of patch from ground plane is 2 mm . Its geometry is shown in Fig. 1 (a, b).

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Fig. 1. Antenna 1 (dimensions are in mm) a. Top View b. Side View (h = 2 mm)

For Case 1: The probe is fed at (X = 2.5 mm, Y = 0) and shorting post is provided at (X = 4mm, Y = 0) from the patch centre. Case 2: The probe is fed at (X = 3 mm, Y=0) from the patch centre without any shorting post (conventional square patch) for resonant frequency comparison. Another conventional square patch antenna (Let us say Antenna 2) with dimensions L = W = 42 mm which resonant frequency is same as of Antenna 1 (case 1) i.e 3.29 GHz is also studied for size or area comparison. The probe is feed at (X = 8 mm, Y = 0) from the patch centre. Air (εr = 1.001) is again used as substrate which will makes fabrication easy. Antennas is fed with probe of 50Ω characteristic impedance having inner conductor of radius 0.6 mm, radius of the shorting post is also 0.6 mm. The foam (εr ≈ 1) can used to support the patch for experimental realization.

3 Analysis and Results For running simulations in IE3D Zeland Software, infinite ground plane is considered to ensure faster convergence. Antenna 1: Case 1. The square patch (L = W = 17.7 mm) with a shorting post gives resonant frequency 3.29 GHz with impedance bandwidth (-10 dB return loss) 2.4% by simulation. The simulated maximum gain of 3.72 dBi and maximum directivity of 4.68 dBi at resonant frequency i.e. 3.29 GHz. The simulated radiation patterns at the

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resonant frequency i.e. 3.29 GHz is shown, E-total at phi =0 (deg) is H-plane and phi = 90 (deg) is E-plane characteristics. All the simulated results for return loss, gain, directivity and radiation patterns are shown in Fig. 2 (a, d).

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Fig. 2. Antenna 1, Case 1 a. Return Loss vs Frequency Graph, b. Maximum Gain vs Frequency Graph, c. Maximum Directivity vs Frequency Graph, d. Radiation Patterns at 3.29 GHz

Case 2 : The same square patch without shorting post gives resonant frequency 7.34 GHz with impedance bandwidth (-10 dB return loss) 5% by simulation as shown in Fig. 3 .a. Antenna 2: Another conventional square patch with dimensions L = W = 42 mm having same substrate and height resonating at 3.29 GHz same as square patch with shorting post (Antenna 1, case 1) is also studied for size or area comparison . It gives impedance bandwidth (-10 dB return loss) of 2.5% by simulation as shown in Fig. 3. b.

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Fig. 3. a. Antenna 1 Case 2 Return Loss vs Frequency Graph. b. Antenna 2, Return Loss vs Frequency Graph.

Antenna 1 with and without shorting post i.e. case 1 and case 2, the resonant frequency is reduced by 55%. The size or area of Antenna 1 (case 1) is reduced by 82% in comparision with Antenna 2 which are having same resonant frequency, substrate and height. The square patch with shorting post has tremendous reduction in size as compared with conventional square patch. As can be seen from the results presented thus far, the shorted patch offers comparable performance with the conventional patch mounted on the same substrate and height with the advantage of significant size reduction.

4 Conclusion The square patch with shorting post offers comparable performance with the conventional patch mounted on the same substrate with the advantage of significant size reduction of 82 % and frequency reduction of 55%. This type of antenna can be used where the application of small size, low gains.

References 1. Waterhouse, R.B.: Small microstrip patch antenna. Electronics Letters 31(8), 604–605 (1995) 2. Sanad, M.: Effect of the shorting posts on short circuit microstrip antennas. In: Proc. IEEE Antenna Propagation Symp., pp. 794–7973 (1994) 3. Waterhouse, R.B., Kokotoff, D.M.: Novel Technique to improve the manufacturing ease of shorted patches. Microwave and optical Technology Letters 17(1), 37–40 (1998) 4. Sharma, S.K., Rattan, M.: Analysis of Broad Banding and Minimization Techniques for square patch antenna. IETE Journal of Research 56(2), 88–93 (2010)

Extraction of Optimal Biclusters from Gene Expression Data J. Bagyamani1, K. Thangavel2, and R. Rathipriya2 1

Department of Computer Science, Government Arts College, Dharmapuri, 636 705, TamilNadu, India [email protected] 2 Department of Computer Science, Periyar University Salem, 636 011, TamilNadu, India [email protected], [email protected]

Abstract. Biclustering is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. In this paper, MAXimal BICluster algorithm (MAXBIC) identifies coherent biclusters of maximum size with high Average Spearman Rho (ASR). This proposed query based algorithm includes three steps viz. three tier pre-processing, identifying a bicluster seed and growing the seed till an optimal bicluster is obtained. Experimental results show the effectiveness of the proposed algorithm. Keywords: Data mining, gene expression data, bicluster seed generation, average spearman rho correlation.

1 Introduction Gene Expression data is arranged as a data matrix where each row stands for the expression of one gene and each column for one sample. Based on the assumption that co-expressed gene may be functionally related, clustering has been widely used for Gene expression data analysis to find groups of genes sharing similar expression patterns across all measured samples. This proposed query based approach will be a useful tool to analyze large and heterogeneous gene expression dataset with larger biclusters and with high ASR. Our contribution is significant, since it identifies optimal biclusters. This paper is organised as follows: Section 2 details the background study. Section 3 explains the proposed algorithm. Section 4 provides the experimental analysis and conclusion is presented in Section 5.

2 Background A bicluster is a set of objects that are similar over only a subset of attributes. Biclustering, which has been applied intensively in molecular biology research recently, provides a framework for finding hidden substructures in large high dimensional matrices. The earliest biclustering algorithm is direct clustering by Hartigan [3] also known as block clustering. This approach is based on statistical analysis of sub-matrices to form the biclusters. Cheng and Church [2] introduced V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 380–383, 2010. © Springer-Verlag Berlin Heidelberg 2010

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biclustering based on minimization of Mean Squared Residue (MSR). In [1, 4] maximum similarity biclusters of gene expression data based on similarity matrix are computed. A gene expression database consists of three parts – the gene expression data matrix, gene annotation and sample annotation. Let G be a set of genes, C a set of conditions, and E(G,C) the n x m expression matrix. The element eij of E(G,C) represents the expression level of gene i under condition j. The aim of biclustering is to extract the sub-matrix E(G',C') of E(G,C) meeting some criteria. The bicluster size is |G’| x |C’| where |.| represents number of elements.

3 Proposed Work 3.1 Three Tier Data Pre-processing Among large number of genes only small part of the genes are functionally important. Hence in the first tier, t - test is applied to remove insignificant genes. In the second tier, cosine similarity measure is used to extract those genes that are relevant to the query gene. In the third tier insignificant attributes (genes) are eliminated as in [5]. Hence each gene expresses only in subset of conditions. 3.2 Seed Generation and Bicluster Formation Phase This step aims to identify the local optimal sub-matrix as seed. A query gene gq, is a small window (gq, bq) of size (1, |bq|), where bq is the subset of conditions corresponding to query gene ‘q ’and |.| is the number of elements. Scan the gene-set to generate sub matrices (gqUgi , bq⋂bi) of size (2, | bq ⋂bi |) where | bq ⋂bi | is the number of conditions common to query gene ‘gq’ and gene ‘gi’. Then those sub matrices whose ASR value is more than the threshold ϴ namely C1,C2...Ck where k<m are selected. Let this collection be C. Among these sub matrices, one sub matrix Cs with high ASR is taken as the valid seed. In seed growing phase, the valid seed Cs= (gi⋃gq, bi⋂bq) is grown by combining the seed with each sub matrix Ci C- Cs to get intermediate seed. In order to arrive at an optimal bicluster the following conditions must hold. i. ii.

The size of intermediate seed should be greater than the size of the seed. The intermediate seed should have ASR greater than the threshold.

Algorithm (MAXBIC) Input: E, the expression matrix, query gene gq and ϴ, the threshold ASR. Output: an optimal bicluster . Step 1. Step 2. Step 3. Step 4. Step 5.

Form bicluster seeds with respect to the query gene . Select seeds with ASR greater than ϴ as in seed formation phase. Select bicluster seed Cs with high ASR. Grow the bicluster seeds as described in bicluster growing phase. Perform step 4 until all the seeds get exhausted.

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4 Experimental Analysis and Discussion 4.1 Datasets The efficiency of the proposed algorithm is tested for the following the bench mark datasets. Yeast Saccharomyces Cerevisiae dataset of size 2884 x 17, Breast cancer dataset of size 7129 x 40 and Colon cancer dataset of size 2000 x 62. 4.2 Performance Analysis ASR measure introduced by Wassim et. al., [5] is used for identifying the bicluster seed as well as for growing the bicluster seed. Table 1. gives the comparative study of the biclusters of MAXBIC and RMSBE[4]. For the same reference gene, the proposed algorithm identifies an optimal bicluster. Table 1. Comparison of biclusters of MAXBIC and RMSBE

Biclusters of MAXBIC algorithm Reference Size of gene i* bicluster

ASR

ACV

Biclusters of RMSBE algorithm Reference Reference Size of gene i* condition j* bicluster

ASR

ACV

288

54 x 8 0.9802 0.9777

288

10

19 x 14 0.8677 0.9224

133

75 x 10 0.9800 0.9765

133

14

20 x 15 0.8261 0.8319

374

60 x 9 0.9800 0.9782

374

1

27 x 16 0.8868 0.9105

93

33 x 12 0.9803 0.9771

93

16

13 x 11 0.9029 0.9092

Bicluster of Yeast Saccharomyces Cerevisiae dataset with 54 genes, 8 conditions when i* is chosen as '288' (gene ID 'YBR198C') has the functional importance of SLIK (SAGA like complex).

5 Conclusion It is observed that the proposed algorithm (MAXBIC) extracts optimal biclusters with high ASR. This algorithm is able to identify coherent biclusters and the performance is justified by ‘p’ value of the bicluster which involve identification of genes that involve in biological process, molecular function and cellular component. Acknowledgment. UGC research project grant No. F-34- 105/2008 is acknowledged gratefully.

References [1] Bagyamani, J., Thangavel, K.: SIMBIC: similarity based biclustering of expression data. Information Processing and management 70, 437–441 (2010)

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[2] Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of 8th International Conference on Intelligent Systems for Molecular Biology, ISMB-2000 Proceedings, pp. 93–103 (2000) [3] Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association (JASA) 67(337), 123–129 (1972) [4] Liu, X., Liu, L.W.X.: Computing maximum similarity biclusters of gene expression data. Bioinformatics 23(1), 50–56 (2007) [5] Ayadi, W., Elloumi, M., Hao, J.-K.: A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data. Biodata Mining

Analysis of Data Warehouse Quality Metrics Using LR Rolly Gupta and Anjana Gosain University School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India [email protected], [email protected]

Abstract. Organizations, these days are deploying Data Warehouse for integrating data from various heterogeneous sources for management of information more efficiently and cost- effectively. Data Warehouse is acting as the main asset of the organization because strategic decision making ability of a business manager largely depends on the effectiveness of data warehouse. One of the main issues that influence the Quality of the information is the Data Warehouse Model Quality. A set of metrics have been defined and validated [9] to measure the Quality of the conceptual Data Model for Data Warehouse. However, this set of metrics contains some redundant metrics. Using Linear Regression (LR), we reduce this set of metrics so as to obtain the effective predictor metrics only. Further, these metrics will be empirically validated so as to use them as Quality indicators. Keywords: Data Warehouse Quality, Dimensional Modelling, Quality Metrics.

1 Introduction Organizations, these days are deploying Data Warehouse for integrating data from various heterogeneous sources for management of information more efficiently and cost- effectively. Data Warehouse is mainly treated as decision support systems for managing data. In data warehouse system quality [4][5], three different aspects are to be considered: DBMSs quality, data model quality and data quality. For such reasons, metrics have been used for the measurement of ‘Quality’ factors. Metrics helps to improve the process of software development (leading to better understanding), building predictive systems for database, maintaining projects and its quality, revealing problematic areas and detecting better ways to assist the researchers. The objective of our work is to empirically validate the defined subset of metrics [9] using stepwise linear regression (LR) technique for decorrelating variables (that explain relations between the variables in a data set), thereby proving the practical utility of the proposed work. Section 2 presents the data warehouse metrics used in our study. Later Sections focuses on the explanation of analysis methodology used, experimental settings and results obtained by analysis. Last section discusses the conclusion and future work. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 384–388, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Metrics for Data Warehouse Taking into account the characteristics expressed previously, we had empirically validated the following metrics [9] for data warehouses. • • • • • • •

NFT(Sc). Defined as a number of fact tables of the schema. NDT(Sc). Number of dimension tables of the schema. NSDT(Sc). Number of shared dimension tables. Number of dimension tables shared for more than one star of the schema. NAFT(Sc). Number of attributes of fact tables of the schema. NADT(Sc). Number of attributes of dimension tables of the schema. NASDT(Sc). Number of attributes of shared dimension tables of the schema. NFK(Sc). Number of foreign keys in all the fact tables of the schema.

3 Methodology In this section we describe the methodology used by Linear Regression, to analyze the metrics data computed from the 41 schemas. In regression analysis, they include linear techniques for modeling and analyzing several variables, focusing on the relationship between a dependent variable and one or more independent variables. Specifically, regression analysis helps to understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.

4 Experimental Setup In this section, we present our empirical validation for the Data Warehouse metrics defined in the previous section. In doing this, we must firstly define the experimental settings. After that we discuss about the collected data. Finally, we analyze and interpret the results to find out if they follow the formulated hypothesis or not. 4.1 Experimental Settings Information is “the” main organizational asset. So, we analyzed a set of valid metrics [9] for measuring data warehouse model quality, which can help designers in choosing the best option among more than one alternative design. This experiment is carried out in order to empirically validate the metrics defined in Section2, as empirical validation plays a vital role in proving the practical utility of metrics. 4.2 Collected Data Forty One Data Warehouse Schema are collected in order to perform this experiment. The domains of the schemas are different and we tried to select examples which represent the real world cases. The values of the Data Warehouse metrics for 41 schemas were computed and analyzed.

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4.3 Analysis and Interpretation In the collected data, there is no missing value and we have analyzed the results by Linear Regression methodology. Linear regression analysis is used to estimate the coefficients of a linear equation, involving one or more independent variables, which best predict the value of the dependent variable. We concentrate on the major findings only. Table 1. Variables Entered/Removed Model

Variables Entered Variables Removed

1

NFT

Method .

Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-Fto-remove >= .100).

2

NSDT

.

Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-Fto-remove >= .100).

a. Predictors: (Constant), NFT, NSDT b. Dependent Variable: Schema No. Table 2. Model Summary

Model

R

R Square

Adjusted R

Std. Error of the

Square

Estimate

1

.698

a

.487

.474

8.692

2

.740

b

.548

.525

8.260

This result reflects that the best predictors are the variables that were entered in the current regression, i.e. NFT, NSDT. Variables were entered into regression in blocks, using stepwise regression. Rest variables were removed from the current regression. 4.4 Conclusion from Study Regression reveals the estimation of linear relationship between dependent variable and one or more independent variable or covariates. It is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. So, regression analysis can be also used to

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infer causal relationships between the independent and dependent variables. Analysis estimates the coefficient of a linear equation involving independent variables (metrics) namely, NFT and NSDT, which best predict the value of the dependent variable.

5 Conclusion and Future Work This paper presented LR technique for de-correlating variables and we found that instead of seven metrics only two principal predictors were extracted which can act as indicators of Data Warehouse Model Quality. So, Linear Regression Analysis estimates that NFT and NSDT are the best predictor for the value of the dependent variable. Concluding this section, the presented Linear Regression Analysis technique (LR) removes the interrelations constructed by independent components. This helps to reduce the effort required for further computation. However, more similar type of studies must be carried out with different data sets and metrics to give generalized results. Moreover, the results will be verified using various other Techniques also. We also plan to see, how these metrics can be used to assess the Quality of Data Warehouse Model objectively.

References 1. Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. In: Data-Centric Systems and Applications. Springer, Heidelberg (2006) 2. Briand, L.C., Morasca, S., Basili, V.: Propertybased software engineering measurement. IEEE Transactions on Software Engineering 22(1), 68–85 (1996) 3. Bouzeghoub, M., Fabret, F., Galhardas, H.: Datawarehouse refreshment. In: Fundamentals of Data Warehouses, ch. 4. Springer, Heidelberg (2000) 4. Calero, C., Piattini, M., Genero, M.: Metrics for controlling database complexity. In: Becker (ed.) Developing Quality Complex Database Systems: Practices, Techniques and Technologies, ch. III, IGPublishing (2001) 5. Caro, A., Calero, C., Caballero, I., Piattini, M.: Defining a Data Quality Model for Web Portals. In: Aberer, K., Peng, Z., Rundensteiner, E.A., Zhang, Y., Li, X. (eds.) WISE 2006. LNCS, vol. 4255, pp. 363–374. Springer, Heidelberg (2006) 6. Gardner, S.R.: Building the data warehouse. Communications of the ACM 41(9), 52–60 (1998) 7. Hammergren, T.: Data Warehousing Building the Corporate Knowledge Base. International Thomson Computer Press, Milford (1996) 8. Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing Data Cubes Efficiently. In: Jagadish, H.V., Mumick, I.S. (eds.) Proc. Of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 205–216 (1996) 9. Calero, C., Piattini, M., Pascual, C., Serrano, M.A., Piattini, M., Genero, M., Calero, C., Polo, M., Ruiz, F.: Towards DW Quality metrics. In: Proceedings of the International Workshop on Design and Management of Data Warehouses (DMDW 2001), Interlaken, Switzerland (June 4, 2001) 10. W.H., Building the Data Warehouse, 3rd edn. John Wiley and Sons, USA (2003) 11. Serrano, M., Calero, C., Piattini, M.: Experimental Validation of Multidimensional Data Model Metrics. In: Proc. HICSS’36. IEEE Computer Society, Los Alamitos (2003)

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12. Serrano, M., Calero, C., Piattini, M.: An experimental replication with data warehouse metrics. International Journal of Data Warehousing & Mining 1(4), 1–21 (2005) 13. Sieniawski, P., Trawiñski, B.: An Open Platform of Data Quality Monitoring for ERP Information Systems. In: IFIP Working Conference on Software Engineering Techniques SET 2006, Warsaw, Poland (2006) 14. Signore, O.: Towards a Quality Model for Web Sites. In: CMG Annual Conference, Warsaw, Poland (2005) 15. Kimball, R., Ross, M.: The Data Warehouse Toolkit, 2nd edn. John Wiley and Sons, Chichester (2002) 16. Ordonez, G.: Consistent Aggregations in Databases with Referential Integrity Errors. In: ACM IQIS Workshop (2006) 17. Pipino, Lee, Wang: Data Quality Assessment. Communications of ACM 45(4) (2002) 18. Scannapieco, Virgillito, Marchetti, Mecella, Baldoni: The DaQuinCIS Architecture. In: A Platform for Exchanging and Improving Data Qualiy in Cooperative Information Systems. IS, vol. 29 (2004) 19. Sieniawski, Trawinski: An open Platform for Data Quality Monitoring for ERP Information Systems. In: IFIP Working Conference on SE Techniques- SET (2006)

Similar - Dissimilar Victor Measure Analysis to Improve Image Knowledge Discovery Capacity of SOM N. Chenthalir Indra1 and E. RamaRaj2 1

Research scholor, Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, India 2 Technology Advisor, Madurai Kamaraj University, Madurai, Tamil Nadu, India

Abstract. Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing on methodologies for extracting useful knowledge from data. Patterns of relations of data and information have the capacity to signify knowledge. Image pattern collection and management is the hottest subject of the digital world. The demand for image recognition knowledge of various kinds of real world images becomes greater. Kohenen’s Self Organizing Maps (SOM) algorithm is one of the particular neural network algorithms, which is used for pattern learning and retrieval. The conventional SOM learning method represents poor knowledge and hence their applicable targets are restricted. In this paper SOM is scrutinizing with various standard distances and remarkable similar measures. Reliable image learning is achieved with City block, Lee, Maximum value distance, Jaccard and Dice coefficient. Image gallery can be mined well by using SOM with the above said measures. The composed knowledge is useful for various significant services. Keywords: Self Organizing Maps, Knowledge management and Data Mining.

1 Introduction Data represents facts or values, and relations between data have the capacity to symbolize information. Patterns of relations of data and information have the capacity to represent knowledge. The patterns representing knowledge have a tendency to be more self-contextualizing. Knowledge Management (KM) includes extraction and organization of knowledge from unstructured and heterogeneous corpora, and the representation of this knowledge in a form that is easily accessible by users. Maintenance and periodic updates of the knowledge base also play an important role in Knowledge Management. Kohenen’s Self Organizing Maps (SOM) algorithm is commonly used by the pattern learning and retrieval process. The capabilities of the SOM technique have been extensively explored in different research areas for more than two decades [1]. General fragile nature of SOM technique are identified in terms of longer time needed to obtain a good map, relatively low accuracy, preciseness, and correctness of the information, and difficulty in interpreting the results. Especially the lack of efficiency and preciseness might be explanations of why business users do not use frequently the SOM tools in their data mining process [2]. Using conventional SOM leads erroneous knowledge collection. Due to this the accumulated knowledge is incapable in V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 389–393, 2010. © Springer-Verlag Berlin Heidelberg 2010

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authentication services. This paper proposes more measures to gain knowledge of authentic image values from the given input image. SOM is an unsupervised competitive learner. Winning vector is selected by competition between unknown and new knowledge vectors. Winner from unknown vector is educated with SOM updater. Selection of winning vector is the deciding factor of Self Organization. By changing this measure SOM learning can be improved. Distance measures are often used to find the similarity between two vector sets in clustering technique like Self-Organizing Maps. Similarity measures are usually applied in classification techniques [6]. Second section describes SOM. Best matching measure is analyzed in two ways one with distance measures and another with similarity measures. The experimental results with proposed measures are dealt with, in the third section. Fourth section proposes suitable measures for image learning.

2 Conventional SOM and Proposed Measures Automatically recognizing complex patterns and making intelligent decisions based on data can be effectively carried out with neural algorithm Self Organizing Maps (SOM). The objective of SOM is to represent high-dimensional input patterns with prototype vectors that can be visualized in a usually two-dimensional lattice structure [3]. Input patterns are fully connected to all neurons via adaptable weights. During the training process, neighboring input patterns are projected into the pattern, corresponding to adjacent neurons. SOM enjoys the merit of input space density approximation and independence of the order of input patterns [4]. The learning algorithm for SOM networks is the following: step1 :The m-dimensional weight vectors w1,w2, . . . ,wm of the m computing units are selected at random. An initial radius r, a learning constant , and a neighborhood function are selected. step 2 : Select an input vector using the desired probability distribution over the input space. step 3 : The unit k with the maximum excitation is selected (that is, for which the is minimal, i = 1, . . .,m). distance between wi and step 4 : The weight vectors are updated using the neighborhood function and wi + (i, k)( wi), for i = 1, . . .,m. the update rule wi step 5 : Stop if the maximum number of iterations has been reached; otherwise and as scheduled and begin from step 1. modify

φ

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ξ ← ηφ

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⎡M 2⎤ d ( x, y ) = ⎢ ∑ x i − y i ⎥ ⎦ ⎣ i =1

1

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(1)

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2.1 Proposed Distance Measures In this paper, winner node selection process is done by the various distance measures. By finding minimum distance node SOM trains almost similar nodes and its neighbors. Proposed distance measures are given in the Eqs. (2), (3) & (4) M

Manhattan distance :

d ( x, y ) = ∑ xi − yi

(2)

i =1

d m ( x, y ) = ∑ {xi − yi , q − xi − yi } M

Lee distance :

(3)

i =1

Maximum value distance :

d m (x, y ) = max{x1 − y1 , x2 − y2 ,... xM − yM } (4)

2.2 Proposed Similarity Measures Similarity measures are generally used by the data mining classification techniques where the classes are predefined. But in the case of clustering (SOM is a clustering technique) it is not as simple as the classification because in clustering classes are not known, in advance [5]. By using similarity measures, images are trained from non similar nodes. The measures chosen for SOM process are given below in Eqs.(5),(6),(7) &(8) Dice coefficient:

Jaccard coefficient:

sim(xi , w j ) =

k

∑

k

k

j

h =1 ih k 2 h =1 jh

k

2 h =1 ih

jh

(6)

k

x w h =1 ih jh

k

h =1 ih

i

Over Lap coefficient:

(5)

x 2 + ∑ h =1 w2jh h =1 ih k

∑ xw sim(x , w ) = ∑ x +∑ w −∑ ∑ xw sim(x , w ) = ∑ x∑ w ∑ tt sim(t , t ) = min (∑ t , ∑ t i

Cosine coefficient:

2∑h =1 xih w jh

j

k

2 h =1 ih

jh

k

h =1

(7)

2 jh

k

i

j

h =1 ih jh k 2 2 h =1 ih h =1 jh k

)

(8)

3 Best Measure Analysis New SOM framework is constructed to learn image gallery. Random numbers are used as initial weight in this experiment. Neighborhood function is set as 1. Threshold value α is set to 1 initially and it is reduced by 0.002 in each iteration. Usually epochs of training is set by the temperament of pattern. This is not fixed in the early SOM. This paper recommends the row size (or) multiple of the row size of the pattern dimension value as its epoch value. Epochs mean points of chance remarkable for

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learning feature of input. The first level tests are done with the random input data of size (100 x100) and its reduced (50x50) output is collected. The 2-D feature Maps of those clustered data are arranged in the Figure 3. Very poor data training is obtained by the cosine and overlap coefficient measures. On the other hand well distributed trained data are acquired by other measures. Manhattan distance self-Organising Feature Map

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The main aim of this analysis is to improve the image learning capacity of SOM. Hence further scrutiny is continued with image data. For each input pattern RGB values are extracted. Primary colors of each pixel is ranged from 0 to 255. The experiment results are recorded with the above tested distance and similarity measures and the gained self organized data are mapped. Euclidean and Maximum value distance measures add 67.8% and 87.5% reliability to the SOM, respectively. Cosine

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Coefficient and Overlap measures show very poor information compilation. Its learning capacity is 3.7% and 6.9% respectively. Lee, Maximum value distance, Jaccard and Dice similarity measures append 99% to 99.9 % knowledge collection. The Fig.4 stands evidence for image learning using SOM.

4 Conclusion SOM is used as input data trainer by Existing Methodologies for various applications. Its Training ability is used for dimension reduction. While attempting a track to implement the SOM trained data for authentication purpose, its target is restricted. So it is understood that the accrued Knowledge is not enough for further feature extractions. Particularly for the image mining, if a pattern is not represented clearly the knowledge can not be mined for future developments. This experiment strongly recommends additional measures to SOM. Distance measures Manhattan and Lee bring 99.5% support to SOM in image learning process. Similarity measures Dice and Jaccard provide 99.98% support to SOM. Small threshold value, epoch corrections are needed while implementing the process for variant data types with various size. Further study will be done by applying the process for network security.

References 1. Marghescu, D., Rajanen, M., Back, B.: Evaluating the Quality of uses of Visual Data Mining Tools. In: Proceedings of 11th European Conference on Information Technology Evaluation, pp. 239–250 (2004) 2. Indra, N., Ramaraj, E.: Self Acquiring Image Knowledge Using Maximum Value Metric based Self Organizing Maps. In: IEEE International Advanced Computing Conference, pp. 649–653 (March 2009) 3. Kohonen, T.: The Self Organizing Map. Proceedings of IEEE 78(4), 1464–1480 (1990) 4. Chandramouli, K.: Particle Swarm Optimisation and Self Organising Maps based Image Classifier. In: Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007), pp. 225–228. IEEE, Los Alamitos (2007) 5. Dunham, M.H.: Data Mining Introductory and Advanced Topics. Pearson Education, London (2006) 6. Yegnanarayana, B.: Artificial Neural networks. Prentice Hall, India (2001)

Cluster-Base Directional Rumor Routing Protocol in Wireless Sensor Network Parvin Eftekhari1, Hamid Shokrzadeh2, and Abolfazl Toroghi Haghighat1 1

Islamic Azad University- Qazvin Branch Islamic Azad University- Pardis Branch {p.eftekhari,haghighat}@qiau.ac.ir, [email protected] 2

Abstract. Predicting occurrence of events in the network has always been a notable problem in computer networks especially in wireless sensor networks. Estimating the location in which the next event occurs, can be used as an important routing factor in these networks. Events that occur randomly in the network can't be modeled with methods like curve fitting, therefore, to reach this goal, other methods like clustering are needed. This paper proposes a novel algorithm using clustering methods with the aim of estimating the future events locations. Simulation results illustrate that using methods like “Complete-Link” can be an efficient help in leading routing agents - in algorithms based on routing agents- toward new events in network. Keywords: Sensor networks, Rumor routing protocol, Agent, Clustering, Event tracking.

1 Introduction Wireless sensor network (WSN) is said to a wireless network of autonomous sensors that are scattered in different distances and are used for grouping measurement of some physical quantities or ambient conditions at different locations in a region [1]. Usually, a sensor network makes a wireless ad-hoc network, it means that, each node uses multi-hop routing algorithm. Many sensor nodes forward a data packet and transmit it to the base station called “sink node” or “BS” standing for Base Station. The main problem that must be resolved by a routing algorithm is to find a path from the sink to the event or vice versa. In some category of routing algorithms, software agents are used to make a route between the source and the sink node. Rumor Routing (RR) is a basic agent-based routing algorithm which is inspired by the motion pattern of the ants [2], [3]. In [4] a second layer routing has been proposed to make the protocol suitable for high traffic environments. This paper proposes a novel routing method base on combination of Directional Rumor Routing (DRR) and Complete-Link Clustering algorithm [5]. The remaining parts of this paper are organized as follow: next section reviews the related work and describes the rumor routing based protocols in detail. Section 3 describes Cluster-base DRR newly presented method. Section 4 describes simulation results and the last section concludes the paper. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 394–399, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Related Work In all rumor-based routing methods, whenever the sink node needs aggregating data from network, it searches the network. In rumor routing [2], when a special event occurs in the network, node or nodes that are witnesses of event's occurrence, propagate some agents named “event agents” in the network. Each node receiving that agent transmits it to its random neighbor and this operation continues while agent's lifetime is not finished. Each agent consists of information about the path that reaches the event's location. This information is stored in the node's event table and is maintained for a limited time. Besides, when the sink node wants to be aware of the location of a special event in the network, it propagates some query agent in the network. These agents move in the network randomly and when encountering a node which has been traversed by an event agent, will follow the event agent path. Directional rumor routing (DRR) [6], [7] was proposed to extend this algorithm by adding a localization device to the sensor nodes. In this method, both event agents and query agents are propagated simultaneously and straightly, with equal angels in networks and it has shown that for example for a network with square topology, it is suitable to have five event agents and five query agents to yield maximum success rate.

3 Cluster-Base DRR The main goal of this algorithm is to propagate all query agents one by one in the network instead of propagating all at once. In case of success of one of the query agents in routing, the algorithm prevents transmitting other agents to prevent waste of node’s energy. Here, the main point is that, the first query agent should move toward a place where the probability of event occurrence is more. To reach this goal, the sink node which becomes aware of event occurrence locations in the network as time goes on, clusters network according to the location of nodes which witnessed a particular event in the network. Then, when the sink node wants to make further routing, transmits the first query agent toward the center of the cluster which has the most members; because the probability of further event occurrence in this cluster is more. To reach this goal, this method makes use of complete-link clustering algorithm [5]. In the complete-link or furthest-neighbor method, the distance between two groups A and B is the distance between the two furthest points, one taken from each group: d AB = max d ij i∈ A, j∈B

(1)

Consider the dissimilarity matrix in Table1 for each pair of objects in a set comprising six individuals: Table 1. Dissimilarity matrix for six individuals (in the first stage of algorithm)

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The closest two groups (which contain a single object each at this stage) are those containing the individuals 3 and 5. These are fused to form a new group {3, 5} and the distances between this new group and the remaining groups calculated according to (1) so that d1,(3,5)= max {d13,d15}=13, d2,(3,5)= max {d23,d25}=11, d4,(3,5)= 7, d6,(3,5)= 9, giving the new dissimilarity matrix in Table2. Table 2. Dissimilarity matrix (in the second stage of algorithm)

This operation continues to arbitrary time according to the threshold value. The complete-link diagram is given in Fig.1.

Fig. 1. Complete-link diagram

Whenever the sink node discovers location of some event using DRR algorithm, adds one row and one column to matrix which exists in its memory by inserting the node that witnessed the event. Executing the complete-link algorithm each time a new event is discovered helps in putting the nodes that are near to each other, and have witnessed the same event, in the same cluster.

4 Simulation Results Simulation results show a considerable improvement of DRR algorithm. Based on proposed simulation scenario, simulation results illustrate that utilizing complete-link method in giving priority to agent transmissions; decreases probable delay in complete-link SDRR quickly and decreases it to the DRR’s (Fig.2). Whereas, Fig.3 shows that, in complete-link SDRR, energy consumption is less than DRR. Also, simulation results in Fig.4 shows that, by increasing node’s density in the network, the average rate of energy consumption (that is considered equal to average number of

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transmissions in the network) decreases about 27% to 32%. According to Fig.2, the decreasing rate of energy consumption raises by increasing node’s density in the network. The simulation was performed by utilizing C# tool and was completely object oriented. The simulation area is considered to have 250m length and 250m width, consisting 2000 to 4000 nodes. The nodes are placed in the network completely

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random and uniformed. Each node has a transmitting radius of 15m and sensing radius of 15m. The lifetime of each agent is considered in a way that it can traverse at least diameter of the network. The events occur in the network with a non uniform distribution function of exponential, with Expected Value equal to 40 (E=40). Each node which witnesses an event propagates five agents to distribute the event in the network. At next step, sink node which is placed in the network in a random manner, propagates query agents one by one in order to find location of event occurrence. In other words, the sink node waits after transmitting the first agent; if it does not receive any answer, it will transmit second agent and so on.

Fig. 4. Decrease of energy consumption by increase of node's density

5 Conclusion In this paper, one of the famous query-driven routing algorithms named “DRR” has improved utilizing a clustering method named “complete-link”. Using this algorithm helped random routing to be replaced with intelligent routing; thus, it caused considerable decrease in energy consumption. Totally, it can be concluded that, using clustering and classification methods can be helpful considerably for decision making contexts in routing algorithms. Simulation results show that, proposed algorithm improves more by increasing node’s density in the network. Also, resulting delay of proposed method will be eliminated after a short time in the network.

References 1. Hac, A.: Wireless Sensor Network Designs. In: The Atrium, Southern Gate, Chichester, Sussex PO19 8SQ. John Wiley & Sons Ltd., England (2003) 2. Braginsky, D., Estrin, D.: Rumor Routing Algorithm For Sensor Networks. In: Conference on Distributed Computing Systems, ICDCS-22 (2001)

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3. Banka, T., Tandon, G., Jayasumana, A.P.: Zonal Rumor Routing for Wireless Sensor Networks. In: IEEE International Conference on Information Technology: Wireless Ad Hoc/Sensor Networks and Network Security (ITCC), Las Vegas, NV (2005) 4. Shokrzadeh, H., Mashayekhi, M., Nayebi, A.: Improving Directional Rumor Routing in Wireless Sensor Networks. In: 4th International Conference on Innovations in Information Technology (Innovations 2007), UAE (2007) 5. Webb, A.R.: Statistical Pattern Recognition, 2nd edn., pp. 364–367. Copyright 2002, John Wiley & Sons, Ltd., Chichester (2002) 6. Shokrzadeh, H., Haghighat, A.T., Nayebi, A.: Directional Rumor Routing in Wireless Sensor Networks. In: 3rd IEEE International Conference in Central Asia on Internet The Next Generation of Mobile, Wireless and Optical Communications Networks, ICI (2007) 7. Shokrzadeh, H., Haghighat, A.T., Nayebi, A.: New Routing Framework Base on Rumor Routing in Wireless Sensor Networks. In: Computer Communications, vol. 32(1), pp. 86– 93. Elsevier, Amsterdam (2009) doi:10.1016/j.comcom

On the Calculation of Coldness in Iowa, a North Central Region, United States: A Summary on XML Based Scheme Sugam Sharma1, Shashi Gadia1, and S.B. Goyal2 1

226 Atanasoff Hall, Department of Computer Science Iowa State University, Ames, Iowa, USA 50011 2 MRCE, Faridabad, India [email protected], [email protected]

Abstract. NC94 dataset is an agricultural dataset which consists of climate, crop, and soil data and is considered as a completely dataset available. The data is organized based on county and is treated as an object. It is adapted in interdisciplinary research and widely used in various research domains and once again has been exploited in our parametric database research. In this research work, we exploit the climate data of NC94 dataset to calculate the cumulative degree of coldness in Iowa in last 30 years. To demonstrate the degree of coldness, an xml based scheme is used considering blue as the base color with the notion that darker shade reflects higher degree of coldness. This work is a baby step towards the generation of climate atlas of North Central Region of USA in one single click. Keywords: NC94, XML, shape file, Coldness, Iowa.

1 Introduction In North Central Region of the United States, an association for agricultural data collection collects various dataset of agricultural data such as NC94. The data in NC94 dataset consists of space (geography) and time as their dimensions implicitly and is considered as a spatiotemporal dataset. The complete NC94 dataset is useful for interdisciplinary research and widely used in various research domains e.g. agriculture, geosciences, and computer etc. and has been exploited (only Climate) in this research. In order to execute any ParaSQL query on NC94 dataset it is first loaded into an in-housed developed local storage called CanStoreX. CanStoreX, Canonical Storage for XML[2] is an XML storage technology. It paginates a large XML document before storing it into the storage and used as a backend for storing NC94 data. The execution of a parametric climate query returns the desired data from NC94 dataset which is used as the input for further processing. The output of a parametric query consists of many attributes and some of them are useful to calculate the degree of coldness of an individual county. The rest of the paper is organized as follows: V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 400–405, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Section 2 describes our approach toward this research work. In section 3 we discuss the formulation view of our approaches. In section 4 we analyze the results. Section 5 discusses the future work and concludes the paper.

2 Approach The motivation behind the research work is to analyze the coldness in all counties in Iowa in 30 years period. Figure 1 shows a complete object oriented architecture of our research methodology and this section elaborates each and every object as follows. 2.1 NC94 Dataset Over 50 years, North Central Regional Association of Agricultural Experiment Station in the United States has been contributing to research community by collecting, developing, verifying, and validating agricultural databases. Out of the huge collection of datasets, NC94 is considered as one of the most important, internally more consistent and is used widely for pest management, crop and risk analysis, and forecasting research and is also available for public access through internet and in many scientific data formats, used with software packages to store and process for environmental development and research. Despite the advantages of the various data formats, public cannot access it directly as unlike commercial database management systems they do not support directly ad-hoc queries [1]. In this research work, NC94 dataset is used in two formats; 1) GML format, and 2) shape file format. Iowa Environmental Mesonet [4] has collected many datasets of atmospheric field, soil and hydrologic for analysis and dissemination and provided the shape file of NC94 dataset which is useful for graphical display. A detailed study on issue arises using Geography Markup Language (GML) for spatial databases and their proposed solutions are discussed in [5].

Fig. 1. Architecture of research methodology

In this research work, we are confined with climatic data of NC94 dataset. Climatic data is in Geographical Markup Language (GML) format.

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2.2 Query In our research work we exploit the following set of commands for loading the NC94 dataset to the storage, parsing the client query and creating a parse tree, building an expression tree, executing a query, and redirecting the resultant tuples to the output text file. LoadNC94Data -This command is used for invoking the loader module for loading the NC94 dataset into the common storage. Out of ‘climate’ or ‘soil’ or ‘crop’, this command takes any one of he relations as input parameter and loads the specific relation into the common storage. z OpenNC94Database - This command opens the NC94 XML catalog and parses using DOM parser for accessing schema of the NC94 relations. It is an initialization step. z SELECT * RESTRICTED TO [[(C.MaxTemp + C.MinTemp)/2 <0]] FROM Climate C || queryop.txt; It takes NC94 query, the output filename where the output tuples should be redirected. The string “||” acts a separator that separates the actual NC94 query from the rest of the command. The rest of the commands have output file name and the log xml filename. z CloseNC94Database Command -This command closes the handler to the NC94 relation in the storage and releases all the resources. z

2.3 Color Coding Scheme In this section we show our proposed color coding scheme. Figure 2 shows the xml representation of the contents needed for color coding. The tag is given the hexadecimal value which corresponds to the dark black color.

Fig. 2. XML representation of color coding scheme

The left most two bits of base tag value correspond to red color, the middle two bits are correspond to green color whereas the last two right most bits are used to manipulate the blue color. Tag Number_of_Ranges shows the range of shades of a base color that we employ in the rendition of the map. The tag Lowest_Value and Highest_Value are the highest and lowest boundaries of degree of coldness, one can

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expect. These values have substantial marginal differences from the actual values of the degree of coldness in any county. Range and Main are two user defined java objects. Range generates a map (a java Collection class) which consists of the set of county FIPs and the corresponding degree of coldness as the key-value pair and injects this map to Main object. Based on the degree of coldness, Main object helps to paint the county with a shade of blue color.

3 Formulations This section describes the mathematical formulation of the model that we use in our research work to calculate the degree of coldness and color coding scheme to render the coldness on the map. 3.1 Algorithm to Calculate the Degree of Coldness The below is the pseudocode to calculate the degree of coldness in a county. Algorithm 1. To calculate the degree of coldness in a county

Line 1 helps to store the set of states extracted from NC94 dataset into an array. As for this research purpose we are interested to confine within Iowa only so the array in line 1 is iterated to extract the spatial data in Iowa only and variable S in line 4 temporarily holds the state name. Line 5 helps to check whether the stored state in variable S is Iowa. Once line 5 returns true, all counties of Iowa are extracted and stored in another array CC as shown in line 7. This array is iterated to calculate the number of days in which the average of maximum temperature and minimum temperature is less than zero and line 10 in this pseudocode is corresponding to that operation. Once the numbers of cold days are calculated, then it is extremely straight

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forward to calculate the degree of coldness over a 30 years period and line 12 in pseudocode helps to return the degree of coldness of a particular county at a time, and finally when the for loop in line 8 is exhausted, we obtain the calculated degree of coldness for all counties in Iowa.

4 Results Figure 3 is a map showing all 99 counties of Iowa. Each county is rendered with a shade of blue color. The darker shade is interpreted a higher degree of coldness. It can be observed that most of the counties up in north of Iowa have more intensified shades indicating higher degree of coldness than that of down side (south). County Palo Alto is one of the counties having highest degree of coldness whereas Cedar joins to that group of counties which has lowest degree of coldness.

Fig. 3. The display of degree of coldness in all counties of Iowa

5 Conclusion and Future Work In this paper the degree of coldness has been depicted using blue as the base color. The increase and decrease in degree of coldness is analogous to the higher and lower intensity of the base color respectively. The future task involves the calculation of other interesting artifacts e.g. GDD which can be used to generate the climate atlas.

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References 1. Seo-Young, N.: Hybrid Storage Design for NC94 Database within the Parametric Data Model Framework. In: Proceedings of the International Conference on Computational Science and Its Applications, Part II, Glasgow, UK, May 8-11, pp. 145–154 (2006) 2. Patanroi, D.: Binary Page Implementation of a Canonical Native Storage for XML. Master’s thesis, Department of Computer Science, Iowa State University, Ames, Iowa (August 2005) 3. Ma, S.: Implementation of a canonical native storage for XML. Master’s Thesis, Department of Computer Science, Iowa State University (2004) 4. Gadia, S.K., Gutowski, W.J., Al-Kaisi, M., Taylor, S.E., Herzmann, D.: Database tools promoting extensive, user-friendly access to the iowa environmental mesonet. Baker Proposal (2004) 5. Sripada, L.N., Lu, C.-T., Wu, W.: Evaluating GML Support for Spatial Databases. In: 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - (COMPSAC 2004), vol. 2, pp. 74–77 (2004)

Development of a Three Layer Laminate for Better Electromagnetic Compatibility Performance at X-Band C. Dharma Raj1, G. Sasibhushana Rao2, P.V.Y. Jayasree1, B. Srinu3, and P. Lakshman1 1

Dept. of ECE, GITAM University, Visakhapatnam, India [email protected] 2 College of Engineering, Andhra University, Visakhapatnam, India 3 Vizag Institute of Technology, Visakhapatnam

Abstract. A three layer laminate consisting of absorbing material, conductive polymer and conductor was developed to enhance the performance of electromagnetic compatibility of a shield. In this paper, equations were developed for the estimation of reflectivity and shielding effectiveness of the three layer laminate and analysis of the laminate was carried out to investigate the electromagnetic compatibility performance. Different types of microwave absorbers and conductive polymer materials were considered for achieving optimum reflectivity and shielding effectiveness. The investigations were carried out in X-band frequency range for the three layer laminate at different thicknesses of the layers. Keywords: Electromagnetic compatibility, reflectivity, shielding effectiveness.

1 Introduction Electromagnetic shields can be developed for optimum performance of compatibility using multilayered laminate of different materials. In open literature, either reflectivity or shielding effectiveness was considered separately for optimizing shielding performance but both the parameters were not considered while formulating shields. A unique shield [15] which addresses the needs of both reflectivity as well as shielding effectiveness was developed in this work. The characteristic of high attenuation with negligible reflection loss of a microwave absorber is considered to be the initial layer in order to optimize reflectivity characteristic of the laminate. The reflectivity of the microwave absorber layer [16] [17] backed by conductive polymerconductor can be determined with the mathematical analysis available in open literature [3][4][5][6]. The shielding performance of high conductive polymeric sheets which have thicknesses small compared to skin depth is very high. Among the various conducting polymers, Polyacetylene doped electro chemically with 80% by weight by iodine [9] [11] [14] has a very high conductivity/weight factor at X-band frequency range. A sandwich of such a layer between two layers of microwave absorber and conductor fulfills the requirement of very high shielding effectiveness. The thicknesses of the three layers in the multilayer laminate can be varied in accordance V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 406–410, 2010. © Springer-Verlag Berlin Heidelberg 2010

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to the specification of electromagnetic compatibility and mechanical constraints of the laminate for a given application. In this paper, mathematical analysis for the estimation of reflectivity and shielding effectiveness of a three layer laminate was carried out.

2 Reflectivity Reflectivity of the microwave absorber backed by conductive polymer-conductor can be estimated using the transmission line analysis for normal incidence. Reflection coefficient [1] at an interface of microwave absorber-conductive polymer can be given as q

(1)

Where is the intrinsic impedance of conductive polymer and is the intrinsic impedance of the microwave absorber. The reflection coefficient at the interface of free space- microwave absorber is very small and is neglected in this analysis. The reflectivity at the interface of microwave absorber -conductive polymer in the laminate, as shown in the fig.1, is defined as the path loss of the electromagnetic energy while it propagates from free space to microwave absorber – conductive polymer interface and back after reflection by the conductive polymer in the interface. Thus, the reflectivity of the laminate can be derived as R

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is the thickness of the microwave absorber and αA is the attenuation Where constant of the microwave absorber. Free space

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Fig. 1. Reflectivity and shielding effectiveness of microwave absorber – conductive polymerconductor laminate

3 Shielding Effectiveness Shielding effectiveness of the three layer laminate of microwave absorber, conductive polymer and conductor is defined as the attenuation loss of the electromagnetic energy while it propagates through the three layers of the laminate. In other words,

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the shielding effectiveness is nothing but effective transmission coefficient across the layers of the laminate. Considering successive re reflections at the interface of the three layers, the shielding effectiveness [2] across the laminate can thus derived to be ܵ ൌ െʹͲ݈‫݃݋‬ଵ଴ ሺ‫݌‬ሾሺͳ െ ‫ݍ‬ଵ ݁ ିଶఊಲ ௧ಲ ሻሺͳ െ ‫ݍ‬ଶ ݁ ିଶఊ೛ ௧೛ ሻሺͳ െ ‫ݍ‬ଷ ݁ ିఊ೎ ௧೎ ሻሿିଵ ݁ ିఊಲ ௧ಲ ିఊ೛ ௧೛ ିఊ೎ ௧೎ ሻ (3)

where p is the transmission coefficient across the different interfaces, , are reflection coefficients[2] at absorbing material - conductive polymer interface, conductive polymer - conductor interface and at conductor- free space interface , are the thicknesses of the absorber, conductive polymer respectively, , are the propagation constants of the and conductor respectively, absorber, conductive polymer [8] and metallic conductor [2] respectively. Reflectivity vs frequency at given thickness(t=5mm)0f different Absorbers

Reflectivity vs thickness of BaFe(12-2x)AxCOxO19(for A=Ru-co,x=0.5) at different frequencies 120

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4 Results and Conclusion The reflectivity of the microwave absorber backed by conductive polymer-conductor in three layer laminate is estimated using the equation (2) for four different microwave

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absorbing materials. Fig. 2 is the plot for the variation of reflectivity with frequency with microwave absorber thickness, = 5 mm. Fig. 3 is a plot for estimation of reflectivity with variation in thickness of absorber in the laminate at a constant frequency for M-Type Barium ferrites. It may be observed that the performance of MType Barium ferrites is optimum in the X-band frequency for reflectivity. Estimations were carried out to determine the variations of shielding effectiveness with frequency at different thicknesses of the conductive polymer and conductor layers and presented in fig. 4 and 5 respectively. From the analysis carried out, it can be deduced that the effect of thickness of absorber layer in the laminate on the shielding effectiveness is minimal and can be neglected. Also, the reflectivity of the laminate is dependent only on the absorber layer material and its thickness. Neither the conductive polymer layer nor the conductor layers have influence on this parameter. One more important conclusion that can be derived is that at resonant frequency of the conducting polymer, the shielding effectiveness of the laminate is independent of the thickness of the conductive polymer layer. The material for microwave absorber and thicknesses of the three layers in the laminate can accurately be predicted and analyzed using the mathematical modeling of the reflectivity and shielding effectiveness for a given application according to the requirement of electromagnetic compatibility and mechanical stability.

References [1] Kodali, V.P.: Engineering Electromagnetic Compatibility, Principles, Measurements and Technologies. S Chand & Company Ltd. (2000) [2] Schulz, R.B., et al.: Shielding Theory and Practice. IEEE Transactions on Electromagnetic Compatibility 30(3), 187–201 (1988) [3] Feng, Y., et al.: Microwave Absorption Properties of the Carbonyl Iron/EPDM Radar Absorbing Materials. Journal of Wuhan University of Technology-Mater. Sci. Ed. 2, 266–270 (2007) [4] Cho, H.-S., Kim, S.-S.: M-Hexa ferrites with Planar Magnetic Anisotropy and Their Application to High-Frequency Microwave Absorbers. IEEE Transactions on Magnetics 35(5), 3151–3153 (1999) [5] Singh, P., et al.: Microwave absorption studies of Ca–NiTi hexaferrite composites in Xband. Materials Science and Engineering B78, 70–74 (2000) [6] Kim, D.Y., et al.: Dependence of Microwave Absorbing Property on Ferrite Volume Fraction in MnZn Ferrite-Rubber Composites. IEEE Transactions on Magnetics 32(2), 555–558 (1996) [7] Zhang, B., et al.: Microwave-Absorbing Properties of De-Aggregated Flake-Shaped Carbonyl-Iron Particle Composites at 2–18 GHz. IEEE Transactions on Magnetics 42(7), 1178–1781 (2006) [8] Jayasree, P.V.Y., et al.: shielding effectiveness of conductive polymers against EM fields-a case study. IE(I)Journal–ET 90 (July 2009) [9] Naishadham, K., Kadaba, P.K.: Measurement of the Microwave Conductivity of a Polymeric Material with Potential Applications in Absorbers and Shielding. IEEE Transactions on Microwave Theory Technol. 39, 1158–1164 (1991) [10] Naarman, H.: Synthesis of New Conductive Electronic Polymers. In: Proceedings of International Congress Synthetic Metals, Kyoto, Japan (June 1986)

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[11] Ehinger, K., Summerfield, S., Bauhofer, W., Roth, S.: DC and Microwave Conductivity of Iodine-doped Polyacetylene. Journal of Phys. Cr Solid State Phys. 17, 3753–3762 (1984) [12] Jarva, W.: Shielding Tests for Cables and Small Enclosures in the 1-to 10-GHz Range. IEEE Transactions on Electromagnetic Compatibility EMC-12 (Feburary 1970) [13] Konefal, T., Dawson, J.F., Marvin, A.C.: Improved Aperture Model for Shielding Prediction. In: IEEE International Symposium on Electromagnetic Compatibility [14] Oussaid, R.: Study of The Materials Improvement In Electromagnetic Compatibility. Journal of Electrical Systems Special Issue (01), 53–56 (2009) [15] Raj, C.D., et al.: Estimation of Reflectivity and Shielding Effectiveness of Three Layered Laminate Electromagnetic shield at X-band. Progress In Electromagnetics Research B 20, 205–223 (2010) [16] Levcheva, V.P., Arestova, I.I., Nikolov, B.R., Dankov, P.I.: Characterization and Modeling of Microwave Absorbers in the RF and Antenna Projects. In: 16th Telecommunications Forum (TELFOR), pp. 547–550 (November 25-27 2008) [17] Min, E.H., Kim, M.S., Koh, J.G.: Microwave Absorption Properties in Absorbers for Mobile Phones. Journal of the Korean Physical Society 52(6), 1850–1853 (2008)

Pre-Confirmation Neural Network for Reducing the Region of Interest in an Image for Face Detection A. Femina Abdulkader and Ajit Joseph Neural Networks Research Group, Department of Electronics and Communication, Rajagiri School of Engineering and Technology, Rajagiri Valley, Kochi, India [email protected], [email protected]

Abstract. In this paper we present a Pre-Confirmation Neural Network (PCNN) to reduce the processing time of the general face detection Neural Networks (NNs) by reducing the region of interest in an image up for face detection. The other algorithms commonly used for most face detection works by applying one or more NNs, directly to portions of the input image, and arbitrating their results. This requires that the whole image be passed several times through different NNs thereby increasing the processing time required for face detection. We present a smaller and less complex PCNN which operates on the image to produce a relatively small set of image portions which have the possibility of being a Face. When only this small set is passed through the NNs, generally used for face detection, the time required to detect faces in an image reduces. Keywords: Face Detection, Image processing, Neural Networks, Pre-Confirmation Neural network, Image correlation.

1 Introduction Face detection from any image implies the task of detecting any number of faces present in the image. It is different from face recognition wherein the task is to identify the face in the image. In a way face detection precedes face recognition. There are many ways in which face can be detected and a lot of them have been tried and tested. Recently proposed algorithms use color segmentation to find the presence of skin color and then to detect the face. This makes the task of face detection much simpler because many colors don’t match the skin color so most of the background in the image can easily be neglected. That is the same reason why grey scale images are not easy to be processed. Detection in grey scale images, even though more complex than color images, is employed here because most of the security cameras operate in grey scale to reduce the data that has to be stored in the process.

2 Neural Networks for Face Detection The detection of faces in image, using Neural Networks (NN), is being tried out since last decade [1], [2]. Most of them successfully attempted use of NN which follows the simple process of training a NN to detect a face from images using different V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 411–416, 2010. © Springer-Verlag Berlin Heidelberg 2010

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algorithms [3]. Some of these algorithms are structurally complex and large hence perform better. But there is a common problem in most of these networks. Normally any image would consist of areas where the image appears to be with fewer variations e.g. Clear sky, background wall, clothes etc which do not require a complex network to be detected as a non face. But still the networks analyze the whole image with the same complexity as would be done for any prospective face image. This increases the time of processing which can be reduced by using a PreConfirmation Neural Network (PCNN). PCNN is also a NN but a structurally simpler one than those that are used for confirming the presence of face. PCNN is used before the final NN confirms the detection of face hence we named it PCNN. PCNN is discussed in detail in section 4.3.

3 Challenges in Face Detection Before we start with problem solving it is rather wise to understand the depth of the problem. The Image that may appear for face detection may contain only one face or many. The size of the face may depend on how the people are positioned in the image. Closer to camera faces will appear large as compared to farther positioned people’s faces, thus giving a range of face sizes. Large faces will also be more focused and clear as compared to the far away faces. Another problem that may be encountered is illumination of faces. The faces may not be illuminated uniformly in most of the cases where light will be available from directions other than from the front. In such cases the face may not be completely visible. The faces may be full frontal and upright or may be oriented in space so as to appear as profiles or non-upright. The use of facial furniture and facial hairs makes the task even more difficult. Facial expressions to a certain extent make the problem of face detection a tedious process. For the sake of simplicity we target only full frontal and upright faces. This paper will frequently refer to the problems discussed above and detail the necessary steps taken to avoid them.

4 Steps in Face Detection The basic steps in face detection start with dividing the whole image to small overlapping blocks so that face would be present in some of these blocks. These blocks are then preprocessed and passed on to a trained NN which verifies them to find if they are a face or not. We have made a simpler NN which would process the blocks to check their validity as a possible face and neglect those which have a very low possibility of being a face. This reduces the number of blocks that have to be analyzed by a more complex and large neural network. The whole process is explained in detail in subsections below. 4.1 Methods of Extracting Blocks from the Image For detecting the face from the whole image it becomes necessary to scan the whole image. Overlapping blocks of the image are taken out separately and analyzed to find out if it’s a face or not. The block has a rectangular shape of size (27 × 18) because

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faces have an oval shape. Thus, an image of size a × b will have {(a-27) · (b-18)} rectangular blocks to be processed by PCNN. So, for a 250 × 250 image we will have 51736 overlapping rectangular blocks. We expect the face to be captured in one of these blocks. But we cannot be sure that it will always happen as we have already said that the position of the people from the camera will decide the size of face in the image. So one of the options is to scan the whole image by a rectangular block of a certain size and then repeat the process for a slightly bigger or smaller sized block a number of times. But this will increase the processing time and also change the number of pixels required as input for the NN. So instead, as in [1], we alter the size of the image each time so that in one of these alterations the face will fit in the box. This process is shown in figure 1, where the image is shown to be altered in size by 20% several times. We change the size of the image by 120% four times and by 80 % four times.

Fig. 1. The image is resized to several different sizes and blocks are taken out of it which is then preprocessed before being sent to Pre-Confirmation Neural Network (PCNN)

So, for an image (250 × 250), we will have eight resized image. Thus, for a total of 9 images there will be approximately 573662 rectangular boxes to be passed through PCNN. Imagine the time that will be required by a complex face detection neural network to process each of these blocks. With the help of much simpler PCNN, this huge number of blocks reduces to almost 15 to 20 rectangular blocks and only these 15 to 20 blocks need to be analyzed using a complex face detection neural network. But before being sent to PCNN, each of the above mentioned 573662 image blocks have to be image processed to have uniform illumination. This is to avoid the problem we had discussed in the first section, that a non-uniformly illuminated face would appear unsymmetrical and hence go undetected. 4.2 Image Preprocessing Non-uniformly illuminated blocks and the processed ones, as per the method suggested by [1], to correct the illumination are shown in figure 1. The image is first processed to find out the best fit linear function. This function is then subtracted from the block to get the light corrected image before being uniformly illuminated by equalizing the histogram. After this preprocessing the blocks are passed to PCNN. 4.3 Pre-Confirmation Neural Network

Pre-Confirmation Neural Network (PCNN) is actually a neural network which has the task of reducing the number of blocks to be passed to a more complex NN which confirms the presence of face in it.

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Fig. 2. left figure shows the mask and the selective area of the block which is passed to the PCNN. The right figure shows some face images used for training PCNN.

In PCNN the input for a face image are the important facial features like eyes, nose and mouth. For this only selected portion of the block is used as shown in figure2. Here a mask covering the entire block, other than a selected area of 5:13 × 7:12, is used which exposes only the facial features to be exposed to the neural network. Thus a total of 270 pixels, as opposed to 486 pixels (27 × 18), pass through PCNN and get analyzed for presence of face. PCNN is trained with images that are very easy to be distinguished as face or non-face. A small set out of total images which are used for training the PCNN is also shown in figure 2. A total of 71 face images and 65 non face images were used for training the PCNN. PCNN was trained using scaled conjugate gradient backpropagation algorithm. 4.4 Increase in Processing Speed Using Correlation PCNN helps to reduce the time required for the total task of detection. But the time required to process the whole image can be further reduced if we send only some blocks which have the higher chance of being a possible face. This is done, as also suggested in [4], with the help of correlation and is shown in figure 3.

Fig. 3. Figure is showing the output of correlation between a dark face and the whole image and that of between a bright face and the whole image. These correlation outputs are then passed through an OR operator and searched for the points of maximum value of correlation (shaded in yellow). Thus only these regions are passed through the PCNN instead of the whole image which reduces the time for detection.

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Fig. 4. The results of applying the PCNN. The green rectangle box shows a block possible to be detected as a face. As can be seen, when only these blocks are sent to the main face detection Neural Network the time required for total face detection reduces.

Here two different colored faces (fair and dark) of size 27 × 13 are correlated, with the whole image, separately. The maximum correlation in a region of around 8 pixels is identified. Blocks centered on this region of maximum correlation is only sent to PCNN for detection. This reduces the number of blocks given to PCNN which saves processing time.

5 Result Analyses PCNN is able to give an output which has most of the face images recognized but with false detection which is perfectly okay since the main job of PCNN was to reduce the number of blocks which will be sent to the main NN for face detection and not to give only faces as the output. The PCNN was able to reduce upto 99% of the blocks. The results are shown in figure 4. We have taken images with different characteristics to

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show the output of PCNN. These images were downloaded from the net randomly as we could not get any database for experimenting.

6 Future Work After PCNN we are going to concentrate on the main Neural Network to detect the faces from the rectangular blocks selected by the PCNN. This NN should be able to clear out all the false detections passed on by PCNN.

References 1. Rowley, H.A., Baluja, S., Kanade, T.: Neural Network based face detection. In: Proc. IEEE Conf. on Computer Vision and Patern Recognition, San Francisco, CA, pp. 203–207 (1998) 2. Zuo, F., Peter, H.N.: Cascaded face detection using Neural Network Ensembles. EURASIP Journal in Advances in Signal Processing (January 2008) 3. Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34–58 (1999) 4. Sakhi, O.: Face Detection Program For Matlab 7.0, http://www.mathworks.com

Low Voltage Low Power Op Amp with Gain Boosting and Frequency Compensation Technique for Battery Powered Applications K. Sarangam and Hameed Zohaib Samad Department of Electronics and Communication Engineering National Institute of Technology Warangal, India {zohaibhameed007,sarangam.nitw}@gmail.com

Abstract. Designing analog circuit to operate at low voltage levels for applications like battery powered analog and mixed mode electronic device is the need of the day. MOS devices are required to be used in the weak inversion region or the sub-threshold inversion region to minimize DC source power. In this paper the design of low voltage low power operational amplifier operating at ±1V with power consumption of 110µW has been implemented in 0.18µm CMOS technology. Simulated results of the amplifier using CADENCE, Spice simulation tool showed a DC gain of around 70dB with GBW of 20MHz, phase margin of 330.The designed op amp has various advantages over its existing counter-parts like high gain, GBW, phase margin with the only relevant drawback being marginal power dissipation enhancement making it suitable to be used in battery-powered applications.

1 Introduction With the growing demand for low power mixed signal integrated circuits for portable or non-portable high performance systems, analog circuit designers are challenged with making analog circuit blocks with lower power consumption with little or no performance degradation. The operational amplifier (op amp), which is without doubt the most versatile analog building block, is also the most vulnerable to the reduction of supply voltage. Most of the available op amp in the market are not concerned about the voltage parameter and have biasing voltage in excess of 3V to achieve the required performance. This may not be a problem in most of the cases but in applications involving battery powered portable devices the magnitude of the voltage as well as the power dissipation plays an important role. Other high gain CMOS op amps have been investigated in the previous work [1-5] but most were unable to achieve gain more than 70dB with low power dissipation. These CMOS op amp designs had used up to 5 cascaded gain stages to achieve the high gain. In general, high gain architectures need more complicated compensation to stabilize the op amp and generally require more than one compensation capacitor. This paper discusses about an op amp architecture suitable for battery powered applications where high gain, high accuracy with less power dissipation is desired. The proposed op amp structure applies composite cascode connections in both the input V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 417–422, 2010. © Springer-Verlag Berlin Heidelberg 2010

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stage and the second stage to achieve a gain of around 70 dB with low power consumption. The op amp employs the traditional two gain stages followed by a near unity gain buffer stage. It also uses simple miller compensation technique to stabilize the op amp. This op amp overcomes some limitations of conventional CMOS cascode by enhancing the gain without using additional bias circuits and requires only a small bias headroom voltage. The proposed mixed mode design methodology, which comprised of mathematical derivation, system level simulation (CADENCE, Spice Simulation) is discussed in this paper.

2 Need for Gain Boosting Techniques Considering an op amp configuration without any gain-boosting technique consisting of rail-rail input stage in order that the whole dynamic range is utilized; biasing circuit so that the trans-conductance (gm) remains constant; summing circuit to sum the output current of each differential pair using balanced configuration so that the output node is not affecting the differential pair directly it was observed that the power dissipation was extremely less but at the cost of reduced gain. The gain was around 20dB with lesser GBW (around 1MHz). This severely limited the application of the balanced operational amplifier circuit where it could not be used for high frequency related applications. Increasing GBW value was possible by increasing the bias current or W/L ratio but this again lead to enhanced power dissipation. The phase response study of such a configuration showed problem of oscillation mainly due to the unstable circuit.

3 Low Voltage Low Power Op Amp with Gain Boosting Several cascode connection techniques such as conventional cascode or folded cascode can be used in op amp designing. The conventional cascode connection offers advantages like increased gain but suffers from disadvantages like restriction of input as well as output voltage swing making it unsuitable for battery-powered applications. To overcome the limited input common mode voltage and possibly improve the output swing of the conventional telescopic cascode connection, a structure called the “folded cascode” can be used [1, 3, 9] but it suffers from major drawback like enhanced power dissipation. There is a need for certain technique that can be used in low voltage low power op amp designing 3.1 Composite Cascode Connection If the conventional cascode structure is changed to bias the upper MOS device in a way that has less effect on the output voltage swing, the output impedance of the connection may be increased with sufficient output swing at low supply voltage. One practice is to make both of the gates of upper and lower MOS driven by the input signal and share a single bias source. This composite cascode approach, which combines the regular active devices with weak inversion devices, is promising in low voltage low power op amp design. In order to reduce the bias headroom voltage required for a conventional cascode, Comer et al. [6, 7, 8] proposed the composite cascode connection.

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4 Designing of Composite Cascode For optimal operation, W/L ratio of upper MOS (M2) is kept larger than that of lower (M1), i.e., m>>1. The effective gm for the composite transistor is approximated gm (effective) = (gm2/m) = gm1 For the composite transistor to be in saturation region M2 have to be in saturation and M1 in linear region. For these transistors, the currents ID1 and ID2 are given as ID1 = β1(Vin - VTN – (VX/2))VX (Ohmic region) 2

ID2 = (β2/2) (Vin - VX - VTN) (Saturation region)

(1) (2)

From the above Eq. (1) and (2) we get ID2 = [(β2/β1)/(2×(β2 + β1))][Vin - VTN]2

(3)

βeffective = (β2/β1)/( β2 + β1) & β2 = m β1 = [m/(m+1)] β1 = [1/(m + 1)] β2 & for m >> 1, βeffective ≈ β1 The operating voltage of regular cascode is much higher than that of self-cascode and hence a self-cascode structure can be used in the low voltage design. The advantage offered by self-cascode structure is that it offers high output impedance similar to a regular cascode structure while output voltage requirements are similar to that of a single transistor. 4.1 Stages in Composite Cascode Op Amp As shown in Fig 1a, the first stage is the differential-to-single ended practical composite cascode stage with current mirror load. The composite cascode connection in both N-channel and P-channel allows the input to swing from the positive power supply to the negative power supply. P-channel input devices provide for good power supply rejection. Moreover, the gain in the weak inversion region is relatively independent of drain current. In the composite cascode configuration, the output transistor is selected to have a much higher W/L aspect ratio than that of the lower transistor. This allows a simple adjustment of the tail current to place the drain connected device M2 in the weak inversion region while the source connected device M1 operates in the strong inversion region. The transistors are scaled with a channel length of 180 nm, while allows better matching. Using CADENCE simulation tool we tried to match the theoretical value of 35dB. From the simulation in Fig. 2a it is evident that the gain was around 32dB when the circuit was simulated using the designed W/L values as shown in Table 1a. Fig. 2b is the simulation of phase response of differential composite cascode stage using CADENCE tool. One can observe that initially the phase is 1800 but with increase in frequency the phase response stabilizes to around 900. The second stage was a common source stage implemented as a composite cascode (Fig. 1b). W/L values were determined (Table 1a) such that the gain of this stage was 35dB.

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(a)

(b)

Fig. 1. (a) Spice Schematic of input differential composite cascode (b) Spice Schematic of common source stage using composite cascode

An output source follower with current sink load can be employed in overall schematic of operational amplifier where the output stage is used as a buffer to drive an external load. A source follower with a current-sink load [1, 3] can be used as shown in Fig. 3. This output buffer stage had low output impedance which allowed loading by a large capacitive load or small resistive load.

(a)

(b)

(c)

(d)

Fig. 2. (a), (c) CADENCE simulation of frequency response of differential composite cascode stage (Gain: 32dB) & final composite cascode respectively (b), (d) CADENCE simulation of phase response of differential composite cascode & final composite cascode respectively.

4.2 Composite Cascode OpAmp The overall schematic shown in Fig. 3 consisted of composite cascode current mirror where the mirror provided the bias current to the input stage and the bias voltage to the second stage. The bias current for the input differential stage was only 3.65μA. This P type current mirror was also connected in the composite cascode structure in order to provide the robust bias current and voltage which can self adjust by tracking of the corresponding variation of the other composite cascode stages.

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Fig. 3 shows the overall schematic of CMOS operational amplifier using composite cascode stages with two gain stages followed by a buffer output stage. The entire op amp design does not use any extra bias voltage or current supplies. The only bias circuit is built inside the op amp chip to allow self adjustment.

Fig. 3. Composite Cascode Operational Amplifier

Fig. 2c shows the overall gain of the amplifier when both the input as well as the second stage is connected which is close to the expected 70dB i.e. 35dB + 35dB. There was also reduction in bandwidth of the circuit which could be due to the compensation capacitor used. The phase response shown in Fig. 2d was when the compensating capacitor is connected so that the phase margin could be increased. One can clearly observe that the phase was quite stable and for higher value of frequencies there was hardly any change in the phase which was fixed at -900. Table 1. (a) Transistor Dimensions of schematic in Fig.7 (b) Specification of overall schematic of composite cascode op amp

(a)

(b)

Transistor

W/L

M1, M2 M3, M4 M5, M6 M7, M8 M11

115 5 50 1 4

Parameter DC Gain (dB) GBW (MHz) PM (0) SR+/SR- (V/µs) Ts+/Ts- (µs)

Result 70 20 33 0.24/0.76 4.30/1.39

5 Conclusion This paper reports simulation results of ±1V CMOS cascode op amp consisting of three stages mainly input stage and common-source stage which provides overall voltage gain high enough for the op amps and a source-follower stage that acts as a buffer. By selecting the appropriate current and choosing the aspect ratios of transistors

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ingenuously, some MOS device were made to operate in sub-threshold while the remainder in the active region making it very useful for low power applications. A phase margin of around 330 was achieved using conventional Miller compensation with a capacitor of only 3.5pF. Spice simulations were mainly used to decide the appropriate architecture while CADENCE simulation in 0.18µm CMOS process showed enhanced gain of around 70dB with much lower power dissipation i.e. 110µW with CMRR and PSRR above 100dB, GBW of 20MHz and phase margin of 330 after employing Miller compensation.

References 1. Johns, D., Martin, K.: Analog Integrated Circuit Design. John Wiley & Sons, Inc., New York (1996) 2. Huijsing, J., Hogervorst, R., de Langen, K.-J.: Low-power low-voltage vlsi operational amplifier cells. IEEE Transactions on Circuits and Systems 42(11), 841–852 (1995) 3. Allen, P.E., Holberg, D.R.: CMOS Analog Circuit Design, 2nd edn. Oxford University Press, New York (2002) 4. Black, W., Allstot, D., Reed, R.: A high performance low power CMOS channel filter. IEEE J. Solid-State Circuits SC-15(6), 929–938 (1980); Rajput, S.S., Jamuar, S.S.: Low Voltage Analog Circuit Design Techniques (2002) 5. Coban, A.L., Allen, P.E.: A 1.75 v rail-to-rail CMOS op amp. In: Proc. of IEEE International Symposium on Circuits and Systems, vol. 5, pp. 497–500 (June 1994) 6. Comer, D.J., Comer, D.T.: Fundamentals of electronic circuit design. John Wiley & Sons Inc., New York (2003) 7. Galup-Montoro, C., Schneider, M.C., Loss, I.J.B.: Series-parallel association of fet’s for high gain and high frequency applications. IEEE J. Solid-State Circuits 29(9), 1094–1101 (1994) 8. Comer, D.J., Comer, D.T., Petrie, C.: The utility of the composite cascode in analog CMOS design. International Journal of Electronics 91(8), 491–502 (2004) 9. Razavi, B.: Design of analog CMOS integrated circits. McGraw-Hill, New York (2001)

Performance of Clustering in Mobile Domain Soumen Kanrar1 and Aroop Mukherjee2 1

DIATM –Durgapur-12, West Bengal, India soumen [email protected] 2 NEFD –TN- India [email protected]

Abstract. The topology of the ad hoc network has a significant impact on its performance. The dense topology has produce high interference and low capacity while the thinly scattered topology is Vulnerable to link failure. Some research work has been done on topology control in wireless networks. The existing topology control algorithms utilize either a purely centralized or purely distributed approach. In this work we have presented the traffic load analysis of the mobile node in the wireless network, by used of cluster concept. The wireless domain has been partitioned into different zones or clusters. Here we present analytically, the hops distance between random cluster heads. We have considered the minimum hops distance between the clusters and shown the traffic load at the gateway node, destination node. We considered that all the nodes randomly move. In this paper we have shown, how clustering affects on the performance with respect to throughput, delay, packet sent and packet received by simulation. Keywords: Topology, Performance, Wireless, Channel, Packet, Cluster.

1 Introduction In ad hoc networks, where nodes are deployed without any preconfigured infrastructure and communicate via multihops wireless links. The network topology is autonomously formed based on the node’s location and communication ranges. The network topology has a huge impact on the performance of the network. A dense topology may induce high interface, which in turn, reduces the effective network capacity due to limited spatial reuse and causes unnecessarily high energy consumption. A sparse topology is vulnerable to network due to the link failure. There has been some research work done on topology control for ad hoc wireless networks. The earlier works of topology control can be found in [1, 2]. In [3], Hou et al. studied the relationship between transmission range and throughput. An analytic model was developed to allow each node to adjust its transmitting power to reduce interference and hence achieve high throughput. Several topology control algorithms based on transmission power adjustment have been proposed. Where topology control is defined as the problem ofïassigning transmission powers to the nodes. So that the resulting topology achieves certain connectivity properties and some function of the transmission powers is optimized. Centralized algorithms [11] [12] [13] relay on the assumption that the locations of all of the nodes are known by a central entity. In order to calculate the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 423–429, 2010. © Springer-Verlag Berlin Heidelberg 2010

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transmission powers that result in a topology with strong connectivity i.e. ( k- connectivity for k ≥ 1). In general the centralized algorithms are not scalable. The distributed algorithms [ 13] [14] [15] are generally scalable and adaptive to mobility due to the fact that each node relies on local information collected from nearby nodes to autonomously compute its appropriate transmission power. The strong connectivity neither easily achieves nor guaranteed. In [1], a distributed algorithm was developed for each node to adjust its transmitting power to construct a reliable high-throughput topology. Minimizing energy consumption was not a concern in both works. Recently, energy efficient topology control has become an important topic in ad hoc wireless networks. Most of the works have been focused on the construction and maintenance of a network topology with required connectivity by using minimal power consumption. The performance of the topology control algorithms achieved by optimizing some of objective function. The major objective functions are the minimize the maximum power used by any node in the network. The other objective function minimizes the total power used by all of the nodes in the network. This is equivalent to the minimizing the average power used by the nodes. In ref [4] Piyush Gupta says theoretical analysis implies that the throughput of each node declines rapidly while the number of nodes increases. Most of them have proposed clustering technique based on the connectivity of nodes [4]-[9]. Many clustering algorithms have been proposed to partition mobile users into clusters to support routing and network management [4]-[10]. Ad hoc network (MANET) is totally wireless and infrastructure less. The packet is forwarded from the source to destination through single hop or multi hops. If the packets are sent to the nodes that belong to the same cluster then the packet covers at most two hops .If the packet is sent to the destination nodes which belongs to the other cluster in the network the packet have to move through at least three hops. The major advantage in the clustering concept is that the spatial reuse of the mobile node. The head node holds only the information about the nodes which belongs to that cluster only and the gateway node holds all the topological information about the other gateway nodes that belongs to whole network. Clearly it will reduce the overhead at the head node. It have been seen that it will reduce the size of the routing table at the gateway nodes. Due to the random movement of the mobile node, it’s required that the position information of the mobile node is updated efficiently and quickly. The important part of the clustering concept, for searching a destination node in the wireless domain is to find the corresponding cluster gateway. The cluster gateway forwards the packet to the head node of that cluster. The destination node is a one hop from the head node. In this paper, we have reduced the number of packets to search the destination node. If there were no clustering concept, the network generates and broadcast the search packet. If the wireless domain contains N number of nodes

N2 − N then at least ( ) numbers of packets have to be generated and broadcasted in 2 the wireless domain to find destination node. The destination node belongs to the different cluster and is more than two hops away. For the large number of N it’s a heavy load and burden to the wireless network. For the wireless domain with

N nodes the maximum number of possible form of clusters are ( 2 N − N − 1 ).

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Each cluster has at least three nodes such that one node is normal node and the other nodes are head node and gateway node.

Fig. 1. Topological View of Clusters

In the figure -1 we see that there are 29 nodes form four clusters in which every cluster has more than three nodes. One of the nodes is the head node and other nodes are gateway node and normal nodes. Figure 1 represents the normal node which sends the data packet to the head node of that cluster. The head node of that cluster forwards packet to the gateway node. The gateway node searches for the gateway node of the destination cluster. If it is neighbor than forwarded otherwise it will forwarded to the gateway node, which has the least hop count number of the destination cluster. Now if the source node belongs to cluster 1 and the destination node belongs to the cluster 3. According to the figure the data packet is forwarded to the nearby cluster gateway i.e. to the cluster2 .The Gateway node of the cluster 2 forwards to the gateway node of the destination node i.e. to the gateway node of the of the cluster 3. The gateway of the cluster node forwards the data packet to the head node of the cluster 3. The destination node is one hop from the head node of the cluster 3. The packet is then forwarded to the destination node. All nodes employ a common range r for all their transmission. When node

X i transmits to a node X j over the mth sub channel this

transmission is successfully received by within the value and

X j if the distance between X i and X j is

r , i.e. | X i − X j |≤ r for i ≠ j and i, j ∈ ` , note that if X i

X j belongs to the same cluster that are one hops distance and if the X i and X j

belongs to different cluster then, channel is

X i simultaneously transmitting over the same sub

| X i − X j |≥ (1 + δ )r ,where δ ; 0 .

2 Analytic Model The wireless network can be modeled as a unidirectional graph G = (V, E), where V is the finite set of nodes and E is the finite set of edges i.e. the link in the wireless network. The geographical area S that contains all the mobile nodes is partitioned into m regular clusters in any shape like cell, hexagon, square or triangle shapes or grid. The node has the transmission range (r). If the total area of wireless domain that the mobile node cover is A square unit and the edge length of the grid is (kr ) , where

k ∈ \;0 . So the number of cluster required is A/(kr)2 to cover the total wireless

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domain. Now if the cluster head is located at the center of the wireless domain then the transmission of the node be considered as ring of circle centered at the head node with radius i.r where, i =1,2,…….k/2. As the nodes are uniformly distributed over the wireless domain then the expected number of hops required from head node to the randomly chosen node is k /2

π ( ir ) 2 − π ( i − 1) 2

i =1

( ik ) 2

∑

.i

(1)

Now if the number of cluster which is ( J ) hops away from some specific cluster we nomenclature it as N ( J ) . Every cluster is marked with the hops distance from the specific cluster. So the expected number of hops count from specific cluster to the randomly selected cluster is l (i ) =

∑

(

j

N ( j)

∑

). j

N (J )

(2)

j

If the wireless domain contains M number of clusters, then the expected number of hops between two clusters in the wireless domain represented as M

1 M

∑ l (i )

(3)

i =1

Let the mean distance to be traversed by a packet be L and r be the range of transmission. Then the mean number of hops traversed by the packets is

≈

L r

(4)

by considering the equation (3) and (4) we get, 1 M

M

∑

i=1

l (i) ≤

L r

(5)

In a random scenario nodes are randomly and uniformly distributed on the wireless domain, each node has a randomly chosen destination in the different cluster to which its wishes to send λ bits/sec. If the channel is broken into several sub channel of capacities W1 , W2 ,…..,

M

WM bits/sec as long as ∑ W m = W , where M is the number m =1

of clusters . To cover the path from source to destination through different cluster, (source and intermediate nodes) it generates at least

L.λ bits/sec of traffic for other r

nodes. Since the total number cluster is M in the wireless domain. The total traffic L .λ .( M + l ) bits /sec for ( M numbers of gateway node generated is not less than r and l number of ordinary and head nodes). Each capable of W bits/see. We

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get, L .λ .( M + l ) ≤ M .W . An upper bound on the throughput is therefore r W .r clearly to increase the throughput the number of hop traversed by each λ ≤ L packet to be reduced.

3 Simulation Result Figure 2 represents the traffic load in the wireless domain. The simulation result represents two curves. One for the packets generated to search the destination node and send data packet from the source, when there were no cluster concept used in the mobile domain. The other curve represents the traffic load in the wireless domain i.e. the packets generates to search the destination node from the source node by using the clustering concepts and minimum number of hops counts via – equation (3) . The figure-2 shows that the traffic load reduced in wireless domain by using the clustering concept.

Fig. 2. Traffic Vs Number of Nodes

In the figure -3 and figure-4 we present the simulation of delay, throughput and traffic load. The figure -3 presents the traffic scenario at the destination node with respect to the packet sent from the source node. During 0 to 220 seconds the mobile node belongs to a particular cluster and data sends through the sharing of channels. During the time 220 to 600 seconds the nodes (source) goes to new cluster and during the time 610 seconds onwards the source node come to its original cluster. The simulation result clearly reflects the scenario of node movement between the clusters with respect to the delay and traffic load curves.

Fig. 3. Traffic Load Scenarios

Figure -4 represents the scenario of traffic load and delay throughput at the intermediate node i.e. at the gateway node. During the time 220 seconds to 600seconds the source node moves to new cluster and after the 610seconds the source node back to the

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old cluster. The delay becomes high and the through put maintain a level due to the fact that data comes to the gateway node via other gateway node of the other clusters.

Fig. 4. Delay-Throughputs

4 Conclusion In this work we have shown that by using efficiently clustering in the wireless domain reduces the traffic load in the wireless network. By simulation we have shown how the delay is increased if the intermediated node i.e. the gateway node and the sender node randomly moves between the clusters. In further work, cost effective algorithm will be designed to reduce the delay and unnecessary traffic load in the network when the source and the gateway node moves randomly in the clusters.

References [1] Zhu, C., Corson, M.S.: QoS routing for mobile ad hoc networks. In: IEEE INFOCOM 2002 (2002) [2] Lin, C.R., Liu, J.S.: QoS routing in ad hoc wireless networks. IEEE Journal on Selected Areas in Communications 17(8), 1426–1438 (1999) [3] Hou, T., Li, V.O.K.: Transmission range control in multihop packet radio networks. IEEE Trans. on Communications 34(1), 38–44 (1986) [4] Gupta, P., Kumar, P.R.: The capacity of wireless networks. IEEE Transactions on Information Theory 46(2) (March 2002) [5] Ramanathan, R., Steeenstrup, M.: Hierarchically-organized, multihop mobile wireless networks for quality of service support. Mobile Networks Appl. 3(1), 101–119 (1998) [6] Lin, C.R., Gerla, M.: Adaptive clustering for mobile wireless networks. IEEE J. Select. Areas Commun. 15, 1265–1275 (1997) [7] McDonald, A.B., Zanti, T.F.: A mobility based frame work for adaptive clustering in wireless ad hoc networks. IEEE J. Select. Areas Commun. 17, 1466–1487 (1999) [8] Vaidya, N.H., Krishna, P., Chatterjee, M., Pradhan, D.K.: A cluster –based approach for routing in dynamic networks. ACM Comput. Commun. Rev. 17(2) (April 1997) [9] Gerla, M., Tsai, J.T.-C.: Multicluster mobile multimedia radio network. ACM J. Wireless Networks 1(3), 255–265 (1995) [10] Ephremides, A., Wieselthier, J.E., Baker, D.J.: A Design Concept for Reliable Mobile Radio Networks with frequency Hopping Signaling. Proc. of IEEE 75(1), 56–73 (1987) [11] Kirousis, L.M., Kranakis, E., Krizanc, D., Pelc, A.: Power Consumption in Packet Radio Networks. In: Reischuk, R., Morvan, M. (eds.) STACS 1997. LNCS, vol. 1200, pp. 363– 374. Springer, Heidelberg (1997)

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[12] Lloyd, E.L., Liu, R., Marathe, M.V., Ramanathan, R., Ravi, S.S.: Algorithmic Aspects of Topology Control Problems for Ad Hoc Networks. In: Proc. IEEE Mobile Ad Hoc Networking and Computing (MOBIHOC) (June 2002) [13] Ramanathan, R., Rosales-Hain, R.: Topology Control of Multihop Wireless Networks Using Transmit Power Adjustment. In: Proc. 19th Ann. Joint Conf. IEEE Computer and Comm. Soc. (INFOCOM), pp. 404–413 (March 2000) [14] Li, L., Halpern, J.Y., Bahl, P., Wang, Y.-M., Wattenhofer, R.: Analysis of a Cone-Based Distributed Topology Control Algorithms for Wireless Multi-Hop Networks. In: Proc. ACM Symp. Principle of Distributed Computing (PODC 2001) (August 2001) [15] Wattenhofer, R., Li, L., Bahl, P., Wang, Y.-M.: Distributed Topology Control for Power Efficient Operation in Multihop Wireless Ad Hoc Networks. In: Proc. Infocom 2001 (April 2001)

Morphological Analyzer for Telugu Using Support Vector Machine G. Sai Kiranmai1, K. Mallika2, M. Anand Kumar3, V. Dhanalakshmi4, and K.P. Soman5 1

2,3,4

Post Graduate Student, Amrita Vishwa Vidyapeetham, Coimbatore Research Associates, Department of CEN, Amrita Vishwa Vidyapeetham, Coimbatore 5 Research Head, Department of CEN, Amrita Vishwa Vidyapeetham, Coimbatore [email protected], [email protected], {m_anandkumar,v_dhanalakshmi,kp_soman}@cb.amrita.edu

Abstract. In this paper, we presented a morphological analyzer for the classical Dravidian language Telugu using machine learning approach. Morphological analyzer is a computer program that analyses the words belonging to Natural Languages and produces its grammatical structure as output. Telugu language is highly inflection and suffixation oriented, therefore developing the morphological analyzer for Telugu is a significant task. The developed morphological analyzer is based on sequence labeling and training by kernel methods, it captures the non-linear relationships and various morphological features of Telugu language in a better and simpler way. This approach is more efficient than other morphological analyzers which were based on rules. In rule based approach every rule is depends on the previous rule. So if one rule fails, it will affect the entire rule that follows. Regarding the accuracy our system significantly achieves a very competitive accuracy of 94% and 97% in case of Telugu Verbs and nouns. Morphological analyzer for Tamil and Malayalam was also developed by using this approach. Keywords: Machine learning, Support Vector Machine, Natural Language Processing, Morphemes, Paradigms and classification.

1 Introduction Dravidian language Telugu is a highly inflectional and agglutinative language providing one of the richest and challenging set of linguistic and stastical features. For the purpose of analysis of such inflectionally rich languages, the root and the morphemes of each word have to be identified. In Telugu each inflected word starts with root and is having so many suffixes. The word suffix is used here is referred to inflections, postpositions and markers, indicating tense, number, person and gender, negatives, imperatives [5].

2 Machine Learning and Support Vector Machine A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data [1]. A machine learning V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 430–433, 2010. © Springer-Verlag Berlin Heidelberg 2010

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methodology called support vector machine (SVM) is used for training the data [3]. One of most important application of machine learning is natural language processing (NLP) and its main aim is to design and build software that will analyze, understand languages. The first step involved in NLP is morphological analysis [6]. In this paper we presented a morphological analyzer for Telugu language by using machine learning approaches (supervised machine learning approach). In supervised learning we are using pair of input data and desired output data for training.

3 Data Creation for Supervised Learning Data creation plays a major role in supervised machine learning is approach. The first step involved in the data creation (corpora development) for morphological analyzer is classifying paradigms for verbs and nouns. All the paradigms of verbs and nouns are classified depending on suffixes. In second step we have to collect the list of words which will fall under the paradigms of verbs and nouns. Third step of Data creation is pre processing. Following Fig. 1 explains the steps involved in preprocessing for creating the corpora of morphological analysis [2].

Table 1. Sample Data Format

Fig. 1. Preprocessing steps

Fig. 2.

The preprocessing steps are Romanization, Segmentation, Alignment and mapping. In Romanization we are using Unicode to Roman mapping file for generating input and output structures. In Segmentation each and every word in the corpora is segmented based on the Telugu grapheme and additionally each syllable in the corresponding word is further segmented into consonants “C” and vowels “V”. Morpheme boundaries are indicated by “*” symbol in output data. The segmented syllables are aligned horizontally is shown in Table 1. Here the input segmented syllables are consequently mapped with labeled output segmented syllables. 3.1 Mismatching It is the key problem in mapping between the input and output data. Mismatching occurs in two cases i.e., either the input units are larger or smaller than that of the output units. This problem is solved by inserting null symbol “$” or combining two units based on the morph-syntactic rules to the output data.

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Case 1: Input sequence

Output sequence (Mismatching)

l-C E -V s t -C u -V n n -C A -V n -C u - V

l E* t u n n A* n u *

(10segments)

(9segments)

Corrected sequence l E* $ t u n n A* n u *

(10segments)

Case 1 shows the input sequence is having more number of segments than the output sequence. Telugu verb lEstunnAnu is having 10 segments in input sequence but in output it has only 9 segments. The occurrence of “s” (3rd segment) in the input sequence becomes null due to the morphosyntactic rule. So there is no segment to map with that “s” (3rd segment) in output sequence. For this reason, in training data “s” (3rd segment) is mapped with “$” symbol ($ indicates null). Now the no. of input units are equal to the no. of output units is shown in corrected output sequence. Case 2: Input sequence:

Output sequence (Mismatching)

A v - C A -V m - C e -V

A v u* A m e*

(5segments)

(6segments)

Corrected sequence A vu * A m e *

(5segments)

Case 2 shows the input sequence is having less number of units than the output units. Telugu noun AvAme is having 5 units in input sequence but output has 6 units or segments. Due to morphosyntactic change the unit “v-C” in the input sequence is mapped to two segments “v, u*” in output sequence is shown in corrected output sequence. For this reason in training “v-C” is mapped with “vu*”. Now the input and output sequences are having equal no. of units. Following example shows the case where case 1 and case 2 will occur together. Example noun Urikeduru. Input sequence: U r-C i-V k -C e -V d -C u -V r-C u -V

(9segments)

Output sequence (Mismatching) U

r u * i* k e d u r u *

(10segments)

Corrected sequence U ru * i* $ e d u r u *

(9segments)

4 Implementation of Morphological Analyzer Using machine learning approach the morphological analyzer for Telugu is developed. We have developed separate engines for nouns and verbs. Morphological analyzer is redefined as a classification task using the machine learning approaches. Three phases are involved in our morphological analyzer. 1) Preprocessing 2) Segmentation of morphemes 3) Identifying morphemes which were explained in [2]. Figure 2 gives a Schematic Representation. 4.1 SVMTool and System Evaluation The SVMTool is a simple and effective generator of sequential taggers based on SVM. Here this tool is used in morphological analyzer for classification task. The three main components of SVMTool is learner (SVMTlearn), tagger (SVMTagger) and the evaluator (SVMTeval) [4]. The morphological analyzer system for verb and noun are trained with 2, 00,000 and 1, 50,000 word forms respectively. This system is

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also tested with 70,000 verb forms and 50,000 nouns forms. The SVM based machine learning tool affords better results compare to others. In case of testing SVM holds good result.

5 Conclusion This paper describes the morphological analyzer based on the Support Vector Machine (SVM) a new state of art. We have not used any morpheme dictionary but from the training model our system has identified the morpheme boundaries. The accuracy obtained from the different machine learning tools shows that SVM based machine learning tool gives better result than other machine learning tools. A GUI to enhance the user friendliness of the morphological analyzer engine was also developed using Java Net Beans.

References 1. Dietterich, T.G.: Machine Learning. In: Wilson, R., Keil, F. (eds.) The MIT Encyclopedia of Cognitive Sciences, pp. 497–498. MIT Press, Cambridge (1999) 2. Anand Kumar, M., Dhanalakshmi, V.: A Novel Approach to Morphological Analysis for Tamil Language. University of Koeln Koln, Germany (October 2009) 3. Soman, K.P.: Machine Learning with SVM and other Kernel methods. Prentice-Hall of India Pvt. (2009) 4. Gimenez, J., Marquez, L.: SVM Tool, Technical Manual, Universitat Politenica deCatalunya (August 18, 2006) 5. Brown, C.P.: The Grammar of the Telugu Language. Laurier Books Ltd., New Delhi (2001) 6. Ritchey, T.: General Morphological Analysis A general method for non-quantified modeling. Presented at the 16th EURO Conference on Operational Analysis Brussels (2002-2006)

Visualization of State Transition Systems in a Parallel Environment Subbu Ramanathan, Haresh Suresh, Amog Rajenderan, and Susan Elias Department of Computer Science and Engineering Sri Venkateswara College of Engineering, Tamil Nadu, India [email protected], [email protected], [email protected], [email protected]

Abstract. Visualizations predominantly require high computational and communication complexity. In extremely large scale, real world applications, the efficiency in question, is debatable. Parallelization, being one of the current methods of increasing efficiency, is pertinent to the divide and conquers strategies used in most visualization algorithms today. Our choice for parallelization, are state transition systems, owing to the simplistic applicability of the aforementioned strategy, and the use of simple data structures. Interactive systems that lend themselves to parallelization, when implemented, drastically reduce computation time and hence greatly improve performance, in concurrence with Amdahl’s law. Keywords: Parallel Algorithms, Visualization, State Transitions, Boost MPI.

1 Literature Review Being a relatively new field, there is very little available literature in the field of transition systems visualization. The principal work in this field of was predominantly laid down by Frank van Ham et al. His work includes large scale graph visualization [1] using ASK-GraphView, a node-link-based visualization system, and small world graph visualization which uses a combination of semantical and geometrical distortions. Particularly relevant to state transition systems, his work involves clustering nodes, forming interactive cone tree structures [2][3].

2 Description of the Visualization Process The aim of the visualization process is to generate a clustered 3D structure of a real world system. One of the ways by which this can be implemented is with the help of cone trees and their associated properties. The created 3D structure is a state transition system that can be used to study both the macro and micro level relations that exist between the system parameters. The transitions from one state to the other in the 3D structure models the dynamics of the real world system. This includes interactive features like rotation, zooming, finding the shortest path between start to the end node and such that aids in understanding the real world system in detail. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 434–436, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. The Program Model as explained in Section 3

3 Concept Implementation MPI Process Manager manages the parallel pathways, via 'send' and 'receive' functions across processes. It initiates the server manager on the server side and the client manager on the client side. Server Process Manager gets the input graph from the input file and starts the event manager. It also uses a pre-defined template ‘data structure’ to be dumped into the database for data analysis. The Client Manager starts the event delegator comparable to the event manager at the server side. The Event Manager fills the events queue with computations that need to be performed and then dispatch them to the event delegator. The Event Delegator then schedules those computations to any of the following modules: Adjacency Matrix Generation, Ranking, Generation of Modified Adjacency Matrix, Formation of Small Clusters, and Formation of Large Clusters. Since each of these steps in the algorithm must be executed only after all its preceding steps have been completed, the parallel environment employs parallelization concept by finding parallel pathways within each step and eventually performing the steps serially.

4 Data Structure i. ii. iii. iv.

File structures have been used as input to the algorithm. Adjacency matrix is used to represent the graph connectivity. In functions ranking and formation of small cluster, a one dimensional linear array of nodes (vertices) are been implemented. In formation of large cluster function, grouping of small clusters is done by implementing linked lists, with the next field pointing to the start node of the next small cluster.

5 Statistical Data Prior analysis indicate an approximate 31% processing time increase during search and sort procedures commensurate with input size when utilizing a serial rendering

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algorithm. The performance of the serial algorithm is therefore, limited by the available memory and the speed of the graphics card utilized. The algorithm that generates the global layouts has a time complexity that is linear in both the number of states and the number of edges. Switching from a single GPU render into a multi-GPU crossfire/SLI configuration has previously shown [4] performance increases by a factor of approximately 1.8. We therefore expect a strong decrease in computational time on implementation using Boost MPI. Further study and implementation of the parallel algorithm along with time analyses constitute our future planned course of research.

6 Conclusion In today's world where results are expected in even nanoseconds we feel that parallelization of serial algorithms a very important aspect of algorithm implementation. Especially in the field of visualization there is much to be gained by parallelizing algorithms which could potentially improve efficiency greatly and aid in handling larger communication and computational loads. Thus our future work will be in this direction, along with the implementation of an interactive visualization model. As well as pursuing parallelization in other technical domains.

References 1. Abello, J., van Ham, F., Krishnan, N.: Ask-GraphView: A large scale graph visualization system. IEEE Transactions on Visualization and Computer Graphics, 1077-2626, 669–676 (2006) 2. van Ham, F., van de Wetering, H., van Wijk, J.J.: Visualization of State Transition Graphs. In: INFOVIS Proceedings of the IEEE Symposium on Information Visualization, 1522404X, pp. 59–56 (2001) 3. van Wijk, J.J., van Ham, F., van de Wetering, H.: Interactive Visualization of State Transition Systems. IEEE Transactions on Visualization and Computer Graphics, 1077-2626 8, 319–329 (2002) 4. Helgeland, A., Elboth, T.: High-Quality and Interactive Animations of 3D Time-Varying Vector Fields. IEEE Educational Activities Department, 1077-2626 12(6), 1535–1546 (2006)

Facilitating Efficient Integrated Semantic Web Search with Visualization and Data Mining Techniques S.K. Jayanthi1 and S. Prema2 1 Associate Professor and Head, Department of Computer Science, Vellalar College for Women (Autonomous), Erode, Tamilnadu, India Tel.: 0-91-9442350901 [email protected] 2 Lecturer, Dept. of Computer Science, K.S.R. College of Arts and Science, Tiruchengode-637209, Namakkal district, Tamilnadu, India Tel.: 0-91-9842979939 [email protected]

Abstract. In the recent years, Data mining has attracted a great deal of attention in the information industry to turn huge volumes of data into useful information and knowledge. In this research work, it has been proposed to build Semantic Web Architecture for effective Information Retrieval and to display the result in visual mode. Hence, the first motivation of this paper is towards clustering of documents. The second motivation is to invent a data structure called BOOKSHELF for community mining in the search engine, using which the storage and time efficiency can be enhanced. The third motivation is to construct a novel semantic search engine to give results in visual mode. This paper proposes a web search results in visualize web graphs, representations of web structure overlaid with information and pattern tiers by providing the viewer with a qualitative understanding of the information contents. Keywords: Visualization, Intelligent Data Visualization, Data mining tool, Visual Data Mining, Data Dimension, Book Shelf Data Structure.

1 Introduction While searching the web, the user is often confronted by a great number of results, generally displayed in a list which is sorted according to the relevance of the results. Facing the limits of existing approach, this paper proposes exploration of new organizations [1] and presentations of search results, as well as new types of interactions with the results to make their exploration more intuitive and efficient. This research work is mainly focused on affording a knowledge mining tool in the form of a search engine that results list in visual mode in spite of Web page URLs as in the case of the existing conventional search engines. The main focus of this paper is the processing of the results coming from an information retrieval system. Although the relevance depends on the results quality, the effectiveness of the results processing represents an alternative way to improve the relevance V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 437–442, 2010. © Springer-Verlag Berlin Heidelberg 2010

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for the user. Then the proposed approach organizes the results according to their meaning using a BookShelf Data Structure, and visualizes them in a 3D scene to increase the representation space. This paper deals with the processing of query results. This processing, still neglected in some information retrieval systems, is becoming more and more important and essential. The two main points to reach this goal are a good document organization and an effective visualization. Concerning these two aspects, the main directions of this paper are a Clustering method and a 3D visualization.

2 Two Issues in Search Results Processing The search results processing has two main issues: results clustering and results visualization. For the first one, the goal is to find an effective method which allows to group similar results together using BookShelf Data Structure and to organize the various clusters. The second one is to find an effective visualization of the organized results. Many works have been done on search results visualization in the last few years (some examples can be found in [2]).A mixed interface for visualizing selforganized results is proposed in this section. This interface is composed of a 2D part (a Java applet) and a 3D scene [3] which represents the metaphor.

3 Literature Review Most previous search engine analysis research involved evaluating search engines using metadata in such areas as size, change over time, overlap, and usage patterns. In 1998 Lawrence et. al. analysed the reporting of search engines in proportion to the total size of the web. In the field of search engine performance evaluation Hawking et. al. [4] compared search engines using web search query logs and methods learned from TREC (Text REtreival Conference).Hawking et. al. [4] further compared the retrieval performance of eleven search engines based on usefulness of search results for finding online services. Chowdhury et. al. [5] compared search engines using known search results, a set of web search query logs, and a corpus with relevance judgments. Beitzel et. al. uses ODP and Looksmart along with a set of web search query logs to evaluate search engines. Gordon and Chowdhury [5] show that some search engines perform better than others for some queries. However, overall the search engines returned statistically similar results. The work in this paper extends this by doing a large scale analysis of the largest search engines and display the result in visual mode in common use today.

4 Proposed Domain Expert Search Engine The main intention of this research work is to implement the best page ranking algorithm with the help of a semantic based search engine and to make an environment through which people belonging to similar interest can share their domain specific

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knowledge. The complete architecture of the unified model of Domain Expert[6] is shown in (Fig.1).There are several major challenges to build this Domain Expert search engine[6] (i) Locating relevant documents from the Web (ii) Filtering semantic documents from the corpus (iii) Forming domain specific documents as Web communities (iv) Providing visualization results as graphs or maps. This unified semantic search engine architecture is designed with main components such as crawler, web repository, e-mail repository, lexicon, bookshelf indexer, barrels of text files and anchor files, sorter, URL resolver, client interface and Page ranker.By implementing all these idea, this semantic search engine entitled Domain Expert has been derived. A new derived data structure [7] named bookshelf shown in Fig.2 have been introduced in this search engine for forming communities.

Web

URL Page Ranking Visual Results

Domain

Result URLs Query Engine

Lexicon Indexer

Link Server

Sorter

Client

Web Page Repository

Crawlers

Bookshelf Data Structure

Index Server Store

Fig. 1. The Proposed Semantic Web Architecture

4.1 Bookshelf Data Structure Bookshelf data structure has been introduced for community formation, which stores the inverse indices of the WebPages. This data structure is formed by combining a matrix and list with dynamically allocated memory. This is an extended data structure of hash table and bi-partite core [7], which is used to store base domain and subdomain indices of various communities. A recent study [7] shows that 81.7%of users will try a new search if they are not satisfied with the listings they find within the first 3 pages of results. However it would be too restrictive to only consider the first 30 results (10 results per page). Indeed this study has been done on search engines with linear results visualization (ordered lists) and users may want to see more results on visualizations like web graphs.

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MBS1

Main Domain

MBS2

Bookshelf Sets

Sub –Domain Sets MBS3

MBS4 MBS5

Fig. 2. Bookshelf Data structure

5 BSDS + K-Means Algorithm In Book Shelf Data Structure each book shelf maintains a matrix Xi = (C1, C2, …, Ci, .., Ck), where Ci represents the ith cluster centroid vector and k is the cluster number. The fitness value is measured by the equation (1) below:

(1)

where mij denotes the jth document vector, which belongs to cluster i; Oi is the centroid vector of ith cluster; d(oi, mij) is the distance between document mij and the cluster centroid Oi.; Pi stands for the document number, which belongs to cluster Ci; Nc stands for the cluster number. 1. 2. 3.

At the initial stage, each shelf randomly chooses k numbers of document vectors from the document collection as the cluster centroid vectors. For each shelf: Calculate the fitness value based on equation (1). Repeating step (2) until one of following termination conditions is satisfied.

The maximum number of iterations is exceeded or the average change in centroid vectors is less than a predefined value.The K-means module will inherit the BSDS module’s result as the initial clustering centroids and will continue processing the optimal centroids to generate the final result. 1. Inheriting cluster centroid vectors from the BSDS module. 2. Assigning each document vector to the closest cluster centroids.

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3. Recalculating the cluster centroid vector cj using equation 2.

(2)

where dj denotes the document vectors that belong to cluster Sj; cj stands for the centroid vector; nj is the number of document vectors belong to cluster Sj. 4. Repeat steps 2 and 3 until the convergence is achieved.

6 Evaluations The Result analysis of the existing web search engines like Google, Yahoo, Alta Vista and MSN will give the result in random manner based on the content and the classifications will be like Definition, Encyclopedia, Applications and Current Technologies is summarized below in Fig.3.

35 30 25 20 15 10 5 0

De fin

iti on Ap pl ica tio ns En cy clo pe di Cu a r re nt N ew s Do wn lo ad s

Result Rate

Search Engine Result Analysis

Categories

Fig. 3. Result Analysis

Using Book Shelf Data Structure if the user is searching the concept for example Operating system then the web page results is given below in graph where each node represents the URLs and links represent the relationship between them in visual mode in spite of web pages as in the existing system. The simulated result is given in Fig.4.

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Applications 8

System Programs 2

15

1

19

10

16 9

1 Top 20 Results

4

1

2

Linux 7 Software

14

Definitions

5 1

18

Encyclopedia 3 Others

6 12

Server Operating

Fig. 4. Simulated Result

7 Conclusion This paper proposes an effective method for organizing and visualizing web search results. Domain Expert architecture for Semantic web mining is proposed and it is in the path of progress. Bookshelf Data Structure for organizing documents is defined for effective information retrieval to display the result in visual mode.

References 1. Dittenbach, M., Merkl, D., Rauber, A.: Using Growing Hierarchical Self-Organizing Maps for Document Classification. In: ESANN, pp. 7–12 (2000) 2. Mann, T.M.: Visualization of Search Results from the World Wide Web. University of Konstanz, Germany (2002) 3. Brath, R., Oculus, M.P.: Spreadsheet Validation and Analysis through Content Visualization (2006) 4. Hawking, D., Craswell, N., Griffiths, K.: Which search engine is best at finding online services. In: WWW Posters (2001) 5. Chowdhury, A., Soboroff, I.: Automatic evaluation of World Wide Web search services. In: SIGIR 2002: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 421–422. ACM Press, New York (2002) 6. Vijaya, K.: E-mail Id harvester to retrieve E-mail addresses of domain experts. In: National Conference on Current Trends in Computer Applications (2009) 7. Zamir, O.: Visualization of Search Results in Document Retrieval Systems. General Examination Report (1998)

Image Object Classification Using Scale Invariant Feature Transform Descriptor with Support Vector Machine Classifier with Histogram Intersection Kernel Biplab Banerjee, Tanusree Bhattacharjee, and Nirmalya Chowdhury* Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India [email protected]

Abstract. Recently much attention have been paid to region of interests in an image as they are useful in bridging the gap between high level image semantics and low level image features. In this paper we have proposed a method for classification of image objects produced by a standard image segmentation algorithm using multiclass support vector machine classifier integrated with histogram intersection kernel. SIFT is a relatively new feature descriptor which describes a given object in terms of a number of interest points. They are invariant to scaling, translation and partially invariant to illumination changes. This paper primarily focuses on the design of a fast and efficient image object classifier by combining the robust SIFT feature descriptor with intersection kernel SVM which is comparatively better than the existing kernel functions in terms of resource utilization. The experimental results show that the proposed method has good generalization accuracy. Keywords: SVM, Histogram Intersection kernel, SIFT.

1 Introduction Advances in earth observations sensors and GIScience have led to the emerging fields of Object-based Image Analysis (OBIA). In Object based Image Analysis, the image is first segmented to extract the regions from the image and after that some analysis is carried out based on the extracted objects. Image object classification is a promising subfield of image analysis where a previously unknown object is assigned its proper class label based on some training instances. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Basically SVM is a binary classifier which separates data points belonging to two different classes by using a hyperplane that maximizes the margin between the closest data points of the two classes. These data we use different kernel functions to map the data into some high dimensional space and try to draw the hyperplane there. Several binary SVM’s can be grouped to support multiclass classification. Tzosos has used SVM for Object based Image Analysis for remotely sensed imagery [1]. He had used multiclass SVM for classification purpose *

Corresponding author.

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integrated with linear, polynomial and radial basis kernel functions. Global and efficient self similarity is also used for object classification and detection [4]. Recently a new kernel function called histogram intersection kernel is proposed by Maji et.al. [2] which is very fast in term of time complexity than the traditional kernels. This paper focuses on the problem of image object classification. The image is first segmented using Otsu’s method of image segmentation which is based on adaptive histogram thresholding. From the segmented image, we extract the objects using the concept of connected component labeling. From the objects thus obtained, feature extraction step is carried out. We have worked with SIFT based descriptors and grey level co-occurrence matrix (GLCM) based texture feature descriptors. The color feature is not taken into account for this purpose as our primary intention was to categorize the objects based on its geometrical patterns. As objects belonging to same class may be of different color values, that is why we have focused on texture and local image features. We have trained the multiclass SVM classifier with the feature set thus obtained. Histogram intersection kernel is used for the purpose of nonlinear mapping of the data points into some higher dimensional space. The novelty of our work lies in combining the SIFT based feature descriptors with histogram intersection kernel SVM for image object classification purpose. The classifier so designed has shown high classification accuracy while speeding up the classification process without loss of generality. The rest of the paper is organized as follows. Section 2 describes the working of SVM classifier along with histogram intersection kernel. Section 3 describes the feature extraction in detail covering SIFT and GLCM based features. Our method is presented in section 4. Section 5 deals with some experimental results and discussion. Section 6 focuses on conclusion and future directions.

2 Support Vector Machine The basic idea of support vector machine classifier is based on structural risk minimization. The idea of structural risk minimization is to find a hypothesis h for which we can guarantee the lowest true error. The SVM approach seeks to find the optimal separating hyperplane between classes of data points by focusing on the training cases that are placed at the edge of the two individual hyper planes which are the class descriptors of those classes. These training cases are called support vectors. Training cases other than support vectors are not taken into account. This way, not only an optimal hyperplane separating the data points is fitted, but also less training samples are effectively used. This is called linear classification which separates the data points from two classes by a separating hyperplane. For non-linearly separable case, the data points are mapped into a higher dimensional space by using a suitable kernel function, and in that space, SVM tries to draw the separating hyperplane. The histogram intersection kernel is a relatively new kernel [2] which is very efficient and requires less time in computation. It is defined as, K (Ha, Hb) = ∑ min (Hai, Hbi), i=1,2,…L .

(1)

This is used as a measure of similarity between histogram Ha and Hb with L number of bins.

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3 Feature Extraction Feature extraction is a necessary step for any classification task. For image object classification purpose, the use of texture and shape features has proved to be quite effective for many applications. There are many ways for calculating texture feature descriptors. GLCM is one of them. Many descriptors can be obtained from the cooccurrence matrix calculated. The SIFT based descriptors describes a given object with respect to a set of interesting points which are invariant to scale, translation, partial occlusion and clutter. These feature descriptors have been used successfully for object recognition, robotic mapping etc. 3.1 GLCM Based Texture Feature Descriptors Texture features based on spatial co-occurrence of pixel values are probably the most widely used texture feature descriptors having been used in several application domains like analysis of remotely sensed images, image segmentation etc. Cooccurrence texture features are extracted from an image into two steps. First, pair wise spatial co-occurrence of pixels separated by a given angular value are computed and stored in a grey level co-occurrence matrix. Second, the GLCM is used to compute a set of scalar quantities that characterizes the different aspects of the underlying texture. We have worked with four GLCM based descriptors, namely, Contrast, Correlation, Homogeneity and Energy. 3.2 SIFT Feature Descriptors In computer vision, SIFT is used to detect and describe local features in an image. SIFT features are used for reliable matching between different views of the same object. The extracted features are invariant to scale, orientation and are partially invariant to illumination changes. The SIFT feature extraction is a four step process. In the first step, locations of the potential interest points are computed in the image by finding the extremas in a set of Difference of Gaussian (DOG) filters applied to the actual image at different scale-space. Then those interest points which are located at the areas of low brightness and along the edges are discarded. After that an orientation is assigned to the remaining points based on local image gradients. Finally local image features based on image gradient is calculated at the neighboring regions of each of the key points. Every feature is defined in the 4 x 4 neighborhoods of the key points and is a vector of 128 elements.

4 The Proposed Method The algorithm proposed in this paper is described in detail in the following section. 4.1 Image Segmentation and Object Extraction At first we segmented the given image using otzu’s method for image segmentation. This method is based on adaptive global thresholding which segment the given image by minimizing the within class variance. Once the image is segmented, we extract the

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objects from the segmented image using the concept of connected component labeling. In this way we separate each image objects and then from each object, we extract features. 4.2 Feature Extraction for Classification Purpose We have worked with GLCM based texture features and SIFT features. For each object, we computed 4 texture features, contrast, homogeneity, correlation and energy. For each object, the SIFT algorithm generates a feature vector of 128 elements. So each image object is now represented by a feature vector of 132 elements. 4.3 Train the Classifier From the feature set calculated in the way described above, we divide the set into two different sets randomly. The sets ate training set and testing set. The elements in the training set is of the form (X, Y) where X€€ R132 and Y= {1, 2, 3}, the class labels. Here a multiclass SVM classifier integrated with histogram intersection kernel is built from the training examples. Once the classifier is built and we obtain the optimal SVM parameters after cross validation, we use it for generalization purpose. 4.4 Testing Phase Now the classifier is tested with some previously unknown data points. As our classifier is a cascaded SVM with one-against-one multiclass support, a max-win operator is used to finally calculate the class label of that data point.

Fig. 1. Some synthetic data used for experimental purpose

5 Experimental Results We implemented the algorithm in matlab and tested it on a Pentium Core 2 duo system with 2 GB of RAM. We have performed the experiment on some synthetic image data. Fig 1 shows some sample images from the database. For experimental purpose, we used three classes of objects (circles, rectangles and rectangles with curved corners). The objects are of different size and they are oriented in different directions as we wanted to analyze the robustness of the SIFT feature descriptors. We

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first segmented a given image using Otsu’s method of histogram thresholding. Then from the segmented image, we extract the objects. After that, we compute some feature descriptors from each objects. Our feature vector consists of a set of 132 feature descriptors. The interest points are summed up dimension wise to compute feature vector of 128 elements for each object. In one of the experiment setup, we train the SVM classifier with a set of 100 objects from those three different classes. Our test set contains 75 image objects. We have used SVM classifier integrated with histogram intersection kernel and compare our result with SVM integrated with linear, polynomial and radial basic and sigmoidal kernel functions. The experimental results show that SVM with histogram intersection kernel provides better classification accuracy than any other kernel. While the histogram kernel based SVM can classify an unknown image object with 97.33% of accuracy, the classification accuracy of linear kernel based SVM is also 97.33%. Polynomial kernel of degree 2 is and that of degree 4 have classification accuracy of 96% and 94.66% respectively. Radial basic kernel based SVM has the accuracy of 88%. The accuracy of sigmoidal kernel based SVM is just 33.33%. The table 1 shows the no of data points correctly classified for each kernel functions. Table 1. Number of correctly classified samples by different kernels Kernel Functions Histogram Intersection Linear Polynomial (degree 2) Polynomial (degree 4) Radial Basis Sigmoidal

Number of correctly classified objects (Out of 75) 73 73 72 71 66 25

6 Conclusion In this paper, we have proposed a method for image object classification using SVM classifier integrated with histogram intersection kernel. We have used GLCM based texture and SIFT feature descriptor. The experimental results show that our method produces better classification accuracy than SVM integrated with linear, polynomial or RBF kernel. The classification accuracy of linear kernel and histogram kernel based SVM are same in our experiment. As the objects are linearly separable, the linear kernel is able to classify them with high accuracy. As soon as the objects become non-linearly separable, then accuracy of linear kernel SVM reduced drastically, but histogram intersection kernel SVM sustains its accuracy. The main advantage with histogram intersection kernel is that it is very fast in terms of time requirement in comparison with linear kernel. The time complexity in the test phase of general kernel based SVM is O (m x n) where m is the number of support vectors and n is the dimension of the data points. Whereas the time requirement of histogram intersection based kernel is O (c x n). The future direction of this work can be to reduce the feature space effectively.

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References 1. Tzotsos, A., Argialas, D.: Support Vector Machine Classification or Object Based Image Analysis. Lecture notes in Geoinformation and Cartography, pp. 663–677. Springer, Heidelberg (2008) 2. Maji, S., Berg, A.C., Malik, J.: Classification using Intersection Kernel Support Vector Machine is Efficient. In: IEEE Computer Vision and Pattern Recognition Anchorage, CVPR 2008 (2008) 3. Lowe, D.G.: Distinctive Image Features from Scale Invariant Key Points. International Journal of Computer Vision (2004) 4. Deselaers, T., Ferrari, V.: Global and Efficient Self-Similarity for Object Classification and Detection. In: IEEE Computer Vision and Pattern Recognition, CVPR 2010 (2010)

Palmprint Recognition System Using Zernike Moments Feature Extraction P. Esther Rani1 and R. Shanmuga Lakshmi2 1

Department of ECE, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai [email protected] 2 Department of CSE, Government College of technology, Coimbatore

Abstract. A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics. One of the difficult problems in feature-based recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents a palmprint recognition using Zernike moments feature extraction. Unsharp filtered palmprint images makes possible to achieve highly robust palmprint recognition. Experimental evaluation using a palmprint image database clearly demonstrates an efficient matching performance of the proposed system. Keywords: Pattern Matching, Unsharp Filter, MATLAB, Zernike Moments, Palmprint.

1 Introduction Biometric authentication has been receiving much attention over the past decade with increasing demands in automated personal identification. Among many biometric techniques, palmprint recognition is one of the most reliable approaches, since a palmprint, the large inner surface of a hand, contains many features such as principle lines, ridges, minutiae points, singular points and texture [1].A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics [1],[2]. One of the difficult problems in feature based palmprint recognition is that the matching performance is significantly influenced by many parameters in feature extraction process (e.g., spatial position, orientation, center frequencies and size parameters for 2D Gabor filter kernel), which may vary depending on environmental factors of palmprint image acquisition. This paper presents an efficient algorithm for palmprint recognition using Zernike moments feature extraction. The proposed system consist of four steps Step 1: Extracts the desired area of palmprint from hand image for processing. (The extraction process is robust to translation and rotation due to placement of the hand on the scanner bed) Step 2: Converts non uniform brightness palmprint to uniform one and enhances its contrast using unsharp filter V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 449–454, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Step 3: The extracted palmprint is divided into small sub-images and sub image features are extracted by using Zernike moments Step 4: Euclidean distance is used to match the Zernike moments of corresponding sub-images of live and enrolled palmprint The proposed system is tested with PolyU and CASIA database sample images. The MATLAB is used to implement the proposed system. The remaining sections are organized as follows: The section 2 describes about Zernike moments and section 3 describes about proposed system implementation details. Experimental results are given in section 4. Finally section 5 describes the concluding remarks

2 Zernike Moments Zernike Moments are useful in pattern recognition and image analysis due to their orthogonality and rotation invariance property. Zernike moments are a class of orthogonal moments and have been shown to be effective in terms of image representation. Zernike moments are rotation invariant and can be easily constructed to an arbitrary order. Although higher order moments carry finer details of an image, they are also more susceptible to noise. Moment functions of image intensity values are used to capture global features of the image in pattern recognition and image analysis [4]. Among many moment based descriptors, Zernike moments have minimal redundancy (due to the orthogonality of basis functions [5]), rotation invariance and robustness to noise; therefore they are used in a wide range of applications on image analysis, reconstruction and recognition [6]. Zernike moments are based on a set of complex polynomials that form a complete orthogonal set over the interior of the unit circle [7]. Zernike moments are defined to be the projection of the image function on these orthogonal basis functions. The basis functions Vn,m (x,y) is given by V

x, y

,

V

.

ρ, θ

R

,

ρ e

(1)

where n is a non-negative integer, m is non-zero integer subject to the constraints n-|m| is even and |m| < n, ρ is the length of the vector from origin to (x, y) , θ is the angle between vector ρ and the x -axis in a counter clockwise direction and Rn,m(ρ) is the Zernike radial polynomial. The Zernike moment of order n with repetition m for a digital image function f(x, y) is given by ,

∑∑

,

,

.

(2)

where V* n,m(x, y) is the complex conjugate of V n,m(x, y). To compute the Zernike moments of a given image, the image center of mass is taken to be the origin. The function f(x; y) can then be reconstructed by the following truncated expansion [8]: (3) where N is the maximum order of Zernike moments we want to use, Cn,m and Sn,m denote the real and imaginary parts of Zn,m respectively.

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3 Proposed System The proposed system has two major blocks. The first block is enrollment of the palmprint to system database. The second block is used to identify the live palmprint captured image with the database. The fig.1 shows the enrollment block diagram. First process is to extract the palmprint – region of interest (ROI) from the captured hand image. The second step converts non uniform brightness in palmprint to uniform one and enhances its contracts by using unsharp filter (MATLAB function).The process of sharpening is related to edge detection. Changes in color are attenuated to create an effect of sharper edges. Using a ‘fspecial’, it create a filter for sharpening an image. The special filter is ironically named 'unsharp'. In the next step, palmprint is divided to small sub-images and Zernike moments are used to extract features from each sub image of the palmprint. Finally the palmprint features are stored in system database.

Uniform brightness with image enhancement

Palmprint -ROI extraction

Database

Feature extraction using Zernike moments

Fig. 1. Enrollment Block Diagram

The fig.2 shows the Identification block diagram. Initial steps are same as enrollment for feature extraction. The proposed system is an efficient method to extract features from sub–images of palmprint which can be used for personal identification. The features are extracted from sub-images of the palmprint using Zernike moments. Euclidean distance is to play the main role to match the Zernike moments of corresponding sub-images of live and enrolled palmprint. Finally the result of the person identification will be displayed based on the matcher unit output.

Palmprint -ROI extraction

Database

Uniform brightness with image enhancement

Feature extraction using Zernike moments

Matcher using Euclidean distance

Identification

Fig. 2. Identification Block Diagram

Result

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Palmprint Extraction Palmprint extraction technique depends on hand image. Hand images can be classified in two categories. This classification is based on image acquisition technique. These categories are 1.

2.

Constraints (peg) free image acquisition: In this category users are free to place there hand on scanner surface without any constraints (pegs). In this case orientation and placement of hand would vary for every incident relative to line of symmetry of the working surface of scanner. An example of this category is CASIA database. Constrained (with pegs) image acquisition: In this category users were asked to place their hand according to pegs. In this case orientation and placement of hand is fixed. Examples of this category are PolyU database.

In this subsection, two algorithms for extracting the region of interest (palmprint) from hand –image have been proposed.First algorithm is used for extracting the region from hand – images which are acquired using constraint free scanner. Algorithm 1. Palmprint extraction techniques for constraint (pegs) free image acquisition. The algorithm is applied on a given hand image, obtained using constraint free scanner (CASIA database). Following are the steps applied on the hand image to extract the palmprint. It consists of 2 major steps. First step is Binarization and second step is region of interest detection Binarization For input I image of the size NxN, global threshold G_Threshold can be determined using

G_Threshold =

N

N

i =1

j =1

∑ ∑ I (i, j )

(7)

NxN

where I(i,j) is intensity value of pixel at position (i,j) of hand image. This threshold is used to obtain the binary image BI shown in fig(3) by using BI

(i, j ) =

⎧ ⎪ 0 ⎨ ⎪1 ⎩

if

I (i, j ) ≤

G

_ Threshold

(8)

otherwise

where intensity value 0 means black pixel and 1 means white pixel. Selecting the Region of Interest (ROI) Fingertips and valley coordinates of hand image are obtained using transition tracker. The image will be traced from left-top to right-bottom to identify the number of transition between low to high or high to low to find the required transition x and y coordinate points . Two reference points in image are (i) (ii)

V1 the valley between little finger and ring finger and V2 the valley between index finger and middle finger are selected from these points.

Let L be the line connecting V1 and V2 and L1,L2 are lines at angle 45 degree and 60 degree to L respectively as shown in fig.3.(a)

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(a)

453

(b)

Fig. 3. (a) Binary Image with V1,V2 ,C1 & C2 points, (b)extracted palmprint

Points C1 and C2 are hand contour points which also lies on line L1 and L2 respectively. M1 and M2 are the middle points of V1, C1 and V2, C2 respectively. ROI is the square region with M1 and M2 as its adjacent corners. Selected region is shown in figure3 (a) This ROI is extracted and addressed as extracted palmprint shown in fig.3 (b) Algorithm 2. Palmprint extraction techniques for constrained (pegs) image acquisition. This algorithm is applied on images, obtained using constrained scanner (PolyU database). To extract the ROI palmprint image is binarized and then traced using contour tracing algorithm explained in subsection of binarizarion. Four reference points P1, P2, P3 and P4 are located on the contour of the palm as shown in fig. 4(b) Square area of 192x192 pixels with its center coinciding with intersection of line segments P1-P2 and P3-P4 is considered as region of interest (ROI). Figure 4(a) shows the region of interest in gray scale hand image, and finally ROI (palmprint) will be extracted

(a)

(b)

Fig. 4. (a) Original PolyU Image (b) Binary Image with P1,P2,P3 & P4

Sub-image Based Feature Extraction In this section the feature extraction from sub-images of the palmprint is described. After palmprint extraction and palmprint enhancement, feature should be extracted for later verification. Since the stable valley point between the fingers are used to extract the ROI, palmprint is invariant to rotation, so sub-image location dependent matching could be performed. The extracted and enhanced palmprint image P of size (nxn) is divided into mxm equal sized sub-images. To verify the live palmprint, Euclidean distance between the Zernike moments computed for the sub–image of enrolled palmprint and Zernike moments of the corresponding sub-images of live palmprint is calculated. Fusion of matching scores of Zernike moments from all subimages is done using non-weighted sum-rule, where average of all scores is considered as final score.

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4 Experiment Result The system is implemented and verified by using CASIA and PolyU sample database images in MATLAB. The table 1 describes about FAR, FRR & accuracy comparison based on the experimental resutl is shown below. Table 1. FAR, FRR & Accuracy comparison

Parameter FAR FRR Accuracy

CASIA image 1% 3% 99.98

PolyU image 1% 1% 99.99

5 Conclusion The palmprint verification system using Zernike moment features from sub-images of palmprint is implemented. The two algorithms are implemented to extract the ROI of the palmprint based on images acquisition technique like method1 for Constraints (peg) free image acquisition and method 2 for constrained (with pegs) image acquisition. Both algorithms are verified by using CASIA and Poly U sample database images. The table1 clearly tells about system accuracy capability. The parallel implementation of the proposed system is possible as feature extraction technique (calculating Zernike moments) from sub-images is independent of feature extraction from other sub-images. This improvement can also be done to increase the speed of the system operation.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Zhang, D.: Palmprint Authentication. Kluwer Academic Publication, Dordrecht (2004) Zhang, D., Kong, W.-K., You, J., Wong, M.: Online palmprint identification. IEEE Trans. Pattern Anal. Machine Intell. 25(9), 1041–1050 (2003) Kuglin, C.D., Hines, D.C.: The phase correlation image alignment method. In: Proc. Int. Conf. Cybernetics and Society, pp. 163–165 (1975) Prokop, R.J., Reeves, A.P.: A survey of moment based techniques for unoccluded object representation. Graph. Models Image Process. CVGIP 54(5), 438–460 (1992) Teague, M.R.: Image analysis via the general theory of moments. J. Opt. Soc. Am. 70(8), 920–930 (1980) Teh, C.H., Chin, R.T.: On image analysis by the method of moments. IEEE Trans. Pattern Anal. Mach. Intell. 10, 485–513 (1988) Zernike, F.: Physica (1934) Khotanzad, A., Hong, Y.H.: Invariant image recognition by zernike moments. IEEE Trans. on Pattern Anal. and Machine Intell. 12, 489–498 (1990) Rattani, A., Kisku, D.R., Bicego, M., Tistarelli, M.: Feature level fusion of face and fingerprint biometrics. Biometrics: Theory, Applications and Systems, 1–6 (2000)

A QOS Framework for Mobile Ad-Hoc in Large Scale Networks A. Boomaranimalany1 and RM. Chandrasekaran2 2

1 Department of Comp. Sci. Engg., Anna University, Thiruchirapalli, India Department of Comp. Sci. Engg., Annamalai University, Chidambaram, India

Abstract. The success of MANET will depend on its ability to support existing, applications and protocols. Such a dynamic setting poses fabulous design challenges at each layer of the network. Hence this paper proposes a new concept of Blending Routing Protocols (BRP) in MANET which provides the advantages of the three types of routing protocols and addresses the QOS requirements and repeatability issues calculated for better scalability in streaming multimedia MANET with Comparison results were appraised. Keywords: QOS, DYMO, Scalability, Streaming, Multimedia.

1 Introduction Manet delivers tremendous application in the field of communication which is an emerging research area. MANET is a self-organizing network were topologies deformed consistently according to their mobility without any vital administration. Many routing protocols are reviewed [3] and proposed for Manet. This proposal is quite different for MANET which provides QoS with CBR traffic distribution [2]. 1.1 Problem Statement Manet is a difficult task to define and scientific research work since 1970. Most important challenges in MANET are to providing quality of service in multimedia. QOS [1] is usually defined as a set of service requirements that needs to be met by the network While, transporting a packet stream from a source to its destination.

2 Proposed Framework Quality of Service [6] refers to the capability of a network to provide better service to selected network traffic over various technologies. This approach is based on a new concept of BRP (Blending Routing protocols). Many approaches like DIFFSERV, INTSERV were applied to provide quality of service. The previous example is SHARP, Which have proactive and reactive process according to their size zones. 2.1 Framework Design Fig 1 shows the first stage conditional set up phase consists of IEEE 802.11 DCF, RWM and RED for congestion control. In this phase checks for all the equipments V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 455–457, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. Framework design

and link connections are in perfect, if there is any fault or failure in any node, connections it skip out the process and starts from the commencement, after the setup was proper then the process switch for the next stage. In the link study phase the three routing protocols proactive, reactive and hybrid protocols as FSR [4], DYMO [5] and ZRP were mixed in the same network. In proactive, the routing information between the nodes is maintained throughout the network whereas in the reactive if there is a need then the information maintained. In hybrid protocols the information processing depends upon the zones divisions. Some nodes in the network processed using proactive protocols, some reactive protocols and some on hybrid protocols. This method utilizes the advantages of three routing protocols in the network which is used for furnish prominent performance. In the performance phase the various recital measures were analyzed according to the different parameter variations.

3 QOS Framework Pseudo Code Implementation STEP 1: SET: Nn (n1,n2,n3…nN) Rp RWP STEP 2: Check nodes condition ok else go to step 1 STEP 3: CHOOSE:N Rp;Go to STEP 6 STEP 4: FACTORS={R,S} CHECK: R {St,Ni,Ne,Rc} CHECK: S {Nw.N} STEP 5: SELECT = {DYMO, FSR, ZRP} STEP 6: If(Rp==DYMO) Route Discover Phase: Rd Rd={xRrq, YRrp} Route Maintenance Phase: Rm UPDATE: {Nl,Error} Rm=pm till the node finishes its communication Else If(Rp==FSR) FUNCTION f(dl)=mi; f(dm)=li; Else (Rp==ZRP) If(S ЄZi && DЄZj ) i==j /* S & D in same Zone*/ “IARP” Else if(SЄZi && DЄZj) i≠j /* S & D in Different Zone */ “IERP” STEP 7: Communication under Process.

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4 Results and Discussion The above fig 2 and fig 3 shows the comparison of proposed QOS frame work with other protocols separately. Other recital metrics with different variations were also calculated and observed. By observing the above results this method delivers high performance compared with the protocols separately.

Fig. 2. Packet delivery fraction comparison

Fig. 3. Average jitter comparison

5 Conclusion and Future Work Our framework consists of three phase conditional, link study and performance phase for the provision of quality of service metrics in mobile ad-hoc for large scale networks. By observing the above chart the new concept QOS framework delivers high packet delivery ratio whereas low in jitter. In future security analyze will be carried out to this framework in mobile ad-hoc networks.

References 1. Chiasserini, C.-F., Srinivasan, V.: Quality of service in ad hoc and sensor networks. In: Performance Evaluation, vol. 64, pp. 377–378. Elsevier, Amsterdam (June 2007) 2. Ivascu, G.I., Pierre, S., Quintero, A.: QoS routing with traffic distribution in mobile ad hoc networks, vol. 32, pp. 305–316. Elsevier, Amsterdam (Feburary 2009) 3. Trung, H.D., Benjapolakula, W., Duc, P.M.: Performance evaluation and comparison of different ad hoc routing protocols. Comcom 30 (September 2007) 4. Yang, C.-C., Tsenga, L.-P.: Fisheye zone routing protocol: A multi-level zone routing protocol for mobile ad hoc networks. Comcom 30, 261–268 (2007) 5. Boomaranimalany, A., Chandrasekaran, R.M.: Quality of service in DYMO protocol mobile adhoc network for small, medium and large scale scenarios. In: IJCEIT, SERC 2009, vol. 3, pp. 38–44 (May 2009) 6. Rubin, R.Z.: Robust throughput and routing for mobile ad hoc wireless networks. Ad Hoc Networks, 265–280 (2009)

An Interactive Content Based Image Retrieval Method Integrating Intersection Kernel Based Support Vector Machine and Histogram Intersection Based Similarity Measure for Nearest Neighbor Ranking Tanusree Bhattacharjee, Biplab Banerjee, and Nirmalya Chowdhury* Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India [email protected]

Abstract. Relevance Feedback is an important tool for grasping user's need in Interactive Content Based Image Retrieval (CBIR). Keeping this in mind, we have build up a framework using Support Vector Machine Classifier in interactive framework where user labels images as relevant and irrelevant. The refinement of the images shown to the user is done using a few rounds of relevance feedback. This relevant and irrelevant set then provides the training set for the SVM for each of these rounds. The framework uses Histogram Intersection kernel with this interactive SVM (IKSVM). It has a retrieval component on top of this which searches for those images for retrieving which falls in the nearest neighbor set of the query image on the basis of histogram intersection based similarity ranking (HISM). The experimental results shows that the proposed framework shows better precision when compared with Active learning based SVMActive implemented with Radial Basis or Polynomial Kernels. Keywords: Histogram intersection similarity measure, support vector machines, intersection kernel, content based image retrieval, active learning.

1 Introduction Relevance feedback and active learning based CBIR has been a favorite research topic recently [2], [3]. SVMActive implemented by Tong et.al. [6] used relevance feedback based SVM with traditional kernels like Radial Basis or polynomial. In [4], [5] Intersection kernel is used with SVM for CBIR. S. Maji et.al. shows in [1] that classification using IKSVM can be made computationally efficient. In this work we have designed an interactive CBIR framework with relevance feedback which uses IKSVM [1] integrated with ranking based retrieval using HISM. The novelty of the method lies in developing a CBIR framework when less number of *

Corresponding author.

V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 458–462, 2010. © Springer-Verlag Berlin Heidelberg 2010

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labeled training instances is available and resource utilization has to be made efficient as well as retrieved images should be more similar to query image. The paper contains 4 following sections. Section 2 and 3 contains the feature extraction module and the proposed method respectively. Experimental results are given in section 4. The paper completes with conclusion and discussions in Section 5.

2 Feature Extraction We calculate both Color and Texture features for each image in the database. For color feature extraction histogram based feature extraction by binning the pixel values based on RGB color space using a novel approach is done. RGB color space does not provide any information of image brightness. So we converted the pixels in each bin from RGB to HSV. In each bin, for each of H, S and V channel statistical moments like mean and variance are calculated. Using Gaussian distribution of average and average variance, spreadness of bin and elongation of bin are calculated. For texture feature extraction purpose, we first obtain the discrete wavelet transformed version of a given image using haar wavelet mask. From an image of resolution M x N, the haar wavelet transform obtains sub images in four different orientation each of resolution M/2 x N/2. From these 4 sub images, we calculate the grey level co-occurrence matrix (GLCM). The energy measures for the 4 GLCMs are calculated as follows. (1) where (i,j) represents the (row, col) pair of each GLCM represented by Pd where d represents displacement of the GLCM. From each GLCM we obtain energy mean, energy variance, energy spreadness and energy elongation. The energy spreadness and energy elongation are calculated from Gaussian distribution of energy mean and energy variance.

3

Proposed Method

Initially we have a parent database of images containing C categories of images, each category having p number of images. The training set Tr is formed from this parent database by selecting randomly r (r
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set F. User is shown the set F which he marks as relevant or irrelevant. Relevant images form set Rr and irrelevant images form set Ri (Rr⊂Tr & Ri⊂Tr). The next phase is the training of IKSVM. Here calculation of feature vector of Rr and Ri is done. Feature vectors of Fr forms the positive set and feature vectors of Fi forms the negative set of data points for training the classifier. When they are input to IKSVM, it draws a hyperplane separating them. The feature set of Tr is calculated and then fed to IKSVM for classification so that the hyperplane formed can separate data points in training set as positive or negative .Those data points which falls on the positive side of the hyperplane forms a set FTr and similarly the negative side forms another set FTi. Since an image is a data point in a feature space we use the term data point and feature vector interchangeably. The selection of sample images for next feedback round is the task of the next phase. In this phase k/2 data points which are the most nearest to the hyperplane are selected from the set FTr and similarly k/2 data points are selected from the set FTi. The images corresponding to these k data points are kept in set F. If this is not the final iteration then the process continues again from step of showing images of the relevance feedback phase up to this step. Otherwise the process continues from the same step up to the completion of training of IKSVM phase and then goes to retrieval phase discussed in next subsection. The advantage of using IKSVM is that in the generalization phase the time complexity can be approximated to be proportional to number of dimensions of the feature vector and devoid of the number of support vectors [1].

Field

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Fig. 1. Sample Images of each category from the database

3.2 Retrieval Using Histogram Intersection Similarity Measure This phase starts by collecting the set FTr from the last iteration of training by IKSVMActive Next calculation of feature vector of the query image is done. Then the query image feature vector is plotted along with all the data points in FTr in a d dimensional space. Now, say user wants g number of images from the database which are most relevant to the query image, to be displayed. So on the basis of HISM calculation of the distance of query data point with data points in the set FTr is done. Lastly sorting of the distances in descending order is done .This follows from the fact that two of the most relevant images will have histogram distance close to 1 and the dissimilar ones will have distance close to 0. Finally retrieval of first g images from that sorted list is done.

An Interactive Content Based Image Retrieval Method Integrating Intersection Kernel

Query Image Q1

First 5 Results with Proposed Method

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Fig. 2. Top 5 images shown against respective query images

4 Experimental Results We have done our experiments on a 6 category image database (uploaded to http://www.flickr.com/photos/48753989@N03/) having 120 images per category. Each category has some overlapping RGB values with other categories. The training set is computed by randomly collecting 80 images from each category. The test set contains remaining 40 images from each category. Fig 2 shows the first 5 retrieved images ordered according to the HISM ranking against two query image from two different categories. One of the performance measures for image retrieval is precision which is defined as (Number of relevant images retrieved)/ (No of images retrieved). The graph in fig 3 shows that the proposed method performs better than other 3 methods for instance when 20 images are retrieved the proposed method has 100% precision whereas SVMActive used with Radial Basis kernel gives 95% precision, with polynomial degree 2 kernel gives 85% precision and with polynomial degree 4 kernel gives 65% precision.

Fig. 3. Comparative study of existing kernel functions used with SVMActive & the Proposed Method after 3 feedback rounds

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5 Conclusion and Discussions Our proposed method performs well off even when the number of images retrieved is close to the number of relevant images in the database. The method is also very efficient in terms of time complexity. The time complexity in the test phase of SVM is reduced due to using histogram intersection kernel based SVM. Also, since the SVM training complexity is dependent on number of training instances, we have sampled the database and reduced the number of training instances by using the relevance feedback rounds. This mechanism is suitable for reducing the training complexity for large databases. A future direction of research may be to use large databases and efficiently sub sampling them for using the proposed method and the results could be verified.

References 1. Maji, S., Berg, A.C., Malik, J.: Classification using Intersection Kernel Support Vector Machine is Efficient. In: IEEE Computer Vision and Pattern Recognition Anchorage, CVPR (2008) 2. Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and Trends of the New Age. ACM Computing Survey 40 (2008) 3. Liu, Y., Zhang, D., Lu, G., Ma, W.: A Survey of Content Based Image Retrieval with HighLevel Semantics. Pattern Recognition 40, 262–282 (2007) 4. Mei, L., Brunner, G., Setia, L., Burkhardt, H.: Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content Based Image Retrieval. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 63–70. Springer, Heidelberg (2005) 5. Setia, L., Ick, J., Burkhardt, H.: SVM-based Relevance Feedback in Image Retrieval using Invariant Feature Histograms. In: IAPR Workshop on Machine Vision Applications, MVA, Tsukuba Science City, Japan (2005) 6. Tong, S., Chang, E.: Support Vector Machine Active Learning for Image Retrieval. In: ACM Multimedia, Ottawa, Canada (2001)

Analysis and Prediction of Blocking Probability in a Banyan Based ATM Switch R. Pugazendi1 and K. Duraiswamy2 1

Faculty of Computer Science, KSR College of Arts and Science, Tiruchengode, Tamilnadu, India [email protected] 2 Dean (Academic), KSR College of Technology, Tiruchengode, Tamilnadu, India [email protected]

Abstract. Banyan network plays an important role in today’s broadband technology. One of the problem faced in the banyan network is the loss of packets and blocking probability. To address this problem, the concept of increasing the queue size to one is applied. The simulation is carried out for both cases i.e for normal queue size and increased queue size. The output obtained is used for plotting graph in MS-Excel. With the obtained graphs, the performance of the network for both cases and a report is prepared. This analysis provides a valuable analysis for network designers and operators in terms of network performance. Keywords: Banyan, Blocking, ATM, Switch, packet, Queue.

1 Introduction 1.1 Banyan Network The banyan network covers a large class of interconnection networks that has only a single path between any input and output. Unfortunately, the banyan is an internal blocking [1] network and its performance degrades rapidly as the size of the network increases. There are several ways to reduce the internal blocking to a level that is acceptable for ATM. The behavior of two queues in banyan switch design could be found and then aggregated to one block. [4] 1.2 Blocking Each switching element (box) is a 2x2 cross bar switch. In banyan switch [3], the types of blocking are as follows: (a) External Blocking, (b) Internal Blocking. It could be due to the following reasons: (i) Drop (ii) Overwriting 1.3 Packets The packet voice transport network, which may be IP, based, Frame Relay, or ATM, forms the traditional “cloud.” At the edges of this network are devices or components V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 463–468, 2010. © Springer-Verlag Berlin Heidelberg 2010

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that can be called voice agents. It is the mission of these devices to change the voice information from its traditional telephony form to a form suitable for packet transmission. The network then forwards the packet data to a voice agent serving the destination or called party.

2 Proposed Algorithm for Simulation Consider the 2 structures assumed: struct pkt{Char d_addr [10]; int data, s,d;}; struct se{ struct pkt *iport0; struct pkt *iport1; struct pkt *oport0; struct pkt *oport1; char iport0p[20]; char iport1p[20]; }bn[4][3]; Intialise I=0, Repeat the Loop for I <= MAX_CLOCK CYCLE 1. free the packets at the last stage(stage=3) by working as below. # Repeat the loop for each output port if( output port is not free){ Output count = outputcount+1; release the output port; }else -do nothing 2. (i) Queue[0] : For stage3 and stage2 switching, consider the stage position value, binary bit position value, and clock cycle value Assign column value= stage position value; #Repeat the loop for each Input Port if(ch is equal to 0 or 1) { if( Oport0 or Oport1 is free corresponding to ch value){ copy Iport0 or Iport1 packet to corresponding Oport[ch] ;

if(Iport0 or Iport1 is not free){ Declaring ch variable; ch =bitposition value; if(ch is equal to 0 or 1){ if( Oport0 or Oport1 is free corresponding to ch value){ copy Iport0 or Iport1 packet to corresponding If(Iportop or Iport1p is not free){ copy Iport0p or Iport1p to corresponding iports link; Release Iport0p or Iport1p;}}else{write Iport0 or Iport1 blocked data to block0 text file; if (Iportop or Iport1p is not free) Write Iport0p or Iport1p lost data to lost0 text file;}} (ii) Queue [1]: For stage3 and stage2 switching, consider the stage position value, binary bit position value, and clock cycle value Assign column value= stage position value; #Repeat the loop for each Input Port if(Iport0 or Iport1 is not free){ Declaring ch variable; ch=bitposition value; }else {Write Iport0 or Iport1 blocked packet to corresponding blocking text file;}} (i)Loading the Packet for Queue [0]

Analysis and Prediction of Blocking Probability in a Banyan Based ATM Switch

Release Iport0 or Iport1; If (Iport0b or Iport1b is not free){ copy Iport0b or Iport1b to corresponding iports; Release Iport0b or Iport1b according to above line operation;} If (Iportop or Iport1p is not free){ copy Iport0p or Iport1p packets to corresponding iports buffer link; Release Iport0p or Iport1p; }}else{ write Iport0 or Iport1 blocked data to block1 text file; if (Iportop or Iport1p is not free){ if (Iport0b or Iport1b is free){ copy Iport0p or Iport1p to corresponding Iports buffer; Release Iport0p or Iport1p;}else { Write Iport0p or Iport1p packet to lost1 text file;} 3. For Stage1 Switching, Consider the Clock cycle Assign column Value =1; #Repeat the loop for each Input Port if(Iport0 or Iport1 is not free){Che = first binary bit position value; if (che is equal to 0 or 1){if(Oport0 or Oport1 is free){ Copy Iport0 to Iport1 packet to corresponding Oport[che]; Release Iport0 or Iport1;

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Assign Column Value =1; #Repeat the loop for each Input Port Reading the Input Packet from the randomly generated file; If(Iport0 or Iport1 is free) copy Input packet to corresponding source port; else - Input packet is lost; (ii)Loading the packet for Queue [1] Reading the Input Packet from the randomly generated file; Assign Column Value =1; #Repeat the loop for each Input Port if (Iport0 or Iport1 is free){ if(Iport0b or Iport1b is not free){ copy Iport0b or Iport1b to corresponding Iports; Release Iport0b or Iport1b; Now copy Random Input packet to Iport0b or Iport1b; }else{ Copy Input packet to Iport0 or Iport1;}} else {if(Iport0b or Iport1b is free){ copy Input packets to corresponding Iport0b or Iport1b; }else {Input packet lost; } 5. Displaying each stage iports and oports packet.

3 Analysis of Blocking The proposed algorithm is simple with its logic and easy to understand. Moreover, the existing algorithm uses a large number of data structures. This consumes a lot of space unnecessarily [2]. In our proposed algorithm, apart from the two structures, there are no other data structures. Routing is effectively done using pointers. Another main thing is that the existing algorithm only gives the number of times the packet is

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blocked at each stage, whereas the proposed algorithm gives the full details of the packets which are blocked and lost. In our approach, the off-line simulation of a whole 3*3 banyan switch is being performed. The simulation is comprised of two different programs. One is with Queue size 0 and the other is with Queue size 1.By Queue size 0, it means that there is a single storage unit or buffer to hold the data. And when there are two buffers it is of Queue size 1.Queue size is also called “Arrival process transfer outside”. The first simulation is that of Queue size 0.The program is run for a particular amount of clock cycles and the output is recorded in a C text file. For a single simulation, there are two output files. One is for the blocked data and the other is for the lost data. The output in the text file consists of the following information: 1) The clock cycle considered presently, 2) The stage at which the block/lost is found, 3) The port where the block/lost is found. (0 or 1), 4) the source address of the packet, 5) the destination address of the packet. Similarly, for the second simulation Queue size is changed to 1.i.e an additional storage unit of one is added. Outputs are obtained in another set of C files (for block and lost).These four text files form the basis for our analysis. The second major part of the work is the plotting of graph of the acquired output using MS-Excel for analyzing the blocking probabilities. The necessary details from the output viz clock cycle; stage and port are alone taken and plotted.

4 Observed Results and Discussion The simulation is done in C language. Every packet is conceptualized as a structure with its members as destination address, source port number and data, The functions used are as follows: Input file creation, Bninitialize( ), Bnfree ( ) , Bnswitch2 (column value, key, time/clock cycle), Bnswitch1 (column number, destination address, time), Bnload ( ) 4.1 Existing Approach

Fig. 1. The graph shows the blocked data and lost data obtained as per the existing approach

4.2 Proposed Approach The first part of the result is that obtained in the C text file. It has the details of the packets dropped and blocked at each stage. The second stage of our work involves the plotting of graph from the output in the text file. Microsoft Excel is used to plot the graph.

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Fig. 2. (a) The Graph shows the blocked data of Queue size 0; (b) The Graph shows the blocked data of Queue size 1

While comparing the above two graphs, in queue size 1, after the 3rd clock cycle, the number of packets blocked is increased to three. After the 5th clock cycle, it again reaches three and becomes nil in the 7th clock cycle. The number of packets blocked in the port 1 of stage 2 and stage 3 are the same.

(a)

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Fig. 3. (a) The Graph shows the lost data of queue size 0; (b) The Graph shows the lost data of queue size 1

In the above graphs, it is clear that when compared to Queue size 0, in Queue size1 there is significant decrease in the loss of packet. The loss observed in stage1 (port 0 an 1) and stage 2 (port 1) is brought to a nil in queue size 1. Loss in Stage 3 port 1 is nil in both cases.

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Fig. 4. (a) The Graph shows the blocked and lost packet in stage3, port1 for queuesize 0; (b) The Graph shows the blocked and lost packet in stage3, port1 for queuesize1

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In the above two diagrams, both the number of packets blocked and packet loss is the same.

5 Conclusion The performance of Banyan based ATM packet switch based on Queue size 0 and Queue size 1 Switching Elements is analyzed by simulations. The blocking probability and packet loss at each stage which proves as an effective analysis tool for a Network designer is being predicted. The Queue size of 1 at input port is introduced to reduce the packet loss. Blocking behavior analysis also provides an effective approach to studying network performance and it finds a graceful compromise among hardware cost, network depth, and blocking probability.

References 1. Hussain, S.S., Yih-Chyun: Analysis and optimization of a Banyan based ATM switch by simulations Local Computer Networks. In: 21st IEEE Conference, pp. 268–277 (1996) 2. Yu, C., Jiang, X., Horiguchi, S., Guo, M.: Overall Blocking Behaviour Analysis of General Banyan-Based Optical Switching Networks Parallel and Distributed Systems. IEEE Transactions, 1037–1047 (2006) 3. Rosenblum, M.: Approximating fluid schedules in crossbar packet-switches and Banyan networks. IEEE ACM Transactions on Networking (2006) 4. Zhang, Z.: A Novel Analytical Model for Switches with Shared Buffer. IEEE/ACM Transactions on Networking (2007)

Performance Evaluation of QoS Aware Routing in OLSR (Optimized Link State Routing Protocol) Using Genetic Algorithm M. Pushpavalli1 and A.M. Natarajan2 1

Lecturer/Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India [email protected], [email protected] 2 Chief Executive & Professor, Bannari Amman Institute of Technology, Sathyamangalam, India [email protected]

Abstract. A MANET is a dynamic multi – hop network established by a group of mobile nodes on a shared Wireless channels by virtue of their proximity to each other. To support mobility to users generally low configure nodes are in use, so limited resources, dynamic network topology and link variations are some of the issues in MANET. Routing in such dynamic environment is a challenge issue, lots of work have been done for routing but still QoS(Quality of Service) requirements of the network is not satisfied because to find an optimal path in dynamic networks is a NP complete problem. The main objective of this paper is to find a feasible path that has sufficient resources to satisfy the network constraints. In this paper we have applied multi object genetic algorithm optimize four QoS parameters such as delay ,bandwidth, traffic from adjacent nodes and number of hops that assists a QoS model in meeting timing requirements and improves in global network performance. Analyze is done for OLSR protocol based on how the protocol finds out its MPR sets and MPR to route a packet from source to destination. Keywords: GA, OLSR protocol, MPR selection algorithm, Delay and bandwidth.

1 Introduction In areas in which a there is limited resources or no communications infrastructure or the existing infrastructure is very expensive or inconvenient to use, mobile users may still be able to communication through the formation of MANET. In such a network, each mobile node operates not only acts as hosts but also as a router, forwarding the packets to other mobile nodes in the network. MANET is also called as infrastructure less network [4]. In 1997, the Internet Engineering Task Force (IETF) [1] created a working group to study the challenges of designing MANET routing protocols. The working group has since separated MANET routing protocols into two distinct classes:Reactive or on-demand protocols are designed to contend with the low bandwidth typical of wireless networks. The reactive nature of the protocol decreases the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 469–474, 2010. © Springer-Verlag Berlin Heidelberg 2010

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amount of control overhead by only initiating a request for a route when it is required. Proactive routing protocols periodically broadcast information that is sent across the network in a controlled flood. The information is used at each node to build a routing table.[3],[2],[4]Multimedia and military applications of MANET technology require explicit performance needs to be met.. A MANET QoS framework must be able to find multiple hop paths with sufficient bandwidth and delay characteristics, despite network changes, low bandwidth links and shifting traffic patterns. Implicit parallelism of genetic algorithm makes it more appropriate to solve problems where the space of all potential solutions is truly huge - too vast to search exhaustively in any reasonable amount of time [1]. Most problems that fall into this category are called as nonlinear. In a linear problem, the fitness of each component is independent, so any improvement in any other part will be the improvement of the whole system. [8].

2 Analysis of Optimized Link State Routing 2.1 Protocol OLSR is a proactive link state routing protocol. Each node periodically broadcasts its routing table allowing each node to build a global view of the network topology. The periodic nature of the protocol creates a large amount of overhead. OLSR addresses this by limiting the number of nodes that forward network-wide traffic. This is accomplished through the use of multi point relays (MPRs) that is responsible for forwarding routing messages. Each node independently elects a group of MPRs from its one hop neighbors. MPRs are chosen by a node such that it may reach each two hop neighbor via at least one MPR. The nodes that have been selected as MPRs are responsible for forwarding the control traffic generated by that node. The below fig is an example of MPR here m1, m2, m3 nodes are selected as MPRs in 1-hop neighbors. MPR is selected such that it covers 2-hop distance thus hop-byhop routing technique is followed here. A node’s knowledge about its neighbors and two-hop neighbors is obtained from HELLO messages which are the message each node periodically generates to declare the nodes that it hears.

Fig. 1. Multipoint relays

The node N, which is selected as a multipoint relay by its neighbors periodically generates TC (Topology Control) messages, announcing the information about who has selected it as an MPR. Apart from generating TCs periodically, an MPR node can

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also originate a TC message as soon as it detects a topology change in the network. A TC message is received and processed by all the neighbors of N, but only the neighbors who are in N’s MPR set retransmit it.[6] Using this mechanism, all nodes are informed of a subset of all links – links between the MPR and MPR selectors in the network.

3 Changing of MPR Selection Using GA Criteria 3.1 QOS in OLSR Protocol To support QoS routing, link state metrics such as bandwidth and, delay in the network should be available and manageable. However, getting and managing such link state information in a MANET is not trivial because the quality of a wireless link changes quite frequently due to mobility and variations in the surroundings.[5],[9] The decision of how each node selects its MPRs is essential to determining the optimal bandwidth route in the network. Whenever there is a need to find MPR sets, GA module is always the initiator to find the set and it provides new set of routes to QoS module (fig 2) and it is stored in the buffer of QoS Module, and if there is no any other node that is present within the neighbor N, then the path from the buffer is followed. For the sake of efficiency, at some periodic time, route from buffer is again evaluated by GA module. To manage modularity concepts, the QoS routing module only has a minimum knowledge of the internals of the GA module and MPR sets are found in the form of simplified individuals (paths). As new sets of routes are exchanged with the QoS routing module is done through the buffer. The data structure sorting the pair of nodes which constitute the 2-hop node and the corresponding measurement information is referred to as a gene here (fig 3). The GA individual each corresponds to a route and their internal representation of this route is a linked list of pointers to genes.

Fig. 2. OLSR Scheme

An individual does not store its fitness as the delays associated with the hops that built up the path will change over time. The individuals are arranged in subpopulations, one for each destination and within these subpopulations. This is done just before the subpopulations are checked for size, so that when the size limit is reached, the less efficient paths are the ones that get dropped.The ACKs generated by smart agents most likely bring back new paths (set of nodes) while the ACKs generated by the

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dumb agent carry updated measurements on known paths. Diversity in the individual population is highly desirable both in order to have a set of alternative routes if the current route becomes saturated and in order to enhance the changes of cross over producing interesting new individuals. For the same reason, the GA module treats the paths brought back by smart agent in different fashion. To make this possible, along with the path itself, the module receives a set of flags, the only currently used being the priority flag which is set if the path was generated by the smart agent.

Fig. 3. GA module based on QoS routing

Thus by using GA based approach ,smart agent finds alternate set of routes which is added as MPR set at that time to find better path. The algorithm selects the MPRs in a way such that all the 2-hop neighbors have the optimal bandwidth path through the MPRs to the current node. 3.2 Metrics Measurement – Delay and Throughput Delay and Throughput metrics are included on each routing table entry corresponding to each destination. IEEE 802.11 b is used as the medium access control to achieve the bandwidth measurement.[8].With the modified OLSR (MOLSR) protocol, a route is immediately available when needed satisfying the QoS requirements. Each node in the adhoc network periodically broadcasts locally its HELLO messages. The delay is measured with the help of below expression, Where, Mtq=Mac queuing time, TtS=Transmission time, CAt=Collision Avoidance phase time, to=Control overhead time , NT=Number of necessary transmissions, BT=Back off time for r. Delay =Mtq + (Tts+ CAt+to) X NT +

BT

The throughput is calculated between a node and its neighbors having direct and symmetric link. Here we consider data packets and signaling traffic that uses the available bandwidth. (e.g.) HELLO messages and Traffic control messages in the OLSR protocol.[5] where, S = Packet size Through put s (Packet) = --------------------------------tq + (Tts+ CAt +to) X NT + B

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4 Performance Evaluation Simulation is carried out by using NS-2.33 OLSR and modified OLSR using GA are analyzed by taking different configurations and scenarios. Nodes vs Packet Delivery Ratio (PDR)(10m/s,20m/s) 90 80 70 P D R

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As the number of nodes increases packet delivery ratio decreases in both the cases due to more number of links but compared to standard OLSR, our protocol (MOLDR) packet delivery ratio is improved about 8%. The fig. 4 shows the number of nodes versus packet delivery ratio for a mobility of 10m/s and 20 m/s. The maximum packet delivery ratio is achieved for low mobility (10m/s). Fig. 5 depicts the comparison of mobility (m/s) versus throughput (Mbps) for standard OLSR and proposed protocol for various pause times 7s and 10s.From the graph we observe that our protocol (MOLSR) has better throughput for both the pause times.

5 Conclusion The problems with adhoc networks are that traditional routing algorithms do not perform well in highly mobile environments. We have proposed the OLSR with GA module in addition to QoS module; here GA module is used to select the MPR Sets,

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which provides QoS guarantees in terms of better packet delivery ratio, low packet loss, less delay and good bandwidth. When we are adding some random mobility to the mobile nodes then QoS OLSR is not efficient so to deal with this Genetic Algorithm based QoS is applied and by applying cross over and fitness functions GA provides better path selection in less time compared to standard QoS OLSR. Simulation results demonstrate that our protocol is effective and efficient in the QoS provisioning. Our future work is the performance evaluation for MPR selection for different threshold values and heuristic algorithms can also be modified to select the MPR nodes.

References 1. Goldberg, D.E.: Genetic Algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989) 2. Marvaha, S., Srinivasan, D., Tham, C.K., Vasilakos: Evolutionary Fuzzy Multi Objective routing for mobile adhoc networks. In: Congress on Evolutionary Computation, vol. 2, pp. 1964–1971 (June 19-23, 2004) 3. Perkins, C., Bhagwat, P.: Routing over Multi hop Wireless Networks of Mobile computing Computers. In: SIGCOMM 1994, Computer Communications review (1994) 4. Wong, J.-H., Leung, V.C.M.: Load Aware On Demand Routing Protocol for Mobile adhoc Networks. In: The 57th IEEE Semi Manual Vehicular Technology Conference 2003, April 22-25, vol. 3, pp. 1753–1757 (2003) 5. Chakrabarti, S., Mishra, A.: QoS Issues in AdHoc Wireless Network. IEEE Communications Magazine (February 2001) 6. Clausen, T., Jacquet, P.: Optimized Link State Routing Protocol. In: IETF Internet Draft, draft ietf - manet - olsr- 11.txt (July 2003) 7. Kuipers, F., van Mieghem, P., Korkma, T., Krunz, M.: An overview of Constraint – Based Path Selection Algorithms for QoS Routing. IEEE Communications Magazine 40(12) (December 2002) 8. Mohapatra, P., Jian, L., Gui, C.: QoS in AdHoc Wireless Network. IEEE Wireless Communications Magazine (March 2003) 9. Pushpavalli, M., Natarajan, A.M.: Fortification of QoS routing in MANETs using Proactive Protocols. In: Conference Proceedings of International Conference on (ICWCSC) held on January 2-4 2010, IEEE Xplore Digital library (February 17, 2010) 10. The NS manual (NS notes and Documentation), http://www.nsnam.com

Handloom Silk Fabric Defect Detection Using First Order Statistical Features on a NIOS II Processor M.E. Paramasivam1 and R.S. Sabeenian2 1

Lecturer – ECE & Research Staff Professor – ECE & Centre Head, Sona SIPRO, Sona Signal and Image PROcessing Research Centre, Advanced Research Centre, Sona College of Technology, Salem, Tamil Nadu {sivam.sct,sabeenian}@gmail.com, {sivam,sabeenian}@sonatech.ac.in 2

Abstract. This paper focuses on identifying defects in a handloom silk fabric using image analysis techniques such as first order statistical features. Any disparity in the knitting process that leads to an unpleasant appearance or dissatisfaction of the customer is termed as a defect in the fabric. Even today, the defect detection in a silk fabric is done using skilled manual labour. An automated defect detection and identification system would naturally enhance the quality and result in improved productivity to meet both customer demands and also reduce the costs associated with off-quality. This paper also classifies about the various defects that can occur in a silk fabric. As a supplementary need for the proposed machine vision based defect detection in textile fabric images, we would require a hardware implementation of the proposed method. This has been done using a soft core processor such as a NIOS processor of Altera Semiconductors. Keywords: Fabric Defect Detection; soft core processor; first order statistical features, NIOS II Processor.

1 Introduction Global Textile Market today is worth more than $500 billion and it is still growing every year [1]. Handlooms constitute the rich cultural heritage of India. The handloom weaving, as an economic activity, provides livelihood to the people. The element of art and craft present in Indian handlooms makes it a potential sector for the upper segments of market in domestic as well as global. In spite of the Government welfare schemes, the number of handlooms is continuously reducing all over the country. Handloom industry in Tamil Nadu plays an important role and provides employment for more than 4.29 lakh weaver households and about 11.64 lakh weavers [1]. 1.2 NIOS Processor Processor cores can be classified as either “hard” or “soft.” This designation refers to the flexibility/configurability of the core. Hard cores are less configurable; however, V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 475–477, 2010. © Springer-Verlag Berlin Heidelberg 2010

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they tend to have higher performance characteristics than soft cores. Hard processor cores use an embedded processor core, in dedicated silicon, in addition to the FPGA's normal logic elements. This paper deals with the NIOS II soft-core processor. An overview about the basic NIOS II processor features can be found in [2]. 1.3 Fabric Defects Literatures [6] generally classify the defects as follows. • • •

Critical Defects Major Defects Minor Defects

The authors carried out an extensive survey with the local handloom industries and sales outlets on the various types of defects that might occur in silk fabrics. Defects can be classified broadly as [4] • •

Manufacturing defects – generally occur during the process of manufacturing and can be rectified during production process itself Handling defects – occur mainly due to the improper handling of the fabric by the customer immediately after sale or by the sales person during the process of the sales.

2 Previous Work Currently, the quality assurance of web processing is mainly carried out by manual inspection, where the reliability is only about 70% and that too with most highly trained inspectors. Numerous techniques have been developed to detect fabric defects, among which using wavelets [4, 5] is also one of the methods. The first survey on fabric defect detection techniques has been carried out in [3] by considering around 160 papers for reference. Most recently Sabeenian and et.al [4] have designed an algorithm for identifying a number of defects in a fabric.

3 Proposed Work The reference image needs to be discrete wavelet transformed and then the first order statistical features, such as mean and standard deviation are obtained and stored in a library. The test image is also discrete wavelet transformed and the first order statistical features are obtained. The obtained value is compared with the reference image value for determining the presence of any kind of defects on the fabric. The flow for the hardware implementation is shown in the figure 1 below.

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Fig. 1. Hardware Implementation methodology

To implement the system, a NIOS II processor is used, which is referred to as a Central Processing Unit (CPU); a On-chip memory, which consists of the memory blocks in the Cyclone II chip of 4-Kbyte memory arranged in 32-bit words; a SRAM for storing the image converted in the form of data (HEX Values); Two parallel I/O interfaces and a JTAG UART interface for communication with the host computer. All the above are instantiated using a SOPC builder.

4 Results and Discussion The compilation report summary shows that the total number of logic elements required would be 3084 with 1955 registers and 64128 Memory bits. It also makes use of 4 Embedded 9 bit Multiplier. It is evident from the design that there is no requirement of PLL’s for the design. The implemented design was downloaded on to the 90nm chip CMOS process to check the functionality and various other design aspects.

References 1. Trichy Handloom Cluster, Annual Report of the Indian Handloom Clusters under the Office of Development Commissioner (Handlooms), Ministry of Textiles, Government of India (2008-2009) 2. Altera Corp., Nios II Processor Reference Handbook, Altera Document NII5V1-1.2, Altera Corp., San Jose, CA (2008) 3. Kumar, A.: Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE Transactions on Industrial Electronics 55(1), 348–363 (2008) 4. Sabeenian, R.S., Paramasivam, M.E.: Defect detection and identification in textile fabrics using Multi Resolution Combined Statistical and Spatial Frequency Method. In: IEEE 2nd International Advance Computing Conference (IACC), Patiala, pp. 162–166 (2010) 5. Acharya, T., Tsai, P.-S.: JPEG 2000 standard for image compression: concepts, algorithms and VLSI architectures. John Wiley and Sons, Chichester (2005) 6. Reddy, R.C.M. (I.A.S): A catalogue on woven fabric defects and visual inspection, In: Quality Appraisal and Export Promotion, & Market Research Wings, Textiles Committee, Mumbai

Performance Modeling of MANET Routing Protocols with Multiple Mode Wormhole Attacks Yogesh Chaba1, Yudhvir Singh1, Kanwar Preet Singh2, and Prabha Rani3 1

Deptt. of CSE, GJUS&T, Hisar, India 2 IBM, New Delhi, India 3 OITM, Hisar, India

Abstract. The lack of any centralized infrastructure in mobile ad hoc networks (MANET) is one of the greatest security concerns in the deployment of wireless networks. MANET functions properly only if the participating nodes cooperate in routing without any malicious intention. However, some of the nodes may be malicious in their behavior by initially attracting a large amount of traffic and later on launching active security attacks like worm hole. Wormhole attack is severe attack in ad hoc networks and particularly challenging to defend against. The wormhole attack is possible even if the attacker has not compromised any hosts and even if all communication provides authenticity and confidentiality. This approach evaluates the impact of various wormhole attacks by evaluating different performance parameters like jitter, frame dropped, end to end delay, throughput and packet delivered on various routing protocols and recommends the safest and weakest routing protocol against wormhole attack. Keywords: Wormhole, MANET, Routing Protocols, Security, Attacks, Computer Networks, Tunneling.

1 Introduction A mobile ad hoc network (MANET) is a collection of mobile devices that can communicate with each other without the use of a predefined infrastructure or centralized administration. In addition to freedom of mobility, a MANET can be constructed quickly at a low cost, as it does not rely on existing network infrastructure. Unlike the conventional network, a MANET is characterized by having a dynamic, continuously changing network topology due to mobility of nodes [1]. This feature makes it difficult to perform routing, so research focused on providing routing service with minimum cost in terms of bandwidth. The wireless links and dynamic topology definitely gives flexibility, but at the same time, security is a major concern in these networks. The wireless channels are vulnerable to various security attacks. Some of the ad hoc nodes may be victimized in the network by malicious nodes and may indulge in various attacks. The lack of security frameworks in these networks are one of the major concerns in their large scale deployment [2]. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 478–483, 2010. © Springer-Verlag Berlin Heidelberg 2010

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In physics, a wormhole is a hypothetical shortcut through space and time that connects two distant regions. In cyber security, the term wormhole was recently adopted to describe an attack on MANET routing protocols in which colluding nodes create the illusion that two remote regions of a MANET are directly connected through nodes that appear to be neighbors, but are actually distant from one another. The wormhole thus creates three artificial traffic choke points that are under the control of the attacker and can be utilized at an opportune future time to degrade or analyze the traffic stream [3]. In this attack, an attacker records a packet or individual bits from a packet, at one location in the network, tunnels the packet to another location, and replays it there. Routing can be disrupted when routing control messages are tunneled. This tunnel between two colluding attackers is referred as a wormhole. For example, when a wormhole attack is used against an on-demand routing protocol such as AODV, the attack could prevent the discovery of any routes other than through the wormhole [4]. The remainder of this paper is organized as follows. Section 2 introduces the wormhole attack modes, threats, impact on the ad hoc networks applications and routing protocols. Section 3 elaborates the implementation environment in details, while in section 4 result and analysis of wormhole attacks with various routing protocols is described. Finally, conclusion is given in section 5.

2 Wormhole Attacks A wormhole attack uses two cooperating corrupted nodes of a network connected by channel to re-route data traffic. Wormholes represent a significant threat to the functionality of wireless networks due to the fact that packets drawn into the wormhole are not routed through the advertised shortest path. Consequently, if no detection mechanism is present, it’s in the interest of the adversaries to create longer tunnels and cause greater delays in the network [5] [6]. On the other hand, if a detection mechanism is present, the adversary faces a tradeoff. If it decides to create a long tunnel, it causes greater damage to the network. None of the authors has studied the impact of wormhole attack on a large set of routing protocols with a large number of parameters. So there is a need to analyze the various performance parameters such as packet delivery ratio, throughput, Average end-to-end delay, jitter time and frame dropped with various routing protocols. 2.1 Wormhole Attack Modes Wormhole attacks can be launched using several modes, among these modes and mentioned [7]: Wormhole using Encapsulation: In this mode a malicious node at one part of the network and hears the RREQ packet. It tunnels it to a second colluding party at a distant location near the destination. The second party then rebroadcasts the RREQ. The neighbors of the second colluding party receive the RREQ and drop any further legitimate requests that may arrive later on legitimate multihop paths. The result is that

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the routes between the source and the destination go through the two colluding nodes that will be said to have formed a wormhole between them. This prevents nodes from discovering legitimate paths that are more than two hops away. Wormhole using Out-of-Band Channel: The second mode for this attack is the use of an out of band channel. This channel can be achieved, for example, by using a longrange directional wireless link or a direct wired link. This mode of attack is more difficult to launch than the previous one since it needs specialized hardware capability. Wormhole with High Power Transmission: Another method is the use of high power transmission. In this mode, when a single malicious node gets a RREQ, it broadcasts the request at a high power level, a capability which is not available to other nodes in the network. Any node that hears the high-power broadcast rebroadcasts it towards the destination. By this method, the malicious node increases its chance to be in the routes established between the source and the destination even without the participation of a colluding node. Wormhole using Packet Relay: Wormhole using Packet Relay is another mode of the wormhole attack in which a malicious node relays packets between two distant nodes to convince them that they are neighbors. It can be launched by even one malicious node. Cooperation by a greater number of malicious nodes serves to expand the neighbor list of a victim node to several hops. Wormhole using Protocol Deviations: A wormhole attack can also be done through protocol deviations. During the RREQ forwarding, the nodes typically back off for a random amount of time before forwarding reduce MAC layer collisions. A malicious node can create a wormhole by simply not complying with the protocol and broadcasting without backing off. The purpose is to let the request packet it forwards arrive first at the destination.

3 Experimental Setup This system consists of a set of mobile nodes which communicate using radio transmission and the radio link between neighbors is bidirectional. There is no any specific assumption about the medium access control protocol used by the nodes to access the radio channel. Further assumption about the transmission power of a wormhole is similar to a normal node in that more powerful transceiver is easily to be detected. Then a dedicated link is assumed between the two end points. Simulation Setup To evaluate the effectiveness of the wormhole attacks, various routing protocol are simulated using QualNet simulator. The goal of this evaluation is to test the effectiveness of wormhole attack variations under normal and attack conditions, implementation modes. As wormhole attacks have different effects and operate under different scenarios, and simulated the number of attacks under appropriate conditions on various routing protocols. The simulation parameters that are same for each variation of wormhole attack are listed in Table 1 and rest of the network parameters are default/standard parameters.

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Table 1. Simulation parameters Parameters Simulator Routing Protocol Simulation time Simulation area Number of nodes Number of malicious nodes Transmission range Movement model Traffic type Seed

Values Taken QualNet AODV 30 to 300 Seconds 1500m x 1500m 30 1 to 5 250m Random Way Point Model CBR 1

The following metrics are used to evaluate the effects of the wormhole attacks. Packet Delivered: This metric count the number of data packets received by the intended node and the number of data packet send to the node. Throughput: This metric measures the network throughput for nodes that generate data packets to be sent to the base station. Average End-to-End delay: Average time for packet to deliver from source node to destination node. Jitter Time: Jitter is expressed as an average of the deviation from the network mean latency. Frame Dropped: The number frame dropped due to wormhole node.

4 Result and Analysis In this section, the simulation results obtained using the QualNet simulator are presented. To illustrate the effect of the wormhole attack, various scenarios with different number of malicious nodes with a heavy CBR (10) server sending traffic between nodes is simulated. Numbers of packets send from sender nodes to receiver nodes are 25. Results have shown that, not only the traffic passes via the malicious nodes, but also intermediate nodes have been bypassed and hence the chosen path appears to have less number of hops. •

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As shown in figure 1, packets delivered in LAR1 are highest and OSPFv2 are lowest for 1 to 5 active malicious nodes.

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As shown in figure 2, throughput in LAR1 is highest, OSPFv2 is second highest and throughput remains constant if number of wormhole attackers is increased. • Average End-to-End Delay AODV( S) 8

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As shown in figure 3, average end-to-end delay in LAR1 are highest, DYNMO is second highest and end to end delay remains constant if number of wormhole attackers are increased. • Jitter Time AODV( S) 0 .5

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As shown in figure 4, Jitter in LAR1 is highest, ZRP is second highest and jitter varies with change in number of wormhole attackers. Jitter is lower in FishEye and OSPFv2 routing protocols against wormhole attack. • Frame Dropped AODV( S)

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As shown in figure 5, Frames Dropped in OSPFv2 is highest and continuously increases with increase in number of wormhole nodes. Frames dropped in Bellman Ford sharply increases when worm hole reaches to 5.

5 Conclusion In this paper new variants of the wormhole attack are identified and analyzed. The attacks identified in this paper only require a single malicious node, thus making it easier for an adversary to launch an attack. These new variants of wormhole attack have serious consequences on the network. To illustrate the effect of attack, simulation results are presented. Result show that LAR routing algorithm gives best packet delivery and throughput against wormhole attack. On the other hand LAR performs worst in case of end to end delay and jitter time. OSPFv2 perform worst in case of frame dropped when network is attacked by wormhole attack. The results obtained can help in putting some constraints on the network topology to design a robust network for such attacks, and in the design of new and more powerful attack countermeasures.

Acknowledgement The financial assistance provided by University Grant Commission, New Delhi in the form of Major Research Project to Dr. Yogesh Chaba is acknowledged with thanks.

References 1. Chun Hu, Y., Perrig, A., Johnson David, B.: Wormhole Detection in Wireless Ad Hoc Networks. In: Ninth International Conference on Network protocol, ICNP 2001 (June 2002) 2. Kannhavong, B., Nakayama, H., Nemoto, Y., Kato, N.: A Survey of Routing Attacks in Mobile Ad Hoc Networks. IEEE Wireless Communications, 85–91 (October 2007) 3. Wu, B., Chen, J., Wu, J., Cardei, M.: ‘A Survey on Attack and Countermeasures in Mobile ad hoc Networks. Wiley Journal Wireless Communication and Mobile Computing, WCMC (2006) 4. Panaousis, E.A., Nazaryan, L., Politis, C.: Securing AODV Against Wormhole Attacks in Emergency MANET Multimedia Communications. In: ICST Mobimedia 2009, London, UK, September 7-9 (2009) 5. Win, K.: Analysis of Detecting Wormhole Attack in Wireless Networks. In: Proc. of World Academy of Science, Engineering and Technology, vol. 36 (December 2008) ISSN 20703740 6. Mahajan, V., Natu, M., Sethi, A.S.: Analysis of Wormhole Intrusion Attack in MANETs. In: Proc. IEEE Military Communication Conference (2008) 7. Alexandrovna, M.: Review of Existing Wormhole Discovery Technique. In: Proc. Conference of the IEEE Computer and Communication Societies, vol. 3 (August 2006)

Mining a Ubiquitous Time and Attendance Application Schema Using Oracle Data Miner: A Case Study Binu Jacob and K.V. Promod Cochin University of Science and Technology, India [email protected], [email protected]

Abstract. Our case study used a Bayesian approach to find out the classification report requirements from a Oracle based Time and attendance application schema. Oracle data Miner was used to get the reports and though our chosen approach was simple, its strong assumption that attributes are independent within each class gave our model remarkably high accuracy. The study was conducted as a part of the process to make an airline profitable by re-structuring.

1 Introduction As a prominent airline of the Middle East, the use of a ubiquitous time and attendance application has been in place to manage time and attendance data of all staff. The application is interfaced with a set of clocks/readers, some of which are mounted with sensors that permeate the parking space and the entry doors of the building of the airline head quarters, hangars etc. The staff make their attendance by punching their unique legic cards which is saved real-time into reader memory buffers and then to a database which distributes the same selective data over to a web portal which is then accessed by staff to check their own attendance status in their offices or via PDA's over a wireless network. A recent decision of the government was to move the airline from a corporation to a private company on the basis of which a data mining project overseen by a committee of airline management and national executives was launched to get a first-hand grip of the situation. The committee asked for reports such as percentage report of attendance of staff punch in their respective shifts for the current quarter, middle management staff with classification of grade, qualification and age having irregular attendance for the current quarter and without requisite skills, and the behavior trend of the top management, middle management and normal staff in attendance. Ubiquitous Data Mining (UDM) is the process of performing analysis of data from ubiquitous computing environments. This environment can be classified as a ubiquitous environment operating in a time-critical environment and including continuous monitoring and analysis of status information received by readers at various locations for intrusion detection as well as time and attendance. This paper is organized as follows: Section 2 describes the related work in the field. Section 3 analyses our proposed route to solve this case study using Oracle data Miner. Section 4 outlines the implementation and assumptions while Section 5 V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 484–489, 2010. © Springer-Verlag Berlin Heidelberg 2010

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handles the experimentation results and analysis. Finally in Section 6 we make our concluding remarks.

2 Related Work Many data management systems maintain statistics on the underlying relation. The method proposed in [1] maintains small space data structures that monitor the transactions on the relation and when required, quickly output all frequent items without rescanning the relation in the database. With user-specified probability all frequent items are correctly reported by this method. Paper [2] considers the problem of clustering data streams, which is important in the analysis a variety of sources of data streams, such as routing data, telephone records, web documents, and clickstreams. [3] provide an improved randomized algorithm for the k-Median problem and [4] deals with the maintaining variance and k-Median clustering. [5] is about an adaptive approach in resource constrained environments like mobiles and another adaptive approach using algorithm output granularity was proposed in [6]. Cost-efficient models were proposed in studies [7] and [8]. Time series data stream studies were proposed in [9], [10] and [11] and heterogeneous data mining was dealt with in [12]. Clustering Binary data streams with K-means was proposed in [13]. Mining ConceptDrifting data streams using Ensemble classifiers was proposed in [14]. Some of the Intrusion detection systems (IDS's) are also closely related with this study since this application is also partly an intrusion detection system. [15,16,17,18] are data mining based IDS's requiring less expert knowledge but providing good performance which are capable of generalizing to new and unknown attacks. [19] points to the fact that successful detection of different types of attacks requires inputs from different audit data sources. [20] provides a Adaptive Model Generation architecture in which a database component plays a key role in the architecture and database is only a centralized data repository where processing does not take place (unlike Database-centric Architecture for Intrusion detection).

3 Architecture, Database Infrastructure and Data Mining Model The architecture in Figure1 shares more common features with the DAID (Databasecentric Architecture for Intrusion Detection) architecture that major operations take place in the centralized data repository, but it differs here that selective data from the database are streamed over to the web portal database which then is distributed to user PC's and PDA's. Since the model generation data mining methods are integrated in the data infrastructure no data movement is required. The major benefits of using such an integrated approach are improved security, speed, data management and ease of implementation. The database of our Time and Attendance application is hosted on a Sun server (Sun Fire V880, 32GB RAM, Solaris 2.8 OS) and the schema of application is implemented

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in Oracle 10g. The Bayes theorem is helpful because it allows us to express the posterior probability in terms of the prior probability P(Y), the class-conditional probability P (X|Y), and the evidence, P(X): P (Y|X) = [P (X|Y) * P(Y)]/P(X).

4 Explaining the Data and the Implementation Method In our study the test data collected for middle management staff was from each different departments of the corporation. The attributes of attendance (equal or greater than 65 percent), native (should be Yes(Y)), total service years (should be 15 years or more) and Graduate/Skilled (should be Yes(Y)) were based to create the column of last appraisal designated as a binary column with a Yes(Y) or No(N) which was considered the class. If the last appraisal was designated to be a No then the staff would be considered for layoff. The data of the middle management was considered as base example which was closely similar to evaluate the other classification requirements. The fields of Native and Graduate/Skilled had binary values whereas Attendance percent was a continuous attribute which was evaluated for the last quarter.

Fig. 1. Snap shot of Mining activity and result viewer in ODM

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Figure 1 shows the model which was created using Oracle Data Miner and result data, later on which the actual data was applied on. The positive target value was chosen as Y for the last appraisal. To predict the attendance trend of top management staff we first created a PL/SQL program to find the Attendance percent of the top management staff for the last two quarter and then used a Gaussian distribution to represent the class conditional probability of the continuous attribute. Here we used the algorithm settings svms_kernel_function provided with the DBMS_DATA_MINING package with the setting value for svms_gaussain which is the Kernel for Support Vector Machine algorithm by amending the defaults as below using the 'Create PL/SQL package' option in ODM. We used a standard deviation value of 0.075 as the threshold Gaussian distribution value. We chose the SVM algorithm for this purpose since it has the maximum prediction accuracy that avoids over-fit.

5 Results and Analysis The middle management records in the database when applied to the test data model had a predictive confidence of 76% (10 records) which was reflected as 74% ( 121 records) in the scoring data. The test data and scoring data set reflected a close relation for the attribute Graduate/skilled in determining the 'last appraisal' which was the target class. 33% of the staff were considered for layoff as their last appraisal showed a 'N' which was close to the 30% reflected in the test model. The average attendance percentage of two quarters of data revealed that the top management had to improve significantly in their attendance as for some departments it was below 60 percent.

Fig. 2. Predictive Confidence

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Fig. 3. Attendance Prediction

6 Conclusion This case study is different from other business environments like product impact analysis, attrition analysis that was applied, Bayes approach in a new domain (restructuring of an organization) as we were able to provide accurate reports based on the classification and predictive algorithms used in ODM to find out the incompetent staff based on the target attribute 'last appraisal' . These reports helped the committee to a significant extent to decide on the staff to be considered and action for lay-off, thus helping the process of privatization of the organization and making it profitable.

References 1. Cormode, G., Muthukrishnan, S.: What’s hot and what’s not: tracking most frequent items dynamically. In: PODS 2003, pp. 296–306 (2003) 2. O’Callaghan, L., Mishra, N., Meyerson, A., Guha, S., Motwani, R.: Streaming data algorithms for high-quality clustering. In: Proceedings of IEEE International Conference on Data Engineering (March 2002) 3. Charikar, M., O’Callaghan, L., Panigraphy, R.: Better streaming for clustering problems. In: Proc. of 35th ACM Symposium on Theory of Computing, STOC 2003 (2003)

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4. Babcock, B., Datar, M., Motwani, R., O’Callaghan, L.: Maintaining Variance and kMedians over Data Stream Windows. To appear Proceedings of the 22nd Symposium on Principles of Database Systems, PODS 2003 (2003) 5. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Towards an Adaptive Approach for Mining Data Streams in Resource Constrained Environments. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 189–198. Springer, Heidelberg (2004) 6. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Adaptive Mining Techniques for Data Streams using Data Streams Using Algorithm Output Granularity. In: The Australasian Data Mining Workshop (AusDM 2003), Held in conjuction with the 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia (December 2003) 7. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: A Cost-Efficient Model for Ubiquitous Data Stream Mining. In: Proceedings of Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Perugia, Italy (July 4-9, 2004)(accepted for publication ) 8. Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Cost-Efficient Mining Techniques for Data Streams. In: Purvis, M. (ed.) Proc. Australasian Workshop on Data Mining and Web Intelligence (DMWI 2004), Dunedin, New Zealand. CRPIT, 32. ACS 9. Guha, S., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering Data Streams. In: Proceedings of the Annual Symposium on Foundations of Computer Science. IEEE, Los Alamitos (November 2000) 10. Hulten, G., Spencer, L., Domingos, P.: Mining Time-Changing Data Streams. In: ACM SIGKDD 2001 (2001) 11. Keogh, E., Lin, J., Truppel, W.: Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. In: Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL (November 2003) 12. Kargupta, H.: CAREER: Ubiquitous Distributed Knowledge Discovery from Heterogeneous Data. In: NSF Information and Data Management (IDM) Workshop (2001) 13. Ordonez, C.: Clustering Binary Data Streams with K-means. In: ACM DMKD (2003) 14. Wang, H., Fan, W., Yu, P., Han, J.: Mining Concept-Driftig Data Streams using Ensemble Classifiers. In: 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Washington DC, USA (August 2003) 15. Barbara, D., Couto, J., Jajodia, S., Popyack, L., Wu, N.: ADAM: A Testbed for Exploring the Use of Data Mining in Intrustion Detection. ACM SIGMOD Record 30(4), 15–24 (2001) 16. Noel, S., Wijesekera, D., Youman, C.: Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt. In: Barbara, D., Jajodia, S. (eds.) Applications of Data Mining in Computer Security, pp. 2–25. Kluwer Academic Publishers, Boston (2002) 17. Markou, M., Singh, S.: Novelty Detection: A review Part 1: Statistical Approaches. Signal Processing 8(12), 2481–2497 (2003) 18. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stoflo, S.J.: A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In: Barbara, D., Jajodia, S. (eds.) Applications of Data Mining in Computer Security, pp. 78–99. Kluwer Academic Publishers, Boston (2002) 19. Lee, W., Stolfo, S.J.: A Framework for Constructing Features and Models for Intrusion Detection Systems. ACM Transactions on Information and System Security 3(4), 227–261 (2000) 20. Honig, A., Howard, A., Eskin, E., Stoflo, S.J.: Adaptive Model Generation, an Arhitecture for the Deployment of Data Mining-Based Intrusion Detection Systems. In: Barbara, D., Jajodia, S. (eds.) Applications of Data Mining in Computer Security, pp. 154–191. Kluwer Academic Publishers, Boston (2002)

4T Carry Look Ahead Adder Design Using MIFG P.H.ST. Murthy1, L. Madan Mohan2, V. Sreenivasa Rao3, and V. Malleswara Rao4 1

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Assitant professor, GIT, Gitam university, TechForce engineering services pvt Ltd, Senior Engineer -physical Design 3 HCL, Engineer 4 HOD, ECE, GIT, GITAM University

Abstract. Low-voltage and low-power circuit structures are substantive for almost all mobile electronic gadgets which generally have mixed mode circuit structures embedded with analog sub-sections. Using the reconfigurable logic of multi-input floating gate MOSFETs, 4-bit full adder has been designed for 1.8V operation. Multi-input floating gate (MIFG) transistors have been anticipating in realizing the increased functionality on a chip. A multi-input floating gate MOS transistor accepts multiple inputs signals, calculates the weighted sum of all input signals and then controls the ON and OFF states of the transistor. This enhances the transistor function to more than just switching. Implementing a design using multi-input floating gate MOSFETs brings down transistor count and number of interconnections. Here in this we have presented how to eliminate the propagate and generate signals This tends the design to become more efficient in area and power consumption. The following information is about Carry look ahead adder circuit, tested with 45nm technology and is extended to ALU. The proposed circuit has been implemented in 45n-well CMOS technology. Keywords: Mirror adder circuit, MIFG.

1 Introduction A floating gate transistor is a kind of transistor in which its driving terminal is electrically isolated from the rest of the device[1],[2],[3]. Since there is no direct internal DC path from the input terminal to the other terminals, the resistance is high. The main advantages of the floating gate transistors are the high input resistance and the simplified driving characteristics of the device operating in voltage mode. The two important floating gate transistors are: the IGBT and the FGMOSFET.

Fig. 1. Structure of multi input floating gate transistors V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 490–494, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Multiple-Input Floating Gate CMOS Inverter Multiple-input floating gate CMOS inverter is shown in Fig. 5. V1, V2, V3,…,Vn are input voltages and C1, C2, C3 ,…,Cn are corresponding input capacitors. Equation 3 is used to determine voltage on the floating gate of the inverter. Weighted sum of all inputs is performed at the gate and is converted into a multiple-valued input voltage, Vin at the floating gate. The switching of the floating gate CMOS inverter depends on whether Vin obtained from the weighted sum, is greater than or less than the inverter threshold voltage or inverter switching voltage (Vin). The switching voltage is computed from the voltage transfer characteristics of a standard CMOS inverter[14].

Fig. 2. Spice equivalent model

Fig. 3. Three input CMOS inverter for carry generation of full adder

As shown in the above figure 6 three input CMOS inverter is constructed. Majority NOT gate or majority NOR gates can be constructed using the above circuits .Here the problem is with delays associated with the circuits that can be adjusted by the proper logic effort. The delays associated with different output capacitance are as follows.

Fig. 4. Carry output using MIFg CMOS inverter different load capacitances

Logic effort: Logic effort is made in such a way Logic 0 must be only from 0-0.25 and logic 1 must be from 0.5-1.1. To get this we have to change length and width of nmos and pmos transistors as shown the below figure logic.

Fig. 5. Voltage transfer characteristics with wp/wn=5

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The lengths Ln and Lp are set to minimum value used in 1.5μm n-well CMOS process. The values for Wp and Wn are calculated as 3.2μm and 10.4μm, respectively to obtain Vinv = VDD/2. The uniqueness of multi- input floating gate inverter lies in the fact that the switching voltage can be varied by selection of those capacitor values through which the inputs are coupled to the gate. Ordinarily, varying the Wp/Wn ratios of the inverter does the adjustment of threshold voltage. In multi- input floating gate inverter, varying the coupling capacitances to the gate can vary the switching point in DC transfer characteristics. The input capacitors are C1, C2 and Cin. Among these Capacitors C1 and C2 are connected permanently to VDD and VSS, respectively, with 1.1V DC voltage applied to input capacitor Cin. In this experiment, the values of C1, C2 and Cin were initially fixed at 100 fF each. The bias voltages, VDD and VSS are 3.0V and 0V, respectively.

3 Carry Look Ahead Adder: 2nd Stage The main novelty of this work is no requirement of propagation and generation signals. The inputs to the first stage of the inverter are a0, b0, c0, a1, b1.Where a0, b0, c0 are the first stage inputs and a1, and b1 are second stage inputs. As shown in the figure number of transistors reduced only 4T.

Table 1. For delay and power consumption

45

8T carry

Sum

Delay

500ps

700ps

Power consumption

0.003mw

0.0034mw

Fig. 6. Two input ex-or gate

The reduction of number of transistors is possible only through the understanding of the five input truth table. There are basically two observations from the table. One is whenever a1,b1 both are one then irrespective of the first stage three will be carry. Second one whenever there is carry from the first stage immediately that will affect the second stage.so considering all these into account C1,C2,C3,C4,C5 values are decided in such a way that it satisfies the following condition.

C1 = 0 . 08 , C1 + C 2 + C 3 + C 4 + C 5

C2 = 0 . 08 , C1 + C 2 + C 3 + C 4 + C 5

C3 = 0 .08 C1 + C 2 + C 3 + C 4 + C 5

C4 = 0.38 , C1 + C 2 + C 3 + C 4 + C 5

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Table 2. Delays associated with 4 bit carry look ahead adder

45 Delay Power consumption

4 bit carry 900ps 0.029mw

Sum 1000ps 0.029mw

Fig. 7. First stage of the output before inverter is obtained with Ln=45n Wn=125n Lp=45n Wp=45n

C1, C2, C3, C4, C5 values can be chosen to satisfy the above equations. Full voltage swing is observed after the second stage of the figure 10.As shown in the figure 11 it is observed that there is very less power consumption than any differential types of logic. For any number of transistors same logic can be applied.

4 Conclusion Carry look ahead full adder is implanted in nano technology. It is observed that the delay has been reduced to many fold (230-500ps).Area has been reduced. Number of transistors has been reduced to only 4 for each stage.

References 1. Modeling multiple-input floating-gate transistors for analog signal pirqcessing. 1997 IEEE International Symposium on Circuits and Systems, Hong Kong (June 9-12, 1997) 2. Tsividis, Y.: Operation and Modeling of The MOS Transistor. Mc Graw-Hill, New York (1999) 3. Shibata, T., Ohmi, T.: A functional MOS transistor featuring gate-level weighted sum and threshold operations. IEEE Trans. on Electron Devices 39(6), 1444–1455 (1992) 4. Srivastava, A., Venkata, H.N., Ajmera, P.K.: A novel scheme for a higher bandwidth sensor readout. In: Proc. of SPIE, vol. 4700, pp. 17–28 (2002) 5. Weber, W., Prange, S.J., Thewes, R., Wohlrab, E., Luck, A.: On the application of the neuron MOS transistor principle for modern VLSI design. IEEE Trans. on Electron Devices 43, 1700–1708 (1996) 6. Yin, L., Embadi, S.H.K., Sanchez-Sinencio, E.: A floating gate MOSFET D/A converter. In: Proc. of IEEE International Symposium on Circuits and Systems, vol. 1, pp. 409–412 (1997) 7. Rodriguez-Villegas, E., Huertas, G., Avedillo, M.J., Quintana, J.M., Rueda, A.: A practical floating-gate Muller-C element using vMOS threshold gates. IEEE Trans. on Circuits and Systems-II: Analog and Digital Signal Processing 48, 102–106 (2001) 8. Rabaey, J.M.: Digital Integrated Circuits- A Design Perspective. Prentice Hall, Englewood Cliffs (1996)

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9. Bui, H.T., Wang, Y., Jiang, Y.: Design and analysis of low-power 10- transistor full adders using novel XOR-XNOR gates. IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing 49, 25–30 (2002) 10. Wang, J.M., Fang, S.C., Fang, W.C.: New efficient designs for XOR and XNOR functions on transistor level. IEEE J. of Solid State Circuits 29, 780–786 (1994) 11. Shams, A.M., Bayoumi, M.A.: A novel high-performance CMOS 1-bit full-adder cell. IEEE Trans. on Circuits and Systems II: Analog and Digital Signal Processing 47, 478– 481 (2000) 12. Rodriguez-villegas, E., Quintana, J.M., Avidillaand, M.J., Rueda, A.: High speed low power logic circuits using floating gates. In: IEEE international symposium on circuits and systems, Transaction on circuits and systems 13. http://www.wikipedia.org/ 14. http://etd.lsu.edu/docs/available/etd-1011103211310/unrestricted/Srinivasan_thesis.pdf

Microcontroller Based Monitoring and Control of Greenhouse Enivironment Gaytri Gupta Department of Electronics and Communication AMITY University Sector-125, Noida, India [email protected]

Abstract. Greenhouses in India are being deployed in the high-altitude regions where the sub-zero temperature up to -40° C makes any kind of plantation almost impossible and in arid regions where conditions for plant growth are hostile. So what we intend to do is to design a device to control and monitor this greenhouse environment. This device has been designed using simple equipments to meet the needs of Indian farmers requiring no skilled knowledge. Keywords: greenhouses, strayed-out parameter.

1 Introduction The proposed system closely monitor and control the microclimatic parameters of a greenhouse on a regular basis round the clock for cultivation of crops or specific plant species which could maximize their production over the whole crop growth season and to eliminate the difficulties involved in the system by reducing human intervention to the best possible extent. Thus, this system eliminates the drawbacks of the existing set-ups mentioned in the previous section and is designed as an easy to maintain, flexible and low cost solution.

2 Hardware Description The block diagram of the system consists of various sensors, namely temperature and light. These sensors sense various parameters- temperature, humidity, soil moisture and light intensity and are then sent to the A/D Converter. The microcontroller constantly monitors the digitized parameters of the various sensors and verifies them with the predefined threshold values and checks if any corrective action is to be taken for the condition at that instant of time. A LCD is used to indicate the present status of parameters and the respective AC devices (simulated using bulbs). Display can be interfaced to the system with respective changes in driver circuitry and code. Monitoring and controlling of a greenhouse environment involves sensing the changes occurring inside it which can influence the rate of growth in plants. The V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 495–497, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. Block Diagram

important parameters are temperature inside the greenhouse which affect the photosynthetic and transpiration processes are humidity, moisture content in the soil, the illumination etc. Since all these parameters are interlinked, a closed loop (feedback) control system is employed in monitoring it.

3 Software Design and Implementation The main features are Easy interface to all microcontrollers, operates ratiometrically, with 5 volt DC or analog span adjusted voltage reference, no zero or fullscale adjust required, 8-channel multiplexer with address logic, 0V to 5V input range with single 5V power supply and outputs meet TTL voltage level specifications.

Fig. 2. Flowchart of software development

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4 Advantages The system is more compact compared to the existing ones, hence is easily portable. Can be used for different plant species by making minor changes in the ambient environmental parameters. Can be easily modified for improving the setup and adding new features. In response to the sensors, the system will adjust the heating, fans, lighting, irrigation immediately, hence protect greenhouse from damage. Malfunctioning of single sensor will not affect the whole system. Natural resource like water saved to a great extent.

5 Conclusion A step-by-step approach in designing the microcontroller based system for measurement and control of the four essential parameters for plant growth, i.e. temperature, humidity, soil moisture, and light intensity, has been followed. The system has successfully overcome quite a few shortcomings of the existing systems by reducing the power consumption, maintenance and complexity, at the same time providing a flexible and precise form of maintaining the environment.

References 1. van Straten, G.: What can systems and control theory do for agriculture? In: Proceedings of 2ndIFAC International Conference Agricontrol, Osijek, Croatia (2007) 2. Farkas, I.: Modelling and control in agricultural processes. Comput. Electron. Agric. 49, 315–316 (2005) 3. Narasimhan, L.V., Arvind, A., Bever, K.: Greenhouse asset management using wireless sensoractor networks. In: Proceedings of IEEE International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, French Polynesia, Tahiti (2007) 4. Sandee, J.H., Heemels, W.P.M.H., van den Bosch, P.P.J.: Event-driven control as an opportunity in themultidisciplinary development of embedded controllers. In: Proceedings of the American Control Conference, Portland, Oregon, USA (2005)

Hand written Text to Digital Text Conversion using Radon Transform and Back Propagation Network (RTBPN) R.S. Sabeenian1 and M. Vidhya2 1

Professor – ECE & Centre Head, 2 Research Associate - ECE Sona SIPRO, Sona Signal and Image Processing Research Centre, Advanced Research Centre, Sona college of Technology, Salem, Tamil Nadu [email protected], {sabeenian,vidhya.aug}@gmail.com

Abstract. Handwritten to Digital Text Conversion Tool is designed using digital image processing technique to make data conversion (hand written scanned paper document to digital document) an easy and cost effective method using MATLAB. The input handwritten text is scanned and its Digital image form is obtained. The image is handled with the help of Enhancement techniques, segmentation, image recognition and neural network with an ultimatum of achieving higher efficiency. The Recognition system is designed along with the multilayer feed forward neural network, so that higher level of efficiency is obtained for the cursive handwriting recognition. The flexibility of this design allows it to extend to other languages easily. Keywords: Statistical Features, Back Propagation Network (BPN), hand written text, radon transform, Thinning, Optical Character Recognition (OCR).

1 Introduction The handwritten database contains images of 26 training images, 26 validation images, and 26 test images. The offline handwriting recognition is the technique involves image capture, enhancement and recognition. The image capture step involves the scanning of the image with 300 dots per inch resolution and 256 gray level scaling. Here we proposed an algorithm using Radon Transform and Back Propagation Network(RTBPN).

2 Proposed Work The gray scale image is scanned and the resultant gray scale image is normalized. Thresholding techniques are used in this normalization process. In this Paper the first step of segmentation is line segmentation and word segmentation followed by baseline estimation [1-3]. The three baselines are the base parameters for the future analysis of the separated word. This baseline estimation is done after splitting the entire image into sub images in the following sequence.Radon transform is the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 498–500, 2010. © Springer-Verlag Berlin Heidelberg 2010

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fundamental tool used in the proposed approach. This transform and its application in estimating slant angle of the word or image. The Radon transform can be used to detect linear trends in images. The principal direction of the image is first estimated from the Radon transform of a disk shape area of the image [5]. The cropped word is then further cropped into ‘n’ reasonable sub words. For each sub word the radon transform is applied separately to calculate the shear angle.

Fig. 1. Baseline estimation

3 Thinning and Representation Thinning is a morphological operation that is used to remove selected foreground pixels from binary images, somewhat like erosion or opening. It can be used for several applications, but is particularly useful for skeletonization. In this mode it is commonly used to tidy up the output of edge detectors by reducing all lines to single pixel thickness. Thinning is normally applied to binary images, and produces another binary image as output. The thinning operation is related to the hit-and-miss transform. Like other morphological operators, the behavior of the thinning operation is determined by a structuring element[1-2]. The thin image that is obtained from thinning process is processed to obtain skeletons. Figure 3 shows the input text image, slope corrected image is shown in figure 4.slant estimated and corrected images are shown in Figure 5 & 6.Skeleton of the input image is shown in figure 7.

Fig. 2. Input text image

Fig. 3. Slope corrected

Fig. 5. Slant corrected

Fig. 4. Slant Estimate

Fig. 6. Skeleton

4 Back Propagation Network The original document is scanned into the computer and saved as an image. The OCR software breaks the image into sub-images, each containing a single character. The sub-images are then translated from an image format into a binary format, where each

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0 and 1 represents an individual pixel of the sub-image [4]. The binary data is then fed into a neural network that has been trained to make the association between the character image data and a numeric value that corresponds to the character. The output from the neural network is then translated into ascii text and saved as a file. As the algorithm's name implies, the errors (and therefore the learning) propagate backwards from the output nodes to the inner nodes. So technically speaking, back propagation is used to calculate the gradient of the error of the network with respect to the network's modifiable weights.

Fig. 7. Flow diagram of proposed algorithm

5 Results and Discussion The image is loaded, the cropped image and the preprocessed image features are compared with database images and then finally the digital text image is classified. We have analyzed 125 samples. The classification rate for the 125 samples are 97.3%.

6 Conclusion The paper deals with recognition of general unconstrained cursive handwriting remain largely unsolved. Hence presented a system for recognizing off-line cursive English text guided in part by global characteristics of the handwriting. A new method for finding the letter boundaries based on minimizing a heuristic cost function is introduced. After a normalization step that removes much of the style variation, the normalized segments are classified by a one hidden layer feed forward neural network.

References 1. Cheriet, M., Kharma, N., Liu, C.-L., Suen, C.: Character Recognition Systems: a guide for students and practioners. Wiley-Interscience, Hoboken (2007) 2. Senior, A.W., Robinson, A.J.: An Off-Line Cursive Handwriting Recognition System. IEEE Transactions on pattern analysis and machine intelligence 20, 309–320 (1996) 3. Haralick, R.M.: Statistical and structural approaches to Texture. IEEE Proceedings 67, 786– 804 (1979) 4. Sabeenian, R.S., Palanisamy, V.: Comparision of Efficiency for Texture Image Classification using MRMRF and GLCM Techniques. Published in the International Journal of Computers Information Technology and Engineering 2(2), 87–93 (2008) 5. Sabeenian, R.S., Palanisamy, V.: Rotation Invariant Texture Characterization and Classification using Radon and Wavelet Transform. Published in the International Journal of Computational Intelligence and Health Care Informatics 1(2), 95–100 (2008)

Resource Allocation and Multicast Routing Protocol for Optical WDM Networks N. Kaliammal1 and G. Gurusamy2 1

Professor, Department of ECE N.P.R college of Engineering and Technology, dindugul, Tamil nadu 2 Dean/HOD EEE, FIE, Bannari Amman Institute of Technology, Sathyamangalam, Tamil nadu

Abstract. In traditional data networks, a multicast tree which is starting at the source is built with branches across all the destinations to admit a multicast session. In a highly dynamic and traffic changing environment, a routing and bandwidth allocation scheme has to be developed in the optical WDM networks. Without requiring detection of traffic changes in real time, the dynamic traffic will be routed with the quality-of-service (QoS) guarantees. In this paper, we propose to develop a Resource Allocation and Multicast Routing (RAMR) protocol. In this protocol, the incoming traffic is sent from the multicast source to a set of intermediate junction nodes and then, from the junction nodes to the final destinations. The traffic is distributed to the junction nodes in predetermined proportions that depend on the capacities of intermediate nodes. Bandwidth required for these paths depends on the ingress– egress capacities, and the traffic split ratios. The traffic split ratio is determined by the arrival rate of ingress traffic and the capacity of intermediate junction nodes. By simulation, we show that our proposed protocol attains increased throughput and bandwidth utilization with reduced delay. Keywords: quality-of-service (QoS) guarantees, Resource Allocation and Multicast Routing (RAMR) protocol, wavelength division multiplexing (WDM) technologies.

1 Introduction 1.1 Wavelength-Division-Multiplexing (WDM) Networks The requirement for on-demand provisioning of wavelength routed channels with service differentiated offerings within the transport layer has become more significant because of the recent emergence of high bit rate IP network applications. In order to achieve the above necessities, diverse optical transport network architectures have been proposed. This approach is determined by the fundamental advances in the wavelength division multiplexing (WDM) technologies. Due to the availability of ultra long-reach transport and all-optical switching, the deployment of all-optical networks has been made possible [1]. By using WDM network, we can transfer a large amount of data to long distance with high rate of speed. [2].. The light path establishment requires similar wavelength V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 501–503, 2010. © Springer-Verlag Berlin Heidelberg 2010

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and it should be used along the entire route of the light path without wavelength conversion. This is commonly considered to the wavelength continuity constraint [3]. 1.2 Multicasting Issues in WDM Networks A network methodology which is used for the delivery of data to a group of destinations is called as multicast addressing..In traditional data networks, in order to permit a multicast session, a multicast tree which is rooted at the source is constructed with branches spanning all the destinations[4] [5][6] Since there is no optical architecture based on WDM approach which can efficiently route the multicast traffic, the residual bandwidth is wasted or more OEO conversions are required [7][8].

2 Resource Allocation and Multicast Routing Protocol

Fig. 1. Multicast Routing Process

The above diagram (Fig. 1) shows the routing process. A predetermined fraction of the traffic entering the network at any node is distributed to every joint node. The corresponding route from the source to the joint node can be denoted as R sj . Then each joint node receives the traffic to be transmitted for different destinations and it routes to their respective destinations. The corresponding route from the joint node to the destination can be denoted as Rjd .The traffic split ratio ( δ ) is determined by the arrival rate of ingress traffic and the capacity of intermediate joint nodes.For a traffic split ratio of 1 for intermediate node k , the traffic on path Pi is I i for all i ≠ k and the traffic on path HQ j is E j for all j ≠ k . Using this, compute the traffic T (l ) on link l per unit split ratio δ k for intermediate node k as T

( l ) =

∑

I i i ≠ k , P i ∈ l

∑

+

j ≠ k , H

E

j

j ∈ l

∀ l ∈ E.

(1)

Now compute the maximum value δ for the traffic split ratio for intermediate node k which is consistent with the link capacity constraints ( Wl ) for sending flow along the paths Pi , H j as δ

=

min

I ∈

E

W l T ( l )

(2)

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2.1 Results - CBR Traffic Rate Vs Thr oughput

Rate V s De lay

1.2 1 0.8 0.6 0.4 0.2 0

D elay(sec)

2000

RA MR DPBMR

1500 RA MR

1000

DPBMR

500 0

2

4

6

8

2

Fig. 2. Rate Vs Throughput

6

8

Fig. 3. Rate Vs Delay Rate

U t i l i z a t i o n

4

Rate (M B)

Rate (M B)

Vs

Utiliz at ion

0.05 0.04 RA MR

0.03 0.02

DPBMR

0.01 0 2

4

6

8

Rate ( M B)

Fig. 4. Rate Vs Utilization

3 Conclusion In this paper, we have developed a Resource Allocation and Multicast Routing (RAMR) protocol for dynamic traffic in optical WDM networks. In this protocol initially, a multicast tree has been constructed using the member only algorithm. Then the incoming traffic is sent from the multicast source to a set of intermediate junction nodes and then, from the junction nodes to the final destinations. By simulation, we have shown that our proposed protocol attains increased throughput and bandwidth utilization with reduced delay.

References 1. Rajkumar, A., Murthy Sharma, N.S.: A Distributed Priority Based Routing Algorithm for Dynamic Traffic in Survivable WDM Networks. IJCSNS International Journal of Computer Science and Network Security 8(11) (2008) 2. Ou, C., Zang, H., Singhal, N., Zhu, K., Sahasrabuddhe, L.H., Macdonald, R.A., Mukherjee, B.: Sub path Protection For Scalability And Fast Recovery In Optical WDM Mesh Networks. IEEE Journal On Selected Areas In Communications 22(9) (2004) 3. Le, V.T., Ngo, S.H., Jiang, X.H., Horiguchi, S., Inoguchi, Y.: A Hybrid Algorithm for Dynamic Light path Protection in Survivable WDM Optical Networks. IEEE (2005) 4. Wikipedia, http://en.wikipedia.org/wiki/Multicasting 5. Fen, Z., Miklos, M., Bernard, C.: Distance Priority Based Multicast Routing in WDM Networks Considering Sparse Light Splitting. In: IEEE 11th Singapore International Conference on Communication Systems (2008) 6. Jia, X.-H., Du, D.-Z., Hu, X.-D., Lee, M.-K., Gu, J.: Optimization of Wavelength Assignment for QoS Multicast in WDM Networks. IEEE Transactions on Communications 49(2) (2001)

An Approximate Algorithm for Solving Dynamic Facility Layout Problem Surya Prakash Singh Department of Management Studies, Viswakarma Bhawan, Indian Instiitute of Technology Delhi, New Delhi-110016, India [email protected]

Abstract. The problem of rearranging manufacturing facilities over time is known as Dynamic Facility Layout Problem (DFLP). The objective is to minimize the sum of the material handling and the rearrangement costs. The problem is NP-hard and has begun to receive attention very recently. In this paper, an approximate algorithm/ heuristic for solving DFLP is presented. The proposed heuristics has been applied to eight different data sets of a problem set (containing 48 data sets) given by Balakrishnan and Cheng [1], and it has been found that the proposed heuristic provide good solutions having about 12% of deviation from the best known solution available in the published literature. Further, as a future scope of research work, an improvement heuristic can be developed or some meta-heuristic approach such as simulated annealing, and tabu search can be further applied to improve the solution quality obtained from the proposed approximate algorithm. Keywords: Approximate Algorithm, Dynamic facility layout problem, Heuristic, Meta-heuristic.

1 Introduction The facility layout problem (FLP) is to arrange facilities within a given space to minimize a given objective. The most common objective considered is the minimization of material-handling cost. Material handling costs are determined based on the amounts of materials that flow between the facilities and the distance between the locations of these facilities. When the flow of material between the facilities is fixed, this problem is known as static facility layout problem (SFLP). When the flow of materials is not static then problem is known as the Dynamic Facility Layout Problem (DFLP). For the DFLP it is assumed that the flow data for each period remains constant through out a given sub-period. We denote the time period by t (t=1,2,…,T); facilities are denoted by i and k, and locations are denoted by j and l, then the material-handling cost for the layout in each time period t is given by the T n n n n expression ∑ ∑ ∑ ∑ ∑ F ikt * D jl * X ijt * X klt where F ik denotes flow of t i =1 j =1 k =1 l =1 materials between facilities i and k; and D jl is the distance between locations j and l V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 504–509, 2010. © Springer-Verlag Berlin Heidelberg 2010

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in the time period t. A ijlt is denoted as the cost of shifting facility i from location j to

l at the beginning of time period t. X ijt is referred as a decision variable and is 1 if the facility i is located in the location j in the time period t, and is 0 otherwise. Decision variable Y ijlt is 1 if the facility i is shifted from location j to location l at the beginning of the time period t and is 0 otherwise. Mathematical model of DFLP can be represented as: T n n n T n n n n ∑ ∑ ∑ ∑ ∑ F ikt * D jl * X ijt * X klt + ∑ ∑ ∑ ∑ A ijlt * Y ijlt . t = 2 i =1 j =1 l =1 t =1 i =1 j =1 k =1 l =1

(1)

n ∑ X ijt = 1 j =1

i = 1,..., n; t = 1,..., T .

(2)

n ∑ X ijt = 1 i =1

j = 1,..., n; t = 1,..., T .

(3)

Y ijlt = X ij (t −1) * X ilt i, j, l = 1,..., n; t = 1,..., T .

(4)

X ijt = {0,1} i, j = 1,..., n; t = 1,..., T .

(5)

Y ijlt = {0,1} i, j, l = 1,..., n; t = 1,..., T .

(6)

The paper is organized as follows. Section 2 gives a brief review on DFLP, and section 3 describes the proposed approximate algorithm. The computational results are given in section 4 followed by conclusion and future research directions.

2 Past Work The first major research on the DFLP was undertaken by Rosenblatt [2] who defined and addressed the dynamic aspect of FLP. Balakrishnan et al. [3] attempted to model a constraint on DFLP proposed by Rosenblatt [2]. He formulated the new problem and named it as Constrained Dynamic Facility Layout Problem (CDFLP). A Dynamic Programming (DP) technique was used to solve CDFLP. Urban [4] proposed a steepest descent pair-wise exchange heuristic for the DFLP. Conway and Venkatratnam [5] were the first to examine the suitability of GA for DFLP. Balakrishnan [6] proposed an improved pair-wise exchanged heuristic based on the pair wise exchange method proposed by Urban [4]. Balakrishnan [7] proposed a hybrid approach of genetic algorithm and DP, and referred as GADP to solve DFLP. They tested GADP for the same data sets generated by them and reported that they found effective results. Erel et al. [8] presented a new heuristic scheme for DFLP. Singh and Sharma [9] proposed four different GA based heuristic approach for solving DFLP where they have considered a random parent selection scheme. They applied their heuristic on the same data sets provided by Balakrishnan and Cheng [1] and found better solutions for few problem sets for 5 time period 6 facility problems. Balakrishnan and Cheng [10]

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reviewed work on DFLP and its solution methodologies. Baykasoglu et al. [11] applied ant colony algorithm to solve DFLP in the presence of budget constraints. After sometime, Baykasoglu and Gindy [12] applied simulated annealing algorithm to solve DFLP. Kaku and Mazzola [13] attempted to solve DFLP using tabu search approach. Mackendall and Shang [14] presented the results of hybrid ant system on DFLP. Mackendall et al. [15] applied modified simulated annealing based approach for solving DFLP. Recently, Balakrishnan and Cheng [16] incorporated uncertainty issues in forecasting and proposed a methodology to solve DFLP in the presence of uncertainty. Mckendall and Hakobyan [17] gave a heuristic approach to solve DFLP. Very recently, Ramazan et al. [18] have applied simulated annealing approach to solve DFLP in the presence of budget constraint. Although many heuristics and metaheuristic approaches have been applied but none of the proposed methods ensures very good solution in a reasonable computational time. In this paper, a new heuristic approach is developed to solve DFLP in a reasonable time based on relaxing binary constraints of DFLP. Currently, the approximate algorithm is tested on limited data sets due to software capacity limitation, and to validate the approximate algorithm, testing on large variety of data sets is proposed as a future scope of research work.

3 An Approximate Algorithm In this section, an approximate algorithm is presented for solving DFLP. The algorithm is based on relaxing the binary constraint of X ijt . We called this a Relaxed Dynamic Facility Layout problem (RDFLP). After relaxing the binary constraint the complexity reduces as the number of binary variables reduces and RDFLP gives solution in comparatively less time. But the solution from RDFLP is real and hence not a feasible solution. Therefore, in order to make a feasible binary solution from real one (obtained from solving RDFLP), we propose a rule based approach that converts real values of X ijt into binary values. If all real values of X ijt gets converted to a binary one then it can be treated as a final solution for DFLP else we put converted real values of X ijt as a set of constraints in the formulation of DFLP and try to solve Extended Dynamic Facility Layout Problem (EDFLP). This section presents all steps of the proposed approximate algorithm and mathematical formulations of RDFLP and EDFLP. Step 1: Drop the binary integer constraint of DFLP to obtain relaxed problem which is formulated as RDFLP and is shown below.

T n n n n T n n n ∑ ∑ ∑ ∑ ∑ F ikt * D jl * X ijt * X klt + ∑ ∑ ∑ ∑ A ijlt * Y ijlt . t =1 i =1 j =1 k =1 l =1 t = 2 i =1 j =1 l =1

(1)

n ∑ X ijt = 1 j =1

i = 1,..., n; t = 1,..., T .

(2)

n ∑ X ijt = 1 i =1

j = 1,..., n; t = 1,..., T .

(3)

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Y ijlt = X ij (t −1) * X ilt i, j, l = 1,..., n; t = 1,..., T .

(4)

X ijt ≥ 0 i, j = 1,..., n; t = 1,..., T .

(7)

Y ijlt = {0,1} i, j, l = 1,..., n; t = 1,..., T .

(6)

Step 2: Solve RDFLP using the commercially available optimization software LINGO 8 to obtain solution. In this step, solution of decision variables X ijt consists

of real (or fractional) values. It may also contain binary values. Step 3: In this step, real values are converted to binary values using a simple procedure. For facility i ∀ i = 1, ... , n search the candidate variable X ijt having the largest fractional value ∀ j = 1, ... , n. If the value of the candidate variable is strictly greater than 0.6 (in this work it is kept 0.6), then assign that variable as 1 else leave as it is. Construct a new set of constraints that contains only X ijt = 1 as Set_of_ones = Set_of_ones + { X ijt }. This new set of constraints will be the additional constraint i.e

eighth constraint and form EDFLP. Step 4: After including Set_of_ones = Set_of_ones + { X ijt } as explained in step 3, the mathematical formulation of EDFLP can be shown as given below. T n n n n T n n n ∑ ∑ ∑ ∑ ∑ F ikt * D jl * X ijt * X klt + ∑ ∑ ∑ ∑ A ijlt * Y ijlt . t =1 i =1 j =1 k =1 l =1 t = 2 i =1 j =1 l =1

(1)

n ∑ X ijt = 1 j =1

i = 1,..., n; t = 1,..., T .

(2)

n ∑ X ijt = 1 i =1

j = 1,..., n; t = 1,..., T .

(3)

Y ijlt = X ij (t −1) * X ilt i, j, l = 1,..., n; t = 1,..., T .

(4)

X ijt = {0,1} i, j = 1,..., n; t = 1,..., T .

(5)

Y ijlt = {0,1} i, j, l = 1,..., n; t = 1,..., T .

(6)

Set_of_ones = Set_of_ones + { X ijt }.

(8)

Step 5: Solve problem EDFLP using commercially available optimization software LINGO 8. The solution now obtained will be consisting of decision variables having only binary values. This solution is treated as a solution obtained from the proposed algorithm and is also compared with the best known solution available in published literature and is discussed in detail in the following section.

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4 Computational Experiments The proposed approximate algorithm is tested only on eight data sets having n=6 and time period t=5 given by Balakrishnan and Cheng [1] on a machine having 2GB RAM and 2.56 GHz microprocessor speed. Table 1 presents objective function value of the proposed algorithm. To validate the performance of the proposed algorithm the solution obtained is also compared with the best known solution taken from the published literature. It is found that the proposed algorithm provides solution within 12 % of deviation from the best known solution. Table 1. Computational results of the proposed approximate algorithm and its comparison with the best known solution. Table also shows % deviation from the best known solution. S.No. 1 2 3 4 5 6 7 8

Instance P1 P2 P3 P4 P5 P6 P7 P8

Best Known Value 106419 104834 104320 106399 105628 103985 106439 103771

Solution Obtained 117339 115264 118271 117417 120408 115943 120673 115527

%Dev. 10.2 9.94 12.3 10.35 12.99 11.49 12.3 11.3

5 Conclusion and Future Research Direction An approximate algorithm is presented in this paper for solving DFLP in a reasonable computational time. Proposed approach provides solution within the deviation of about 12% from the best known solution. Due to the capacity limitation of Lingo 8 software the proposed approximate algorithm has been only tested on a few data sets and in future it is proposed to apply in all data sets of Balakrishnan and Cheng [1] in order to validate the performance of an approximate algorithm. In order to improve the solution quality some improvement heuristic based approach such as a pair-wise exchange can be developed and applied. Also, meta-heuristic approaches such as simulated annealing, and tabu search can also be applied to further improve the solution quality obtained from the proposed approximate solution.

References 1. Balakrishnan, J., Cheng, C.H.: Genetic search and the dynamic layout problem. Comp. and Oper. Res. 27(6), 587–593 (2000) 2. Rosenblatt, M.J.: The dynamics of plant layout. Manage. Sci. 32(1), 76–86 (1986) 3. Balakrishnan, J., Jacobs, R.F., Venkataraman, M.A.: Solution for the constrained dynamic facility layout problem. Eur. J. Oper. Res. 57, 280–286 (1992) 4. Urban, T.L.: A heuristic for dynamic facility layout problem. IIE Trans. 12, 200–205 (1980)

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5. Conway, D.G., Venkatratnam, M.A.: Genetic search and dynamic facility layout problem. Comput. Oper. Res. 21(8), 955–960 (1994) 6. Balakrishnan, J., Cheng, C.H., Conway, G.: An improved pair-wise exchange heuristic for the dynamic facility layout problem. Int. J. Prod. Res. 38(13), 3067–3077 (2000) 7. Balakrishnan, J., Cheng, C.H., Conway, G., Lau, C.M.: A hybrid genetic algorithm for dynamic plant layout problem. Int. J. Prod. Econ. 86, 107–120 (2003) 8. Erel, E., Ghosh, J.B., Simon, J.T.: New heuristics for dynamic facility layout problem. J. Oper. Res. Soc. 54, 1275–1282 (2003) 9. Singh, S.P., Sharma, R.R.K.: Genetic algorithm based heuristics for dynamic facility layout problem. Eur. J. Oper. Manag. 8(1), 128–134 (2008) 10. Balakrishnan, J., Cheng, C.H.: Dynamic layout algorithms: a state-of-the-art-survey. Omega 26, 507–521 (1998) 11. Baykasoglu, A., Dereli, T., Sabuncu, I.: Ant colony algorithm for solving budget constraint and unconstraint dynamic facility layout problem. Omega 34(4), 385–396 (2006) 12. Baykasoglu, A., Gindy, N.N.Z.: A simulated annealing algorithm for dynamic facility layout problem. Comp. Oper. Res. 28(14), 1403–1426 (2001) 13. Kaku, B.K., Mazzola, J.B.: A tabu search heuristic for the dynamic plant layout problem. Informs J. Comp. 9(4), 374–384 (1997) 14. Mackendall Jr., A.R., Shang, J.: Hybrid ant systems for the dynamic facility layout problem. Comp. Oper. Res. 33(3), 790–803 (2006) 15. Mackendall Jr., A.R., Shang, J., Kupuswamy, S.: Simulated annealing heuristics for the dynamic facility layout problem. Comp. Oper. Res. 33, 2431–2444 (2006) 16. Ramazan, S., Kadir, E., Orhan, T.: A Simulated annealing heuristics for the dynamic layout problem with budget constraint. Comp. Ind. Engg. (in press, 2010) 17. Balakrishnan, J., Cheng, C.H.: Dynamic Plant Layout Problem: Incorporating rolling horizons and forecast uncertainty. Omega 37(1), 165–177 (2009) 18. McKendall Jr., A.R., Hakobyan, A.: Heuristics for the Dynamic Plant Layout Problem with unequal-area department. Eur. J. Oper. Res. 201(1), 171–182 (2010)

Homology Modeling and Protein Ligand Interaction to Identify Potential Inhibitor for E1 Protein of Chikungunya C.S. Vasavi, Saptharshi, R. Radhika Devi, Lakshmi Anand, Megha. P. Varma, and P.K. Krishnan Namboori Computational Chemistry Group (CCG), Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Ettimadai, Coimbatore-641105 [email protected], [email protected]

Abstract. Chikungunya fever is an overwhelming, but non-fatal viral illness that has been reported in many parts of the country. The E1 domain of Q1El92_CHIKV virus that helps in binding with the host has been determined by using comparative homology modeling program MODELLER based on crystal structure of the homotrimer of fusion glycoprotein E1 from Semliki Forest virus as a template protein and it had 63% sequence identity. The modeled structure‘s energy was minimized and validated using structure validation server in which 82.8% of the residues were present in the most favored regions of the Ramachandran plot. Disulphide bonds which help in protein folding of the proteins were analyzed and it was found to be conserved for both the homologous and the modeled structures. The ion pairs which contribute to fusion of viral membranes and that help in solvent protein interactions were analyzed. Docking studies was carried out with various phytochemicals and it was found that osltamivir had the most stable interaction with the E1 domain of the Q1El92_CHIKV virus. Thus from the complex scoring and binding ability it was interpreted that Osltamivir could be a promising inhibitor for E1 domain of Q1El92_CHIKV virus as the drug target yet pharmacological studies have to confirm it. Keywords: Homology modeling, chikungunya, E1 protein, Docking.

1 Introduction Arthropod-borne viruses (arboviruses) are the causative agents of some of the most important budding infectious diseases and are accountable for major global public health problems [1]. The contributing agent, Chikungunya virus (CHIKV), is a singlestranded positive RNA-enveloped virus, a member of the genus Alphavirus of the Togaviridae family and is spread from primates to humans generally by Aedes aegypti [2, 3]. This virus can target human epithelial and endothelial cells, fibroblasts and macrophages [4] and muscle progenitor cells, to cause an extensive range of clinical manifestations including fever, headache, rash, nausea, vomiting, myalgia, and especially, disabling joint pain. CHIKV can damage collagen and alter connective tissue V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 510–513, 2010. © Springer-Verlag Berlin Heidelberg 2010

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metabolism in cartilage and joints to produce severe acute arthritis [5]. The 3’ region encodes the three major structural proteins (the capsid and two envelope proteins). Virions consist of 5 structural proteins located in the envelope (i.e.) nucleocapsid. The virions located on the surface of the cell membrane enter the host cells by fusion and endocytosis of viral envelope. The structural envelope proteins E1 and E2 help in binding and fusion of the membranes respectively. At present there is no specific treatment for Chikungunya and hence the identification of the potential drug required for the treatment of Chikungunya. In this paper, the structural proteins of the Chikungunya virus and their interactions with the selected phytochemicals were studied through computational analyses. This would provide an insight into finding a suitable inhibitor for Chikungunya E1 envelope protein that would prevent binding of the virus with the host cells.

2 Theory A molecular dynamics simulation computes the behavior of a system as a function of time. It involves solving Newton's laws of motion, principally the second law (Equation 1). mi

∂2y = Fi ∂x 2

(1)

where Fi is the force of the ‘i’th particle and is related to potential energy by Equation 2. F i = − ∇ i V ( ri , . , r N )

(2)

The time progression or evolution of the system is then characterized by solving the second order differential equation for each particle in the system. The forces can be considered as varying smoothly with time for a realistic modeling of interaction of particles. A continuous potential head results in aggregation of interacting particles, making the analytical solution difficult. In such cases, finite difference numerical method can be used to solve the equations of motion. In this approach, total time span will be split up into discrete steps of length δt. Knowing velocities and position at a time‘t’, time evolved properties of the particle at (t + δt) can be efficiently computed. With these new positions and velocities, those at time (t + 2δt) will be calculated. Hence the equations of motion can be solved on a step-by-step basis.Homology modeling for proteins was done using Modeller .

3 Methods The E1 domain of Q1El92_CHIKV virus was subjected to sequence analysis, structural analysis, and computational modeling in order to characterize them. There were no 3D protein structures available for the E1 protein. Hence the primary structure of the envelope protein E1 which helps in fusion of Chikungunya virus to the host cells was BLAST with the available PDB [7] structures. From the obtained BLAST [8] output 1RER protein had the highest score. Homology modeling of the E1 protein was done by using Modeller. The modeled E1 protein was validated using Structural validation server [9] which helps in validating and assessing the protein

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structure using 6 programs (PROCHECK, WHATCHECK, ERRAT, VERIFY3D, PROVE). Structural analysis of the protein was done using PSAP. All together 9 phyto chemicals were taken for analysis. Interaction studies of these Potential drugs with modeled E1 protein have been carried out with CDOCKER tool of Accelry’s Discovery Studio.

4 Results and Discussion The primary structure of E1 domain in Q1El92_CHIKV protein was subjected to sequence analysis with the help of different expasy tools . From the analysis it was found that there were a total of 439 amino acid residues .The percentage of coils was more than the other secondary structures. The presence of coils indicates that the protein is denatured. This Denaturation of proteins might be due to the interaction with the host cells and their requirement to survive in a different environment. Homology modeling of the E1 protein was done by using the structure 1RER (SFV E1 protein) as the template. Thus the modeled structure was validated and it had an accuracy of 82.8% (Fig.1). The structural analysis of the protein was done using Protein structure analysis package. The disulphide bonds were analyzed conserved for both 1RER and E1 domain. This would help in protein folding and maintaining the stability of the protein. Ion pairs contribute to several functions including the activity of catalytic triads, fusion of viral membranes and solvent-protein interactions. Furthermore, they have the ability to affect the stability of protein structures and are also a part of the forces that act to hold monomers together. The ion pairs ASP 992--ARG 1056, ASP 997--LYS 985, GLU 859--LYS 1050 were conserved except for the residues D-992, R-1056, E-918 and E-926 in E1 domain of the Q1El92_CHIKV Protein. Docking of the ligands for the E1 domain was done using c-docker. It was found that out of the 8 phytochemicals (Table 1), the overall interaction energy for osltamivir was found to be low, which proves that it has more stable interaction compared to the other phytochemicals. This particular phytochemical would help in preventing the spread of virions from infected cells to new cells, and thus it could act as a good inhibitor for the viral envelope protein from being bound to new cells. Table 1. Phytochemicals and their interaction energy S.No 1 2 3 4 5 6 7 8

Phytochemicals

Osltamivir Zanamivir Fisetin Kaempferol Andrographalide Rhamnose Myricetin Quercitin

Interaction Energy (kcal/mol)

-41.339 -77.289 -80.173 -86.486 -93.374 -123.07 -193.738 -352.63

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Fig. 1. Modeled E1 Domain of CHIKV(Q1EL92) Protein

5 Conclusion The E1 domain of the CHIKV (Q1EL92) protein had highest percentage of random coils, which indicates that, the protein is denatured. Homology modeling of E1 domain was carried out and it had an accuracy of 82.8%. Structural analysis points out the presence of ion-pairs and disulphide-bonds. Ion-pairs help in fusion of viral membranes, and Di-sulphide bonds help in maintaining the stability. The modeled protein was subjected to docking and osltamivir that helps in preventing the spread of virions from infected cells to new cells had stable interaction compared to the other phytochemicals.

References [1] Gubler, D.J.: Human arbovirus infections worldwide. Ann. N. Y. Acad. Sci 951, 13–24 (2001) [2] Ann, M.P., Aaron, C.B., Robert, B.T., Scott, C.W.: Re-emergence of chikungunya and o’nyong-nyong viruses: evidence for distinct geographical lineages and distant evolutionary relationships. J.G.V. 81, 471–479 (2000) [3] Khan, A.H., Morita, K., Mdel, M., Parquet, C.F., Hasebe, M.E.G., Igarashi, A.: Complete nucleotide sequence of Chikungunya virus and evidence for an internal polyadenylation site. J. Gen. Virol., 3075–3084 (2002) [4] Soumahoro, M.K., Gerardin, P., Boelle, P.Y., Perrau, J., Fianu, A., et al.: Impact of Chikungunya Virus Infection on Health Status and Quality of Life: A Retrospective Cohort Study. PLoS ONE 4(11) (2009) [5] Lokireddy, S., Vemula, S., Vadde, R.: Connective tissue metabolism in chikungunya patients. J.Virol. 5, 31 (2008) [6] Sali, A., Blundell, T.L.: Modeller. J.M.B. 234, 779 (1993) [7] Helen, M.B., John, W., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Ilya, N.S., Philip, B.: The Protein Data Bank. Nucl.Acids Res. 28, 235–242 (2000) [8] Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. J.M.B. 215, 403–410 (1990) [9] Laskowski, R.A., MacArthur, M.W., Moss, D.S., Thornton, J.M.: PROCHECK - a program to check the stereochemical quality of protein structures. J. App. Cryst. 26, 283– 291 (1993)

Tied Mixture Modeling in Hindi Speech Recognition System R.K. Aggarwal and M. Dave Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, India {rka15969,mdave67}@gmail.com

Abstract. The goal of automatic speech recognition (ASR) is to accurately and efficiently convert a speech signal into a text message independent of the device, speaker or environment. In ASR, the speech signal is captured and parameterized at front-end and evaluated at back-end using the Gaussian mixture hidden Markov model (HMM). In statistical modeling, to handle the large number of HMM state parameters and to minimize the computation overhead, similar states are tied. In this paper we present a scheme to find the degree of mixture tying that is best suited for the small amount of training data, usually available for Indian languages. In our proposed approach, perceptual linear prediction (PLP) combined with Heteroscedastic linear discriminant analysis (HLDA) was used for feature extraction. All the experiments were conducted in general field conditions and in context of Indian languages, specifically Hindi, and for Indian speaking style. Keywords: ASR, HMM, PLP, HLDA, Mixture Gaussian, State Tying.

1 Introduction Automatic speech recognition is a task performed by a machine in which the textual message is extracted from the acoustic signals. State-of-the-art speech recognition systems use mixture Gaussian output probability distributions in HMM together with context dependent phone models [1]. To handle the large number of state parameters of HMM, many similar states of the model are tied and the data corresponding to all these states are used to train one global state [2]. HMMs with this type of sharing were proposed in [3,4] under the names semi-continuous and tied-mixture HMMs. Speech recognition is a pattern classification problem based on training and testing phases. To train the acoustic model (i.e. HMM) in ASR, standard speech databases are required to get the best results [5]. For the design and development of European languages ASR systems, where large and standard databases (e.g. TIMIT, Switchboard corpus) are available to model acoustic variability, higher degrees of mixture tying (i.e. 4000 to 8000 total tied states) have been applied [6]. However, the same convention cannot be followed for Indian languages as the databases, prepared by various research groups, are relatively small and phonetically not very rich. In this V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 514–519, 2010. © Springer-Verlag Berlin Heidelberg 2010

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paper we present a solution to find the right degree of mixture tying by observing empirically the performance of Hindi speech recognition system using a self prepared small database. Rest of the paper is organized as follows: Section 2 presents the architecture and functioning of proposed ASR. Section 3 describes the used feature extraction and reduction technique. Framework for statistical modeling is explained in section 4. Experimental results are given in section 5. Conclusions are drawn in section 6.

2 System Overview The process of speech recognition can be subdivided into preprocessing, feature extraction, model generation and pattern classification steps as shown in Fig. 1. The analog speech signal is digitized and segmented into overlapping blocks (known as frames) with the help of Hamming window [5]. To compute the feature vectors on a frame by frame basis various techniques are available in literature like Mel frequency cepstral coefficient (MFCC) [7], perceptual linear prediction (PLP) [8], temporal patterns (TRAPs) [9] and wavelets [10]. Training Speech Preprocessing Input

Model Generation

Feature Extraction

Testing

Pattern Classification

Recognized Words

Fig. 1. Speech recognition system components

The acoustic feature vectors extracted from speech signals are evaluated at back end with the help of mathematical models like HMM [11], dynamic Bayesian networks (DBN) [12], artificial neural networks (ANN) [13], and support vector machines (SVM) [14]. Among them, HMM has been widely used due to its efficient training techniques based on maximum likelihood estimation (MLE) [15] or discriminative methods [16]. The acoustic and language models resulting from the training procedure are used as knowledge sources during decoding.

3 Feature Extraction and Reduction The most widely used feature extraction techniques are Mel frequency cepstrum coefficient (MFCC) and perceptual linear prediction (PLP). Both techniques employ auditory-like warping of short-term spectrum of speech, yielding higher spectral resolution at lower frequencies. In PLP, to obtain the auditory spectrum 17 band pass filter outputs are used. Their center frequencies are equally spaced in Bark domain z :

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(

z = 6 log ( f

/ 600 ) +

(f

/ 600 ) + 1 2

)

(1)

where f is the frequency in Hz and z covers the range 0-5 KHz, into the range 0-17 Bark (i.e.0 ≤ z ≤ 17 Bark). Briefly, the main steps followed in this technique are spectral analysis, critical-band spectral resolution, equal loudness pre-emphasis, inverse discrete Fourier transform, and solution for autoregressive coefficients. Finally, the feature vector consists of 39 values including the 13 cepstral coefficients (with energy), 13 delta cepstral coefficients and 13 delta delta coefficients. For feature reduction HLDA, a linear transformation scheme is used which projects the features into low dimensional subspace, while preserving discriminative information. It assumes that n-dimensional original feature space can be split into two statistically independent subspaces [17]. Let x be an n-dimensional feature vector. The goal of HLDA is to find the transform matrix A that maps x to a new y.

Y = Ax =

⎡ Am x ⎤ ⎡ ym ⎤ ⎢ A x⎥ = ⎢ y ⎥ ⎣ n−m ⎦ ⎣ n−m ⎦

(2)

Where Am is a matrix consisting of first m rows of n×n matrix A, An-m consists of remaining n-m rows, ym are useful dimensions in the rotated space and yn-m are the nuisance dimensions.

4 Framework for Statistical Modeling The key issues related with HMM are model topology, acoustic-phonetic information representation, number of Gaussian mixtures and tied states [18]. 4.1 Mixture Gaussian HMM HMM acts as a stochastic finite state machine in which states are interconnected by links describing the conditional probabilities of a transition between the states. Each of these states is also associated with a multivariate Gaussian distribution, defined as: N ( x; μ , ∑ ) =

1 (2π )

n/2

∑

1/ 2

⎡ 1 ( x − μ )t ∑ −1 ( x − μ ) ⎤ ⎢⎣ 2 ⎥⎦

exp −

(3)

where μ is the n dimensional mean vector, Σ is the n×n covariance matrix, and |Σ| is the determinant of covariance matrix Σ. The probability density for observable data y is the weighted sum of each Gaussian component:

p ( y | λ ) = ∑ kM=1 ck N k ( y | μ k ∑ k )

(4)

Where ck, the mixture weight associated with kth Gaussian component is subject to the following constraint

ck ≥ 0 and ∑ mk=1 ck = 1 , and p ( y | λ ) is an utterance level

score of y given the model λ [19].

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4.2 State Tying Tying of similar states leads to a large amount of data for each state hence parameters are well estimated. The choice of which states to tie is commonly made using decision trees [20]. The appropriate degree of tying for a particular task depends on the difficulty of the task, the amount of available training data, and the available computational resources for recognition, since systems with a smaller degree of tying have higher computational demands during recognition [21].

5 Experimental Results The experiments were performed on a set of speech data consisting of four hundred words of Hindi language recorded by 10 male and 10 female speakers. Testing of randomly chosen sentences spoken by different speakers is made and recognition rate (i.e. accuracy) is calculated, where accuracy = detected words / total words in test set. The input speech was sampled at 12 kHz and then processed at 10 ms frame rate with a Hamming window of 25 ms to obtain the feature vectors. Triphone model of HMM with linear left-right topology is used to compute the score against a sequence of features for their phonetic transcription. All experiments were performed using an open source toolkit HTK-3.4.1 [22] in the following two phases: • Standard 39 PLP features (i.e. PLP +Δ+ΔΔ) were used at front-end. • 13 extra triple delta features were added in standard features (i.e. PLP +Δ+ΔΔ+ ΔΔΔ) forming a feature vector of 52 values and then reduced to 39 by HLDA. 5.1 Experiment with Rate of Speech Experiments were performed with different types of speech, that is, for slow speech (less than 60 words per minute), for normal speech (80 to 140 words per minute) and for fast speech (more than 160 words per minute). Maximum accuracy is achieved for normal speech as shown in Fig. 2.

Fig. 2. Accuracy versus speech rate

Fig. 3. Accuracy versus SNR

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5.2 Experiment with SNR In normal field condition SNR remains between 20-35. Above 35, clean speech is assumed. In noisy environment, when SNR is less than 20, speech recognition is a difficult problem. Experiments were performed by mixing additive noise (ambient noise) and accuracy was calculated by varying SNR as shown in Fig. 3. 5.3 Experiment with Tied States Experiments were performed with different number of tied states as shown in Table 1. The number of Gaussian mixtures used for each case of tied states is four [23]. Table 1. Accuracy versus tied states No. of Genones Accuracy (%)

50 82

350 86

740 91

1250 90.5

1700 88

2400 84

6 Conclusion There is no doubt that new developments in the fields of signal processing and pattern recognition, have improved the performance of ASR systems, still there exists number of problems that need to be solved. Parameter tying of the HMM states in Gaussian mixture model is one such problem. To avoid over fitting and to minimize computation overhead appropriate degree of mixture tying is very important. By experimental results we conclude that the tied states of order one thousand gave the maximum accuracy with 4 Gaussian mixtures in the context of small databases used for Hindi language. The results also illustrate that if PLP combined with HLDA is used for feature extraction, the accuracy can be improved by 2-4%. During the experiments speech rate should be between 80 to 140 words per minute to achieve best results.

References 1. O’Shaughnessy, D.: Interacting with Computers by Voice-Automatic Speech Recognitions and Synthesis. Proceedings of the IEEE 91(9), 1272–1305 (2003) 2. Young, S.J.: The General Use of Tying in Phoneme-Based HMM Speech Recognizers. In: Proceeding of ICASSP (1992) 3. Huang, X.D., Jack, M.A.: Performance Comparison between Semi-continuous and Discrete Hidden Markov Models. IEE Electronics Letters 24(3), 149–150 (1988) 4. Bellegarda, I.R., Nahamoo, D.: Tied Mixture Continuous Parameter Modeling for Speech Recognition. IEEE Trans. ASSP 38(12), 2033–2045 (1990) 5. Becchetti, C., Ricotti, K.P.: Speech Recognition Theory and C++ Implementation. John Wiley, Chichester (2004) 6. Gales, M., Young, S.: The Application of Hidden Markov Models in Speech Recognition. Foundations and Trends in Signal Processing 1(3), 195–304 (2007)

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7. Vergin, R., O’Shaughnessy, D., Farhat, A.: Generalized Mel Frequency Cepstral Coefficients for Large-Vocabulary Speaker-Independent Continuous-Speech Recognition. IEEE Trans. on Speech and Audio Processing 7(5), 525–532 (1999) 8. Hermansky, H.: Perceptually Predictive (PLP) Analysis of Speech. Journal of Acoustic Society of America 87, 1738–1752 (1990) 9. Hermansky, H., Sharma, S.: Temporal Patterns (TRAPs) in ASR of Noisy Speech. In: Proc. of IEEE Conference on Acoustic Speech and Signal Processing (1999) 10. Sharma, A., Shrotriya, M.C., Farooq, O., Abbasi, Z.A.: Hybrid Wavelet Based LPC Features for Hindi Speech Recognition. International Journal of Information and Communication Technology, Inderscience publisher 1, 373–381 (2008) 11. Jelinek, F.: Statistical Methods for Speech Recognition. MIT Press, Cambridge (1997) 12. Deng, L.: Dynamic Speech Models: Theory, Applications, and Algorithms. Morgan and Claypool, San Francisco (2006) 13. Rao, K.S., Yegnanarayana, B.: Modelling Syllable Duration in Indian Languages Using Neural Networks. In: Proceeding of ICASSP, Montreal, Canada, pp. 313–316 (May 2004) 14. Guo, G., Li, S.Z.: Content Based Audio Classification and Retrieval by SVMs. IEEE Trans. Neural Networks 14, 209–215 (2003) 15. Juang, B.-H.: Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov Chains. AT & T Tech. Journal 64(6), 1235–1249 (1985) 16. Juang, B.-H., Katagiri, S.: Discriminative Learning for Minimum Error Classification. IEEE Transactions on Signal Processing 14(4) (1992) 17. Kumar, N., Andreou, A.G.: Heteroscedastic Disciminant Analysis and Reduced Rank HMMs for Improved Speech Recognition. Speech Communication 26, 283–297 (1998) 18. Young, S.: A review of Large Vocabulary Continuous Speech Recognition. IEEE Signal Processing Magazine 13, 45–57 (1996) 19. Huang, X., Acero, A., Hon, H.W.: Spoken Language Processing: A Guide to Theory Algorithm and System Development. Prentice Hall-PTR, New Jersy (April 2001) 20. Young, S.J., Odell, J.J., Woodland, P.C.: Tree-based State Tying for High Accuracy Acoustic Modeling. In: Proc. of Human Language Technology Workshop, pp. 307–312 (1994) 21. Digalakis, V.V., Monaco, P., Murveit, H.: Genones; Generalized Mixture Tying in Continuous Hidden Markov Model-Based Speech Recognizers. IEEE Transactions On Speech And Audio Processing 4(4), 281–289 (1996) 22. Hidden Markov Model Toolkit (HTK-3.4.1), http://htk.eng.cam.ac.uk 23. Aggarwal, R.K., Dave, M.: Effects of Mixtures in Statistical Modeling of Hindi Speech Recognition System. In: Proceedings of the 2nd International Conference on Intelligent Human Computer Interaction, IIIT, Allahabad, January 16-18, pp. 224–229. Springer, Heidelberg (2010)

Propagation Delay Variation due to Process Induced Threshold Voltage Variation Krishan Gopal Verma1, Brajesh Kumar Kaushik2, and Raghuvir Singh1 2

1 School of Electronics, Shobhit University, Meerut, Uttar Pradesh, India Deptt. of Electronics and Computer Engg., Indian Institute of Technology-Roorkee, India [email protected], [email protected]

Abstract. Process variation has emerged as a major concern in the design of circuits including interconnect pipelines in current nanometer regime. Process variation results in uncertainties of circuit performances such as propagation delay, noise and power consumption. Threshold voltage of a MOSFET varies due to changes in oxide thickness; substrate, polysilicon and implant impurity level; and surface charge. This paper provides a comprehensive analysis of the effect of threshold variation on the propagation delay through driver-interconnect-load (DIL) system. The impact of process induced threshold variations on circuit delay is discussed for three different technologies i.e 130nm, 70nm and 45nm. The comparison of results between these three technologies shows that as device size shrinks, the process variation issues becomes dominant during design cycle and subsequently increases the uncertainty of the delays. Keywords: Process variation, interconnects, VLSI, systematic variation, propagation delay.

1 Introduction Variability in modern nanometer circuits has not scaled down in proportion to the scaling down of their feature sizes [1]. Manufacturing process variations (e.g. threshold voltage, effective channel length), environmental variations (e.g., supply voltage, temperature), and device fatigue phenomenon contribute to uncertainties [2]. Uncertainty due to parametric variations deeply impacts the timing characteristics of a circuit and makes timing verification extremely difficult [3-5]. This necessitates the consideration of the parametric variations in timing analysis for accurate timing estimation [3]. The function of interconnects or wiring systems is to distribute clock and other signals and to provide power/ground to and among the various circuits/systems functions on the chip [6]. The performance s.a. time delay and power dissipation of a high-speed chip is highly dependent on the interconnects, which connect different macro cells within a VLSI chip [7-10]. This paper analyzes the effect of threshold voltage variation due to process variation on the propagation delay of Driver-Interconnect-Load (DIL) system [6] for different technologies i.e 130nm, 70nm and 45nm. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 520–524, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Monte Carlo (MC) Analysis The analysis carried out in this work takes into account a DIL system [6] shown in Figure 1. The driver is an inverter gate driving the interconnect.

m11

z=ℓ τ

RLC Interconnect

Vin

CL

m12

Fig. 1. Driver Interconnect Load (DIL) System

The threshold voltage of the driver transistor is described by the following equation 2

2

Here in equation (1): VTO= Threshold voltage for VSB=0V;

(1) ;γ=

2 ; NA = doping Fabrication –process parameter and is given as concentration of p-type substrate; and Cox = Gate oxide capacitance. The threshold voltage of a device is dependent on various physical parameters which are prone to process variation. In this analysis, the driver is subjected to process variations in reference to threshold voltage for three different fabrication technologies. To obtain statistical information on how much the characteristics of a circuit can be expected to scatter over the process, Monte Carlo analysis is applied. In order to obtain reasonable statistical results, a large number of simulations are needed, leading to quite long simulation times. 3 Effect of Threshold Voltage Variation on Delay of DIL System Monte Carlo simulations are run for threshold voltage variations in 130nm, 70nm and 45nm fabrication technology. Figure 2 shows the SPICE input and output voltage for a variation of 30% in threshold voltage in NMOS and PMOS transistors in 130nm technology. It is observed that the output varies significantly due to the process variation parameter. Table-1 accounts for NMOS threshold voltage (Vtn); PMOS threshold voltage (Vtp); the delay due to driver and interconnect line; the percentage variation in NMOS and PMOS threshold voltage and percentage variation in delay of driver and line. It is observed that the variation in delay ranges from -2.39% to 4.60% for 130nm technology. Similarly, MC simulations are run for threshold voltage variations in 70nm fabrication technology also. Figure 3 shows the SPICE input and output voltage variations for variation in threshold voltage for NMOS and PMOS transistors of the driver in 70nm technology. It is observed that the output varies appreciably higher than the results observed for 130nm technology due to the process variation parameter.

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Table 1. Variation in delay due to change in threshold voltage of NMOS & PMOS for 130nm fabrication process technology Vtn (V)

Vtp (V)

0.044 0.049 0.064 0.066 0.067 0.070 0.071 0.073 0.074 0.075 0.085

Driver and Line Delay (ps)

Variation in Vtn (%)

Variation in Vtp (%)

59.88 60.36 61.60 61.64 61.82 62.08 62.28 62.40 62.61 62.56 63.43

-34.15 -26.11 -4.06 -0.48 0.00 4.33 6.21 8.81 10.54 12.72 26.69

2.39 -5.68 -3.89 41.53 0.00 1.99 -19.00 -10.14 -31.83 9.22 7.98

-0.218 -0.201 -0.205 -0.302 -0.213 -0.217 -0.173 -0.191 -0.145 -0.233 -0.230

Variation in Delay of Driver and line (%) -2.39 -2.23 -0.47 -0.62 0.00 0.61 1.30 1.54 2.31 1.87 4.60

70nm 130nm

v(out2,gnd) v(vin,gnd) v(out2,gnd) v(vin,gnd)

1.5 1.5

1.0

Voltage (V)

Voltage (V)

1.0

0.5 0.5

0.0

0.0

0

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250

300

350

0

400

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50

100

150

200

250

300

350

400

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Fig. 2. SPICE Input and Output waveforms through DIL system for 130nm Technology Driver

Fig. 3. SPICE Input and Output waveforms through DIL system for 70nm Technology Driver

Table 2. Variation in delay due to change in threshold voltage of NMOS & PMOS for 70nm fabrication process technology Vtn (V) 0.132 0.148 0.192 0.199 0.200 0.209 0.212 0.218 0.221 0.225 0.253

Vtp (V) -0.225 -0.208 -0.211 -0.311 -0.220 -0.224 -0.178 -0.198 -0.150 -0.240 -0.238

Driver and Line Delay (ps) 44.907 45.974 48.879 49.051 49.421 50.014 50.456 50.738 51.186 51.137 53.124

Variation in Vtn (%) -34.15 -26.11 -4.06 -0.48 0.00 4.33 6.21 8.81 10.54 12.72 26.69

Variation in Vtp (%) 2.39 -5.68 -3.89 41.53 0.00 1.99 -19.00 -10.14 -31.83 9.22 7.98

Variation in Delay of Driver and line (%) -9.13 -6.97 -1.10 0.75 0 1.20 2.09 2.66 3.57 3.47 7.49

Table-2 accounts for NMOS threshold voltage (Vtn); PMOS threshold voltage (Vtp); the delay due to driver and interconnect line; the percentage variation in NMOS and PMOS threshold voltage and percentage variation in delay of driver and line. It is observed that the variation in delay ranges from -9.13% to 7.49% for 70nm technology.

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Table 3. Variation in delay due to change in threshold voltage of NMOS & PMOS for 45nm fabrication process technology Vtn (V)

Vtp (V)

0.145 0.163 0.211 0.219 0.220 0.230 0.234 0.239 0.243 0.248 0.279

-0.225 -0.208 -0.211 -0.311 -0.220 -0.224 -0.178 -0.198 -0.150 -0.240 -0.238

Driver and Line Delay (ps)

Variation in Vtn (%)

63.055 65.425 71.976 72.613 73.22 74.656 75.603 76.337 77.263 77.414 82.393

-34.15 -26.11 -4.06 -0.48 0.00 4.33 6.21 8.81 10.54 12.72 26.69

Variation in Vtp (%)

Variation in Delay of Driver and line (%)

2.39 -5.68 -3.89 41.53 0.00 1.99 -19.00 -10.14 -31.83 9.22 7.98

-13.90 -10.60 -1.70 -0.83 0 1.96 3.25 4.26 5.52 5.73 12.5

45nm

15% % Change in Delay

v(out2,gnd) v(vin,gnd)

1.5

10%

1.0

5%

V oltage (V)

0%

-40%

0.5

-20%

-5% 0%

-15%

0.0

0

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Fig. 4. SPICE Input and Output waveforms through DIL system for 45nm Technology Driver

20%

40%

% Threshold Voltage Variation

-10%

130nm 70nm 45nm

-20%

Fig. 5. Comparison of percentage change in delay due to variations in threshold voltage for 130nm, 70nm and 45nm technologies

Figure 4 demonstrates the Monte Carlo SPICE simulation input and output voltage variations due to variation in threshold voltage of NMOS and PMOS transistors of the driver in 45nm technology. It is observed that the output varies drastically due to the process variation parameter in 45nm technology compared to 130nm and 70nm technologies.Table 3 accounts for NMOS threshold voltage (Vtn); PMOS threshold voltage (Vtp); the delay due to driver and interconnect line; the percentage variation in NMOS and PMOS threshold voltage and percentage variation in delay of driver and line. It is observed that the variation in delay ranges from -13.9% to 12.5% for 45nm technology. The comparison between three technologies shows that as device size shrinks, the process variation becomes dominant and subsequently gives rise in variation of delays. Figure 5 demonstrates this claim by comparing the percentage change in delay due to variations in threshold voltage for 130nm, 70nm and 45nm technologies. It is observed that as feature reduces the variation in delay performance increases due to change in threshold voltage. Thus these simulation results reveals that process variation has large effect on the driver delay due to variation in threshold voltage.

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4 Conclusion Process variation represents a major challenge to design system-on-chip using nanometer technologies. In this paper, we have evaluated process variation effects on the delay of Driver-interconnect-load system due to threshold voltage variations. Variations in the driver and interconnect geometry of nanoscale chips deciphers to variations in their performance. The resulting diminished accuracy in the estimates of performance at the design stage can lead to a significant reduction in the parametric yield. Thus, determining an accurate statistical description of the DIL response is critical for designers. The random or systematic part of variations plays an important role in deviating electrical parameter. In the presence of significant variations of device model parameters the variations in performance parameter such as delay is severely affected. The comparison between three technologies shows that as device size shrinks the process variation becomes a dominant factor and subsequently raises the variation in delays.

References 1. Borkar, S., Karnik, T., Narendra, S., Tschanz, J., Keshavarzi, A., De, V.: Parameter variations and impact on circuits and microarchitecture. In: Proceedings of the 40th IEEE Design Automation Conference, pp. 338–342. IEEE Press, New York (2003) 2. Krstic, A., Wang, L.C., Cheng, K.T., Liou, J.J.: Diagnosis of delay defects using statistical timing models. In: Proceedings of the 21st IEEE VLSI Test Symposium, Washington, DC, USA, pp. 339–344. IEEE Computer Society, Los Alamitos (2003) 3. Vrudhula, S., Wang, J.M., Ghanta, P.: Hermite Polynomial Based Interconnect Analysis in the Presence of Process Variations. IEEE Trans. on CAD of Integrated Circuits and Systems 25(10), 2001–2011 (2006) 4. Verma, K.G., Kaushik, B.K., Singh, R.: Effects of Process Variation in VLSI Interconnects- a Technical Review. Microelectronics International, vol. 26(3, pp. 49–55. Emerald Pub., U.K. (2009) 5. Verma, K.G., Kaushik, B.K.: Effect of Process Based Oxide Thickness Variation on the Delay of DIL System Using Monte Carlo Analysis. International Journal of Recent Trends in Engineering 3(4), 27–31 (2010) 6. Kaushik, B.K., Sarkar, S.: Crosstalk Analysis for a CMOS-Gate-Driven Coupled Interconnects. IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems 27(6), 1150–1154 (2008) 7. Kaushik, B.K., Sarkar, S., Agarwal, R.P.: Waveform Analysis and Delay Prediction for a CMOS Gate Driving RLC Interconnect Load. Integration. VLSI Journal 40(4), 394–405 (2007) 8. Kaushik, B.K., Sarkar, S., Agarwal, R.P., Joshi, R.C.: Crosstalk Analysis of Simultaneously Switching Interconnects. International Journal of Electronics 96(10), 1095–1114 (2009) 9. Kaushik, B.K., Sarkar, S., Agarwal, R.P., Joshi, R.C.: Effect of Line Resistance and Driver Width on Crosstalk in Coupled VLSI Interconnects. In: Microelectronics International, vol. 24(3), pp. 42–45. Emerald Pub., U.K. (2007) 10. Mishra, D., Mishra, S., Agnihotry, P., Kaushik, B.K.: Crosstalk Scenario in Multiline VLSI Interconnects. International Journal of Recent Trends in Engineering 3(4), 80–83 (2010)

Biomedical Image Coding Using Dual Tree Discrete Wavelet Transform and Iterative Projection Sanjay N. Talbar1 and Anil K. Deshmane2 1

Department of Electronics & Telecommunication Engineering, SGGS Institute of Engineering and Technology, Nanded, India 2 TCT’s College of Engineering, Osmanabad, India [email protected], [email protected]

Abstract. The aim of the paper is to explore the application of 2-D dual tree discrete wavelet transform (DDWT) which is directional and redundant over the critically sampled transform like discrete wavelet transform (DWT) for image coding. In this paper image coding application is investigated with DDWTs along with iterative projection based noise shaping (IP-NS) algorithm. IP-NS is one of sparsifying method for DDWT coefficients used to modify large coefficients to compensate for the loss of small coefficients, without substantially changing the original image. Promising results are compared with DWT and DWT with noise shaping also. After thorough investigations, it is proposed that by employing DDWT along with noise shaping algorithm significantly improve the performance over DWT. Keywords: Discrete Wavelet Transform, Image coding, redundant transform, sparse representation.

1 Introduction Digital Image compression techniques have played an important role in the world of telecommunication and multimedia system where bandwidth is still a valuable commodity. Uncompressed multimedia data requires considerable storage capacity and transmission. Amongst all transforms developed in the past decade, Wavelet Transform have very intensively used for image compression. Image coding is one of the most visible applications of wavelets as it is a non redundant, separable transform. DWT efficiently captures point singularities but fails to capture 1-D singularities like edges and counters in images which are not aligned in horizontal or vertical direction. 2-D DWT has poor directionality. Therefore 2-D DWT cannot provide efficient approximation for directional features of image which ultimately affects the coding performance based on DWT. Thus many researchers started to develop methods having improved directionality. One effective approach for directional selectivity first introduced in [5] called dual tree discrete wavelet transform (DDWT). DDWT has many advantages over DWT like improved directionality; it is nearly shift invariant, aliasing. In this work we explored the possibility of dual tree DWT for biomedical image coding with iterative projection based noise shaping algorithm. In section 2 the dual tree DWT is described V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 525–528, 2010. © Springer-Verlag Berlin Heidelberg 2010

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while iterative projection based noise shaping scheme is presented in section 3. The experimental results are demonstrated with biomedical images in section 4.

2 The Dual Tree Discrete Wavelet Transform The idea behind the dual tree is very simple. The DDWT employs two real DWTs. The first DWT gives the real part of the transform while the second DWT gives the imaginary part. The filter banks (FB) used to implement the DDWT. The two real wavelet transforms use two different sets of filters, with each satisfying the perfect reconstruction (PR) conditions. The filters are themselves real, no complex arithmetic is required for the implementation of the DDWT. The inverse of the DDWT is as simple as the forward transform. To invert the transform, the real part and the imaginary part are each inverted the inverse of each of the two real DWTs are used to obtain two real signals. These two real signals are then averaged to obtain the final output. The DDWT is 2m: 1 redundant for an m-dimensional signal. It turns out that the degree of redundancy can be reduced without sacrificing perfect reconstruction by simply discarding the complex parts of the coefficients, resulting in 2:1 redundancy for a 2D DDWT.

3 Iterative Projection Based Noise Shaping This method of image coding using Iterative Projection based Noise Shaping (IP-NS) was first demonstrated in [1]. Noise shaping is one of sparsifying method for DDWT coefficients [5]. Now days overcomplete transforms are used very intensively than the critically sampled transforms. In this method we reduce the number of DDWT coefficients by discarding small magnitude coefficients and refining the remaining coefficients to compensate without much more reconstruction quality loss.

Fig. 1. Iterative Projection based Noise Shaping

Fig.1 shows the block diagram for iterative projection compression system. Let x be input image. Apply DDWT analysis operator A to the original image. We get transformed image . Now apply threshold to the transformed image we get thresholded coefficients . These noise coefficients are then projected back into the image domain. Using these thresholded coefficients we can reconstruct the image by applying the synthesis operator R. This means we are simply applying inverse of transform. At the end of synthesis we get reconstructed image . Due to thresholding signals energy reduces. This error can be calculated by subtracting the reconstructed

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image from original image x. A gain factor 1 is applied to the error signal to replace energy lost in the system. These error coefficients are then again transformed by using DDWT analysis operator A. This error is projected into DDWT domain and added to the noisy coefficients to form the wavelet coefficients . On the ith iteration, some non-linear operation, such as thresholding or quantizing, is applied to wavelet coefficient . This operation can be represented as the addition of noise . When the system starts the input to the non-linear operator is the DDWT of image x. After this, the input to the non-linear operator switches to the feedback loop.

4 Results and Discussion A. Experimental Setup The choice of k affects noise components in the system. For k =1, the number of nonzero coefficients remaining after thresholding decreases with each iteration, while the PSNR of the reconstructed image increases, until the signal converges. We consider that the signal has converged when . The problem is that, because of the energy loss, the reconstruction quality attained when the system converges is limited. When k>1, the energy of the final reconstructed image is closer to the energy of the original. This improves the reconstructed image’s PSNR, but at the expense of more non-zero coefficients. We consider the number of non-zero coefficients to be a measure of efficiency because zero coefficients are very efficient to code, and so bit rate tends to be proportional to the number of non-zero coefficients. For implementation of the method we used k=1.6 for better results. Initially we consider a large threshold =64 and decreasing it at each iteration until our target threshold, 32 is reached. A large starting threshold causes many coefficients to be eliminated initially. With the right balance between the energy gain k and the amount by which the threshold decreases each iteration, most insignificant coefficients remain significant, while the signal’s energy is maintained and image reconstruction improves. The control system, where the initial threshold 64 is reduced by 1 each iteration until =32, performs best. Comparing the efficiency–distortion data for this system with the data corresponding to a threshold reduction of 4/ iteration, the system with the slower threshold reduction rate produces a DDWT signal with fewer non-zero coefficients but the same reconstruction image quality. For the experiments test images are decomposed up to 2 levels. With respect to DDWT, there is no strong constraint on the filters for the first level decomposition. We choose the CDF 9/7 filter bank since this filter bank shows excellent performance, and is widely used in image coding. The 6-tap Qshift filters [6] are used for the remaining stages of DDWT. Table 1 shows the results for CT_brain image (256 256). Initially PSNR is calculated for DWT and DDWT reconstructed image. Then IP-NS algorithm is applied to the DWT and DDWT and PSNR is calculated for the same. Finally the result for DWT, DWT with IP-NS, DDWT, DDWT with IP-NS is displayed. The results show that the reconstruction quality of the DDWT with IP-NS is better than DDWT without noise shaping, DWT and DWT with IP-NS since the PSNR value for DDWT with noise shaping is greater than other methods. Visual quality for CT_brain is shown in Fig 2.

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64

33.41

PSNR for DDWT with IPNS 37.90

56

37.48

43.57

42.02

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38.10

44.79

43.11

44

38.69

43.27

42.89

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38.91

44.52

43.96

36 32

39.22 39.51

44.84 44.17

43.04 40.12

Thres hold

Fig. 2. Visual Quality for CT brain image (Threshold=32)

PSNR for DDWT

PSNR for DWT with IP-NS 36.32

5 Conclusion The threshold reduction rate affects the performance results. With decreasing threshold at each iteration PSNR value increases that means with decreasing threshold the quality of image increases. And we get best results at 32 . The choice of gain factor k affects the noise components in the system. It is always better to choose k greater than one. When, 1 the energy of the final reconstructed image is closer to the energy of the original. This improves the reconstructed image PSNR, but at the expense of more non-zero coefficients and we obtain best results at k=1.6.

References 1. Reeves, T.H., Kingsbury, N.G.: Overcomplete image coding using iterative projectionbased noise shaping. In: Proc. Int. Conf. Image Pro., NY, pp. 597–600 (2002) 2. Yang, J., Wang, Y., Xu, W., Dai, Q.: Image coding using Dual Tree Discrete Wavelet Transform. IEEE Tras.on Image Processing 17(9) (September 2008) 3. Nguyen, T.T., Oraintara, S.: Multiresolution direction filter banks: Theory, design and application. IEEE Trans. Signal Process. 53(10), 3895–3905 (2005) 4. Kingsbury, N.G., Reeves, T.H.: Redundant representation with complex wavelets: How to achieve sparsity. In: Proc. Int. Conf. Image Process, Barcelona (September 2003) 5. Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22, 123–151 (2005)

Privacy-Preserving Naïve Bayes Classification Using Trusted Third Party and Offset Computation over Distributed Databases B.N. Keshavamurthy, Mitesh Sharma, and Durga Toshniwal Department of Electronics & Computer Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India [email protected], [email protected], [email protected]

Abstract. Privacy-preservation in distributed databases is an important area of research in recent years. In a typical scenario, multiple parties may wish to collaborate to extract interesting global information such as class labels without revealing their respective data to each other. This may be particularly useful in applications such as car selling units, medical research etc. In the proposed work, we aim to develop a global classification model based on the Naïve Bayes classification scheme. The Naïve Bayes classification has been used because of its simplicity and high efficiency. For privacy-preservation of the data, the concept of trusted third party with two offsets has been used. The data is first anonymized at local party end and then the aggregation and global classification is done at the trusted third party. The proposed algorithms address various types of fragmentation schemes such as horizontal, vertical and arbitrary distribution. Keywords: privacy-preservation, distributed database, partitions.

1 Introduction Due to the advancement of computing and storage technology in recent times, digital data can now be easily collected. It is very difficult to analyze the entire data manually. Thus a lot of work is going on for mining and analyzing such data. In many real world applications such as hospitals, direct marketing, design-firms and university databases, data is distributed across different sources. The distributed database consists of horizontal, vertical or arbitrary fragments. In case of horizontal fragmentation, each site has complete information on a distinct set of entities. An integrated dataset consists of the union of these datasets. In case of vertical fragments each site has partial information on the same set of entities. An integrated dataset would be produced by joining the data from the different sites. Arbitrary fragmentation is a hybrid of previous two. Normally, the data sources of distributed database scenarios are willing to go for the global instance of their integrated data for their mutual benefits. Thus preserving privacy of personal sensitive data is a non-trivial issue. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 529–534, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Initially, for privacy-preserving data mining, randomization methods were used [2] [3]. In [4] [5] [6], the usage of the approach for classification was discussed. A number of other techniques [6] [7] have also been proposed for privacy preservation which work on different classifiers. The work in [8] [9] describes the methods for improving the effectiveness of classification. In [9] it proposes a method which eliminates the privacy breach and increase utility of the released database. In case of distributed environment, the most widely used technique in privacy preservation mining is secure sum computation [10]. Naive Bayes approach for classification is described in [1]. A few of research papers have discussed the privacy preserving mining across distributed databases. The key research gap with the existing methods of computation is that the global pattern computation is done at one of the data source itself. Our paper addresses this issue effectively by using a trusted third party who is responsible for computing result for aggregate classification data of the collaborating parties. Secondly all the class types need not be present at every collaborating party. This issue is addressed by providing two offsets, one for the newly initiating class instances at each collaborating par and other for the class instances which are initiated by predecessor. The rest of the paper is organized as follows: Section 2 briefs about related research work followed by proposed work, privacy-preserving Naïve Bayes classification using trusted third party computation with two offset over distributed databases. Section 3 gives experimental results. Section 4 includes conclusion.

2 Proposed Work The Naïve Bayes algorithm for different distribution scenarios such as horizontal, vertical and arbitrary is proposed for distributed databases. 2.1 Horizontal Partitioning Scenario Here each party locally computes the local count of instances. The global count is given by the sum of the local counts. To count global values by summing all local values we use modified secure sum algorithm which sends the sum to the trusted third party. The following steps are followed in computing global class instances for the collaborating parties using trusted third party computation: 2.1.1 At Trusted Third Party Following steps were followed: 1. The collaborating parties interested in global pattern computation send their request to trusted third party. 2. Trusted third party sends offset and address to collaborating parties to perturb his class instances and send perturbed class instances to logical adjacent one. 3. Finally after receiving integrated class instances from the last collaborating party, trusted third party will compute the integrated class instances by subtracting his random values form the class instance values obtained from last party and will get actual class instances and same will be conveyed to collaborating parties

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2.1.2 At Collaborating Parties The following steps were followed: 1. Third party will compute the integrated class instances by subtracting his random values form the class instance values obtained from last party and will get actual class instances and same will be conveyed to collaborating parties. 2. The randomly selected initial party receives one offset called newoffset because instance of class items starts from here. He sends perturbed values its logical neighbor 3. For all data sources except the initial collaborator. If There is no new instance of classes in compare with old list of class instances send by previous collaborator then collaborator needs only one offset oldoffset to perturb his class instances, then add his perturbed values to the values received from his predecessor and then send the resulting instance of class to to logical successor else collaborator needs oldoffset for class instances already initiated by its predecessor and newoffset for class instances initiates form the here and then send the resulting instance of class to logical successor. 4. Finally, the logical last collaborating party after performing step c operation it will sends the class instances to the trusted third party. The necessary algorithms are given follows: Here each party locally computes the local count of instances. The global count is given by the sum of the local counts. To count global values by summing all local values we use modified secure sum algorithm with trusted third party.

Algorithm Assumptions: n parties, r class values, z attribute values, jth attribute contain lj different values ,S– Supervisor/Trusted Third Party and P – Parties/collaborating parties, C il.r = no of instances with party, Pi having classes r and attribute values l and N ir = no of instances with party Pi having classes r. At P:

For all class values y do For all z, Party Pi locally computes ciyz Party Pi locally computes niy EndFor Encrypt values and send to third party

At S: Receive values and decrypt them;

From all parties, we can get all possible C1l.r and N1r Parties calculate the required probabilities from global C1l.r and N1r , on that basis will predict the class. Fig. 1. Algorithm for Horizontal Scenario

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2.2 Vertical Partitioning Scenario Each party computes nac and nc, and send them to the trusted third party. Here we have been making use of encryption and decryption techniques correspondingly at collaborating and trusted third party to preserve the privacy of import sensitive information. The following steps are used to compute the aggregate values of the collaborating parties: 2.2.1 At Trusted Third Party 1. Trusted third party will decrypt all the values and integrate them to get global instances of all required values, and used to predict classes. 2. Send the integrated values to all the collaborating parties. 2.2.2 At Collaborating Parties 1. The collaborating parties interested in global pattern computation send their encrypted values to the third party. 2. After receiving the integrated values from trusted third party, the actual values will be interpreted using private key. The corresponding algorithm is given in Fig. 2 is as follows: In nominal attributes, each party calculates their local instances of nac and na, of the attribute values they have. As each party have different attributes, so no parties have same value of instance of attribute and class. Hence there is no need to calculate the sum of values. At a particulate timestamp, we calculate the local values of nac and nc, and send them to the trusted third party. Algorithm Assumptions: n parties, r class values, z attribute values, jth attribute contain lj different values ,S– Supervisor/Trusted Third Party and P – Parties/collaborating parties, C il.r = no of

instances with party, Pi having classes r and attribute values l and N ir = no of instances with party Pi havig classes r. At P:

For all class values y do For all z, Party Pi locally computes ciyz Party Pi locally computes niy EndFor Encrypt values and send to third party

At S: Receive values and decrypt them;

From all parties, we can get all possible C1l.r and N1r Parties calculate the required probabilities from global C1l.r and N1r , on that basis will predict the class.

Fig. 2. Algorithm for Vertical Fragmentation

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2.3 Arbitrary Partitioning Scenario

It is an inter mix of the vertical and the horizontal partition. In arbitrary fragmentation over distribute databases; the trusted third party well in advance knows the collaborating parties who are belong to horizontal and vertical partition. To compute the global class instances the following steps were used: 1. If the collaborator is horizontally distributed, follow the procedure mentioned in section 2.1, to collect, integrate, compute global instances of class values and disseminate the global class instances for to the different collaborating parties. 2. If the collaborator is vertically fragmented, follow the procedure mentioned in section 2.2 to collect, integrate, compute the global integrated values from different collaborating parties and finally at collaborating parties the actual values of the class instances are found using the private key.

3 Results The dataset used for the purpose of experimentation is car-evaluation [11]. The algorithm was applied on non-distributed database and the percentage accuracy was obtained. The percentage accuracy for the distributed scenario should intuitively be less than or equal to non-distributed scenario. The accuracy is coming out to be same as that to non-distributed scenario which is best case result. The analysis results of different partitions of distributed databases are as follows in table 1: Table 1. Classification of distributed and non-distributed databases

S. No.

Description

1

Classification of data is at single party (No distribution)

2

Number of Parties

Total Number of Records

% Accuracy

1

1728 Records Party1: 500 Records Party2: 500 Records Party3: 728 Records

85

Classification of data distributed in horizontal scenario

2

Classification of data distributed in vertical scenario

3

3

4

Classification of data distributed in arbitrary scenario

3

Party1: 2 Attributes Party2: 2 Attributes Party3 : 3 Attributes Party1: 500 Records Party2: 4 Attributes Party3: 3 Attributes

85

85

85

4 Conclusion In our proposed work, we have proposed a set of algorithms for classifying data using Naïve Bayes from a group of collaborative parties without breaching privacy. The non-distribution and various distribution scenarios such as horizontal, vertical and arbitrary scenarios are compared and their accuracy is calculated on the data. The accuracy comes out to be the same showing that the algorithm is giving best case

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results. Privacy is also preserved using privacy preservation techniques such as offset computation and encryption. The third party concept is also introduced to calculate global classification results with privacy preservation. In this case, Naïve Bayes algorithm is applied to static data but the algorithm can also be extended for dynamic databases in future work. Also the algorithm can be modified for numeric data to widen its scope.

References 1. Vaidya, J., Kantarcıoğlu, M., Clifton, C.: Privacy-Preserving Naïve Bayes Cassification. International Journal on Very Large Data Bases 17(4), 879–898 (2008) 2. Leew, C.K., Choi, U.J., Liew, C.J.: A data distortion by probability distribution. ACM TODS, 395–411 (1985) 3. Warner, S.L.: Randomized Response: A survey technique for eliminating evasive answer bias. Journal of American Statistical Association, 63–69 (1965) 4. Agarwal, R., Srikanth, R.: Privacy–preserving data mining. In: Proceedings of the ACM SIGMOD conference (2005) 5. Agarwal, D., Agarwal, C.C.: On the design and Quantification of Privacy–Preserving Data Mining Algorithms. In: ACM PODS Conference, pp. 1224–1236 (2002) 6. Zhang, P., Tong, Y., Tang, D.: Privacy–Preserving Naïve Bayes Classifier. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 744–752. Springer, Heidelberg (2005) 7. Zhu, Y., Liu, L.: Optimal Randomization for Privacy–Preserving Data Mining. In: KDD ACM KDD Conference (2004) 8. Gambs, S., Kegl, B., Aimeur, E.: Privacy –Preserving Boosting, Journal (to appear) 9. Poovammal, E., Poonavaikko: An Improved Method for Privacy Preserving Data Mining. In: IEEE IACC Conference, Patiala, India, pp. 1453–1458 (2009) 10. Yao, A.C.: Protocol for secure sum computations. In: Proc. IEEE Foundations of Computer Science, pp. 160–164 (1982) 11. Bohanec, M., Zupan, B.: UCI Machine Learning Repository (1997), http://archive.ics.uci.edu/ml/datasets/Car+Evaluation

Extraction of Pose Invariant Facial Features Singh R. Kavita1, Zaveri A. Mukeshl2, and Raghuwanshi M. Mukesh3 1

Department of Computer Technology, YCCE, Nagpur, 441 110, India Computer Engineering Department, S.V.National Institute of Technology, Surat, 329507, India 3 NYSS College of Engineering and Research, Nagpur, 441 110, India [email protected], [email protected], [email protected] 2

Abstract. In this paper, we describe a method for extraction of facial features of 2D still faces with variations in view in a certain viewing angle range. The images we have considered vary beyond left 30 degrees to right 30 degree out of plane rotation. Our technique applies skin separation and corner detection for extraction of features of faces in different poses. Just detecting the location of two facial points namely the corner of eyes and location of nose tip, the other features will be derived from them automatically; thus saving the time during the feature extraction. Keywords: feature extraction, skin segmentation, corner detection.

1 Introduction Pattern recognition finds its application in many face processing techniques such as face recognition. In general, any face recognition system consists of three different modules; viz: face detection, feature extraction and recognition [1]. Face recognition on a broader spectrum uses mainly the following techniques: facial geometry, based on geometrical characteristics of the face; skin pattern, based on visual skin pattern and lastly facial thermogram, based on an infrared signals to map the face. Recognizing someone based on facial geometry makes human recognition a more automated process. Based on this fact, feature extraction plays a significant role. So, it becomes important to extract the prominent features in order to perceive human faces. In the last so many years, the approaches proposed for feature extraction can be broadly categorized as (i) global approach [2],[3],[4]; (ii) template based approach [5],[6]; (iii) local feature based approach [7],[8] and (iv) geometry based approach[9],[10],[11]. In addition, many approaches [12], [13] used eyes as the only facial features for the initialization of faces for any face recognition technique (FRT). All the approaches cited above suffers from a major drawback and that is, their decreasing accuracy as the subject face has pose angles more than 60 degrees or so. However, in general for faces which are rotated out of plane where both eyes are not visible, techniques may require more features than just the eyes. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 535–539, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Our proposed technique takes an account of the geometry based features which makes use of skin separation and corner detection for extraction of feature points on faces in different poses. Just detecting the location of two facial points namely the corner of eyes and location of nose tip, the other features will be derived from them automatically; thus saving the time during the feature extraction. The paper is organized as follows: in Sect. 2, discuss about the proposed methodology for feature extraction. Experimental results are presented in Sect. 3. Finally, a conclusion is given.

2 Our Approach for Feature Points Extraction Faces which are out of plane something other than eye balls is required to localize the face before extraction of features. Base on this fact, we have considered nose as the localizing component on face. Moreover, the nose profile can be characterized as the set of points with the highest symmetry and high luminance values; therefore we can identify the nose tip as the point that lies on the nose profile, above the nose baseline, and that corresponds to the brightest gray level. These considerations allow localizing the nose tip robustly shown in Fig 1.

Fig. 1. Localized Nose tip

As we know the geometry-based technique works efficiently on the frontal faces, however; we take an opportunity to extend this approach for feature extraction for faces under variations in pose. The overall methodology for feature extraction is described in Fig 2. Selecting nose tip

the

Image Normalization

Skin segmentation

Input Image Corner Detection

Feature Vector

Fig. 2. Our approach to feature extraction

In order to detect facial feature candidates properly, the unnecessary information in a face image must be removed in advance. So in our approach, the first stage is

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cropping the face area as soon as the image is read from the database and the remaining part of preprocessing is performed on that cropped images. In this respect, when an image is selected we first localize the nose tip as mentioned earlier. Based on the threshold value computed from the located nose tip, a frame is designed for cropping the faces. Accordingly we design the three different efficient frames for the frontal, left oriented and right oriented images. Cropped faces from the images are shown in Fig 3.

Fig. 3. Sample images from database top row (cropped frontal faces), middle row (cropped left oriented faces), and bottom row (cropped right oriented faces).

To the cropped images we further employ segmented skin region candidates that satisfy the homogeneity property of the human skin. If corner detection is directly applied to cropped images the fiducially points on the face are not predominant. Once the position of the extracted points as in red in Fig 4.b is located, features are inferred on the basis of simple geometrical consideration. The features that we have considered are eye_lip_distance, eye_nose_distance, nose_lip_distance, angle_eye_lip, angle_eye_nose and angle_nose_lip.

(a)

(b)

Fig. 4. (a) Skin segmented faces, (b) Corner detected faces

3 Experimentation and Results In our experiment, we have used subset of Indian database [14]. This database is a rich set of 611 colored images of size 567 X 334 with different expressions, varying illumination and varying orientation. We have worked with 183 images of 61 different subjects (22 female and 39 male). For each subject we have selected, randomly, three images irrespective of illumination or expression consideration. For feature extraction we have taken into consideration three images per subject (one frontal, one right oriented and one left oriented). Our algorithm is not restricted for rotation range to be in between +30 and –30 degrees. We have considered even those faces where only single eye is visible. The features are extracted from cropped images of dimension 301 × 351 which contains only the faces, using the procedure explained in Fig 2.

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Once the features have been extracted with our proposed method and PCA, we have analyzed that the features extracted from PCA varies from 0 to some greater range. Moreover, features are more consistent in case of our approach between specific ranges, except for few images. It implies that features we have considered are almost consistent for variations in poses of faces. For the faces from each orientation, the correct features were extracted in 98 % of the faces. However, for 2% of faces features were not extracted correctly due effect of illumination. For testing, we have selected any image randomly from the test database. While deciding that test image should go to which cluster, a simple tactic is used and that is finding nose tip as the pixel under a threshold in the test image so that it can be said to be at right or left of the origin. For the selected pixel within the threshold limit, the image has been considered to have a frontal face. In this case, the pixel corresponding to tip of the nose has been considered as it gave clear idea of the face angle ( since the only information required for clustering is whether the pixel is located left or right of the origin, if none, it is frontal face.) Mathematically, the above explained method can be represented as, , 40 ,,

,

40 40

40

where, It =test image under consideration, (µ= threshold value, selected X-coordinate320). Now, when the system knows the clusters and has classified the test image as native of one of those, the subsequent tasks are to select and match feature vectors and produce the correct match accurately. For this, any of the classical methods can be used but here we have concentrated on Euclidean distance classifier as an ease of implementation without any breach in accuracy. Hence the feature vectors contain only those points which are common to all the clusters. The results of the experiments show quite a fascinating success rate. The extracted features have been used to classify 183 images with variation in pose and we achieved an 85% success rate on an average with equation. We have also evaluated the performance of feature extraction algorithm against the established PCA algorithm as shown in Table 1. Table 1. Comparative result of two approaches

PCA

Number of images 183

Error rate 0.37

Success percentage 63

Our

183

0.15

85

Database

Approach

The overall results however are very encouraging and hold good prospects of further research. The other factor which decides the efficiency of the algorithm is time taken to find a match for the subject image. Although the results below indicate time

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efficiency for only near hundred images which gave correct matches and about four dozen images which produced wrong match or no match at all, they will vary only slightly for an increase in database.

4 Conclusion and Future Work Although the results presented in this paper represent an initial analysis of the pose invariant geometry based approach, we are extremely encouraged that our approach is competitive with current algorithms. The experiment result shows that the system can work in the different orientation and expression.

References [1] Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM, Computing Surveys 35(4), 399–458 (2003) [2] Turk, M., Pentland, A.: Eigenfaces for Recognition. Cognitive Neuroscience 3(1), 71–96 (1991) [3] Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D.L., Weng, J.: Discriminant analysis of principal components for face recognition. In: face recognition:From Theory to applications, pp. 73–85. Springer, Heidelberg (1998) [4] Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711– 720 (1997) [5] Yuille, A., Cohen, D., Hallinan, P.: Facial feature extraction from faces using deformable templates. In: Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, pp. 104–109 (1989) [6] Feris, R.S., De Campos, T.E., Cesar Jr., R.M.: Optimization Techniques 1973. LNCS (LNAI), vol. 4, pp. 127–135 (2000) [7] Manjunath, B.S., Chellappa, R., Von Der Malsburg, C.: A Feature Based approach to Face Recognition. In: Proc. of IEEE Computer society Conference on Computer Vision and Pattern Recognition, pp. 373–378 (1992) [8] Okada, K., Steffens, J., Maurer, T., Hong, H., Elagin, E., Neven, H., von der Malsburg, C.: The Bochum/U. SC Face Recognition System and How it Fared in the FERET phase III Test In face Recognition:from Theory to applications. Springer, Heidelberg (1998) [9] Kanade, T.: Computer Recognition of Human faces. Basel and Stuttgart, Birkhauser 19(7), 721–732 (1997) [10] Gu, H., Su, G., Du, C.: Feature Points Extraction from Faces. Image and Vision Computing NZ, 154–158 (2003) [11] IoanNou, S., Caridakis, G., Karpouzis, K., Kollias, S.: Robust Feature Detection for Facial Expression Recognition. EURASIP Journal on Image and Video Processing, vol. 2007, Article ID 29081 (2007) [12] Kapoor, A., Picard, R.W.: Real-Time, Fully Automatic Upper Facial Feature Tracking. In: Proceedings from 5th International Conference on Automatic Face and Gesture Recognition, pp. 10–15 (2002) [13] Gourier, N., James, D.H., Crowley, L.: Facial Features Detection Robust to Pose, Illumination and Identity. In: SMC 2004, pp. 617–622 (2004) [14] http://viswww.cs.umass.edu/~vidit/IndianFaceDatabase/

On the Segmentation of Multiple Touched Cursive Characters: A Heuristic Approach Tanzila Saba1, Ghazali Sulong2, Shafry Rahim2, and Amjad Rehman1 1

Department of Computer Science B.Z.University Multan Pakistan 2 Graphics and Multimedia Department FSKSM UTM

Abstract. Heuristics are based on the experiences and solves problems approximately that cannot be solved exactly. In handwritten documents recognition, the most difficult phase is touched character segmentation as incorrectly segmented characters cannot be recognized correctly. Accordingly, this paper presents a heuristic approach for multiple touched cursive characters. Initially, a possible segmentation zone is detected using peak to valley function. Next, greedy best search algorithm is implemented in the possible segmentation zone for touched characters segmentation. Experimental results on a test set extracted from the IAM benchmark database exhibit high segmentation accuracy up to 91.63%. Moreover, proposed approach is very fast and can handle multiple cursive touching characters. Keywords: OCR, touched characters segmentation, heuristics, peak to valley function, greedy best search.

1 Introduction Inventions of modern technologies have brought significant changes in cursive handwriting. Touched cursive character segmentation is a current issue for optical character recognition (OCR) systems and is the main cause of low accuracy. Although literature is replete with many character segmentation techniques, their feasibility is for machine printing, hand printing and well written cursive scripts only. All these techniques failed for touched cursive character segmentation [1]. In this regard detailed review can be viewed in [2].

2 Touched Character Segmentation: Proposed Approach This section elaborates proposed strategy for multiple touched cursive characters segmentation using heuristics. The methodology comprises of preprocessing, possible segmentation zone detection and heuristic segmentation of cursive touched characters. Multiple touched cursive characters are extracted from IAM forms scanned in grayscale format at 300 dpi [3]. However, more than three touched cursive characters are unavailable. Therefore, for the sake of experiments, two and three touched cursive characters are considered. Prior to character segmentation, V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 540–542, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. Preprocessing results: Original sample, Thresholding, Core-region

digital images are binarized using Otsu method. Furthermore, the core-region is detected to avoid ascenders and descenders of the overlapped characters [4]. Fig 1 exhibits preprocessing results. Possible segmentation zone (PSZ) is defined as an area that most probably occupies the segmentation path between the touching boundaries of the characters. In this research, possible segmentation zone is detected using peak to valley function (see Fig. 2) V (l p ) − 2 *V ( x) + V (rp ) (1) Vp x = V ( x) + 1

In equation (1), V p x is the vertical projection function at

location.

l p is peak

position on the left side of x, rp is the peak position on the right side of x . The zone between l p and rp is the possible segmentation zone. Here, w is the width of image and x is determined heuristically as below. ⎧ w / 2, for two touched characters x=⎨ ⎩ w / 3, for threetouched characters

(2)

Fig. 2. Possible segmentation zone detection (PSZ)

Following possible segmentation zone detection, the next step is segmentation of touched characters. Accordingly, within PSZ, from upper baseline to lower baseline, numbers of transactions from background (white pixels) to foreground (black pixels) are counted in each column. The columns with transactions value 2 are termed as candidate segmentation columns and are stored in a queue. However, to locate the best segmentation column among the candidates, greedy best search algorithm is implemented [5]. The greedy best search algorithm evaluated all candidate segmentation columns and finally column with minimum stroke thickness (vertically) selected.

3 Results and Discussion In the proposed approach, PSZ is detected using peak to valley function. Finally, successful touched character segmentation is performed in the PSZ using greedy best

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search algorithm. For experiments, two hundred touched characters were extracted from IAM forms. Segmentation accuracy rate 91.63% obtained in this research. Segmentation rate is computed as below. (For segmentation results see Fig 4). % Segmentation accuracy = (correct segmentation/ total test words) * 100

Fig. 4. Touched character segmentation using proposed approach

The proposed approach remained successful for solving single and multipletouched characters segmentation problems. The promising results are due to the clustering algorithm that worked on the whole image rather than on specific features. In addition, proposed algorithm is independent of stroke’s thickness.

4 Conclusion This paper presented a new heuristic algorithm for cursive touched character segmentation. The algorithm handles multi touching characters successfully. Proposed algorithm searches within the search area (PSZ) that makes its search narrow and fast. The promising experimental results achieved up to 93.61%.

References [1] Kurniawan, F., Rehman, A., Dzulkifli, M., Mariyam, S.: Self Organizing Features Map with Improved Segmentation to Identify Touching of Adjacent Characters in Handwritten Words. In: IEEE Ninth International Conference on Hybrid Intelligent Systems, HIS 2009, China, pp. 475–480 (2010) [2] Saba, T., Sulong, G., Rehman, A.: A Survey on Methods and Strategies on Touched Characters Segmentation. International Journal of Research and Reviews in Computer Science 1(2), 103–114 (2010) [3] Marti, U., Bunke, H.: The IAM database: An English sentence database for off-line handwriting recognition. International Journal of Document Analysis and Recognition 15, 65–90 (2002) [4] Rehman, A., Dzulkifli, M., Sulong, G., Saba, T.: Simple and Effective Techniques for Core Zone Detection and Slant Correction in Script Recognition. In: The IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2009), pp. 15–20 (2009) [5] Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 94–95. Prentice Hall, Upper Saddle River (2003), http://aima.cs.berkeley.edu/

Context Representation and Management in a Pervasive Environment B. Vanathi and V. Rhymend Uthariaraj Ramanujan Computing Centre, Anna University Chennai, Chennai, Tamil Nadu, India [email protected] and [email protected]

Abstract. Integrating computing and computing applications into surroundings instead of having computers as discrete objects is the objective of pervasive computing. Applications must adjust their behavior to every changing surroundings. Adjustment involves proper capture, management and reasoning of context. This paper proposes representation of context in a hierarchical form and storing of context data in an object relational database than an ordinary database .Semantic of the context is managed by ontology and context data is handled by object relational database. These two modeling elements are associated to each other by semantics relations build in the ontology. The separation of modeling elements loads only relevant context data into the reasoner therefore improving the performance of the reasoning process.

1 Introduction The continuing technical progress in computing and communication lead to an all encompassing use of networks and computing power called ubiquitous or pervasive computing. Pervasive computing system targets at constantly adapting their behavior in order to meet the needs of users within every changing physical, social, computing and communication context. Pervasive devices make ad-hoc connections among them and may be connected to different types of sensors to capture changes in the environment. Fig. 1. Shows the flow in the evolution chain from centralized computing to pervasive computing as presented by [1] [2].Context awareness is at the heart of pervasive computing problems. Context can be defined as an operational term whose definition depends on the intension for which it is collected and the interpretation of the operations involved on an entity at a particular time and space rather than the inherent characteristics of the entities and the operations themselves according to Dey & Winogards [3, 4].The complexity of such problems increases in multiplicative fashion rather than additive with the addition of new components into the chain. Pervasive Context aware computing has three major basic components: pervasive environment, Context management modeling and context-aware service. Pervasive environment is characterized by dynamicity, heterogeneity and ubiquity of users, devices and other computing resources, ad-hoc connection among the devices and existence of hardware and software sensors. Context management modeling deals with how context data is collected, organized, represented, stored and presented to the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 543–548, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. Evolution Chain

reasoning module. Context aware service performs context reasoning and decisions about the actions to be triggered. In this paper special emphasize on context data management which can be further used in the development of collaborative context application is proposed. Proper modeling, specification and definition of context and its management are essential for efficient reasoning, interpretation, and utilization of context data.

2 Related Works in Context Management Modeling Data required for modeling are obtained from the applications using sensors .Sensors can be physical, virtual or logical sensors. After collecting the data from the application; it has to be represented in a suitable way for processing. Various context management and modeling approaches are introduced to present context for reasoning in different application area. Data from the sensors are presented using any of the following modeling approaches like key-value-pair modeling, Graphical modeling, Object Oriented modeling, logic based modeling, Mark up scheme modeling and Ontology modeling. Among all the modeling approaches ontology based context model is more suitable for context aware computing [2].Ontology is defined as explicit specification of a shared conceptualization [4].Context is modeled as concepts and facts using ontology. Some context aware systems that use this approach are discussed. CONON (CONtext Ontology) [5] is based on treatment of high-level implicit contexts that are derived from low-level explicit context. It supports interoperability of different devices. It defines generic concepts regarding context and provides extensibility for adding domain specific concepts. Context reasoning in pervasive environment is time-consuming but is feasible for non-time-critical applications. For time-critical applications the data size and rule complexity must be reduced. This is an infrastructure based environment. CoBrA-ONT [6] is a context management model that enables distributed agents to control the access to their personal information in context-aware environments. It is designed to overcome the interoperability of different devices. It is central part of CoBrA, broker-centric agent architecture in smart spaces. CoBrA-ONT assumes that there always exists a contextbroker server known by all the participants. It is infrastructure-centric and is not for pervasive computing. SOUPA (Standard Ontology for Ubiquitous and Pervasive Applications) [7] includes modular component vocabularies to represent intelligent agents with associated beliefs, desires and intension, time, space, events, user profiles, actions and policies for security and privacy. SOUPA is more comprehensive than CoBrA-ONT because it deals with more areas of pervasive computing and ontology

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can be reused. GAS Ontology [8] is ontology designed for collaboration among ubiquitous computing devices. The basic goal of this ontology is to provide a common language for the communication and collaboration among the heterogeneous devices that constitute these environments. The GAS Ontology also supports the service discovery mechanism that a ubiquitous computing environment requires.

3 Limitations of Ontology Based Context Management Context aware systems are based on ad-hoc models of context, which causes lack of the desired formality and expressiveness. Existing models do not separate processing of context semantics from processing and representation of context data and structure. Ontology representation tools are suitable for statically representing the knowledge in a domain. They are not designed for capturing and processing constantly changing information in dynamic environment in a scalable manner. Existing ontology languages and serialization formats are test based (xml/rdf/owl) and not designed for efficient query optimization, processing and retrieval of large context data. The main drawbacks of pure ontological approaches are low performance and data throughput.

4 Proposed Work The proposed context aware system has context acquisition layer, context middleware (representation layer, context management layer and decision layer) and application layer. Context acquisition layer gathers the context from the environment using sensors. Context representation layer represents context as entity relation hierarchy form. In the context management layer context is further classified as inactive context and active context. Predicates are used to decide the inactive context. For example context defined using predicates like ownedby are inactive and context defined using predicates like locatedIn are active. Inactive context are stored in Object relational database and active context are stored in Ontology. In the middleware, rules learned or rules derived from other rules are also maintained. Using the rules, relevant context from database and ontology are forwarded to the reasoning component. From reasoning layer appropriate context is sent to user in the application layer. 4.1 Context Representation Dey and Winogards define Context in terms of a statement that is made about the characteristics of the entities, their relationships and properties of the relationships [4]. Context can be personal, device, physical, activity, network, location etc. Personal entity provides contexts like person's identity, address, activity, location etc. Context can be represented as Entity, Hierarchy, Relation, Axiom and Metadata [9]. Hierarchy is a set of binary relations that form an inversely directed acyclic graph. Relation is union of set of binary relations. Relation can be either entity relation or attribute relation. Entity relation has a set of binary relations having either its domain or range from the set of entity. SubEntityOf relation, SubPropertyOf, domain, range etc is some relations used to link entities. Root of the hierarchy is a global entity called ContextEntity .Entities and relations are sources of context data. Relation can be generic or domain based. The flow of context is shown in Fig. 2.

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S ta rt C on te xt A cqu is ition (se n s o rs , b a d g e s, C a m e ra ,W iFi e tc)

C o n te xt re p res en ta tion

Is C on tex t in active

Y es

S to re C o n text in O b ject R e latio n al D a tab a se(O R D B M S)

No S to re A ctiv e C o n text in O n tolo g y R ep o s ito r

R u le s C a p tu re d / D e rive d

S e le ct A p p ro p riate C o n text u sin g re as o n e r an d se n t to th e u s er End

Fig. 2. Flow of Context

For example in a generic level, relation is defined as person isLocatedIn Location and in a domain level, relation is defined as Student isLocatedIn Class .Attribute relation is the set of binary relations defined from set of entities to set of literals. Axiom is the axiomatic relations. Few generic level axiomatic relations are sameAs, inverse, symmetric and transitive. Meta data are information about a defined relation instance. Information like time of occurrence, precision, source of data can be a part of Meta information. Consider an example, Student isLocatedIn Class at a given time t. 4.2 Storing Context Data in an Relational Database Context represented in entity relation hierarchy form is stored in a relational database using the algorithm steps mentioned below. The attributes CEntity stores name of context entities. Attribute that stores name of the entity one step above in the hierarchy isa relation. Layer stores whether an entity is in the generic or domain layer. Relation stores name of relations. Persistence of the relation stores whether an entity can be inactive or active. Values of relations with static persistence are stored in the persistent context repository and values with dynamic persistence are stored temporarily for immediate use in the field Persistence. ValueForm stores source of value as a context entity or as a Literal. An attribute that stores name of instances is EInstance. Value is an attribute that stores value of the relation after applied to the instance. Timestamp stores context time. Source stores source of context. Precision stores precision of the context .Structure of the relational table is shown below: Step 1: Entity table (Context Entity, direct hierarchy relation, Layer) Step 2: Relation table (Relation, Persistence)

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Step 3: Relation Instance table (Relation name, Context Entity, Value) Step 4: Entity Instance table (Entity Instance, Context Entity) Step 5: Context Instance table (Entity Instance, Relation, Value, Time Stamp, Context Source and Context Precision) 4.3 Advantages of ORDBMS and RDBMS Relational models provide standard interfaces and query optimization tools for managing large and distributed context database or receive and send notification on context changes. Relational models are not designed for semantic interpretation of data. Relational database alone cannot be used to represent context in a pervasive environment. For semantic interpretations, ontology is used along with relational database. The Table 1 below summarizes the appropriateness of the approaches in relation to the necessary features. All approaches have strong and weak sides with respect to features for context management modeling. Best of three worlds are combined to form a hybrid context management model. Ordbms approach is more suitable than rdbms approach. It ensures large storage capacity, quick access speed. Table 1. Comparison of rdbms, ordbms and ontology approach

Necessary Feature Semantic Support Ease of transaction Query optimization Reasoning Support Formality Scalability

Relational Approach No Yes Yes No Yes Yes

Ontology Approach Yes No No Yes Yes No

Object Relational Approach No Yes Yes No Yes Yes

Object relational database supports several storage units like collection list, arrays, types and UDTs (User defined data types) and most of them are represented as objects arrays. Ordbms have a massive scalability compared to relational approach and excellent manipulation power of object databases. It supports rich data types by adding a new object-oriented layer. The systems are initially implemented by storing the inactive context to a relational database and active context to an ontology. Then the response time to get the relevant time is noted. Further system is implemented by replacing the storage of inactive context to relational database by object relational database. Then appropriate service can be provided to the user using service discovery [10]. 4.4 Metrics Basic metrics used to evaluate the relational database and object relational database are throughput or response time, memory and/or storage usage/requirements. Throughput is the number of queries per time. Evaluation time can be wall-clock (real), Central Processing Unit (user), Input/output (system), server side versus client

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side. To compare the relational database and object relational database with respect to ontology complexity of complex columns (CCC) is considered.

5 Conclusion Context is represented using layered and directed graph. Layered organization helps to classify and tag context data as generic domain independent or as domain dependent. A combination of context model using ontology and object relational database is proposed. This paper focuses on context representation and storage of context. Reasoning and decision making of the context obtained from the context management are the future work.

References 1. Satyanarayanan, M.: Pervasive Computing Vision and Challenges. IEEE Personal Communications, 10–17 (2000) 2. Strang, T., LinnhoPopien, C.: A Context Modeling Survey. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205. Springer, Heidelberg (2004) 3. Winograd, T.: Architectures for Context. Human-Computer Interaction 16(2-4), 401–419 (2001) 4. Dey, A.K., Abowd, G.D.: Towards a Better Understanding of Context and Context Awareness. In: Proceedings of the CHI Workshop on the What, Who, Where and How of Context- Awareness, The Hague, The Netherlands (2000) 5. Wang, X., Zhang, D., Gu, T., Pung, H.K.: Ontology Based Context Modeling and Reasoning using OWL, workshop on context modeling and reasoning. In: IEEE International Conference on Pervasive Computing and Communication, Orlando, Florida (2004) 6. Chen, H.: An Intelligent Broker Architecture for Pervasive Context-Aware Systems. PhD Thesis University of Maryland, Baltimore Country, USA (2004) 7. Chen, H., Perich, F., Finin, T., et al.: SOUPA: Standard Ontology for Ubiquitous and Pervasive Applications. In: International Conference on Mobile and Ubiquitous Systems: Networking And Services, Boston, USA (2004) 8. Christopoulou, E., Kameas, A.: GAS Ontology: ontology for collaboration among ubiquitous Computing devices. International Journal of Human-Computer Studies 62(5), 664–685 (2005) 9. Ejigu, D., Scuturi, M., Brunie, L.: An Ontology Based Approach to Context Modeling and Reasoning in Pervasive Computing. In: 5th IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 14–19 (2007) 10. Vanathi, B., Rhymend Uthariaraj, V.: Ontology based service discovery for context aware computing. In: 1st IEEE International Conference on Advanced Computing. IEEE Computer Society, Chennai (2009)

A Comparative Study and Choice of an Appropriate Kernel for Support Vector Machines R. Sangeetha and B. Kalpana Department of Computer Science, Avinashilingam University for Women, Coimbatore [email protected], [email protected]

Abstract. Support Vector machine (SVM) has become an optimistic method for data mining and machine learning. The exploit of SVM gave rise to the development of a new class of theoretically refined learning machines, which uses a central concept of kernels and the associated reproducing kernel Hilbert space. The performance of SVM largely depends on the kernel. However, there is no premise about how to choose a good kernel function for a particular domain. This paper focuses in this issue i.e. the choice of the Kernel Function is studied empirically and optimal results are achieved for binary class SVMs. The performance of the Binary class SVM is illustrated by extensive experimental results. The experimental results of the datasets show that RBF Kernel or any other kernels is not always the best choice to achieve high generalization of classifier although it is often the default choice. Keywords: Support Vector Machine, Pattern Classification, Kernel Function, Support Vectors, Mercer Kernels.

1 Introduction Support Vector machines are based on Statistical Learning Theory developed by Vapnik [1] and designed originally for binary classification. The formulation embodies the Structural Risk Minimization principle has given by the authors in [5-6], which has been shown to be superior to traditional Empirical Risk Minimization principle employed by conventional neural networks. SRM minimizes an upper bound on the generalization error but ERM minimizes the error on the training data. Use of Kernel outlined in [3] enables the curse of dimensionality to be addressed and the solution implicitly contains Support Vectors. In training a SVM we need to select a Kernel and its parameters. There is no method to determine how to choose an appropriate Kernel and its parameters for a given dataset to achieve high generalization of classifier. Furthermore, the choice of the regularization parameter C is crucial to obtain good classification results. This paper is organized as follows. Section 2 gives the brief overview of support vector machines. Section 3 discusses the various types of kernels. This is followed by experimental results. Section 5 concludes the work. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 549–553, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Support Vector Machines Traditional optimization algorithms such as Newton Method or Quasi-Newton Method cannot work any more due to the memory problem. A special property of SVM [2-4] is it simultaneously minimizes the empirical classification error and maximizes the geometric margin, called as Maximum Margin Classifiers. SVM maps input vector to a higher dimensional space where a maximal separating hyperplane is constructed. Two parallel hyperplanes are constructed on each side of the hyperplane that separates the data. One of the hyperplanes that maximizes the margin is named as the optimal separating hyperplane. Consider the problem of separating the set of training vectors belonging to binary classes or dichotomization (xi, yi), i = 1,…. l, xi ∈ Rn, yi ∈ {+1, −1}, where the Rn is the input space, xi is the feature vector and yi is the class label of xi. The separating hyperplanes are linear discriminating functions as follows, f ( x ) = wT x + b

(1)

where w is a weight vector and b is called the bias value.

3 Kernel Induced Feature Space SVM uses an efficient mathematical function for mapping the classification data called as kernel trick and a dot product given in [7] for mapping higher dimension. 3.1 Mercer Kernel In statistical learning theory, if kernels are positive definite then they satisfy Mercer’s condition [8] in Hilbert space as a dot product and called as Mercer Kernel. 3.1.1 Mercer Theorem [9] 2 Suppose that K ∈ L∞ ( χ ) is a symmetric real-valued kernel such that the integral

operator T K : L 2 ( χ ) → L 2 ( χ )

TK f ( x) =

∫ K ( x , y ) f ( y ) dy

(2)

x

is positive,i.e.., for all f ∈ L2 (χ ) we have ,

∫ K ( x , y ) f ( x ) f ( y ) dxdy

≥ 0 . If any

kernel function satisfies equation (2), then the kernel K maps data in feature space. 3.2 Reproducing Kernel Hilbert Spaces A Hilbert Space H (an infinite dimensional linear space endowed with a dot product) is a Reproducing Kernel Hilbert Space (RKHS) [8] ,if the evaluation functional bounded i.e., there exists a M such that

A Comparative Study and Choice of an Appropriate Kernel for Support Vector Machines

Ft [ f ] = f ( t ) ≤ M f

H

∀f ∈ H .

551

(3)

4 Experimental Results In this section, we use the different type of kernels depicted in table 1 to three bench mark datasets (Liver Disorder, Iris, Heart) from UCI database repository. Our aim is to make the kernels generalized for every domain. In classification tasks Ten-fold cross validation is used for generalization performance. The table 2 suggests that the few kernels give good classification performance and low error rate. After comparing all the features of the kernels, the appropriate kernels for small binary class datasets are spline and polynomial which has minimum number of support vectors, minimum value as error rate and good classification rate. Table 1. Types of Kernels Kernels Linear

Function

K ( xi , x j ) = 1 + xi x j T

Polynomial

K ( xi , x j ) = (1 + xi x j ) p

Radial Basis Function (RBF) Gaussian RBF

K(xi , x j ) = exp(−γ xi − x j )

Exponential RBF Sigmoid Spline

T

2

K ( x i , x j ) = exp( −

K ( x i , x j ) = exp( −

xi − x j

2

2σ 2 xi − x j

2σ

2

)

)

K ( xi , x j ) = tanh(kxi x j − δ ) T

k spline (u , v ) = 1 + uv + ∫ (u − t )+ (v − t )+ dt 1

0

Anova Spline Additive

K ( u , v ) = 1 + k ( u 1 , v 1 ) + k ( u 2 , v 2 ) + k (u 1 , v 1 ) k (u 2 , v 2 )

∑

K (u , v ) =

K i (u , v )

i

Tensor product

n

K (u , v ) = ∏ K m (u m , v m ) m =1

Generally kernel functions are classified as Translation invariant kernel and Rotation invariant Kernel. From the above study, it is proposed that hybrid kernels are obtained by combining more than one invariant kernel, which can find numerous applications in practice. It works as an efficient classifier while comparing with standard kernel classifier and gives high accuracy for classification. In the hybrid kernel function, the parameters can be easily modified for different domains and data can be classified with low execution time.

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Table 2. Comparing Kernels with datasets. (Support Vectors,Classification and Error Rate) (a) Iris dataset Kernel Function (Parameters) Polynomial RBF Exponential RBF Gaussian RBF Sigmoid Spline Anova Spline

C=10,p=1 C=50000, p=2 C=5, γ=5 C=50000, γ =2 C=10, σ =2 C=80000, σ =5 C=10, σ =2 C=5000, σ =5 C=10,k=0.5 δ=0 C=inf, k=2, δ=4 C=10 C=1000000 C=10,k=5 C=50000,k=10

Kernel Function (Parameters) Polynomial RBF Exponential RBF Gaussian RBF Sigmoid Spline Anova Spline

RBF Exponential RBF Gaussian RBF Sigmoid Spline Anova Spline

SV%

CR

ER

33 29 55 40 36 34 43 42 60 60 31 29 44 47

55.0 48.3 91.7 66.7 60.0 56.7 71.7 70.0 100 100 51.7 48.3 73.3 78.3

0.7500 0.7500 0.9835 0.9667 0.7500 0.9833 0.9667 1.0 0.7600 0.7600 0.7500 0.7700 0.9998 0.9997

0.2500 0.2500 0.0165 0.0333 0.2500 0.0167 0.0333 0.0 0.2400 0.2400 0.2500 0.2300 0.0002 0.0333

(b) Liver dataset SV SV%

C=10,p=5 C=500000,p=2 C=10, γ=0.05 C=1000000, γ =1 C=10, σ =2 C=100000, σ =5 C=10, σ =2 C=50, σ =5 C=5,k=1, δ=1 C=500000,k=5, δ=10 C=10 C=1000000 C=10,k=5 C=100,k=10

Kernel Function (Parameters) C=10,p=3 Polynomial

SV

59 54 62 54 61 58 75 74 56 56 55 54 56 56

54.6 50.0 57.4 50.0 56.5 53.7 69.4 68.5 51.9 51.9 50.9 50.0 51.9 51.9

(c) Heart dataset SV SV%

C=100, p=5 C=10, γ =2 C=1000000, γ=5 C=10, σ =2 C=1000000, σ =5 C=10, σ =2 C=1000000, σ =5 C=10,k=1, δ=3 C=100000, k=5, δ=10 C=10 C=1000000 C=10,k=2 C= 100000, k=10

73 72 115 70 85 70 75 70 138 70 71 70 75 74

52.9 52.2 83.3 50.7 61.6 50.7 54.3 50.7 100 50.7 51.4 50.7 54.3 53.6

CPU (s) 0.1 0.1 0.1 0.3 0.1 0.3 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1

CR

ER

0.6852 0.6853 0.6852 0.6854 0.6852 0.6852 0.6857 0.6852 0.6852 0.6759 0.6759 0.6852 0.6667 0.6667

0.3148 0.3147 0.3148 0.3146 0.3148 0.3148 0.3143 0.3148 0.3148 0.3241 0.3241 0.3148 0.3333 0.3333

CPU (s) 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.2 0.1 0.2 0.3 0.2 0.1 0.2

CR

ER

CPU(s)

0.5580 0.5580 0.5581 0.5580 0.5581 0.5581 0.5000 0.6154 0.5714 0.6154 0.4286 0.7143 0.5714 0.3571

0.4420 0.4420 0.4419 0.4420 0.4419 0.4419 0.5000 0.3846 0.4286 0.3846 0.5714 0.2857 0.4286 0.6429

0.5 0.5 0.6 0.5 0.4 0.6 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.4

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5 Conclusion The experimental results of the three datasets show that opting the Kernels randomly is not always the best choice to achieve high generalization of classifier. It illustrates that the crucial kernels effectively improves the classification performance and generializability by optimizing the result. We exhibit the dependency of classifier accuracy on the different Kernel Functions of the Binary-class SVM using different datasets. It will be interesting and practically more useful to determine some method for determining the Kernel Function and its parameters based on statistical properties of the given data. In this paper we propose a Hybrid kernel method for classification of data. A systematic methodology and optimization technique is needed for the construction of Hybrid kernel in SVM. Particle swarm optimizations, Genetic Algorithm are the areas which provide a feasible solution for the optimization problems. So, they could be applied as an optimization technique for constructing hybrid kernel function based on large margin learning theory. Therefore, the crisis on choosing the right kernels and the best systematic method of combining them would be our research work in future.

References 1. Vapnik, V.: An overview of statistical learning theory. IEEE Trans. on Neural Networks 10(5), 988–999 (1999) 2. Cristianini, N., Shawe-Taylor, J.: Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000) 3. Schölkopf, B., Smola, A.: Leaning with Kernels. MIT Press, Cambridge (2001) 4. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 56–89 (1998) 5. Cortesand, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20 (1995) 6. Gunn, S.R.: ” SVM for Classification and Regression”, Technical Report, Image Speech and intelligent system groups (1998) 7. Herbrich, R.: Learning kernel classifiers: theory and algorithms. MIT Press, Cambridge (December 2001) 8. Xia, G.-E., Shao, P.-J.: Factor Analysis Algorithm with Mercer Kernel. IEEE Second International Symposium on Intelligent Information Technology and Security Informatics (2009) 9. Schölkopf, B., Mika, S., Burges, C., et al.: Input Space versus Feature Space in KernelBased Methods. IEEE Transactions on Neural Networks (5), 1000–1017 (1999) 10. Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel Methods in Machine Learning. The Annals of Statistics 36(3), 1171–1220 (2008)

Color Image Restoration Method for Gaussian Noise Removal J. Harikiran and R. Usha Rani Department of Information Technology, GIT, GITAM University, Visakhapatnam [email protected], [email protected]

Abstract. A new approach to the restoration of color images corrupted by Gaussian noise is presented. The proposed technique adopts a multipass processing approach that gradually reduces the noise in the color information components of the image. Two different models for data smoothing are proposed based on the different classes of noisy pixels. The subsequent algorithm for edge detection is designed to better appraise the noise cancellation behavior of our filter from the point of view of human perception. This method does not require any “a priori” knowledge about the amount of noise corruption. Experimental results show that the filtering performance of the proposed approach is very satisfactory and accurate edge maps are achieved even in the presence of highly corrupted data. Keywords: Gaussian noise, image enhancement, nonlinear filter, image restoration, image processing.

1 Introduction Image noise is the random variation of brightness or color information in images produced by the sensor and circuitry of a scanner or digital camera. The development of techniques for noise removal is of paramount importance for image-based measurement systems [1]. In-order to smooth out from noise many filtering architectures have been proposed in the literature [2]. Indeed, noise can significantly decrease the accuracy of very critical operations such as feature extraction and object recognition. The goal of the filtering action is to cancel noise while preserving the integrity of edge and detail information, non-linear approaches generally provide more satisfactory results than linear techniques. However, a common drawback of the practical use of these methods is the fact that they usually require some “a priori” knowledge about the amount of noise corruption. Unfortunately such information is not available in real time applications. In this paper, a new approach to filtering of Gaussian noise and edge detection in color images is presented. The filtering behavior is based on the classification of noisy pixels into two different (fuzzy) classes. 1) Pixels corrupted by noise with amplitude not two different from that of neighbors. (Type A pixels). 2) Pixels corrupted by noise with amplitude much larger than that of the neighbors. (Type B pixels). V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 554–560, 2010. © Springer-Verlag Berlin Heidelberg 2010

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555

The noisy color image is processed by first converting it from RGB to YIQ domain. Then prefiltering is applied (for both Type A and Type B pixels) only to the color information (I and Q) components of the image and the filtered image is converted from YIQ into RGB domain resulting a noise free image. The images generated by our method have been obtained without taking into account the original uncorrupted data, in order to simulate a real application where only noise data is available. This is the key advantage of the proposed method. This paper is organized as follows. Section 2 presents conversion of color image from RGB to YIQ domain. Section 3 presents filtering architecture for Type A pixels, Section 4 presents filtering architecture for Type B pixels, Section 5 presents the edge detection algorithm, Section 6 presents the experimental results and finally Section 7 report conclusions.

2 Conversion from RGB to YIQ Domain The early approaches to color image processing are performed by processing each RGB components separately. A disadvantage of these methods is the loss of correlation between the color channels resulting in color shifts [4][5][6]. That is a substitution of a noisy pixel color through a new false color, which does not fit into the local neighbourhood. This means that a noisy pixel is replaced by another noisy pixel. In our work, the YIQ system is used to process color image. The principle advantage of this space in image processing is that the color information components (I and Q) are processed leaving the luminance component (Y) It will be a need to convert RGB to YIQ system for this purpose. The conversion from RGB to YIQ and YIQ to RGB [3] is given as follow: Y I Q

0.299 =

0.587

0.114

R

R

1

0.596 -0.274

-0.322

G

G

= 1

0.312

B

B

1

0.211

-0.523

Fig. 1. Conversion from RGB to YIQ

0.9563

0.6210

Y

-0.2721 -0.6474

I

-1.1070

Q

1.7046

Fig. 2. Conversion from YIQ to RGB

3 Type A Prefiltering Pixels corrupted by noise with amplitude not too different from that of the neighbors are Type A pixels. The filter, we are using in our approach is called “zed filter”. Let us suppose we deal with digitized images having L levels (color images). Let x(n) be the pixel luminance at location n=[n1,n2] in the noisy image. Let x1(n), x2(n) ……, xN(n) be the group of N=8 neighboring pixels that belong to a 3X3 window around x(n). The output y(n) of the filter is defined by the following relationship: y(n) = x(n) + Where

1 N

N

∑ ζ ( x (n), x(n)) i

i =1

(1)

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u–v

ζ

(u,v)=

|u – v| ≤ p

3 p− | u − v | sgm(u-v) p<|u-v| ≤ 3p 2 0

|u-v|>3p

(2)

and “p” is an integer ( 0
4 Type B Prefiltering After completion of Type A prefiltering, perform Type B prefiltering for I and Q components. Pixels corrupted by noise with amplitude much larger than that of the neighbors are Type B pixels. If all the differences between pixel to be processed and its neighbors are very large, the pixel is (possibly) an outlier to be cancelled. For this reason, the noise removal procedure adopts a different nonlinear model. It is briefly summarized as follows: x( i ,j ) = x ( i, j ) – (L-1) ∆ ( i, j ) where x(i,j) is the pixel value at location (i,j) , ∆ ( i, j ) = MIN (µ LA( x( i, j ) , x ( i+m , j+n))) m=-1, 0, 1 n=-1, 0, 1 (m,n) ≠ (0,0)

(3) MIN (µ LA( x( i+m, j+n) , x( i , j))) m=-1, 0, 1 n=-1, 0, 1 (m,n) ≠ (0,0)

and µ LA (u,v) denotes the membership function that describes the fuzzy relation: ”u is much larger than v”. u-v/L-1 0< u-v ≤L-1 µ LA (u,v) = 0 u-v ≤ 0 After performing the Type B prefiltering action, convert the image from YIQ domain to RGB domain using figure 2.(for 24 bit image L=36.140)

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5 Edge Detection Algorithm Edge detector is one of the most important tools in computer vision [8]. A color edge detection algorithm is proposed in this paper. The proposed method consists of three steps as follows: 1.

Directional color difference calculation: Edges in a color image exist in four directions 00, 900, 450 and 1350 , where abrupt changes of RGB values occur. So to detect proper edges, first the abrupt color differences in an image must be pointed out. To reduce the computational overhead we have calculated a transformed value (weighted addition of three components Red, Green and Blue) for each pixel which converts three component valued pixels into a single valued attribute. The transformation is given by pixel(i,j)=2*r(i,j)+3*g(i,j)+4*b(I,j)

(4)

All the four masks shown in figure 5 are moved over the transformed pixel values one by one to calculate the color differences in four directions (00, 450, 900, 1350). From these four directional color differences calculate the maximum directional color difference by considering each pixel at the centre of the mask. 0

0

0

4

0

-4

0

0

0

0

4

0

4

0

0

0

0

-4

0

0

0

0

0

0

0

0

0

0

-4

0

0

0

-4

4

0

0

00

900

1350

450

Fig. 3. Directional masks

0

0

0

0

-1

0

-1

2

-1

0

2

0

0

0

0

0

-1

0

Fig. 4. Thinning masks

2.

Threshold Technique: Threshold technique is very important task in edge detection algorithms. The accuracy of an algorithm is dependent on the choice of threshold parameters. We have already calculated the maximum color difference for each pixel. Next the average value of the maximum color difference is computed given by “t”. t= maximum color difference / (row *col). Thus the threshold value is set at T=1.2 *t.

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3.

Edge Thinning: Edge map produced from above two steps contain thick edges. So a thinning technique is applied to create more thin edges which will be accurate and visibly smoothing. Two masks as shown in figure 6 are moved over the edge image(produced after thresholding) to create thin edges.

6 Experimental Results This section presents the simulation results illustrating the performance of the proposed filter. The test image employed here is the true color image “parrot” with 290×290 pixels. For the addition of noise, the source image was corrupted by additive Gaussian noise with standard deviation σ =10, 20,30 and 40. The noise model was computer simulated. The performance of our two filtering models is compared with the traditional mean filter shown in Table (1). All filters considered operate using 3×3 processing window. The performance of filters was evaluated by computing the mean square error (MSE) between the original image and filtered image as follow: MSE= (

1 ) ∑ (I(x,y) – I1(x,y))2 M nεF

(5)

Where F denotes the set of M processed pixels, I(x,y) denotes the vector pixel value in the original image and I1(x,y) denotes the vector pixel value in the filtered image. Figure 7 shows the results of filtering the color image parrot which is corrupted by Gaussian noise with σ=10. First the color image is converted from RGB to YIQ domain. The proposed Type A and Type B prefiltering is applied for the removal of noise in I and Q components and the resultant image is converted from YIQ to RGB domain.

Fig. 5. a, b, c, d take left to right direction from the first figure

5a) Original noise free color image with 290×290 pixels 5b) Noise image with σ=10 5c) filtered image obtained by using mean filter 5d) Resultant noise free obtained by using proposed method. Table (1) shows the results of MSE for both proposed filter and mean filter for different noise levels. As it can be seen, the minimum mean square error (MSE) was obtained by our proposed filtering approach for all cases. In order to better appraise the noise cancellation behavior of our filter from the point of view of human perception, we perform edge detection mechanism for the

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filtered image using the edge detection algorithm described in section V. Figure 8 shows that our filter significantly reduces Gaussian noise and the image details have been satisfactorily preserved. Table 1. Experimental results mean square error(MSE)

Noise

σ=10

σ=20

σ=30

σ=40

39.113

39.5747

39.6598

39.6865

26.125

26.235

26.578

26.789

18.5163

18.3276

18.2927

18.2806

Image Mean Filter Proposed Filter

(a)

(b)

(c)

Fig. 6. Edge detection using our method 6a) Original noise free image 6b) Noise image and 6c) Resultant image by our method

7 Conclusion A new technique for the restoration of color images degraded by Gaussian noise has been presented. The proposed filtering approach is a simple and effective algorithm for noise cancellation. A method for edge detection is also presented to better appraise the noise cancellation behavior of our filtering models. This restoration of image data is very likely to find potential applications in a number of different areas such as electromagnetic imaging of objects, medical diagnostics, remote sensing, robotics, etc. Experimental results show that the proposed method yields very satisfactory results.

References 1. VanderHeijden, F.: Image Based Measurement Systems. Wiley, NewYork (1994) 2. Jain, K.: Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs (1989) 3. Pitas, I., Venetsanopoulos, A.N.: Nonlinear Digital Filters: Principles and Applications. Kluwer, Norwell (1990)

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4. Kao, O.: A Parallel Triangle Operator for Noise Removal in True Colour Images, Department of Computer Science, Technical University of Clausthal, 38678 Clausthal – Zellerfeld Germany 5. Yang, C., Rodriguez, J.: Efficient Luminance and Saturation Processing Techniques for bypassing Color Coordinate Transformations. Electrical and Computer Engineering Department, Arizona University (1997) 6. Baker, M.N., Al-Zuky, A.A.: Color Image Noise Reduction Using Fuzzy Filtering. Journal of Engineering and Development 12(2) (June 2008) 7. Nadernejad, E.: Edge Detection Techniques: Evaluations and Comparisions. Applied Mathematical Sciences 2(31), 1507–1520 (2008) 8. Canny, J.F.: A computational approach to edge detection. IEEE Trans.Pattern.Anal. Machine Intell. 8(6), 679–698 (1986) 9. Russo, F.: A Method for Estimation and Filtering of Gaussian Noise in Images. IEEE Transactions on Instrumentation and Measurement 52(4) (August 2003)

A Decision Tree Approach for Design Patterns Detection by Subgraph Isomorphism Akshara Pande, Manjari Gupta, and A.K. Tripathi DST- Centre for Interdisciplinary Mathematical Sciences, Banaras Hindu University, Varanasi-221005, India [email protected], [email protected], [email protected]

Abstract. In many object oriented softwares, there are recurring patterns of classes. Design pattern instances are important for program understanding and software maintenance. Hence a reliable design pattern mining is required. Here we are applying decision tree approach followed by subgraph isomorphism technique for design pattern detection. Keywords: design pattern, decision tree, UML, row-column element, subgraph isomorphism.

1 Introduction Design patterns [1] increasingly being applied in object oriented software design processes as a part of many solutions to Software Engineering difficulties and thus extensively used by software industries. In this paper we are using a decision tree approach for design pattern identification by applying subgraph isomorphism technique. Here we are taking two graphs, one is corresponding to the system design (i.e. system under study) and other is corresponding to the design pattern graph. A decision tree is made for system design, and then try to find out is there any isomorphism between the subgraphs of system design and design patterns. Decision tree approach uses adjacency matrix representation of the corresponding relationship graphs. For adjacency matrix we are only considering those nodes in which relationships exist. Decision tree approach is applicable for all 23 GoF [1] design patterns detection. In section 2 representation of system design and design patterns are shown. Decision tree approach for design pattern detection by subgraph isomorphism has been described in section 3. Related works are discussed in section 4. Lastly we concluded in section 5.

2 Representations of System Design and Design Patterns Firstly, all the relationship graphs are extracted from the UML diagrams of the system design and design patterns and the write their adjacency matrices [8]. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 561–564, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. UML Diagram of System Design and corresponding relationship graphs and adjacency matrices

Fig. 2. UML Diagrams of Façade and Abstract Factory Design Pattern and their corresponding relationship graphs and adjacency matrices

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3 Decision Tree Approach for Design Pattern Detection by Subgraph Isomorphism It is necessary to detect design pattern that all the possible subgraphs of system design should be extracted first. It can be found out by generating row-column elements for all the possible permutations of adjacency matrix (figure 3) [4].

Fig. 3. Formation of decision tree for direct association relationship of system design by using row-column element method of adjacency matrices All the possible subgraphs of system design can be found by decision tree, we only take out the design pattern relationship matrix and represent in row-column elements format. Then starting from root node traverse the decision tree of system design, if for any row-columns the elements matched, design pattern exists. For example if façade design pattern is considered (figure 2), the corresponding row-column element would be (p), (1, q, 0), which will match the decision trees row column elements (i.e. (a), (1, b, 0) or (a), (1, c, 0) or (c), (1, b, 0)). Hence façade design pattern exists. Similarly, abstract factory design pattern (for each relationship) also exists in system design.

4 Related Work The first effort towards automatically detect design pattern was achieved by Brown [5]. In this work, Smalltalk code was reverse-engineered to facilitate the detection of four well-known patterns from the catalog by Gamma et al. [1]. Antoniol et al. [6] gave a technique to identify structural patterns in a system with the purpose to

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observe how useful a design pattern recovery tool could be in program understanding and maintenance. Nikolaos Tsantalis [2], proposed a methodology for design pattern detection using similarity scoring. But the limitation of similarity algorithm is that it only calculates the similarity between two vertices, not the similarity between two graphs. To solve this Jing Dong [3] gave another approach called template matching, which calculates the similarity between subgraphs of two graphs instead of vertices. In our earlier work [7-11], we have shown how to detect design patterns using various techniques.

5 Conclusion In this paper we took the system design and a design pattern, and tried to find out whether design pattern matches to any subgraph of system design by using decision tree approach. Firstly, we made a decision tree with the help of row-column elements, and then traverse the tree. By applying the decision tree approach, the complexity is reduced.

References 1. Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns Elements of Reusable Object-Oriented Software. Addison-Wesley, Reading (1995) 2. Tsantalis, N., Chatzigeorgiou, A., Stephanides, G., Halkidis, S.: Design Pattern Detection Using Similarity Scoring. IEEE Transaction on Software Engineering 32(11) (2006) 3. Dong, J., Sun, Y., Zhao, Y.: Design Pattern Detection By Template Matching. In: The Proceedings of the 23rd Annual ACM, Symposium on Applied Computing (SAC), Ceará, Brazil, pp. 765–769 (March 2008) 4. Messmer, B.T., Bunke, H.: Subgraph isomorphism detection in polynomial time on preprocessed model graphs. In: Second Asian Conference on Computer Vision, pp. 151– 155 (1995) 5. Brown, K.: Design Reverse-Engineering and Automated Design Pattern Detection in Smalltalk, Technical Report TR-96-07, Dept. of Computer Science, North Carolina State Univ. (1996) 6. Antoniol, G., Casazza, G., Penta, M.D., Fiutem, R.: Object- Oriented Design Patterns Recovery. J. Systems and Software 59(2), 181–196 (2001) 7. Pande, A., Gupta, M.: Design Pattern Detection Using Graph Matching. International Journal of Comuter Engineering and Information Technology (IJCEIT) 15(20), 59–64 (2010) 8. Pande, A., Gupta, M.: A New Approach for Design Pattern Detection Using Subgraph Isomorphism. In: Proc. of National Conference on Mathematical Techniques: Emerging Paradigm for Electronics and IT Industries, MATEIT-2010 (2010) 9. Gupta, M., Pande, A.: A New Approach for Detecting Design Patterns Using Genetic Algorithm. Presented in International Conference on Optimization and its Application, organized by Deptt. of Mathematics. Banaras Hindu University (2010) 10. Pande, A., Gupta, M., Tripathi, A.K.: Design Pattern Mining for GIS Application using Graph Matching Techniques. In: 3rd IEEE International Conference on Computer Science and Information Technology, Chengdu, China, July 09-11 (2010) (accepted) 11. Pande, A., Gupta, M., Tripathi, A.K.: A New Approach for Detecting Design Patterns by Graph Decomposition and Graph Isomorphism. In: Proc. of Third International Conference on Contemporary Computing(IC3), Noida, India, August 09-11, Springer, Heidelberg (2010) (to be published) (accepted)

Realisation of Various EBG Structures B. Bhuvaneswari1 and K. Malathi2 1

Research Scholar, Dept. of Electronics and Commn Engg, 2 CEG, Anna University, Chennai-25, Meenakshi College of Engineering, West. K.K. Nagar, Chennai-78 [email protected] 2 Assistant Professor, Dept. of Electronics and Commn Engg, CEG, Anna University, Chennai-25 [email protected]

Abstract. Patch antenna arrays are used extensively due to their low profile structure, light weight and low cost. Patch antenna arrays have been widely used for a variety of wireless applications. However, a major drawback of this type of antenna arrays is mutual coupling and bandwidth. Mutual Coupling losses can be reduced effectively by placing Electromagnetic band gap (EBG) structures, also called photonic band gap (PBG) structures. In this paper, different types of Electromagnetic Band Gap (EBG) structures are proposed to be placed in between the patch antenna arrays to reduce the mutual coupling loss. These EBG structures are designed as small as possible because of system compactness. Hence the design of novel compact hybrid EBG structures are more challenging for wireless applications. In this paper various hybrid EBG structures showed with and without vias are compared with the defined antenna parameters. Keywords: EBG structures, patch antenna arrays, mutual coupling loss, structure realization, band pass – stop band, compact size, bandwidth, vias.

1 Introduction Wireless applications are growing very rapidly in recent years. Wireless devices are needed to be very smart and small[1]. Single antenna systems are small in size but the efficiency is not much sufficient as required[2]. So the designs of more than one antenna system called antenna arrays are proposed. The antenna arrays are having high directivity and gain. In antenna arrays energy is wasted in backward radiation as well as losses due to mutual coupling[3]. To reduce the mutual coupling losses EBG arrays are placed in between antenna elements. The band gap feature of EBG structures has found useful applications in suppressing the surface waves in various antenna designs[4]. Conventional EBG structures are mushroom like shape; increasing the width of the EBG patches which will also increase the bandwidth. Instead of increasing the width of EBG patch, EBG gap should be increased so that capacitance is increased which will narrow the bandwidth[5]. Different EBG structures are proposed which is simulated using MoM solver[6]. Parameters like mutual coupling, return loss, gain, V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 565–570, 2010. © Springer-Verlag Berlin Heidelberg 2010

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directivity, efficiency and bandwidth are measured[7]. According to the requirement and application EBG structure can be chosen. H like EBG, Hybrid H EBG and Double E shaped fork like EBG structures shows good performance in mutual coupling and return loss. Low profile antennas are useful in wireless devices, aircraft, satellite and missile applications, where size, weight, cost, performance, ease of installation, and aerodynamic profile are strict constraints[8]. The array antennas are very good in directivity where it can extend longer distance and coverage. In array antennas the major disadvantages are backward radiation and mutual coupling losses[9]. By placing appropriate EBG structure, mutual coupling losses can be reduced in array antennas [10]. To increase the capacitance effect of the equivalent LC circuit and to improve the compactness in EBG structures, different shapes with significant gaps are designed [11]. Hence the shape and size of the EBGs structures have become more challenge in the recent technology of antennas design.

2 EBG Structure Realization In WLAN (2.4-2.55Ghz) systems which has IEEE 802.11b and 802.11g standards are utilizing the 2.4 GHz ISM band where high gain and low cost adaptable antennas promise increase in capacity and coverage by having greater directivity[4]. The reduction of mutual coupling is considered as important by means of periodic structure becomes particularly efficient when grating lobes must be avoided[5]. The patch antenna has a size of approximately 0.5λ unless high permittivity materials are used, and the space between the edges of two adjacent patches is very small under the no grating lobes constraint. This is the reason for the use of high permittivity dielectric material as antenna substrate. EBG seeds are inserted between patch antenna array to reduce the mutual coupling loss[6]. EBG structures are needed to be small in size. These EBG structures are used to increase the directivity of antenna major lobe, there by obtaining the high gain in antenna arrays[7].

3 Proposed Work In this paper the proposed work is realization of various EBG structures with different shapes and sizes. The performance of the EBG structures can be improved by, i. increasing the EBG seed thickness ii. Increasing substrate height, iii. Introducing more gaps instead increasing width of EBG structure. In this paper the proposed work, the EBG seed thickness is kept as 0.5mm. Conventional EBG structures are mushroom shape and uni-planar EBG structures.

4 Initial Design The electromagnetic properties of the EBG seeds can be described using lump circuit elements, capacitors and inductors with designed resonant frequencies. In the frequency range where the surface impedance is very high of an EBG structure, the

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equivalent LC circuit acts as a two-dimensional electric filter to restrict the flow of the surface waves. The central frequency of the band gap is calculated using, fo= 1 / 2π√LC. The inductor L results from the current flowing through the vias, and the capacitor C due to the gap effect between the adjacent patches. 4.1 Mushroom Shape Conventional EBG structures are mushroom shape EBG structure with via and without via, realized for the following dimensions in figure 1. Vias are important W =8x12mm, r = 0.125mm, EBG seed thickness = 0.5mm. The mushroom like EBG structure has a wider stopband and compact nature.

Fig. 1. Muahroom shape EBG array

The dielectric substrate used is FR4 with dielectric constant of 4.55. By increasing the thickness of the EBG seeds, performance of the structures are improved. 4.2 Hybrid EBG Structures EBG structures are needed to more compact in nature since the antenna system will also get reduced in size. The main objective of the work is to summarize various EBG structures with increased gaps so that the capacitance increased which narrows the bandwidth. The EBG structures are analyzed for simulated parameters S11(return loss in dB) and S21(mutual coupling in dB). H EBG structures EBG structures are designed by distributed LC network with specified resonant frequencies hence surface impedance of EBG structures is frequency sensitive. When the surface impedance is very high in particular frequency range, the equivalent LC circuit acts as filter to block the flow of surface waves. Vias are placed to provide the current flow.

H=12mm W=8mm

The H shape EBG structure is more compact and it can made into hybrid structures. The band gap characteristics is given by the formula, ω=1/√LC.

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This H like EBG structure provides additional capacitance between the neighbouring edges of the slot and also formed stretched strip with adjacent patches. This property enhances the performance of EBG structure. E-E Tunable EBG structures Another novel EBG structure is proposed in this paper is E-E tunable EBG structures. Tunable property is wanted in antenna devices because it introduces adaptability in beam steering, in the desired direction. Each element of EBG lattice consists of a square metal patch with a slot etched on it and a stretched strip.

H=12mm W=8mm

With the above described EBG structures more hybrid EBG structures are proposed in this paper. Various different EBG structures with and without vias are listed below. The corresponding values of S11(return loss in dB) and S21(mutual coupling in dB) are also mentioned in the tabular column. Table 1. Results listed with via in EBG structure

SHAPE

DESIGN

MUTUAL COUPLINGLOSS (dB)

RETURN LOSS(dB)

MUSHROOM

H=12mm W=8mm

42.8

5.7

SINGLE H

H=12mm W=8mm

49

5.9

HALLOW H

H=12mm W=8mm

45.8

5.6

HYBRID H – I

H=12mm W=8mm

34.8

9.5

HYBRID H– II

H=12mm W=8mm

33

6.6

DOUBLE E

H=12mm W=8mm

56.8

14.7

HYBRID E

=12mm W=8mm

39

6.8

E-E

=12mm W=8mm

46

26

68

35

65

28

E-TUNABLE EVEN ETUNABLE

W=8mm

W=8mm

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Table 2. Results listed without via in EBG structure:

MUTUAL COUPLING LOSS(dB)

RETURN LOSS(dB)

H=12mm W=8mm

39

3.9

SINGLE H

H=12mm W=8mm

49

8

HALLOW H

H=12mm W=8mm

47

8

HYBRID H– II

H=12mm W=8mm

52

8

DOUBLEE

H=12mm W=8mm

45

32

34

25

57

28

67

30

70

32

SHAPE MUSHROOM

HYBRID E E-E E-TUNABLE EVEN E-TUNABLE

DESIGN

=12mm W=8mm H=12m W=8mm W=8mm W=8mm

5 Conclusion In this paper, we have discussed various structures of EBG structures and the simulated results are listed in tabular column. The EBG structures are more compact and reduce the mutual coupling loss. In future EBG structures are made as tunable structures so that the system can operate in tunable narrow bandwidth. EBG loss is due to the current from via to the substrate material. The EBG structure without via shows slightly higher results but for fabrication EBG with via is more appropriate. Wireless devices need multilayered tunable EBG substrates in which antenna array size is reduced as well beam steerability is achieved. Also patch array antennas can work in narrow bandwidth. These tunable EBG structures are more challenging nowadays because of mentioned advantages.

References 1. Book on, Electromagnetic Band Gap Structures in Antenna Engineering, Fan Yang University of Mississippi Yahya Rahmat-SAMII University of California at Los Angeles 2. Amman, M.: Design of Rectangular Microstrip Patch Antennas for the 2.4 GHz Band. Applied Microwave &Wireless, 24–34 (November/December 1997) 3. Garg, R., Bhartia, P., Bahl, I., Ittipibon, A.: Microstrip Antenna Design Handbook. Artech House, Boston (2001) 4. Azad, M.Z., Ali, M.: Novel wide band directional dipole antenna on a mushroom like EBG structure. IEEE Trans. Antennas and Propaga 56(5) (May 2008)

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5. Yang, L., Fan, M., She, F.C.J., Feng, Z.: A novel compact EBG structure and its application for Microwave circuits. IEEE Trans. Microw. Theory Tech. 53(1) (January 2005) 6. Antenna Theory by Constantine Balanies Handbook, II edn. Wiley Publications, Chichester 7. Elsheakh, D.N.: Hawaii Center for Advanced Communications University of Hawaii Honolulu, USA H. A. Elsadek and E. A. Abdallah Electronics Research Institute Cairo, Egypt M. F. Iskander Hawaii Center for Advanced Communications University of Hawaii Honolulu, USA H. ElhenawyFaculty of Engineering Ain Shams University Cairo, Egypt, ‘Ultra-wideband and Miniaturization of the Monopole Patch Antenna(MMPA) with modified plane for wireless applications’, Progres. Electromagnetics Research Letters 10, 171–184 (2009) 8. Rajo-Iglesias, E., Quevedo-Teruel, O., Inclan-Sanchez, L.: Mutual coupling reduction in patch antenna arrays by using a Planar EBG structure and a multilayer dielectric substrate. IEEE Trans., Antennas and Propaga. 56(6) (June 2008) 9. Amman, M.: Design of Rectangular Microstrip Patch Antennas for the 2.4 GHz Band. Applied Microwave &Wireless, 24–34 (November/December 2008) 10. Yang, A.A.L., Fan, M., Feng, Z.: A Spiral Electromagnetic Bandgap (EBG) Structure and its Application in Microstrip. State Key Lab on Microwave & Digital Communications (2008) 11. Liang, J., David Yang, H.Y.: Microstrip Patch Antennas on Tunable Electromagnetic Band-Gap Substrates. IEEE Transaction on Antenna and Propagation (2009)

Identification of Melanoma (Skin Cancer) Proteins through Support Vector Machine Babita Rathore1, Sandeep K. Kushwaha1, and Madhvi Shakya2 1

Department of Bioinformatics, MANIT, Bhopal 462051, India Department of Mathematics, MANIT, Bhopal 462051, India [email protected], [email protected], [email protected] 2

Abstract. Melanoma is a form of cancer that begins in melanocytes. The occurrence of melanoma continues to rise across the world and current therapeutic options are of limited benefit. Researchers are studying the genetic changes in skin tissue linked to a life-threatening melanoma through SNP genotyping, Expression microarrays, RNA interference etc. In the spectrum of disease, identification and characterization of melanoma proteins is also very important task. In the present study, effort has been made to identify the melanoma protein through Support Vector Machine. A positive dataset has been prepared through databases and literature whereas negative dataset consist of core metabolic proteins. Total 420 compositional properties of amino acid dipeptide and multiplet frequencies have been used to develop SVM model classifier. Average performance of models varies from 0.65-0.80 Mathew’s correlation coefficient values and 91.56% accuracy has been achieved through random data set. Keywords: Skin Cancer, Support Vector Machines, Melanoma, and Compositional property.

1 Introduction After several decades, Cancer is still burning research area due to its diversity in the sites, origin and patho-physio mechanism. Mainly, skin cancer arises in the outer most layer of the skin i.e. epidermis. Especially, Epithelial cells which is produces three cutaneous barriers to the environmental factors and a number of neoplasm originate from cutaneous epithelial cell [1]. Melanoma is one of the popularly known types of skin cancer. Melanoma is a form of cancer that begins in melanocytes. It may begin in a mole (skin melanoma), but can also begin in other pigmented tissues, like eye or intestines. Melanoma causes the majority (75%) of skin cancer related deaths. The incidence of melanoma has rapidly risen in developed countries during the past several decades [2]. In 2000, World statistical analysis of cancer has been reported the 47,700 new case of melanoma and 7700 deaths [3]. Estimated new cases and deaths from melanoma in the United States in year 2007 are 59,940 and 8,110 respectively. The life time risk of developing melanoma is now 1 in 49 for men and 1 in 73 for women [4]. In 2009, there will be an estimated 68,720 new cases of melanoma diagnosed with 8650 deaths [5]. Melanoma can be classifies into four main classes: Superficial spreading melanoma, V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 571–575, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Nodular melanoma, Lentigo maligna melanoma and Acral lentiginous melanoma [6]. The major risk factor for developing melanoma are intrinsic (genetic) and environmental (sun exposure) [7]. Initial signs and symptoms of melanoma are much diversified which includes the asymmetry in size or shape, change in colour, itching, tenderness, bleeding, and ulceration of the suspicious lesion [8]. Melanoma diagnosis at any stage of disease, people with melanoma may have treatment to control pain and other symptoms of the cancer, to relieve the side effects of therapy. Medically, melanoma treatment includes surgery, chemotherapy, biological therapy, or radiation therapy or a combination of treatments. [9]. Researchers are studying the genetic link in melanoma by employing high-tech techniques including SNP, Micro array, and RNA interference etc [4]. In recent past researchers are focusing towards the identification of melanoma protein, which contribute to the development of melanoma. The ccompletion of human genome project and computational advancement have facilitated the identification of various kinds of human proteins. The availability of genomic sequences from genome project accelerated the experimental identification and characterization of predicted proteins. Identification and characterization can be simplify through application of tools and techniques, algorithms and prediction methods. In the present study we elucidate the method to identify melanoma causing protein by implying Support Vector Machine. SVMs have demonstrated highly competitive performance in numerous real-world applications, such as bioinformatics, text mining, face recognition, and image processing, which has established SVMs as one of the state-of the-art tools for machine learning and data mining, along with other soft computing techniques, e.g., neural networks and fuzzy systems. [10]. But SVM has been proved to perform much better in many classification problems than ANN [11].

2 Materials and Methods 2.1 Preparation of Datasets and Redundancy Removal Melanoma protein sequences have been retrieved from the NCBI, SWISS-PROT databases [12]. Retrieved sequences are filtered at first level i.e. remove the sequences through keyword filters "Probable", “Putative", "Hypothetical", "Unknown" and "Possible". Non-redundancy verification of prepared datasets has been done through BLASTclust. The strict criterions of BLASTclust (i.e. 100% Sequence coverage, 100% Sequence identity) have been used to verify non-redundancy of datasets. 76 sequences in positive dataset and 304 sequences in negative dataset have been found after the filtering of second level. 2.2 Used Compositional Properties a.

Dipeptide frequencies: The frequency of a dipeptide (i,j) 100 2 Where i, j = 1–20. There are 20*20 = 400 possible dipeptides.

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b.

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Multiplet frequencies: Multiplets are defined as homo-polymeric stretches of (Xn), where X is the amino acid and n (integer) ≥ 2. After identifying all the multiplets, the frequencies of the amino acids in the multiplets were computed as follows:

Where, L is the length of the sequence. There are 20 possible values for fi (m) for 20 amino acids. For each sequence, 420 compositional frequencies have been used as input as numerical features [13]. 2.3 Support Vector Machine Support vector machine (SVM) is a supervised machine learning technique applicable to both classification and regression. In 1995, Vladimir Vapnik and co-workers developed the statistical learning technique at AT&T Bell Laboratories. SVM classifiers for melanoma protein identification have been developed through freely downloadable package of SVM, SVMlight (http://www.cs.cornell.edu/People/tj/svm_light/). Selection of kernel and supporting parameters were optimized for best performance through cross validation techniques [14]. 2.4 Performance Evaluation Performance evaluation has been done by calculating specificity (SP), sensitivity (SN) and Mathew’s correlation coefficient (MCC) [15].

.

.

3 Result and Discussions Various models have been generated to identify melanoma proteins through Support Vector Machine. Fourteen SVM classifier models have been identified through an exhaustive search from 255 kernel parameters set. Most popularly used kernels for classification tasks are polynomial function and radial basis function (RBF). For polynomial kernel, all the SVM parameters have been used default, except d and c, the trade-off between training error and margin. The scalable memory parameter (m) have fixed to 200 [16]. The values for d and c have been incremented stepwise

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through a combination of d and c. For polynomial the range for d was between 1 - 7 and c was 1e-07 -1e+07. Similarly, In RBF gamma (g), the value of g has been incremented stepwise with combination of parameter c. [12]. For RBF range of g was between 1e-07 – 100 and c was between 0.01 -1e+12. For evaluation of method, five-fold cross validation has been adopted. 255 classification models have been created from each of the five training data-sets. Performance assessments of models have been done through Mathew’s correlation coefficient (MCC) and these models show the better performance from 0.65-0.80 MCC values [17]. Best performing models has been shown in the table-1. Table 1. Shows the parameter sets and performance of selected models to identify the melanoma proteins

S.N.

Identified Model No. (Classifiers)

1. 2. 3. 4. 5 6. 7. 8. 9. 10. 11. 12. 13. 14.

7 20 114 127 128 141 142 155 156 169 170 183 184 198

Kernel Type Polynomial Polynomial RBF RBF RBF RBF RBF RBF RBF RBF RBF RBF RBF RBF

Parameters

d=1 c= 0.1 d=2 c = 0.001 g =1e-07 c = 106 g =1e-06 c = 104 g =1e-06 c = 105 g =1e-05 c =103 g =1e-05 c = 104 g =0.0001c= 100 g=0.0001 c= 103 g =0.001 c = 10 g =0.001 c = 100 g =0.01 c = 1 g =0.01 c = 10 g =0.1 c = 1

Mean MCC for the parameters across five test subset 0.93 0.90 0.96 0.83 0.96 0.83 0.96 0.83 0.95 0.84 0.96 0.83 0.96 0.96

Accuracy

92% 91% 94% 86% 94% 88% 95% 88% 95% 88% 95% 86% 95% 95%

4 Conclusion The occurrence of melanoma cases continues to rise across the world. The life time risk of developing melanoma in men are relatively high than women and the current therapeutic options for metastatic melanoma appear to be of limited benefit. So, identification of new therapeutic methods is needed. In this order, identification and characterization of melanoma proteins is also very important task. In this work, melanoma protein prediction method using Support Vector Machine is proposed and it classifies melanoma sequences from non-melanoma proteins with an accuracy of 91.56%. Fourteen best model have been identified for melanoma protein prediction which shown in table-1. In the appealing background of the work, the identification of several proteins of unknown function as melanoma-like proteins could generate new leads for further characterization.

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References 1. Owens, D.M., Watt, F.M.: Contribution of stem cells and differentiated cells to epidermal tumours. Nat. Rev. Cancer 3, 444–451 (2003) 2. Berwick, M., Erdei, E., Hay, J.: Melanoma epidemiology and public health. Dermatol. Clin. 27, 205–214 (2009) 3. Greenlee, R.T., Murray, T., Bolden, S., Wingo, P.A.: Cancer statistics. CA Cancer J. Clin. 50, 7–33 (2000) 4. Sanjiv, S., Agarwala, M.D.: Metastatic melanoma: an AJCC review. Community Oncology 5(8), 441–445 (2008) 5. Jemal, A., Siegel, R., Ward, E., Hao, Y., Xu, J., Thun, M.J.: Cancer statistics. CA Cancer J. Clin. 59, 225–249 (2009) 6. Hartleb, J., Arndt, R.: Cysteine and indole derivatives as markers for malignant melanoma. Journal of chromatography B 764, 409–443 (2001) 7. Wang, S., Setlow, R., Berwick, M., Polsky, D., Marghoob, A., Kopf, A., Bart, R.: Ultraviolet A and melanoma: a review. J. Am. Acad. Dermatol. 44(5), 837–846 (2001) 8. Elwood, J.M., Gallagher, R.P.: The first signs and symptoms of melanoma: a populationbased study. Pigment Cell Res. 9, 118–130 (1988) 9. Oliveria, S.A., Christos, P.J., Halpern, A.C., Fine, J.A., Barnhill, R.L., Berwick, M.: Patient knowledge, awareness, and delay in seeking medical attention for malignant melanoma. Journal of Clinical Epidemiology 52(11), 1111–1116 (1999) 10. Kecman, V.: Support Vector Machines – An Introduction. Stud. Fuzz. 177, 1–47 (2005) 11. http://www.imtech.res.in/raghava/ctlpred/about.html 12. Bairoch, A., Apweiler, R.: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acid Res. 28(1), 45–48 (2002) 13. Ansari, F.A., Naveen, K., Subramanyam, M.B., Gnanamani, M., Ramachandran, S.: MAAP: Malarial adhesins and adhesin-like proteins predictor. Proteins 70, 659–666 (2008) 14. Joachims, T.: Making large-scale SVM learning particle. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods Support Vector Learning, pp. 42–56. MIT Press, Cambridge (1999) 15. Kushwaha, S.K., Shakya, M.: Neural Network: A Machine Learning Technique for Tertiary Structure Prediction of Proteins from Peptide Sequences, act. In: 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies, pp. 98–101 (2009) 16. Bhasin, M., Raghava, G.P.S.: Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci. 13, 596–607 (2004) 17. Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442–451 (1975)

BPNN and Lifting Wavelet Based Image Compression Renu Singh1, Swanirbhar Majumder2, U. Bhattacharjee1, and A. Dinamani Singh2 1

Rajiv Gandhi University, Arunachal Pradesh, India NERIST (Deemed University), Nirjuli, Arunachal Pradesh, India [email protected], [email protected], [email protected], [email protected] 2

Abstract. Compression of data in any form is a large and active field as well as a big business. Image compression is a subset of this huge field of data compression, where the compression of image data is taken specifically. Wavelet transform is one of the popular transforms used in this field and its lifting based variant has become very popular for its easy hardware implementability. For images, the inter-pixel relationship is highly non-linear and unpredictive in the absence of a prior knowledge of the image itself. The back propagation based neural network (BPNN) takes into account the psycho visual features, dependent mostly on the information contained in images. Thereby preserving most of the characteristics of the data while working in a lossy manner and maximize the compression performance. So here image compression based on the lifting wavelet transform is taken in to account along with the BPNN based adaptive technique. Firstly by varying quantization levels for the lifting wavelet transform and number of hidden neurons for the BPNN an optimized compression percentage is reached for suitable adaptive hardware implementation of image compression with both the techniques. Keywords: Lifting Wavelet Transform, Back Propagation based Neural Network (BPNN), quantization level, hidden neuron, hardware implementation.

1 Introduction With the advance in the development of Internet and multimedia technologies, the amount of information that is processed by computers has grown exponentially over the past decades. This information requires large amount of storage space and transmission bandwidth which the current technology is unable to provide economically. One of the possible solutions to this problem is to compress the information so that the storage space, transmission time and transmission bandwidth can be reduced. This work focuses on the compression techniques of still images to address the aforesaid problems. A common characteristic that can be found in most images is the presence of redundant information classified as Spatial or Spectral redundancy [1-3]. A lot of work on image compression has been done in both the systems i.e removal of redundancies in spectral or spatial domain, but with their own pros and cons. Moreover implementation on hardware makes any operation faster than what it is when operated in software domain. Therefore instead of employing traditional V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 576–579, 2010. © Springer-Verlag Berlin Heidelberg 2010

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spectral domain characteristics like FFT, DCT or DWT here the lifting based integer wavelet transform has been implemented [5] [6]. The ease of implementation on hardware, taking into account the problems like word length and floating point has been this algorithms added advantage. Along with this spectral domain compression an adaptive compression scheme in spatial domain has been also included. The back propagation algorithm based neural network has been used earlier for compression as it is adaptive on account of the psycho visual features just like human visual system. Here instead of implementing the lifting based wavelet transform alone the BPNN based compression has been also attached along with it [4] [7]. Both the schemes were initially implemented separately as per the algorithms of Dutta et. al. [4] and Majumder et. al.[8] , then they have been optimized to achieve a particular percentage of compression for which both percentages of compression meet. Then they were implemented together to achieve the algorithm for adaptive hardware implementation.

2 Compression Scheme The lifting discrete wavelet transform (LDWT) scheme used was that of Majumder et. al.[8] and BPNN based compression scheme used was that of Dutta et. al.[4] The compression percentage versus the Peak Signal to Noise Ratio (PSNR) had been analyzed using curve fitting polynomials and estimating the goodness of fit. Both the schemes were found to coincide at a particular percentage of compression for which the one level lifting based wavelet transforms has quantization level of QLO with the Cohen-Daubechies-Feauveau (CDF) 9/7 wavelet, which is the name 'cdf97' specifically and BPNN based method required the multi-layer feedforward network have HNO hidden neurons after 8x8 block processing for the 256x256 ‘lena’ image.

Fig. 1. Adaptive hardware compression scheme using optimized BPNN and LDWT

The optimized quantization level is referred to as QLO and optimized number of hidden neurons as HNO for any image in figure 1. The optimization that has been used

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is via simple curve fitting polynomials. The percentage of compression has been decided based on the coinciding of the PSNR versus Compression curves of both the schemes. Later QLO and HNO is deduced out from the curves of the individual schemes of quantization(for LDWT) or number of hidden neuron (for BPNN) versus Compression percentage.

3 Result Analysis The LDWT based compression algorithm of Majumder et. al has been used for the variation of PSNR and compression percentage with respect to number of quantization level is plotted for the CDF wavelet, single decomposition as in figure 2(a). Similarly the algorithm of Dutta et al. has been used with the same for variation of number of hidden neurons has been plotted in figure 2(b).

(a)

(b)

Fig. 2. Variation of percentage of compression and PSNR with respect to (a) quantization levels for LDWT (b) hidden neurons for BPNN Optimization of percentage of compression v/s PSNR for lifting wavelet and BPNN schemes PSNR_BPNN vs. Compression Fit for PSNR_BPNN PSNR_LDWT vs Compression Fit for PSNR_LDWT

45

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Fig. 3. Curve fitted data of Compression versus PSNR for BPNN and LDWT techniques

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Once the details of variation of compression and PSNR of both the schemes are achieved, the compression percentage is considered as the independent variable. Thus variations of PSNR with respect to the compression of both LDWT and BPNN are plotted as in figure 3. Here it has been seen that their point of intersection is at 79.2% compression. This corresponds to 12 hidden neurons for BPNN and 32 quantization levels of LDWT after Loess quadratic fit smoothing of the data and fitting of polynomial curves of order 3 for BPNN and order 4 for LDWT. These are the optimized values. Therefore using QLO =32 and HNO=12, for adaptive hardware implementation using a hybrid technique of both BPNN and LDWT can be implemented.

4 Conclusion A method of utilizing the advantage of lifting based discrete wavelet transform (LDWT) for hardware implementation and that of BPNN for adaptivity has been used here. The optimization of the number of quantization levels and number of hidden neurons has been done using curve fitting polynomials and loess quadratic fit. When implemented on hardware successfully the hybrid variant would be providing a good quality output of PSNR of around 30-35 dB. Moreover the compression percentage too will be good as it will be providing above 80% compression with higher speed due to hardware implementation.

References 1. Junejo, N., et al.: Speech and Image Compression Using Discrete Wavelet Transform. In: IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication (2005) 2. Akintola, A.A., et al.: Evaluation of Discrete Cosine Transform (DCT) for Reconstructing Lost Blocks in Wireless Video Transmission. In: Proceeding ACIT - Signal and Image Processing (2005) 3. Andrew, J.P., et al.: Modified discrete wavelet transform for odd length data appropriate for image and video compression applications. IEEE, Los Alamitos (2001) 4. Dutta, et al.: Digital Image Compression using Neural Networks. In: International Conference on Advances in Computing, Control and Telecommunication Technologies’2009 (ACT 2009), pp. 116–120. IEEE Computer Society, Trivendum (December 2009) ISBN 978-0-7695-3915-7 5. Claderbank, R., et al.: Wavlet Transforms that map integers to integers. Applied and Computational Harmonica Analysis 5(3), 332–369 (1998) 6. Shnayderman, A., et al.: An SVD-based grayscale image quality measure for local and global assessment. IEEE Transactions on Image Processing 15(2), 422–429 7. Anna Durai, S., Anna Saro, E.: Image Compression with Back-Propagation Neural Network using Cumulative Distribution Function. International Journal for Applied Science and Engg. Technology, 185–189 8. Majumder, S., et al.: Image Compression using Lifting Wavelet Transform. In: Proceedings of International Conference on Advances in Communication, Network, and Computing – CNC 2010 (2010)

Combined Off-Line Signature Verification Using Neural Networks D.R. Shashi Kumar1, R. Ravi Kumar2, K.B. Raja2, R.K. Chhotaray3, and Sabyasachi Pattanaik4 1

Department of CSE, Cambridge Institute of Technology, Bangalore, India 2 Department of ECE, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India 3 Department of CSE, Seemanta Engineering College, Mayurbhanj, Orissa, India 4 Department of Computer Science, F.M. University, Balasore, Orissa, India [email protected], [email protected]

Abstract. In this paper, combined off-line signature verification using Neural Network (CSVNN) is presented. The global and grid features are combined to generate new set of features for the verification of signature. The Neural Network (NN) is used as a classifier for the authentication of a signature. The performance analysis is verified on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm. Keywords: Signature, Neural Network, FAR, FRR, Grid features and Global feature.

1 Introduction Handwritten signature is one of the most widely accepted personal attributes for identity verification. The signatures are unique to a person since it incorporates the intrapersonal and interpersonal differences. Handwritten signature recognition can be divided into on-line or dynamic and off-line or static recognition. On-line recognition refers to a process where the signer uses a special pen called stylus to create his or her signature, producing the pen locations, speed and pressure, while off-line recognition deals with signature images acquired by a scanner or a digital camera. In general, offline signature recognition is a challenging problem, unlike the on-line signature where dynamic aspects of the signing action are captured directly as the handwriting trajectory. As a symbol of consent and authorization, especially in the applications like credit cards and bank cheques, legal documents and security systems handwritten signature has long been used. The features like grid, global and texture [1] have been extracted and Euclidean distance was used for signature verification. In [2] the distance properties such as furthest, nearest, template and central were calculated and combined using mean, maximum and minimum rule. The grid features were extracted using deformable grid partition technique for the signature modeling based on Hidden Markov model was discussed in [3]. The concept of Radon Transform for feature extraction and Support Vector Machine has been suggested by [4]. The K-nearest V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 580–583, 2010. © Springer-Verlag Berlin Heidelberg 2010

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neighbor classifier was also proposed in [5]. The NN based on back propagation algorithm for signature classification was used in [6]. Along with local and global features Gaussian empherical rule, Euclidean and Mahalanobis distance based classifiers [7] were used for signature identification. NN using multilayer perceptrons for Conic functions and Radio basic functions [8] were used to achieve computational performance of signature recognition. Bezier curves for feature extraction and an ensemble of classifiers were proposed for classification in [9].

2 Proposed CSVNN Algorithm Problem Definition: Given a test signature for the verification of authenticity. The objectives are (i) the database signatures and test signature are preprocessed to eliminate noise. (ii) The NN is trained by the feature of each data base signature. (iii) The test signature features are compared with the database signature features using NN to authenticate the test signature. Signature is a behavioral biometric used to authenticate a person in day to day life and are verified using signature features such as, (i) Grid Feature: The skeletonized image is divided into 120 rectangular segments (15x8), and for each segment, the area (the sum of foreground pixels) is calculated. The results are normalized so that the lowest value i.e., the rectangle with the smallest number of black pixels would be zero and the highest value i.e., the rectangle with the highest number of black pixels would be one. (ii) Global Feature viz., (a) Signature height: It is the height of the signature image, after width normalization. (b)Image area: It is the number of black pixels in the skeletonized signature images. (c)Pure width: The width of the image with horizontal blank spaces removed. (d)Pure height: The height of the signature image after vertical blank spaces removed. (e) Aspect Ratio: The ratio of signature pure height to pure width. Multilayer feed forward NN is used for verification of off-line digitized signatures. The Back propagation NN consists of 30 input variables to which the extracted signature features are fed to train, and is designed to verify one signature at a time. CSVNN algorithm is implemented using the following steps. 1. 2. 3. 4. 5. 6.

Noise Reduction, Normalization and skeletonization are performed on database signature and test signature in the preprocessing. Global Features and Grid Features of database signatures are extracted. Neural Network is trained by features of database signatures. Global Features and Grid Features of test signature are extracted. Compare the features of test signature and database signatures using NN. The test signature is verified based on the results of NN.

3 Performance Analysis The test and the database signatures of 96-dpi resolution are considered for the performance analysis. It is observed from the Table 1 that the values of FRR and FAR are improved in the proposed CSVNN method compared to existing [1] grid and global feature method. The FAR is improved by a factor 25-30% compared to grid

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Feature using KNN Classification and 50-60% as compared to Global Feature using KNN Classification method. The FRR is improved by a factor 7-12% in the proposed algorithm compared to grid Feature using KNN Classification and 30-35% as compared to Global Feature using KNN Classification method. A group of 20 persons are used to collect 30 specimens from each person’s resulting in 600 signature samples. The genuine signatures are collected from 10 persons and the forged signatures are collected from the remaining 10 persons. Table 1. FAR and FRR for different classifiers Method Grid Feature using KNN Classification Global Feature using KNN Classification Offline Signature Verification using NN Classification

FRR

FAR

8.07%

5.91%

11.26%

9.53%

7.51%

4.16%

4 Conclusion We proposed the CSVNN algorithm which combines both the global and grid features to yield better results as compared to individual global and grid features. The Back Propagation Neural Network is used for verification of offline signatures. In future signature may be converted into transform domain for the verification of the performance analysis.

References 1. Mahar, J.A., Khan, M.K., Mumtaz Hussain Mahar, C.: Off-Line Signature Verification of Bank Cheque having Different Background colors. In: IEEE/ACS International Conference on Computer Systems and Applications, pp. 738–745 (2007) 2. Milena, R.P.S., Leandro, R., George, A., Cavalcanti, D.C.: Combining Distance through an Auto Encoder Off Line Signature. In: 10th Brazilian Symposium on Neural Networks, pp. 63–72 (2008) 3. Hai Rong, L.V., Yin, W.J., Yin Dong, C.: Off Line Signature Verification Based on Deformable Grid Partition and Hidden Markov Models. In: IEEE International Conference on Machine Intelligence and Electronics Systems, pp. 374–377 (2009) 4. Kiani, V., Poureza, R., Hamid Raza Pourreza, C.: Offline Signature Verification using Local Radon Transform and Support Vector Machine. International Journal of Image Processing 3(5), 184–251 (2010) 5. Abdulla Ali, A.A., Zhirkov, V.F.: Offline Signature Verification using Radon Transform and SVM/KNN Classifiers. Transactions of the Tambov State Technical University 1, 62–69 (2009)

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6. Karki, M.V., Indira, K., Sethun Selvi, S.C.: Off Line Signature Recognition and Verification using Neural Network. In: International Conference on Computational Intelligence and Multimedia Application, pp. 307–312 (2007) 7. Kistu, D.R., Gupta, P., Sing, J.K.: Offline Signature Identification by Fusion of Multiple Classifiers using Statistical learning Theory. In: International Conference on Future Generation Information Technology, pp. 34–39 (2009) 8. Erkmen, B., Kahraman, N., Vural, R.A., Yildirim, T.: Conic Section Function Neural Network Circuitry for Offline Signature Recognition. IEEE Transactions on Neural Networks 21(4) (2010) 9. Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Reducing forgeries in writerindependent off-line signature verification through ensemble of classifiers. Journal of Pattern Recognition 43(1), 387–396 (2010)

Distribution of Continuous Queries over Data Aggregators in Dynamic Data Dissemination Networks Mahesh Gadiraju1 and V. Valli Kumari2 1

Dept. of C.S.E., S. R. K. R. Engg. College, Bhimavaram, India [email protected] 2 Dept. of C.S.&S.E., A.U.C.E, Andhra University, Waltair, India [email protected]

Abstract. Selecting a query plan for executing continuous aggregate queries over dynamic data using data dissemination networks by optimally distributing the query among different data aggregators is the key issue in improving performance of dynamic data dissemination networks. Optimal execution of continuous queries over dynamic data using data dissemination networks is useful in many online applications like stock market applications. In this paper, we propose a better algorithm, Enhanced greedy algorithm with withdrawals (EGAWW), for dividing the client query into sub-queries and assigning these sub-queries to different data aggregators of dynamic data dissemination networks. Keywords: Dynamic data dissemination networks, Data aggregator, Incoherency bound, Enhanced greedy algorithm with withdrawals, Continuous aggregate queries.

1 Introduction The Internet and the Web are increasingly used to disseminate fast changing data such as sensor data, weather information, and stock prices. Many data intensive applications delivered over the web suffer from performance and scalability issues. Dynamic data dissemination networks can be used to address these issues. In dynamic data dissemination networks a hierarchical network of data aggregators is used as shown in Fig.1. A data aggregator (DA) is repository that caches and serves different data items to the clients. In applications such as stock quotes, client requests a continuous aggregate query with some incoherency bound which includes dynamic data items. Query incoherency bound is the maximum allowed difference in value of query result obtained at the source and that obtained at the client. The query is to be executed by obtaining data items from data aggregators. Many data aggregators may disseminate the same data item and all the data items may not be disseminated by one DA. Here we have to divide the query into sub-queries and we have to decide which sub-query is executed by which DA. So, a query plan has to be obtained for execution. Now let us discuss how the data aggregators are refreshed according to the changes of data at the source. In data dissemination schemes proposed in literature [3, 4], a V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 584–589, 2010. © Springer-Verlag Berlin Heidelberg 2010

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hierarchical network of DA’s is employed such that each DA serves the data items at some guaranteed incoherency bound. Incoherency of a data item at a given node is defined as the difference in value of the data item at the data source and the value at that node. For maintaining a certain incoherency bound, a DA gets data updates from the data source or some higher level DA so that the data incoherency is not more than the data incoherency bound. In a hierarchical data dissemination network a higher level aggregator guarantees a tighter incoherency bound compared to a lower level aggregator. Thus data refreshes are pushed from the data sources to the clients through the network of aggregators. [1] Sources

( , 0.6), ( , 0.24)

( , 1.2), ( , 0.12) DA2

DA1 Network of DA’s

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180 60

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Clients 60 Fig. 1. Dynamic Data Dissemination Networks

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Fig. 2. Distribution of a query over DA’s

2 Distribution of Continuous Queries Continuous aggregate queries are long running aggregate queries with ever changing data items. The user is interested in notification when the results of these queries change beyond a limit. Responses to these queries are to be refreshed continuously according to the coherence requirements of these queries. The general form of a portfolio query used in stock market is ∑ (ni × vi ). Where ni and vi are number of shares and share price (data item) of a company i.[5,6] Example: 60 × v1 + 240 × v2 + 180 × v3 For executing continuous aggregate queries using dynamic data dissemination networks a query plan has to be selected. There are three ways of distributing the continuous query among DA’s. 1. 2. 3.

Getting one data item from one DA Assigning the whole query to one DA Dividing the query into sub-queries and assigning each sub-query to one DA.

In the first option we can select the aggregator which can disseminate a data item optimally. But, the disadvantage is that collecting and aggregating each and every

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single data item from different aggregators is costly. The second option is a better option as there is no over head of collecting and aggregating data items from different DA’s. But it is not always possible to have such DA which can disseminate all the data items. The third option is the best option.[1] 2.1 Executing Continuous Queries Using Sub-queries For a given client query Q with incoherency bound B, for given set D of data aggregators and set V of data items and one to many mapping D (V,B), where B is a sub-set of real numbers representing incoherency bound for data items at the data aggregator D, we have the following tasks.[1] Task1: To divide the client query Q into sub-queries (q) such that . Task2: To allocate each sub-query with an incoherency bound Bk, to data aggregators, while fulfilling the following conditions 1) The sum of incoherency bounds of the sub-queries should be less than or equal to query incoherency bound i.e., ∑ Bk ≤ B 2) The chosen DA should be able to provide all the data items appearing in the sub-query assigned to it. 3) Data incoherency bounds at the chosen DA should be such that the subquery incoherency bound can be satisfied at the chosen DA. i.e., Bk ≥ Xk . Xk is tightest incoherency bound that the data aggregator can ensure for the given sub-query. It is calculated by the formula Xk =∑bjnjq . Where bj and njq are incoherency bound and weight (Number of shares in the portfolio . query) of jth data item appearing in sub-query, Objective: Query execution cost should be minimum. Example 1: Let the given client query be Q=60 × v1 + 240 × v2 + 180 × v3 with query incoherency bound B as 120. Let us assume that there are two data aggregators DA1{(v1,0.6) ,(v3,0.24)} and DA2{(v1,1.2) ,(v2,0.12)}. i.e., DA1 can disseminate v1 and v3 with incoherency bounds 0.6 and 0.24. DA2 can disseminate v1 and v2 with incoherency bounds 1.2 and 0.12.Then, the problem is to select one of the following set of sub-queries with least cost and which can disseminate all the data items (task1and condition 2 of task2). It is illustrated in Figure2. Set1: q1=60 × v1 + 180 × v3 from DA1 X1 = 60× 0.6 + 180×0.24=79.2 Set2: q3=180 x v3 from DA1 X3 = 43.2

and q2=240 × v2 from DA2. and X2 = 28.8 and q4=60 × v1 + 240 × v2 from DA2. and X4 = 100.8

Now let us allocate incoherency bounds to sub-queries. B1, B2 should be at least 79.2, 28.8(condition 3) and ∑Bk = B1+B2=108 < 120. B3, B4 should be at least 43.2, 100.8and ∑Bk = B3+B4=144 > 120. Since condition1 is satisfied for Set1 but not satisfied by Set2, only Set1 is the feasible solution. If more than one feasible solution is there then the set of sub-queries with least cost is selected. Cost of a sub-query at a given DA is minimum if number of refreshes at the DA is minimum. No. of refreshes at a DA is proportional to a data dynamic measure called

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sumdiff . If there is a query with two data items p and q with values pi, pi-1, qi, qi-1 at time instances i and i-1, then sumdiff of the query is defined as: [1] Rquery =∑ | np ( pi - pi-1) + nq (qi - qi-1) |. 2.2 Optimizing Query Cost Using EGAWW Algorithm Before solving the actual problem, we can consider the simple problem of getting the set of sub-queries minimizing the query cost with given values of Bk. This problem is weighted set cover problem which can be solved by greedy algorithm as given in [1]. Algorithms better than simple greedy algorithm was proposed in [2] .We use an improved algorithm, Enhanced greedy algorithm with withdrawals, to get the set of subqueries with minimum query cost. In the weighted set cover problem, we are given a set of elements G and a collection F of subsets of G, where and each such that has a positive cost pS. The goal is to compute a sub-collection and its cost ∑ is minimized. Such a sub-collection of subsets is called a weighted set cover. When we consider instances of the weighted set-cover such that each Sj has at most k elements, we obtain the weighted k-set cover problem. EGAWW Algorithm 1.

2. 3.

4.

Initialize the solution collection of subsets (Z) and the set of Uncovered items( ) and let α 1 (a) (b) / For every subset assign cost : where is the sumdiff of the sub-query formed by the elements of S , add S ′ to with cost = If For every subset and every ′ , and , , then , let , ,….., be the resulting extended collection, and denote the cost of Sj as pj. Iteration: While do: | |. If 0, (a) For every , let and every sub-collection C F of at most k subsets such (b) For every , let ( ) | | be the number of still unthat covered items that belong to the subsets in C. If (c) Let (

( ) ̃

0, let (

, )

∑:

be an index such that r , ) is minimized.

( ) j*

is minimized, and let

̃,

be such that

(d) Greedy step: If ( ̃ , ) then add Sj* to the solution and define the price of the newly covered items as . Formally, do the following: , let price(e) := i. For every e ii. \ . iii.

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(e) Withdrawal step: Otherwise . . , ( ̃ , ), replace ̃ by the subsets in and define the price of the newly covered items as ( ̃ , ). Formally, do the following: , let price(e) := ( ̃ , ) i. For every e . ii. U := U \ iii. ( \ ̃ ) Return Z.

The EGAWW algorithm can be used for the problem of getting the optimal set of sub-queries by assigning the data items of the given client query to G and the data items of different options of sub-queries to F and the number data items in the client query to k, since there won’t be any restriction on size of sub-queries. The problem given in example 1 can be solved by substituting G as {v1, v2, v3}, F as { {v1, v3}, {v2}, { v3}, {v1, v2}}. The main difference between Greedy algorithm and this algorithm is withdrawal step. In each iteration, a sub-set S is withdrawn from the solution. This step improves the performance of the algorithm. Since the size of the U decreases until U= Ф.So, the number of iterations is at most k. Each iteration takes at most |F| + |F|k and because k is a constant, each iteration takes polynomial time. This is a Polynomial time algorithm for every value of k. [2] Now we consider the overall problem of selecting optimal set of sub-queries and dividing incoherency bound among them. In this algorithm we first get the optimal set of sub-quires without considering the conditions1 and 3. i.e., apply the EGAWW algorithm using the cost of subsets (pS) as Rquery1/3. Then allocate incoherency bound among them using condition1 and condition 3. In some situations optimal set of sub-queries may not satisfy conditions1 and 3. So, there should be a compromise between the query satisfiability and performance. We are selecting the sub-queries without considering the data incoherency bounds for the selected data aggregators. We correct that by applying the EGAWW algorithm using the cost of subset (pS) as

.

Where β is the tuning parameter .It is used to balance the objectives of both minimizing query execution cost and meeting the query coherency requirements. B is is the sumdiff of the sub-query [1]. incoherency bound of client query and

3 Conclusion Division of query into sub-queries and distribution of sub-queries over DA’s is the key issue for improving performance of dynamic data dissemination networks and it is solved by using a better algorithm for obtaining better query plan selection. In this paper a new approximation algorithm, EGAWW, is proposed. In this paper optimal execution of continuous queries by minimizing query execution cost is considered. Optimization of continuous queries by maximizing gain of executing the query using sub-queries also can be done in the same way using the EGAWW algorithm.

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References [1] Gupta, R., Ramamritham, K.: Optimized Query Planning of Continuous Aggregation Queries in Dynamic Data Dissemination Networks. In: WWW 2007, Banff, Alberta, Canada, May 8-12 (2007) [2] Hassin, R., Levin, A.: A Better-Than-Greedy Approximation Algorithm for the Minimum Set Cover Problem. SIAM Journal on Computing 35(1) (2005) [3] Shah, S., Ramamritham, K., Shenoy, P.: Resilient and Coherence preserving Dissemination of Dynamic Data using Cooperating Peers. IEEE Transactions on Knowledge and Data Engineering 16(7) (July 2004) [4] Vander Meer, D., Datta, A., Dutta, K., Thomas, H., Ramamritham, K.: Proxy-Based Acceleration of Dynamically Generated Content on the World Wide Web. ACM Transactions on Database Systems (TODS) 29 (June 2004) [5] Ramamritham, K.: Maintaining Coherent Views over Dynamic Distributed Data. In: Janowski, T., Mohanty, H. (eds.) Distributed Computing and Internet Technology. LNCS, vol. 5966, pp. 13–20. Springer, Heidelberg (2010) [6] Shah, S., Ramamritham, K.: Handling Non-Linear Polynimial Queries over Dynamic Data. In: Proc. of IEEE International Conference on Data Engineering (April 2008)

Formal Verification of IEEE802.16m PKMv3 Protocol Using CasperFDR K.V. Krishnam Raju1, V. Valli Kumari2, N. Sandeep Varma2, and K.V.S.V.N. Raju2 1

Dept. of Computer Science Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India 2 Dept. of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India, 5300 03 {kvkraju.srkr,vallikumari,snvarma9,kvsvn.raju}@gmail.com

Abstract. IEEE 802.16m is the standard representing the security architecture for multi hop relay of broadband wireless access. The security sublayer is provided within IEEE 802.16m MAC layer for privacy and access control, which includes the Privacy and Key Management (PKM) protocol. This paper models the PKMv3 key agreement protocol using CasperFDR and analyzes the output. A few Attacks are found in this version. The specifications through which these attacks are found are presented. Keywords: Verification, CasperFDR, PKMv3.

1 Introduction IEEE 802.16 is the standard for specifying the air interface of Wireless Metropolitan Area Network (Wireless MAN). IEEE 802.16-2004 is sometimes also referred as WiMAX. IEEE 802.16e [1] adds mobility functionality to Broadband Wireless Access (BWA) and is also known as mobile WiMAX. The IEEE 802.16m [6] standard specifies a security sub layer at the bottom of the MAC layer, to provide initial key agreement between Mobile Stations (MS) and Base Station (BS) and key information transfer between Base Station (BS) and Relay Station (RS)during multi hop relay. There are two component protocols in the security sub layer: an Encapsulation protocol and Privacy Key Management protocol. The Encapsulation protocol is used for encrypting packet data across BWA. The Privacy and Key Management protocol is used for secure distribution of keying information from the BS to the RS. In IEEE 802.16m a new protocol called PKM version 3 (PKMv3) is proposed, it adds an extensible framework for previous PKM protocols for supporting key agreement in multi hop relay for broad band wireless access. In this paper we analysed key information exchange between MS and RS and attacks are found. Modeling and analysis of security protocols with Communicating Sequential Process (CSP) and Failure Divergence Refinement (FDR) have been proven to be effective and have helped the research community find attacks in several protocols. Lowe V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 590–595, 2010. © Springer-Verlag Berlin Heidelberg 2010

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thus designed Casper [5], which takes more abstract descriptions of protocols as input and translates them into CSP. CSP was first described by Hoare in [2] [3], and has been applied in many fields. First, we formally model and analyze the PKMv3 protocol with CasperFDR. Next, we use CasperFDR to show that there are no other known attacks on PKMv3 protocol. The rest of the paper is organized as follows. Section II deals with related work. In Section III, PKMv3 is modeled with CasperFDR and is analyzed and finally we conclude in Section IV.

2 Related Work Johnson and Walker are among the first researchers who discussed the security issues in IEEE 802.16 [4]. They proposed to enhance PKMv1 protocols with mutual authentication to enable the SS to authenticate the BS as well as the BS authenticating the SS, and with the addition of nonce’s to counter replay attacks. Sen Xu, Chin-Tser Huang, Manton M. Matthews [7], have analyzed security issues on the PKMv1, PKMv2 protocols using Casper FDR and proposed solutions. Following that, PKMv3 was proposed in the 802.16m standard. To our knowledge we did not find much research on this protocol till now.

3 Modeling and Analysis of PKMv3 Protocol 3.1 IEEE 802.16m PKMv3 Protocol Structure The above message sequence for the protocol PKMv3 is based on [6]. After initial or Re-Authentication between Mobile Station(MS) and Relay Station(RS) an Authentication Accounting and Auditing Server (AAA Server) begins key information exchange between RS and MS by sending Base station nonce value(Nb) to RS. Now RS sends Nb to MS. Subsequently MS sends its identification information (MSID), Nb and newly generated nonce value (Nm) to RS by encrypting with Cipher-based Message Authentication code (CMAC) key. Since RS cannot decrypt this message until it derives CMAC keys from Authorization Key (AK) that is received from AAA Server.

Message 1. S → RS : Message 2. RS → MS : Message 3. MS → RS : Message 4. RS → S : Message 5. S → RS : Message 6. RS → MS :

Nb Nb MSID, Nb, Nm MSID, Nm AK Nb, Nm

Fig. 1. PKMv3 key agreement Protocol [6]

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For receiving AK from AAA Server the RS sends MSID and Nm values to AAA Server. After receiving these values from RS the AAA Server sends AK to RS. Now the RS generates CMAC keys and decrypts the message that was previously received from MS. Finally RS sends information containing Nb and Nm by encrypting with CMAC key. After receiving this information by MS both MS and RS generates Transmission Encryption Key (TEK) and starts the communication. 3.2 Modeling PKMv3 Protocol in CasperFDR The modeled PKMv3 key agreement protocol in CasperFDR is shown in Fig.2. In the specification the initiator MS and Responder RS represents Mobile Station and Relay station. Sam(S) represents AAA Server. 3.3 Analysis of PKMv3 Key Agreement Protocol with CasperFDR After compiling and checking the above model in CasperFDR tool, attacks are found for two of the seven properties declared in specification part in Fig.2. Out of six property1 and property2 are related to secret specifications, property3 to property6 are related to authentication specifications. No attacks are found for secret specifications in the specification part, but CasperFDR tool found two attacks on authentication specifications in the specification part. The top level trace generated by CasperFDR is Relay Station believes that it is running the protocol, taking role RESPONDER, with Mobile Station, using data items Nb, Nm. Mobile Station also believes that it is running the protocol. An attack on the property4 Agreement (rs, ms, [nb, nm]) as shown in Fig.3 can be explained in the following steps. I. Initially the intruder acts as a relay station (I_RelayStation) when the Server sends Nb value to RS as shown in the above messages 0 and first instance of message 1 of Fig.3. II. During the second instance of message 1 the intruder acts as server and sends Nb value to RS. III. The intruder also acts as Mobile Station (I_MobileStation) and receives information from RS. IV. Now the intruder acts as a legitimate Mobile Station during the 4th message instances. V. However the communication between Relay Station and Server is also captured by the intruder by using man-in-the-middle attack. The top level trace generated by CasperFDR is Relay Station believes that it is running the protocol, taking role RESPONDER, with Mobile Station, using data items Nb, Nm. Mobile Station also believes that it is running the protocol. After receiving the entire information the intruder will be in a position such that it can derive CMAC and TEK’s. After performing these operations not only RS and MS but the Intruder can also derive CMAC key from AK and subsequently can generate TEK that is the key used between RS and MS for message encryption.

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#Free variables ms, rs : Agent s : Server nm, nb : Nonce krm, ka, kc : SessionKey ServerKey : Agent ĺ ServerKeys InverseKeys = (krm, krm), (ka, ka), (ServerKey, ServerKey), (kc, kc) #Processes INITIATOR(ms,s,nm,nb,krm,kc) knows ServerKey(ms) RESPONDER(rs,nm,nb,krm,kc) knows ServerKey(rs) SERVER(s,nb,ka,krm) knows ServerKey(rs), ServerKey(ms) #Protocol description 0. ĺ s : rs [rs != s] 1. s ĺ rs : {nb}{ServerKey(rs)} 2. ĺ rs : ms [rs != ms] 3. rs ĺ ms : {nb}{krm} 4. ms ĺ rs : {{ms,nb,nm}{kc}%v}{krm},{ms,nm}{krm} 5. rs ĺ s : {ms,nm}{ServerKey(rs)} 6. s ĺ rs : {ka}{ServerKey(rs)} 7. rs ĺ ms : v%{ms,nb,nm}{kc} #Specification Secret(s, nb, [rs]) Secret(rs, nb, [ms]) Agreement(s, rs, [nb,ka]) Agreement(rs, ms, [nb, ns]) Agreement(ms, rs, [ms, nm,nb]) Agreement(rs, s, [ms, nm]) #Actual variables MobileStation, RelayStation, Mallory : Agent Sam : Server Krm, Ka, Kc : SessionKey Nm : Nonce Nb : Nonce Ni : Nonce InverseKeys = (Krm, Krm), (Ka, Ka), (Kc, Kc) #Inline functions symbolic ServerKey #System INITIATOR(MobileStation, Sam, Nm, Nb, Krm, Kc) RESPONDER(RelayStation, Nm, Nb, Krm, Kc) SERVER(Sam, Nb, Ka, Krm) #Intruder Information Intruder = Mallory IntruderKnowledge = {MobileStation, RelayStation, Mallory, Sam, Ni, ServerKey(Mallory)}

Fig. 2. PKMv3 key agreement Protocol Specification

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0. 1. 1. 2. 3. 3. 4. 4. 5. 5. 6. 6. 7. 7.

-> Sam : RelayStation Sam -> I_RelayStation : {Nb}{ServerKey(RelayStation)} I_Sam -> RelayStation : {Nb}{ServerKey(RelayStation)} -> RelayStation : MobileStation RelayStation -> I_MobileStation : {Nb}{Krm} I_MobileStation -> MobileStation : {Nb}{Krm} MobileStation -> I_MobileStation : {{MobileStation, Nb, Nm}}{Krm}, {MobileStation, Nm}{Krm} I_MobileStation -> MobileStation : {{MobileStation, Nb, Nm}}{Krm}, {MobileStation, Nm}{Krm} RelayStation -> I_Sam : {MobileStation, Nm}\ {ServerKey (RelayStation)} I_RelayStation -> Sam : {MobileStation, Nm}\ {ServerKey (RelayStation)} Sam -> I_RelayStation : {Ka}{ServerKey(RelayStation)} I_Sam -> RelayStation : {Ka}{ServerKey(RelayStation)} RelayStation -> I_MobileStation : {MobileStation, Nb, Nm}{Kc} I_MobileStation -> MobileStation : {MobileStation, Nb, Nm}{Kc} Fig. 3. Attack generated by CasperFDR for property4

0. 1. 1. 2. 3. 3. 4. 4. 5. 5. 6. 6. 7.

-> Sam : RelayStation Sam -> I_RelayStation : {Nb}{ServerKey(RelayStation)} I_Sam -> RelayStation : {Nb}{ServerKey(RelayStation)} -> RelayStation : MobileStation RelayStation -> I_MobileStation : {Nb}{Krm} I_Mallory -> MobileStation : {Nb}{Krm} MobileStation -> I_Mallory : {{MobileStation, Nb, Nm}}{Krm}, {MobileStation, Nm}{Krm} I_MobileStation -> Relay Station : {{MobileStation, Nb, Nm}}{Krm}, {MobileStation, Nm}{Krm} RelayStation -> I_Sam : {MobileStation,Ns}{ServerKey(RelayStation)} I_RelayStation -> Sam : {MobileStation,Ns}{ServerKey(RelayStation)} Sam -> I_RelayStation : {Ka}{ServerKey(RelayStation)} I_Sam -> RelayStation : {Ka}{ServerKey(RelayStation)} RelayStation -> I_MobileStation : {MobileStation, Nb, Nm}{Kc} Fig. 4. Attack generated by CasperFDR for property5

From the Fig.4 it is found that there is another attack on property5 and there are no attacks on remaining properties on specification part in Fig.2. The attack on the property5 Agreement (ms, rs, [ms, ns, nb]) can be explained with the message sequence seen in Fig.4. I. Initially the intruder acts as a relay station (I_RelayStation) when the Server sends Nb value to RS as shown in the above messages 0 and first instance of message 1 of Fig.4. II. During the second instance of message 1 the intruder acts as server and sends Nb value to RS.

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III. The Intruder Mallory directly communicates with the MobileStation in message 3. IV. Now the intruder acts as a legitimate Mobile Station during the 4th message instances. V. However the communication between Relay Station and Server is also captured by the intruder by using man-in-the-middle attack. VI. After receiving the entire information the intruder will be in a position such that it can derive CMAC and TEK’s. After performing these operations not only RS and MS but the Intruder can also derive CMAC key from AK and subsequently they generate TEK that is the key used between RS and MS for message encryption.

4 Conclusions and Future Work In this paper, the PKMv3 protocol is modeled using CasperFDR. The compilation was done with CasperFDR. Attacks are found in this version. The attacks are interpreted by CasperFDR and the message sequence results are reported. In future we will fix the attacks found in the IEEE 802.16m PKMv3 protocol.

References 1. IEEE std 802.16e–2005: Air interface for fixed broadband wireless access system – amendment: Physical and medium access control layers for combined fixed and mobile operation in licensed bands. IEEE, Los Alamitos (2006) 2. Hoare, C.A.R.: Communicating sequential processes. Communications of ACM 21(8), 666– 677 (1978) 3. Hoare, C.A.R. (ed.): Communicating Sequential Processes. Prentice Hall International, Englewood Cliffs (1985) 4. Johnston, D., Walker, J.: Overview of IEEE 802.16 security. IEEE Security & Privacy (2004) 5. Lowe, G.: Casper: A compiler for the analysis of security protocols. Journal of Computer Security 6, 53–84 (1998) 6. IEEE 802.16 Broadband Wireless Access Working Group. IEEE std 802.16m–09_2057, http://ieee802.org/16 7. Xu, S., Matthews, M.M., Huang, C.-T.: Modeling and Analysis of IEEE 802.16 PKM Protocols using CasperFDR. In: IEEE ISWCS (2008)

Digital Image Steganography Based on Combination of DCT and DWT Vijay Kumar1 and Dinesh Kumar2 1

CSE Department, JCDMCOE, Sirsa, Haryana, India [email protected] 2 CSE Department, GJUS&T, Hisar, Haryana, India [email protected]

Abstract. In this paper, a copyright protection scheme that combines the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is proposed. The proposed scheme first extracts the DCT coefficients of secret image by applying DCT. After that, image features are extracted from cover image and from DCT coefficients by applying DWT on both separately. Hiding of extracted features of DCT coefficients in the features of cover image is done using two different secret keys. Experimentation has been done using eight different attacks. Experimental results demonstrate that combining the two transforms improves the performance of the steganography technique in terms of PSNR value and the performance is better as compared to that achieved using DWT transform only. The extracted image has good visual quality also. Keywords: Discrete Cosine Transform, Discrete Wavelet Transform, Steganography.

1 Introduction The rapid growth of multimedia processing and Internet technologies in recent years has made it possible to distribute and exchange huge amount of multimedia data more easily and quickly than ever at low cost. The data can be easily edited with almost negligible loss using multimedia processing techniques. Therefore, the need for the copyright protection of digital data has emerged. Nowadays, Steganography has become the focus of research for copyright protection. There are two approaches related to steganography i.e., spatial-domain and frequency-domain approach [13]. In the former approach, the secret messages are embedded into least significant pixels of cover images. They are fast but sensitive to image processing attacks. The latter approach contains transforming the cover image into the frequency domain coefficients before embedding secret messages in it. The transformation can be either Discrete Cosine transform (DCT) or Discrete Wavelet Transform (DWT) etc. Though these methods are more difficult and slower than spatial domain, yet they have an advantage of being more secure and noise tolerant [12]. Among these methods, DWT has been widely used in digital image steganography due to its multi-resolution characteristics. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 596–601, 2010. © Springer-Verlag Berlin Heidelberg 2010

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In this paper, steganography based on combination of two transforms DWT and DCT has been described. The proposed technique showed high robustness against many image processing attacks. The remainder of this paper is organized as follows. Section 2 presents the related work. Section 3 presents the proposed DCT-DWT based digital image steganography approach. Section 4 shows the experimentation and results followed by conclusions in section 5.

2 Related Work Least Significant Bit Substitution (LSB) [1] is the most commonly used steganography technique. Sinha and Singh [2] proposed a technique to encrypt an image for secured transmission using digital signature of the image. Digital signatures enable the recipient of a message to verify the sender of a message and validate that the message is intact. In [3], a spatial domain approach, the authors proposed the exploitation of correlation between adjoining pixels for determining the bit number to be embedded at certain specific pixel. In [4], a frequency domain approach, the authors proposed that embedding is realized in bit planes of subband wavelet coefficients obtained by using the Integer Wavelet Transform. In [5], authors proposed an algorithm that utilized the probability density function to generate discriminator features fed into a neural network system which detected hidden data in this domain. Tsuang-Yuan et al. [6] proposed a new method for data hiding in Microsoft word documents by a change tracking technique. Kisik et al. [7] proposed a stegnographic algorithm which embeded a secret message into bitmap images and palette-based images. The algorithm divided a bitmap image into bit plane images from LSB-plane to MSB-plane for each pixel. Satish et al. [8] proposed a chaos based spread spectrum image steganography method. The majority of LSB steganography algorithm embed message in spatial domain such as pixel value differencing [9]. McKeon [10] proposed a methodology for steganography based on fourier domain of an image by using the properties of zero-padding. These zeros can be changed slightly where the change in the image is not noticeable. In [11], authors discussed the effects of steganography in different image formats and DWT. They also introduced the number of payload bits and the place to embed. In [14], authors proposed method to spread hidden information within encrypted image data randomly based on the secret key. Li et al. [17] proposed loseless data hiding using difference value of adjacent pixels instead of the whole image. Tsai et al. [15] divide the image into blocks where redual image was calculated using linear prediction. Then, the secret data was embedded into the residual values, followed by block reconstruction. Chao et al. [16] presented the embedding scheme that hide secret messages in the vertices of 3D polygen models.

3 DWT-DCT Based Digital Image Steganography Approach In this paper, we combine the algorithm [12] with Discrete Cosine Transform (DCT). The proposed algorithm is as follows.

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3.1 Embedding Procedure The steps of embedding procedure are as follows: 1. 2.

3.

Apply DCT to the secret image S to get DCT coefficients. Decompose the cover image (I matrix) and the DCT coefficients of secret image into four sub-images (ICA, ICH, ICV, ICD) and (CCA, CCH, CCV, CCD) respectively using DWT. Each of CCA, ICA, and ICH are partitioned into blocks of 4 × 4 pixels and can be represented by:

CCA = {BS i ,1 ≤ i ≤ ns}

(1)

ICA = {BC j ,1 ≤ j ≤ nc}

(2)

ICH = {BH k ,1 ≤ k ≤ nc}

(3)

where BS i , BC j ,and BHk represent the i

th

block in CCA, the j

th

block in

th

4.

ICA and the k block in ICH respectively. ns is the total number of the 4 × 4 blocks in CCA and nc is the total number of the 4 × 4 blocks in each of ICA and ICH. For each block BS i in CCA, the best matched block BC j of minimum error in

5.

ICA is searched by using the root mean squared error (RMSE).The first secret key K1 consists of the addresses j of the best matched blocks in ICA. Calculate the error block EBi between BS i and BC j as follows:

EBi = BC j − BS i 6.

For each error block EBi , the best matched block using the RMSE criteria as before and that

7. 8.

(4)

BH k in ICH is searched for

BH k is replaced with the error

block EBi . The second secret key K2 consists of the addresses k of the best matched blocks in ICH. Repeat the steps 4 to 6 until all the produced error blocks are embedded in ICH. Apply the inverse DWT to the ICA, ICV, ICD, and the modified sub-image ICH to obtain the stegano-image G.

3.2 Extraction Procedure

The steps of secret image extraction procedure are as follows: 1.

Decompose the stegano-image G into four sub-images (GCA, GCH, GCV, GCD) using DWT transform.

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Extract the block BC j from the sub-image GCA by using the first secret key K1. Use the second secret key K2 to extract the error blocks. The secret blocks BS i can be obtained by:

BS i = BC j − EBi 3. 4. 5.

(5)

Repeat step 2 until all the secret blocks are extracted and form the sub-image CCA. Using detail coefficients from sender such as CCD, CCV, CCH and extracted CCA from above step, apply the inverse DWT to obtain the DCT coefficients. Apply the inverse DCT on DCT coefficients obtained from step 4.

4 Experimentation and Results 4.1 Experiment 1 and Results

We evaluate the performance of the combined DCT-DWT based digital image steganography using four cover images: Peppers, Lena, Goldhill and Boat, each of size 256 × 256 and four secret images: Redfort, Watch, C.V. Raman and Taj Mahal, each of size 128 × 128. Figure 1 shows all the secret images.

(a)

(b)

(c)

(d)

Fig. 1. Secret images (a) Redfort (b) Watch (c) Raman (d) Taj Mahal

We compare the two techniques, LSB [1] and Ahmed A. Abdelwahab [12] with proposed method using above mentioned four cover images and Redfort as secret image. The PSNR values of stegano-images after embedding secret image for above said methods are tabulated in table 1. The results reveal that proposed method has higher PSNR value than the other two methods. Table 1. Comparison between LSB [1], Ahmed [12] and Proposed methods in terms of PSNR using Redfort as secret image Image Cover Image (256x256) Peppers Lena Goldhill Boat

LSB[1]

PSNR Ahmed[12]

10.75 09.66 11.16 12.91

31.59 31.86 31.86 32.37

Proposed method 42.09 41.93 41.84 42.45

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4.2 Experiment 2 and Results

The next experiment was performed to see the effect of chosen attacks such as Gaussian Noise, Sharpening, Median Filtering, Gaussian Blur, Histogram Equalization, Gamma Correction, Transform and Cropping. The PSNR values for four different stegano images and extracted secret images after different image processing attacks are illustrated in table 2. The results reveal that after applying attacks on stegano images, the secret images have high value of PSNR and hence the good visual quality. Table 2. PSNR of stegano-images and extracted secret images under different image processinh attacks Image G. Noise Stegano-Peppers 19.72 Extract-Redfort 19.50 Stegano-Lena 19.89 Extract-Watch 19.37 Stegano-Goldhill 18.69 Extract-Raman 18.66 Stegano-Boat 19.61 Extract-Taj 15.52

Sharp. 17.15 17.10 13.65 31.01 11.95 31.64 16.10 22.05

PSNR with different attacks Hist. G. Blur Gamm. TransEqul. Corr. form 20.01 26.19 52.80 11.75 13.26 25.36 12.44 38.92 16.25 25.42 40.48 11.78 27.08 37.65 36.34 40.03 16.51 25.06 25.54 12.61 30.06 32.68 20.19 38.66 16.16 19.61 38.42 13.26 20.12 15.52 12.35 38.23

Median Filter. 27.61 23.32 25.37 37.27 24.87 32.38 26.63 21.19

Crop. 08.69 09.51 08.70 30.64 10.07 16.97 08.83 15.84

5 Conclusion This paper presented a digital image steganography technique in which DCT was combined with DWT. The experimentation was done using different attacks. The simulation results depict that there is substantial increase in the PSNR value of the stegano images. Further a comparison of proposed technique with the earlier existing techniques [1] and [12] establishes supremacy of the proposed algorithm.

References 1. Chan, C.K., Chang, L.M.: Hiding data in image by simple LSB substitution. Pattern Recognition 37, 469–471 (2003) 2. Sinha, A., Singh, K.: A technique for image encryption using digital signature. Optics. Communications 218(4), 229–234 (2003) 3. Chang, C.C., Tseng, H.W.: A Steganographic method for digital images using side match. Pattern Recognition 25, 1431–1437 (2004) 4. Tarres, S., Nakano, M., Perez, H.: An Image Steganography Systems based on BPCS and IWT. In: 16th International Conference on Electronics, Communications and Computers, pp. 51–56 (2006) 5. Manikopoulos, C., Yun-Qing, S., Sui, S., Dekun, Z.: Detection of block DCT based stegnography in gray scale images. In: IEEE Workshop on Multimedia Signal Processing, pp. 355–358 (2002)

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6. Tsung-Yuan, L., Wen-Hsiang, T.: A New Steganography method for data hiding in Microsoft Word documents by a Change Tracking Technique. IEEE Transaction on Information Forensics and Security 2(1), 24–30 (2007) 7. Kisik Chang, E., Chango, J., Sangjin, L.: High Quality Perceptual Steganography Techniques. In: Kalker, T., Cox, I., Ro, Y.M. (eds.) IWDW 2003. LNCS, vol. 2939, pp. 518–531. Springer, Heidelberg (2004) 8. Satish, K., Jayakar, T., Tobin, C., Madhavi, K., Murali, K.: Chaos based spread spectrum image steganography. IEEE transactions on consumer Electronics 50(2), 587–590 (2004) 9. Zhang, X., Wang, S.Z.: Vulnerability of pixel-value differencing steganography to histogram analysis and modification for enhanced security. Pattern Recognition, 331–339 (2004) 10. McKeon, R.T.: Steganography Using the Fourier Transform and Zero-Padding Aliasing Properties. In: IEEE International Conference on Electro/Information Technology, pp. 492–497 (2006) 11. Mastronadri, G., Castellano, M., Marino, F.: Steganography Effects in various Formats of Images-A preliminary study. In: International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 116–119 (2001) 12. Abdelwahab, Ahmed, A., Hassan, Lobha, A.: A Discrete Wavelet Transform based Technique for Image Data Hiding. In: National Radio Conference, Egypt, pp. 1–9 (2008) 13. Chang, C.C., Chen, T.S., Chang, L.Z.: A Steganographic method based upon JPEG and Quantization table modification. Information Science 141, 123–138 (2002) 14. Younes, M.B., Jantan, A.: A New Steganography approach for image encryption exchange by using the Least Significant Bit Insertion. Computer Science and Network Security 8, 247–253 (2008) 15. Tsai, P., Hu, Y.C., Yeh, H.L.: Reversible image hiding scheme using predictive coding and histogram shifting. Signal Processing 89(6), 1129–1143 (2009) 16. Chao, M.W., Lin, C.H., Yu, C.W., Lee, T.Y.: A high capcity 3D steganography algorithm. IEEE Trnasctions on Visualtion and computer Graphics 15(2), 274–284 (2009) 17. Li, Z., Chen, X., Pan, X., Zeng, X.: Lossless data hiding scheme based on adjacent pixel difference. In: International Conference on Computer Engineering and Technology, pp. 588–592 (2009)

Mobile Robot Pose Estimation Based on Particle Filters for Multi-dimensional State Spaces J. Divya Udayan 1, T. Gireesh Kumar 2, Roshy M. John3, K.J. Poornaselvan4, and S.A. Lakshmanan5 1,5

Dept. of Electrical & Electronics, Amrita Vishwa Vidyapeetham, Coimbatore, India [email protected] 2 Dept. of Information Technology, Amrita Vishwa Vidyapeetham, Coimbatore, India [email protected] 3 Dept. of Instrumentation & Control, Wipro Technologies,Banglore, India [email protected] 4 Dept. of Electrical & Electronics, Govt. College of Technology, Coimbatore, Tamilnadu, India [email protected]

Abstract. Perception and pose estimation are still some of the key challenges in the area of robotics, and hence the basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measurements to localize itself with respect to a global reference frame. For this purpose the odometric values or the sensor measurements have to be fused together by means of particle filters. Earlier particle filters were limited to low-dimensional estimation problems, such as robot localization in known environments. More recently, particle filters are used in spaces with as many as 100,000 dimensions. This paper presents some of the recent innovations on the use of particle filters in robotics. Keywords: Particle filters, Localization, Sensor fusion, Pose estimation.

1 Introduction This paper surveys some of the recent developments and points out some of the opportunities and pitfalls specific to robotic problem domain. Particle filters [4],[7] comprise a broad family of sequential Monte Carlo algorithms for approximate inference in partially observable Markov chains [2]. Early successes of particle filter implementations can be found in the area of robot localization, in which the robot’s pose has to be recovered from sensor data. These advances have led to a critical increase in the robustness of mobile robots, and the localization problem is now widely considered to be solved. However, a range of problems still exist with probabilistic techniques. In robotics, all models lack important state variables that systematically affect sensors and actuator noise. This paper is organized in the following way. Particle filters are described in Section 2. Particle filters in low-dimensional spaces reported in Section 3. Section 4 contains description of particle filters in high-dimensional state spaces, followed by the detailed discussion in Section 5. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 602–605, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Particle Filters Particle filters are approximate techniques for calculating posteriors in partially observable controllable Markov chains with discrete time. Suppose the state of the Markov chain at time k is given by xk where xk is the state vector describing the location and orientation of the robot, uk is the control vector at time k - 1 to drive the robot to a state xk at time k. mi is a vector describing the location of the ith landmark whose true location is assumed time invariant, zik is an observation taken from the robot of the location of the ith landmark at time k (Fig. 1).

Fig. 1. Black path is the planned trajectory, the red path is the realized trajectory: (a) localization with only odometric measures (b) localization with particle filters

3 Particle Filters in Low – Dimensional State Space Particle filters are commonly known as Monte Carlo Localization [1]. MCL has been implemented with as few as 100 samples as shown in Fig.3. The convergence of landmark uncertainty is shown in Fig.2. The Monte Carlo method is just one of the many methods for analyzing uncertainty propagation, where the goal is to determine how random variation, lack of knowledge, or error affects the sensitivity, performance, or reliability of the system that is being modelled.

Fig. 2. The convergence of landmark uncertainty. The plot shows the comparison of estimated distribution of localization uncertainty to observed distribution of localization uncertainty.

Fig. 3 (a). Distribution of observed standard deviations in the localization estimate (b) Distribution of estimated standard deviations in the localization estimate

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4 Particle Filters in High-Dimensional State Spaces Simultaneous localization and mapping problem (SLAM) as shown in Fig. 4 addresses the problem of building a map of the environment with a moving robot [3]. The absence of an initial map in the SLAM problem makes it impossible to localize the robot during mapping using algorithms like MCL. Furthermore, the robot faces a challenging data association problem (Fig.5) determining whether two environment features, observed at different point in time correspond to the same physical feature in the environment. The solution to the above stated problem was based on Extended Kalman Filters or EKFs. The EKF techniques are based on some fixed values of the input and measurement noise covariance matrices. Recent research [5],[6],[8] has led to a family of so-called Rao-Blackwellized particle filters that in the context of SLAM, lead to solutions that are significantly more efficient than the EKF. These particle filters require time of O (n log n) instead of O (n2) where m is the number of particles and n is the number of features.

Fig. 4. The structure of SLAM problem. A simultaneous estimate of both robot and landmark locations is required. The thin solid line shows the observed values and thick solid lines shows the estimated values of robot motion.

Fig. 5. Data association graph represents associations possible between individual nodes. The edges indicate compatible associations and the clique is a set of mutually compatible associations. (eg. , the clique 2,6,10 implies that associations a1- > b2, a2 - > b3, a4 - > b1 may coexist.

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5 Discussion This paper has described some of the recent successes of particle filters in the field of robotics. Earlier, particle filters were applied to low-dimensional state spaces. Recently, particle filters have provided new solutions to challenging higherdimensional problems such as the problem of multi-robot tracking. Despite this progress, there exist plenty of opportunities for future research. Appearance and pose-based SLAM methods offer a radically new paradigm for mapping and location estimation without the need for strong geometric landmark descriptions.

References 1. Liu, J., Chen, R.: Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93, 1032–1044 (1998) 2. Pitt, M., Shephard, N.: Filtering via simulation: auxiliary particle filter. Journal of the American Statistical Association 94 (1999) 3. Gutmann, Burgard, W., Fox, D., Konolige, K.: An experimental comparison of localization methods. In: International Conference on Intelligent Robots and Systems (1998) 4. Doucet, A., de Freitas, J.F.G., Gordon, N.J. (eds.): Sequential Monte Carlo Methods In Practice. Springer, Heidelberg (2001) 5. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM.: A factored solution to the simultaneous localization and mapping problem. In: AAAI 2002 (2002) 6. Montemerlo, M., Whittaker, W., Thrun, S.: Conditional particle filters for simultaneous mobile robot localization and people-tracking. In: ICRA 2002 (2002) 7. Abrate, F., Bona, B., Indri, M.: Monte Carlo Localization of mini-rovers with low-cost IR sensors. In: IEEE/ASME international conference on Advanced intelligent mechatronics, pp.1–6 (2007) 8. Armesto, L., Ippoliti, G., Longhi, S., Tornero, J.: Probabilistic Self-Localization And Mapping. IEEE Robotics & Automation Magazine 11(2), 67–77 (2008)

Neural Networks Based Detection of Purpose Data in Text P. Kiran Mayee, Rajeev Sangal, and Soma Paul Language Technologies Research Centre, International Institute of Information Technology, Hyderabad, India [email protected],{sangal,soma}@iiit.ac.in

Abstract. Purpose is an inherent relation in artifact-related text. This information is available as a pair of components, called ‘purpose-action’ and ‘purpose-upon’ in text. This paper presents the Neural Networks as a possible solution to detecting the existence of ‘purpose_action’ in corpus. Two types of Neural Networks, i.e., RBF Networks and Multilayer Perceptron Neural Network have been tried and compared with a Naïve Bayes approach to detection. The corresponding results have been tabulated. The MLP method is found to be more efficient among the two. Keywords: Classification, RBF Network, Multilayer Perceptron Neural Network.

1 Introduction Semantic analysis is an essential component of any Natural Language Processing task. Therefore, there is nowadays a growing interest within the Natural Language Processing community on the extraction of semantic relations from large text volumes. One such relation of interest is the ‘purpose’ relation. Purpose is important because it is inherently the reason for existence of artifacts. It also determines why an artifact looks the way it does or has a particular set of physical characteristics. The actions that can be performed by an artifact are also influenced by its purpose. But, this vital information tends to be hidden in text in most natural languages. These facts point to a need to ascertain purpose information. The presence or absence of this information can be determined using certain cues or feature sets within text. These cues can be used to extract the purpose of artifacts from given corpora. 1.1 Previous Work Semantic relations have been studied in several fields. (White, 1990) provides an overview of theories within the fields of Philosophy and Psychology. In Cognitive Linguistics, one of the most important theories is Force Dynamics (Talmy, 2000). There have been many attempts in the computational linguistic communities to define and understand the Causality relation. Nastase in (10) defines causality as a general class of relations that describe how two occurrences influence each other. Further, the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 606–609, 2010. © Springer-Verlag Berlin Heidelberg 2010

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author proposes the following sub-relations of causality: cause, effect, purpose, entailment, enablement, detraction and prevention. Some results (Kang et al., 2004) have shown improvements by the use of semantic knowledge, where others have not. Some papers (Khoo et al., 2000) have focused on the medical domain. Some papers (Girju and Moldovan, 2002) have defined a set of semantic constraints to rank possible causations. Newer approaches use Machine Learning (ML) techniques (Girju, 2003; Chang and Choi, 2006). Those systems are more robust and yield higher performance, with F-measures around 0.8. In our work, we have used WordNet for purpose data expansion with tried and tested pattern extraction methodologies. On the other hand, there have been authors who have explored the use of neural networks as efficient methodologies for classification (Nabney, 1999). 1.3 Purpose as an Underlying Principle for Organizing Data One of the senses defined for the term 'purpose' in WordNet 2.1 is: 'what something is used for'. Example – 'the function of an auger is to bore holes'. The use of the term 'Purpose' in WordNet is rare ( polysemy count = 2). But, there exists rich implicit data on artifact purpose in WordNet. Purpose is something like – “A pen is used to write on paper”. It is therefore understood that the purpose of the pen or any other artifact is in the performance of an action. Therefore, artifacts are used in the fulfillment of some human need. ‘Write’ is a need. But then, one could write on the wall or on a paper as suggested by the sentence or write on a bench. Depending on the base on which the function is performed, one would probably have a different artifact. For example one would probably write on the wall with a paint-brush or an airbrush, all other parameters remaining the same. It is therefore imperative that all artifact purposes should be explained by the two-pair template consisting of ‘action’ and ‘action_upon’.

2 Approach We approach the problem top-down, namely identify and study first the distinguishing patterns of each linguistic pattern, and then develop models for their semantic classification. The distribution of the semantic relations is studied across different NP patterns and the similarities and differences in the relations is studied across different NP patterns. The standard RBF network with regression analysis, and ten-fold cross-validation was used. The architecture of the multilayer perceptron neural network perceptron network used had three layers, with 182 neurons in the input layer, 2 neurons in the hidden layer and 2 neurons in the output layer. The number of neurons in the hidden layer was determined by network size evaluation using 4-fold cross-validation. An optimal size of 2 neurons was obtained for the least misclassification of 28.05%. The logistic function was used as the hidden and output layer activation function. The type of analysis used is classification. The type of validation used was the 10-fold cross-validation.nd the similarities and differences among resulting semantic spaces are analyzed.

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3 Tools and Data Used We have searched the web for corpora rich in artifact data. We zeroed in on the WordNet artifact corpus, since it is not only rich in artifact data, but 70% of the data was found to contain purpose information. A total of 10,000 sentences were picked at random, out of which sentences containing four types of cues were extracted. We, thus, had 4 sets of data for testing and comparison. The data set contained 4460 positive and 5547 negative examples. The DTREG and Weka tools were used to obtain the implementation results. The corresponding statistics were then tabulated and efficiency of the method, computed.

4 Results The RBF neural network was constructed with a clustering seed value 1, with a mean standard deviation of 0.1, two clusters (since this is a binary classification problem) and ridge value of 1.0E-8. Similarly, the mlp neural network was constructed with autobuild, one hidden layer (to enable comparison with RBF network), learning rate as 0.3 (the standard value), standard momentum (0.2) and validation threshold as 20. The confusion matrix for the multilayer perceptron neural network method is presented in table 1. Table 1. Comparison of Results of the RBF, MLP and Naive Bayes Approaches Method

RBF

MLP

Naïve Bayes 2786

True Positives

2598

3914

True Negatives

4327

3924

1579

False Positives

1767

546

4338

False Negatives

1220

Precision F-Measure

1623

1196

0.716

0.78

0.733

0.83

0.87

0.688

The confusion matrix for the RBF Network with 10-fold cross validation is presented in column1 of table 1. Among the three possible methods of purpose detection, it is found that the MLP method has the highest accuracy for true positives, followed by the Naïve Bayes method. For the RBF network, the accuracy was higher for true negatives . The same observation also holds for the false positives. From these results, we might conclude that the RBF network may not always be the best possible choice for purpose data detection in text. The precision values for the two methods were found to be 0.716 and 0.78 respectively. On the other hand, the Fmeasure for MLP neural network was higher (0.87) compared to the same for RBF network (0.83).

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5 Conclusions In this paper, Neural Networks have been suggested as a possible method to extract purpose data from text. Purpose relations have been determined using the RBF and Multilayer Perceptron methods and the corresponding efficiencies have been compared. The efficiency of the MLP neural network method has been found to be higher at 0.78, favoring the use of this type of classification.

6 Future Scope Additional features may also be detected by this approach. The extraction of purpose data may also be implemented using some of the other semi-supervised and unsupervised machine learning approaches. Newer approaches may be suggested for tackling cases such as ‘action_upon’. A priority scheme needs to be formulated for the ‘multiple’ result cases.

References 1. Girju, R.: Automatic detection of causal relations for question answering. In: Proceedings of the 41st ACL, Workshop on Multilingual Summarization and Question Answering (2003) 2. Ghanem, M.M., Guo, Y., Lodhi, H., Zhang, Y.: Automatic Scientific Text Classification Using Local Patterns. In: KDD CUP 2002, Task 1 (2002) 3. Kaplan, R.M., Berry-Rogghe, G.: Knowledge based acquisition of causal relationships in text. Knowl. Acquis. 3(3), 317–337 (1991) 4. Khoo, C.S.G., Chan, S., Niu, Y.: Extracting causal knowledge from a medical database using graphical patterns. In: ACL 2000: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, Morristown, NJ, USA, pp. 336–343 (2000) 5. Kiran Mayee, P., Rajeev, S., Soma, P.: PurposeNet: An Ontological Resource. In: ICON 2008: Proceedings of the 8th International Conference on Natural Language Processing, CDAC, Pune (2008) 6. Nastase, V.: Semantic Relations Across Syntactic Levels. PhD Dissertation, University of Ottawa (2003) 7. Nabney, I.T.: efficient training of RBF Networks for classification. In: Proceedings of Ninth International Conference on artificial Neural Networks. Edinburg, UK 8. Yegnanarayana, B.: Artificial Neural Networks. PHI

Comparative Performance Analysis of QoS-Aware Routing on DSDV, AODV and DSR Protocols in MANETs Rajneesh Gujral1 and Anil Kapil2 1

Assoc. Professor, Computer Engineering Department, M. M. Engineering College, M. M. University, Ambala, India [email protected] 2 Professor, M.M. Institute of Computer Technology and Business Management, M. M. University, Ambala, India [email protected]

Abstract. Mobile ad hoc networks (MANETs) appear nowadays as one of the most promising architectures to flexibly provide multimedia services in multiple wireless scenarios. However, the dynamic nature of this environment complicates the supporting of the heavily demanded QoS. In this paper, an attempt has been made to performing the individual and comparative performance analysis of QoS-aware routing on proactive protocol (DSDV) and two prominent on-demand source initiated routing protocols: AODV and DSR protocols using network simulator NS-2.34.The performance matrix includes the following QoS parameters such as PDR (Packet Delivery Ratio), Throughput, End to End Delay and Routing overhead. We are also analyzing the effect in performance of QoS parameters on these routing protocols when packet size changes, when time interval between packet sending changes, when mobility of nodes changes. Keywords: QoS, Performance, Ad hoc Routing Protocols, NS-2.34.

1 Introduction Mobile ad hoc networks (MANETs) are self-created and self organized by a collection of mobile nodes, interconnected by multi-hop wireless paths in a strictly peer to peer fashion [1]. The increase in multimedia, military application traffic has led to extensive research focused on achieving QoS guarantees in current networks. The goal of QoS provisioning is to achieve a more deterministic network behaviors, so that information carried by the network can be better delivered and network resources can be better utilized. The QoS parameters differ from application to application e.g., in case of multimedia application bandwidth, delay jitter and delay are the key QoS parameters [2].After receiving a QoS service request the main challenges to maintain the mobile ad hoc network are: no central controlling authority, limited power ability, continuously maintain the information required to properly route traffic. Some studies [3],[4],[5],[6][7] evaluated the performance of the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 610–615, 2010. © Springer-Verlag Berlin Heidelberg 2010

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proposed routing algorithms. A detailed packet simulation comparative study of AODV,DSR,TORA and DSDV was reported in [3] highlighting the DSR and AODV achieve good performance at all mobility speed whereas DSDV and TORA perform poorly under high speeds and high load conditions respectively. In [7] three routing protocols were evaluated in a city traffic scenarios and it was shown that AODV outperforms both DSR and the proactive protocol FSR.A simulation study of AODV, DSR and OLSR was done in [4] and it was shown that AODV and DSR outperform OLSR at higher speeds and lower number of traffic streams and OLSR generates the lowest routing load. In this paper, an attempt has been made to perform the individual and comparative performance analysis of QoS-aware routing on proactive protocol (DSDV) and two prominent on-demand source initiated routing protocols: AODV and DSR protocols. Section 2 of this paper describes Simulation Model and Performance Metrics of the ad hoc routing protocols. Section 3 presents the comparative performance analysis of the study Section 4 concludes the paper and future works.

2 Simulation Model and Performance Metrics This section starts with a framework and overview of simulation techniques used for network performance evaluation and that is the most suitable technique to get more details that can be incorporate and less assumption is required compared to analytical modeling. 2.1 Performance Metrics In this paper, the performance matrix includes the following QoS parameters. Packet Delivery Ratio (PDR): also known as the ratio of the data packets delivered to the destinations to those generated by the CBR sources. The PDR shows how successful a protocol performs delivering packets from source to destination. The higher for the value give you the better results. PDR

=

∑ ∑

n

CBRrece 1 n

*

100

CBRsent 1

Average End to End Delay: Average End to End delay is the average time taken by a data packet to reach from source node to destination node. First we have calculated total delay by subtracting the time when packets was sent from the time when the packet was received. Then find the ratio of total delay to the number of packets received. n

Avg _ End

_ to _ End

_ Delay

=

∑

( CBRrecetim

e − CBRsenttim

1

e) * 100

n

∑

CBRrece

1

Throughput: Throughput is the ratio of total number of delivered or received data packets to the total duration of simulation time. Like, we start the packet sending at time 1 and finish at time 10 so total duration of simulation is 9 (10-1). This is calculated by calculating total number of received packets divided by 9 (simulation time).

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=

Throughput

∑

CBRrece

1

simulation

time

Normalized Protocol Overhead/ Routing Load: Routing Load is the ratio of total number of the routing packets to the total number of received data packets at destination. First we calculated total routing packets (RTR) then we have divided this total number of RTR by total data packets received at destination. Routing

_ Load

=

∑ RTRPacket ∑ CBRrece

2.2 Analysis of Trace File with Grep and Gawk Filters To filter the data from trace files, we have used simple grep and awk filter both. We have used grep command to count lines containing cbr and AGT and starting with S from AODV.tr file. Syntax is: $ cat AODV8.tr | grep AGT | grep cbr | grep ^s | wc-l awk is a good filter and a report writer. It is a pattern searching and processing language. We have written pd.awk filter to find Total Delay, Average End to End Delay and Packet Delivery Ratio (PDR). The syntax of command to find total delay, average delay and packet delivery ratio: $ gawk –f pd.awk AODV8.tr The simulation parameters are considered when building the TCL script and pd.awk code has shown in figure 1 and figure2. Parameters No of Node Simulation Time Environment Size Traffic Size Packet Size Queue Length Source Node Destination Node Mobility Model Antenna Type Simulator Mobility speed Packet Interval Operating System

Value 25 10 sec 1200x1200 CBR (Constant Bit Rate ) 100,500 and 1000 bytes 50 Node 0 Node 2 Random Waypoint Omni directional NS-2.34 500,1000,2000 m/s 0.015,0.15 and 1.5 sec Linux Enterprises version -5

Figure 1: Simulation Parameter

BEGIN { npacketsent=0; npacketrece=0; rtotaldelay=0.0; } { strEvent=$1; rTime=$2; strAgt=$4; idPacket=$6; strType=$7; if( strAgt =="AGT" && strType =="cbr"){ if(strEvent =="s") { npacketsent+=1; nSentTime[IdPacket]=rTime; } if(strEvent =="r") { npacketrece+=1; nReceivedTime[idPacket]= rTime; rtotaldelay+=nReceivedTime[idPacket]-nSentTime[idPacket]; } } } END { rTime=rEndTime-rStartTime; rpacketDeliveryRatio=(npacketrece/npacketsent)*100; if(npacketrece !=0) rAverageDelay=rtotaldelay/npacketrece; printf("total= %f",rtotaldelay); printf(" Averagedelay:%15.5f packetdeliveryratio :%10.6f" rtotaldelay,rAverageDelay,rpacketDeliveryRatio); }

Figure 2: AWK filter (pd.awk code)

3 Comparative Performance Analysis on DSDV, AODV and DSR Detail of analysis are focusing on Packet delivery Ratio, Average end to end delay, Throughput and Normalized Protocol Overhead/ Routing Load .We are also analyzing the effect in performance of these QoS parameters when packet size changes, when time interval between packet sending changes, when mobility speed of nodes changes. We have taken 25 nodes and simulation time=10 sec in our scenario file. Source node is 0 and destination node is 2. Node 2 is moving in nature so path to destination is changing. Figure-3 shows data transfer from source to destination and dropping

613

N oof P acketR eceived

Comparative Performance Analysis of QoS-Aware Routing

Simulation Time (Sec) Figure-4 (Packets Received when packet size=1000 bytes, interval=0.015 sec) Figure-3 (Simulation showing packet transfer)

Table-1 (Performance Matrix packet size=1000 bytes, interval=0.015 sec)

Table-2 (Performance Matrix packet size=1000 bytes, interval=0.15 sec)

Table 1

Table 2

Packets

Packets

(PDR)

Average

Packet

End to

Sent

Received

AODV

600

184

DSDV

600

74

12.33%

DSR

600

158

26.33%

Delivery

End

Ratio

Delay

30.67%

2.51

Throughput

Routing Load

Packets

Packets

Averag

Throug

Routing

Sent

Receive

Packet

e End

hput

Load

d

Deliver

to End

y Ratio

Delay

60

38

63.33%

3.95

4.2

5.81

9

15%

2.20

1

9.44

31

51.67%

3.19

3.44

4.22

20.44

4

AODV

2.28

8.22

7.85

DSDV

60

2.18

17.55

7.11

DSR

60

(PDR)

packets are the packets lost during transfer.Figure-4 shows packets received in each protocol when packet size is 1000 bytes and interval between packet sending is 0.015 sec. In graphs, X-Axis shows the simulation time and Y-Axis shows the number of packets received in each three protocols. Table-1 shows the performance matrix of three protocols designed after filtering the data from trace files generated after simulation. Table-1 shows the number of the packets received, PDR, Average end to end delay, throughput and routing load of three protocols separately. Similarly we have also simulated the graph for different situations like by changing packets size, by changing time interval between packet sending and by changing mobility speed. The performances of three protocols in different environment have been shown in table-2 to table-8. After analyzing all the tables at different packet sizes, at different time interval between packet sending and different mobility, we can say that DSR protocol is performing little bit better than AODV protocol which is performing much better than DSDV protocol. From table-1, table-2 and table-3 we are analyzing that as time interval between packets sending is

Packets

Packets

(PDR)

Average

Sent

Received

Packet

End to

Delivery

End

Ratio

Delay

Routin g Load

AODV

6

3

50%

0.33

44

DSDV

6

2

33.33%

1.75

0.22

15.5

DSR

6

4

66.67%

0.81

0.44

14.25

Simulation Time (Sec) Figure-5 (Packets Received when packet size=1000 bytes, interval=0.15 sec)

Noof PacketReceived

Noof Packet R eceived

3

Throughput

N oofP acket R eceived

Table-3 (Performance Matrix packet size=1000 bytes, interval=1.5 sec)

Simulation Time (Sec) Figure-7 (Packets Received when packet size=100 bytes, interval=0.015 sec)

Simulation Time (Sec) Figure-6 (Packets Received when packet size=1000 bytes, interval=1.5 sec)

Table-4 (Performance Matrix packet size=100 bytes, interval=0.015 sec Table 4

AODV

Packets

Packets

Sent

Received

600

307

(PDR)

Average

Packet

End to

ng

Delivery

End

Throughput

Routi

Load

Ratio

Delay

51.17%

Table-5 (Performance Matrix packet size=500 bytes, interval=0.015 sec Table 5

Packets

Packets

Averag

Throug

Routing

Sent

Receive

Packet

e End

hput

Load

d

Deliver

to End

y Ratio

(PDR)

Delay

3.38

34.11

2.73

AODV

600

201

33.50%

2.59

22.33

4.05

DSDV

600

74

12.33%

2.28

8.22

7.85

DSDV

600

74

12.33%

2.28

8.22

7.85

DSR

600

279

46.50%

3.10

31

2.91

DSR

600

219

36.50%

2.62

24.33

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N o o f P a c k e t R e c e iv e d

No of Packet Received

614

Simulation Time (Sec) Figure-8 (Packets Received when packet size=500 bytes, interval=0.015 sec)

N o of Packet Received

No of Packet Received

Simulation Time (Sec) Figure-9 (Packets Received when packet size=1000 bytes, interval=0.015

Simulation Time (Sec) Figure-11(Packets Received packet size=1000 bytes, interval=0.015 sec mobility=2000)

Simulation Time (Sec) Figure-10 (Packets Received packet size=1000bytes interval=0.015sec mobility=500)

Table-6 (Performance Matrix packet size=1000 bytes, interval=0.015sec, mobility=1000 m/sec)

(PDR)

Average

Packet

End to End

End

Delivery

Delay

Ratio

Delay

Ratio

111

18.50%

1.83

12.33

6.39

Packets

(PDR)

Average

Sent

Received

Packet

End to

600

Throughput

Routing

Packets

Packets

Load

Sent

Received

DSDV

600

56

9.33%

2.15

6.22

10.37

DSR

600

108

18%

1.80

12

8.60

AODV

Table-8 (Performance Matrix packet size=1000 bytes, interval=0.015sec, mobility=2000) Packets

Packets

Sent

Received

(PDR)

Average

Packet

End to End

Delivery

Delay

Throughput

Routing Load

Ratio AODV

600

DSDV

600

DSR

600

103

17.17%

1.76

11.44

54

9%

103

17.17%

7.49

2.13

6

10.75

1.76

11.44

9.35

600

123

20.50%

Throughput

1.93

Routing Load

13.66

3.67

DSDV

600

60

10%

2.18

6.66

9.68

DSR

600

122

20.33%

1.92

13.55

8.54

Noof Packet Received

AODV

Table-7 (Performance Matrix packet size=1000 bytes, interval=0.015sec, mobility=500)

Delivery

Packets

Simulation Time (Sec)

increasing, numbers of packets received are decreasing. We have analyzed the performance of DSDV protocol at different packet size. From table-1, table-4 and table-5, we can say that there is no effect on the performance of DSDV protocol at varying packet size. At packet size 100, 500 and 1000 bytes, number of packet received is 74.Performance of DSDV protocol a different packet size is shown in figure-12. As the number of packets received in AODV is decreasing with increasing packet size, packet delivery ratio (PDR) is also decreasing. Average delay between packet sending is also decreasing with increasing packet size. Throughput is also decreasing with packet increasing packet size. Thus, performance of AODV protocol is decreasing with increasing packet size as shown in performance matrices table-1, table-4 and table-5. We can analyze the performance of DSR protocol at variable packet size from table-1, table-4 and table-5. We can see that packet delivery ratio

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(PDR) is decreasing with increase in packet size. Throughput of DSR protocol is decreasing as packet size is increasing. Routing overhead is also increasing with increase in packet size. Average end to end delay is decreasing with increase in packet size. It means the performance of DSR protocol is high at less packet size except the routing load.

4 Conclusions and Future Work From all the graphs and tables, we analyzed that performance of DSDV protocol is not good as throughput is very low and routing load is very high as compared to AODV and DSR protocols. AODV performed good in some situations than DSR protocol but overall DSR is performing better than AODV protocol like if we compare average end to end delay. There is no effect on the performance of DSDV protocol if packet size varies as shown in figure-12. AODV and DSR protocols perform better at less packet size. Performance of all three protocols decreasing as mobility of nodes increasing. As observed from analysis AODV is more desirable in highly mobile networks and DSR protocol becomes mediocre in highly mobile networks due to use of caching and its inability to expire stale routes. In the future, some other protocols Comparative Performance Analysis (TORA, OLSR etc) could be studied too with taking few other QoS parameters (Security, Power consumptions etc.) in our performance matrix.

References [1] Qasim, N., Said, F., Aghvami, H.: Mobile Ad Hoc Networks Simulations Using Routing Protocols for Performance Comparisons. In: Proceedings of the World Congress on Engineering 2008, London, U.K, July 2-4. WCE 2008, vol. I (2008) [2] Gujral, R., Kapil, A.: Secure QoS Enabled On-Demand Link-State Multipath Routing in MANETS. In: Proceeding of BAIP 2010, Trivandrum, Kerala, India, March 26-27. LNCSCCIS, pp. 250–257. Springer, Heidelberg (2010) [3] Broch, J., Maltz, D., Johnson, D., Hu, Y.C., Jetcheva, J.: A performance comparison of multihop wireless ad hoc network routing protocols. In: MOBICOM 1998, October 1998, pp. 85–97 (1998) [4] Clausen, T., Jacquet, P., Viennot, L.: Comparative study of Routing Protocols for Mobile Ad Hoc Networks. In: The first Annual Mediterranean Ad Hoc Networking Workshop (September 2002) [5] Usop, N.S.M., Abdullah, A., Abidin, A.F.A.: Performance Evaluation of AODV, DSDV & DSR Routing Protocol in Grid Environment. International Journal of Computer Science and Network Security (IJCSNS) 9(7), 261–268 (2009) [6] Lakshmi, M., Sankranarayanan, P.E.: Performance Analysis of Three Routing Protocols in Wireless Mobile Ad Hoc Networks. Information Technology Journal 5(1), 114–120 (2009) [7] Jaap, S., Bechler, M., Wolf, L.: Evaluation of Routing Protocols for Vehicular Ad Hoc network in city traffic scenarios. In: International Conference on ITS Telecommunications, France (2005)

Steering Control of an Automated Vehicle Using Touch Screen with Simulation Result Apeksha V. Sakhare1, V.M. Thakare2, and R.V. Dharaskar3 1

Master of Engineering [ESC], Computer Science Department, GHRCE Nagpur [email protected] 2 P.G. Depatrment of Computer science, Faculty of Engg. and Tech. S.G.B. Amravati University, Amravati [email protected] 3 Dept. of CSE, GHRCE, Nagpur [email protected]

Abstract. The need for graphical user interfaces in industrial and consumer applications is steadily increasing. To address this need, free scale is introducing a family of cost-effective and highly integrated Atmega microcontrollers that feature an integrated touch screen controller module. The idea behind the selection was channelization of human thoughts to automated realization. It is decided to implement the theme of automatic maneuvering of vehicles and the unanimous choice of sensor was touch screen. It was started with the thought of being able to replace the steering of a car completely by a touch screen. It is drawn on the experience of driving to reach at the choice of touch screen as a drive interface. Another innovation was the touch screen controller being wireless. The idea is application of the user input tracking capability of the touch screen to an RC car control scheme. The Radio controlled car should follow the path drawn by user on a touch screen in real time. Keywords: touch screen, automated vehicle, steering control, AVR microcontroller, wireless communication.

1 Introduction Driving system relied on man-machine-environment cooperation is presented. With this evaluation architecture, there is the reliability of two driving modes in automated driving system. The touch screen provides users with fewer response options thus simplifying the decision making involved in current driving scheme. The Working Principle When the screen is touched, it pushes the conductive ITO coating on the PET film against the ITO coating on the glass. That results the electrical contact, producing the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 616–618, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Fig. 1. With the 4-wire touch screen panel, use the two active areas of the resistive touch screens to sense the X and Y pressure points (a). The equivalent circuit is simply a voltagedivider (b).

voltages. It presents the position touched. The pins (X left) and (X right) are on the glass panel, and the pins (Y up) and (Y down) are the PET film. The microprocessor applies +5V to pin (X left) on the glass panel, and the voltage is uniformly decreasing to pin (X right) for 0V because of the resistive ITO coating on the glass substrate, and the PET film is grounded. When the touch screen is not touched, the controller detects the voltage on the PET film is zero. When the touch screen is not touched, the controller detects the voltage on the glass panel is zero.When the touch screen is touched, a voltage on the glass substrate proportional to the X (horizontal) position of the touch appears on the PET film. This voltage is digitized by the A/D Converter and subjected to an averaging algorithm. Then it is stored and transferred to the host.

2 An Algorithm Background Math The touchpad is quantized into a 50 X 25 grid of points (based entirely on the real life dimensions of the screen itself). If the user speeds up the stylus, naturally the points will be further spaced apart. There are 50 rows and 25 columns are designed in the Touch screen So, 1 line = 50 milli volt And variation = 0 to 25 volt. = 25000 milli volt The screen when touched the voltage is converted into digital points. The Points are taken by program and given to the receiver by transmitter. According to the program designed if touch screen get continuous forward points then it will return r1 to go forward, for continuous down points it will return r2, for continuous left points it will return r3, and for continuous right points it will return r4.touched points will be shown on the LCD on Transmitter side. Proteus7 Professional is used for the simulation.

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Simulation for the Transmitter

Simulation for the Receiver

References [1] Atmel Corporation, ATmega16(L) Preliminary Complete Datasheet, 2490G-AVR-03/04 (2001) [2] Downs, R.: Using resistive touch screens for human/machine Interface. Analog Applications Journal, Texas Instruments (2005) [3] Osgood, S., Ong, C.K., Downs, R.: Touch screen controller tips. Application Bulletin (LIT # SBAA036), Texas Instruments (April 2000) [4] Chen, C., Guldner, J., Kanellakopoulos, I., Tomizuka, M.: Nonlinear damping in vehicle lateral control: Theory and experiment. In: Proc. American Control Conf., Philadelphia, PA, pp. 2243–2247 (June 1998) [5] Patwardhan, S., Tan, H.S., Guldner, J.: A general framework for automatic steering control: System analysis. In: Proc. American Control Conf., Albuquerque, NM, pp. 1598– 1602 (June 1997)

A Low Cost GPS Based Vehicle Collision Avoidance System Samarth Borker and R.B. Lohani Department of Electronics and Telecommunication Engineering, Goa College of Engineering, Farmagui-Ponda, Goa [email protected], [email protected]

Abstract. This paper presents a novel system for vehicle collision avoidance based on microcontroller which will be very effective for reducing accidents. In order to develop automobile collision avoidance system, the vehicles involved should be able to exchange the information in real time. The system adopts ultrasonic sensor and light intensity meter to monitor the distance of approaching vehicle. Based on simulation and tests conducted a light intensity threshold is set. Upon exceeding this threshold, the device triggers the relay which in turn dips the headlights of vehicles involved. Apart from this, it will have a life saving capability. The system is also equipped with GSM and GPS modules. In case the collision occurs, then the exact location of collision will be sent by GPS to the stored numbers through GSM circuit, so that medical care is attended in short time. Keywords: Time-to-collision, GPS, GSM, Sensors.

1 Introduction Goa has one of the highest accident rate in the country and this in a country that is among the most accident – prone in the world [1]. An accident takes place every 3 minutes and a person is dead every 10 minutes on Indian roads. The Indian share in world vehicle population is only 1% while its share in road accidents is as high as 6%, contributing heavily to the suffocation of economic growth due to high cost related to them1. The estimated annual loss to the country is to the tune of Rs. 550 Billion annually. This is around 3% of the GDP [2]. With the lowering cost of sensors and embedded systems, vehicle collision avoidance systems are becoming increasingly attractive option for improving roadway safety. The past decade has seen emergence of many promising technologies that enhance the driving safety of a vehicle. One such technique is a collision avoidance system (CAS) that detects the surrounding objects, estimates their distances from the host vehicle, and predicts the time-to-collision (TTC). 1

Please see article Bridging the gap- GIS a key in crash data display and analysis.mht for the email address (mail: [email protected]).

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Generally, Collision is caused on account of following reasons: 1. Recognition errors

2. Decision errors

3. Performance errors

2 Literature Survey Vehicular safety system can be classified as passive safety system and active safety system [3]. The system designed needs to meet several criteria: 1. System warnings should result in a minimum load on driver attention. 2. The system should be universal for high effectiveness and applicability. 3. Automatic control of the controlling parameter/ actuator should not interfere with normal driving operation.

3 Implementation The implemented model uses two main underlying concepts. These are GPS and GSM. The main application of this system in this context is tracking the vehicle to which the GPS is connected, giving the information about its position whenever required. This is done with the help of the GPS satellite and the GPS module attached to the vehicle which needs to be tracked. This GPS module comes as a shield which can be interfaced with Arduino microcontroller. The model combines the GPS’s ability to pin-point location along with the ability of the Global System for Mobile Communications (GSM) to communicate with a control centre in a wireless fashion. Interfacing of sensor components with Arduino is as shown in fig 1.

Fig. 1. Interfacing of sensor components with Arduino

4 Simulation Design The safety distance model according to relative velocity and itself velocity, the safety distance can be calculated by formula [4,5].

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(1) Where, V (V=V1-V2) is relative velocity(v 1 is the host vehicle velocity ,v2 is the is brake delay time ( =1~2s), ‘a’ is retarded approaching vehicle velocity), velocity, ‘R0’ is the distance(between self/host and forward/approach vehicle, when self vehicle is at a standstill). The analytical curves from simulated model are as shown in figure 2[4,5]. This existing simulation will be incorporated in the hardware implementation.

Fig. 2. Safety distance curves

References 1. Lohani, R.B., Borker, S.: A Novel Model to avoid vehicle collision at night for the demography of Goa. In: International Conference and Workshop on emerging trends in Technology, ICWET2010, pp. P921–P922 (2010) 2. Jain, L.K.: Walking On Delhi Roads Is A Pedestrian Nightmare 3. Borker, S., Lohani, R.B.: A Novel aaroach for GSM based intelligent vehicle with location tracking and collision avoidance. In: 2nd International conference on Intelligent science and Technology IIST 2010, pp. P401–P406 (2010) 4. Jingcheng, X., Wang, X.: Fire control radar technology, vol. 24(9), pp. P15–19 (1995) 5. Gang, L., Dezao, H., Keqiang, L.: Warning algorithm of vehicle collision avoidance system. Tsinghua. univ. (Sci.&Tech.) 44(5), P697–P700 (2004)

Human Skin Region Identification Using Fusion Technique Vijayanandh Rajamanickam1 and Balakrishnan Ganesan2 1

Department of Computer Applications, J.J. College of Engineering & Technology and Research Scholar, Research and Development Centre, Bharathiar University, Tamil Nadu, India 2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tamil Nadu, India {rvanandh,balakrishnan.g}@gmail.com

Abstract. Face recognition is very important in many research areas from machine vision to complex security systems and the important cue to detect the human face is skin color. The proposed paper focuses on isolating the regions of an image corresponding to human skin region through the use of fusion technique. Fusion is performed on the skin region detected from RGB, YCbCr and CIEL*a*b color spaces of the given input image. Then the fused image is filtered by the median filter, in order to avoid the noise. This technique will be able to clearly identify skin region of the image. Keywords: RGB, YCbCr, CIEL*a*b color spaces, Fusion, Median filter.

1 Introduction As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years [1]. Face recognition research could be based on still and video based. The factors of challenges associated with face detection are pose [2], presence or absence of structural components, facial expression [3], occlusion and image orientation. Face recognition could also be used with the skin-tone approach and twinkle approach, which follow the statistical approach [4]. Statistical approach uses two independent techniques based on skin color model by histograms and skin color distribution as a Gaussian. Skin color detection from complex background natural images was performed by combining color thresholding, skin color density estimation and skin region growing [5]. Hence, color plays an important role in the detection of skin region or faces in images. There are a variety of color spaces available. Among them RGB, HSV, YCbCr and CIEL*a*b are commonly used for skin region detection applications. Also CIEL*a*b color space was used to obtain the improved performance of skin color segmentation among other color spaces. The performance was measured using metrics, single color component, color without luminance and color with luminance in six different color spaces of 40 evaluations [6]. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 622–625, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Proposed Segmentation Technique Human skin color has been used and proven to be an effective feature in the applications of face detection and robust visual cue for detection. The proposed segmentation method has been performed on RGB, YCbCr and CIEL*a*b color spaces. 2.1 Skin Region Segmentation RGB and YCbCr color spaces were used to segment the skin region with the skin color conditions. The RGB skin color detection was based on the following set of conditions [7]: (R, G, B) is classified as skin if R>95 and G>40 and B>20 and (max{R,G,B}-min{R,G,B})>15 and |R-G|>15 and R>G and R>B. In YCbCr color space, the range of chrominance for skin gives a rectangular region spanning 77 ≤ Cb ≤ 127 and 122 ≤ Cr ≤ 173. Then CIEL*a*b color components were converted from RGB color components by using the following equations [6]. ⎧116(Y / Yn)1 / 3 −16 ; Y / Yn > 0.008856 L=⎨ 903.3(Y / Yn) ; otherwise ⎩

(1)

a = 500[ f ( X / Xn) − (Y / Yn)]

(2)

b = 200[ f (Y / Yn) − ( Z / Zn)]

(3)

where ⎡ X ⎤ ⎡0.412453 0.357800 0.180423⎤ ⎡ R ⎤ ⎢ Y ⎥ = ⎢ 0.212671 0.715160 0.072169⎥ ⎢G ⎥ and ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢⎣ Z ⎥⎦ ⎢⎣0.019334 0.119193 0.950227⎥⎦ ⎢⎣ B ⎥⎦ ⎧ t1 / 3 f (t ) = ⎨ ⎩7.787t + (16 / 116)

; t > 0.008856 ; otherwise

The converted CIEL*a*b color image was segmented by HillClimbing segmentation with K-Means clustering. The initial seed of the K-Means clustering algorithm was selected from local maximum of the 3D color histogram of the CIEL*a*b color space and its number of histogram bins in each dimension was set to 10. The labeled cluster obtained by the K-Means clustering algorithm reduced the number of clusters and the formed clusters were human perception based one, because it tried to move an object to another cluster. Finally the label of the skin region was identified from the labeled image. 2.2 Fusion Technique The methods discussed in section 2.1, fail to locate the skin region in the input color image for different reasons. Therefore, the proposed method is to combine the three color space segmentation methods in a systematic way to overcome the weaknesses of

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Image size 128x170 113x170

RGB / p

YCbCr / p

CIEL*a*b / p

9627 / 44.24 637 / 3.32

9972 / 45.83 2014 / 10.48

9768 / 44.89 1224 / 6.37

Fusion technique / p 7831 / 35.99 335 / 1.74

the individual methods and result in a method that is more accurate than each of the individual methods. The output image is more informative than any of the input images. Actually digital images are prone to a various types of noise. So, the median filter was applied on the fused image for denoise. The resulted image produced more skinned region. Table 1 shows the performance of the skin region segmentation based on the number of pixels detected on the proposed method. Here p is the percentage of skin region detected from an input image.

3 Experimental Results This section presents some experimental results obtained by the proposed human skin detector, which is implemented with Matlab7.0 using the image processing toolbox and clustering algorithms. The fusion based segmented images are more refined than the one that is obtained from RGB, YCbCr and CIEL*a*b based segmentation methods. The individual segmentation methods produced non-skin regions like hair, eyes, background, etc. and the images were also having noise. Therefore the output of the individual segmentation methods was not upto the expected results. The proposed fusion based segmentation method was experimented with 170 different images and it

(a)

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Fig. 1. (a) & (f) An original image, (b) & (g) RGB based segmented image, (c) & (h) YCbCr based segmented image, (d) & (i) CIEL*a*b based segmented labeled image, (e) & (j) Fused image

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is evident that the method provides more accurate skin region than the existing skin detector. The sample results are shown in Fig. 1.

4 Conclusion and Future Work This paper, which was presented, provides a new method to detect human skin region from the color images. Different tools, such as thresholding, three color spaces, K-Means clustering, fusion technique and median filtering for making the process robust were integrated. From the results obtained, it is evident that the proposed method offers high accuracy and speed in a large variety of images. In future work, the spatial and texture information will be integrated into pixel features for clustering and the segmented images will be used for fusion.

References 1. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Surveys (2003) 2. Lee, M.W., Nevatia, R.: Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(1) (2009) 3. Praseeda Lekshmi, V., Sasikumar, M.: Facial Expression Recognition from Global and a Combination of Local Features. IETE Technical Review 26(1) (2009) 4. Ren, J., Jiang, J.: Statistical Classification of Skin Color Pixels from MPEG Videos. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 395–405. Springer, Heidelberg (2007) 5. Laurent, C., Laurent, N., Bodo, Y.: A Human Skin Detector combining Mean Shift Analysis and Watershed Algorithm. In: IEEE Proceedings of the International Conference on Image Processing (2003) 6. Porle, R.R., Chekima, A., Wong, F., Sainarayanan, G.: Performance of Histogram-Based Skin Colour Segmentation for Arms Detection in Human Motion Analysis Application. International Journal of Electronics, Communications and Computer Engineering 1(3), 164– 169 (2009) 7. Ravichandran, K.S., Ananthi, B.: Color Skin Segmentation Using K-Means Cluster. International Journal of Computational and Applied Mathematics 4(2), 153–157 (2009) ISSN 1819-4966

Avoidance of Electromagnetic Interference in Modem with Minimal Bit Error Rate Using Tele Typewriter Signals C. Annadurai1, D. MuthuKumaran2, C. Charithartha Reddy2, and M. Kanagasabapathy2 1

Assistant Professor, 2 Final Year Students, Department of ECE, SSN College of Engineering, Kalavakkam, Tamil Nadu [email protected]

Abstract. A Wireless Data Modem using Tele Typewriter Signals and Frequency Shift Keying (FSK) Modulation Technique is proposed here in order to neglect Electromagnetic Interference with minimal Bit Error Rate. The wireless modem designed here consists of 5 stages namely a) Modulation stage, b) Transmission stage, c) Reception stage, d) Filtering stage and e) Demodulation stage. The Modulation stage consists of IC555 Timer generating FSK signal. Transmission is wireless and is achieved using IRLED. The transmitted signal is received using a spectrally matched Phototransistor. The Filtering stage consists of a second order low pass filter. Demodulation is achieved using IC565. The modem designed here makes use of Tele Typewriter signals which neglects conduction and radiation interference from external environment. The Probability of error for this wireless data modem is better with minimal Bit Error Rate. Hence the whole setup is stable and accurate device for transmitting digital data. Keywords: Frequency Shift Keying (FSK), Tele Typewriter signals, Electromagnetic Interference, Phase Locked Loop, Probability of Error and Bit Error Rate.

1 Introduction Wireless Data Modem plays a very vital role in the field of digital data communication. In this scientific era, data transfer is very crucial and most of the data transfer is done using Modems in which wireless data communication is very popular. For the transfer of digital data using Tele Type writer signals whose frequency range is from 1070Hz to 1270Hz, a wireless data modem is proposed. The Tele Typewriter includes a Start Signal, Data information defined by signal transitions between 1070Hz to 1270Hz and a stop signal which are operated upon so that they are converted to signals adopted for use in digital logic circuits in a unique manner to neglect Electromagnetic Interference of both the radiation and conduction type. So for transmitting digital information using such signals, the basic modulation and demodulation technique used is Frequency Shift Keying (FSK). So V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 626–631, 2010. © Springer-Verlag Berlin Heidelberg 2010

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for transmitting digital data using Tele Typewriter signals, we use fH=1070Hz and fL=1270Hz, where fH corresponds to logic 1 and fL corresponds to logic 0 of the input binary message data to be transmitted. The major reason for choosing FSK modulation technique is due to the small bandwidth requirement of the Tele Typewriter signals. The Bandwidth is 200Hz which is very low. The usage other modulation techniques makes the signal distorted and hence it does not resemble the input message signal. The accuracy of FSK technique is better than other modulation techniques. The Probability of Error of FSK technique is also good. So because of these reasons FSK has been selected as the modulation technique in our modem.

2 Block Diagram The basic block diagram of the Wireless Data Modem is shown in figure 1. It consists of five stages namely a) Modulator Stage, b) Transmitter Stage, c) Receiver Stage, d) Filter stage and e) Demodulator Stage.

Fig. 1. Block Diagram

The aim is to transmit the input digital data effectively from one network to another network or from one network to other peripheral devices. The first step is to modulate the input digital data with the carrier signal using FSK Modulation Technique. This is achieved using IC555 Timer, which produces the FSK modulated signal whose fH=1070Hz and fL=1270Hz. The modulated signal is then transmitted wirelessly using IRLED. The transmitted signal is received at the receiver side by means of a spectrally matched Phototransistor which then sends the obtained modulated signal to the Filter Stage. The Filter stage consists of a Second order low pass filter. The final stage is the Demodulator stage which consists of Phase Locked Loop and a RC ladder Filter. The Phase Locked Loop is implemented using IC 565. Thus at the demodulator Stage the digital data signal is obtained successfully.

3 Modulation Modulation is the process of altering the characteristics of the carrier signal in accordance to the message signal. Here FSK signal is obtained using IC555 Timer. The circuit diagram for FSK Signal generation using IC555 Timer is shown in figure 2. It consists of an IC555 Timer which works in astable mode. The resistors Ra, Rb and capacitor C determines the Frequency of the FSK modulated signal. The standard digital data input frequency is usually 150Hz. When the input is HIGH i.e. when the input binary data is of logic 1, the PNP transistor Q is off and IC555 Timer works in the normal astable mode of operation. The frequency of the output FSK modulated signal is given by the equation

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f0= 1.45/(Ra+2Rb)C. (1) The Resistors Ra, Rb and capacitor C are selected in such a way that the value of f0 is 1070Hz. Here the values of Ra= 50K ohm, Rb= 47K ohm and C=10nF. When the input is LOW i.e. when the input binary data is of logic 0, the PNP transistor Q is on and it connects the resistance Rc across the resistance Ra. Thus now the frequency of the output FSK modulated signal is given by the equation f0= 1.45/((Ra||Rc)+2Rb)C.

(2)

The Resistors Rc is selected in such a way that the value of f0 is 1270Hz. Here the value of Rc=50K ohm. The capacitor C1 is used to bypass noise and ripples from the supply.

Fig. 2. FSK Generation using IC 555 timer

4 Transmission An infrared emitter is an LED made from Gallium Arsenide, which emits near infrared energy at about 880nm. The FSK signal from the Modulator is now wirelessly transmitted to the receiver side using IRLED. The IRLED used as a transmitter is shown in figure 3. Hence the value of R is about 100 ohms.

Fig. 3. Transmitter using IR LED

5 Reception The infrared Phototransistor acts as a transistor with the base voltage determined by the amount of light hitting the transistor. Hence it acts as a variable current source.

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Greater amount of IR light cause greater current to flow through the collector-emitter leads. The signal from the Transmitter side should be received at the Receiver side by a spectrally matched Phototransistor. The Phototransistor used as a Receiver is shown in the figure 4. Thus the whole process of wireless transfer of data is achieved using the combination of IRLED and a spectrally matched Phototransistor. The important parameter of a Modem, which is the Probability of Error, is good for this combination.

Fig. 4. Receiver using Phototransistor

6 Filter The low pass filter is used to eliminate the unnecessary high frequency components from any low frequency signal and hence helps in improving the Probability of Error of a system. Here a second order low pass filter as shown in the figure 5 is used to remove the high frequency Noise components introduced by the Wireless Channel.

Fig. 5. Low Pass Filter

The timing resistors are calculated using the following formulae fL=1/(2*pi*R1*C1).

(3)

fH=1/(2*pi*R2*C2).

(4)

So for fL=1070Hz and fH=1270Hz, the values are R1=745 ohm, R2=1.25K ohm, C1=200nF, C2=100nF.

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7 Demodulation The process of eliminating the carrier signal and recovering the base band message signal from the modulated signal is termed as Demodulation. Here the process of demodulation is achieved using Phase Locked Loop IC565. The circuit diagram for achieving the process of demodulation using PLL IC565 is shown in figure 6.

Fig. 6. Demodulator using PLL 565

In the 565 PLL the frequency shift is usually accomplished by driving a Voltage Controlled Oscillator (VCO) with the digital data signal so that the two resulting frequencies correspond to the logic 0 and logic 1 states of the digital data signal. The frequencies corresponding to logic 1 and logic 0 states are commonly called the mark and space frequencies. The demodulator receives a signal at one of the two distinct carrier frequencies 1,270 Hz or 1,070 Hz representing logic levels of mark (- 5 V) or space (+ 14 V), respectively. Capacitance coupling is used at the input to remove a dc level. As the signal appears at the input of 565 PLL, the PLL locks to the input frequency and tracks it between the two possible frequencies with a corresponding dc shift at the output. Resistor R1 and capacitor C1 determine the free-running frequency of the VCO. Capacitor C2 is a loop filter capacitor that establishes the dynamic characteristics of the demodulator. Capacitor C2 is chosen smaller than usual one to eliminate overshoot on the output pulse. A three-stage RC ladder filter is employed for removing the sum frequency component from the output. The VCO frequency is adjusted with R1 so that the dc voltage level at the output (pin 7) is the same as that at pin 6. An input at frequency 1,070 Hz drives the demodulator output voltage to a more positive voltage level, driving the digital output to the high level (space or + 14 V). An input at 1,270 Hz correspondingly drives the 565 dc output less positive with the digital output, which then drops to the low level (mark or – 5 V). The comparator prevents the amplitude changes occurring at the output stage. Thus the demodulator stage effectively reproduces the input digital data at 150Hz.

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8 Probability of Error The most important parameter that defines the efficiency of a Modem is the Probability of Error and Bit Error Rate (BER). BER means the amount of error introduced in the transmitted signal by the channel’s noise. Usually the noise considered in a wireless medium is Additive White Gaussian Noise with zero mean and power Spectral density as No/2. The expression for Probability of Error for this wireless data modem which uses Non Coherent FSK demodulation is given as pe = ½*exp(-Eb/(2*No)).

(5)

Where Eb is the transmitted signals energy per bit. From the above equation it is very clear that the Bit Error Rate for this wireless data Modem is very low.

9 Conclusion Thus in this paper a Wireless Data Modem using Tele Typewriter signals is designed and its importance to transmit digital data wirelessly with minimal Bit Error Rate without getting affected by electromagnetic interference is analyzed.

References 1. McDermott, T.: Wireless Digital Communications: Design and Theory, Tucson Amateur Packet Radio Corporation, Tucson, Arizona (1996) 2. Peebles, P.Z.: Digital Communication Systems. Prentice-Hall, Englewood Cliffs (1987) 3. Haykins, S.: Communication Systems, 4th edn. John Wiley, Chichester (2001) 4. Proakis, J.G.: Digital Communication, 3rd edn. McGraw-Hill, New York (1995) 5. Taub, Schilling: Principles of Digital Communication. Tata McGraw-Hill, New York (2003) (28th reprint)

A State Variables Analysis for Emerging Nanoelectronic Devices K. Vaitheki1 and R. Tamijetchelvy2 1

Assistant Professor, Department of Computer Science and Engineering, Pondicherry University, Puducherry, India [email protected] 2 Assistant Professor , Department of Electronics and communication, Perunthalaivar Kamarajar institute of Technology, Karaikal, India [email protected]

Abstract. A collection of technologies that operates, analyzes, and controls materials at the nanoscopic level, is now merging into the main stream of electronics with examples ranging from quantum-electronic lasers to memory devices, even Nano Electro Mechanical Systems (NEMS) known as the Nanotechnology. State variables are physical representations of information used to perform information processing via memory and logic functionality. Advances in material science, emerging nanodevices, nanostructures, and architectures have provided hope that alternative state variables based on new mechanisms, nanomaterials, and nanodevices may indeed be plausible. The review and analysis of the computational advantages that alternate state variables may possibly attain with respect to maximizing computational performance via minimum energy dissipation, maximum operating switching speed, and maximum device density is performed. An outlook of some important state variables for emerging nanoelectronic devices is suggested in the work. Keywords: Nanodevices, nanoelectronics, nanotechnology, State variable, Device density.

1 Introduction The deep insides of the macro- systems like intelligent buildings, electronic cars are being steadily filled with more and more micro semiconductors than before, as technology advances into the nanoelectronics era [4]. The revenues of the semiconductor industry over the last 30 years achieve a compound annual growth of about 15% per year [1]. A physical state variable should be able to reside in two distinguishable states that are controlled between states (write), read, and initialized in a defined state (erase) for a Boolean computation to occur. The information is also required to be transmitted from one physical location to another[11]. Computer users executing these applications generally demand maximum computer speed (time to perform a function), minimum physical area/volume (high device density per area), minimum computation errors (reliability), minimum energy dissipation (reduced heat dissipation), and maximum value [dollars per device or dollars per microprocessor without V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 632–637, 2010. © Springer-Verlag Berlin Heidelberg 2010

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interlocked pipeline stages (MIPS)]. The examples of alternative state variables beyond charge-based devices include magnetism (magnetic domain) used in magnetic data storage, chalcogenide-phase-change-based memory used in CDs/DVDs, and photon-based communication used for broadband applications [3].

2 Electron Charge Silicon-based electronics commonly referred to as electron charge- based electronics, is fundamentally considered as a variable that alter between various states of electron population (charging/discharging of capacitance). A simple charge-based switch can be approximated as a capacitor. The energy dissipated in charging and is charging (one full cycle) a capacitor C is given by E dissipated = CV 2 = Nq VDD. The supply voltage VDD is at about 1.1 V in today’s 90-nm node ICs. At 2020 time frame, the International Technology Roadmap for Semiconductors (ITRS), the expected VDD will reduce down to 0.7 V [1].

Fig. 1. Energy dissipation versus switching time for different energy separation (different error rates)

3 Single Spin Other than representing information by the presence/absence of electrons, electrons do posses intrinsic angular momentum, more commonly known as spin[3]. Spin is the fundamental building block of magnetism, and invoke on demand electron spin

Fig. 2. Two-level single-spin system

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orientation that requires a magnetic field B to be generated to manipulate splitting between energy levels, which could be done via spin–orbit (SO) coupling or by an external magnetic field.

4 Single Domain A single domain consists of various spins that make up a region of aligned spins called a spin domain. The collective behavior of a spin domain differentiates it from the single-spin primarily due to indirect exchange coupling that causes spins to interact and mass-align in a polarized fashion [4]. The stability achieved from this collective behavior has enabled the long-term success of magnetic media storage.

5 Spin Waves Spin waves, otherwise known as magnons, are collective oscillations of spins in a spin lattice around the direction of magnetization [8]. The coherence length of the spin waves may exceed tens of micrometers at room temperature which makes spin waves an attractive candidate for application in logic devices. Spin waves can be used to transmit information among spin-based cells and achieve logic functionality exploiting wave interference. One important feature of the spin-wave-based circuitry is that the main power dissipation may not occur in the ferromagnetic waveguide, but in the external electric contour that modulates the magnetic field.

Fig. 3. Numerical simulations showing energy dissipated per switch in a spin wave modulator

6 Molecular Movement and Conformation The ever growing interest and fascination are molecular based devices for beyondCMOS augmentation. Molecular switches, such as bistable rotaxanes, constitute a state variable for which information storage relies on physical changes of the molecule. The ionization potential of the rotaxane molecule constitutes the minimum energy required for information storage, i.e., one electron per molecule at the voltage at which the redox event occurs (energy diagram presented in this oxidation process occurs at about 0.8 V, corresponding to an activation energy of 1.2 × 10−19 J/molecule.

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7 Discussion The estimates within the table are to serve as optimistic performance estimates in switching speed, power dissipation, and device density. It could be found that the “alternative” state variables do not profoundly outperform electron charge from the outset. The current challenge in microprocessor technology development is to minimize energy dissipation, which fundamentally requires the state variable to compete with the thermal noise bath (less than kT ln 2 in the most idealized case) [6][4]. If energy dissipation is to be less than this limit, distinguishability will be compromised, and elaborate fault-tolerant and redundancy mechanisms would need to be implemented. From our analysis, we see that electron and spin regimes may offer the most optimistic minimum power dissipation scenario in approaching the kT ln 2 limits. It is evident that material, device, interconnect, and patterning techniques are required to be developed to attain such an optimistic performance. Another aspect of practicality lies in variability benefits between spin and electron charge, in particular toward thermal and quantum fluctuations. The spintronic devices can improve variability compared with today’s chargebased devices as variability of spintronic devices has been limited only by thermal fluctuations, and not by quantum fluctuations as for electronic devices. A major drawback in a practical spin-based device is the lack of any demonstrated spin “gain” device that is required for spin circuit fan-out. Conceptual spin gain devices that has been proposed is still not clear if such logic devices require carrier charge transport [14]. If that is so, scattering and losses as a result of electron carrier transport would remain and justifying power dissipation benefits using spin would be negated. Molecular-based state variables offer unique advantages, particularly due to their larger mass (compared to an electron) that translates to longer retention times in memory applications. The interesting benefits that include molecular design and synthesis is that it allows preferential molecular self-assembly onto metals and semiconductors for interconnection. Bottom-up assembly coupled directly to the state variables offer major advantages, particularly as top-down lithography approaches are more challenging and variability conditions are beyond the 22-nm node. In the progress of employing alternate state for information processing, technology development will continue to counteract the slower rate of CMOS scaling which is referred to as the “demise of Moore’s law” [10]. The other interesting and relevant technology developments related to the use of alternative state variables that are foreseen include the following. 7.1 Use of Collective Phenomena to Increase Functionality Such phenomenon could be the rise of a new state variable such as orbitronics. Orbitronics aims at controlling the electric current and possibly spin by triggering changes in d-electron orbital hybridization that propagate as a wave. Others include strongly correlated electron systems that exhibit correlated functions via their charge, spin, and orbital symmetry. The key is to create a phase change through an external stimulus that will result in a dramatic change in electrical or magnetic properties [8].

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7.2 Operating State Variables Devices faster than the relaxation and coherence times T1 and T2 driving into a nonequilibrium regime, where it has been shown that energy dissipation is less than kT ln 2, could be achieved. 7.3 Appropriate Thermal Engineering of Devices and Materials Along with our optimistic estimates, thermodynamic limits pertaining to nanoelectronics have also been explored [14], showing maximum thermal flux dissipation reaching megawatts per square centimeter. In comparison, the most advanced water cooling systems achieve heat dissipation up to 1000W/cm2. Hence, a clear gap exists in the practicality of evacuating such a large thermal flux. This being the case, material design of nanoscale devices to maximize phonon transport efficiency is the key, including new materials with high thermal conductivity such as new carbon-based materials. System management of thermal hot spots is also critical, controlling subsystem and device duty cycles, done similarly in today’s microprocessor implementations that average 10–20% of transistor duty cycle. Table 1. Summary of State Variable Performance Estimates

8 Conclusion The vision of the nanoelectronics Research and Development aims at the enhancement of the technology development of next generation semiconductor memories for the products of high-speed, high-density, and low-power applications, and to establish the world-class nanoelectronics platform supporting local semiconductor

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manufacturers to develop advanced technology for next generation semiconductor ICs[9] .Some of the alternate state variables that are being considered for use within high-performance information processing have been analyzed and reviewed. The study presents some more popular variables that are currently being considered and explored in the research community. Some variables that were not mentioned include photons, nanoelectromechanical (NEMS) variables, domain walls, plasmonics, and others. But based on the performance estimates, it could be concluded that the electron-charge based state variable implemented in popular logic devices such as CMOS offers the industry a competitive solution based on our comparison with alternative state variables. Research and development into novel areas such as thermal engineering, non equilibrium device operation, and other state variables, such as orbitronics, could offer possibilities that would further improve performance metrics of alternative state variable operation. In conclusion, based on our most optimistic estimates, the challenge remains in deriving a physical state variable that surpasses the status quo (electron charge) in terms of energy dissipation and practical implementation.

References 1. Haselman, M., Hauck, S.: The Future of Integrated Circuits: A Survey of Nanoelectronics. Proceedings of the IEEE 98(1) (2010) 2. Welser, J.: The Semiconductor Industry’s Nanoelectronics research Initiative: Motivation and Challenges. In: 46th ACM/IEEE Design Automation Conference, DAC 2009 (2009) 3. Sung, C.Y.: Post CMOS Nanoelectronics Research for the Next Generation Logic Switches VLSI Technology. In: IEEE International Symposium on Systems and Applications (2008) 4. Wang, K.L., Galatsis, K., Ostroumov, R., Ozkan, M., Likharev, K., Botros, Y.: Nanoarchitectonics: Advances in nanoelectronics. In: Handbook of Nanoscience, Engineering and Technology 5. Neizvestny, I.G.: Trends in Development of Modern Silicon Nanoelectronics. In: IEEE Proceeding of 7th Annual 2006 International Workshop and Tutorials on Electron Devices and Materials (2006) 6. Snodgrass, W., Hafez, W., Harff, N., Feng, M.: Pseudomorphic InP/InGaAs heterojunction bipolar transistors (PHBTs). In: Electron Devices Meeting, San Francisco, CA, December 11-13 (2006) 7. Wey, C.-L.: Nanoelectronics: Silicon Technology Roadmap and Emerging Nanoelectronics Technology in Taiwan. In: IECON 2005, 31st Annual Conference of IEEE Industrial Electronics Society (2005) 8. International Technology Roadmap for Semiconductors, ITRS (2005) 9. Chin, D.: Nanoelectronics for an ubiquitous information society. In: IEEE International Solid-State Circuits Conference, 2005, Digest of Technical Papers, ISSCC (2005) 10. Ostroumov, R., Wang, K.L.: On power dissipation in information processing. Presented at the Amer. Phys. Soc. Meeting, Los Angeles, CA (March 2005) 11. Ostroumov, R., Wang, K.L.: Fundamental power dissipation in scaled CMOS and beyond. Presented at the SRC Techcon Conf., Portland, OR (October 2005) 12. Likharev, K.K.: Electronics below 10 nm. In: Nano and Giga Challenges in Microelectronics, pp. 27–68. Elsevier, Amsterdam (2003)

Unconditional Steganalysis of JPEG and BMP Images and Its Performance Analysis Using Support Vector Machine P.P. Amritha, Anoj Madathil, and T. Gireesh Kumar Centre for Cyber security, Amrita vishwa vidyapeetham, Coimbatore {pp_amritha,t_gireeshkumar}@cb.amrita.edu, [email protected]

Abstract. A feature based steganalytic method used for detecting both transform and spatial domain embedding techniques was developed. We developed an unconditional steganalysis which will automatically classify an image as having hidden information or not using a powerful classifier Support Vector Machine which is independent of any embedding techniques. To select the most relevant features from the total 269 features extracted, they apply Principal Component Analysis. Experimental results showed that our steganalysis scheme blindly detect the images obtained from six steganographic algorithms- F5, Outguess, S-Tool, JP Hide & Seek, LSB flipping and PVD. This method is able to detect any new algorithms which are not used during the training step, even if the embedding rate is very low. We also analyzed embedding rate versus detectability performances. Keywords: Steganalysis, Feature vectors, DCT, Spatial domain, Support Vector Machine.

1 Introduction Steganography is a method of information hiding in any media. The detection of hidden messages in an image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics. Firstly using six steganographic tools [2] we obtained stego images with embedding capacity 25%, 50 % and 100%. Unconditional Steganalysis mainly concentrated on both spatial and transform domain features. From cover and stego images 269 features was extracted. To evaluate the usefulness of a feature, and find the optimal feature subset to improve the algorithm efficiency Principal Component Analysis (PCA) was exploited. Then Support Vector Machine (SVM) was trained by the extracted features and then it was subjected to classify images. Performance study was done by classifying images to six known JPEG and BMP steganographic techniques and also by extracting quantized Discrete Cosine Transform (DCT) features using four JPEG quality factors (75, 80, 90 and 100). Unconditional steganalysis follow certain assumptions: V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 638–640, 2010. © Springer-Verlag Berlin Heidelberg 2010

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1. The hidden message contains approximately any amount of payload. 2. Spatial and transform domain technique is used by the steganographic algorithm for embedding. 3. Images can be in any of the format but here considered only for JPEG, BMP and PNG images. Performance analysis holds true for any stego scheme which satisfies the above assumptions.

2 Feature Eight first order statistics which include mean, standard deviation, skewness, kurtosis of pixels intensity, DCT coefficients and two pixel difference which gives which gives the deviation between the inner pairs and border pair pixels [4]. Next six features were obtained from chi-square test [3]. These features are mainly used for images embedded with LSB flipping steganography. 22 DCT features, 149 Extended DCT features [1], 81 Markov features are the remaining features for steganalysis. 269 features are summarized in Table 2. Table 2. Total Extracted Features of 269

3 Classification Based on Statistical Measures and SVM 3.1 Experimental Result Data set used: 600 cover images were taken and each 100 images were made stego images using six steganographic algorithms with different embedding rate (for e.g. using F5 50% of the payload was embedded in 50 images and remaining 50 was embedded with 25% of payload). We concluded that for JPEG images, the DCT features perform better than spatial features. Another advantage of DCT features is that the training is faster because the features have lower dimension and better seperability compared to spatial domain features. This is not surprising as the DCT features were built specifically for JPEG files while the spatial features are more used for steganographic methods that embed directly in pixels and in other image formats.

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The least detectable stego program among tested algorithm is the PVD and F5. The SVM was trained with parameters γ = 0.25, 0.5 and C = 100, 128, 64 determined using the multiplicative grid search.

4 Conclusion A new set of features for steganalysis was developed. We considered features that take into account the numerical changes in the pixel values and DCT coefficient introduced by embedding. The new merged feature set provides significantly better results than previous result. Classifier is capable of not only detecting stego images but also classifying them to appropriate stego algorithms. SVM classifier gave 75100% accuracy with some misclassification.

References 1. Pevny, T.: Merging Markov and DCT features for multi-class JPEG steganalysis. In: Delp, E.J., Wong, P.W. (eds.) SPIE, San Jose, CA, vol. 6505, pp. 311–314. Electronic Imaging, California (2007) 2. Steganography Software, http://www.jjtc.com/Steganography/tools.html 3. Westfeld, Pfitzmann, A.: Attacks on Steganographic Systems. LNCS, vol. 1768, pp. 61–75. Springer, Berlin (2000) 4. Wang, Y., Moulin, P.: Steganalysis of Block DCT image steganography. In: IEEE workshop on Statistical Signal Processing, IEEE Computer Society Press, Los Alamitos (2003) 5. Wu, D.C., Tsai, W.H.: A steganographic method for images by pixel-value differencing. Pattern Recognition Letters 24, 1613–1626 (2003)

ASSR Fair Load Distribution Using Efficiency Division Factor with Greedy Booster Approach for MANET Ahmad Anzaar1, Husain Shahnawaz1, Chand Mukesh1, S.C. Gupta2, and R. Gowri3 1 Research Scholar, Graphic Era University, Dehradun (U.K) India {anz.hmd,shahnawaz.husain,mukesh.geu}@gmail.com 2 Prof. IITRoorkee, India 3 Prof. Graphic Era University, Dehradun (U.K) India

Abstract. Mobile Adhoc Network Environment poses its unique challenges to the existing Transaction models, which are fail to solve. The main challenges of mobile computing environment are its heterogeneous environment, low and width and power resources. The transaction must be able to handle frequent disconnection because mobile user can move anywhere. In this paper, we presented a greedy booster approach with fair task distribution for MANET.Network topology is highly dynamic in mobile environment and no restriction is applicable to the nodes, if any node is performing some important transaction, it may leave the network then performance degraded abruptly. There is no mechanism and work is available, to control the performance in this critical situation. Our approach is able to overcome this situation and to utilize the maximum efficiency and resources of efficient node. Also applying this approach a Fair load can be distributed among nodes according to their efficiency by using Work Division Factor. The Fair load Distribution approach improves the performance of overall MANET . Keywords: Adhoc Networks. CH, EDF, WDF, Fair load Distribution.

1 Introduction Mobile Adhoc Network is a future technology; various challenges are superimposed by this technology. MANET inherited the challenges from cell architecture in addition bandwidth and highly dynamic topology and battery back up problem. MANET is used where no infrastructure is available for communication, such like disastrous area, military application. A mobile Transaction is structured as a Distributed transaction [2, 3]. In which the transaction is completed by the help of mobile nodes, providing different services. The mobile environment produces the significant challenges to transaction processing. The wireless network provides limited bandwidth so network bandwidth is a scarce resource. Battery power drains with data transmission and transaction processing. Primary applications of MANET are military tactical application, oceanography and the situation where a human being is unable to go and we have the condition to access the information about the climate and environment of V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 641–646, 2010. © Springer-Verlag Berlin Heidelberg 2010

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that location. Various approaches have been given to manage a MANET. Cluster based technique is one of them.

2 Related Work Dunham [4] suggests a model that is known as Kangaroo Transaction Model, where mobile nodes are basic unit for transaction initiation. Data access agent further extends this to handle data source. Data access Agent resides on mobile support station (MSS) and work on behalf of mobile units which lie in the range of host MSS. The Transaction normally hops from one MSS to another MSS as Mobile units move. However model does not discuss about Recovery. Crysanthis [1] considers the mobile transaction as a multidatabase transaction and introduces the additional notion of reporting and co-transactions. [1] Introduces a transaction proxy concept. Here a proxy run at MSS corresponding to each transaction and ensures the backup at the mobile hosts. Pitoura and Bhargava [7, 8] propose a transaction wherein they consider mobile transaction as an issue of consistency in a global multidatabase, which is divided into clusters. In [9] the model works on semantics based transaction and consider mobile transaction as a cache coherency problem and concurrency in a distributed database. Yeo and Zaslavksy [10] suggest for disconnected transaction processing and allow the local management of transaction via Compacts. If we consider these works of transaction in MANET, then any node that is assigned for a task and goes out from the cluster for a very short period then for connecting to that cluster after coming in the range it process the messaging to connect the cluster which is cumbersome and create congestion inside the network, but there are no tactics which fix this kind of the problem.In this paper we proposed a method for decreasing the messaging and congestion in the network, which is created by these types of the nodes (disconnected for short period).In [1] EDF is calculated on behalf of Resource dominant but in that approach EDF computation was not so effective. In A Refined Fair Load Distribution Using Efficiency Division Factor with Greedy Booster Approach for MANET model this problem is reduced.

3 Working of Proposed Model 3.1

Efficiency Division Factor

In the model we have assumed that each node in MANET is booster enabled, a threshold value is used to activate and deactivate the booster. The threshold (TMIN) value is related to strength of signal. All the nodes in the cluster send the performance table (as shown in table 1) using DSDV protocol to the cluster head (CH). According to efficiency of the node CH maintains a Performance table. On the basis of efficiency the CH assign the task to the nodes in the cluster. If any node goes out from the cluster and receiving a low signal (equivalent to link failed), then outgoing node checks the value of signal strength, if signal strength is less than TMIN then booster becomes active, the booster amplifies the signal, the node which is somehow connected to outgoing node also activate its booster when it realize the equivalence of link failure

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Table 1. Parameters for calculating efficiency division Node ID 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Battery Back Up (BB) 20% 30% 30% 60% 72% 68% 70% 80% 75% 94%

Resources(R)

Service Type

2 3 2 4 7 3 1 4 3 4

*abc *abc *abc *abc #xyz *abc #xyz $qwe #xyz #xyz

Processing Speed (%P) 56 46 65 49 59 78 58 49 67 55

Fig. 1. Comparison of Nodes of Same Services

We can calculate the resources in percentage from table K

Ri = Ri *100 / (• Rj)

(1)

j=1

Where K is the set of nodes of same type services. %EDF = %R+% P+%BB

(2)

From the equations (1, 2) the efficiency division factor (shown in Table 2)can be calculated, in which service type shows the services provided by the node, here we considered the same type of services shown in figure 2. 3.2 Fair Allocation Approach After computed the table 2, cluster head (CH) divide the work to particular nodes according to WDF shown in table 3. For example if WDF for node 1 is 30% of the work for the service *abc then for node 2 it is again 30% of the work for the service *abc and for node 4 it is 40% of task can be assigned. CH has the authority to distribute the work among the nodes of same services, but it should be below the %age of the EDF or equal to that.

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Battery Back Up(BB) 20% 30% 30% 60% 72% 68% 70% 80% 75% 94%

Resources (R)

Service Type

Resources (%R)

2 3 2 4 7 3 1 4 3 4

*abc *abc *abc *abc #xyz *abc #xyz $qwe #xyz #xyz

14.28 21.42 14.28 28.57 46.66 21.42 6.66 100 20 26.6

Processing Speed (%P) 56 46 65 49 68 78 59 49 69 58

%EDF

30.09 32.47 36.42 45.85 62.22 55.80 45.22 76.33 54.66 59.53

Fig. 2. %EDF for Same Services Node Table 3. Work Division Factor(Algorithm can be designed)

Node ID

%EDF

%WDF

1 2 3 4 6

30.09 32.47 36.42 45.85 55.80

30 32 36 44 55

Fig. 3. Load division on behalf of WDF

4 Result Analysis In Figure [2] node 6 is more efficient in comparison to other nodes of same type of services. In the proposed method the node 6 utilized fully in the cluster, so it increases

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the overall working efficiency of complete Network. Because node 6 has very good efficiency so cluster head would not like to relinquish that node until and unless that node goes very far away. From section 3.2 a work can be distributed among nodes of same services according to fair load distribution by using table 3, request generated for completion of the some application of service (*abc) which node 1,2,3,4 and 6 are provider, then work should be divided according to the work division factor and shown in Figure 3, if we provided 60% of the work to node 1 , and 40 % of the work to node 4, and table 2 shows that node 2 is much efficient comparison to node 1 then node 4 will complete before the node 1 complete, in this situation cluster head has to wait for node 1 to complete the work. But with the fair allocation approach node 1, 2 and node 4 will submit the work allotted to them within approximately same time, thus cluster head need not to wait for any other node, and hence overall performance of the network system will increase.

5 Conclusion and Future Work In this paper, we have provided the approach in which cluster head always try to tie up with the efficient node within its cluster, through the use of signal booster technology; Efficiency of node can be calculated by the use of efficiency division formulae, which we have simulated in efficiency division table. It will be very helpful for dividing the work among the nodes. No such type of signal booster and a fair allocation work division approach is available in MANET .Our approach will provide the new directions & dimensions in MANET. Future work will contribute the strength to the various QoS models in MANET, & designing of low weighted and less battery consumption enabled boosters and optimization of efficiency table and efficient algorithms for fair load distribution.

References 1. Husain, S., Ahmad, A., Chand, M.: A Fair Load Distribution Using Greedy Booster Approach in MANET. In: 3rd International Conference on Data Mining (ICDM 2010), jointly organized by University of Saskatchewan Canada, IMT Gaziabad, Nanyang Technological University, Singapore, India, March 11-12 (2010) 2. Navidi, W., Camp, T.: Stationary distributions for the random waypoint mobility model. IEEE Transactions on Mobile Computing, 99–108 (2004) 3. Fife, L.D., Gruenwald, L.: Research Issues for Data Communication in Mobile Ad-Hoc Network Database Systems. SIGMOD Record 32(2) (June 2003) 4. Dunham, M.H., Helal, A., Balakrishnan, S.: A mobile transaction that captures both data and movement behavior. ACM-Baltzer Journal on Mobile Networks and Application 2, 149–162 (1997) 5. Gruenwald, L., Javed, M., Gu, M.: Energy- Efficient Data Broadcasting in Mobile Ad-Hoc Networks. In: Proc. International Database Engineering and Applications Symposium (IDEAS 2002) (July 2002) 6. Pei, G., Gerla, M., Hong, X., Chiang, C.: A wireless hierarchical routing protocol with group mobility. In: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), pp. 1538–1542 (1999)

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7. Pitoura, E., Bhargava, B.: Revising Transaction concept for mobile computing. In: First IEEE Workshop on Mobile Computing Systems and Applications, June 1995, pp. 164–168 (1995) 8. Pitoura, E., Bhargava, B.: Maintaining consistency of data in mobile distributed environments. In: 15th International Conference of Distributed Computing System, pp. 404–413 (1996) 9. Walborn, G., Chrysanthis, P.: Supporting semantics based transaction processing in mobile database application. In: 14th IEEE Symposium on Reliable Distributed System, pp. 31–40 (1995) 10. Yeo, L., Zaslavksy, A.: submission of transaction from workstation in a cooperative multidatabase processing environment. In: 14th ICDCS 1994 (1994) 11. Spentzouris, P., Amundson, J.: FNAL Booster Experiment and Modeling. In: Proc. of the 2003 Particle Accelerator Conference, IEEE computer society, Los Alamitos 12. Chrysanthis, P.: Transaction Processing in Mobile Computing Environments. In: IEEE Workshop on Advances in Parallel and Distributed Systems (1993) 13. Mohan, C., Harderle, D., Lindsat, B., Pirahesh, H., Schwarz, P.: Aries: A Transaction Recovery Method supporting fine granularity locking and partial rollback using write ahead logging. ACM Transactions on Database Systems 17(1), 94–162 (1992) 14. Acharya, A., Badrinath, B.R., Imielinski, T.: Checkpointing Distributed Applications on Mobile Computing. In: Proceedings of the Third International Conference on Parallel and Distributed Information Systems (September 1994) 15. Pitoura, E., Bhargava, B.: Maintaining Consistency of Data in Mobile Distributed Environments. Technical Report TR-94-025, Purdue University, Dept. of Comp. Sciences (1994)

Secret Sharing Scheme for Image Encryption Using new Transformation Matrix A. Kalai Selvi1 and M. Mohamed Sathik2 1

Associative Professor in computer science, S. T. Hindu College, Nagercoil, India [email protected] 2 Associative Professor in Computer Science, Sadakathullah Appa College, Tirunelveli, India [email protected]

Abstract. This paper proposes image encryption and decryption process using new transformation matrix. The image is divided into zones .The zones are constructed from blocks of size 3 x 3 using secret key. All the zones are combined together to form a new transformation matrix and is used for encryption purpose. The sender sends the secret in the form of polynomial’s function value for their ID value. The receiver reconstructs the secret from the polynomial’s function value. Then form the transformation matrix and decrypt it to get the original image. The comparison of the proposed method with the Cross Chaotic map image encryption method [7] reveals that the proposed method is higher in security and superior in encryption quality and also the share to be sent is smaller. Keywords: Secret , Transformation matrix, Zone, Encryption, decryption.

1 Introduction The basic ideas can be classified into three major types: position permutation [2] and value transformation [3] and [4] and the combined form. This paper tells the shares of the participants[1]. In the present paper an innovative technique for image encryption is proposed based on transformation matrix. The new algorithm generates the transformation matrix from secret key and hence security against the image is achieved at low computational overhead.

2 Secret Process Secret sharing refers to the method for distributing a secret amongst a group of participants, each of which is allocated a share of the secret. A (t , n) threshold secret sharing scheme is used to distribute a secret “s” to “n” participants in such a way that a set of “t” or more participants can recover the secret “s” and a set of (t-1) or fewer participants cannot recover the secret “s”. In this method, the coefficients a1,… a t-1 are randomly generated . The constant term F(0) is a secret. Let the registered V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 647–650, 2010. © Springer-Verlag Berlin Heidelberg 2010

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participant be “n” and let t < n, where t is a threshold value. Each participant has their own identity value IDi ( i = 1 to n ) .The ID values and the encrypted image are in public. The function value of the polynomial for the input of participant’s ID value is performed. The function value for ID1 is share1; the function value for ID2 is share2 and so on. The sender sends share1 to the participant for ID1, share2 for ID2 and so on. The sender only sends the secret to the receiver.

3 Zone Construction The matrix of any size is divided into zones of size 6 x 6. The size of the image matrix with respect to zones is calculated by using the following calculation. R=

m / 6 +1 if mod(m,6) ≠ 0 m/6 if mod(m,6) = 0 (m , n) Æ matrix size ,

C=

n / 6 +1 if mod(n,6) ≠ 0 n/6 if mod(n,6) = 0

R Æ row zones , C Æ column zones

Each zone is partitioned into four blocks of size 3 x 3. The block matrix B is of size 3 x 3 is calculated using the following formula . B = ( b i j ) = S * power ( t , p ) mod 256

…………………(1)

where p = ( i – 1) * 3 + ( j – 1) , 1 ≤ i , j ≤ 3 , t Æ scalar , S Æ secret Each zone is constructed from the block of size 3 x 3. The zone is expressed in terms of blocks and is represented in the matrix form as follows. b11

b12

Z = b 21 b 22

where b11 = B mod 256 b12 = (Zsize * I + B) mod 256 b 21 = ( t * U − B ) mod 256 b 22 = −B mod 256, B Æ Block matrix

I Æ Identity matrix of size 3 x 3 U Æ 3 X 3 Unit matrix , Zsize Æ Zone size = 6

4 Encryption Process The original image is XOR with the new transformation matrix to get the encrypted image and is put in public. 4.1 New Transformation Matrix The block value is constructed with different secret values for each zone. The secret value for the zone Z11 is the initial secret S1, Z12 is S1+1and so on. If the secret value exceeds the value 255 then set the secret value t * S1 ( S1 is an initial secret) and then the next zone onwards increment this secret value by one. After constructing all the zones a new transformation matrix of size m x n is constructed from this matrix.

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5 Decryption Process The receiver accepts the share from the sender for reconstructing the secret. The receiver who is having the identification value ID1 accepts the share y(1), the receiver for ID2 receives the share y(2) and so on. Only t shares are enough to reconstruct the secret. The combiner receives t shares y(1), y(2), ..y(t) and the polynomial is reconstructed by using Lagrange interpolation formula. The constant term from the polynomial is taken as the secret value. t −1

F(x)=

∑

y (i )

∏

0 ≤ k ≤ t−1

i=0

k ≠ i

x − xk xi − xk

…………………(2)

where x1,x2, …xt Æ IDs, tÆThreshold value, y(1)= f(ID1) , y(2) = f(ID2) and so on. 5.1 Decryption The secret S can be obtain from the reconstructed polynomial F(i.e. S = F(0)). The new transformation matrix is formed from secret. The receiver takes the encrypted image and is decrypted with the transformation matrix to get the original image. It is implemented in MATLAB.

6 Sample Input Output

Fig. 1. Input Image

Fig. 2. Encrypted Image

Fig. 3. Output Image

7 Analysis 7.1 Correlation Analysis From the table result, two adjacent pixels from plain image is highly correlated and the correlation coefficient is approximately equal to 1; however, the correlation Table 1. Correlation coefficient of adjacent pixels in plain and encrypted image

Direction

Plain

Horizontal Vertical Diagonal

0.9775 0.9715 0.9713

Encrypted [7] 0.00261 0.00371 0.00403

Encrypt Proposed 0.0386 0.0012 0.0013

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coefficient of two adjacent pixels from encrypted image is approximately equal to 0, so they have little correlated relationship. Compared to the result from [7] the experimental result is better. 7.2 Information Entropy The entropy H(S) = −

N −1

∑ i =0

p( si ) log 2 p(si ) , where p(si) Æ the probability of si.

By calculation, the information entropy of encrypted image is equal to 7.9973, which means that information leakage in the encrypted process is negligible and the encryption system is secure from the entropy attack.

8 Conclusion In the new algorithm, the image is divided into zones and the zones are divided into blocks. Each zone value is performed with different key values. So it is almost impossible to extract the original image. The image can be decrypted only when they know both the key and the algorithm. Thus the proposed method is higher in security and superior in encryption quality and also the size of the share is far smaller than the original image.

References 1. Runhua, S., Hong, Z., Liusheng, H., Yonglong, L.: A (t,n) Secret Sharing Scheme for Image encryption. In: 2008 Congress on Image and Signal Processing Proceeding of IEEE (2008) 2. Shamir, A.: How to Share a Secret. Communication. ACM 22, 612–613 (1979) 3. Naor, M.: Visual Cryptography. In: De Santis, A. (ed.) EUROCRYPT 1994. LNCS, vol. 950, pp. 441–449. Springer, Heidelberg (1994) 4. Zhenfu, C.: A threshold key escrow based on public key cryptosystem. In: Science in China (Series E ), pp. 441–448 (2001) 5. Lerma, M.A.: Modular Arithmetic (2005), http://www.math.northwestern.edu/~mlerma/problem_solving/ results/modular_arith.pdf 6. Lukac, R., Plataniotis, K.N.: A Secret Sharing Scheme for Image Encryption. In: 46th International Symposium Electronics in Marine, EIMAR 2004, pp. 549–554 (2004) 7. Wang, L., Ye, Q., Xiao, Y., Zou, Y., Zhang, B.: An Image Encryption Scheme Based on Cross Chaotic Map. In: Proc. IEEE 2008 Congress on Image and Signal Processing, pp. 22–26 (2008)

Measuring the Reusability Level of Software Packages Using Reusability Testing Scripts R. Kamalraj1, A. Rajiv Kannan2, P. Ranjani3, and R. Hemarani4 1

AP / CSE, PPG Institute of Technology, Coimbatore, Tamil Nadu, India 2 AP / CSE, KSR College of Engg., Tiruchengode, Tamil Nadu, India 3 Lecturer / IT, Avinasilingam University for Women, Coimbatore, Tamil Nadu, India 4 Lecturer / MCA, KSR College of Engineering, Tiruchengode, Tamil Nadu, India

Abstract. Software Testing approaches are playing essential role to satisfy the clients’ needs without defects in delivered systems. Different testing methods will be used to filter the systems defects and improve the system quality. In testing method wise here proposed ‘Reusability testing scripts’ can be considered for finding the reusable packages in the current system development to fulfill the requirement of future requirement. To find the reusable level, the different package metrics such as ‘Coupling’, ‘Cohesion’, ‘Stability’, and ‘Complexity’ are analyzed to define the reusability level of software packages. Keywords: Software Testing, Reusability Testing scripts, Regression Testing, Design Metrics.

1 Introduction Software Engineering is composed of ‘Requirement Analysis’, ‘Design’, ‘Implementation’, ‘Testing’ and ‘Maintenance’ phases. First three phases are used to start and construct the software system. In testing phase, system will be tested to find the defects and rectify the problem hidden in the system.

2 Reusability of Software Package Reusability means that the developed packages can be reusable or not [10]. Reusability plays a very important role to reduce the efforts on software project management [1]. To identify the similar packages from the existing system Domain Analysis, Package Analysis and System Analysis will be done. Here we proposed a Testing approach named as ‘Reusability Testing (IRR)’ to measure the above mentioned metrics to find the reusable level of a package. 2.1 Coupling (C) The strength of link between two modules is called ‘Coupling’. If a package is Highly Coupled (C >20) with other system modules then it may be difficult to separate the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 651–653, 2010. © Springer-Verlag Berlin Heidelberg 2010

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module or code from the whole system. Only Low Coupled (C <20) modules can be separated without any problem. 2.2 Stability of a Package By measuring the ‘Import (IC)’ and ‘Export Coupling (EC)’ [4] of a package the ‘Stability’ can be defined. If one package has only ‘Import Coupling’ then it could not be alive without supportive modules. Instability (I) = IC / (IC + EC)

(1)

When I = 0, then Stability = 1and When I = 1, then Stability = 0. 2.3 Cohesion (Co) The logical relationship among modules in a software system is known as ‘Cohesion’. One element in the package may have a tie up with other element to provide a service from the package.

3 Reusability Testing The ‘Software Testing’ in system development is most important block/phase to feel the performance of system developers and developed software systems. The ‘Integration Testing’ and ‘Regression Testing’ used to check the connection and change on system behavior when different input is given to system. 3.1 Testing Scripts for Reusability Testing The following questions have to be placed for checking the ‘Reusability Testing’ metrics. The testing script number 1, 2, 4 and 5 may be used in ‘Integration Testing’ to get respective answers. Those outputs will be matched with requirement of reusability. The suggested ‘Reusability Testing’ Scripts are 1 2 3 4 5

What is the type and level of ‘Coupling’ of an individual Package? What is the value of ‘Import Coupling’ and ‘Export Coupling’ of a Package? What is the value of ‘Stability’ metric of a package? What is the ‘Complexity’ level of code used for a software package? What is the ‘Size’ of software package?

The scripts may be used to check the reusable level of developed packages. If packages found, the list of reusable package is regularly updated to help for further system development activities.

4 Results and Discussions The ‘Reusability Testing’ which requires less effort from ‘Integration’ and ‘Regression’ testing types to find metrics value to verify, whether those values can satisfy the

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requirement of package reusability. The merits of proposed ‘Reusability Testing’ approach are it can be followed to categorize the packages and extending the information in ‘Knowledge Based System’ with identified ‘reusable packages ’Software Project Management efforts may be reduced for ‘Quality Management’, ‘Resource Management’, ‘People Management’ and ‘Change Management’ through using reusable packages in current development activities [5].

5 Conclusion The proposed ‘Reusability Testing’ method can support for product development by introducing existing matched packages. The test scripts of ‘Reusability Testing’ may be worth full for further enhancement like ‘Quality Improvement’ or ‘Adaptive Maintenance’ of a product through analyzing and modifying available functionalities in the package(s) that are very easy to modify.

References 1. Cheng, J.: A reusability-based software development Environment. In: International Conference on System Sciences, vol. 19(2), pp. 57–62 (1994) 2. Wang, A.J.A.: Reuse Metrics and Assessment in Component-Based Development. In: Proceedings of Software Engineering and Applications, vol. 47, pp. 693–707 (2002) 3. Kamalraj, R., Geetha, B.G., Shyamaladevi, V.: Checking Reusability of Software Packages using Software Integration Testing. In: IJRE, vol. 1 (2009) 4. Kamalraj, R., Geetha, B.G., Singaravel, G.: Reducing Efforts of Software Project Management using Software Package Reusability. In: IACC 2009, vol. 1, pp. 1624–1627 (2009) 5. Kamalraj, R., Rajiv Kannan, A., Hemarani, R.: Metrics of Reusable Packages for Enhancing the Blocks of Software Project Management. CIIT International Journal of Software Engineering and Technology (August 2009) 6. Nunamaker Jr., J.F., Chen, M.: Software productivity: a framework of study and An approach to reusable components. In: Proceedings of the 22nd Annual Hawaii International Conference on System Sciences, Software Track, January 3-6, vol. 2 (1989) 7. Basili, V.R., Bri, L.C., Thomas, W.M.: Domain Analysis for the Reuse Of Software Development Experiences. Experimental Software Engineering Group (ESEG) 11, 86–95 (1994) 8. Jones, C.: Software Tracking: The Last Defense Against Failure. Crosstalk – Journal of Defense Software Engineering 21 (April 2008) 9. Abdullah, K., Kimble, J., White, L.: Correcting for unreliable Regression Integration testing. In: Proceedings of Software Maintenance International Conference, vol. 1, pp. 232– 241 (1995) 10. Bazilchuk, N., Mohagheghi, P.: The Advantages of Reused Software Components. R&D and Technology Transfer (January 2005)

A New Threshold Calculation Approach in the Performance Enhancement of Spread Spectrum System Using Double Density Discrete Wavelet Filter Arunarasi Jayaraman1 and Indumathy Pushpam2 1

Electronics and Communication Engg. Magna College of Engineering, Chennai 2 Department of Electronics Engg. MIT Campus, Anna University, Chennai [email protected]

Abstract. Wavelets are considered as powerful signal processing tools that provide an effective alternative to conventional signal transforms. Spread spectrum plays an important role in CDMA communication system. Because of its unique characteristics, it has been first used for military applications. So spread spectrum communication via, discrete wavelet transform and thresholding becomes effective now a days. This paper describes a near optimal threshold estimation technique for signal denoising in spread spectrum communication using Double Density Discrete Wavelet filter. A new threshold calculating formula is utilized. Performance of the proposed system is found to be better than the conventional spread spectrum receiver. Keywords: Denoising, Spread Spectrum, Double Density Discrete wavelet filter.

1 Introduction CDMA works on the basis of spread spectrum techniques where each user occupies the whole bandwidth accessible. A digital signal is spread (i.e., multiplied by) at the transmitter with the aid of a pseudo-random noise (PN) code [1]. A receiver dispreads the signal obtained, so as to recover the original information, by employing a locally generated PN code. Now days, wavelet transform have become more effective tools for correlation than the conventional signal transform. Denoising refers to the reduction of noise level by processing the wavelet coefficients. In this paper Double density discrete wavelet transform [2] is used. At each decomposition level threshold have been calculated and soft thresholding is applied. The use of wavelets is to identify the presence of a spread spectrum signal that can increase accuracy. In [3] authors used a new method of denoising in spread spectrum systems in which noise signal and code are replaced by wavelet coefficients. In [4] and [5] wavelets for denoising in an attempt to enhance CDMA signal for time delay estimation and code tracking is used. In this paper, a near optimal threshold estimation technique using wavelet transform for signal denoising in spread spectrum communication in the presence of V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 654–659, 2010. © Springer-Verlag Berlin Heidelberg 2010

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Additive White Gaussian Noise (AWGN) is proposed. Double density discrete wavelet transform (DD-DWT) is used for signal decomposition with three sub bands. This paper is organized as follows: in section 2: Introduction of DD DWT is discussed, in section 3: soft threshold technique used in spread spectrum is discussed, in section 4: proposed denoising method with threshold calculating parameters and denoising algorithm is explained. In section 5: simulation results are discussed. Here, the system (BER) performance is evaluated as a function of energy per bit to noise power spectral density (Eb/No) when the various excision techniques are applied to the DSSS prior to the detection. Finally in section 6: conclusions are drawn from the work contained in the paper.

2 Double - Density Discrete Wavelet Transform To implement the double-density DWT, an appropriate filter bank structure is to be selected. The filter bank proposed in Figure 1 illustrates the basic design of the double-density DWT.

Fig. 1. A 3-Channel Perfect Reconstruction Filter Bank

The analysis filter bank consists of three analysis filters—one lowpass filter denoted by h0(-n) and two distinct highpass filters denoted by h1(-n) and h2(-n). As the input signal x(n) travels through the system, the analysis filter bank decomposes it into three subbands, each of which is then down-sampled by 2. From this process, the signals c(n), d1(n) and d2(n) are obtained which represent the low frequency (or coarse) subband and the two high frequency (or detail) subbands respectively. In general signal decomposition and reconstruction can be done as explained here. But in this paper, only a single level of decomposition without up and down sampling has been used.

3 Soft Thresholding Technique The standard technique for denoising is soft thresholding of the wavelet coefficient wt via,

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δS η (wt) = sign(wt)(| wt|- η)+. where, sign(wt) = +1 if wt > 0, = 0 if wt=0, = -1 ifwt < 0, and x+ = x ,if x > 0, = 0 ,if x <0.

(1)

sign (wt) is the signum function .Instead of forcing wt to zero or leaving it untouched, soft thresholding pushes all the coefficients towards zero. And hence the smoothing effect is better in soft thresholding than the hard thresholding.

4 Proposed Wavelet Based Denoising Method for Spread Spectrum Systems Wavelet transform works well in the lower dB values and hence it is more advantageous than the conventional one. This section compares the conventional CDMA receiver with the wavelet - based denoising spread spectrum receiver (fig.2). The main components of the conventional receiver is shown, the signal received by the antenna is directly multiplied by the spreading code, prior to correlator. The signal obtained at the output of the decision device is given by (2) in which we have supposed a perfect synchronization condition

, S and C

are the received noisy signal and spreading code respectively.

Fig. 2. CDMA receiver using Double density Discrete wavelet Transform

In the proposed method shown in fig.3 the received signal goes through the Double density discrete wavelet transform. From the filtered coefficients threshold values have been calculated for each subband of decomposition. Soft thresholding is adopted in which noise levels are almost reduced and the signal obtained after thresholding is multiplied by the same code which is used in the transmitter. The signal obtained is called despreaded signal which has been demodulated to obtain the final denoised signal. If S(t-Td) be the received noisy signal. The wavelet expansion of the received signal is given by

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(3) where sk and rjk are approximation and detail coefficients of the noisy signal. Фk(t) and ψk(t) are the scaling functions and the wavelet functions respectively. 4.1 Signal Denoising Algorithm This section describes the signal denoising algorithm which achieves near optimum soft thresholding in the wavelet domain for recovering original signal from the noisy one. The algorithm is very simple to implement and computationally more efficient. Denoising algorithm has the following steps: • • • • •

Perform single level decomposition of the signal corrupted by AWGN using Double density discrete wavelet transform. In the above step, the input signal is filtered with the FIR filter having the impulse response of h0(-n ), h1(-n ) and h2(-n ) ,which denotes the three columns of a matrix By robust median estimator, noise variance σ2 can be found using equation (6). For each subband of decomposition, the scale parameter β can be calculated using equation (5) For each subband, Compute standard deviation σy Compute threshold using the equation (4) Apply soft threshold to the sub band coefficients

4.2 Estimation of Parameters for Calculating Threshold This section describes the method for computing the various parameters used to calculate the threshold. T=(0.05128*β*σ2)/(√3.5*σy) (4) where, the scale parameter β is computed using the following equation β=√(log(L/4))

(5)

2

L is the length of the filtered coefficient, σ is the noise variance, which is estimated from the subband HH1, using the formula σ2=(median(|Yij|)/0.6745)2,Yij Є subband HH1

(6)

σy is the standard deviation of the subband under consideration computed by standard matlab command.

5 Simulation Results This section presents the results of computer simulation to evaluate the performance of the proposed signal detection method in spread spectrum systems. An AWGN channel has been assumed. The CDMA signal used in simulations were generated by

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QPSK modulation and m-sequence of 64-bit spreading code is used for spreading. Daubechies wavelet with DD-DWT is used, single level of decomposition is utilized. Accordingly after denoising the final reconstructed signal output is calculated followed by a decision about the received symbols by comparing these summations (3) by the defined threshold value. Thresholding have been done for the wavelet coefficients which is obtained by the double density discrete wavelet transform. From the simulation the proposed method of denoising is found better than the other methods. Table 1. Bit Error rate for different dB values dB

sf=64

sf=32

sf=16

-4 -3.5 -3 -2.5 -2 -1.5 -1 -.05 0

.0004 .0003 .0003 .0002 .0001 .0001 .0001 .0001 .0001

.0165 .0130 .0100 .0084 .0064 .0046 .0034 .0026 .0021

.0700 .0626 .0554 .0478 .0421 .0371 .0315 .0262 .0220

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-3

-4

-5

-4

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-2 -1.5 Eb/No(dB)

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0

Fig. 3. BER Vs Eb/No for the proposed method

6 Conclusion In this paper, a new wavelet threshold technique in spread spectrum communication is discussed. Performance shows the advantages of the proposed method in terms of lower bit error rate. In the proposed method Double density discrete wavelet transform has been used. The signal and code have been substituted by their wavelet expansion for correlation operation of a conventional spread spectrum receiver. Simulation result shows the advantage of the proposed method compared to other methods.

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In future, performance of spread spectrum communication can be improved by manipulating different denoising techniques and selecting appropriate wavelet families.

References 1. Dinan, E.H., Jabbari, B.: Spreading codes for direct sequence CDMA and wideband CDMA cellular networks. Communications Magazine 36(9), 48–54 (1995) 2. A New Method in Spread Spectrum Signal Detection Based on wavelet transform – CCECE/CCGEI, Saskatoon, IEEE (May 2005) 3. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, London (1998) 4. Li, L., et al.: High Resolution Time-Delay Estimation Based on Wavelet Denoising. In: ICCCAS 2004 (2004) 5. Burley, S., et al.: Enhanced PN Code Tracking and Detection Using Wavelet Packet Denoising. In: IEEESP Int. Sym. (1998)

Recent Trends in Superscalar Architecture to Exploit More Instruction Level Parallelism Ritu Baniwal and Kumar Sambhav Pandey Computer Science and Engineering Department NIT Hamirpur [email protected], [email protected]

Abstract. Today’s architectures are moving towards to exploit more and more parallelism. Instruction level parallelism (ILP) is where multiple instructions are executed simultaneously. Superscalar architecture was one of such evolutions. To exploit ILP superscalar processors fetch and execute multiple instructions in parallel thereby reducing the clock cycles per instruction (CPI). ILP can be exploited either statically by the compiler or dynamically by the hardware. In this paper the basic superscalar approach and the improvements made to the superscalar architectures to exploit more parallelism in execution have been discussed. Keywords: Superscalar architectures, Instruction level parallelism, CPI.

1 Introduction Parallel processing is the need of today’s architectures. Parallel processing reduces the execution time taken by any program. The execution time taken by any program can be determined by three factors: First, the number of instructions executed. Second, number of clock cycles needed to execute each instruction and the third is the length of each clock cycle. Instruction-level parallelism (ILP) is where multiple instructions from one instruction stream are executed simultaneously. ILP can be exploited by: pipelined execution (overlapping of instructions), superscalar execution (fetch and execute multiple instructions per clock cycle) and out-of-order execution (in-order commit). Superscalar architectures have exploited instruction-level-parallelism (ILP). Superscalar machines dynamically extracted ILP from a scalar instruction stream. Superscalar architectures fetched and executed multiple instructions simultaneously in one clock cycle reducing the number of clock cycles per instructions thus reducing the execution time. The CDC 6600 [9] used a degree of pipelining, but achieved ILP through parallel functional units. Another remarkable processor of the 1960s was the IBM 360/91 [3]. The 360/91 was deeply pipelined, and provided a dynamic instruction issuing mechanism, known as Tomasulo’s algorithm [10]. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 660–665, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Motivation As scalar processors fetched one instruction at a time so the throughput was limited to only one instruction. To enhance the throughput and performance parallelism in instruction execution can be implemented superscalar pipeline. Since the instructions were executed in parallel so the Clock cycles Per Instruction (CPI) was reduced. The Figure 1 shows the difference between the two approaches.

(a)

(b)

Fig. 1. (a) Scalar approach (b) Superscalar approach

3 Superscalar Implementations ILP can be exploited either statically by the compiler or dynamically by the hardware. Dynamic instruction scheduling was an important feature of superscalar processor. Each individual instruction takes some time to fetch and execute which is called instruction’s latency. To reduce the time to execute a sequence of instructions i.e. a program, two things can be done. First, individual instruction latencies can be reduced. Second, more instructions can be executed in parallel. Superscalar processors exploited ILP by considering the latter method. Parallel instruction processing required: the determination of the dependencies between instructions, adequate hardware resources to execute multiple operations in parallel, strategies to determine when an operation is ready for execution, and techniques to pass values from one operation to another. The various stages implemented in the superscalar architecture were: First, the fetch stage which was capable of fetching multiple instructions at a time. Second, the decode stage; the dependencies between different instructions were detected at this stage. The next stage, the issue stage; the decision about what instruction should be issued and at what time, has been taken at this stage. At the execution stage, the instructions were executed in parallel by the redundant functional units. When the instructions were completed, these were reordered and were committed in order. 3.1 Compile Time Scheduling Methods In case of direct issue, the instructions were issued to the functional units in-order. The simple superscalar approach implementing direct issue is shown in Figure 2. In

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case of indirect issue as shown in Figure 3, the instructions were issued to various reservation stations (RSs) from the issue unit. Reservation station fetched and buffered an operand as soon as it was available thereby eliminating the need to get the operand from a register [5]. The results were passed directly to the functional units from the reservation stations where they were buffered, rather than going through the registers. This bypassing was done with a common data bus (CDB) that allowed all units waiting for an operand to be loaded simultaneously.

Fig. 2. Superscalar Approach (Direct Issue)

Fig. 3. Superscalar Approach (Indirect Issue)

3.2 Dynamic scheduling methods To exploit more parallelism the limitation of control dependence should be overcome. This can be done by speculating on the outcome of branches and executing the program as if guesses were correct. Hardware based speculation combined the three ideas: dynamic branch prediction, speculation to allow the execution of instructions before the control dependencies are resolved and dynamic scheduling [5]. Speculation allowed the instructions to be executed in out-of-order fashion but forced them to commit in order. Adding this commit phase to the instruction execution required an

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additional hardware buffer named reorder buffer (ROB) that could hold the results of instructions that have finished execution but have not been committed.

4 Improvements 4.1 Multithreaded Superscalar Architecture The out-of-order and speculative superscalar architectures needed changes in instruction set. In the multithreaded architecture, instructions from each thread were issued non-speculatively and in-order thus reducing the complexity of the processor. In traditional multithreaded architecture, round robin policy was adopted for the execution of instructions which switched the instruction execution to a new thread in each cycle. In other implementations, when more than one active threads were available multithreading kept the pipeline busy by immediately switching to another thread upon encountering a hazard [6]. The performance has been increased when more threads were available for execution. 4.2 Superscalar Processors with Multibank Register File To exploit more instruction level parallelism, the number of ports and entries in the register file has been increased. Due to the increase in the number of ports and entries, the access time and power consumption was increased which lowered down the performance of the processor. The use of multibank register file avoided this problem. This architecture is shown in the Figure 4. But there was a problem in this approach. In case of bank conflicts, the access time was increased due to which the execution time has also been increased. There were two methods to solve this problem: First, register renaming, so that access would not concentrate on a particular bank. Second, the reduction in the number of register accesses to decrease the register bank access conflicts. This can be done by forwarding in which result of an instruction may be forward to the succeeding instruction [7]. Multibank register file in superscalar processor achieved higher clock rate.

Fig. 4. Multibank clustered architecture

4.3 Wide Issue Superscalar Processors As number of instructions issue has been increased, the number of read and write ports has also been increased. The number of ports on each physical register has been

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reduced by specializing the write access ports and read access ports. In register write and read specialization, different functional unit groups could have write access to only a subset of physical register file. Thus reducing the number of write and read ports to each individual register. The main problem with this approach was that there was a deadlock issue [8] which may arise when register write specialization was used in situations where number of physical registers in some register subsets was smaller than the number of logical registers in the ISA. 4.4 Two-Dimensional Superscalar Processor This architecture also known as Grid ALU Processor (GAP) used the advantages of a superscalar processor and of a coarse grained dynamically reconfigurable system [15]. This architecture used the two dimensional arrangement of functional units. The data dependent instructions can be issued to the functional units in same clock cycle. GAP as an in-order issue processor, provided throughput similar to that is provided by an out-of-order processor. It does not require any out-of-order logic like issuing buffers, reorder and commit stages, as well as register renaming [15].

5 Conclusion and Future Work Superscalar processors were introduced to exploit instruction level parallelism by reducing the CPI. Various improvements have been made to these architectures to exploit more parallelism and to get more throughput with the maximum utilization of available resources. Multithreaded superscalar processor has given better performance when multiple active threads were available. To reduce the number of ports multibank register file concept has been introduced. The number of ports on each physical register can also be reduced by specializing the read and write access ports. In future, the superscalar features can be incorporated in vector processor to get the benefits of both; the superscalar processor and the vector processor. Vector processor used deeply pipelined functional units and all the operations were performed concurrently until all the elements were processed. Superscalar architectures reduced execution time by fetching and dispatching multiple instructions per clock cycle thus reducing the number of CPI. Vector architectures, on the other hand, reduced execution time by reducing the number of instructions executed. Vector processor operated on a vector and superscalar processor issued multiple instructions at a time. Multiple words of a short vector can be operated in parallel by using the superscalar issue in vector processing. This increases the throughput and performance.

References 1. Smith, J.E., Sohi, G.S.: The Microarchitecture of Superscalar Processors. In: IEEE, vol. 83, no. 12 (December 1995) 2. Yeager, K.: The MIPS R10000 Superscalar Microprocessor. IEEE Micro. 16(2), 2840 (1996) 3. Anderson, D.W., Sparacio, F.J., Tomasulo, R.M.: The IBM System/360 Model 91: Machine Philosophy and Instruction-Handling. IBM Journal of Research and Development 11, 8–24 (1967)

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4. Keckler, S.W., Dally, W.J.: Processor Coupling: Integrating Compile Time and Runtime Scheduling for Parallelism. In: ACM (1992) 5. Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach, 4th edn. 6. Kish, B.J., Preiss, B.R.: Hobbes: A Multithreaded Superscalar Architecture 7. Hironaka, T., Maeda, M., Tanigawa, K., Sueyoshi, T., Aoyama, K., Koide, T., Mattausch, H.J., Saito, T.: Superscalar Processor With Multibank Register File. In: Innovative Architecture for Future Generation High- Performance Processors and Systems, IWIA 2005 (2005) 8. Seznec, A., Toullec, E., Rochecouste, O.: Register Write Specialization Register Read Specialization: A Path to Complexity-Effective Wide-Issue Superscalar Processors. In: 35th Annual IEEE/ACM International Symposium on Microarchitecture (2002) 9. Thornton, J.E.: Parallel Operation in the Control Data 6600. Fall Joint Computers Conference 26, 33–40 (1961) 10. Tomasulo, R.M.: An Efficient Algorithm for Exploiting Multiple Arithmetic Units. IBM Journal of Research and Development, 25–33 (January 1967) 11. Vajapeyam, S., Mitra, T.: Improving Superscalar Instruction Dispatch and Issue by Exploiting Dynamic Code Sequences. In: ACM (1997) 12. Glass, D.N.: Compile-time Instruction Scheduling for Superscalar Processors. In: IEEE (1990) 13. Bing, Y., Zhigang, M., Chuhui, G.: A Microarchitecture of Clustered Superscalar Processor. In: IEEE (2007) 14. Patterson, D.A., Hennessy, J.L.: Appendix G: Computer Organization and Design. In: The Hardware and Software Interface, 4th edn., 15. Uhrig, S., Shehan, B., Jahr, R., Ungerer, T.: A Two-dimensional Superscalar Processor Architecture. In: Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, IEEE, Los Alamitos (2009)

A Neural Network Based Solution to Color Image Restoration Problem Satyadhyan Chickerur and M. Aswatha Kumar Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore, India [email protected], [email protected]

Abstract. In this paper, the problem of color image restoration using a neural network learning approach is addressed. Instead of explicitly specifying the local regularization parameter values, we modify the neural network weights, which are considered as the regularization parameters. These are modified through the supply of appropriate training examples. The desired response of the network is in the form of estimated value for the current pixel. This estimate is used to modify the network weights such that the restored value produced by the network for a pixel is closer to this desired response. In this way, once the neural network is trained, images can be restored without having prior information about the model of noise/blurring with which the image is corrupted. Keywords: Color Image Restoration, Neural Networks, Regularization, Ill Posed Problem.

1 Introduction Restoration of blurred and noisy images belongs to the class of inverse problems, which requires the adoption of many different approaches and one of them is the regularization approach [1][2][3][4]. In the presence of both blur and noise, the restoration process is ill conditioned and requires the specification of additional smoothness constraints on the solution [5]. This is generally achieved by adding a regularization term in the associated cost function, in addition to the usual least square error term. Regularization parameter controls the relative contribution of these two terms. Due to the non-stationary nature of images, adaptive processing techniques have been applied to images degraded by noise and blur to allow adequate preservation of important image features such as edges and textures. It has been generally seen that a technique suited for restoring edges is not efficient for restoring texture portion of the image and vice versa. In this paper, a neural network based approach to solve this adaptive regularization problem [6] for color images is undertaken. Here the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 666–671, 2010. © Springer-Verlag Berlin Heidelberg 2010

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local regularization parameters are regarded as neural network weights and are then modified through the supply of appropriate training examples. The updated pixel value in the current iteration is regarded as the network output. This is expressed as a function of both the neighboring pixel values in the previous iteration, and the regularization parameter value. A weighted-order statistic (WOS) filter is used to predict the most probable value for the current pixel. This is considered as a desired network output, which forms part of a training example, and is used to adjust the network weight. Unlike conventional WOS noise filtering applications, where we replace the previous pixel value with the predicted value, we now adopt this WOS estimate output as a desired network output to guide the adaptation of the regularization parameter. As a result, instead of choosing this parameter by trial and error, we can now explicitly incorporate neighboring pixel value information to guide its selection. This makes it possible to automatically adjust the regularization parameters under different degradation conditions. A salient aspect of our solution is the local estimation of the restored image based on gradient descent approach. This organization of the paper is as follows: the next section introduces color image restoration using neural networks, followed by the section, which presents some experimental data and results from this investigation. The last section concludes this paper.

2 Color Image Restoration Using Neural Networks The application of neural networks to image restoration problem is receiving a lot of attention in literature these days [7][8][9]. In this section, application of a neural network using back-propagation algorithm to image restoration problem is presented. 2.1 Image Restoration-Using Regularization Regularized image restoration methods aim to minimize the constrained least-squares error measure

1 1 E = || y − Hx ||2 + λ || Dx ||2 . 2 2

(1)

where y vector represents blurred image pixels, x vector is the restored image pixel estimate, λ represents the regularization parameter and D is the regularization matrix, which is generally a high pass operator. A salient aspect of our solution is the local estimation of the restored image based on gradient descent approach. This equation can be further used for restoring images using neural network as this equation and back propagation neural network equation can be considered equivalent in this scenario.

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2.2 Regularization Using Neural Network

The key to solving this problem is to minimize the degradation measure which is represented by Ei.j defined for each pixel (i, j) in an M X N image and is given by 2 2 Ei, j = 1 ⎡⎢ yi, j − h ∗ x ⎤⎥ + 1 λi, j ⎡⎢⎣d ∗ x ⎤⎥⎦ . ⎦ 2⎣ 2

(2)

Where h*x denotes the convolution between a blur filter h centered at point (i, j) and the restored image x; d*x represents the convolution between a high pass filter d centered at a point (i, j) and the restored image x. The Variable λi, j represents the local regularization parameters. In general, large values of λ are required for smooth regions and small λ for edges. Iterative update approaches are generally used to minimize this cost function by gradient optimization approaches as

∂Ei, j = h ∗ x − yi, j h− a,−b + λ (d ∗ x)h− a,−b ∂x p,q

(

)

(3)

This gradient optimization is done using a back propagation neural network, which is a supervised neural network. The neural network design is as shown in the Fig.1.

Fig. 1. Image Restoration approach using neural network based approach

2.3 Color Image Restoration Using Neural Network

To minimize the cost function, we need to equate the above derivative to zero for the updated pixel x′p ,q in which the previous color value x p ,q is replaced by x′p ,q .Thus the required amount of update for the pixel is given as

A Neural Network Based Solution to Color Image Restoration Problem

Δx p,q = x′p,q − x p,q

669

(4)

in addition, this can be further enumerated as for red value of the pixel

Δx( p,q ) = x(′ p,q ) − x( p,q )

for green value of the pixel

Δx( p ,q ) = x(′ p ,q ) − x( p , q )

for blue value of the pixel

r

r

g

g

Δx( p ,q ) = x(′ p ,q ) − x( p ,q ) b

b

b

r

g

(5)

(6)

(7)

It is thus seen that the required amount of update depends on the local regularization parameter λi, j . To train the neural network we need two sets of data. One set of data is the input vector of the blurred /corrupted image and the other set denotes the individual color level change Δx dp ,q for each pixel for satisfactory restoration. In other words, the training example is of the form

(x

d p ,q , Δx p , q

)

(8)

The training data given in Equation 6 above is used to train the neural network and thus, we can update the parameter λi, j . Since we are using supervised learning approach, for effective restoration we require a target image, which is the expected output of the restoration process. This target image is generated using the WOS filtering as a preprocessing step applied to the blurred/noisy image.

3 Experimentation and Results The restoration algorithm using ANN was implemented on Intel core 2-duo processor. The algorithms were implemented using C and Matlab on Suse Linux. Different images like Lena, Airplane, Bridge, Bird and Boat were used to evaluate performance of the above said approach to image restoration. The images were degraded with noise; blur and both blur and noise. The results are encouraging and the quality of images restored with this approach is of good visual quality as shown in Fig.2. It also shows the snapshots during the training of the neural network and the error reduction for one of the cases of the proposed approach of image restoration. In Fig.3 PSNR and RMSE values for various approaches is compared with the proposed approach. The Graphs indicate that the proposed approach performs better than the various other approaches shown.

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Fig. 2. Neural network based restoration results for various color image data sets and shots of the training, goal achieved of the proposed neural network

snap-

A Neural Network Based Solution to Color Image Restoration Problem

671

Fig. 3. PSNR and RMSE of the Proposed approach in comparison of various other approaches

4 Conclusion A new approach to color image restoration has been presented in this paper. Advantages of this method is that once the neural network is trained, the images can be restored without any information about the noise/blurring model. In addition to this, the proposed method gives encouraging results as compared to other conventional methods.

References 1. Banham, M.R., Katsaggelos, A.K.: Digital Image Restoration. IEEE Sig. Proc. Mag. 1053(5888), 24–41 (1997) 2. Andrews, H.C.M., Hunt, B.R.: Digital Image Restoration. Prentice Hall, NJ (1977) 3. Jain, A.K.: Fundamentals of digital Image Processing, PHI, New Delhi (2001) 4. Kang, M.G., Katsaggelos, A.K.: Simultaneous Iterative Image Restoration and Evaluation of the Regularization Parameter. IEEE Trans. Sig. Proc. 40, 2329–2334 (1992) 5. Miller, K.: Least Squares Methods for Ill-Posed Problems with a Prescribed Bound. J. Math. Anal. 1, 52–74 (1970) 6. Wong, H.S., Chan, L.: A neural learning approach for adaptive image restoration using a fuzzy model based network architecture. IEEE Trans. Neu. Net. 12(3), 516–531 (2001) 7. Zhou, Y.T., Chellappa, R., Vaid, A., Jenkins, B.K.: Image restoration using a neural network. IEEE Trans. Acoust. Sp. Sig. Proc. 36(7), 1141–1151 (1988) 8. Perry, S., Guan, L.: Weight assignment for adaptive image restoration by neural networks. IEEE Trans. Neu. Net. 11, 156–170 (2000) 9. Anand, R., Mehrotra, K., Mohan, C.K., Ranka, S.: Efficient classification for multiclass problems using modular neural networks. IEEE Trans. Neu. Net. 6, 117–124 (1995)

Spline Biorthogonal Wavelet Design T. Arathi1, K.P. Soman2, and Latha Parameshwaran3 1

Research Associate, Department of CEN 2 Research Head, Department of CEN 3 Professor, Department of Computer Science, Amrita Vishwa Vidyapeetham, Coimbatore {t_arathi,kp_soman,p_latha}@cb.amrita.edu

Abstract. This paper gives a simple and straightforward method for designing spline based biorthogonal wavelets. Biorthogonal wavelets differ from orthogonal wavelets in that the former has more flexibility in its design. This is because, they enable the design of wavelets which are symmetric and smooth, which is not possible in the case of orthogonal wavelets (except Haar wavelet). However, the compromise made to achieve the symmetry property is that the non-zero coefficients in the analysis and synthesis filters are not the same for biorthogonal wavelets. The existing algorithm for spline biorthogonal wavelet design involves complex formulas, whose proof is also not easily understandable. In this paper, we present a very simple way of constructing spline based biorthogonal wavelets, which results in the same nonzero coefficients for the analysis and synthesis filters. Keywords: Biorthogonal wavelets, B-splines, Vanishing moments.

1 Introduction Wavelets have replaced conventional Fourier transform in many applications like, signal compression, noise removal, image processing etc. This resulted from the fact that wavelets could represent a signal compactly both in time and frequency domain.i.e. they could localize a signal in both time and frequency, simultaneously.A disadvantage with conventional orthogonal wavelets is that their construction is not flexible and hence is very restrictive in design. They are incapable of designing symmetric filters, except for the Haar wavelet system, which is symmetric. However, many filtering applications require symmetrical filter coefficients to achieve linear phase. Biorthogonal wavelets,have the flexibility to design wavelets which are symmetric, regular and smooth. But, the non-zero coefficients in the analysis and synthesis filters [1] are no longer the same.

2 Conventional Biorthogonal Wavelet Design Assume the design of a 5/7 biorthogonal and symmetric scaling filters. The 5-tap filter is represented as: . The first factor in the above equation ensures two vanishing moments [2] and the second factor is such that V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 672–674, 2010. © Springer-Verlag Berlin Heidelberg 2010

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673

0. The resulting filter is a one-parameter

H(1) =1 and

symmetric low pass filter with 5 taps. The dual filter is chosen as: . has two vanishing moments. It is formed such that the resulting filter is a low pass symmetric filter of length 7. Imposing double shift orthogonality conditions between the two sets of filters gives ‘b’ and ‘c’ in terms of ‘a’.

3 Spline Biorthogonal Wavelet Design B-spline function [3] is a good choice as a scaling function, because of its remarkable property that B-spline of a given order can be expressed as a linear combination of scaled and translated versions of itself. Also, since a B-spline of any order is symmetric, it can act as the scaling filter coefficients. We make use of these properties of splines to design spline- based biorthogonal filters.Spline-based biorthogonal wavelet construction has been outlined in [4]. However, to reach at the formulas shown in [4] for the filter design, rigorous mathematics is required, which is not mentioned. The methodology proposed in this paper, doesn’t require any heavy mathematical proofs. An intuitive knowledge about B-splines and their properties is enough for its design. Here, the first scaling filter h is taken as the B-spline coefficients. This is possible because of the symmetric property of B-splines and because it satisfies the refinement relation, which is expected of any scaling function. Assuming the design of a 2/6 biorthogonal filter. The first filter of length 2 is obtained as h The

dual

H% ( z ) =

scaling 2 z

−1

~ h is

filter

⎛ 1 + z ⎞ ⎜ ⎟ 2 ⎝ ⎠

3

⎛ 1 − a z ⎜ 2 ⎝

obtained

by

1 − a ⎞ + a + z ⎟ 2 ⎠

−1

=

{h 0 , h 1 } =

assuming

1 ⎫ ⎧ 1 , ⎨ ⎬ 2 ⎭ ⎩ 2

a

form

.

like:

.The coefficients of the above

~

expression form the dual scaling filter h , of length 6.An example of designing a 3/5 biorthogonal spline filter is explained below [5]: Let 2 1 ⎫ ⎧ 1 1 1 ⎫ ⎧ 1 h = {h − 1 , h 0 , h1 } = ⎨ , , , , ⎬= ⎨ ⎬ and h% = h%−2 , h%−1 , h%0 , h%1 , h%2 . 2 2 2⎭ ⎩2 2 2 2 2 2 ⎭ ⎩2 2

(

Let , H%

(z) =

2 z

−1

2

⎛ 1 + z ⎞ ⎜ ⎟ 2 ⎝ ⎠

⎛ 1 − a z ⎜ 2 ⎝

− 1

+ a +

)

1 − a ⎞ z ⎟ 2 ⎠

Here, we can only apply one orthogonality condition. On expanding and simplifying we obtain,

h% =

( h%

−2

.

Initial filter positions are:

h%

h h%

− 2

shift orthogonality conditionyields: 2 4

are: %

h =

2

×

( h%

−2

1 2

2

+

1− a 4

, h% − 1 , h% 0 , h%1 , h% 2

)=

, h% − 1 , h% 0 , h%1 , h% 2

2

×

1 2

) = ⎛⎜⎝ 4− 12

− 1

0

. h%

− 2

h 1 h%

0

. h% 0

− 1

. h% 2

1

h

− 1

h% 1

Applying doubleh 0 h%

.

2

This implies a=2. Therefore the filter coefficients

1 2

h h%

− 1

. h%

= 0 ,

2 2 + 2a 2 1 − a ⎞ ⎛ 1 − a , , , , ⎜ ⎟ 4 2 4 2 4 2 ⎠ ⎝ 4 2 4 2

2

,

3 2

2

,

1 2

2

,

4

−1 ⎞ ⎟ 2 ⎠

The normality condition is .

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automatically satisfied. That is, with no shift, if we multiply the filter coefficients at the corresponding positions and add, the result is 1. The two sets of scaling filter coefficients are obtained as: . 1

0

−

4

2

1

1

2

2

2

1

2

1

2

3

2

2

0

2

−

1

2

2

2

4

1

2

4 Conclusion A simple and straightforward approach of designing spline based biorthogonal wavelets has been presented here. The design methodology has been elaborated by taking the example of a 3/5 spline biorthogonal wavelet system design. This can be extended to any set of biorthogonal filters, provided, the lengths of the analysis and synthesis filters are either both odd or both even. The method doesn’t require any heavy mathematical proofs to understand the design. An intuitive knowledge about splines and its properties is enough for its design. This design also ensures that the non-zero coefficients in the analysis and synthesis filters are the same. The wavelet filters thus formed are symmetric as well.

References 1. Herley, C., Vetterli, M.: Wavelets and Recursive Filter Banks. IEEE Transactions on Signal Processing 41(8) (August 1993) 2. Butzer, P.L., Fischer, A., Ruckforth, K.: Scaling functions and wavelets with vanishing moments. Computers & Mathematics with Applications 27(3), 33–39 (1994) 3. Olsen, L., Samavati, F.F., Bartels, R.H.: Multiresolution B-Splines based on Wavelet Constraints. In: Eurographics Symposium on Geometry Processing, pp. 1–10 (2005) 4. Godavarthy, S.: Generating Spline Wavelets. In: Proceedings of the 36th Annual Southeast Regional Conference, pp. 8–14 (1998) ISBN: 1–58113-030-9 5. Soman, K.P., Ramachandran, K.I.: Insight into wavelets – From theory to Practice, 2nd edn. PHI (2005)

Securing Password File Using Bezier Curves Sunil Khurana1 and Sunil Kumar Khatri2 1

Department of Computer Applications, Lingaya’s Institute of Mgt. & Tech., Faridabad [email protected] 2 Department of Computer Applications, Lingaya’s University, Faridabad [email protected]

Abstract. Password security has emerged as a promising field in the Computer science and technology. The innovative strategies are found to be costly and also require expertise to use them. The widely used methods of password security are pass-faces and biometrics password authentication schemes. Though they serve their purpose but are found to be cost ineffective.This paper looks at the new concepts of password security based on text-based authentication, ensuring the security from dictionary attacks. It is based on the principle of conversion of the characters of password in some control points, an unrecognizable form for intruders. Keywords: Bezier Curve, Password Authentication, System Security, Control Points.

1 Introduction Security system in any computer protects the sensitive data by concealing it and keeping the non sensitive data tamper proof [1] using the passwords. It is an ongoing process and works as long as it is in active state. Passwords are the weakest link in the authentication process, as they can be forgotten, stolen, sniffed or cracked. They are subjected to multiple attacks [2] such as eavesdropping, replay, man-in-the-middle, guessing attacks, dictionary attacks, brute force attack and hybrid attack. Attackers concentrate on the cracking of password stored in some or other text. To protect the systems from such attacks, there exist password security schemes like alphanumeric password, graphical password and other schemes. These security schemes have implications at two levels [3]: 1. The “unmotivated user” who bypasses security. 2. To realize the users that security tasks are worth investigating. As in all the above discussed methods of cracking, one finds that the text characters emerge as the most vulnerable. This paper aims to develop a security system based on the technique of converting the text characters of the password into the Bezier curves.

2 Literature Survey Jablon [1], showed how leaking information can be prevented from intruders in Password-only method. Lomas et al [5], introduced the first password protocol LGSN, V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 675–680, 2010. © Springer-Verlag Berlin Heidelberg 2010

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which became the basis for many password authentication protocols. It served solution to some poorly chosen keys only. Kwon [6] proposed the protocol u-AMP which combines the various functions, such as Password-verifier based authentication [7], Diffie-Hellman key agreement [8] and Easy generalization [9]. The u-AMP is used with the hardware-supported authenticated schemes, and requires some specification for implementation. Bellare and Rogawayy [10], introduced advancement to password authenticated key protocol with human memorable AuthA protocol in which there are two entities a client A and a server B. Yan [11], proposed Entropy Based Proactive Password Checking. The limitation of this technique lies in the weak password patterns for entropy based checking. Later he developed the 7-character case-insensitive alphanumeric passwords [12], focusing on algorithm efficiency and storage space. Chiasson and Biddle [3] in their study showed that the graphical passwords provide the best security. The limitations lie in ecological validity. Jermyn et al [13] proposed a technique, called “Draw - a - secret (DAS)”, which allows the user to draw their unique password in the form of simple picture on a grid. “Passface” authentication technique was developed by Real User Corporation [14]. The user is authenticated if he/she correctly identifies the four faces. Limitation that the Passface based log-in process took longer than the text passwords. The implementation of Blonder’s approach was given by Boroditsky [4]. The user chooses several objects as the password. The limitation of the scheme is that the number of available click regions is small. Wiedenbecka et al [15] overcame the limitation of the Blonder’s scheme by providing the flexibility of choosing the images either by the system or by the user. In this passwords are saved in hashed form. Hartanto et al [16] proposed the technique called moveable frame graphical password. It is based on fact that the user has to build straight line by arranging the picture passwords. Kumar and Katti [17] proposed the method based on hash-function which is resistant to the user anonymity, online and offline guessing attack, stolen verifier attack, reply attack and DoS attack and also resolved problems mentioned by Chen and Ku [18]. (i) The SHF does not allow an adversary to impersonate the user login even though he has stolen the user’s verifier. (ii) c5 = hKEY(h2(P _ N0)||h(A||N0||P)||h(P _ N)) protects the integrity of the transmitted message. Abdalla et al [19] proposed the model based on Gateway-based PasswordAuthenticated Key Exchange which worked on dictionary attacks, measures such as no separate proof for each security property, corruptions of participants, key privacy with respect to the server, were focused. Abdalla et al [20] presented an idea based on smooth projective hash functions for more complex languages. Abdalla et al [21] presented encryption and decryption schemes of an ideal tweakable cipher scheme, in which they assumed the sFGPAKE be the multi-session extension of the split functionality sFGPAKE and let FRO and FITC be the ideal functionalities that provide a random Oracle and an ideal tweakable cipher to all parties. Lashkari and Farmand [22] had given the survey on problems arising with the graphical authentication system and also the security features provided by the graphical authentication system. Itoh and Ohno [23] had given the curve fitting algorithm for character fonts, their four step algorithm extracts the control points from the image and then divides the

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points into a number of segments at the corner points following the step for fitting a piecewise cubic Bezier curve to each segment. Perumal and Ramaswamy [26], showed how Bezier curve has its own impact in fingerprint password authentication schemes for data compression reducing the problem of storage and transmission. Pal, Biswas and Abraham [27], used the Bezier curves in face recognition password authentication. The two control points of a Bezier curve are interpolated which helped them in face recognition.

3 Bezier Curve Based Password Protection When we log on to computer, either offline or online, on any system (comprising hard drives or on any diskless workstation) in any situation, the intruder with the help of Trojan software accesses the password file and secured data. The aim of this research is to protect the data by using a new kind of encryption technique to which researcher has named as curveagraphy. This research will focus on developing a new security system operating at the log in stage itself. It secures system at two stages: 1. 2.

When the user enters the characters of the password, they will be encrypted, which in turn will help to form the respective Bezier curve of the particular character. Instead of containing the password characters in normal text form, password file will contain the data in the form of Bezier curves which will prevent the intruders from interpreting the characters.

Curveagraphy is different from the encryption techniques such as Cryptography, Steganography and Hash functions. 3.1 Researcher Would Like to Quote an Example Here When we press any character of a password, let us say ‘A’, it gets converted into its binary equivalent and gets stored into the password file. This is the point where the code of the intruder can detect the password. To avoid this, we convert the password character into Bezier curve before it is being stored. The input character is mapped using a special character ‘.’. The mapped character prepended and appended by the special character ‘.’ will be used in storing the corresponding Bezier curve for the input character using control points. The Bezier curve of the mapped characters will make it difficult for the intruder to analyze the password. The character shown in the Figure 1 is ‘A’, which will get converted into its binary equivalent 01000001 and will be moved into the register. The conversion process will be applied in which the 8-bit binary equivalent of dot ‘.’ character is added on right and left of the binary equivalent of character (A) and also the actual binary equivalent of character ‘A’ will be replaced by the difference of character ‘A’ from the dot ‘.’ character as follows: ‘A’ ‘.’

65 - 46 19

01000001 - 00101110 00010011

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The 24-bit number for ‘A’ will be 001011100001001100101110. The two 8-bits for dot ‘.’ character equivalent is appended to the right and left. Three control points will be required to generate the Bezier curve for the character ‘A’ as shown below:

8-bit binary equivalent of ‘A’ is converted to the 24-bit binary equivalent due to the fact that the three control points are required to form the Bezier curve equivalent of character. When adding the 8-bits mapped for character with either the left-most or right-most 8-bits, i.e., with the dot ‘.’ character, 8-bits mapping the character will yield the actual character, with the help of which the match of the Bezier curve with its corresponding character will be performed. Password of any character length can be converted with each character being mapped into the separate Bezier curve. The curve design however differs on the basis of character. The conversion of character to 16, 24, 32 or 40 binary digits depend upon the number of contour and control points required to form its equivalent Bezier curve. The Bezier curve will get stored in the password file instead of the characters. The Bezier curve for all characters will be different either on the basis of the number of control points to form the curve or on the basis of the angle at which the curve of the respective character is formed. It is easy to form the curve on the basis of uppercase letter. So if user enters the small case character in password, then in first step, character has to be converted into the uppercase and secondly the curve will be formed.

4 Algorithm 1. 2.

3. 4. 5.

Capturing of the password when entered from the key strokes Conversion of the characters of the password into the required number of contour and control points using its binary equivalent prior to its storage in the memory using algorithm. Extraction of contour points and control points. Movement of the contour points and the control points of the password characters into memory. Matching algorithm character per curve stored in the password file.

Instead of extraction of contour points from the grey-level images using Avrahami’s algorithm [24], or extracting supposed corner points using Davis’ algorithm [25] this algorithm is based on the extraction of contour points and control points from the password character fitting the curve which is a new concept in all .

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5 Conclusion Dictionary attacks are the most common types of attacks on the network. The solution we have provided for it would be the feasible and can be taken into consideration after implementation and performing the comparative study with the current security mechanism for the dictionary attacks. The research will provide security against dictionary attacks. The Bezier curves do not occupy large space as comparative to the graphical password schemes. Keeping in view the major considerations of organization increasing the number of computer systems, such as time, expertise, memory space and cost this can be a feasible method of protection especially against dictionary attacks.

References 1. Jablon, D.P.: Strong Password-Only Authenticated Key Exchange. In: ACM Computer Communication Review, Westboro (1996) 2. Albataineh, M., En-Nouaary, A.: Strengthening Password Authentication Systems. In: Sixth International Network Conference, Plymouth (2006) 3. Chiasson, S., Biddle, R.: Issues in User Authentication. In: CHI Workshop Security User Studies Methodologies and Best Practices (2007) 4. Boroditsky, M.: Passlogix password schemes, http://www.passlogix.com 5. Lomas, M., Gong, L., Saltzer, J., Needham, R.: Reducing risks from poorly chosen keys. In: Proceedings of the 12th ACM Symposium on Operating System Principles, ACM Operating Systems Review, pp. 14–18 (1989) 6. Kwon, T.: Ultimate Solution to Authentication via Memorable Password. In: A Proposal for IEEE, p. 13631a. IEEE Press, Los Alamitos (2000) 7. Bellovin, S., Merritt, M.: Augmented encrypted key exchange: a password-based protocol secure against dictionary attacks and password file compromise. In: ACM Conference on Computer and Communications Security, pp. 244–250 (1993) 8. Diffie, W., Hellman, M.: New directions in cryptography. IEEE Transactions on Information Theory 22(6), 644–654 (1976) 9. Menezes, A., van Oorschot, P., Vanstone, S.: Handbook of applied cryptography. CRC Press, Inc., Boca Raton (1997) 10. Bellare, M., Rogawayy, P.: The AuthA Protocol for Password-Based Authenticated Key Exchange. In: Contribution to IEEE, p. 1363. IEEE Press, California (2000) 11. Yan, J.: A Note on Proactive Password Checking. In: New Security Paradigms Workshop (2001) 12. Yan, J.: A Note on Proactive Password Checking. In: New Security Paradigms Workshop (2001) 13. Jermyn, I., Mayer, A., Monrose, F., Reiter, M.K., Rubin, A.D.: The Design and Analysis of Graphical Passwords. In: Proceedings of the 8th USENIX Security Symposium (1999) 14. RealUser, http://www.realuser.com 15. Wiedenbecka, S., Watersa, J., Birgetb, J., Camille, B.A., Memon, N.: PassPoints Design and Longitudinal Evaluation of a Graphical Password System. J. Human Computer Studies 63, 102–127 (2005) 16. Hartanto, B., Santoso, B., Welly, S.: The Usage of Graphical Password As a Replacement To The Alphanumerical Password. J. Informatika 7(2), 91–97 (2006)

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17. Kumar, M., Katti, R.: A Hash-based Strong Password Authentication Protocol with User Anonymity. J. International Journal of Network Security 2(3), 205–209 (2006) 18. Chen, M., Ku, W.: Stolen-verifier attack on two new strong-password authentication protocols. IEICE Transactions on Communications E85-B(11), 2519–2521 (2002) 19. Abdalla, M., Izabachène, M., Pointcheval, D.: Anonymous and Transparent Gatewaybased Password-Authenticated Key Exchange. In: Franklin, M.K., Hui, L.C.K., Wong, D.S. (eds.) CANS 2008. LNCS, vol. 5339, pp. 133–148. Springer, Heidelberg (2008) 20. Abdalla, M., Chevalier, C., Pointcheval, D.: Smooth Projective Hashing for Conditionally Extractable Commitments. In: Halevi, S. (ed.) Advances in Cryptology - CRYPTO 2009. LNCS, vol. 5677, pp. 671–689. Springer, Heidelberg (2009) 21. Abdalla, M., Catalano, D., Chevalier, C., Pointcheval, D.: Password-Authenticated Group Key Agreement with Adaptive Security and Contributiveness. In: Preneel, B. (ed.) AFRICACRYPT 2009. LNCS, vol. 5580, pp. 254–271. Springer, Heidelberg (2009) 22. Lashkari, A., Habibi, F.S.: A Survey On Usability And Security Features In Graphical User Authentication Algorithms. J. Computer Science and Network Security 9(9) (September 2009) 23. Itoh, K., Ohno, Y.: A curve fitting algorithm for character fonts. Electronic Publishing 6(3), 195–205 (1993) 24. Avrahami, G., Pratt, V.: Sub-pixel edge detection in character digitization. In: Morris, R., Andre, J. (eds.) Raster Imaging and Digital Typography II, pp. 54–64. Cambridge University Press, Cambridge (1991) 25. Davis, L.: Shape matching using relaxation techniques. IEEE Trans. PAMI 1, 60–72 (1979) 26. Perumal, V., Ramaswamy, J.: An Innovative Scheme For Effectual Fingerprint Data Compression Using Bezier Curve Representations (IJCSIS) International Journal of Computer Science and Information Security 6(1) (2009) 27. Pal, S., Biswas, P.K., Abraham, A.: Face Recognition Using Interpolated Bezier Curve Based Representation. In: IEEE Proceedings of the International Conference on Information Technology: Coding and Computing, ITCC 2004 (2004) 0-7695-2108-8/04

Reading a 4-Bit SLC NAND Flash Memory in 180nm Technology Nilesh Shah, Rasika Dhavse, and Anand Darji SVNIT, Surat, India

Abstract. The basic device that is used in a Flash memory block is a floating gate metal oxide semiconductor field effect transistor (FGMOS). Storage of the charge on the floating gate allows the threshold voltage (VT ) to be electrically altered between a low and a high value to represent logic 0 and 1, respectively. Typically, 8 or 16 cells are connected together in series to manufacture SLC NAND ah memory. In this paper experimentation has been done on string of 4 such cells using UMC 0.18 m CMOS process technology. Transient analysis is performed for measurement of Read access time operation. This also includes Design of Sense amplifier, Decoder and Buffer.

1 Introduction Two major forms of Flash memory :NAND Flash and NOR Flash are compared in Fig.1[1]. NAND Flash can be further categorized in Single level cell(SLC) and Multi level cell(MLC) as shown in Table.1[2].

Fig. 1. NAND Vs. NOR flash[1] Table 1. SLC Vs. MLC[2]

Density Cost per bit Endurance Power Consumption Write/Erase speed

SLC Low High High Low High

MLC High Low Low High Low

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2 FGMOS: Structure, Model and Operating Principle Basic cell used in NAND ash memories is an FGMOS transistor[3]. The neutral state of the FGMOS is considered as logic 1 and the negatively charge state (corresponds to stored electrons) is considered as a logic 0 state.

(a)

(b)

Fig. 2. (a)FGMOS structure[4](b)Theoretical I-V curves of a floating-gate MOS tran-sistor with strong capacitive coupling effect[5]

DC analysis for FGMOS is priorly done and threshold voltage variation with different control gate and floating gate voltage is demonstrated.

3 NAND Ash Memory Architecture and Operations NAND ash memory organizational blocks are memory cell array, row and column decoders, sense amplifiers, data input/output buffer, control circuit and NAND ash controller as shown in Fig.3[7]. NAND ash memory chips have a two-level hierarchical structure. At the lowest level, bits are organized into pages. Pages are the unit of read and write locality in NAND ash. Pages are grouped into higher-level structures

Fig. 3. NAND ash Memory Blocks

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called erase blocks. While pages are the units of reads and writes, erase blocks are the units of erasure. Writing to a page can only clear bits (make them to zero), not set them. In order to set any bit on a given page to 1, an expensive erase operation must be executed on the erase block containing the page; this sets all bits on all pages in the erase block to 1[13][14]. Finally,the number of erase cycles per erase block is limited, and typically ranges from 10,000 to 1,000,000[6]. The cells are connected in series as shown in Fig.4. The biasing of each cell is obtained by using passive switches. The erased cells have negative threshold while the programmed cells have positive threshold. The unselected cells operate as pass transistors so as to transfer the read voltage to the selected cell. The potential of the selected word line is equal to the ground potential. The erase is completely driven by the substrate that is biased at high voltage whereas the word lines are tied to ground. The program operation is carried out by applying very high voltage to the word line and grounding the channel through the bit line. The unselected gates have an intermediate potential and the substrate is tied to ground. Operating voltages[10][11] for NAND ash in Read, program and erased mode are summarized in Table.2. Table 2. Operating voltages for NAND flash

Read Bitlines Data Selected Wordline Moderate Unselected Wordline Higher then selected Source Ground Substrate Ground

Program Data High Moderate Ground Ground

Erase Open Ground Ground Open High

4 Simulation and Results The overall integrated circuit is as shown in Fig.4. Sense Amplifier Schematic is as shown in Fig.5.

Fig. 4. Schematic of NAND ash used for simulation purpose: Row decoder gives delay of 25ns. Output buffer gives delay of 14ns.

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Fig. 5. Sense amplifier Schematic: NMOS clamp transistors with W/L ratio of 1.694

(a) delay of 24ns

(b) Gain 19dB with 2MHz Bandwidth and CMRR=20

Fig. 6. (a)Sense amplifier transient analysis (b)Sense amplifier AC analysis

(a)

(b)

Fig. 7. (a)Program-read for cell 1 (b)Block Erase operation

5 Conclusion In program-verify operation it is observed that delays are more dependent on cell position in string. As cell is down in string, delay is more. Delay is also dependent on Table 3. Program-verify delays for different inputs

Selected cell in string from top Delay (in ns) cell 4 100 cell 3 85 cell 2 71 cell 1 61

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bitline capacitance, supply voltage and peripheral blocks. The read access operation is found to be completed in 100ns which supports USB standards. Further optimization in delay can be achieved by reducing delays in peripheral blocks.

References 1. NAND Vs. NOR Flash Memory, Technology Overview, TOSHIBA, 25/4/2006 2. SLC vs. MLC: An Analysis of Flash Memory, SLC vs. MLC: Whitepaper, Super Talent Technology 3. Kahng, D., Sze, S.M.: A floating gate and its application to memory devices. Bell Systems Technical Journal 46, 1286 (1967) 4. Jalabert, A., Amara, A., Clrmidy, F.: Molecular Electronics Materials. In: Devices and Applications. Non-Volatile Memory Cells, ch. 3. Springer, Heidelberg 5. Wang, S.T.: On The I-V Characteristics Of Floating-Gate MOS Transistors. IEEE Transactions On Electron Devices Ed-26(9) 6. NAND Flash 101: An Introduction to NAND Flash and How to Design It In to Your Next Product, Technical Note, MICRON 7. Pierce, R.: Design Consideration for High-Speed NAND Flash, Flash Memory Summit. US/2009/0147581 A1 8. Campardo, G., Micheloni, R., Novesel, D.: VLSI-Design of Non Volatile memory. ch. 4 NAND Flash memories, ch. 19 The Output Buffer. Springer, Heidelberg 9. Krishna, S.R.: Nanoparticles based Flash Memory, ECG 453/653 Introduction to Nanotechnology 10. Kang, S.-M., Yoo, S.-M.: CMOS Digital Integrated Circuits, ch. 10 Semiconductor Memory 11. Brewer, J.E., Gill, M.: Nonvolatile Memory Technologies with Emphasis on Flash. Introduction to Nonvolatile Memory. IEEE, Los Alamitos, 12. Conte, A., Giudice, G.L., Palumbo, G., Sig-norello, A.: A High-Performance Very LowVoltage Current Sense Amplifier for Non-volatile Memories. IEEE Journal of Solid-state Circuits 40(2), 507 (2005) 13. (July 2009), http://www.explainthatstu.com/flashmemory.html 14. (September 2009), http://en.wikipedia.org/wiki/Flashmemory

Keystroke Dynamics Authentication Using Neural Network Approaches Venkateswaran Shanmugapriya1 and Ganapathi Padmavathi2 1 Lecturer, Dept. of Information Technology, Avinashilingam Deemed University for women, Coimbatore, India [email protected] 2 Prof and Head, Dept. of Computer science, Avinashilingam Deemed University for women, Coimbatore, India [email protected]

Abstract. Securing the sensitive data and computer systems by allowing ease access to authenticated users and withstanding the attacks of imposters is one of the major challenges in the field of computer security. Traditionally, ID and password schemes are most widely used for controlling the access to computer systems. But, this scheme has many flaws such as Password sharing, Shoulder surfing, Brute force attack, Dictionary attack, Guessing, Phishing and many more. Biometrics technologies provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working with a keyboard. In this paper, neural network approaches with three different passwords namely weak, medium and strong passwords are taken into consideration and accuracy obtained is compared. Keywords: Biometrics, Keystroke dynamics, Back propagation neural network, cascade forward back propagation neural network, Radial basis function.

1 Introduction Almost all the people rely on computers at certain level in day today life. Many of these systems store highly sensitive, personal, commercial, confidential or financial data. Unauthorized access to such data will lead to loss of money or unwanted disclosure of highly confidential data by threatening the Information security. The first and foremost step in preventing unauthorized access of information for providing information security is user authentication. Biometric-based authentication is one of the authentication technique which is based on something you are and depends on behavioral and physiological characteristics of individuals. In this authentication, the person presents a characteristic that cannot be forged and nor be forgotten. Biometrics is classified into physiological and behavioral biometrics [11]. Physiological biometric refers to what the person is and the Behavioral biometrics are related to what a person does, or how the person uses the body. Keystroke dynamics is considered as a strong behavioral biometric based authentication system [1]. It is a process of analyzing the way a user types at a terminal by monitoring the keyboard in order to identify the V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 686–690, 2010. © Springer-Verlag Berlin Heidelberg 2010

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users based on habitual typing rhythm patterns. Moreover, unlike other biometric systems [1], which may be expensive to implement, keystroke dynamics is cheap and does not require any sophisticated hardware as the only hardware required is the keyboard. Keystroke analysis contains two approaches: static and dynamic [14]. In static approach, the system checks the user one time that is at authentication time. In the dynamic approach, the system checks the user continuously throughout the session. Our approach is static since the authentication is done only during login time. The remaining of this paper is organized in four sections. Section 2 tabulates the works published in the area. In the section 3 the methodology is discussed. The experiments are presented and discussed in the section 4, and finally the conclusions are found in the section 5.

2 Literature Review A number of studies [3,5,7,8,12-14,17] have been performed in the area of keystroke analysis since its inception. Table 1 illustrates a summary of the main research approaches performed in till date using neural network approaches. Table 1. Keystroke dynamics Approaches Study Brown & Rogers [4] Bleha & Obaidat) [17] Obaidat & Sadoun [15] Cho et al. [5]

Classification Technique Static Neural network Static Neural network Static Neural network Static Neural network

Users 25 24 15 21

Ord & Furnell [16] Yu & Cho [7] Gunetti & Picardi) [8] Clarke & Furnell [6] Lee and Cho [10]

Static Static Static Static Static

Neural network Neural network Neural network Neural network Neural network

14 21 205 32 25

Hawang et al [18]

Static

Neural network

25

FAR %) 0 8 0 0

FRR (%) 12.0 9 0 1

9.9 30 0 3.69 0.005 5 5 (Equal error rate) 0.43 (Average Integrated Error) 4 (Equal error rate)

3 Methodology In the proposed methodology, there are three important phases involved in keystroke dynamics. First, a user registers or enrolls his/her timing Vector patterns. Second, a preprocessing is done. Third, neural network classifier is built using the timing vector patterns to measure the accuracy. 3.1 Registration or Enrollment During the registration phase 26 users were asked to type three different passwords. Each user typed each password 10 times. Totally 780 samples were collected within a week time. Age group of users is between 18-21. The three passwords used are

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‘pass_tie.R’, ‘.tie5Roanl’ and ‘nopassword’. The password-strength checker [9] rated the above passwords as strong, medium and weak respectively. 3.2 Feature Extraction The main function of feature extraction is to extract vital features from the timestamp collected from raw keystroke data for template generation. There are many types of features that can be extracted from a human keystroke such as Duration, Latency, Digraph, Tri-graph, Pressure of keystroke, Force of Keystroke, Difficulties of typing text, Frequency of word errors, Typing rate, etc. However, not all kinds of the abovementioned features are useful and widely used. In the experimentation all the timing features such as Dwell time or Duration, Flight time or Latency, Digraph and Trigraph are measured. 3.3 Preprocessing The typing pattern of the user varies from time to time even for the same user. The figure 1 shows the difference in typing pattern of same user. Four sample-typing patterns are shown.

3

15 s ampl e4 10 s ampl e3 s ampl e2

5

s ampl e1

T i mi ng f eat ur e

2. 5

bef or e

2

Nor mal i z at i on 1. 5 T i mi ng f eat ur e 1

A f t er

0. 5

0 1

Nor mal i z at i on

0

6 11 16 21 26 31 36 41 46 51 56 61 66 71

1

7 13 19 25 31 37 43 49 55 61 67

E xt r act ed Feat ur es Feat ur es

. Fig. 1. Difference in typing pattern of a User

Fig. 2. Effect of Min-max normalization

In the preprocessing phase, cleaning up of data is done in order to improve the performance of the system. Among the many normalization techniques that have been proposed in the literature [2], min-max normalization is used in the paper. The minmax normalization can be done using the below formula (1).

Xi ' =

X i − min max − min

(1)

The effect of preprocessing of sample of a user using the week password is shown in figure 2. From the above graph it is shown clearly that the preprocessing brings the score range between 0 and 1, which remove the ambiguity of the obtained scores. 3.4 Classification The preprocessed timing vectors are classified using three different neural network approaches namely back propagation neural network, Cascade forward back

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propagation neural network and radial basis function and obtained classification accuracy are compared.

4 Experimental Results The collected samples of all the three passwords are preprocessed using min-max normalization and feed into BPN, CFBP and RBF for classification. The following Figure 5 shows the obtained accuracy. Using the strong password 91.5%, 90.3%, 83.8% accuracy is obtained using BPN, CFBP, and RBF respectively. The results show that keystroke dynamics with traditional strong passwords provide more security for user authentication. Table 2. Accuracy obtained using Neural network Approches using all categories of passwords Successfully Number Unrecognized Recognized of Keystrokes Keystrokes samples

Password

Password Category

Neural network

238

22

91.5

Strong

BPN CFBP RBF

260

Pass_tie.R

260 260

230 237

30 23

88.4 91.1

235

25

90.3

Medium

BPN CFBP RBF

260

.tie5Roanl

260 260

230 233

30 27

88.4 89.6

218

42

83.8

Weak

BPN CFBP RBF

260

nopassword

260 260

210 215

50 45

80.7 82.6

Accuracy (%)

5 Conclusions A system is designed for user multifactor authentication combining the traditional password with the keystroke dynamics. Keystroke of strong, medium and weak passwords are measured and the accuracy is calculated using neural network approaches and results obtained show that keystroke in combination with strong password gives accuracy better than the medium and weak passwords and Back propagation neural network gives better accuracy. Since the strong passwords are hard to remember, user may store it in a database or write down the passwords, which may lead to dictionary attacks when hacked by imposters. A password combined with keystroke dynamics provides more security. Even when the other person knows the passwords, he cannot steal the typing rhyme of the user, which adds more security.

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References 1. Ahmed, A.A.E., Traore, I.: Anomaly Intrusion Detection based on Biometrics. In: Proceedings of 6th IEEE Information Assurance Workshop, pp. 452–453 (2005) 2. Jain, A., NandaKumar, K., Ross, A.: Score normalization in multimodal biometric systems. IEEE Pattern Recognition 38, 2270–2285 (2005) 3. Bergando, et al.: User Authentication through keystroke Dynamics. ACM transaction on Information System Security (5), 367–397 (2002) 4. Brown, M., Rogers, J.: User Identification via Keystroke Characteristics of Typed Names using Neural Networks. International Journal of Man-Machine Studies 39, 999–1014 (1993) 5. Cho, et al.: Web based keystroke dynamics identity verification using neural network. Journal of organizational computing and electronic commerce 10(4), 295–307 (2000 ) 6. Clarke, N.L., Furnell, S.M.: Authenticating mobile phone users using keystroke analysis. International Journal of Information Security 6(1), 1–14 (2007) 7. Yu, E., Cho, S.: Keystroke dynamics identity verification and its problems and practical solutions. Computers & Security (2004) 8. Gunetti, Picardi,: Keystroke analysis of free text. ACM Transactions on Information and System Security, 312–347 (2005) 9. http://www.microsoft.com/protect/yourself/password/ checker.mspx 10. Lee, H., Cho, S.: Retraining a keystroke dynamics-based authenticator with impostor patterns. Computers & Security 26(4), 300–310 (2007) 11. O’Gorman, L.: Comparing Passwords, Tokens, and Biometrics for User Authentication. Proceedings of the IEEE, 2019–2040 (2003) 12. Monrose, F., Reiter, M., Wetzel, S.: Password Hardening Based on Keystroke Dynamics. IIJS, 1–15 (2001) 13. Monrose, F., Rubin, A.: Authentication via Keystroke Dynamics. In: Proceedings of the 4th ACM Conference on Computer and Communications Security, pp. 48–56 (1997) 14. Monrose, R., Rubin, A.: Keystroke Dynamics as a Biometric for Authentication. Future Generation Computer Systems, 351–359 (1999) 15. Obaidat, M.S., Sadoun, B.: Verification of Computer User Using Keystroke Dynamics. IEEE Transactions on Systems, Man and Cybernetics – Part B: Cybernetics 27(2) (1997) 16. Ord, T., Furnell, S.: User Authentication for Keypad-Based Devices using Keystroke Analysis, MSc Thesis, University of Plymouth, UK (2000) 17. Bleha, S., Obaidat, M.S.: Computer user verification using the perceptron. IEEE Trans. Systems, Man, and Cybernetics 23(3), 900–902 (1993) 18. Hwang, S.-S., Cho, S., Park, S.: Keystroke dynamics based authentication for mobile phones. Computers & Securit, 85–93 (2009)

Moving Object Tracking Using Object Segmentation Sanjay Singh1, Srinivasa Murali Dunga1, A.S. Mandal1, Chandra Shekhar1, and Anil Vohra2 1

Central Electronics Engineering Research Institute (CEERI) / Council of Scientific and Industrial Research (CSIR), Pilani-333031, Rajasthan, India {sanjay298,smdunga}@gmail.com, {atanu,chandra}@ceeri.ernet.in 2 Electronic Science Department, Kurukshetra University, Kurukshetra, Haryana, India [email protected]

Abstract. Research in motion analysis has evolved over the years as a challenging field, such as traffic monitoring, military, automated surveillance system and biological sciences etc. Tracking of moving objects in video sequences can offer significant benefits to motion analysis. In this paper an approach is proposed for the tracking of moving objects in an image sequence using object segmentation framework and feature matching functionality. The approach is

amenable for SIMD processing or mapping onto VLIW DSP. Our C implementation runs at about 30 frames/second with 320x240 video input on standard Window XP machine. The experimental results have established the effectiveness of our approach for real world situations. Keywords: Object Tracking, Motion Analysis.

1 Introduction Tracking moving objects over time is a complex problem in computer vision and has many potential applications in the fields of intelligent robots [1], monitoring and automated surveillance [2], human computer interfaces [3], vehicle tracking [4], biomedical image analysis [5], video compression [6], etc [7]. Object tracking has been an active research area in the vision community in recent years. Numerous approaches have been proposed to track moving objects in image sequences. Impressive tracking systems have been developed for some specific applications. We assume, initially, that we just want to track one object in the scene, but there may be other moving objects present in the scene. The basic condition imposed by our method is to maintain the few important features of the object in a sequence of images. In this algorithm we extract all the objects from an input image by object segmentation. The result of object segmentation is a binary frame containing connected clusters of blocks that represent different objects. To be able to separate and distinguish between these clusters, they have to be labeled. We have used Equivalence Table Based Algorithm for labeling the connected components. Next we extract simple object features. Then we compare the features of extracted objects in the current frame with that of tracked in the previous frame. The most similar object (best feature matching) between the successive frames is marked as object to be tracked. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 691–694, 2010. © Springer-Verlag Berlin Heidelberg 2010

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2 Proposed Scheme The proposed tracking scheme involves three major operations: Object Segmentation, Object Labeling, and Object Tracking. 2.1 Object Segmentation Object Segmentation can be achieved by building a representation of the scene called the background model and then finding deviations from the model for each incoming frame. Any significant change in an image region from the background model signifies a moving object. The various approaches have been presented for segmenting the moving objects from the background [8 - 12]. We have used computationally much simpler object segmentation scheme proposed in [13]. 2.2 Object Labeling After segmentation, a binary frame is produced containing connected clusters of blocks that represent different objects. To be able to separate and distinguish between these clusters, they have to be labeled. Various labeling algorithms have been proposed. A typical label collision occurs in a binary image when a u shaped object is encountered. The method to handle this problem is Equivalence Table Based Algorithms. Equivalence table based algorithm [14] scans through the memory writing every label collision into an equivalence table. In the first label scan each pixel is compared with its neighbors to the left and above. After the first scan all pixels are assigned a label and all collisions have been detected. The second scan resolves all collisions. 2.3 Object Tracking Features are used to separate the tracking part of the system from the image stream. In this scheme we have two types of features (color centroid and size). Matching is performed between stored object and all new clustered objects. These two types of features are sufficient to perform tracking of moving objects in the video stream. However, they are not reliable to keep track of an object that walks out from the scene and reenters at a later time. If two or more persons enter the scene with similar clothing occlusion handling between those persons becomes unreliable. The solution to these problems is to include more and better features.

3 Results and Discussion We implemented the proposed method in C. Our implementation runs at about 30 frames/second with 320x240 video input on a standard window XP machine. Figure 1 shows the tracking results of a moving person moving on road. There is no effect of other moving object (person wearing white shirt on cycle) on tracking results. We experimented about 20 video sequences of average length 500 frames (from 350 frames to 1000 frames). Our scheme has tracked about 90% of true objects.

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Frame 307

Frame 321

Frame 360

Frame 385

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Fig. 1. Sequence of tracking a person which shows movement of other object (person wearing white shirt on cycle) in background

4 Conclusion In this paper we have presented an approach for the tracking of moving objects in an image sequence using object segmentation framework and feature matching functionality. The computation is structured for easy implementation in embedded environment. Each block can be mapped to a processing element (PE) in VLIW DSP processor because there exists no dependency between the blocks. The proposed scheme runs in real-time (30 frames/second) for 320x240 video. The intended use of this approach is for our Automated Surveillance System.

Acknowledgments This work was supported by Ministry of Communications and Information Technology (MCIT) / Department of Information Technology (DIT), Government of India.

References 1. Kwok, C., Fox, D., Meila, M.: Adaptive Real-Time Particle Filters for Robot Localization. In: IEEE International Conference on Robotics & Automation, pp. 2836–2841 (2003) 2. Cui. Y., Samarasekera. S., Huang. Q., Greiffenhangen, M., Enhagen, M.G.: Indoor Monitoring Via the Collaboration Between a Peripheral Sensor and a Foveal Sensor. In: IEEE Workshop on Visual Surveillance, pp. 2–9 (1998)

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3. Bradski, G.R.: Real Time Face and Object Tracking as a Component of a Perceptual User Interface. In: IEEE Workshop on Applications of Computer Vision, pp. 214–219 (1998) 4. Betke, M., Haritaoglu, E., Davis, L.S.: Multiple Vehicle Detection and Tracking in Hard Real-Time. In: IEEE Intelligent Vehicles Symposium, pp. 351–356 (1996) 5. Pan, L., Prince, J.L., Lima, J.A.C., Osman, N.F.: Fast Tracking of Cardiac Motion Using 3D-HARP. IEEE Transactions on Biomedical Engineering 52(8), 1425–1435 (2005) 6. Eleftheriadis, A., Jacquin, A.: Automatic Face Location Detection and Tracking for Model-Assisted Coding of Video Teleconference Sequences at Low Bit Rates. Signal Processing - Image Communication 7(3), 231–248 (1995) 7. Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 142–149 (2000) 8. Seed, N.L., Houghton, A.D.: Background updating for Real-time Image Processing at TV Rates. In: Proceeding of SPIE, vol. 901, pp. 73–81 (1988) 9. Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient Moving Object Segmentation Algorithm using Background Registration Technique. IEEE Transactions on Circuits and Systems for Video Technology 12(7), 577–586 (2002) 10. Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-time Tracking. In: Proceedings of CVPR, pp. 246–252 (1999) 11. KaewTraKulPong, P., Bowden, R.: An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection. In: 2nd European Workshop on Advanced Video Based Surveillance Systems (2001) 12. Butler, D.E., Bove Jr., V.M., Sridharan, S.: Real-time Adaptive Foreground/Background Segmentation. EURASIP Journal on Applied Signal Processing, 2292–2304 (2005) 13. Singh, S., Dunga, S.M., Mandal, A.S., Shekhar, C., Vohra, A.: Real-time Object Segmentation in Smart Camera for Remote Surveillance Scenario. In: 2010 International Conference on Advances in Computer Engineering, pp. 360–362 (2010) 14. Suzuki, K., Isao, H., Noboru, S.: Linear-time Connected-component Labeling based on Sequential Local Operations. J. of Computer Vision and Image Understanding 89, 1–23 (2003)

Mobile Health Care System for Patient Monitoring Titir Santra Future Institute of Engineering and Management Kolkata, West Bengal [email protected]

Abstract. The wireless body area network (WBAN) allows the data of a patient’s vital body parameters and movements to be collected by wearable or implantable sensors and communicated using short-range wireless communication techniques. WBANs provide unprecedented opportunities to monitor the patient’s health status with real- time updates to the physician. Furthermore, these devices are used to collect life-critical information and may operate in hostile environments, so they require strict security mechanisms to prevent the malicious interaction with the system. In this paper the technique that can be used to monitor patients by the use of body area networks is reviewed. Also, the current secure strategies that can impede the attacks faced by wireless communications in healthcare systems and improve the security of mobile health care are discussed. Keywords: Mobile health (m-health), body sensor network (BSNs), wireless body area network (WBAN), health monitoring, security, privacy.

1 Introduction M-Health is defined as mobile computing, medical sensor, and communications technologies for healthcare [1] The use of the M-health terminology relates to applications and systems such as telemedicine[2], telehealth[3]. The MobileHealth system provides a complete end-to-end m-health platform for ambulant patient monitoring[5]. The MobileHealth patient/user is equipped with different portable and wearable sensors that can monitor a patient‘s health status in real time and automatically transmit the sensed data to patient healthcare management centers. Patient monitoring takes advantage of typical wireless and mobile networks, such as the mobile ad hoc network (MANET) and the body sensor network (BSN). Chung-Chih Lin et al[6] proposed a scheme for assessment and safety monitoring of dementia patients using these networks. When two medical sensors are in each other‘s transmission ranges, they can directly communicate with each other. Otherwise, other sensors or devices can cooperate to relay the transmitted data. Security is one of the most important aspects of any system; a mobile healthcare system with patient monitoring is no exception[7]. A patient‘s medical data can be exposed to malicious intruders or eavesdroppers [8] Shu-Di Bao et al [9] proposed a scheme where he used the timing information of heartbeats as an entity identifier to secure body sensor network. V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 695–700, 2010. © Springer-Verlag Berlin Heidelberg 2010

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In this article I highlight the issues of patient monitoring by the use of mobile healthcare technology and different security strategies that are applied to achieve the security and privacy requirements. I throw some light on related work in section 2. In the section 3 I briefly describe BSNs. In section 4 I focus on the solutions for data security and privacy respectively. Finally I draw the conclusion.

2 Related Work Early clinical trials was conducted in the mid-nineties by the National Institute of Health in the Mobile Telemedicine project [10] .The result has led to a proliferation of health-care projects, including CodeBlue [11], PPMIM [12], MobiCare [13], LiveNet [14], PCN [15], UbiMon [16], MobiHealth [17], AMON [18], and PadNET [19]. Various types of wearable health monitoring sensor devices are integrated into patients’ clothing [20], an armband [21], or wristband [22]. Project aims to provide a continuous monitoring system for patients in order to capture transient but life threatening events [23]. CodeBlue [24] is the only existing project that employs wireless sensor networks in emergency medical care as an emergency message delivery system. A wireless health monitoring system [25] has been proposed that presents a three-tier architecture integrated to smart homes.

3 Body Sensor Networks and the Technology A body sensor network (BSN), also known as a body area network (BAN) [26, 27], consists of wearable or implantable biosensors and continuously monitors a patient [8].

Fig. 1. Integration of personalized BSN in a telemedicine system

RF technology has been widely adopted to interconnect in-body and on-body sensors. Figure 1 illustrates a simplified example of using BSNs in a telemedicine system. Each BSN connects all sensors and each sensor being connected to a microprocessor, wireless transceiver, and battery forms a BSN node complex capable of seamlessly integrating with outside environments via various wireless access technologies .

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Performance characteristics of typical wireless sensors, such as processing power and available memory, severely limit real-time capabilities of a typical WBAN. In addition, a personal server must have (a) enough local storage to log hours of sensor data and (b) the ability to upload these data wirelessly to a remote medical record repository using the Internet when a hub becomes available. At this point, the data will be available for remote access by physicians and researchers that wish to extract physiologic parameters, apply state-of-health assessment algorithms, note trends in patient data over time, and even predict health crises. A BSN uses wireless communication technologies such as Bluetooth, IEEE 802.x, and General Packet Radio service (GPRS). Communication between entities within a BAN is referred to as intra-BAN communication. IEEE 802.15.4/Zigbee technology[28] is utilized by a BAN proposed in [29] Captured biosignals undergo a series of processes starting with low level signal processing and ending with high level clinical analysis and interpretation to various levels of abstraction in order. Interpretation tasks require a more sophisticated approach [30]. 3.1 ZigBee for Wireless Sensing and Transmission of Medical Data Established standards for wireless applications, such as Bluetooth and IEEE 802.11, allow high transmission rates, but poses disadvantages such as high power consumption, application complexity, and cost. ZigBee networks on the other hand, are primarily intended for low duty cycle sensors, those active for less than 1% of the time for improved power consumption. The network name comes from the zigzagging path a bee (a data packet) follows to get from flower to flower (or node to node) [31]. The ZigBee Alliance chose to use an existing data link and physical layer specifications. Complete descriptions of the protocols used in ZigBee can be found in [28, 32]

4 Data Security and Privacy in WBANs There are many concerns about security and privacy that need to be solved to protect the users’ information in m-healthcare systems. Typical concerns include how to prevent the disclosure of a patient’s data, who should have the right to access the patient’s medical record, and how to protect the privacy of the patient.Most current solutions protect data confidentiality by using cryptography. Symmetric keys, such as secret key, session key, or private key, are generally distributed to a user of mhealthcare when he/she registers in the system. Asymmetric keys are more able to protect data confidentiality [33], so elliptic-curve cryptography (ECC) is implemented to reduce the computational costs of public key cryptography. An HIPAA-compliant key management solution [34] considers two crucial regulations of HIPAA: security and privacy. Chessa et al. [35] proposed secure distributed data storage and sharing scheme for mobile wireless networks, based on the Redundant Residue Number System (RRNS). Wang et al [36] proposed a secure and dependable distributed data storage scheme. In Wang‘s scheme data confidentiality, dependability, and dynamic integrity assurance are achieved simultaneously. However, the above techniques incur high communication and storage overhead, which makes it less practical in energyconstrained WBANs.

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4.1 Fine Grained Distributed Data Access Control In a WBAN access rights to patient-related data are often granted to users based on their professional roles. Fine-grainedness can be achieved through designating the users’ roles appropriately. SKC-Based Schemes : A solution is proposed by Morchon et al. [37], which utilizes Blundo‘s key predistribution scheme to support RBAC. In order to achieve both fine-grained access control and efficiency, it is more desirable to encrypt once and for all (i.e., encrypt the file once so that all the authorized users can have access).PKC-Based Schemes: Attribute-Based Encryption (ABE), is an effective primitive method to achieve fine-grained access control [38]. ABE is a one-tomany encryption method, where the ciphertext is meant to be readable only by a group of users that satisfy a certain AP. Ciphertext Policy ABE (CP-ABE) [38] perfectly matches the model of RBAC. Each user is assigned a set of attributes (roles), and a patient can freely choose a set of users/roles that are allowed to gain access to his/her medical data, from which the AP is derived. Whenever a node in the WBAN generates some data, the AP is built into the ciphertext. Accountability and revocability: Yu et al. studied this attack in [39] and proposed a technique to defend against it. The pirate device is tricked to decrypt a value that is encrypted under its ID, which will not succeed. Yu et al. proposed a broadcast-based revocation scheme in wireless sensor networks [40], where key updates are done using only one broadcast message. Anonymity in Access Control : Recently, Nishide et al. proposed two constructions of CP-ABE with a partially hidden access policy [41]. They achieve recipient anonymity by hiding which subset of attributes is specified in the AP. However, their complexities are high, which limits the applicability to WBANs.

5 Conclusion In this article the diversity of factors that take part in the design of reliable, intelligent, secure patient monitoring and management systems has been presented. I describe the issues concerning BSNs. I demonstrate the techniques used by current secure mhealthcare system to ensure security and privacy of WBAN. The WBAN is an emerging and promising technology that will change people‘s healthcare experiences revolutionarily. Data security and privacy in WBANs and WBAN-related e-healthcare systems is an important area, and there still remain a number of considerable challenges to overcome. The research in this area is still in its infancy now, but I believe it will draw an enormous amount of interest in coming years.

References 1. Istepanian, R.S.H., Zhang, Y.T.: Guest editorial introduction to the special section on mhealth: Beyond Seamless Mobility and Global Wireless Health-care Connectivity. IEEE Transactions on Information Technology (2004) 2. Istepanian, R.S.H., Wang, H.: European Telemedicine Glossary of Concepts, Standards Technologies. In: European Commission Information Society Directorate General (ed.), Brussels, Belgium (2003)

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3. Istepanian, R.S.H., Lacal, J.: M-Health systems: Future Directions. In: Paper presented at the 25th Annual International Conference of IEEE Engineering Medicine and Biology,Cancun, Mexico (2003) 4. Ren, Y., Pazzi, R.W.N., Boukerche, A.: Monitoring Patients via a Secure and Mobile Healthcare System. In: IEEE Wireless Communications (February 2010) 5. Lin, C., et al.: A Healthcare Integration System for Dissease Assessment and Safety Monitoring of Dementia Patients. IEEE Trans. Info. Tech. Biomedicine 12, 579–586 (2008) 6. Li, M., Lou, W., Ren, K.: Data Security and Privacy in Wireless Body Area Networks·. In: IEEE Wireless Communications (February 2010) 7. Li, M., Lou, W., Ren, K.: Data Security and Privacy in Wireless Body Area Net-works? IEEE Wireless Communications (February 2010) 8. Tentori, M., Favela, J., Ridriguez, M.D.: Privacy-Aware Autonomous Agents for Pervasive Healthcare. IEEE Intelligent Sys. 21, 55–62 (2006) 9. Bao, S.-D., Poon, C.C.Y., Zhang, Y.-T., Shen, L.F.: Using the Timing Information of Heartbeats as an Entity Identifier to Secure Body Sensor Network. IEEE Transaction on Information Technology Biomedicine 12(6) (November 2008) 10. Final Report, “Mobile Telemedicine Testbed for National Information Infrastructure,” National Institute of Health, project, N0-1-LM-6-3541, http://collab.nlm.nih.gov/tutorials 11. Malan, Fulford-Jones, D.T., Welshm, M., Moulton, S.: Codeblue: an Ad-hoc Sensor Network Infrastructure for Emergency Medical Care. In: Proceedings of the 1st International Workshop on Wearable and Implantable Body Sensor Networks(BSN 2004), London, UK (April 2004) 12. Jea, D., Srivastava, M.B.: A Remote Medical Monitoring and Interaction System. In: Proceedings of the 4th International Conference on Mobile Systems, Uppsala, Sweden (June 2006) 13. Chakravorty, R.: A Programmable Service Architecture for Mobile Medical Care. In: Proceedings of the 4th Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom 2006), Pisa, Italy, pp. 532–536 (March 2006) 14. Benbasat, A.Y., Paradiso, J.A.: A Compactmodular Wireless Sensor Platform. In: Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN 2005), Los Angeles, Calif., USA, pp. 410–415 (April 2005) 15. Patient-Centric Network, http://nms.lcs.mit.edu/projects/pcn/ 16. Ng, J.W.P., Lo, B.P.L., Wells, O., et al.: Ubiquitous Monitoring Environment for Wearable and Implantable Sensors (UbiMon). In: Proceedings of the 6th International Conference on Ubiquitous Computing (UBICOMP 2004), Nottingham, UK (September 2004) 17. European MobiHealth Project, http://www.mobihealth.org/ 18. Anliker, U., Ward, J.A., Lukowicz, P., et al.: AMON: A Wearable Multiparameter Medical Monitoring and Alert System. IEEE Transactions on Information Technology in Biomedicine 8(4), 415–427 (2004) 19. Junker, H., Stäger, M., Troster, G., Blttler, D., Salama, O.: Wireless Network in Context Aware Wearable Systems. In: Proceedings of the 1st European Workshop on Wireless Sensor Networks (EWSN 2004), Berlin, Germany, pp. 37–40 (January 2004) 20. Park, S., Jayaraman, S.: Enhancing the Quality of Life through Wearable Technology. IEEE Engineering in Medicine and Biology Magazine 22(3), 41–48 (2003) 21. BodyMedia, http://www.bodymedia.com/ 22. IBM Linux Wrist Watch, http://www.research.ibm.com

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23. Lo, B.P.L., Yang, G.-Z.: Implementations of Body Sensor Networks. In: Proceedings of the 2nd InternatiInternational Workshop on Body Sensor Networks (BSN 2005), London, UK (April 2005) 24. CodeBlue project, http://www.eecs.harvard.edu/mdw/proj/codeblue/ 25. S. Dağtaş, G. Pekhteryev, Z. Sa̧ahinoğglu: Multi-stage Real Time Health Monitoring via ZigBee in Smart Homes. In: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW 2007), vol. 1, Niagara Falls, Canada, pp. 782–786 (May 2007) 26. Jones, V., van Halteren, A., Widya, I., Dokovsky, N., Koprinkov, G., Bults, R., Kons, D., Herzog, R.: MobiHealth: Mobile Health Services based on Body Area Networks. In: Istepanian, R.H., Laxminarayan, S., Pattichis, C.S. (eds.) M-Health: Emerging Mobile Health Systems, pp. 219–236, 624. Springer, Heidelberg (2006) 27. MobiHealth, project, http://www.mobihealth.org/ 28. ZigBee Alliance Document 02130, Network Layer Specification (July 2004) 29. Monton, E., et al.: Body Area Network for Wireless Patient Monitoring. IET Commun., 215–222 (2008) 30. MYOTEL, http://www.myotel.eu/ 31. ZigBee Networks Open the Door to More Wireless Medical Devices, Medical Design (April 2005) 32. IEEE Standard for Part 15.4, Wireless Medium Access Control (MAC) and Physical Layer (PHY) specificatios for low rate Wireless Personal Area Networks, WPANs (2003) 33. Malhotra, K., Gardner, S., Patz, R.: Implementation of Elliptic-Curve Cryptography on Mobile Healthcare Devices. In: Proc. IEEE Int‘l. Conf. Net., Sensing, and Control, pp. 239–244 (2007) 34. Lee, W.-B., Lee, C.-D.: A Cryptographic Key Management Solution for HIPAA Privacy/Security Regulations. IEEE Trans. Info. Tech. Biomedicine 12, 34–41 (2008) 35. Chessa, S., Maestrini, P.: Dependable and Secure Data Storage and Retrieval in Mobile. In: Wireless Networks: Int‘l. Conf. Dependable Sys. Net., pp. 207–216 (June 2003) 36. Wang, Q., et al.: Dependable and Secure Sensor Data Storage with Dynamic Integrity Assurance. In: Proc. IEEE INFOCOM 2009 (April 2009) 37. Morchon, O.G., Baldus, H.: Efficient Distributed Security for Wireless Medical Sensor Networks. In: Int‘l. Conf. Intelligent Sensors, Sensor Net., Info. Processing, pp. 249–254 (December 2008) 38. Bethencourt, J., Sahai, A., Waters, B.: Ciphertext-Policy Attribute-Based Encryption. In: Proc. IEEE Symp. Security and Privacy (May 2007) 39. Yu, S., et al.: Defending against Key Abuse Attacks in KP-ABE Enabled Broadcast System SecureComm 2009 (September 2009) 40. Yu, S., Ren, K., Lou, W.: FDAC: Toward Fine grained Distributed Data Access Control in Wireless Sensor Networks. In: IEEE INFOCOM 2009 (April 2009) 41. Nishide, T., Yoneyama, K., Ohta, K.: Attribute-Based Encryption with Partially Hidden Encryptor-Specified Ac-cess Structures. In: Bellovin, S.M., et al. (eds.) ACNS 2008, May 2008. LNCS, pp. 111–129. Springer, Heidelberg (2008)

Author Index

Abhraham, Annamma 313 Agarwal, Ajay 297 Aggarwal, R.K. 514 Aghila, G. 287 Aghila, V. 62 Akasapu, Ajaya K. 180 Amir, Mohd. 228 Amritha, P.P. 638 Anand Kumar, M. 430 Anand, Lakshmi 510 Annadurai, C. 626 Anzaar, Ahmad 641 Arathi, T. 672 Arunachalam, V.P. 55 Arun Balaji, S. 165 Arun, K.S. 319 Ashraﬁ Payaman, N.A. 330 Aswatha Kumar, M. 666 Bagyamani, J. 380 Banerjee, Biplab 443, 458 Banerjee, Prasun 237 Baniwal, Ritu 660 Basha, P. 108 Bhaskar, Manju 243 Bhattacharjee, Tanusree 443, 458 Bhattacharjee, U. 576 Bhattacharya, Sukriti 237 Bhattacharyya, Balaram 339 Bhoyar, Dinesh B. 267 Bhuvaneswaran, R.S. 116 Bhuvaneswari, B. 565 Boomaranimalany, A. 455 Borker, Samarth 619 Chaba, Yogesh 478 Chakraborty, Sandip 201 Chandra Sekaran, K. 83 Chandrasekaran, R.M. 455 Charithartha Reddy, C. 626 Chattopadhyay, Matangini 201 Chattopadhyay, Samiran 201 Chauhan, Sameer Singh 136 Chenthalir Indra, N. 389 Chhotaray, R.K. 580

Chickerur, Satyadhyan 666 Chithralekha, T. 243 Choudhury, Prasenjit 22 Chowdhury, Nirmalya 443, 458 Dakhole, Pravin 50 Darji, Anand 681 Dasari, Manohar Babu 22 Das, Minu M. 243 Dave, M. 514 D’Costa, Stephan 237 Derhami, Vali 326 Deshmane, Anil K. 525 Devaraj, P. 116 Dhanalakshmi, V. 430 Dhanumjaya, K. 13 Dharaskar, R.V. 616 Dharma Raj, C. 406 Dharmar, Vydeki 100 Dhavse, Rasika 681 Dinamani Singh, A. 576 Divya Udayan, J. 602 D’Souza, Rio G.L. 83 Dubey, Ashutosh K. 157 Dunga, Srinivasa Murali 691 Duraiswamy, K. 463 Eftekhari, Parvin 394 Elias, Susan 165, 434 Esther Rani, P. 449 Femina Abdulkader, A.

411

Gaddam, Rajasekhar 22 Gadia, Shashi 400 Gadiraju, Mahesh 584 Ganesan, Balakrishnan 622 Ganesh, D. 108 Gireesh Kumar, T. 602, 638 Giriprasad, M.N. 13 Gopalakrishnan, K. 344 Gorpuni, Pavankumar 45 Gosain, Anjana 384 Govindarajan, Kannan 223 Gowri, R. 641

702

Author Index

Goyal, S.B. 400 Gujral, Rajneesh 610 Gupta, Bhaskar 376 Gupta, Gaytri 495 Gupta, Manjari 561 Gupta, Rolly 384 Gupta, S.C. 641 Gurusamy, G. 501 Haghighat, Abolfazl Toroghi Harikiran, J. 554 Harishankar, Vrinda 62 Hasan, Mohd. 215 Hemarani, R. 651 Islam, Aminul

394

215

Jabin, Suraiya 68 Jacob, Binu 484 Jain, Rajat Sheel 1 Jain, Satbir 129, 371 Jayanthi, S.K. 437 Jayanthy, K. 100 Jayaraman, Arunarasi 654 Jayasree, P.V.Y. 406 John, Roshy M. 602 Joseph, Ajit 411 Joshi, Ramesh C. 124, 136, 142 Joy James Prabhu, A. 303 Kalai Selvi, A. 647 Kaliammal, N. 501 Kalpana, B. 549 Kalpana, G. 350 Kamalraj, R. 651 Kanagasabapathy, M. 626 Kandasamy, A. 83 Kanrar, Soumen 423 Kapil, Anil 186, 610 Karthik, S. 55 Kaur, Amarjeet 249 Kaushik, Atul Kant 124 Kaushik, Brajesh Kumar 520 Kavita, Singh R. 535 Keshavamurthy, B.N. 529 Khanaa, V. 171 Khare, Ashish 281 Khare, Manish 281 Khatri, Sunil Kumar 675 Khurana, Sunil 675

Kiran Kumar, G. 13 Kiran Mayee, P. 606 Krishnam Raju, K.V. 590 Krishnan Namboori, P.K. 62, 510 Kumar, Brijesh 1 Kumar, Dinesh 596 Kumar, Mala S. 62 Kumar, Neeraj 1 Kumar, R. Ravi 580 Kumar, Vijay 596 Kushwaha, Sandeep K. 571 Lainu, K.L. 62 Lakshmanan, S.A. 602 Lakshman, P. 406 Lohani, R.B. 619 Lokanadham Naidu, V. 108 Madan Mohan, L. 490 Madathil, Anoj 638 Mahapatra, K.K. 45 Maheswara Rao, V.V.R. 90 Majumder, Swanirbhar 576 Malathi, K. 565 Malleswara Rao, V. 490 Mallika, K. 430 Mandal, A.S. 691 Mandal, Byomkesh 339 Manju, A. 29 Masram, Bharati Y. 267 Meenakshi, V.S. 206 Meshram, B.B. 292 Miangah, Tayebeh Mosavi 307 Mishra, Debahuti 356 Mohamed Sathik, M. 78, 647 Mona Subramaniam, A. 29 Mukesh, Chand 641 Mukeshl, Zaveri A. 535 Mukesh, Raghuwanshi M. 535 Mukherjee, Aroop 423 Munwar, Sk. 108 Murthy, P.H.S.T. 490 MuthuKumaran, D. 626 Nalavade, K.C. 292 Natarajan, A.M. 469 Nezarat, Amin 307 Nigam, Madhav J. 29 Niranjan, Utpala 171 Niyogi, Rajdeep 142

Author Index Padmavathi, Ganapathi 206, 686 Pande, Akshara 561 Pandey, Kumar Sambhav 274, 660 Paramasivam, M.E. 475 Parameshwaran, Latha 672 Parisi, Rajesh Babu 22 Patnaik, Tushar 281 Pattanaik, Sabyasachi 580 Paul, Soma 606 Penchala, Sathish Kumar 287 Pilli, Emmanuel S. 124, 142 Poornaselvan, K.J. 602 Pradhan, Srikant 258 Prakash Singh, Surya 504 Prasad, E.V. 36 Prema, S. 437 Priyadarshini, Sadhana 356 Promod, K.V. 484 Pugazendi, R. 463 Punithavalli, M. 350 Purkayastha, Bipul Syam 365 Purushothaman, Dhanya 62 Pushpam, Indumathy 654 Pushpavalli, M. 469 Radhika Devi, R. 510 Rahim, Shafry 540 Raja, K.B. 580 Rajamanickam, Vijayanandh 622 Rajaram, Kanchana 362 Raja Reddy, M. 13 Rajavel, Rajkumar 223 Rajenderan, Amog 434 Rajiv Kannan, A. 651 Raju, K.V.S.V.N. 590 Ramadass, Sathya 313 Ramanathan, Subbu 434 Ramani, K. 36, 108 RamaRaj, E. 389 Rana, Sanjeev 186 Rani, Prabha 478 Ranjani, P. 651 Rathi, Manisha 195 Rathipriya, R. 380 Rathore, Babita 571 Raval, Gaurang 258 Ravichandran, T. 55 Rehman, Amjad 540 Rhymend Uthariaraj, V. 344, 543

703

Saadatjoo, Fatemeh 326 Saadatjoo, Mohammad Ali 326 Saba, Tanzila 540 Sabeenian, R.S. 475, 498 Sai Kiranmai, G. 430 Sakhare, Apeksha V. 616 Samad, Hameed Zohaib 417 Sandeep Varma, N. 590 Sangal, Rajeev 606 Sangeetha, R. 549 Santra, Titir 695 Sanyal, Debarshi Kumar 201 Saptharshi, 510 Sarangam, K. 417 Sarath Chandar, A.P. 165 Sarath, K.S. 319 Sarkar, Partha P. 376 Sarma, Kandarpa Kumar 8 Sarma, Manash Pratim 8 Saruladha, K. 287 Sasibhushana Rao, G. 406 Schieder, Simon 180 Shahnawaz, Husain 641 Shah, Nilesh 681 Shakya, Madhvi 571 Shandilya, Shishir K. 157 Shanmuga Lakshmi, R. 449 Shanmugapriya, Venkateswaran 686 Sharma, Lokesh K. 180 Sharma, Mitesh 529 Sharma, Neha 274 Sharma, Sugam 400 Sharma, T.P. 249 Shashi Kumar, D.R. 580 Shatheesh Sam, I. 116 Shekhar, Chandra 691 Shokrzadeh, Hamid 394 Singh, Kanwar Preet 478 Singh, L. Lolit Kumar 376 Singh, Raghuvir 520 Singh, Renu 576 Singh, Sanjay 691 Singh, Sarangthem Ibotombi 148 Singh, Yashpal 297 Singh, Yudhvir 478 Sinha, Smriti Kumar 148, 365 Sinha, Subrata 365 Solanki, Paresh 258 Soman, K.P. 430, 672 Somasundaram, Thamarai Selvi 223

704

Author Index

Sreenivasa Rao, V. 490 Srinu, B. 406 Subramanyam, R.B.V. 171 Sujatha, S.S. 78 Sulong, Ghazali 540 Suresh, Haresh 434 Talbar, Sanjay N. 336, 525 Tamijetchelvy, R. 632 Thakare, V.M. 616 Thangavel, K. 380 Tijare, Ankita 50 Toshniwal, Durga 228, 529 Tripathi, Arun Kumar 297, 561 Usha Kiruthika, S. 362 Usha Rani, R. 554

Vaitheki, K. 632 Valarmathi, M.L. 55 Valli Kumari, V. 90, 584, 590 Vanathi, B. 543 Varadarajan, S. 36 Varma, Megha. P. 510 Varma, Satishkumar L. 336 Varun Gopal, K. 62 Vasavi, C.S. 510 Velagaleti, Silpakesav 45 Venkatesh, G. 165 Verma, Krishan Gopal 520 Vidhya, M. 498 Virmani, Deepali 129, 371 Vohra, Anil 691 Vuppala, Satyanarayana 22 Vyas, Om Prakash 180

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