Editor In Chief
Dr. Shiv K Sahu
Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT)
Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Dr. Shachi Sahu
Ph.D. (Chemistry), M.Sc. (Organic Chemistry)
Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Vice Editor In Chief
Dr. Vahid Nourani
Professor, Faculty of Civil Engineering, University of Tabriz, Iran
Prof.(Dr.) Anuranjan Misra
Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University,
Noida (U.P.), India
Chief Advisory Board
Prof. (Dr.) Hamid Saremi
Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran
Dr. Uma Shanker
Professor & Head, Department of Mathematics, CEC, Bilaspur(C.G.), India
Dr. Rama Shanker
Professor & Head, Department of Statistics, Eritrea Institute of Technology, Asmara, Eritrea
Dr. Vinita Kumari
Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., India
Dr. Kapil Kumar Bansal
Head (Research and Publication), SRM University, Gaziabad (U.P.), India
Dr. Deepak Garg
Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India, Senior Member of IEEE,
Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of Technical
Education (ISTE), Indian Science Congress Association Kolkata.
Dr. Vijay Anant Athavale
Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India
Dr. T.C. Manjunath
Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India
Dr. Kosta Yogeshwar Prasad
Director, Technical Campus, Marwadi Education Foundation’s Group of Institutions, Rajkot-Morbi Highway, Gauridad, Rajkot,
Gujarat, India
Dr. Dinesh Varshney
Director of College Development Counceling, Devi Ahilya University, Indore (M.P.), Professor, School of Physics, Devi Ahilya
University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India
Dr. P. Dananjayan
Professor, Department of Department of ECE, Pondicherry Engineering College, Pondicherry,India
Dr. Sadhana Vishwakarma
Associate Professor, Department of Engineering Chemistry, Technocrat Institute of Technology, Bhopal(M.P.), India
Dr. Kamal Mehta
Associate Professor, Deptment of Computer Engineering, Institute of Technology, NIRMA University, Ahmedabad (Gujarat), India
Dr. CheeFai Tan
Faculty of Mechanical Engineering, University Technical, Malaysia Melaka, Malaysia
Dr. Suresh Babu Perli
Professor& Head, Department of Electrical and Electronic Engineering, Narasaraopeta Engineering College, Guntur, A.P., India

Dr. Binod Kumar
Associate Professor, Schhool of Engineering and Computer Technology, Faculty of Integrative Sciences and Technology, Quest
International University, Ipoh, Perak, Malaysia
Dr. Chiladze George
Professor, Faculty of Law, Akhaltsikhe State University, Tbilisi University, Georgia
Dr. Kavita Khare
Professor, Department of Electronics & Communication Engineering., MANIT, Bhopal (M.P.), INDIA
Dr. C. Saravanan
Associate Professor (System Manager) & Head, Computer Center, NIT, Durgapur, W.B. India
Dr. S. Saravanan
Professor, Department of Electrical and Electronics Engineering, Muthayamal Engineering College, Resipuram, Tamilnadu, India
Dr. Amit Kumar Garg
Professor & Head, Department of Electronics and Communication Engineering, Maharishi Markandeshwar University, Mulllana,
Ambala (Haryana), India
Dr. T.C.Manjunath
Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India
Dr. P. Dananjayan
Professor, Department of Department of ECE, Pondicherry Engineering College, Pondicherry, India
Dr. Kamal K Mehta
Associate Professor, Department of Computer Engineering, Institute of Technology, NIRMA University, Ahmedabad (Gujarat), India
Dr. Rajiv Srivastava
Director, Department of Computer Science & Engineering, Sagar Institute of Research & Technology, Bhopal (M.P.), India
Dr. Chakunta Venkata Guru Rao
Professor, Department of Computer Science & Engineering, SR Engineering College, Ananthasagar, Warangal, Andhra Pradesh, India
Dr. Anuranjan Misra
Professor, Department of Computer Science & Engineering, Bhagwant Institute of Technology, NH-24, Jindal Nagar, Ghaziabad,
India
Dr. Robert Brian Smith
International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie
Centre, North Ryde, New South Wales, Australia
Dr. Saber Mohamed Abd-Allah
Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Yue Yang Road, Shanghai,
China
Dr. Himani Sharma
Professor & Dean, Department of Electronics & Communication Engineering, MLR Institute of Technology, Laxman Reddy Avenue,
Dundigal, Hyderabad, India
Dr. Sahab Singh
Associate Professor, Department of Management Studies, Dronacharya Group of Institutions, Knowledge Park-III, Greater Noida,
India
Dr. Umesh Kumar
Principal: Govt Women Poly, Ranchi, India
Dr. Syed Zaheer Hasan
Scientist-G Petroleum Research Wing, Gujarat Energy Research and Management Institute, Energy Building, Pandit Deendayal
Petroleum University Campus, Raisan, Gandhinagar-382007, Gujarat, India.
Dr. Jaswant Singh Bhomrah
Director, Department of Profit Oriented Technique, 1 – B Crystal Gold, Vijalpore Road, Navsari 396445, Gujarat. India
Technical Advisory Board
Dr. Mohd. Husain
Director, MG Institute of Management & Technology, Banthara, Lucknow (U.P.), India

Dr. T. Jayanthy
Principal, Panimalar Institute of Technology, Chennai (TN), India
Dr. Umesh A.S.
Director, Technocrats Institute of Technology & Science, Bhopal(M.P.), India
Dr. B. Kanagasabapathi
Infosys Labs, Infosys Limited, Center for Advance Modeling and Simulation, Infosys Labs, Infosys Limited, Electronics City,
Bangalore, India
Dr. C.B. Gupta
Professor, Department of Mathematics, Birla Institute of Technology & Sciences, Pilani (Rajasthan), India
Dr. Sunandan Bhunia
Associate Professor & Head,, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West
Bengal, India
Dr. Jaydeb Bhaumik
Associate Professor, Dept. of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia, West Bengal, India
Dr. Rajesh Das
Associate Professor, School of Applied Sciences, Haldia Institute of Technology, Haldia, West Bengal, India
Dr. Mrutyunjaya Panda
Professor & Head, Department of EEE, Gandhi Institute for Technological Development, Bhubaneswar, Odisha, India
Dr. Mohd. Nazri Ismail
Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia
Dr. Haw Su Cheng
Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya
Dr. Hossein Rajabalipour Cheshmehgaz
Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi
Malaysia (UTM) 81310, Skudai, Malaysia
Dr. Sudhinder Singh Chowhan
Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India
Dr. Neeta Sharma
Professor & Head, Department of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India
Dr. Ashish Rastogi
Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Santosh Kumar Nanda
Professor, Department of Computer Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa),
India
Dr. Hai Shanker Hota
Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Sunil Kumar Singla
Professor, Department of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India
Dr. A. K. Verma
Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India
Dr. Durgesh Mishra
Chairman, IEEE Computer Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis
Institute of Technology, Indore (M.P.), India
Dr. Xiaoguang Yue
Associate Professor, College of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China
Dr. Veronica Mc Gowan
Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman
China

Dr. Mohd. Ali Hussain
Professor, Department of Computer Science and Engineering, Sri Sai Madhavi Institute of Science & Technology, Rajahmundry
(A.P.), India
Dr. Mohd. Nazri Ismail
Professor, System and Networking Department, Jalan Sultan Ismail, Kaula Lumpur, MALAYSIA
Dr. Sunil Mishra
Associate Professor, Department of Communication Skills (English), Dronacharya College of Engineering, Farrukhnagar, Gurgaon
(Haryana), India
Dr. Labib Francis Gergis Rofaiel
Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,
Mansoura City, Egypt
Dr. Pavol Tanuska
Associate Professor, Department of Applied Informetics, Automation, and Mathematics, Trnava, Slovakia
Dr. VS Giridhar Akula
Professor, Avanthi's Research & Technological Academy, Gunthapally, Hyderabad, Andhra Pradesh, India
Dr. S. Satyanarayana
Associate Professor, Department of Computer Science and Engineering, KL University, Guntur, Andhra Pradesh, India
Dr. Bhupendra Kumar Sharma
Associate Professor, Department of Mathematics, KL University, BITS, Pilani, India
Dr. Praveen Agarwal
Associate Professor& Head, Department of Mathematics, Anand International College of Engineering, Jaipur (Rajasthan), India
Dr. Manoj Kumar
Professor, Department of Mathematics, Rashtriya Kishan Post Graduate Degree, College, Shamli, Prabudh Nagar, (U.P.), India
Dr. Shaikh Abdul Hannan
Associate Professor, Department of Computer Science, Vivekanand Arts Sardar Dalipsing Arts and Science College, Aurangabad
(Maharashtra), India
Dr. K.M. Pandey
Professor, Department of Mechanical Engineering,National Institute of Technology, Silchar, India
Prof. Pranav Parashar
Technical Advisor, International Journal of Soft Computing and Engineering (IJSCE), Bhopal (M.P.), India
Dr. Biswajit Chakraborty
MECON Limited, Research and Development Division (A Govt. of India Enterprise), Ranchi-834002, Jharkhand, India
Dr. D.V. Ashoka
Professor & Head, Department of Information Science & Engineering, SJB Institute of Technology, Kengeri, Bangalore, India
Dr. Sasidhar Babu Suvanam
Professor & Academic Cordinator, Department of Computer Science & Engineering, Sree Narayana Gurukulam College of
Engineering, Kadayiuruppu, Kolenchery, Kerala, India
Dr. C. Venkatesh
Professor & Dean, Faculty of Engineering, EBET Group of Institutions, Kangayam, Erode, Caimbatore (Tamil Nadu), India
Dr. Nilay Khare
Assoc. Professor & Head, Department of Computer Science, MANIT, Bhopal (M.P.), India
Dr. Sandra De Iaco
Professor, Dip.to Di Scienze Dell’Economia-Sez. Matematico-Statistica, Italy
Dr. Yaduvir Singh
Associate Professor, Department of Computer Science & Engineering, Ideal Institute of Technology, Govindpuram Ghaziabad,
Lucknow (U.P.), India
Dr. Angela Amphawan
Head of Optical Technology, School of Computing, School Of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia

Dr. Ashwini Kumar Arya
Associate Professor, Department of Electronics & Communication Engineering, Faculty of Engineering and Technology,Graphic Era
University, Dehradun (U.K.), India
Dr. Yash Pal Singh
Professor, Department of Electronics & Communication Engg, Director, KLS Institute Of Engg.& Technology, Director, KLSIET,
Chandok, Bijnor, (U.P.), India
Dr. Ashish Jain
Associate Professor, Department of Computer Science & Engineering, Accurate Institute of Management & Technology, Gr. Noida
(U.P.), India
Dr. Abhay Saxena
Associate Professor&Head, Department. of Computer Science, Dev Sanskriti University, Haridwar, Uttrakhand, India
Dr. Judy. M.V
Associate Professor, Head of the Department CS &IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham,
Brahmasthanam, Edapally, Cochin, Kerala, India
Dr. Sangkyun Kim
Professor, Department of Industrial Engineering, Kangwon National University, Hyoja 2 dong, Chunche0nsi, Gangwondo, Korea
Dr. Sanjay M. Gulhane
Professor, Department of Electronics & Telecommunication Engineering, Jawaharlal Darda Institute of Engineering & Technology,
Yavatmal, Maharastra, India
Dr. K.K. Thyagharajan
Principal & Professor, Department of Informational Technology, RMK College of Engineering & Technology, RSM Nagar,
Thiruyallur, Tamil Nadu, India
Dr. P. Subashini
Asso. Professor, Department of Computer Science, Coimbatore, India
Dr. G. Srinivasrao
Professor, Department of Mechanical Engineering, RVR & JC, College of Engineering, Chowdavaram, Guntur, India
Dr. Rajesh Verma
Professor, Department of Computer Science & Engg. and Deptt. of Information Technology, Kurukshetra Institute of Technology &
Management, Bhor Sadian, Pehowa, Kurukshetra (Haryana), India
Dr. Pawan Kumar Shukla
Associate Professor, Satya College of Engineering & Technology, Haryana, India
Dr. U C Srivastava
Associate Professor, Department of Applied Physics, Amity Institute of Applied Sciences, Amity University, Noida, India
Dr. Reena Dadhich
Prof.& Head, Department of Computer Science and Informatics, MBS MArg, Near Kabir Circle, University of Kota, Rajasthan, India
Dr. Aashis.S.Roy
Department of Materials Engineering, Indian Institute of Science, Bangalore Karnataka, India
Dr. Sudhir Nigam
Professor Department of Civil Engineering, Principal, Lakshmi Narain College of Technology and Science, Raisen, Road, Bhopal,
(M.P.), India
Dr. S.Senthilkumar
Doctorate, Department of Center for Advanced Image and Information Technology, Division of Computer Science and Engineering,
Graduate School of Electronics and Information Engineering, Chon Buk National University Deok Jin-Dong, Jeonju, Chon Buk, 561-
756, South Korea Tamilnadu, India
Dr. Gufran Ahmad Ansari
Associate Professor, Department of Information Technology, College of Computer, Qassim University, Al-Qassim, Kingdom of
Saudi Arabia (KSA)
Dr. R.Navaneethakrishnan
Associate Professor, Department of MCA, Bharathiyar College of Engg & Tech, Karaikal Puducherry, India

Dr. Hossein Rajabalipour Cheshmejgaz
Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi Skudai,
Malaysia
Dr. Veronica McGowan
Associate Professor, Department of Computer and Business Information Systems, Delaware Valley College, Doylestown, PA, Allman
China
Dr. Sanjay Sharma
Associate Professor, Department of Mathematics, Bhilai Institute of Technology, Durg, Chhattisgarh, India
Dr. Taghreed Hashim Al-Noor
Professor, Department of Chemistry, Ibn-Al-Haitham Education for pure Science College, University of Baghdad, Iraq
Dr. Madhumita Dash
Professor, Department of Electronics & Telecommunication, Orissa Engineering College , Bhubaneswar,Odisha, India
Dr. Anita Sagadevan Ethiraj
Associate Professor, Department of Centre for Nanotechnology Research (CNR), School of Electronics Engineering (Sense), Vellore
Institute of Technology (VIT) University, Tamilnadu, India
Dr. Sibasis Acharya
Project Consultant, Department of Metallurgy & Mineral Processing, Midas Tech International, 30 Mukin Street, Jindalee-4074,
Queensland, Australia
Dr. Neelam Ruhil
Professor, Department of Electronics & Computer Engineering, Dronacharya College of Engineering, Gurgaon, Haryana, India
Dr. Faizullah Mahar
Professor, Department of Electrical Engineering, Balochistan University of Engineering and Technology, Pakistan
Dr. K. Selvaraju
Head, PG & Research, Department of Physics, Kandaswami Kandars College (Govt. Aided), Velur (PO), Namakkal DT. Tamil Nadu,
India
Dr. M. K. Bhanarkar
Associate Professor, Department of Electronics, Shivaji University, Kolhapur, Maharashtra, India
Dr. Sanjay Hari Sawant
Professor, Department of Mechanical Engineering, Dr. J. J. Magdum College of Engineering, Jaysingpur, India
Dr. Arindam Ghosal
Professor, Department of Mechanical Engineering, Dronacharya Group of Institutions, B-27, Part-III, Knowledge Park,Greater Noida,
India
Dr. M. Chithirai Pon Selvan
Associate Professor, Department of Mechanical Engineering, School of Engineering & Information Technology, Amity University,
Dubai, UAE
Dr. S. Sambhu Prasad
Professor & Principal, Department of Mechanical Engineering, Pragati College of Engineering, Andhra Pradesh, India.
Dr. Muhammad Attique Khan Shahid
Professor of Physics & Chairman, Department of Physics, Advisor (SAAP) at Government Post Graduate College of Science,
Faisalabad.
Dr. Kuldeep Pareta
Professor & Head, Department of Remote Sensing/GIS & NRM, B-30 Kailash Colony, New Delhi 110 048, India
Dr. Th. Kiranbala Devi
Associate Professor, Department of Civil Engineering, Manipur Institute of Technology, Takyelpat, Imphal, Manipur, India
Dr. Nirmala Mungamuru
Associate Professor, Department of Computing, School of Engineering, Adama Science and Technology University, Ethiopia
Dr. Srilalitha Girija Kumari Sagi
Associate Professor, Department of Management, Gandhi Institute of Technology and Management, India

Dr. Vishnu Narayan Mishra
Associate Professor, Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Dumas
Road, Surat (Gujarat), India
Dr. Yash Pal Singh
Director/Principal, Somany (P.G.) Institute of Technology & Management, Garhi Bolni Road , Rewari Haryana, India.
Dr. Sripada Rama Sree
Vice Principal, Associate Professor, Department of Computer Science and Engineering, Aditya Engineering College, Surampalem,
Andhra Pradesh. India.
Dr. Rustom Mamlook
Associate Professor, Department of Electrical and Computer Engineering, Dhofar University, Salalah, Oman. Middle East.
Dr. Ramzi Raphael Ibraheem Al Barwari
Assistant Professor, Department of Mechanical Engineering, College of Engineering, Salahaddin University – Hawler (SUH) Erbil –
Kurdistan, Erbil Iraq.
Dr. Kapil Chandra Agarwal
H.O.D. & Professor, Department of Applied Sciences & Humanities, Radha Govind Engineering College, U. P. Technical University,
Jai Bheem Nagar, Meerut, (U.P). India.
Dr. Anil Kumar Tripathy
Associate Professor, Department of Environmental Science & Engineering, Ghanashyama Hemalata Institute of Technology and
Management, Puri Odisha, India.
Managing Editor
Mr. Jitendra Kumar Sen
International Journal of Soft Computing and Engineering (IJSCE)
Editorial Board
Dr. Soni Changlani
Professor, Department of Electronics & Communication, Lakshmi Narain College of Technology & Science, Bhopal (.M.P.), India
Dr. M .M. Manyuchi
Professor, Department Chemical and Process Systems Engineering, Lecturer-Harare Institute of Technology, Zimbabwe
Dr. John Kaiser S. Calautit
Professor, Department Civil Engineering, School of Civil Engineering, University of Leeds, LS2 9JT, Leeds, United Kingdom
Dr. Audai Hussein Al-Abbas
Deputy Head, Department AL-Musaib Technical College/ Foundation of Technical Education/Babylon, Iraq
Dr. Şeref Doğuşcan Akbaş
Professor, Department Civil Engineering, Şehit Muhtar Mah. Öğüt Sok. No:2/37 Beyoğlu Istanbul, Turkey
Dr. H S Behera
Associate Professor, Department Computer Science & Engineering, Veer Surendra Sai University of Technology (VSSUT) A Unitary
Technical University Established by the Government of Odisha, India
Dr. Rajeev Tiwari
Associate Professor, Department Computer Science & Engineering, University of Petroleum & Energy Studies (UPES), Bidholi,
Uttrakhand, India
Dr. Piyush Kumar Shukla
Assoc. Professor, Department of Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal (M.P.), India
Dr. Piyush Lotia
Assoc.Professor, Department of Electronics and Instrumentation, Shankaracharya College of Engineering and Technology, Bhilai
(C.G.), India
Dr. Asha Rai
Assoc. Professor, Department of Communication Skils, Technocrat Institute of Technology, Bhopal (M.P.), India
Dr. Vahid Nourani
Assoc. Professor, Department of Civil Engineering, University of Minnesota, USA

Dr. Hung-Wei Wu
Assoc. Professor, Department of Computer and Communication, Kun Shan University, Taiwan
Dr. Vuda Sreenivasarao
Associate Professor, Department of Computr And Information Technology, Defence University College, Debrezeit Ethiopia, India
Dr. Sanjay Bhargava
Assoc. Professor, Department of Computer Science, Banasthali University, Jaipur, India
Dr. Sanjoy Deb
Assoc. Professor, Department of ECE, BIT Sathy, Sathyamangalam, Tamilnadu, India
Dr. Papita Das (Saha)
Assoc. Professor, Department of Biotechnology, National Institute of Technology, Duragpur, India
Dr. Waail Mahmod Lafta Al-waely
Assoc. Professor, Department of Mechatronics Engineering, Al-Mustafa University College – Plastain Street near AL-SAAKKRA square- Baghdad - Iraq
Dr. P. P. Satya Paul Kumar
Assoc. Professor, Department of Physical Education & Sports Sciences, University College of Physical Education & Sports Sciences,
Guntur
Dr. Sohrab Mirsaeidi
Associate Professor, Department of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor, Malaysia
Dr. Ehsan Noroozinejad Farsangi
Associate Professor, Department of Civil Engineering, International Institute of Earthquake Engineering and Seismology (IIEES)
Farmanieh, Tehran - Iran
Dr. Omed Ghareb Abdullah
Associate Professor, Department of Physics, School of Science, University of Sulaimani, Iraq
Dr. Khaled Eskaf
Associate Professor, Department of Computer Engineering, College of Computing and Information Technology, Alexandria, Egypt
Dr. Nitin W. Ingole
Associate Professor & Head, Department of Civil Engineering, Prof Ram Meghe Institute of Technology and Research, Badnera
Amravati
Dr. P. K. Gupta
Associate Professor, Department of Computer Science and Engineering, Jaypee University of Information Technology, P.O. Dumehar
Bani, Solan, India
Dr. P.Ganesh Kumar
Associate Professor, Department of Electronics & Communication, Sri Krishna College of Engineering and Technology, Linyi Top
Network Co Ltd Linyi , Shandong Provience, China
Dr. Santhosh K V
Associate Professor, Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal, Karnataka,
India
Dr. Subhendu Kumar Pani
Assoc. Professor, Department of Computer Science and Engineering, Orissa Engineering College, India
Dr. Syed Asif Ali
Professor/ Chairman, Department of Computer Science, SMI University, Karachi, Pakistan
Dr. Vilas Warudkar
Assoc. Professor, Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, India
Dr. S. Chandra Mohan Reddy
Associate Professor & Head, Department of Electronics & Communication Engineering, JNTUA College of Engineering
(Autonomous), Cuddapah, Andhra Pradesh, India
Dr. V. Chittaranjan Das
Associate Professor, Department of Mechanical Engineering, R.V.R. & J.C. College of Engineering, Guntur, Andhra Pradesh, India

Dr. Jamal Fathi Abu Hasna
Associate Professor, Department of Electrical & Electronics and Computer Engineering, Near East University, TRNC, Turkey
Dr. S. Deivanayaki
Associate Professor, Department of Physics, Sri Ramakrishna Engineering College, Tamil Nadu, India
Dr. Nirvesh S. Mehta
Professor, Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, South Gujarat, India
Dr. A.Vijaya Bhasakar Reddy
Associate Professor, Research Scientist, Department of Chemistry, Sri Venkateswara University, Andhra Pradesh, India
Dr. C. Jaya Subba Reddy
Associate Professor, Department of Mathematics, Sri Venkateswara University Tirupathi Andhra Pradesh, India
Dr. TOFAN Cezarina Adina
Associate Professor, Department of Sciences Engineering, Spiru Haret University, Arges, Romania
Dr. Balbir Singh
Associate Professor, Department of Health Studies, Human Development Area, Administrative Staff College of India, Bella Vista,
Andhra Pradesh, India
Dr. D. RAJU
Associate Professor, Department of Mathematics, Vidya Jyothi Institute of Technology (VJIT), Aziz Nagar Gate, Hyderabad, India
Dr. Salim Y. Amdani
Associate Professor & Head, Department of Computer Science Engineering, B. N. College of Engineering, PUSAD, (M.S.), India
Dr. K. Kiran Kumar
Associate Professor, Department of Information Technology, Bapatla Engineering College, Andhra Pradesh, India
Dr. Md. Abdullah Al Humayun
Associate Professor, Department of Electrical Systems Engineering, University Malaysia Perlis, Malaysia
Dr. Vellore Vasu
Teaching Assistant, Department of Mathematics, S.V.University Tirupati, Andhra Pradesh, India
Dr. Naveen K. Mehta
Associate Professor & Head, Department of Communication Skills, Mahakal Institute of Technology, Ujjain, India
Dr. Gujar Anant kumar Jotiram
Associate Professor, Department of Mechanical Engineering, Ashokrao Mane Group of Institutions, Vathar, Maharashtra, India
Dr. Pratibhamoy Das
Scientist, Department of Mathematics, IMU Berlin Einstein Foundation Fellow Technical University of Berlin, Germany
Dr. Messaouda AZZOUZI
Associate Professor, Department of Sciences & Technology, University of Djelfa, Algeria
Dr. Vandana Swarnkar
Associate Professor, Department of Chemistry, Jiwaji University Gwalior, India
Dr. Arvind K. Sharma
Associate Professor, Department of Computer Science Engineering, University of Kota, Kabir Circle, Rajasthan, India
Dr. R. Balu
Associate Professor, Department of Computr Applications, Bharathiar University, Tamilnadu, India
Dr. S. Suriyanarayanan
Associate Professor, Department of Water and Health, Jagadguru Sri Shivarathreeswara University, Karnataka, India
Dr. Dinesh Kumar
Associate Professor, Department of Mathematics, Pratap University, Jaipur, Rajasthan, India
Dr. Sandeep N
Associate Professor, Department of Mathematics, Vellore Institute of Technology, Tamil Nadu, India
Dr. Dharmpal Singh
Associate Professor, Department of Computer Science Engineering, JIS College of Engineering, West Bengal, India

Dr. Farshad Zahedi
Associate Professor, Department of Mechanical Engineering, University of Texas at Arlington, Tehran, Iran
Dr. Atishey Mittal
Associate Professor, Department of Mechanical Engineering, SRM University NCR Campus Meerut Delhi Road Modinagar, Aligarh,
India
Dr. Hussein Togun
Associate Professor, Department of Mechanical Engineering, University of Thiqar, Iraq
Dr. Shrikaant Kulkarni
Associate Professor, Department of Senior faculty V.I.T., Pune (M.S.), India
Dr. Mukesh Negi
Project Manager, Department of Computer Science & IT, Mukesh Negi, Project Manager, Noida, India
Dr. Sachin Madhavrao Kanawade
Associate Professor, Department Chemical Engineering, Pravara Rural Education Society’s,Sir Visvesvaraya Institute of Technology,
Nashik, India
Dr. Ganesh S Sable
Professor, Department of Electronics and Telecommunication, Maharashtra Institute of Technology Satara Parisar, Aurangabad,
Maharashtra, India
Dr. T.V. Rajini Kanth
Professor, Department of Computer Science Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India
Dr. Anuj Kumar Gupta
Associate Professor, Department of Computer Science & Engineering, RIMT Institute of Engineering & Technology, NH-1, Mandi
Godindgarh, Punjab, India
Dr. Hasan Ashrafi- Rizi
Associate Professor, Medical Library and Information Science Department of Health Information Technology Research Center,
Isfahan University of Medical Sciences, Isfahan, Iran
Dr. Golam Kibria
Associate Professor, Department of Mechanical Engineering, Aliah University, Kolkata, India
Dr. Mohammad Jannati
Professor, Department of Energy Conversion, UTM-PROTON Future Drive Laboratory, Faculty of Electrical Enginering, Universit
Teknologi Malaysia,
Dr. Mohammed Saber Mohammed Gad
Professor, Department of Mechanical Engineering, National Research Centre- El Behoos Street, El Dokki, Giza, Cairo, Egypt,
Dr. V. Balaji
Professor, Department of EEE, Sapthagiri College of Engineering Periyanahalli,(P.O) Palacode (Taluk) Dharmapuri,
Dr. Naveen Beri
Associate Professor, Department of Mechanical Engineering, Beant College of Engg. & Tech., Gurdaspur - 143 521, Punjab, India
Dr. Abdel-Baset H. Mekky
Associate Professor, Department of Physics, Buraydah Colleges Al Qassim / Saudi Arabia
Dr. T. Abdul Razak
Associate Professor, Department of Computer Science Jamal Mohamed College (Autonomous), Tiruchirappalli – 620 020 India
Dr. Preeti Singh Bahadur
Associate Professor, Department of Applied Physics Amity University, Greater Noida (U.P.) India
Dr. Ramadan Elaiess
Associate Professor, Department of Information Studies, Faculty of Arts University of Benghazi, Libya
Dr. R . Emmaniel
Professor & Head, Department of Business Administration ST, ANN, College of Engineering & Technology Vetapaliem. Po, Chirala,
Prakasam. DT, AP. India

Dr. C. Phani Ramesh
Director cum Associate Professor, Department of Computer Science Engineering, PRIST University, Manamai, Chennai Campus,
India
Dr. Rachna Goswami
Associate Professor, Department of Faculty in Bio-Science, Rajiv Gandhi University of Knowledge Technologies (RGUKT) District-
Krishna, Andhra Pradesh, India
Dr. Sudhakar Singh
Assoc. Prof. & Head, Department of Physics and Computer Science, Sardar Patel College of Technology, Balaghat (M.P.), India
Dr. Xiaolin Qin
Associate Professor & Assistant Director of Laboratory for Automated Reasoning and Programming, Chengdu Institute of Computer
Applications, Chinese Academy of Sciences, China
Dr. Maddila Lakshmi Chaitanya
Assoc. Prof. Department of Mechanical, Pragati Engineering College 1-378, ADB Road, Surampalem, Near Peddapuram, East
Godavari District, A.P., India
Dr. Jyoti Anand
Assistant Professor, Department of Mathematics, Dronacharya College of Engineering, Gurgaon, Haryana, India
Dr. Nasser Fegh-hi Farahmand
Assoc. Professor, Department of Industrial Management, College of Management, Economy and Accounting, Tabriz Branch, Islamic
Azad University, Tabriz, Iran
Dr. Ravindra Jilte
Assist. Prof. & Head, Department of Mechanical Engineering, VCET Vasai, University of Mumbai , Thane, Maharshtra 401202, India
Dr. Sarita Gajbhiye Meshram
Research Scholar, Department of Water Resources Development & Management Indian Institute of Technology, Roorkee, India
Dr. G. Komarasamy
Associate Professor, Senior Grade, Department of Computer Science & Engineering, Bannari Amman Institute of Technology,
Sathyamangalam,Tamil Nadu, India
Dr. P. Raman
Professor, Department of Management Studies, Panimalar Engineering College Chennai, India
Dr. M. Anto Bennet
Professor, Department of Electronics & Communication Engineering, Veltech Engineering College, Chennai, India
Dr. P. Keerthika
Associate Professor, Department of Computer Science & Engineering, Kongu Engineering College Perundurai, Tamilnadu, India
Dr. Santosh Kumar Behera
Associate Professor, Department of Education, Sidho-Kanho-Birsha University, Ranchi Road, P.O. Sainik School, Dist-Purulia, West
Bengal, India
Dr. P. Suresh
Associate Professor, Department of Information Technology, Kongu Engineering College Perundurai, Tamilnadu, India
Dr. Santosh Shivajirao Lomte
Associate Professor, Department of Computer Science and Information Technology, Radhai Mahavidyalaya, N-2 J sector, opp.
Aurangabad Gymkhana, Jalna Road Aurangabad, India
Dr. Altaf Ali Siyal
Professor, Department of Land and Water Management, Sindh Agriculture University Tandojam, Pakistan
Dr. Mohammad Valipour
Associate Professor, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
Dr. Prakash H. Patil
Professor and Head, Department of Electronics and Tele Communication, Indira College of Engineering and Management Pune, India
Dr. Smolarek Małgorzata
Associate Professor, Department of Institute of Management and Economics, High School of Humanitas in Sosnowiec, Wyższa
Szkoła Humanitas Instytut Zarządzania i Ekonomii ul. Kilińskiego Sosnowiec Poland, India

S.
No
1.
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Authors:
Volume-2 Issue-5, November 2012, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.
Ugwuishiwu C.H., Inyiama H.C., Ezema M.E.
Paper Title: A Model of NAFDAC Real-Time Crime Information System
Abstract: NAFDAC is a government agency that was established to protect and promote public health by ensuring the wholesomeness, quality and safety of food and drugs consumed in Nigeria. The problems of fake and substandard products proliferation in Nigeria have affected the credibility of the healthcare system and can exert very harmful effects on the consumer resulting to illness, disability and even death. These make it imperative to fight against fake and substandard products, hence NAFDAC regulations. The objective of this paper is to design a model that will help to achieve NAFDAC stipulated objectives through digital data capture from informants, timely crime information dissemination (ie. Just-in-time crime information) and efficient crime information sharing by the various branches of
NAFDAC offices in Nigeria. This model if adopted will help NAFDAC to a great extent bring crime to its minimal rate through instant flow of information.
Keywords: NAFDAC, Crime, Information system, informant, instant information flow, food and drug regulated product.
References:
1.
National Agency for Food and Drug Administration and Control retrieved from en.wikipedia.org/.../ National_Agency_for_ Food_and_
Drug_Administration_and_Control –
2.
Dada (2010) allAfrica.com: Nigeria: NAFDAC - a Broader Mandate available at allafrica.com/stories/201005170569.html
3.
Bill Gates and Collins Hemingway (1999), Business @ the speed of thought: Succeeding in the Digital Economy
4.
Olike ( 2008)The fight against fake drugs by NAFDAC in Nigeria retrieved from apps.who.int/medicinedocs/documents/s18405en/s18405en.pdf accessed on 14/04/2011
5.
World Health Organization (2006), Counterfeit Medicines: an update on estimate, Available Online http://www.who.int/medicines/services/counterfeit/impact/TheNewEstimatesCounterfeit.Pdf accessed 1/1/11
6.
Guidelines for prospective exporters of regulated food and drug www.ngex.com/business/public/newsinfo.php?nid=40 - Cached
7.
WHO (2008) General information on counterfeit medicines. Available at www.who.int/medicines/services/counterfeit/overview/en/ accesses20/1/08
8.
Shelly G.B, Cashman T.J and Rosenblast H.J. Systems Analysis and Design Thomson, Boston, Massachusetts course Technology,
2006.pp16.
Authors: Abolfazl Rajabi, Mostafa Tavassoli, Sasan Mohammadi, Mehrdad Javadi
Paper Title: RFID for Cargo and Passenger Automation and Control in Airline Industry
Abstract: Air industry still relies on manual methods for controlling cargos. The manual procedure results in sustaining great deal of waste of time and human errors. There are many solutions for more efficient controls and barcode-based RFID (Radio Frequency Identification) is one among many. This article deals with control using
RFID technology, where each passenger and items is marked by a RFID tag. The tag bears unique traits of the passenger for identification. Different types of RFID readers will be used from issuance of flight cart until boarding.
Tags are identified by the key inside RFID Tag. Before implementation, the system was studied through simulation and as the results showed, RFID tags were read with 99% accuracy and better performance was achieved. The minimum delay time for passenger and cargo in the simulation was acceptable.
Keywords: RFID Tag, RFID Reader, Baggage, Passenger, Downlink, Uplink.
References:
1.
P.D. Devries, in: The state of RFID for effective baggage tracking in the airline industry, International Journal of Mobile Communications, vol. 6, iss. 2, pp. 151-164, 2008.
2.
T.S. Rangarajan, A. Vijaykumar and S.S. Suraj, in: Stop getting strangled by your supply chain, Forrester Research, 2004.
3.
H.C.W. Lau, W.T. Tsui, C.K.M. Ho and A. Ning, in: Development of a profit-based air cargo loading information system, IEEE
Transactions on Industrial Informatics, vol. 2, no. 4, pp. 303-312,2006.
4.
Sasan Mohammadi, Abolfazl Rajabi and Mostafa Tavassoli, in: Controlling of Traffic lights Using RFID Technology and Neural Network.
Advanced Materials Research Vols. 433-440 (2012) pp 740-745© (2012) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMR.433-440.740
5.
Chi-Kong Chan, Harry K. H. Chow, Alex K. S. Ng, Henry C. B. Chan and Vincent T. Y. Ng, in: An RFID Case Study for Air Cargo Supply
Chain Management , Proceedings of the International Multi Conference of Engineers and Computer Scientists 2012 Vol I, IMECS 2012 ,
March 14 – 16, 2012 .
6.
Dong Li, Yuhao Wang , Linli Hu , Jing Li , Xuezhao Guo , Jiance Lin and Jie Liu , in: Client/Server Framework-Based Passenger
Line Ticket System Using 2-D Barcode on Mobile Phone ,IEEE E-Business and E-Government (ICEE), 2010 .
7.
AeroAssist company : RFID in Aviation:airport luggage control , An AeroAssist white paper , 2008.
8.
Ting Zhang, Yuanxin Ouyang and Yang He, in: Traceable Air Baggage Handling System Based on RFID Tags in the Airport , Journal of
Theoretical and Applied Electronic Commerce Research ISSN 0718–1876 Electronic Version VOL 3 ,2008 .
9.
SITA Company: Baggage Report 2011 , Specialists in air transport communications and IT solutions , 2011.
10.
SITA Company: Baggage Report 2012 , Specialists in air transport communications and IT solutions , 2012.
11.
Jacinto, C. , Romano, M. , Montes, A. , Sousa, P. and Nunes, M.S., in:RFID tuning methodology applied on airport baggage tracking ,
Emerging Technologies & Factory Automation, 2009 .
12.
Yuanxin Ouyang , Yao Hou , Ling Pang , Dongwei Wang and Zhang Xiong , in: An Intelligent RFID Reader and its Application in Airport
Baggage Handling System , Wireless Communications, Networking and Mobile Computing, 2008.
13.
Aman khan : United Airlines Tests RFID to Speed Baggage and Passenger Check-In , The world’s RFID Authority , 2009.
14.
Oberli, C. Torres-Torriti, M. and Landau, D. , in: Performance Evaluation of UHF RFID Technologies for Real-Time Passenger Recognition in Intelligent Public Transportation Systems , Intelligent Transportation Systems, IEEE Transactions on, 2010.
15.
Oberli, C. Landau, D. , in: Performance evaluation of UHF RFID technologies for real-time passenger tracking in intelligent public transportation systems , Wireless Communication Systems. 2008. ISWCS ‘08. IEEE International Symposium on , 2008.
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McCoy, T. Bullock, R.J. and Brennan, P.V. , in: RFID for airport security and efficiency , Signal Processing Solutions for Homeland
Page
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1-4
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Security, 2005. The IEE Seminar on (Ref. No. 2005/11108), 2005.
17.
Chang, Y.S. Oh, C.H. , Whang, Y.S. , Lee, J.J. , Kwon, J.A. , Kang, M.S. , Park, J.S. and Ung, Y.P. , in: Development of RFID Enabled
Aircraft Maintenance System , Industrial Informatics, 2006 IEEE International Conference on , 2006.
18.
Hasan, M.F.M. Tangim, G. , Islam, M.K. , Khandokar, M.R.H. and Alam, A.U. , in: RFID-based ticketing for public transport system:
Perspective megacity Dhaka , Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference,2010.
19.
Simon, L. ,Saengudomlert, P. and Ketprom, U. , in: Speed Adjustment Algorithm for an RFID Reader and Conveyor Belt System
Performing Dynamic Framed Slotted Aloha ,RFID IEEE International Conference on , 2008.
20.
Jiexiao, Y., Kai, H. L. and Peng, L. A, in: Mobile RFID Localization Algorithm Based on Instantaneous Frequency Estimation. IEEE, 2011
Authors: Khaleel Ahmad, Nitasha Varshney
On Minimizing Software Defects during New Product Development Using Enhanced Preventive
Paper Title:
Approach
Abstract: Software defects have a major impact of software development life cycle. Software defects are expensive. Moreover, the cost of finding and correcting defects represents one of the most expensive software development activities. For the foreseeable future, it will not be possible to eliminate defects. While defects may be inevitable, we can minimize their number and impact on our projects. To do this development teams need to implement a defect management process that focuses on preventing defects, catching defects as early in the process as possible, and minimizing the impact of defects. The purpose of this paper is to develop guidance for software managers in the area of defect management and to introduce the defect management model. This defect management model is not intended to be a standard, but rather a starting point for the development of a customized defect management process within an organization. Companies using the model can reduce defects and their impacts during their software development projects.
Keywords: Defects, software, defect management model.
References:
1.
Brad Clark, Dave Zubrow, “How Good Is the Software: A review of Defect Prediction Techniques” version 1.0, pg 5, sponsored by the U.S. department of Defense 2001 by Carnegie Mellon University.
2.
The Software Defect Prevention /Isolation/Detection Model drawn from www.cs.umd.edu/~mvz/mswe609/book/chapter2.p df
3.
Jeff Tian “Quality Assurance Alternatives and Techniques: A Defect-Based Survey and Analysis” SQP Vol 3, No 3/2001, ASQ by
Department of Computer Science and Engineering, Southern Methodist University.
4.
Terence M Colligan “Nine Steps to delivering Defect Free Software” copyright 1997, 1998.
5.
Elfriede Dustin, Jeff Rashka, John Paul “Automate Software Sting” Chapter 4 Automate Testing Introduction Process pg 144, ISBN 7-
89494-044-5.
6.
R Geoff Dromey, “Software Control Quality – Prevention Verses Cure?” Vol 11, pg 197 – 21, Issue 3 July 2003, year of publication 2003
ISSN: 0963-9314.
7.
S.Vasudevan, “Defect Prevention Techniques and Practices” proceedings from 5th annual International Software Testing Conference in
India, 2005.
8.
Orthogonal Defect Classification – A concept for In-Process Measurements, IEEE Transactions on Software Engineering, SE-18.p.943-956.
9.
Jon.T “A Comparison of IBM’s Orthogonal Defect Classification to Hewlett Packard’s Defect Origins, Types, and Modes”, pg 13-16,
Hewlett Packard company Metrics, 1999.
10.
Bary Boehm, Victor R. Basili, “Software Defect Reduction Top 10 List”, article at CeBASE, Jan 2001. Also see http://www.cebase.org/defectreduction/top10.
11.
Defect Prevention by SEI’s CMM Model Version 1.1.http://ww.dfs.mil/technology/pal/cmm/lvl/
12.
Asad Ur Rehman, “cost of quality analysis”. www.feditec.com/downloads/Cost%20of%20Quality.pdf
13.
ftp://ftp.software.ibm.com/software/rational/info/do-more/ RAW14109USEN.pdf
14.
V, Suma, Nair, T.R.Gopalakrishnan, “Defect Prevention Approaches In Medium Scale It Enterprise“, National Conference on Recent
Research Trends in Information Technology, 2008
15.
David N. Card, “Myths and Stratergies of Defect Causal Analysis”, Proceedings from Pacific Northwest Software Quality Conference,
October.
16.
K.S. Jasmine, R. Vasantha ,”DRE – A Quality Metric for Component based Software Products”, proceedings of World Academy Of Science
17.
Purushotham Narayan, “Software Defect Prevention in a Nut shell”, Copyright © 2000-2008 iSixSigma LLC. See also software.isixsigma.com/library/content/c030611a.asp - 73k –
18.
S.Vasudevan, “Defect Prevention Techniques and Practices” proceedings from 5th annual International Software Testing Conference in
India, 2005.
19.
Chillarege, I.S. Bhandari, J.K. Chaar, M.J. Halliday, D.S. Moebus, B.K. Ray, M.-Y. Wong, "Orthogonal Defect Classification-A Concept for
In- Process Measurements," IEEE Transactions on Software Engineering, vol. 18, no. 11, pp. 943-956, Nov., 1992 .
20.
Craig Borysowich, “Inspection/Review Meeting Metrics”, 2006. See also blogs.ittoolbox.com/eai/implementation/archives/sampleinspectionreview- metrics-13640 - 184k
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Halling M., Biffl S. (2002) "Investigating the Influence of Software Inspection process Parameters on Inspection Meeting Performance", Int.
Conf. on Empirical Assessment of Software Engineering (EASE), Keele, April 2002.
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Stefen Biffl, Michael Halling, “ Investingating the Defect Detection Effectiveness and Cost Benefit of Nominal Inspection Teams “, IEEE
Transactions On Software Engineering, Vol 29, No.5, May 2003
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Defect Prevention by SEI’s CMM Model Version 1.1., http://ww.dfs.mil/techlogy/pal/cmm/lvl/dp.
24.
Watts S. Humphrey, "Managing the Software Process", Chapter 17 – Defect Prevention, ISBN-0-201-18095-2.
9-12
Authors: Khaleel Ahmad, Amit Kumar
Paper Title: Forecasting Risk and Risk Consequences on ERP Maintenance
Abstract: There are many studies which show that a number of IT projects fail. If focus is made on costs, it is possible that 80% of overall IT investment is waste of money, in other words, 20 per cent benefits and 80 per cent waste. Great efforts are made to adopt and implement Enterprise Resource Planning (ERP) Systems but success cannot be guaranteed. Successful implementation also depends on properly maintained ERP system. ERP is said to be a backbone of an organization. Due to this fact, ERP systems should be maintained using proper strategy to drive the ERP system towards the successful implementation. A number of risks threaten these projects. There is very limited publication regarding risks related to ERP maintenance risks and how to manage maintenance failure. To address this, we are proposing a strategy to foresee the risks and achieving maintenance goals. It will be helpful for
ERP managers and professionals to manage ERP projects. It will also fill the existing gap in literature.
13-18

5.
Keywords: ERP, Risks, Management, Managing failure, Maintenance.
References:
1.
D. Chand, G. Hachey, J. Hunton, V. Owhoso, and S. Vasudevan, “A Balanced Scorecard Based Framework for Assessing the Strategic
Impacts of ERP Systems,” Computers in Industry, vol. 56, no. 6, pp. 558-572, 2005.
2.
V. Botta-Genoulaz and P.A. Millet, “An Investigation into the Use of ERP Systems in the Service Sector,” Int’l J. Production Economics, vol. 99, nos. 1/2, pp. 202-221, 2006.
3.
D. _OLeary, Enterprise Resource Planning Systems, Life Cycle, Electronic Commerce and Risk. Cambridge Univ. Press, 2000.
4.
M. Al-Mashari, “Enterprise Resource Planning (ERP) Systems: A Research Agenda,” Industrial Management and Data Systems, vol. 102, no. 3, pp. 165-170, 2002.
5.
Booz Allen & Hamilton, “Insights Enterprise Resource Planning, Big Money Down the Drain?” Information Technology Group, http://www.boozallen.com/publications/article/658713, 2000.
6.
A.I. Nicolaou, “Quality of Postimplementation Review for Enterprise Resource Planning Systems,” Int’l J. Accounting Information Systems, vol. 5, no. 1, pp. 25-49, 2004.
7.
C.-S. Yu, “Causes Influencing the Effectiveness of the Post-Implementation System,” Industrial Management and Data Systems, vol. 105, no. 1, pp. 115-132, 2005.
8.
S. Jaconson, J. Sheperd, M. D’Aquila, and C. Carter, “The Enterprise Resource Planning Spending Report 2006-2011,” AMR Research, http://www.amrresearch.com/, 2007.
9.
C.S.P. Ng, G. Gable, and T. Chan, “A Revelatory Case Study into the Adequacy of Standard Maintenance Models in an ERP Context,” Proc.
Seventh Pacific Asia Conf. Information Systems, pp. 1039-1054, 2003.
10.
C.S.P. Ng, G.G. Gable, and T. Chan, “An ERP Maintenance Model,” Proc. 36th Ann. Hawaii Int’l Conf. System Science, pp. 1-10, 2003, doi:10.1109/HICSS.2003.1174609.
11.
T.H. Willis and A.H. Willis-Brown, “Extending the Value of ERP,” Industrial Management and Data Systems, vol. 102, no. 1, pp. 35-38,
2002.
12.
C.C.H. Law, C.C. Chen, and B.J.P. Wu, “Managing the Full ERP Life-Cycle: Considerations of Maintenance and Support Requirements and
IT Governance Practice as Integral Elements of the Formula for Successful ERP Adoption,” Computers in Industry, vol. 61, no. 3, pp. 297-
308, 2010.
13.
O.B. Kwon and J.J. Lee, “A Multi-Agent Intelligent System for Efficient ERP Maintenance,” Expert System with Applications, vol. 21, no.
4, pp. 191-202, 2001.
14.
M.R. Imtihan, M.S. Ngadiman, and H. Haron, “An Alternative Model for ERP Maintenance Strategy,” Proc. Ninth ACIS Int’l Conf.
Software Eng., Artificial Intelligence, Networking, and Parallel/ Distributed Computing, pp. 785-793, 2008, doi: 10.1109/SNPD.2008. 135.
15.
R. Mookerjee, “Maintaining Enterprise Software Applications,” Comm. ACM, vol. 48, no. 11, pp. 75-79, 2005.
16.
A. Teltumbde, “A Framework for Evaluating ERP Projects,” Int’l J. Production Research, vol. 38, no. 17, pp. 4507-4520, 2000.
17.
J.L. Salmeron and C. Lopez, “A Multicriteria Approach for Risks Assessment in ERP Maintenance” J. Systems and Software, vol. 83, no.
10, pp. 1941-1953, 2010.
18.
C. Peng and M.B. Nunes, “Identification and Assessment of Risks Associated with ERP Post-Implementation in China,” J. Enterprise
Information Management, vol. 22, no. 5, pp. 587-614, 2009.
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R. Axelrod, Structure of Decision: The Cognitive Maps of Political Elites. Princeton, 1976.
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B. Kosko, “Fuzzy Cognitive Maps,” Int’l J. Man-Machine Studies, vol. 24, no. 1, pp. 65-75, 1986.
21.
E.I. Papageorgiou, C. Stylios, and P.P. Groumpos, “Unsupervised Learning Techniques for Fine-Tuning Fuzzy Cognitive Map Causal
Links,” Int’l J. Human-Computer Studies, vol. 64, no. 8, pp. 727-743, 2006.
22.
K-M. Osei-Bryson, “Generating Consistent Subjective Estimates of the Magnitudes of Causal Relationships in Fuzzy Cognitive maps,”
Computers and Operations Research, vol. 31, no. 8, pp. 1165-1175, 2004.
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L.M. Fu, “CAUSIM: A Rule-Based Causal Simulation System,” Simulation, vol. 56, no. 4, pp. 251-256, 1991.
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G. Xirogiannis and M. Glykas, “Intelligent Modeling of E-Business Maturity,” Expert Systems with Applications, vol. 32, no. 2, pp. 687-
702, 2007.
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G. Xirogiannis and M. Glykas, “Fuzzy Cognitive Maps in Business Analysis and Performance-Driven Change,” IEEE Trans. Eng.
Management, vol. 51, no. 3, pp. 334-351, Aug. 2004.
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B.P. Lientz and E.B. Swanson, “Problems in Application Software Maintenance,” Comm. ACM, vol. 24, no. 1, pp. 763-769, 1981.
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S. Dekleva, “Delphi Study of Software Maintenance Problems,” Proc. Conf. Software Maintenance, pp. 10-17, 1992.
28.
S.A. Sherer, “The Three Dimensions of Software Risk: Technical, Organizational, and Environmental,” Proc. 28th Hawaii Int’l Conf.
Systems Science, pp. 369-378, 1995, doi: 10.1109/HICSS.1995. 375618.
29.
M. Keil, P. Cule, K. Lyytimen, and R. Schmidt, “A Framework for Identifying Software Project Risk,” Comm. ACM, no. 41, vol. 11, pp. 76-
83, 1998.
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P. Cule, R. Schmidt, K. Lyytinen, and M. Keil, “Strategies for Heading Off IS Project Failure,” Information Systems Management, vol. 17, no. 2, pp. 1-9, 2000.
31.
D.X. Houston, G.T. Mackulak, and J.S. Collofello, “Stochastic Simulation of Risk Factor Potential Effects for Software Development Risk
Management,” J. Systems and Software, vol. 59, no. 33, pp. 247-257, 2001.
32.
A. Mursu, K. Lyytinen, H.A. Soriyan, and M. Korpela, “Identifying Software Project Risks in Nigeria: An International Comparative
Study,” European J. Information Systems, vol. 12, no. 3, pp. 182- 194, 2003.
33.
B.W. Boehm, “Software Risk Management: Principles and Practices,” IEEE Software, vol. 8, no. 1, pp. 32-41, Jan. 1991.
34.
L. Wallace, M. Keil, and A. Rai, “Understanding Software Project Risk: A Cluster Analysis,” Information and Management, vol. 42, no. 1, pp. 115-125, 2004
35.
W.-M. Han and S.-J. Huang, “An Empirical Analysis of Risk Components and Performance on Software Projects,” J. Systems and Software, vol. 80, no, 1, pp. 42-50, 2007.
36.
S.S. Yau and J.S. Collofello, “Some Stability Measures for Software Maintenance,” IEEE Trans. Software Eng., vol. 6, no. 6, pp. 545-552,
Nov. 1980.
37.
S.L. Pfleeger and S.A. Bohner, “A Framework for Software Maintenance Metrics,” Proc. Conf. Software Maintenance, pp. 320- 327, 1990, doi: 10.1109/ICSM.1990.131381.
38.
E. Stark, “Measurements for Managing Software Maintenance,” Proc. Int’l Conf. Software Maintenance, pp. 152-161, 1996, doi:10.1109/ICSM.1996.565000.
39.
L. Wallace, M. Keil, and R. Arun, “How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model,” Decision Sciences, vol. 35, no. 2, pp. 289-321, 2004.
40.
H.M. Sneed and P. Brossler, “Critical Success Factors in Software Maintenance: A Case Study,” Proc. Int’l Conf. Software Maintenance, pp.
190-198, 2003, doi: 10.1109/ICSM.2003.1235421.
41.
Salmeron, J.L. , “Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps”, Sch. of Eng., Univ. Pablo de
Olavide, Seville, Spain, pp 439-452, 2012 March-April 2012
Authors: Ankit Kapil, Arpan Shah, Rekha Agarwal, Sandhya Sharma
Analysis and Comparative Study of Different Parameters of Operational Amplifier Using Bipolar
Paper Title:
Junction Transistor and Complementary Metal Oxide Semiconductor Using Tanner Tools
Abstract: Operational amplifier (Op-Amp) is used in linear AC operation and is widely used in electronic industry.
19-23
What follows are two operational amplifier designs using bipolar junction transistor (BJT) technology and metal

6. oxide field effect transistor (CMOS) technology. Presented designs will focus on the characteristics of both BJT and
CMOS.BJT invented in 1940’s and CMOS came in 1960’s and became popular due to low power because it draws current when switching states. In this analysis I will cover various aspects of an op-amp like power consumption, offset voltage for small signal and large signal mode, input bias current, input offset current, gain and frequency response, maximum and minimum voltage swing, slew rate etc. Operational Amplifier are important building blocks for a wide range of electronic circuits. They used in many linear, non-linear and frequency-dependent circuits so they are most widely used electronic device ,being used in a vast array of consumer, Industrial and Scientific devices-
MOS amplifiers can be operated with power supplies down to 1.5 to 2.0 volts. This is using now a days in several advanced technologies like cell phones, smart phones ,I-pod and many more because it is many advantageous from electronic industry point of view. In my work I will cover the following points.
1. I will analyze and compare the different parameters of Op-amp using BJT and CMOS using tanner tools.
2. Different waveforms will be finding out by manipulating different parameters in the circuit design and mathematical calculation and comparison will be done.
3. At last all the results of different parameters which is obtained by different simulations using programming on T-
Spice window will be summarized in a table then conclusion will be presented.
Keywords: Op-amp, voltage swing, offset voltage, bias current, offset current, slew rate, power dissipation, gain and frequency response.
References:
1. www. Oup-usa.org
2. Spice “Gordon W Roberts and Adel S. Sedra”. Sedra and Smith publication.
3. Jacob Millman, Microelectronics: Digital and Analog Circuits and Systems, McGraw-Hill, 1979, ISBN 0-07-042327-X,
4. Horowitz, Paul; Hill, Winfield (1989). The Art of Electronics. Cambridge, UK: Cambridge University Press. http://books.google.com/books?id=bkOMDgwFA28C&pg=PA177&lpg=PA177#v=onepage&q&f=false.
5. D.F. Stout Handbook of Operational Amplifier Circuit Design (McGraw-Hill, 1976).
6. Thomas H. (November 18, 2002), IC Op-Amps through the Ages, Stanford University.
7. Jung, Walter G. (2004). "Chapter 8: Op Amp History". Op Amp Applications Handbook. Newnes. p. 777. ISBN 978-0-7506-7844-5.
Retrieved 2008-11-15.
8. http://www.philbrickarchive.org
9. Op-Amps and Linear Integrated Circuits; 4th Ed; Ram Gayakwad; 543 pages; 1999; ISBN 978-0-13-280868-2.
10. Basic Operational Amplifiers and Linear Integrated Circuits; 2nd Ed; Thomas L Floyd; David Buchla; 593 pages; 1998; ISBN 978-0-13-
082987-0
11. A.P. Malvino, Electronic Principles (2nd Ed. 1979. ISBN 0-07-039867-4)
12. Design with Operational Amplifiers and Analog Integrated Circuits; 3rd Ed; Sergio Franco; 672 pages; 2002; ISBN 978-0-07-232084-8.
(book website)
13. Operational Amplifiers and Linear Integrated Circuits; 6th Ed; Robert F Coughlin; 529 pages; 2000; ISBN 978-0-13-014991-6.
Authors: Ranjeet Singh, Chiranjit Dutta
Paper Title: Duplicity Detection System for Digital Documents
Abstract: Plagiarism detection is a challenging problem. Today thousands of documents are present on the net but there are no proper tools to guarantee their uniqueness in such a great domain. PDF documents form a significant portion of this vast database. Copy detection in digital document database may provide necessary guarantees for publishers and newsfeed services to offer their valuable work for others perusal. We consider the case of comparing a
Query Document with a Registered Document .Plagiarism detection techniques are applied by making a distinction between natural and programming language. In this paper we have implemented SCAM (standard Copy Analysis
Mechanism) which is relative measure to detecting copies based on comparing the words and lines frequency occurrences of the new document against those of registered documents. These tests involve comparisons of various articles and show that in general this scheme performs pretty well in detecting documents that have Exact, Partial and
Trivial overlap.
Keywords: Plagiarism, SCAM, WordNet, Registered Document, Query Document
References:
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C. Justicia de la Torre, Maria J. Martn-Bautista, Daniel Sanchez, and Mara Amparo Vila Miranda.Text mining: intermediate forms on knowledge rep-resentation.
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Manu Konchady. Building Search Applications:Lucene, Lingpipe, and Gate. Mustru Publishing, Oakton, Virginia, 2008.
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SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’02),2002.
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Information Processing Systems (NIPS ’04),2004.
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H. Pasula, B. Marthi, B. Milch, S.J. Russell, and I. Shpitser,“Identity Uncertainty and Citation Matching,” Advances in Neural Information
Processing Systems (NIPS ’02), pp. 1401-1408, 2002
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S. Chaudhuri, V. Ganti, and R. Motwani, “Robust Identification of Fuzzy Duplicates,” Proc. 21st IEEE Int’l Conf. Data Eng. (ICDE ’05), pp.
865-876, 2005.
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Glatt Plagiarism Services home page: http://www.plagiarism.com/.
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Authors: Rajat Setia, K. Hans Raj
Paper Title: Quantum Inspired Evolutionary Algorithm for Optimization of Hot Extrusion Process
Abstract: Quantum Inspired Evolutionary Algorithm (QIEA) is a probability based optimization algorithm which applies quantum computing principles such as qubits, superposition, quantum gate and quantum measurement to enhance the properties of classical evolutionary algorithms. This work presents the application of QIEA, for the optimization of hot extrusion process i.e., for finding optimum value of die angle, co-efficient of friction and temperature of billet for minimizing the extrusion load. The optimal process parameters are compared with Finite
Element (FE) simulation results conducted in FORGE-3 environment, which is a domain specific software designed to simulate hot, warm and cold forging. The results show the efficacy of QIEA in terms of good global search ability and fast convergence to the best solution due to its highly probabilistic nature and better characteristics of population diversity since it can represent linear superposition of states.
Keywords: Finite Element Simulation, FORGE-3, Optimization, QIEA.
References:
1. Han, K. H., Kim, J.H., "Quantum-inspired evolutionary algorithm for a class of combinatorial optimization," IEEE Transactions on
Evolutionary Computation, vol. 6(6), 2002, pp. 580- 593.
2. Han, K. H., Kim, J.H., "Quantum-inspired evolutionary algorithm with a new termination criterion, Hє gate, and two-phase scheme," IEEE
Transactions on Evolutionary Computation, vol. 8(2), 2004, pp. 156-169.
3.
Narayanan, A., Moore, M., “Quantum-inspired genetic algorithms,” In: Proc. CEC, 1996, pp. 61–66.
4. Talbi, H., Draa, A., Batouche, M., "A new quantum-inspired genetic algorithm for solving the travelling salesman problem," 14th
International Conference on Computer Theory and Applications, Alexandria, Egypt, 2004.
5. Han, K., Kim, J., “Genetic quantum algorithm and its application to combinatorial optimization problem,” In: Proc. CEC, vol. 2, 2000, pp.
1354–1360.
6.
Zhang, G.X., Li, N., Jin, W.D., Hu, L.Z., “Novel quantum genetic algorithm and its applications,” Front. Electr. Electron. Eng. China 1(1),
2006, pp. 31–36.
7. Abs da Cruz, A., Vellasco, M., Pacheco, M., “Quantum-inspired evolutionary algorithm for numerical optimization,” In: Proc. CEC, 2006, pp. 2630–2637.
8. Xing, H., Ji, Y., Bai, L., Liu, X., Qu, Z., Wang, X., “An adaptive-evolution-based quantum-inspired evolutionary algorithm for QOS multicasting in IP/DWDM networks,” Computer Communications, 32(6), 2009, pp. 1086 – 1094.
9.
Xing, H., Liu, X., Jin, X., Bai, L., Ji, Y., “A multi-granularity evolution based quantum genetic algorithm for QOS multicast routing problem in WDM networks,” Computer Communications, 32(2), 2009, pp. 386–393.
10. Li, B.B., Wang, L., “A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling,” IEEE Trans. Syst. Man
Cybern., Part B, Cybern.,37(3), 2007, pp. 576–591.
11.
Vlachoglannis, J.G., “Quantum-inspired evolutionary algorithm for real and reactive power dispatch,” IEEE Transactions on Power
Systems, 23(4), 2008, pp. 1627–1636.
12. Zhao, S., Xu, G., Tao, T., Liang, L., “Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks,”
Computational Mathematics Applied, 57(11–12), 2009, pp. 2009–2015.
Authors: M.Syed Mohamed, M.Mohamed Sathik, K.Senthamarai Kannan
Paper Title: Coal Burn Detection Using Wireless Sensor Network with Evidence Combination
Abstract: It is a well known fact that the coal required for the operation of the Thermal Power Plants is stored in a
Coal Yard. But, the difficulty of using the Coal yard is the nature of the self burning of the coal. At atmospheric temperature, the coal burns itself and becomes ash reducing the quality of the coal. A lot of de–ashing methods are available such as pouring water which further reduces the quality of coal. The present method of de-ashing by adding water in large quantity leads to decrease the quality of coal. Hence, it is necessary to detect and predict fire in
Coal yard more promptly and accurately to minimize the quality of coal. This paper introduces an efficient and intelligent smart system for coal fire detection using wireless sensor network with evidence combination.
Keywords: Coal burn, SVM, Dempster Shafer , Classifer, Wireless Sensor Network
References:
1.
Akyildiz.I.F, Sankarasubramaniam.Y, and Cayirci. E, “Wireless sensor networks: a survey,” Computer Networks, vol. 38, no. 4, pp. 393-
422, March, 2002.
2.
Guan Xin, Yi Xiao “An Improved Dempster–Shafer algorithm for resolving the conflicting Evidences”
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http://modis.gsfc.nasa.gov/, MODIS Web.
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IvorW. Tsang, James T. Kwok “Very Large SVM Training using Core Vector Machines”
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Jair Cervantes, Xiaoou Li, Wen Yu “Multi-Class SVM for Large Data Sets Considering Models of Classes Distribution”
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Jair Cervantes, Xiaoou Li, Wen Yu “Multi-Class SVM for Large Data Sets Considering Models of Classes Distribution”
8.
Liyang Yu, Neng Wang, Xiaoqiao Meng “Real time Forest Fire detection using wireless sensor network” ©2005 IEEE
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Liu Rujie , Yuan Baozong “ D.S based Multi channel Information Fusion Method using classifiers Uncertainty measurement“ Proceedings of
ICSP2000 pp 1297 – 1300.
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Mohamed Sathik.M, Syed Mohamed.M, Balasubramanian.A “Fire detection Using Support Vector Machine in Wireless Sensor Network and
29-34
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Rescue Using Pervasive Devices”, International Journal of Advanced Networking and Applications.
11.
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Authors: Ali EL-Desoky, Marwa Fayz, Doaa Samir
Paper Title: A smart Dictionary for the Arabic Full-Form Words
Abstract: Avery long time is required when searching for a given word in the full-form words dictionary if these words are stored in a single database. A smart enough way is required when designing these type of dictionary in order to decrease the search time as much as possible to speed up the whole process execution time. This paper introduces a smart dictionary in which the full-form words are distributed into a large number of tables(files). This dictionary distributes the full-form words over 30 tables each of which holds the words start with the same alphabetic character this make the search easier and faster.
Keywords: Arabic, human language technologies (HLT), hybrid, morphological analysis, morphology, natural language processing (NLP), phonetic transcription, phonological analysis, statistical language model (SLM), statistical, syntax.
References:
1. Otakar Smr, "Yet Another Intro to Arabic NLP",2005, http://ufal.mff.cuni.cz/~smrz/ANLP/anlp-lecture-notes.pdf
2. Mohsen RASHWAN, Sherif ABDOU, Ahmed RAFEA" ,Stochastic Arabic Hybrid Diacritizer", IEEE trans. Natural Language Processing and Knowledge Engineering, pp.1-8, 24-27 Sept. 2009
3. Mohamed Attia, Mohsen A. A. Rashwan, and Mohamed A. S. A. A. Al-Badrashiny, " Fassieh®, a Semi-Automatic Visual Interactive Tool for Morphological, PoS-Tags, Phonetic, and Semantic Annotation of Arabic Text Corpora", IEEE trans. AUDIO, SPEECH, AND
LANGUAGE PROCESSING, VOL. 17, NO. 5, pp.916-925, JULY 2009
4. http://Archimedes.fas.Harvard.edu/docs/Arabic. ", A Hybrid System for Automatic Arabic Diacritization", is found at http://www.rdieg.com ,pp.54-60.
5.
I. Zitouni, J. S. Sorensen, and R. Sarikaya, “Maximum entropy based restoration of Arabic diacritics,” in Proc. 21st Int. Conf. Comput.
Linguist. and 44th Annual Meeting Assoc. for Comput. Linguist. (ACL); Workshop Comput. Approaches to Semitic Lang., Sydney,
Australia, Jul. 2006 [Online]. Available: http://www.ACLweb.org/anthology/P/ P06/P06-1073
6. Attia, M., Rashwan, M., Khallaaf, G., "A Formalism of Arabic Phonetic Grammar and Application on the Automatic Arabic Phonetic
Transcription of Transliterated Words", NEMLAR int’l conference in Cairo, Sept. 2004. This paper is downloadable from http://www.RDIeg.com/RDI/Technologies/paper.htm.
7. Mansour Alghamdi, Zeeshan Muzafar ,"KACST Arabic Diacritizer",2012, http://www.mghamdi.com/KAD.pdf
8. Mansour Alghamdi, Muhammad Khursheed, Mustafa Elshafei; Fayz Alhargan,Muhammed Alkanhal, Abu Aus Alshamsan, Saad Alqahtani,
Syed Zeeshan Muzaffar, Yasser Altowim, Adnan Yusuf; Husni Almuhtasib , " Automatic Arabic Text Diacritizer", KACST, KFUPM,KSU,
MODA, 18-6-2006
9. KHALED SHAALAN,"Arabic Natural Language Processing: Challenges and Solutions", ACMTransactions on Asian Language
Information Processing, Vol. 8, No. 4, Article 14, Pub. date: December 2009.
10. Attia,"Automatic-Full-Phonetic-Transcription-of-Arabic-Script-with-and-without-Language-Factorization", http://www.rdieg.com/rdi/technologies/arabic_nlp.htm
11. Mohsen A. A. Rashwan, Mohamed A. S. A. A. Al-Badrashiny, Mohamed Attia, Sherif M. Abdou, and Ahmed Rafea, "A Stochastic Arabic
Diacritizer Based on a Hybrid of Factorized and Unfactorized Textual Features" IEEE trans. Auduio, speech, and language processing, vol.19, no.1, pp.166-175, junuary2011.
12. M. Al-Badrashiny, “Automatic diacritization for Arabic texts,” M.Sc. thesis, Dept. of Electron. Elect. Commun, Faculty of Eng., Cairo
Univ., Cairo, Egypt, Jun. 2009.
13. M. Attia, M. Rashwan, and G. Khallaaf, “On stochastic models, statistical disambiguation, and applications on Arabic NLP problems,” in
Proc. 3rd Conf. Lang. Eng.; CLE’2002, by Egypt. Soc. Lang. Eng. (ESoLE) [Online]. Available: www.ESoLE.org
14. M. Attia and M. Rashwan, “A large-scale Arabic PoS tagger based on a compact Arabic PoS tags—Set, and application on the statistical inference of syntactic diacritics of Arabic text words,” in Proc. Arabic Lang. Technol. Resources Int. Conf.; NEMLAR, Cairo, Egypt, 2004.
15. M. Attia, “A large-scale computational processor of the Arabic morphology, and applications,” M.Sc. thesis, Dept. of Comput. Eng.,
Faculty of Eng., Cairo Univ., Cairo, Egypt, 2000.
16. Muhammad Atiyya ,Khalid Choukri ,Mustafa Yaseen (AU),"NEMLAR Specifications of the Arabic Written Corpus ", http://www.medar.info/The_Nemlar_Project/Publications/WC_design_final.pdf
17. Indiana University,"Learning Arabic Morphology Using Statistical Constraint Satisfaction Models",April 2nd, 2005, http://www.casl.umd.edu/sites/default/files/rodrigues_ALS05_Learning-Arabic-Morphology-Using-Statistical-Constraint-Satisfaction-
Models.pdf
18. M. Attia, M. Rashwan, A. Ragheb, M. Al-Badrashiny, H. Al-Basoumy, and S. Abdou, "A Compact Arabic Lexical Semantics Language
Resource Based on the Theory of Semantic Fields". Berlin, Heidelberg, Germany: Springer-Verlag, Aug. 2008, vol. 5221 [Online].
Available: www.SpringerOnline.com, Lecture Notes on Computer Science (LNCS): Advances in Natural Language Processing,
LNCS/LNAI.
19. M. Attia, “Theory and implementation of a large-scale Arabic phonetic transcriptor, and applications,” Ph.D. dissertation, Dept. of Electron. and Elect. Commun., Faculty of Eng., Cairo Univ., Cairo, Egypt, Sep. 2005.
Authors: G.Y.Rajaa Vikhram, S.Latha
39-42
Design of Power System Stabilizer for Power System Damping Improvement with Multiple Design
Paper Title:
Requirements
Abstract: In this paper the optimization problem of tuning of lead-lag Power System Stabilizer (PSS) for damping of oscillations in single machine infinite bus power system with multiple design requirements is considered. The design requirements are considered as both time domain and frequency domain specifications which are initially specified before designing the PSS. The optimization based linear control design technique is used to determine the optimal controller parameters. The stabilizer contributes to the electro mechanical torque components without
43-48 affecting the synchronizing torque in the system. The proposed design of power system stabilizer is tested for various disturbance conditions for damping of oscillations while satisfying the design requirements.

11.
12.
Keywords: Power System Stabilizer, Damping of oscillations, Time domain and Frequency domain specifications,
Optimization based Linear control design.
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E. V. Larsen and D.A. Swann, 'Applying Power System Stabilizers, Part I; General Concepts, Part II; Performance Objectives and Tuning
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CA, Jun. 2005, pp. 1720–1726.
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G. E. Boukarim, S. Wang, J. H. Chow, G. N. Taranto, and N. Martins,“A comparison of classical, robust, and decentralized control designs for multiple power system stabilizers,” IEEE Trans. Power Syst., vol. 15, no. 4, pp. 1287–1292, Nov. 2000.
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Lefebvre S. Tuning of stabilizers in multimachine power systems. IEEE Trans PAS 1983;102(2):290–9.
Authors: Avneesh Vashistha, Pervez Ahmed
Paper Title: SaaS Multi-Tenancy Isolation Testing- Challenges and Issues
Abstract: Cloud computing is broadly categorized as Infrastructure as a Service (IaaS), Platform as a Service
(PaaS) and Software as a Service (SaaS). Multi-tenancy becomes an important feature of SaaS, designing and building of multi-tenancy aware applications introduces several new challenges, central one is tenant. A service provider can support multiple tenants at the same time, while from a customer point of view; the tenants are isolated and customized for their specific needs. In this paper we present multi-tenant isolation testing challenges and issues.
Keywords: Multi-tenancy, IaaS, PaaS, SaaS, Isolation.
References:
1.
http;//www.ibm.com/developerworks/library/ws-middleware/
2.
F. Chong and G. Carraro, “Architecture Strategies for catching the Long Tail”, Microsoft Corporation, http://blogs.msdn.com/gianpaolo,April 2006.
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Management” proceeding of 9th IEEE International Conference on E-Commerce Technology and the 4th IEEE Conference on Enterprise
Computing, E-Commerce and E-Services, 2007, pp. 1-8.
6.
Mietzner R., Unger T., Titze R., Leymann F., “ Combining Different Multi-Tenancy Patterns in Service-Oriented Applications”, proceedings of IEEE International Enterprises Distributed Object Computing Conference,2009, pp. 131-140. SP800-145.pdf.
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Harris I.S., Ahmed Z., “An Open Multi-Tenant Architecture to Leverage SMEs”, published in European Journal of Scientific Research,
2001, pp. 601-610.
8.
http://csrc.nist.gov/publications/nistpubs/800-145/
49-50
Authors: Charu Pandey, Satish Kumar, Rajinder Tiwari
An Innovative Approach towards the Video Compression Methodology of the H.264 Codec: Using
Paper Title:
SPIHT Algorithms
Abstract: The video signal is an integral part of multimedia which has a tremendous importance in most of the applications involving the concept of the multimedia i.e. video conferencing; video-on-demand, broadcast digital video, and high-definition television (HDTV), etc. which are expected to have a substantial end user or the consumer market. It means that these applications which are based on the basic principle of the digital signals having both component of the audio and video, are using the most dominant approach of the video compression techniques so as to make the best and efficient use of the available bandwidth. The existing video coding techniques such as those used in MPEG-1 (ISO), MPEG-2 (ISO), MPEG-3 (ISO), H.261 (ITU), and H.263 (ITU) are the typical and most commonly used standards that employ the basic and most essential schemes or techniques that uses the intra frame coding or inter frame coding techniques so as to achieve the high desired compression rates. In this discussion, the authors put forward an innovative approach of the video signal compression that makes the use of the advanced
H.264 algorithms which are based on the application of DCT & wavelet based video compression of the multimedia based signal. The simulation of the considered image has been carried out with a comparative analysis of the results that has been obtained with the traditional approach as well as proposed one i.e. SPIHT (Set Partitioning in
Hierarchical Trees) algorithm for video compression of the signal.
Keywords: MPEG, JPEG, Frame, Video Compression Efficiency, H.264 Algorithms, DCT, SPIHT, Video
51-56

13.
Compression, Wavelet, Block Matching, Low Bit Rate, AVC.
References:
1. S.M. Akramullah, I. Ahmad, M.L. Liou, A data-parallel approach for real-time MPEG-2 video encoding, Journal of Parallel and Distributed
Computing 30
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Authors: R Gopakumar, Robert Jesuraj K
Paper Title: Simulation of Non-Equilibrium Transport of Suspended Sediment in Open Channels
Abstract: Present paper deals with development of a new mathematical model to simulate non-equilibrium transport of suspended sediment in open channels, with special emphasis on sediment overloading. Equilibrium transport of bed sediment is also included. Sources of the suspended sediment are from the catchment (such as construction sites, mining areas etc.). This sediment is brought into rivers and canals by flowing rainwater. Its overloading can result in large changes in bed and water levels of the rivers and also in heavy silting of the canal beds. A new mathematical model to simulate this effect is derived based on the control volume approach. The model is tested using data available in literature and results are found satisfactory. The developed model is then applied to a hypothetical flood and sediment routing problem in a river and analyses of the results are given.
Keywords: Open channels, Suspended sediment, Non-equilibrium transport, Mathematical model, Simulation.
References:
1. N.K. Khullar, U.C. Kothyari and K.G. Ranga Raju. “Suspended wash load transport of nonuniform sediments”, Journal of Hydraulic
Engineering, ASCE, vol. 136(8), 2010, pp. 534-543.
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131(5), 2005, pp. 413-418.
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Engineering, ASCE, vol. 118(3), 1992, pp. 476-494.
5. B.G. Krishnappan. MOBED: Users manual update II, Hydraulic division, National water research institute, Ontario. December 1986
6. U S Army Corps of Engineers.. River analysis system HEC-RAS. Hydrologic Engineering Center, Davis, California. 2002.
7. A.J. Raudkivi. Loose Boundary Hydraulics, Taylor &Francis Group. 1998.
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F.M. Holly and J.L. Rahuel. “New numerical/physical phramework for mobile-bed modelling, part 1: Numerical and physical principles”,
Journal of Hydraulic Research, vol 28(4), 1990, pp. 401-416.
9. F.M. Holly and J.L. Rahuel. “New numerical/physical phramework for mobile-bed modelling, part 2: Test applications”, Journal of
Hydraulic Research, vol 28(5), 1990, pp. 545-564.
10. V.T. Chow, D.R. Maidment and L.W.Mays. Applied Hydrology, McGraw-Hill International Editions. 1988.
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I. Celik and W. Rodi. “Suspended sediment transport capacity for open channel flows”, Journal of Hydraulic Engineering, ASCE, vol.
117(2), 1991, pp. 191-204
12. J.A. Cunge and N. Predreau. “Mobile bed fluvial mathematical model”, La Houille Blanche, No. 7, 1973, pp. 561-580.
13. M.B. Abbott. Computational hydraulics; elements of the theory of free surface flows. Pitman. London. 1979.
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D.A. Lyn. and P. Goodwin. “Stability of a general Priessmann scheme”, Journal of Hydraulic Engineering, ASCE, Vol. 113(1), 1987, pp.
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17. H.E. Jobson and W.W. Sayre. “Vertical transfer in open channel flow”, Journal of Hydraulics Division, ASCE, vol. 96(3), 1970, pp. 703-
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Authors: J. Swapna Priya, Sk. Abdul Kareem, M. Gargi
A New Scheme for Minimizing Software Piracy Using Combination of Smart Card and Physical
Paper Title:
Attribute with applied Crypto System
Abstract: Software Companies make huge effort in developing a software product and licenses it, illegal use of these software products is commonly termed as Software Piracy. It is a difficult problem in Information Technology
Industry since inception. Our software distribution channel must be flexible for both the new users and the existing users. The denial of service is the bad effect of the complex methods involved in the security structures. So, we have taken care of the above factor seriously and came up with a unique solution. Secondly, depending on the number of legalized installation, the system of security must be flexible, this we have addressed in the paper. A smart card and physical token address combination produces the unique identification carries to a back end process which builds an extremely flexible and robust solution in software piracy eradication. This paper presents a robust software protection scheme based on the use of smart cards, physical serializer and cryptographic techniques.
Keywords: Software protection, smart cards, cryptography, information commerce.
References:
1.
[AuGo99] Aura, T.; Gollman, D. Software License Management with Smart Cards. Proceedings of the Usenix Workshop on Smartcard
Technology (Smartcard’99), pp. 75-86. 1999.
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[Be94] Bennet S. Yee. Using Secure Coprocessors.PhD thesis CMU-CS-94-149, Carnegie Mellon University, 1994.
3.
[CDHP00] Castellá-Roca, J.; Domingo-Ferrer, J.; Herrera-Joancomartí, J.; Planes, J. A Performance Comparison of Java Cards for
Micropayment Implementation. Proccedings of CARDIS’2000, pp 19-38. Kluwer Academic Publishers. 2000.
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[Cons97] Constantas, D. et al. Architecture for Electronic Document Commerce. 4th CaberNet Radicals Workshop, 1997. Available online at http://www.newcastle.research.ec.org/cabernet/research/radicals/1997/ppers/edc-constanta.html
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[CoTh00] Collberg, C.; Thom Orson, C. Watermarking, Tamper-Proofing, and Obfuscation - Tools for Software Protection. University of
Auckland Technical Report #170. Available online at http://www.cs.auckland.ac.nz/~collberg/Res earch/Publications/CollbergThomborson2000a/index.html. 2000.
6.
[CoTh99] Collberg, C.; Thomborson, C. Software watermarking: Models and dynamic embeddings. Proceedings of POPL'99 -26th
ACM Symposium on Principles of Programming Languages. 1999. Available online at http://www.cs.arizona.edu/~collberg/Resear ch/Publications/CollbergThomborson99a/in dex.html. 1999.
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[DDB89] Davida, G. I.; Desmedt, Y.; Blaze, M. J. Defending Systems Against Viruses Through Cryptographic Authentication.
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[FHS97] Forrest, S.; Hofmeyr, S.; Somayaji, A. Computer immunology. Communications of the ACM, Vol. 40, No. 10, pp. 88-96. 1997.
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[Fünf99] Fünfrocken, S. Protecting Mobile Web-Commerce Agents with Smartcards Proceedings of ASA/MA'99. 1999.
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[Gold97] O. Goldreich, towards a theory of software protection, Proc. 19th Ann. ACM Symp. On Theory of Computing, pp. 182-194. 1987.
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[HePi87] Herzberg, A.; Pinter, S. S. Public Protection of Software. ACM Transactions on Computer Systems, 5(4)-87, pp. 371-393. 1987.
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[Hohl98] Hohl F. Time Limited Blackbox Security: Protecting Mobile Agents from Malicious Hosts. In Giovanni Vigna (Ed.), Mobile
Agent Security, LNCS
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[Kent80] Kent, S. Protecting Externally Supplied Software in Small Computers. PhD thesis, Massachusetts Institute
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[KLK97] Kohl, U.; Lotspiech, J.; Kaplan M. A. Safeguarding Digital library Contents and Users: Protecting Documents rather Than
Channels. DLib Magazine, Sept-97 ISSN 082-9873.1997.
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[LoMo99] Loureiro, S.; Molva, R. Function hiding based on error correcting codes. Proceedings of Cyptec’99 - International Workshop on
Cryptographic techniques and Electronic Commerce. 1999.
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[Mana00] Maña, A. Una Solución Segura Basada en Java para la Comercialización de Contenidos Digitales. (In Spanish). Proceedings of the
Sixth Spanish Conference on Cryptography and Information Security. Ra-Ma, isbn 84-7897-431-8, pp-243-252. 2000.
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[LMP00] López, J.; Maña, A; Pimentel, P. Un Esquema Eficiente de Protección de Software Basado en Tarjetas Inteligentes. Technical
Report 14/2000, Department of Computer Science, University of Malaga. 2000
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[Pren00] Preneel, B. El Estado de las Funciones Hash. (In panish). Proceedings of the Sixth Spanish Conference on Cryptography and Information Security. Ra-Ma, ISBN 84-7897-43 1-8,pp- 3-38.2000.
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[Samu95] Samuelson, P. A Manifesto Concerning the Legal Protection of Computer Programs: Why Existing Laws Fail To
Provide Adequate Protection. Proceedings of KnowRight '95, pp 105-115. 1995.
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[SaTs98] Sander, T.; Tschudin C.F. On Software Protection via Function Hiding. Proceedings of information Hiding ’98. Springer-Verlag.
LNCS 1525. pp 111-123. 1998. [ScPi84] Schaumüller-Bichl1, I.; Piller, E. A Method of Software Protection Based on the Use of Smart
Cards and Cryptographic Techniques. Proceedings of Eurocrypt’84. Springer-Verlag. LNCS 0209, pp. 446- 454. 1984.
62-67
Authors: Rashid Hussain, J L Sahgal, Purvi Mishra, Babita Sharma
Paper Title: Application of WSN in Rural Development, Agriculture Water Management
Abstract: India ranks second in agriculture activities. It supports the employment of several households. Being such a big industry it is important to increase the overall productivity. Today India faces several problems and one of the major problems is the shortage of water for irrigation purposes. Farmers depend heavily on the rains because they lack the access to irrigation facilities. Their crop yields are highly unreliable due to the variability in both rainfall amount and its distribution. Also these farmers depend heavily on the prediction values of various factors such as weather, water, soil, etc. Here we describe the use of sensor networks for improved water management and for controlling other parameters. The target population is the resource poor farmers in the semi-arid areas of rural India.
There is a major use of Information and Communication Technology (ITC). Sensor network and other agricultural techniques might help them to store and utilize the rain water, increase their crop productivity, reduce the cost for cultivation and make use of real time values instead of depending just on prediction.
Keywords: WSN, clustering, agriculture, water management.
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16.
References:
1.
“Wireless Sensor Networks for marginal farming in India” by Jacques Panchard, École Polytechnique Fédérale DeLausanne,Switzerland. http://commonsense.epfl.ch/Resources/thesis.pdf.
2.
R. Matthews and W. Stephens, Eds., Crop-Soil Simulation Models:Applications in Developing Countries. CaB Intl, 2002.
3.
B.A. Keating et al., “An overview of APSIM, a model designed for farming systems simulation,” European Journal of Agronomy, vol.
18,pp. 267–288, January 2003.
4.
R. McCown, G. Hammer, J. Hargreaves, D. Holzworth, and D. Freebairn,“APSIM: a Novel Software System for Development,Model
Testing and Simulation in Agricultural Systems Research,” Agricultural Systems, vol. 50, no. 3, pp. 255–271, 1996.
5.
Santosh Simon,K Poulose Jacob”Preliminary design on the development of WSN for water table monitoring in paddy field”IJDPS
Vol.2,no.5,september2011
6.
Yoshisuke Nakano, “Water Saving Practices in Rice Paddy Cultivation
7.
Ossama Younis, Marwan Krunz, and Srinivasan Ramasubramanian, Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges
Authors: Rashmi Sharma, Kirti Vyas
Paper Title: A Novel Compact Monopole Antenna for C band/Wi-Fi/IEEE 802.16 Systems
Abstract: A novel compact rectangular monopole antenna with four rectangular slots in radiating patch is proposed for wireless fidelity (Wi-Fi) applications and in IEEE 802.16 systems. The proposed antenna has simple and compact structure, and the prototype of the proposed antenna has been fabricated and measured. The designed antenna, fed by a 50Ω microstrip transmission line, is only 10mm in length and 3.2mm in width. The dual band antenna has operating bands with 10-dB return –loss bandwidth of about (46%) 3.1 GHz centered at
6.639 GHz and (24%) 2.9GHz centered at 12.02 GHz. Also, good radiation performance and antenna gain over the two frequency ranges have been obtained.
Keywords: C-Band, Microstrip patch, resonating Frequency, satellite communication.
References:
1.
Lee, Y. C. and J. S. Sun, “Compact printed slot antennas for wireless dual- and multi-band operations," Progress In Electromagnetic
Research, Vol. 88, 289-305, 2008.
2.
K. Guney, “A simple and accurate expression for the bandwidth of electrically thick rectangular microstrip antennas,” Microwave and
Optical Technology Letters, vol. 36, no. 3, pp. 225–228, 2003.
3.
Balanis, C.A. 2005. “Antenna Theory Analysis and Design”. Textbook 3rd Edition. New Jersey : John Wiley and Sons.
4.
Yu Xinfeng et al., Computer Simulation Design of Double-Layer Wide band Microstrip Antenna, 2nd International Conference on
Mechanical and Electronics Engineering (ICMEE 2010)
5.
J. Liang, C. Chiau, X. Chen and J. Yu, Study of a rectangular patch monopole antennas for ultra wideband applications, International
Symposium on Antennas and Propagation, 17-21 August, 2004.
6.
I.Govardhani, K.Rajkamal, M.Venkata Narayana, S.Venkateswarlu published a paper on “Phased Array Antenna for Millimeter Wave Radar in W-band using Liquid Crystal Substrate” VOL. 2, NO. 12, December 2011 ISSN 2079-8407Journal of Emerging Trends in Computing and
Information Sciences
7.
Chitra Singh, R.P.S. Gangwar,” Design and Analysis of a Compact Low Cost Patch Antenna for Different Wireless Operations”, 978-1-
4577- 0240-2/11/$26.00 ©2011 IEEE,pg 19-22.
8.
Lukasz Dudzinski*, Grzegorz Jaworski,” BROADBAND, MICROSTRIP, SHAPED BEAM, C-BAND ANTENNA FOR APPLICATION
IN WIRELESS LAN”
73-76
9.
M. Venkata Narayana, I.Govadhani, K.P.Sai Kumar, K. Pushpa Rupavathipublished paper on “Comparative Analysis of Exponentially
Shaped Microstrip-Fed Planar Monopole Antenna With and Without Notch “VOL. 2, NO. 11, October 2011 ISSN 2079-8407 Journal of
Emerging Trends in Computing and Information Sciences
10.
M. Venkata Narayana, A.Vikranth, I. Govardhani, Sd. Khaja Nizamuddin, Ch. Venkatesh published paper on”A Novel Design of a
Microstrip Patch Antenna with an EndFire Radiation for SAR Applications” Volume 2 No.1, January 2012 ISSN 2224-3577 International
Journal of Science and Technology .
11.
I.Govardhani , M.Venkata Narayana, Prof S.Venkateswarlu, K.Rajkamal Published paper in International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 1,Jan-Feb 2012, pp.764-767 on Microstrip patch antenna using holographic structure for WLAN and Ku Band application.
12.
I.Govardhani , M.Venkata Narayana Published paper in International Journal of Computer Science & Communication Networks,Vol 2(1),
375-380, ISSN:2249-5789 on Rectangular Patch Array Antenna with Liquid Crystal Substrate for Ka and Q Band Applications
13.
Dr.K.S.N Murthy, Venkata Raviteja.K, I.Govardhani, M.Venkata Narayana Published a paper on Multi-band Ladder-shape Microstrip Patch
Antenna IJSER Volume 3, Issue 3,
14.
Chen, W. S., B. Y. Lee, and P. Y. Chang, “A compact microstrip-line-fed slot antenna with dual-band notched for WiMAX operation,"
Progress In Electromagnetic Research C, Vol. 16, 13-23, 2010.
15.
Song, K., Y. Z. Yin, and B. Chen, “Triple-band open L-slot antenna with a slit and a strip for WLAN/WiMAX applications," Progress In
Electromagnetic Research Letters, Vol. 22, 139-146, 2011.
16.
Fan, S. T., Y. Z. Yin, H. Li, S. J. Wei, X. H. Li, and L. Kang, “A novel tri-band printed monopole antenna with an embedded ᴒ-shaped slot and a parasitic ring resonator for WLAN and WiMAX applications," Progress In Electromagnetic Research Letters, Vol. 16, 61-68, 2010.
Authors: K. M. Nithya, D. N. Arnepalli, S. R. Gandhi
17.
Paper Title: Role of Sorption Characteristics of Geomaterials on Long Term Performance of Landfill Barrier
Abstract: Among the methodologies adopted for safe disposal of various hazardous municipal and industrial solid wastes into environment, the land-filling is considered to be most safe and cost-effective method. For this purpose, compacted clay liners, CCL’s, made up of different geomaterials are popularly used as a barrier to contain the waste and to isolate it from the surrounding environment. The efficiency of the engineered landfills in minimizing the contamination of ground water aquifers highly depends on the hydraulic conductivity, sorption capacity and chemical compatibility of the liner materials. In general, the hydraulic conductivity of the liner material is considered while selecting material and designing the engineered landfill liner system, however, sorption characteristics of the geomaterials play a predominant role in mitigating the transport of the contaminants through the liner system and determine its long term performance. In view of the above facts, three different locally available geomaterials have been selected to evaluate their suitability as liner material based on the physical, chemical and hydraulic characteristics. The sorption characteristics of these geomaterials were established by conducting batch sorption experiments, for different heavy metal contaminants and suitable isotherm has been identified to represent the
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geomaterial-contaminant interaction. Further, the study illustrated its potential application for obtaining the design thickness of landfill liner based on the sorption characteristics of the geomaterial by using the simple numerical tool,
POLLUTE.
Keywords: Landfill barrier, heavy metal, geomaterial, sorption isotherms, distribution coefficient
References:
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Environmental Engineering, 127(5), 2001, pp.420-429.
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Soil Pollution, 53, 1990, pp. 53-67.
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6.
L.H. Mollins, D. Steward, and T.W. Cusens, “Predicting the properties bentonite sand mixtures”, Clay Mineralogy, 31, 1996, pp. 243-252.
7. K.M. Nithya, Evaluation of geomaterials for their application as landfill liner, Ph.D Thesis, IIT Madras, Chennai, India, 2011.
8. U. Holzlohner, H. August, and T. Meggyes, Contaminant transport, fundamentals and minimization, In: August H, Holzlohner U, Meggyes
T (eds) Advanced landfill liner, Thomas Telford, London, 1997, pp. 31–37.
9. D.L. Spark, Environmental soil chemistry, Academic Press, Amsterdam, 2003.
10. G. Sposito, The chemistry of soils, Oxford University Press, New York, 1989.
11. M.B. McBride, Chemisorption and precipitation reactions, In: Sumner ME (ed-in-chief) Handbook of Soil Science, CRC Press, Boca
Raton, 2000, pp. B265–B302.
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ASTM Standard D854, “Standard test methods for specific gravity of soil solids by water pycnometer method”, Annual Book of ASTM
Standards, 04.08, ASTM International, West Conshohocken, PA, USA, 2006.
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International, West Conshohocken, PA, USA, 1994.
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Standards, 04.08, ASTM International, West Conshohocken, PA, USA, 1994.
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ASTM Standard D427, “Standard test method for shrinkage factors of soils by mercury method”, Annual Book of ASTM Standards, 04.08,
ASTM International, West Conshohocken, PA, USA, 1994.
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ASTM Standards, 04.08, ASTM International, West Conshohocken, PA, USA, 1994.
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ASTM Standard D698, “Standard test method for laboratory compaction characteristics of soils using light effort”, Annual Book of ASTM
Standards, 04.08, ASTM International, West Conshohocken, PA, USA, 2007.
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ASTM D5084, “Standard test methods for measurement of hydraulic conductivity of saturated porous materials using a flexible wall permeameter”, Annual Book of ASTM Standards, 04.08, ASTM International, West Conshohocken, PA, USA, 2010.
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1976, pp. 3-10.
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ASTM Standard D2974, “ Standard test methods for moisture, ash, and organic matter of peat and other organic soils”, Annual Book of
ASTM Standards, 04.08, ASTM International, West Conshohocken, PA, USA, 2007.
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Conshohocken, PA, USA, 2001.
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28. R.N. Yong, and Y. Phadungchewit, “pH influence on selectivity and retention of heavy metals in some clay soils”, Canadian Geotechnical
Journal, 30, 1993, pp. 821-833.
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Science and Technology, 29, 1995, pp. 2317-2321.
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87/006-F, US Environmental Protection Agency, Washington, DC, 1991.
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Book of ASTM Standards, 04.11, ASTM International, West Conshohocken, PA, USA, 2004.
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Conference, ASCE, New Orleans, LA, USA, 1995, pp. 568-590.
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34. V. Autier, and D. White, “Examination of cadmium sorption characteristics for a boreal soil near Fairbanks Alaska”, Journal of Hazardous
Materials, 106B, 2004, pp.149–155.
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Z. Chen, B. Xing, and W.B. McGill, “A unified sorption variable for environmental application of the Freundlich isotherm”, Journal of
Environmental Quality, 28, 1999, pp. 1422-1428.
37. Y.S. Ho, J.F. Porter, and G. McKay, “Equilibrium isotherm studies for the sorption of divalent metal ions onto peat: copper, nickel and lead single component systems”, Water, Air and Soil Pollution, 141, 2002, pp. 1-33.
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18.
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Authors: T.Balamurugan, S.Manoharan
Paper Title: Fuzzy Controller Design using Soft Switching Boost Converter for MPPT in Hybrid System
Abstract: In this paper, a fuzzy logic control (FLC) is Proposed to control the maximum power point tracking
(MPPT) for a photovoltaic (PV) system and to control the maximum power point for a wind turbine (WT) system.
Hybrid integrated topology, fed by photovoltaic (PV) and wind turbine (WT) sources and suitable for distributed generation applications, is proposed. It works as an uninterruptible power source that is able to feed a certain minimum amount of power into the grid under all conditions. PV is used as the primary source of power operating near maximum power point (MPP), with the WT section (block), acting as a current source, feeding only the deficit power. The unique “integrated” approach obviates the need for dedicated communication between the two sources for coordination and eliminates the use of a separate, conventional dc/dc boost converter stage required for PV power processing. The conventional boost converter decreases the efficiency because of hard switching, which generates losses when the switches are turned on/off. During this interval, all switches in the adopted circuit perform zerocurrent switching by the resonant inductor at turn-on, and zero-voltage switching by the resonant capacitor at turnoff. This switching pattern can reduce the switching losses, voltage and current stress of the switching device. The
Hybrid systems have been simulated in MATLAB Simulink software and their Control Schemes have been analyzed and validated.
Keywords: Photovoltaic (PV) systems, Wind Turbine (WT), Fuzzy Logic Controller, Boost Converter, single phase inverter, Triggering Pulses, Perturb and Observe (P&O).
References:
1. Sang-hoom park, Gil-Ro Cha, Yong-Chae Jung and Chung-yuen won .”Design and Application for PV Generationb System Using a Soft-
Switching Boost Converter With SARC,” IEEE Transaction on Industrial Electronics., vol. 57, NO.2, Feb 2010.
2. S.Lalouni, D. Rekioua, T. Rekioua, and E. Matagne, “Fuzzy logic control of stand photovoltaic system with battery storage”, Journal of
Power Sources, Volume 193, Issue2, 5 September 2009, pp. 899
3. M.S. Ait Cheikh, C. Larbes, G. F. Tchoket Kebir, and A. Zerguerras, “Maximum power point tracking using a fuzzy logic control scheme”,
Revue des energies Renouvelables, Vol. 10, 2007, pp. 387-395
4. Subiyanto, Azah Mohamed, M. A. Hanan, and Hamimi Fadziati Adb Wahab, “Photovoltaic maximum power point tracking using logic controller”, Proceed Engineering Postgraduate Conference, 2009.
5.
T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on
Energy Conversion, vol. 22, no. 2, pp. 439–449,
6. Thomas Ackermann, Lennart Soder, “Wind energy technology and current status: a review,” Renewable and Sustainable Energy Reviews,
Elsevier, 2000.
7. T. Burton, D. Sharpe, N. Jenkins, E. Bossanyi, Wind Energy Handbook. John Wiley & sons, ltd, 2001.
8. Munteanu, L., Bratcu, A.I., Cutululis, N. A,. And Ceanga, E., Optimal Control of Wind Energy Systems, Springer, 2008.
9. M. A. Eltawil and Z. Zhao, "Grid-connected photovoltaic power systems: Technical and potential problems—A review", Renewable and
Sustainable Energy Reviews, vol. 14, pp. 112-129, 2010.
10. V. Salas, E. Olias, A. Barrado, and A. L. Zaro, "Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems", Solar Energy Materials & Solar Cells, vol. 90, pp. 1555–1578, 2006..
11. Jaime Alonso-Martinez, Santiago Arnaltes,”A Three-Phase Grid- Connected Inverter For PhotoVoltaic Applications Using Fuzzy MPPT,”
International Journal of Advanced Research in Artifical Intelligence, vol.1,no 4, 2011
12. Subiyanto, Azah Mohamed, Husasin Shareef,” Hopfield Neural Network Optimized Power Point Tracking In a PhotoVoltaic System,”
International Journal of Photo energy Vol. Article Id 798361, 13 pages,2012.
13. Power Electronics; circuits, Devices and Applications. by M.Rashid.
Authors: Himmat Lal Kumawat, Sandhya Sharma
87-94
An Implementation of a Forward Error Correction Technique using Convolution Encoding with
Paper Title:
Viterbi Decoding
Abstract: This paper, as the name suggests, shows the working of a forward error correction (FEC) coding technique using convolutional encoding with Viterbi decoding. It can be used by anyone interested in designing or understanding wireless digital communications Technique systems. This paper initially explains the working of a convolutional encoder[1]. The encoded bit stream, is then passed through an additive white Gaussian noise (AWGN) channel, quantized and received at the decoder. Finally, the original data stream is recovered by either a hard decision Viterbi decoder or a soft decision Viterbi decoder. This entire FEC technique is demonstrated, both practically, using Matlab, and theoretically. Also shown are simulation plots, characterizing the performance factors affecting the FEC coding technique. These factors include primarily the noise level as well as the encoder memory size. Convolutional encoding is widely used in modern communication systems. A software tool for simulating the processes of convolutional encoding and decoding is developed and presented in the paper. The convolutional encoders are investigated in respect of the parameter bit error rate. The decoding of convolutional codes is based on
Viterbi algorithm[3]. The software tool is developed in MATLAB and Communication Toolbox. In mathematics, computer science, telecommunication, and information theory, error detection and correction has great practical importance in maintaining information (data) integrity across noisy channels and less-than-reliable storage media. A channel code is a broadly used term mostly referring to the forward error correction (FEC) code and bit interleaving in communication and storage where the communication media or storage media is viewed as a channel[6]. The FEC code is used to protect data sent over the channel for storage or retrieval even in the presence of noise (errors). There exist two main forms of channel codes – convolutional codes and block codes. Convolutional codes are often used to improve the performance of digital radio, mobile phones, satellite links, and Bluetooth implementations. Unlike block encoders, convolutional encoders are not memoryless devices. A convolutional encoder accepts a fixed number of message symbols and produces a fixed number of code symbols, but its computations depend on the current set of input symbols and on some of the previous input symbols[13].
95-99

20.
Keywords: Channel Coding, Convolutional Codes, Non- Systematic Convolutional Codes, Viterbi Algorithm,
MATLAB, AWGN and BPSK.
References:
1.
B.Sklar, Digital Communications Fundamentals and Applications, 2nd edition, Prentice Hall, Upper Saddle River, New Jersey, 2001.
2.
A. J. Viterbi, “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm,”IEEE Transactions on
Information Theory, vol.IT-13, pp. 260-269, April, 1967.
3.
J.A.Heller and I. M. Jacobs, “Viterbi Decoding for Satellite and Space Communications,”IEEE Transactions on Communication
Technology, vol. com-19, pp. 835-848, October 1971.
4.
J.G.Proakis,Digital Communications, 3rd edition, WCB/McGraw-Hill, Boston,Massachusetts, 1995.
5.
S.Haykin, Communication Systems, 3rd edition, John Wiley & Sons, New York,1994.
6.
L. W. Couch, II,Digital and Analog Communication Systems, 4th edition, Macmillan Publishing Company, New York, 1993.
7.
T . Mc Dermott, Wireless Digital Communications: Design and Theory, Tuscon Amateur Packet Radio Corporation, Tuscon, Arizona, 1996.
8.
M.S. Roden, Digital Communication Systems Design, Prentice Hall, Englewood Cliffs, New Jersey, 1988.
9.
S.Lin and D.J. Costello, Error Control Coding, Prentice Hall, Englewood Cliffs,New Jersey, 1982.
10.
A.M. Michelson and A. H. Levesque, ErrorControl Techniques for Digital Communication, John Wiley & Sons, New York, 1985.
11.
V.Pless, Introduction to the Theory of Error-Correcting Codes, 3rd edition, John Wiley & Sons, New York, 1998.
12.
K.J. Larsen, “Short Convolutional Codes with Maximal Free Distance for Rates1/2, 1/3, and 1/4,”IEEE Transactions on Information Theory, vol. IT-19, pp. 371-372, May 1973.
13.
G.C.Clark, Jr., and J. B. Cain,Error-Correction Coding for Digital Communications, Plenum Press, New York, 1981.
14.
John.M.Geist, “Viterbi Decoder Performance in Gaussian Noise and PeriodicErasure Bursts,”IEEE Transactions on Communications, vol. com-28, no. 8, pp.1417-1422, August 1980.
15.
J.K.Omura, “On the Viterbi Decoding Algorithm,” IEEETransactions onInformation Theory, vol. IT-15, pp. 177-179, January 1969.
Authors: Rakesh Kumar, Tapesh Parashar, Gopal Verma
Paper Title: Background Modeling and Subtraction Based People Counting for Real Time Video Surveillance
Abstract: This paper presents a multiple people counting using only single camera, entering or exiting in a region of interest. Sigma-Delta background modeling and subtraction is used to segment the people from region.
Background subtraction provides blobs as a result with connected component. CG of blobs was used to track and count the entry and exit of people. This paper also proposed method for counting two or more people based on blob size and distance between floor and camera. People will be counted if and only if they passed through the three regions consecutively. Experiments shows that proposed method is robust and provides approximate accurate counts.
Keywords: Edge based Local Thresholding, Background Subtraction, Mathematical Morphological Processes,
Connected Component.
References:
1.
S. Stillman, R. Tanawongsuwan, and I. Essa, “Tracking multiple people with multiple cameras”, College of Computing, GVU C enter,
Georgia Institute of Technology Atlanta, GA 30332-0280 USA
2. H. Goldstein, “People counting using image processing”, Technion - Israel Institute of Technology, Department of Computer Science,
Advanced Programming Lab B. July 1999.
3.
Segen, J. and Pingali, S., “A Camera-based System for Tracking People in Real Time”, IEEE Proc. of Int. Conf. Pattern Recognition, 1999,
Vol.3, pp.63-67.
4.
Stefan Huwer and Heinrich Niemann, “Adaptive Change Detection for Real-Time Surveillance Applications”, Third IEEE International
Workshop on Visual Surveillance - VS-2000, Dublin, Ireland.
5. K. Terada, D. Yoshida, S. Oe, J. Yamaguchi, “A counting method of the number of passing people using a stereo camera”, IEEE Proc. o f
Industrial Electronics Conf., Vol. 3, pp.1318-1323, 1999.
6.
C. Stauffer and W. Grimson, “Adaptive background mixture models for real-time tracking,” presented at the IEEE Computer Society Conf.
Computer Vision and Pattern Recognition, June 1999.
7. A. M. Elgammal, D. Harwood, and L. S. Davis, “Nonparametric Model for Background Subtraction”. In Proc. Of the 6th European Conf. on Computer Vision-Part II, pages 751–767, 2000.
8.
K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: Principles and practice of background maintenance”, In Int. Conf. on
Computer Vision, volume 232, 1999.
9.
C. Ridder, O. Munkelt, and H. Kirchner, “Adaptive Background Estimation and Foreground Detection using Kalman Filtering”, In O.
Kaynak, M. ¨Ozkan, N. Bekiro˘glu, and ˙I. Tunay, editors, Proceedings of International Conf. on recent Advances in Mechatronics,
ICRAM’95, pages 193–199, Istanbul, TURKEY, Aug. 1995. UNESCO.
10. J. Richefeu, A. Manzanera, “robust and computationally efficient motion detection algorithm based on sigma-delta background estimation,” in ICVGIP, IEEE, 2004.
11.
Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Pearson Education, Third Edition, 2009.
Authors: Kamaljit Singh Arora, Randhir Singh, Parveen Lehana
100-102
21.
Paper Title: Evaluation of the Quality of Synthesized Calls of Alexandrine Parakeet (Psittacula Eupatria)
Abstract: Speech synthesis is one of the interesting research areas now-a-days and it has been explored for various applications including man-machine interaction and telecommunications. Moreover, it can be significantly used as an aid for vocally and blindly handicapped persons, etc. Birds produce vocal calls for different meaningful purposes; for e.g. contact call is used for making a vocal coordination with the movement of flock members for the search of a particular type of vegetation, begging call is generally used by baby birds when they are hungry and thirsty, etc.
Synthesis of vocal calls has many applications for e.g. synthesizing an alarm call may be used to protect the bird from any harmful situation, etc. LPC model has the ability to find out speech parameters for efficient speech synthesis. It has been used for speech coding applications because it has the property of low bit rate speech coding.
Since the basic mechanism of vocal sound production mechanism in birds is almost similar to the human speech production mechanism in many aspects. LPC based synthesis may be used for the synthesis of the vocal calls of birds. In this paper, LPC based synthesis approach has been successfully used for the vocal calls of Alexandrine parakeet (Psittacula Eupatria) by estimating different speech parameters. The quality of synthesized speech output has been evaluated using visual analysis of spectrograms, subjective and objective assessment methods keeping in view different MOS and PESQ scores. In order to measure the quality of synthesized output, MOS and PESQ scores
103-110

22. should be compared to identify the required quality level.
Keywords: LPC synthesis, MOS scores, PESQ, Vocal calls.
References:
1.
T. Zhang, “Advances in Intelligent and soft computing (Instrumentation, measurements, circuits and systems)” Springer, 2012.
2.
L.R.S Lazaro, L.L Policarpio and R.C.L Guevara, “Incorporating duration and intonation models in Filipino speech synthesis” in APSIPA
Annual Summit and conference, Sapporo, Japan, October, 2009.
3.
M.A Karjalainen and U.K Laine, “Development of communication aids for the handicapped based on speech synthesis techniques” DIGEST of the 11th international conference on medical and biological engineering, Ottawa, 1976.
4.
V.J Heuven and L.C Pols, “Analysis and synthesis of speech” 1993, pp. 279-280.
5.
A. Harma, "Automatic identification of bird species based on sinusoidal modeling of syllables," in Proc. ICASSP, Hong Kong, 2003.
6.
S. Fagerlund, “Acoustics and physical models of bird sounds, '' in Seminar in acoustics, HUT, Laboratory of Acoustics and Audio Signal
Processing, (Espoo), April 2004.
7.
V.V. Tynes, “Behaviour of exotic pets” ch. 1, psittacines, Blackwel publishing ltd., U.S.A, 2010, pp. 1-10.
8.
J. Murray, “Parrots” United States, ABDO publishing company, 2002, pp. 4-9.
9.
D. M. Warren, “Small animal care and management” ch. 19, 2002, pp. 305-322.
10.
http://www.allaboutbirds.com.au/index.
11.
P. Marler and H. Slabbekoorn, “How birds sing and why it matters,” in Nature’s music: the science of birdsong, vol.1, Elsevier Academic
Press, California, ch.9, 2004.
12.
N. Moustaki, “Bird Brains: Parrot intelligence,” in Parrot for Dummies, Wiley publishing, Hoboken, ch. 15. 2005.
13.
http://flickrhivemind.net/User/wolvenom/Recent
14.
F.B Waal, and P.L Tyack, “Vocal communication in wild parrots,” in Animal social complexity, Harvard University Press, ch.11, 2003, pp.293-303.
15.
J. Bradbury and T.Wright, “Different Calls of Parrots,” [Online] Available:http://www.acguanacaste.ac.cr/loras_acg/parrots.home.html
16.
S.Taylor, and R.P Michael, “Vocalizations of the brown headed parrot Poicephalus cryptoxanthus: their general form and behaviourial context”, OSTRICH, 2005, pp. 61-72.
17.
D.O Shaughnessy, “Speech Analysis,” in Speech Communications, 2nded., Hyderabad, Universities Press Private Limited, ch.6,2001, pp.192-209.
18.
V.B Kura, “Novel pitch detection algorithm with application to speech coding,” M.S.Thesis, Dept. of Elect. Engineering, University of New
Orleans, 2003.
19.
R. Gupta, A. K Mehta and V. Tiwari, "Vocoder (LPC) Analysis by Variation of Input Parameters and Signals" ISCA Journal of Engineering
Sciences, vol. 1, July 2012, pp. 57-61.
20.
N. A Meseguer, “Speech Analysis for automatic speech recognition” Norwegian university of science and technology, Master’s thesis master of science in Electronics, Dept. of electronics and telecommunication 2009.
21.
http://health.tau.ac.il/Communication% 20Disorders/noam/lowbit/lpc10.html.
22.
L. Gu, J.G Harris, R. Shrivastav and C. Sapienza, “Disordered speech evaluation using objective quality measures” in ICASSP, 2005, pp.
321-324.
23.
http://www.pal-acoustics.com/index.php?a=services&id=143
24.
A. W. Rix, J. G. Beerends, M. P. Hollier and A. P. Hekstra, “PESQ - the new ITU standard for end-to-end speech quality assessment” AES
109th Convention, LOS Angeles, 2000, pp. 22-25.
25.
C.L Philipos, “Speech quality Assesment”, Multimedia, Processing, Analysis and communications, University of Texas-Dallas, U.S.A, 2011, pp. 623-654.
26.
D. Deepa, “Dual channel speech enhancement using Hadamard-LMS algorithm with DCT pre-processing technique” International journal of
Engineering science and technology, vol.2, no. 9, 2010, pp.4418-4423.
27.
A.Oka, “Objective evaluation of speech quality” November, 2010, pp. 1-7.
28.
J.G Beerends,, S.V Wijngaarden, R.V Buuren, “ Extension of ITU-T Recommendation P.862 PESQ towards Measuring Speech
Intelligibility with Vocoders”The NATO research and technology organization, new Directions for Improving Audio Effectiveness, meeting
Proceedings, RTO-MP-HFM-123, France, 2005, pp.1 – 6.
29.
R. Blatnik, G. Kandus and T. Sef , “ Influence of the perceptual speech quality on the performance of the text-independent speaker recognition system” International journal of circuits, systems and signal processing, vol. 5, 2011.
Authors: Mostafa Tavassoli, Abolfazl Rajabi, Mehrdad Javadi, Sasan Mohammadi
Paper Title: Baggage Traffic Control in Airports making use of RFID Technology
Abstract: Nowadays, automatic identification of components is one of the most important research fields for researchers and scientists in different arenas. RFID Technology is one of the technologies widely used in this arena.
For this purpose, the present research has dealt with this technology as a means of tracking and identifying baggage automatically in a traffic control system in an airport. The specification of RFID Technology helps distinguish between man and objects with high precision. This paper makes use of this capability for identifying and tracking baggage in an airport and plane in order to increase the efficiency of luggage transport system, which, as a result, increases the precision of the system, decreases the damages arising from the missing of the luggage and its wrong transport and decreases the errors of the manpower. In order to obtain an intelligent system and automatically identify objects, a unique number is required for tracking and control each object. This research has defined a new model with four choices (that is, airline name, flight number, nature of tag and chair number) in order to develop a unique identity number. This number is stored in the tag memory. With such tags passing through RFID Readers embedded at different places including the conveyor belt or at the gates, passengers’ luggage may be easily tracked.
This way, any problem can be easily dealt with. Thanks to its adaptability, this technology can be easily used in computerized network for sending and receiving information; and an information bank has also been used to instantaneously analyze and study the information.
Keywords: BHCS (Baggage Handling Control System), Tag Patterns, Unique Identity Number.
References:
1.
“IATA 2010 annual report,” Int. Air Transport Assoc., Montreal, QC, Canada, 2010 [Online]. Available: http://www.iata.org/pressroom/Documents/IATAAnnualReport2010.pdf.
2. “Improving Airport Operations and Security with RFID”, Motorola industry brief – ref: World AirTransport Statistics, 2008.
3.
A.Khosravi, S.Nahavandi, D. Creighton,”Interpreting and Modeling Baggage Handling System as a System of Systems”, ICIT '09
Proceedings of the 2009 IEEE International Conference on Industrial Technology, Pages 1-6, and ISBN: 978-1-4244-3506-7, 2009.
111-116

23.
4. Michael Johnstone, Doug Creighton, and Saeid Nahavandi, Senior Member, IEEE,”Status-based Routing in Baggage Handling Systems:
Searching Verses Learning”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND
REVIEWS, VOL. 40, NO. 2 MARCH 2010.
5.
Janson Hui “RFID in Airports – Baggage and Passenger Tracking”, 188 – Intro to RFID June 8, 2008.
6. L. Riley. IATA Introduces RFID Standard for BaggageTags Annual industry savings projected at US$760 Million. [Online].Available: http://www.iata.org/pressroom/briefings/2005-11-18-01.htm (2005, Nov. 18).
7. The History of RFID Technology. RFID Journal.www.rfidjournal.com/article/articleview/1338/1/129/. Accessed July, 2008. ALOK
MISHRA, Deepti Mishra “APPLICATION OF RFID IN AVIATION INDUSTRY: AN EXPLORATORY REVIEW”, .Traffic and
Transportation, Vo; 22, 2010, No. 5, 363-372.
8. F.A. Radio Frequency Identification Technology in the Department of Defense Supply Chain —Now and Future. Presented at Research
Opportunities in Radio Frequency Identification (RFID) Transportation Applications Conference, October 17-18, 2006.
9. Erdem, E. Application of RFID Technology for Quality Management in Production and Distribution of Frozen Food Products. Presented at
Research Opportunities in Radio Frequency Identification (RFID) Transportation Applications Conference, October 17-18, 2006.
10. Hont, S. 2001. The Cutting Edge of RFID Technology and Applications for Manufacturing and Distribution, Texas Instrument TIRIS White
Paper.
11. Curtin, J., Kauffman, R. J., Riggins, F. J. Making the ‘‘MOST’’ out of RFID Technology: A Research Agenda for the Study of the
Adoption, Usage, and Impact of RFID, Information Technology and Management, 8 (2), pp. 87-110, 2007.
12.
T.Zhang, Y.Ouyang, Y.He,”Traceable Air Baggage Handling System based on RFID tags in the airport”, Journal of Theoretical and
Applied Electronic Commerce Research,Vol 3 , Aipril 2008 , 106-115.
13. T.Zhang, Y.Ouyang, Y.He,”Traceable Air Baggage Handling System based on RFID tags in the airport”, Journal of Theoretical and
Applied Electronic Commerce Research,Vol 3 , Aipril 2008 , 106-115.
14. Finkenzeller, K. RFID handbook: Fundamen¬tals and applications in contactless smart cards and identification. Chichester: Wiley 2003.
15. Want, R. The magic of RFID. Queue, 2(7), 40–48, 2004.
16.
G. Marrocco, “The art of UHF RFID antenna design: Impedancematching and size-reduction techniques,” IEEE Antennas Propag. Mag., vol. 50, no. 1, pp. 66–79, Feb. 2008.
17. Tzeng Shiou-Fen, Chen Wun-Hwa, Pai Fan-Yun, Evaluating the Business Value of RFID: Evidence from Five Case Studies, International
Journal of Production Economics, Volume 112, Issue 2, April 2008, pp. 601-613.
18.
Carla R. Medeiros, Jorge R. Costa, Senior Member, IEEE, and Carlos A. Fernandes, Senior Member, IEEE,”Passive UHF RFID Tag for
Airport Suitcase Tracking and Identification”, IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, VOL. 10, 2011.
19. C. Medeiros, J. Costa, and C. Fernandes, “UHF tag for suitcase RFID application in airports,” in Proc. EUCAP, Barcelona, Spain, Apr.
2010, pp. 1–5.
20. C. Medeiros, J. Costa, and C. Fernandes, “UHF RFID smart conveyor belt with confined detection range,” in Proc. IEEE AP-S/URSI Int.
Symp., Charleston, SC, Jun. 2009, pp. 1–4.
21.
S. Delichatsios, D. Engels, L. Ukkonen, and L. Sydanheimo, “Albano multidimensional UHF passive RFID tag antenna designs,” Int. J.
Radio Freq. Identif. Technol. Appl., vol. 1, no. 1, pp. 24–40, 2006
22. Patrick Jaska, Don Bosco Adarsh , Jithender Nalla, Nandikonda Vinod Kumar Reddy, Raghavender Tadisina,"Improved Customer Service
Using RFID Technology",2010 EABR & ETLC Conference Proceedings.
23. Hogan, Bernie and Linster Marc. “Using EPCglobal Network to track baggage around the world.” PowerPoint. EPCGlobal US Conference.
15 Sep. 2005.
Authors: Haneef Khan, Amit
Transient And DC Analysis Microcontroller 8051 at 0.5 micro Technology for High Speed
Paper Title:
Applications
Abstract: This paper presents the different types of analysis such as Transient and DC. The Schematic Design of
8051 microcontroller is based upon 0.5µm CMOS Technology using Tanner Tools and focused mainly on four components (SRAM, Interrupt Controller, ALU, CPU). An efficient and practical 8051 microcontroller has been proposed for solving the problem like to minimize the time delay during the operation and minimize the noise margin using different module level, by reducing the number of transistor in 8051 microcontroller its functioning speed goes rises, size & cost reduce.
Keywords: Microcontroller, Schematic, layout, CMOS,VLSI circuit.
References:
1. DESIGN OF LOW POWER 8 BIT SRAM ARCHITECTURE USING LEAKAGE FEED BACK WITH STACK & SLEEP STACK WITH
KEEPER, V.G. Santhi Swaroop, B.Murali Krishna, M.Vijaya Bhaskar, B.Raghu kanth, V.SAI PRAVEEN**/ International Journal of
Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 2,Mar-Apr 2012, pp.192-201.
2. Designing and Analysis of 8 Bit SRAM Cell with Low Subthreshold Leakage Power, Atluri.Jhansi rani*, K.Harikishore**, Fazal Noor
Basha**,V.G.Santhi Swaroop*, L. VeeraRaju*, International Journal of Modern Engineering Research (IJMER), www.ijmer.com,Vol.2,
Issue.3, May-June 2012 pp-733-741 , ISSN: 2249-6645.
3. The power dissipation comparison of different ALU architectures ;Junkai Sun;AnpingJiangMechanical and Electrical Technology
(ICMET), 2010 2nd International Conference on Digital Object Identifier: 10.1109/ICMET.2010.5598395 ;Publication Year: 2010 ,
Page(s): 430 - 433 ;IEEE Conference Publications.
4. An Interrupt Controller for FPGA-based Multiprocessors, Date of Conference: 16-19 July 2007, Author(s): Tumeo, A. Politecnico di
Milano, Milano ,Branca, M. ; Camerini, L. ; Monchiero, M. ; Palermo, G. ; Ferrandi, F. ; Sciuto, D. ,Page(s): 82 - 87
5. Design of a high performance microcontroller; Hu Yue-li; Cao Jia-lin; Ran Feng; Liang Zhi-jian; High Density Microsystem Design and
Packaging and Component Failure Analysis, 2004. HDP '04. Proceeding of the Sixth IEEE CPMT Conference on ;Digital Object Identifier:
10.1109/HPD.2004.1346667 ;Publication Year: 2004 , Page(s): 25 - 28 ; IEEE Conference Publications.
6. Design of a superconducting ALU with a 3-input XOR gate ; Takahashi, K.; Nagasawa, S.; Hasegawa, H.; Miyahara, K.; Takai, H.;
Enomoto, Y.; Applied Superconductivity, IEEE Transactions on Volume: 13, Issue:2 ,Part:1Digital Object Identifier:
10.1109/TASC.2003.813944 Publication Year: 2003 , Page(s): 551 - 554 Cited by: 3 ;IEEE Journals & Magazines .
7. World Academy of Science, Engineering and Technology 39 2008, A Novel Four-Transistor SRAM Cell with LowDynamic Power
Consumption,Arash Azizi Mazreah, Mohammad T. Manzuri Shalmani, Hamid Barati, and Ali Barati.
8. The 8051 Microcontroller and Embedded Systems; Second Edition; Muhammad Ali Mazidi, Janice Gillispie Mazidi, Rolin D. McKinlay.
9. Using the 8051 Microcontroller with Resonant Transducers, Industrial Electronics, IEEE Transactions on,Date of Publication: Nov.
1985,Author(s): Williamson, Tom ,Intel Corporation, Chandler, AZ 85224. Volume: IE-32 , Issue: 4 .Page(s): 308 – 312.
10. Susceptibility studies on an 8051 microcontroller mounted on single- and multi-layer Printed Circuit Boards, Date of Conference: 26-27
Nov. 2008, Author(s): Talwalker, A. Dept. of Electr. Eng., Indian Inst. of Technol.-Bombay, Mumbai, India ,Chandramouli, C. ; Praveen,
C. ; Agarwal, V. Page(s): 155 – 161.
11. A low-energy low-voltage asynchronous 8051 microcontroller core, Date of Conference: 21-24 May 2006Author(s): Kok-Leong Chang
Centre for Integrated Circuits & Syst., Nanyang Technol. Univ.,Singapore Bah-Hwee Gwee ,Page(s): 4 pp. – 3184.
12. A design of low power 8-bit ALU ;Beom Seon Ryu; Jung Sok Yi; Kie Young Lee; Tae Won Cho TENCON 99. Proceedings of the IEEE
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13. Application Specific Low Power ALU Design ;Yu Zhou; Hui Guo Embedded and Ubiquitous Computing, 2008. EUC '08. IEEE/IFIP
International Conference on Volume: 1 Digital Object Identifier: 10.1109/EUC.2008.81 Publication Year: 2008 , Page(s): 214 - 220 Cited by: 1; IEEE Conference Publications.
Authors: S. Chaitanya Rami Reddy, P. Ravinder Kumar
Paper Title: Implementation of Data Aggregation and Authentication in Wireless Sensor Networks
Abstract: Wireless sensor networks are vulnerable to many types of security attacks, including false data injection, data forgery and eavesdropping. Sensor nodes can be compromised by intruders and the compromised nodes can distort data integrity by injecting false data. The transmission of false data depletes the constrained battery power and degrades the bandwidth utilization [1]. False data can be injected by compromised sensor nodes in various ways, including data aggregation and relaying. In this paper it has been assumed that some sensor nodes are selected dynamically as data aggregators and the nodes between two consecutive data aggregators are called forwarding nodes simply because they forward data. To detect false data injected by a data aggregator while performing data aggregation, some neighbouring nodes of the data aggregator (called monitoring nodes) also perform data aggregation and compute MACs for the aggregated data to enable their pair mates to verify the data later. This project presents a Data Aggregation and Authentication protocol, called DAA, to integrate false data detection with data aggregation and confidentiality. To support data aggregation along with false data detection, the monitoring nodes of every data aggregator also conduct data aggregation and compute the corresponding small-size message authentication codes for data verification at their pair mates. This project introduces a data aggregation and authentication protocol (DAA) to provide false data detection and secure data aggregation against up to T compromised sensor nodes, for T>=1. The value of T depends on security requirements, node density, packet size and the amount of tolerable overhead. Microsoft C#.NET programming language is used to implement the Data aggregation and authentication protocol. The front end of the protocol is designed using Visual Studio2005.
Keywords: Authentication, Data aggregation, Data integrity, Network- level security, Sensor networks.
References:
1.
Suat Ozdemir and Hasan Çam, Senior Member, “Integration of False Data Detection With Data Aggregation and Confidential Transmission in Wireless Sensor Networks”, IEEE/ACM Trans. on networking, Vol. 18, No. 3, June 2010.
2.
I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “A survey on sensor networks”, IEEE Commun. Mag., Vol. 40, No. 8, pp.
102–114, Aug 2002.
3.
F. Ye, H. Luo, S. Lu and L. Zhang, “Statistical en-route detection and filtering of injected false data in sensor networks”, in Proc. IEEE
INFOCOM, Vol. 4, pp. 2446– 2457, 2004.
4.
S. Zhu, S. Setia, S. Jajodia and P. Ning, “Interleaved hop-by-hop authentication against false data injection attacks insensor networks”, ACM
Trans. Sensor Netw., Vol. 3, No. 3, Aug 2007.
5.
H. Yang and S. Lu, “Commutative cipher based en-route filtering in wireless sensor networks”, in Proc. IEEE VTC, Vol. 2, pp. 1223–1227,
2004.
6.
Z. Yu and Y. Guan, “A dynamic en-route scheme for filtering false data in wireless sensor networks”, in Proc. IEEE INFOCOM, pp.1 12,
Apr 23–27, 2006.
7.
L. Hu and D. Evans, “Secure aggregation for wireless networks”, in Proc. Workshop Security Assurance Ad hoc Netw., pp. 384–394, Jan.28,
2003,.
8.
B. Przydatek, D. Song, and A. Perrig, “SIA: Secure information aggregation in sensor networks”, pp. 255–265, in Proc. SenSys, 2003.
Authors: Neha Gupta, Garima Saini
25.
Paper Title: Performance Analysis of BFO for PAPR Reduction in OFDM
Abstract: Partial transmit sequence (PTS) is one of the attractive techniques to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) system. As conventional PTS technique requires an exhaustive searching over all the combinations of the given phase factors, which results in the computational complexity increases exponentially with the number of the sub-blocks. In this paper, we aim to obtain the desirable PAPR reduction with the low computational complexity. Since the process of searching the optimal phase factors can be categorized as combinatorial optimization with some variables and constraints, we propose a novel scheme, which is based on a bacteria foraging optimization, to search the optimal combination of phase factors with low complexity. To validate the analytical results, extensive simulations have been conducted, showing that the proposed schemes can achieve significant reduction in computational complexity while keeping good PAPR reduction.
Keywords: Bacteria foraging optimization (BFO), orthogonal frequency division multiplexing (OFDM), partial transmit sequences (PTS), peak-to-average power ratio (PAPR).
References:
1.
Taewon Hwang, Chenyang Yang, Gang Wu, Shaoqian Li, Geoffrey Ye Li, “OFDM and Its Wireless Applications: A Survey,” IEEE
Transactions on Vehicular
2.
S. H. Han and J. H. Lee, “An overview of peak-to-average power ration reduction techniques for multicarrier transmission,” IEEE Wireless
Communication, Vol. 12, pp. 56-65, April 2005.
3.
Marco Lixia, Maurizio Murroni, Vlad Popescu, “PAPR reduction in Multicarrier Modulations using Genetic Algorithms,” Proceedings of
12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), pp. 938-942, May 2010.
4.
Seog Gcun Kang, Jeong Goo Kim, Eon Kyeong Joo, “A Novel Subblock Partition Scheme for Partial Transmit Sequence OFDM,” IEEE
Transactions on Broadcasting, Vol. 45, No. 3, pp. 333-339, Sep. 1999.
5.
Ho-Lung Hung, Yung-Fa Huang, Cheng-Ming Yeh, Tan-Hsu Tan, “Performance of Particle Swarm Optimization Techniques on PAPR
Reduction for OFDM Systems,” Proceedings of IEEE International Conference on Systems, Man, Cybernetics (SMC), pp. 2390-2395, Oct.
2008.
6.
Tao Jiang, Weidong Xiang, Paul C. Richardson, Jinhua Guo, Guangxi Zhu, “PAPR reduction of OFDM signals using partial transmit sequences with low computational complexity,” IEEE Transactions on Broadcasting, Vol. 53, No. 3, pp. 719-724, Sep. 2007.
122-126
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26.
7.
Jung-Chieh Chen, “Partial Transmit Sequences for PAPR Reduction of OFDM Signals with Stochastic optimization Techniques,” IEEE
Transactions on Consumer Electronics, Vol. 56, No. 3, pp. 1229-1234, Aug. 2010.
8.
Jung-Chieh Chen, “Partial Transmit Sequences for Peak-to-Average Power Ratio Reduction of OFDM Signals with the Cross-Entropy
Method,” IEEE Signal Processing Letters, Vol. 16, No. 6, pp. 545-549, June 2009.
9.
Jung-Chieh Chen, “Application of Quantum-Inspired Evolutionary Algorithm to Reduce PAPR of an OFDM Signal Using Partial Transmit
Sequences Technique,” IEEE Transactions on Broadcasting, Vol. 56, No. 1, pp. 110-113, March 2010.
10.
Yajun Wang, Wen Chen, Chintha Tellambura, “A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM
Signals,” IEEE Transactions on Wireless Communications, Vol. 9, No. 10, pp. 2994-3000, Oct. 2010.
11.
Sung-Soo Kim, Myeong-Je Kim, T. Aaron Gulliver, “A New PTS for PAPR Reduction by Local Search in GA,” Proceedings of
International Joint Conference on Neural Networks Canada, pp. 2370-2373, July 2006.
12.
Hsinying Liang, Yan-Ru Chen, Yung-Fa Huang, Chia-Hsin Cheng, “A Modified Genetic Algorithm PTS Technique for PAPR Reduction in
OFDM Systems,” Proceedings of the 15th Asia-Pacific Conference on Communications (APCC), pp. 182-185, Oct. 2009.
13.
L. J. Cimini, N. R. Sollenberger, “Peak-to-Average Power Ratio Reduction of an OFDM Signal Using Partial Transmit Sequences,” IEEE
Communications Letters, Vol. 4, No. 3, pp. 86–88, March 2000.
14.
C. Pradabpet, S. Yoshizawa, Y. Miyanaga, K. Dejhan, “Searching Phase Optimize in PTS-APPR Method by GA for PAPR Reduction in
OFDM-WLAN Systems,” Proceedings of Conference on Innovative Technologies in Intelligent Systems and Industrial Applications
(CITISIA) Malaysia, pp. 393-398, July 2009.
15.
Xiaodong Zhu, Tao Jiang, Guangxi Zhu, “Novel Schemes Based on Greedy Algorithm for PAPR Reduction in OFDM Systems,” IEEE
Transactions on Consumer Electronics, Vol. 54, No. 3, pp. 1048-1052, August 2008.
16.
S. H. Muller, J. B. Huber, “OFDM with Reduced Peak–to–Average Power Ratio by Optimum Combination of Partial Transmit Sequences,”
IEEE Electronics Letters, Vol. 33, No. 5, pp. 368–369, Feb. 1997.
17.
Wang Ke, Hao Jiuyu, Wang Lei, Li Huimin, “Phase Factor Sequences Algorithm in Partial Transmit Sequence,” Transactions of Tianjin
University and Springer-Verlag, Vol. 15, No. 1, pp. 23-26, Feb. 2009.
18.
Tian Ya-fei, Ding Rong-hua, Yao Xiao-an, Tang Hai-wei, “PAPR Reduction of OFDM Signals using Modified Partial Transmit
Sequences,” Proceedings of 2nd International conference on Image and Signal Processing (CISP), pp. 1-4, Oct. 2009.
19.
Peng Liu, W. P. Zhu, M. O. Ahmad, “A Phase Adjustment based Partial transmit sequence scheme for PAPR reduction,” Springer
Transactions on Circuits systems signal processing, Vol. 24, No. 3, pp. 329–337, Feb. 2004.
20.
D. W. Lim, S. J. Heo, J. S. No, H. Chung, “A new PTS OFDM scheme with low complexity for PAPR reduction,” IEEE Transactions on
Broadcasting, Vol. 52, No. 1, pp. 77–82, March 2006.
21.
S. H. Han and J. H. Lee, “ PAPR reduction of OFDM signals using a reduced complexity PTS technique,” IEEE Signal Processing Letters,
Vol. 11, No.11, pp. 887-890, Nov. 2004.
22.
M. Breiling, S. H. Muller and J. B. Huber, “SLM peak power reduction without explicit side information,” IEEE Communication Letters,”
Vol. 5, No. 6, pp. 239-241, June 2001.
23.
C. Tellambura, “Improved phase factor computation for the PAR reduction of an OFDM signal using PTS,” IEEE Communication Letters,
Vol. 5, No. 4, pp. 135–137, April 2001.
24.
A. Alavi, C. Tellambura, I. Fair, “PAPR reduction of OFDM signals using partial transmit sequence: An optimal approach using sphere decoding,” IEEE Communication Letters, Vol. 9, No. 11, pp. 982–984, Nov. 2005.
25.
J. H. Wen, S.H. Lee, Y. F. Huang, H. L. Hung, “A suboptimal PTS algorithm based on particle swarm optimization for PAPR reduction in
OFDM systems,” Transactions on Wireless Communication Networking, Vol. 14, pp. 42-49, Sep. 2008.
26.
S. H. Han, J. H. Lee, “PAPR reduction of OFDM signals using a reduced complexity PTS technique,” IEEE Signal Processing Letters, Vol.
11, No. 11, pp. 887–890, Nov. 2004.
27.
G. R. Hill, M. Faulkner, J. Singh, “Reducing the peak-to-average power ratio in OFDM by cyclically shifting partial transmit sequences,”
IEEE Electronics Letters, Vol. 36, No. 6, pp. 560–561, March 2000.
28.
Liu Y., Passino K.M., “Biomimicry of Social Foraging Bacteria for Distributed optimization: Models, Principles, and Emergent Behaviors,”
IEEE Journal of Optimization Theory and Applications, Vol. 22, No. 3, pp. 52-67, June 2002.
29.
Dong Hwa Kim, Ajith Abraham, Jae Hoon Cho, “A hybrid genetic algorithm and bacterial foraging approach for global optimization,”
International journal on Information sciences, Vol. 177, pp. 3918–3937, April 2007.
30.
P.D. Sathya, R. Kayalvizhi, “Image Segmentation Using Minimum Cross Entropy and Bacterial Foraging Optimization Algorithm,”
Proceedings of International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), pp. 500-506, March
2011.
31.
Tanumay Datta and Iti Saha Misra, “A Comparative Study of Optimization Techniques in Adaptive Antenna Array Processing -The
Bacteria-Foraging Algorithm and Particle-Swarm Optimization,” IEEE Antennas and Propagation Magazine, Vol. 51, No. 6, pp. 69-81,
December 2009.
32.
A D Hanumantharao, T. Panigrahi, U K Sahoo, G. Panda, B Suresh, “Exact Maximum Likelihood Direction Of Arrival Estimation Using
Bacteria Foraging Optimization,” IEEE Transactions on global optimization,Vol. 42, No. 2, pp. 33-38, March 2009.
33.
Shiva Gholami Boroujeny, Mohammad Eshghi, “Active Noise Control using Bacterial Foraging Optimization Algorithm,” Proceedings of
10th International Conference on Signal Processing (ICSP), pp. 2592 – 2595, Oct. 2010.
34.
M. Tripathy and S. Mishra, “Bacteria Foraging-Based Solution to Optimize Both Real Power Loss and Voltage Stability Limit,” IEEE
Transactions on Power Systems, Vol. 22, No. 1, pp. 240-249, Feb. 2007.
35.
Arijit Biswas, Sambarta Dasgupta, Swagatam Das, Ajith Abraham, “Synergy of PSO and Bacterial Foraging Optimization – A Comparative
Study on Numerical Benchmarks,” Innovations in Hybrid Intelligent Systems, Vol. 44, pp. 255–263, March 2007.
36.
Muwaffaq I. Alomoush, “Coordinated Tuning of IPFC and PSS to Improve Power System Stability Using BFO,” Proceedings of 45th
International Universities Power Engineering Conference (UPEC), pp.1-6, August 2010.
37.
M. Marsaline Beno, N.Albert Singh, M.Ciba Therase, M.Mohamed Syed Ibrahim, “Design of PSS for damping low frequency oscillations using Bacteria foraging tuned non-linear Neuro-Fuzzy Controller,” IEEE GCC Conference and Exhibition, pp. 653 – 656, Feb. 2011.
38.
K. M. Bakwad, Dr. S.S. Pattnaik, Dr. B. S. Sohi, Dr. S. Devi, Sastry V. R. S. Gollapudi, Chintakindi Vidya Sagar, P. K. Patra, “Synchronous
Bacterial Foraging Optimization for Multimodal and High Dimensional Functions,” Proceedings of International Conference on Computing,
Communication and Networking (ICCCN), pp. 1-8, Dec. 2008.
39.
S. Mishra1, C.N.Bhende, L.L.Lai, “Optimization of a distribution static compensator by Bacterial Foraging Technique,” Proceedings of the
Fifth International Conference on Machine Learning and Cybernetics, Dalian, pp.4075-4083, 13-16 August 2006.
40.
M. Tripathy and S. Mishra, “Bacteria Foraging-Based Solution to Optimize Both Real Power Loss and Voltage Stability Limit,” IEEE
Transactions on Power Systems, Vol. 22, No. 1, pp. 240-249, Feb. 2007.
Authors: Mustapha Ben Saidi, Abderrahim Marzouk
Paper Title: Access Control Protocol for Cloud Systems Based On the Model TOrBAC
Abstract: The challenge that arises by the arrival of cloud computing is to carefully control the data that are no longer in possession of the company alone, but may be in the hands of third parties (TTP). Managing user trust is a major concern related to the management of migrated data in a Cloud. Dealing with this issue, our paper contributes to this process by defining a security policy based on trust, followed by the description of a security protocol for a
TTP monitor attempts to violations of this policy by users of an organization's cloud. This protocol is based on ordered policies established by the AS and assigned to each user during its connections to the cloud.
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27.
Keywords: Security, Cloud, Access Control, TOrBAC, OrBAC.
References:
1. M. Ben Saidi, A. Abou El Kalam, A. Marzouk, TOrBAC: A Trust Organization Based Access Control model for Cloud computing systems,
International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-4, September 2012
2. A. Abou El Kalam, “A Research Challenge in Modeling Access Control Policies: Modeling Recommendations”, IEEE International
Conference on Research Challenges in Information Science, 3-6 Jun 2008, Marrakech, Morocco.
3. www.ORBAC.org
4. Livre Cloud : Cloud Computing - 2ème éd - Une rupture décisive pour l'informatique d'entreprise : Guillaume PLOUIN.
5. Commentary : Cloud computing a security problem or solution? P.G. Dorey a, *, A. Leite b a Royal Holloway, University of London, UK b KPMG LLP, UK
6. M. Ben Saidi, A. Abou El Kalam , A. Marzouk Politique de contrôle d’accès au Cloud Computing Recommandation à base de confiance,
JNS2, IEEE Xplore 2012. 0.1109/JNS2.2012.6249249 .
7. Recherche Portio pour Colt TelecomGroup, 2009
8. N. Essaouini, A. Abou El Kalam, A. Ait ouahman, “Modeling Security Policies with Recommendations”, International Journal of
Computer Science and Network Security (IJCSNS), octobre 2011, ISSN : 1738-7906, Volume Number: Vol.11, No.10, http://paper.ijcsns.org/07_book/201111/20111121.pdf .
9. N. Essaouini, A. Abou El Kalam, A. Ait ouahman, “Access Control Policy: A Framework to Enforce Recommendations”, International
Journal of Computer Science and Information Technologies (IJCSIT), Vol. 2 (5) , 2011, 2452-2463 , Septembre 2011, ISSN : 0975-9646,
Pages: 2452-2463, disponible à http://www.ijcsit.com/docs/Volume%202/vol2issue5/ijcsit20110205128.pdf .
10. A. Abou El Kalam, P. Balbiani, S. Benferhat, F. Cuppens, Y. Deswarte, R. El-Baida, A. Miège, C. Saurel, G. Trouessin “Organization-
Based Access Control”, 4th International Workshop on Policies for Distributed Systems and Networks (Policy’03), Côme, Italie, 4-6 june
2003, IEEE Computer Society Press, pp. 120-131.
11. http://www.ijcsit.com/docs/Volume%202/vol2issue5/ijcsit20110205128.pdf .
12. A. Abou El Kalam, P. Balbiani, S. Benferhat, F. Cuppens, Y. Deswarte, R. El-Baida, A. Miège, C. Saurel, G. Trouessin “Organization-
Based Access Control”, 4th International Workshop on Policies for Distributed Systems and Networks (Policy’03), Côme, Italie, 4-6 june
2003, IEEE Computer Society Press, pp. 120-131.
Authors: Mahalakshmi C, Ramaswamy M
Paper Title: Tree Routed Contingency Retrieval Mechanism for Virtual Private Networks
Abstract: The paper embarks a retrieval methodology on the occurrence of a contingency to support a continuous transfer of data in the wired infrastructure of a virtual private network (VPN). The scheme envisages a new route between the source and destination in the architecture of a shared provider network to augment the disruption in the flow of traffic. It ensembles the minimum use of bandwidth in its formulation with a view to effectively avail the services and ensure a reliable delivery procedure. The philosophy bestows the creation of a viable alternate path in the form of a tree to carry forth the stream of data and acquire the best performance in terms of measurable indices.
The results obtained through NS2 simulation illustrate the merits of the proposed strategy and emphasize its suitability for large scale transmission to meet the emerging needs.
Keywords: Bandwidth, Performance metrics, Tree routing, VPN.
References:
1. N.G.Duffield, P.Goyal, A.Greenberg, P.Mishra, K.K.Ramakrishnan, and J.E. Van Der Merwe, 2002. “Resource Management with Hoses:
Point-to- Cloud Services for Virtual Private Networks”, IEEE/ACM Transactions on Networking, 2002, pp. 679 – 692.
2. Jian Chu and Chin-Tau Lea, “New Architecture and Algorithms for Fast Construction of Hose-Model VPNs”, IEEE/ACM Transactions on
Networking, vol. 16, June 2008, pp.670-679.
3.
N. Duffield, P. Goyal and A. Greenberg, “A flexible model for resource management in virtual private networks,” in ACM SIGCOMM,
1999.
4. A. Kumar, R. Rastogi, A. Silberschatz, and Bulent Yener, “Algorithms for Provisioning Virtual Private Networks in the Hose Model”,
IEEE/ACM Transactions on Networking, August 2002, pp. 565 – 578,.
5. A. Gupta, J.Kleinberg, A.Kumar, R.Rastogi, and B.Yener, “Provisioning a Virtual Private Network: A Network Design Problem for
Multicommodity Flow,” ACM Symp. Theory of Comp., 2001, pp. 389–398.
6.
Satish Raghunath and K. K. Ramakrishnan, “Resource Management for Virtual Private Networks”, IEEE Communications Magazine, April
2007, pp.38-44.
7. Y. L. Liu, Y. S. Sun and M. C. Chen, “MTRA: An On-Line Hose-Model VPN Provisioning Algorithm”, Technical report TR-IIS-04-020,
IIS, Academia Sinica, 2004.
8. Yi Yang, L. Zhang, J. K. Muppala, and S. T. Chanson, “Bandwidth-delay Constrained Routing Algorithms”, Computer Networks, vol. 42,
July 2003, pp. 503-520.
9.
A. Gupta, A. Kumar and R. Rastogi, “Exploring the Trade-off between Label Size and Stack Depth in MPLS Routing”, in Proc.of IEEE
INFOCOM, 2003, pp. 544-554.
10. Chun Tung Chou, “Traffic Engineering for MPLS-based Virtual Private Networks”, Computer Networks, vol. 44, March 2004, pp.319–
333.
11.
R. Ravi and S. Radhakrishnan, “Provisioning QoS in Virtual Private Network using Dynamic Scheduling”, Journal of Computer Science, vol. 4, January 2008, pp. 1-5.
12. Yu-Liang Liu, S.Yeali, Sun, and Meng Chang Chen, “Traffic Engineering for Hose-Model VPN Provisioning”, IEEE Globecom, vol. 2,
2005, pp.1080-1085.
13.
Zied Ben Houidi and Mickael Meulle, “A new VPN routing approach for large scale networks”, 18th Int. conf on Network Protocols, 2010 pp.124-133.
14.
Rebecca Isaacs and Ian Leslie, “Support for Resource-Assured and Dynamic Virtual Private Networks”, IEEE Journal on Selected Areas in
Communications, vol. 19, March 2001, pp. 460-472.
15. Gee-Swee Poo, and Haibo Wang, “Multi-path routing versus tree routing for VPN bandwidth provisioning in the hose model”, Journal of
Computer Networks, vol. 51, June 2007, pp. 1725–1743.
16. Sahel Alouneh, Abdeslam En-nouaary, Anjali Agarwal, “A Multiple LSPs Approach to Secure Data in MPLS Networks”, Journal of
Networks, vol. 2, August 2007, pp.51-58.
Authors: Richa Bhatnagar, Mariya Khurshid Ansari, Sakshi Bhatnagar, Harshbardhan Barik
139-143
28.
Paper Title: An Expert Anti-Malware Detection System
Abstract: The malware expert system is an enhance approach for analyzing malware and other kinds of software.
So, it is necessary to develop an effective malware expert system that can analyze, detect, classify and remove the
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29. malware codes. This system is necessary because it removes the errors done by human intervention in determining whether the files to be scanned contain any malicious data or not. There are various diverse approaches that were previously used to find and eradicate the malicious codes. But there were some loop- holes in existing strategies like the systems detect false positive malwares. The objective of malware detection expert system is to evaluate sample as malware or non-malware.
Keywords: Anomaly, Adware, False Positive, False Negative, Hit Ratio, PUI (Program Under Inspection),
Spyware, Trojan, Virus, Worms.
References:
1.
Mihai Christodorescu and Somesh Jha,” Testing Malware Detectors”, in Proc. ISSTA’04, July 11 - 14, 2004.pages 33-44, Boston, MA USA,
ACM Press.
2.
J.Xu, A.H.Sung, P.Chavez, S.Mukkamala,” Polymorphic Malicious Executable Scanner by API Sequence Analysis”, Proceeding of 4th
IEEE Symposium of International Conference on Hybrid Intelligent Systems (HIS ’04).
3.
Arun Lakhotia , Aditya Kapoor , Eric Uday, “Are Metamorphic Viruses Really Invincible Part 2” , Virus Bulletin ,January 2005.
4.
Abhishek Karnik, Suchandra Goswami and Ratan Guha,” Detecting Obfuscated Viruses Using Cosine Similarity Analysis”, in The
Proceeding of IEEE Symposium First International Conference on Modelling & Simulation (ASM ’07).
5.
Heng Yin, Dawn Song, Manuel Egele, Christopher Krugel, and Engin Kirda, “Panorama: Capturing System – wide Information Flow for
Malware Detection and Analysis”, in Proc CCS’07, October 29 November 2, 2007, Alexandria, Virginia, USA,ACM Press.
6.
Gerard Wagener, Radu State, Alexandre Dulaunoy,” Malware Behavior Analysis”, Springer-Verlag, France 2007.
7.
Greoigre Jacob, Herve Debar, Eric Fillol,”Behavioral detection of malware: from a survey towards an established taxonomy”, Springer-
Verlag, France 2008.
8.
http://download.microsoft.com/download/a/b/e/abefdf1c-96bd-40d6- a138-e320b6b25bd3 /understandingantimalwaretechnologies.pdf.
9.
http://www.security.iitk.ac.in/contents/events/workshops/iitkhack09/ papers/vinod.pdf.
10.
http://www.fortiguard.com/sites/default/files/DetectingMalware Threats.pdf.
Authors: R. HariKumar, T.Vijayakumar, M.G.Sreejith
Comprehensive Analysis of Hierarchical Aggregation Functions Decision Trees and Minimum
Paper Title:
Relative Entropy as Post Classifiers in the Classification of Fuzzy Based Epilepsy Risk Levels
Abstract: The objective of this paper is to compare the performance of Hierarchical Soft (max-min) decision trees and Minimum Relative Entropy (MRE) in optimization of fuzzy outputs in the classification of epilepsy risk levels from EEG (Electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Hierarchical Soft decision tree and Minimum Relative Entropy (post classifiers with max-min criteria) four types are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient’s risk level. The efficacy of the above methods is compared based on the bench mark parameters such as Performance Index (PI), and Quality Value (QV).
Keywords: EEG Signals, Epilepsy Risk Levels, Fuzzy Logic, Hierarchical Decision Trees, Minimum Relative
Entropy.
References:
1. Joel.J etal, 2004 “Detection of seizure precursors from depth EEG using a sign periodogram transform”, IEEE Transactions on Bio Medical
Engineering, vol, 51 no,4, pp-449-458.
2.
D.Zumsteg and H.G.Wieser, 2000 “Presurgical evaluation: current role of invasive EEG,”Epilepsia,vol.41, No. suppl 3,pp, S55-60.
3.
K P Adlassnig, 1986 “Fuzzy Set Theory in Medical diagnosis”, IEEE Transactions on Systems Man Cybernetics, 16 pp 260-265.
4.
Alison A Dingle et al, “A Multistage system to detect epileptic form activity in the EEG”, IEEE Transactions on Biomedical Engineering,
40(12) pp 1260-1268, December 1993.
5. R.Harikumar and B.Sabarish Narayanan, 2003 “Fuzzy Techniques for Classification of Epilepsy risk level from EEG Signals”, Proceedings of IEEE Tencon – 2003, Bangalore, India, pp 209-213, 14-17.
6.
Haoqu and Jean Gotman, 1997 “A patient specific algorithm for detection onset in long-term EEG monitoring-possible use as warning device”, IEEE Transactions on Biomedical Engineering, 44(2) pp 115-122.
7. R.Harikumar, Dr.(Mrs). R.Sukanesh, P.A. Bharathi, 2005 “Genetic Algorithm Optimization of Fuzzy outputs for Classification of Epilepsy
Risk Levels from EEG signals,” I.E. India Journal of Interdisciplinary panels,vol.86, no.1.
8. Ronald.R.Yager, 2000 “Hierarchical Aggregation Functions Generated From Belief structures,” I.E.EE Transaction of Fuzzy Systems,vol.8, no.5, pp 481-490.
9.
Cezary.Z Janikow, 1998 “Fuzzy Decision Tree: Issues and Methods,” I.E.E.E Trans SMC, vol.28, no.1, pp 1-14.
10. S.Rasoul Safavian, David Landgrebe, 1991 “A Survey of Decision Tree Classifier Methodology,” IEEE Trans SMC,vol.21, no.3, pp-660-
674.
11.
W. De Clereq etal., 2003. “Characterization of interictal and ictal scalp EEG signals with the Hilbert Transform”, Proc of the 25th Annual
International conference of the IEEE EMBS Cancun, Mexico, September 17-21, pp-2459-2462.
12. Jianwei Wang,Eliza Yingzi Du and Chein-I Chang, 2002 “Relative Entropy –Based methods for Image Thresholding”, Proceedings of
IEEE International Conference on Image processing, pp. II 265-II 268.
13. C B Gupta and Vijay Gupta, 2001. “An Introduction to Statistical Methods”, 22nd Ed., Vikas Publishing House Lt.,
14. Aexei.A, 2004 “Algorithms for estimating information distance with application to Bioinformatics and Linguistics,” Proceedings of IEEE,
CCECE.
15.
Gleb Beliakov and Jim Warren, 2001 “Appropriate choice of Aggregation Operators in Fuzzy Decision Systems,” IEEE Transactions on
Fuzzy Systems, vol 9, no 6, pp 773-784.
16. Ronald R.Yager, 1998 “On Ordered Weighted Averaging Aggregation Operators in Multi Criteria Decision Making”, IEEE Trans on
SMC,vol.18,no.1,pp 183-190.
17.
P.Filev and R.R.Yager, 2003 “Context Dependent Information Aggregation”, Proceedings of IEEE International Conference on Fuzzy
Systems, pp 672-677.
18. R.R.Yager, “Including Importance’s in OWA Aggregations Using Fuzzy Systems Modeling”, IEEE Trans. on Fuzzy Systems, vol.6, no2,pp
286-294,1996.
19.
Gleb Beliakov, 2000 “Definition of General Aggregation Operators Through Similarity Relations”, Fuzzy sets and Systems, vol 114, pp437-453.
20. R.R.Yager, 2004 “On role of Anxiety in Decisions Under Possibilistic Uncertainty,” IEEE Trans on SMC, vol 34, no 2, pp1224-1234.
21. Tomasa Calvo, Radko Mesiar, and R.R.Yager, 2004 “Quantitative Weights and Aggregation,” IEEE Transactions on Fuzzy Systems, vol
12, no 1, pp 62-69.
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31.
Authors: Nur Izzati Jamahir, Hairudin Abd Majid, Azurah A.Samah
Prediction of Warranty Cost and Warranty Period using Neuro-Fuzzy Approach: Case Study of
Paper Title:
Automobile Warranty Data
Abstract: Nowadays, warranty has its own priority in business strategy for a manufacturer to protect their benefit as well as the intense competition between the manufacturers. In fact, warranty is a contract between manufacturer and buyer in which the manufacturer gives the buyer certain assurances as the condition of the property being sold. In industry, an accurate prediction of optimal warranty period and warranty costs is often counted by the manufacturer.
A warranty period may be unprofitable for the manufacturers if the choice of duration given is either too short or too long. Same thing goes to warranty cost which is an underestimation or overestimation of the warranty cost may have a high influence to the manufacturers. This paper presents a methodology to adapt historical maintenance warranty data with neuro-fuzzy approach. The main motivations for conducting this paper are simplicity and less computational mass of the neuro-fuzzy based on previous studies on this method compared to single method such neural network and fuzzy logic.
Keywords: neuro-fuzzy, two-dimensional warranty, warranty cost, warranty period.
References:
1. Ang J.C..Application of Artificial Neural Network in Two- Dimensional Warranty Modelling,Universiti Teknologi Malaysia, Skudai:
Projek Sarjana.2011.
2. Boyacioglu M.A., Avci D. (2010). An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return:
The case of the instanbul stock exchange. Expert Systems with Application.7908-7912.
3. K.Rai B. and Singh N., Reliability Analysis and Prediction with Warranty Data, U.S:CRC . 2009.
4. Kim, H. G. and Rao, B. M. (2000). Expected Warranty Cost of Two-attribute Free-replacement Warranties Based on a Bivariate
Exponential Distribution. Computers & Industrial Engineering.
5. Lee S.H., Lee J.H., Park S.B., Lee M.T., Lee S.J., Kim B.K.(2008), A Fuzzy Reasoning Model of Two-dimensional Warranty System.
International Conference on Advanced Language Processing and Web Information Technology. 287-292.
6. Majid H.A., Kasim N.H., Jamahir N.I., Samah A.A. (2012). Soft Computing Methods in Warranty Problems: Review and Recent
Applications (2003-2012). International Journal of Computer Science Issue.190-196.
7. Murthy D. N. P. (2006). Product Warranty and Reliability. 133-146.
8. Murthy D.N.P and Djamaludin I. (2002). New product warranty: A literaturwe review. 231-260.
9. Yadav O. P., Singh N., Chinnam R. B., and Goel P. S. (2003). A fuzzy logic based approach to reliability improvement estimation during product development. Reliability engineering & system safety. 63-74.
10. Yang G. and Zaghati Z. (2002). Two-Dimensional Reliability Modeling From Warranty Data.272-278.
155-158
Authors: Purnima Pandit
Paper Title: Fully Fuzzy System of Linear Equations
Abstract: In real life applications which are represented by system of linear equations, that can arise in various fields of Engineering and Sciences like Electrical, Civil, Economics, Dietary etc., there may be situation when the values of the parameters are not known or they cannot be stated precisely only their estimation due to experts knowledge is available. In such situation it may be convenient to represent such parameters by fuzzy numbers, as in
[5]. Klir in [4] gave the results for the existence of solution of linear algebraic equation involving fuzzy numbers. On the similar lines we give the results for system of linear equations with fuzzy parameters. The α-cut technique is well known in obtaining weak solutions, as in [1] for fully fuzzy systems of linear equations (FFSL). However, our results state and prove the conditions for the existence and uniqueness of the fuzzy solution.
Keywords: α-cut, Fully Fuzzy linear equations, Fuzzy numbers, level cut, weak solutions.
References:
1. T. Allahviranloo ,M. Ghanbari, A. A. Hosseinzadeh, E. Haghi, R. Nuraei, A note on Fuzzy linear systems, Fuzzy Sets and Systems vol.
177 (1), 2011,pp. 87-92.
2. D. Dubois, H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press,New York, 1980.
3. M. Friedman, M. Ming, A. Kandel, Fuzzy linear systems, Fuzzy Sets and Systems,vol. 96, 1998, pp. 201-209.
4. G. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic Theory and Applications, Prentice Hall, 1997,ch. 4 .
5. L.A. Zadeh, Fuzzy sets, Information and Control, vol. 8 ,1965, pp. 338-353.
Authors: Nur Hayati Kasim, Hairudin Abdul Majid, Azurah A. Samah
159-162
32.
Paper Title: Parameter Estimation of Warranty Cost Model Using Genetic Algorithm
Abstract: of some unknown parameters in which the objective function is minimized or maximized. Several methods can be used in estimating parameter including Genetic Algorithm (GA). GA has been widely used in many applications specifically in optimization problem. This paper propose GA as a method to estimate the parameter of warranty cost model with warranty claim data collected from Malaysian automotive industry. Various combinations of GA operators are carried out. Maximum Likelihood Estimation (MLE) method is employed in order to have a comparable solution for the model parameters of warranty cost. The performance of GA in warranty cost model is measured based on Residual Sum of Squares (RSS) and Mean Squared Error (MSE).
Keywords: Genetic Algorithm, parameter estimation, likelihood function, warranty.
References:
1.
A. S. Abutaleb, “A genetic algorithm for maximum likelihood estimation of the parameter of sinusoids in a noisy environment,” Circuits
Systems Signal Processing, Vol. 16, No. 1, 1997, pp. 69-81.
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B. Wu and C. Chang, “Using genetic algorithm to parameters (d, r) estimation for threshold autoregressive models,” Computational
Statistics & Data Analysis, Vol. 38, 2002, pp.315-330.
163-166

33.
3. B. S. Chen, B. K. Lee and S. C. Peng, “Maximum likelihood parameter estimation of F-ARIMA processes using genetic algorithm in the frequency domain,” IEEE Transactions on Signal Processing, Vol. 50, No. 9, Sept. 2002, pp. 2208-2220.
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H. Ozturkler and S. Altan, “A genetic algorithm approach to parameter estimation in nonlinear econometric models,” 2008, pp. 67-76.
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J. G. Restrepo and C. M. V. Sanchez, “Parameter estimation of a predator-prey model using a genetic algorithm,” unpublished.
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K. Sersie and I. Urbiha, “Parameter identification problem solving using genetic algorithm,” Proceedings of the 1. Conference on Applied
Mathematics and Computation Dubrovnik, Croatia, Sept. 1999, pp. 253-261.
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8.
P. A. Scarf and H. A. Majid, “Modelling warranty extensions: a case study in the automotive industry,” Proceedings of the Institution of
Mechanical Engineers, Part O: Journal of Risk and Reliability, 2011, pp. 225- 251.
9. R. Rashid, H. Jamaluddin and N. A. Saidina Amin, “Parameter estimation of tapioca starch hydrolysis process: application of least squares and genetic algorithm,” Journal - The Institution of Engineers, Malaysia, Vol. 66, No. 4, Dec. 2005, pp. 51-60.
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S. Chen and Y. Wu, “Genetic algorithm optimisation for maximum likelihood joint channel and data estimation,” IEEE, 1998, pp. 1157-
1160.
11. S. Halim, I. N. Bisono, D. Sunyoto and I. Gendo, “Parameter estimation of space-time model using genetic algorithm,” IEEE IEEM, 2009, pp. 1371-1375.
12. Y. H. Lee, S. K. Park, and D. E. Chang, “Parameter estimation using the genetic algorithm and its impact on qualitative precipitation forecast,” Ann. Geophys., Vol. 24, 2006, pp. 3185–3189.
Authors: Deman Kosale, Anita Khanna
Modified Variable Step Size Normalized Least Means Square Algorithm In The Context Of Acoustic
Paper Title:
Echo Cancellation
Abstract: In this paper, we present a new approach to cancelling echoes, mostly occurrence in today’s telecommunication system due to acoustic coupling between loudspeaker and a microphone. The proposed algorithm is a Modified VSS-NLMS-UM algorithm the popularity of this algorithm due to the solve a conflicting requirement of fast convergence and low misadjustment. Most of the algorithm was design under-modeling scenario the proposed algorithm doesn’t require any prior information about acoustic environment. Due to the specific characteristic of this algorithm are equipped with good robustness feature against the near end signal variation and has a low computational complexity and low level data storage. So it’s a reliable candidate for real world application.
Keywords: Acoustic Echo Cancellation, Adaptive Filtering Algorithm (AFA), UMSI (Undermodeling System
Identification).
References:
1. S. Hakin, “Adaptive filter theory”, 4th edition, Englewood Cliffs, NJ: Prentice Hall, 2002.
2. B.Widro and S.D. Stearns, Adaptive Signal Processing, Englewood Cliffs, NJ: Prentice-Hall, 2002.
3.
B.Widrow, Mac.Cool, and M.ball, “The Complex LMS Algorithm”, proc.IEEE, vol.63, pp. 719-720, 1975.
4. Alecander D. Poularikas and Zayed M.Ramadan, “Adaptive Filtering Primer with MATLAB”, CRC Press, 2006.
5. A. I. Sulyman and A. Zerguine, “ Convergence and Steady-State Analysis of a Variable Step-Size Normalized LMS Algorithm,” Seventh
International Symposium on Signal Processing and Its Applications, vol. 2, .pp.591-594, 2003.
6.
M. I. Troparevsky and C. E. D'Attellis,”On the convergence of the LMS algorithm in adaptive filtering,” Signal processing, vol.84,
Issue.10, pp.1985-1988, 2004.
7. On a Class of Computationally Efficient, Rapidly Converging, Generalized NLMS Algorithms,” IEEE Signal Processing Letter, vol. 3, issue. 8, pp.245-246, 1996.
8. J. Benesty, H. Rey, R.L.Vega and S. Tressens, “A VSS NLMS Algorithm”, IEEE, Signal Processing Letters, vol. 13, issue.10, pp. 581-584.
9. B.Evans, P.Xue, and B.Liu,”Analysis and implementation variable step size adaptive algorithm”, IEEE Trans. Signal Processing, vol. 41, pp.2517-2535, Aug.1993.
10.
C.Paleologu, J. Benesty, S. Ciochina and C.Vladeanu,”Practical variable step-size adaptive Algorithms for echo cancellation,” 5 th conference on Speech Technology and Human- Computer Dialogue, SpeD-2009, PP. 1-6.
11. Leonardo Rey Vega, Hernán Rey, Jacob Benesty, Senior Member, IEEE, and Sara Tressens A New Robust Variable Step-Size NLMS
Algorithm, IEEE transactions on signal processing, vol. 56, no. 5, may 2008.
12.
L.Ning, Z. Yonggang, H.Yanling and C.A.Jonathon,” A new variable step-size LMS algorithms, designed for applications with exponential decay impulse responses,” Signal processing,pp.4 2008.
13. D. I. Pazaitis and A. G. Constantinides, “A novel kurtosis driven variable step-size Adaptive Algorithm,” IEEE, Trans. Signal Processing, vol. 47,pp. 864–872, Mar. 1999.
14.
Asif Iqbal, M.: Grant and S.L.; “Novel variable step size nlms algorithms for echo cancellation,” IEEE, Acoustics, Speech and Signal
Processing, pp. 241-24,2008.
15. Udrea, R.M.; Paleologu, C.; Benesty, J.; Ciochina, S.”Estimation of the noise power In NPVSS-NLMS Algorithm,” International
Symposium on Electronics and Telecommunication (SETC -2010), PP.385-388.
16. L.C.Jian,Y.Xia and L.Hong-ru,” A nonparametric variable step-size NLMS algorithm for transversal filters,” Applied Mathematics and computation, Vol. 217, Issue.17, pp.7365-7371, 2011.
17. L.C.Jian, Y.Xia and L.Hong-ru,” A nonparametric variable step-size NLMS algorithm for transversal filters,” Applied Mathematics and computation, Vol. 217, Issue.17, pp.7365-7371, 2011.
18. C.Paleologu, S. Ciochina, J.Benesty,“ Variable Step-Size NLMS Algorithm for Under-Modeling Acoustic Echo Cancellation” IEEE,
Signal Processing Letter. 2008, vol.15, PP. 5-8.
19.
C Paleologu, S.Ciochina and J. Benesty “Double-talk robust VSS-NLMS algorithm for under- modeling acoustic echo,” IEEE, international conference Acoustics, Speech and Signal Processing, ICASSP- 2008, pp. 245-248.
20. B. Y.Long, L.F.Chang and Y.Y.Zheng,” Double-talk robust VSS-NLMS algorithm for under-modeling acoustic echo cancellation, IEEE,
Youth Conference on Information, Computing and Telecommunication (YC-ICT - 2009), PP.283-286.
21. P. Ahgren,”Acoustic echo cancellation and double talk detection using a loudspeaker Using loudspeaker impulse responses,” IEEE,
Transaction on Speech and Audio Processing, vol. 13, issue. 6, pp. 1231- 1237, 2005.
22. H.C. Chin, A.H. Sayed, and W.J. Song, “Variable step size nlms and affine projection Algorithm,” IEEE Signal Processing Letter, vol. 11, pp. 132–135, Feb 2004.
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Cancelation”, IEEE, Digital signal processing workshop Proceeding, pp. 354-357, 1997.
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O. Hoshuyama, R.A.Goubran and A. Sugiyama,”A generalized proportionate variable step-size algorithm for fast changing acoustic environment,” IEEE, International conference on Acoustic, Speech, and Signal Processing, Proceedings.(ICASSP - 2004) vol. 4, pp. 161-
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25. S. Weinstein, “Echo cancellation in the telephone network,” IEEE, Communications Society Magazine, vol. 15, issue. 1, pp. 8-15, 1977.
26. S. J. Ben and H. Basbes,” Variable step size filtered sign algorithm for acoustic echo cancellation”, Electronics Letters, vol. 39, issue. 12, pp. 936-938, 2003.
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Y. Zhou and X. Li,” A Variable step-size for frequency- domain acoustic echo cancellation ” IEEE, Workshop on application of signal
167-173

34. processing to audio and acoustics, 2007.
28. R.M. Alaharif, K. Shaoli and C. Rui,”Echo and background music canceller by smart Acoustic room system for car hand- free telephone”,
1st Internation Conference on Information Science and Engineering (ICISE- 2009), pp. 546-549.
29.
M.A. IQBAL AND S.L.GRANT,” A NOVEL NORMALIZE CROSS-CORRELATION BASED ECHO- PATH CHANGE DETECTOR,”
IEEE, REGION 5 TECHNICAL CONFERENCE, PP.249-251, 2007.
30. HAIJUN HU; MANGMANG LIN; ZHIJUN ZHANG; HUI LI; PENG YI,”A LOW COMPLEXITY ACOUSTIC ECHO
CANCELLATION,” INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT- 2006) PP. 1-5.
31. S.Christian, L. Fredric, L. Haibo and C. Ingvar,”Adaptive filter length selection for acoustic echo cancellation”, Journal Signal processing, vol.89, issue.6, pp. 1185-1194, 2009.
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Descent and random Search”, IEEE Transaction on antennas and propagation, vol.AP-24, No.5, Sept 1976.
33. Rainer and Gold, “Theory and Applications of Digital Signal Processing”, Prentic Hall, Inc., 1975.
Authors: Deman Kosale, Anita Khanna
Paper Title: Performance, Monitoring and Analysis of OWC Link at Different Atmospheric Conditions
Abstract: In this paper we present a new and gifted technology Optical wireless communication (OWC) systems.
It undergoes from average optical signal power loss and random power fading due to the different atmospheric condition. For focused laser beams along direct line of sight propagation, the averages received optical signal power and Bit Error Rate (BER) as functions of the high data rates and are studied via large scale numerical simulations. In that case we evaluate the performance of OWC we consider a link of distance 500 meter, optical window 1550nm and three different atmospheric condition 10dB/Km, 40dB/Km and 60dB/Km. The VCSEL laser is used for direct line of sight communication and at the receiver side the APD is used for power reception. The NRZ – OOK modulation format is used for laser beam modulation.
Keywords: Optical Communication Link (OWC), VCSEL, APD, Bit Error Rates, Optical Received Power.
References:
1.
J. M. Kahn, J.R. Barry “Wireless Infrared Communications” IEEE, February 1997.
2.
J.B. Carruthers,“Wireless Infrared Communications”wiley encyclopedia of telecommunications, 2002 (preprint).
3.
M.S. Khan, M.S. Awan, S.S Muhammad, M..Faisal, Marzuki, F. Nadeem, E. Leitgeb, “Probabilistic Model for Free-Space Optical Links under Continental Fog Conditions Radioengineering, Vol. 19, No. 3, September 2010.
4.
D. J. Heatley, D. R. Wisely, I. Neild, and P. Cochrane, “Optical wireless:The story so far,” IEEE Commun. Mag., vol. 36, no. 12, pp. 72–82,
Dec. 1998.
5.
A. A. Farid, S.Hranilovic, “Outage Capacity Optimization for Free-Space Optical Links With Pointing Errors” journal of lightwave technology, vol. 25, no. 7, july 2007.
6.
S. Changyu, F. Huajun, Xu Zhihai, J. Shangzhongt, “High Speed Free Space Optical Communication System for l km communication”
IEEE, 2007.
7.
H. Henniger, O. Wilfert “An Introduction to Free space Optical Communications,” Radio Engineering, Vol. 19, No. 2, June 2010.
8.
G. Hansel, E. Kube, J. Becker, J. Haase, P. Schwarz, “Simulation in the design process of free space optical transmission systems” Proc. 6th
Workshop, Optics in Computing Technology, 2001.
Authors: K. Umapathy, D. Rajaveerappa
174-176
35.
Paper Title: Low Power 128-Point Pipeline FFT Processor using Mixed Radix 4/2 for MIMO OFDM Systems
Abstract: In this paper, an area and power efficient 128- point pipeline FFT processor is proposed for MIMO -
OFDM systems based on mixed-radix 4/2 multipath delay commutator architecture (R2MDC) in terms of lower complexity and higher memory utilization. A conventional mixed radix 4/2 multipath delay commutator FFT processor will increase the hardware capacity and can be used to change the order of the input sequences. The processor is characterized with capable power-consumption for different FFT/IFFT sizes. Unlike the general mixed radix-based architectures which use a larger internal word length to achieve a high signal to noise ratio (SNR), our processor keeps the internal word length the same as the word length of the input data while adopting the blockfloating point (BFP) approach to maintain the SNR. The proposed FFT processor uses different commutators which can be used to decrease the delay elements and integrate with other MIMO-OFDM processing blocks. The designed
128-point FFT processor provides 49% reduction in count of logic gates and 67% in power dissipation on 90-nm
CMOS technology.
Keywords: FFT, MIMO, OFDM, MDCA.
References:
1.
Bruno Fernandes, Helena Sarmento “Implementation of a 128 FFT for a MB-OFDM Receiver” 978-972-789- 304-1 REC'2010 page no 45-
48.
2.
Tai-cheng Lee, Yen-chuan Huang” The design and analysis of a Miller-divider-based clock generator for MBOA-UWB application” solid state circuit, .IEEE journal v41,Isu 6 page no.1253-1261.
3.
“A High-Speed Low-Complexity Modified FFT Processor for High Rate WPAN Applications “Very Large Scale Integration (VLSI)
Systems, IEEE Transactions on volu :pp,Issue :99 page no 1-5
4.
K.J. Cho, K.C. Lee, J.G. Chung, and K.K. Parhi, “Design of low-error fixed-width modified booth multiplier,” IEEE Trans. Very Large
Scale Integr. (VLSI) Syst., vol.12, no.5, pp.522–531, 2004.
5.
A. M. Despain, “Very fast Fourier transform algorithms hardware for implementation”, IEEE Trans. Comput., vol. C-28, no. 5, pp. 333 –
341, May 1979.
6.
K. Maharatna, E. Grass and U. Jagdhold, “A 64-point Fourier transform chip for high-speed Wireless LAN application using OFDM”, IEEE
J. Solid-State Circuits, vol. 39, no. 3, pp. 484 – 493, March 2004
7.
G. Zhang F. Chen, “Parallel FFT with cordic for ultra wide band”, 15 th IEEE International Symposium on Personal, Indoor and Mobile
Radio Communications, PIMRC, 2004.
8.
Chu Yu, Member, IEEE, Mao-Hsu Yen, Pao-Ann Hsiung, Senior Member, IEEE, and Sao-Jie Chen, Senior Member, IEEE, “A Low-Power
64-point Pipeline FFT/IFFT Processor for OFDM Applications” in 2011 IEEE .
9.
Guihua Liu1, Quanyuan Feng, Institute of Microelectronics, Southwest Jiaotong University, Southwest University of Science and
Technology, “ASIC Design of Low-power Reconfigurable FFT Processor” in 2007 IEEE pp 44-46.
10.
M. K. A. Aziz, P. N. Fletcher, and A. R. Nix, ―Performance analysis of IEEE 802.11n solutions combining MIMO architectures with
177-179

36. iterative decoding and sub-optimal ML detection via MMSE and zero forcing GIS solutions, ‖ IEEE Wireless Communications and
Networking Conference,2004, vol. 3, pp. 1451–1456, Mar. 2004.
11.
A. Ghosh and D. R. Wolter, ―Broadband wireless access with WiMax/802.16: current performance benchmarks and future potential, ‖ IEEE
Communications Magazine, vol. 43, no. 2, pp. 129–136, Feb. 2005.
12.
Sangmin Lee, Yunho Jung, Jaeseok Kim, “Low Complexity Pipeline FFT Processor for MIMO-OFDM Systems”, IEICE Electronics
Express, Vol.4, No.23, Pages 750-754, December 2007.
13.
Yoshizawa, S “An Area & Power Efficient Pipeline FFT Processor for 8x8 MIMO OFDM Systems”, IEEE International Symposium on
Circuits & Systems, Pages 2705-2708, May 2011.
14.
J. A. Hidalgo, J. Lopez, F. Arguello, and E. L.Zapata,―Area-efficient architecture for fast Fourier transform, ‖ IEEE Trans. Circuits Syst. II,
Analog Digit. Signal Process. vol. 46, no. 2, pp. 187–193, Feb. 1999.
15.
Chitra MP, Srivatsa SK, “Design of Low Power Mixed Radix FFT Processor for MIMO-OFDM Systems “ACT 2009 Proceedings of
International Conference on Advances in Computing, Control & Telecommunication Techniques, Pages 591-595, 2009.
Authors: Nitisha Payal, Nidhi Chaudhary, Parma Nand Astya
JigCAPTCHA: An Advanced Image-Based CAPTCHA Integrated with Jigsaw Piece Puzzle using
Paper Title:
AJAX
Abstract: With an increasing number of automated software bots and automated programs that abuse and corrupt public web services, the user is primarily required to go through and solve a Turing test problem, before they are allowed to use web applications and web services. This Turing test is termed as CAPTCHA. In this paper, JIGSAW puzzle based CAPTCHA (‘JigCaptcha’) is introduced. The paper introduces a drag and drop image based CAPTCHA by integrating image-based CAPTCHA with AJAX and JIGSAW puzzle for the easy access of web services in lesser time.
Keywords: CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), Turing
Test AJAX(Asynchronous JavaScript and XML) , IMAGINATION (Image Generation for Internet Authentication),
OCR( Optical Character Recognition), XML(Extended Markup Language).
References:
1.
Simard, P., Chellapilla, K.: Using machine learning to break visual human interaction proofs(hips). In: Advances in Neural Information
Processing Systems (2004)
2.
Chellapilla, K., Larson, K., Simard, P., Czerwinski, M.: Designing human friendly human interaction proofs (hips). In: Proc. Of SIGCHI, pp.
711–720 (2005)
3.
Huang, S., Lee, Y., Bell, G., Ou, Z.: A projection-based segmentation algorithm for breaking msn and yahoo captchas. In: Proc. of the international Conference of Signal and Image Engineering (2008)
4.
Elson, J., Douceur, J.R., Howell, J., Saul, J.: Asirra: a captcha that exploits interest-aligned manual image categorization. In: Proc. of the
14th ACM Computer and Communications Security, pp. 366–374 (2007)
5.
http://en.wikipedia.org/wiki/CAPTCHA
6.
Graig Sauer , Jonathan Holman , Jonathan Lazar ,Harry Hochheiser, Jinjuan Feng:Accessible privacy and security: a universally usable human-interaction proof tool , Published online: 25 March 2010_ Springer-Verlag 2010
7.
www.captcha.net/captchas/bongo
8.
www.captcha.net/captchas/pix
9.
http://en.wikipedia.org/wiki/Jigsaw_puzzle
10.
www.adaptivepath.com/publications/essays/archives/000385.php
11.
en.wikipedia.org/wiki/CAPTCHA
12.
www.jigsaw.org/
180-185
Authors: Jaspreet Singh, Er. Sandeep Singh Kang
37.
Paper Title: Security Enhancement in WEP by Implementing Elliptic Curve Cryptography Technique
Abstract: Wireless Network has been gaining rapid popularity over the years because they provide a significant advantage over the traditional Wired Network. Many wireless networks are based on security scheme Wired
Equivalent Privacy (WEP). Despite employing the well-known and believed-secure RC4 cipher, WEP falls short in accomplishing its security goals. In this paper weaknesses and possible attacks on the RC4 stream cipher in WEP have analyzed and we proposes more secure WEP Protocol that offers secure encrypted communication by using
Elliptic Curve Cryptography (ECC) Technique. Point Multiplication is the core operation performed in ECC. NAF
(Non – Adjacent Form) is the efficient method used for Point Multiplication. We implemented both Standard and
Block method for computing NAF of ECC and done the comparative study of these methods by taking several parameters in WEP. The proposed ECC Technique will ensure secure encryption in WEP and will enhance its security.
Keywords: Elliptic Curve Cryptography, ECC in WEP, Security of WEP, Standard NAF in ECC, Block method in
ECC.
References:
1.
ARASH HABIBI LASHKARI, MIR MOHAMMAD SEYED DANESH, “A Survey on Wireless Security protocols (WEP,
WPAWPA2/802.11i)”, IEEE 2009.
2. Rajni Pamnani, Pramila Chawan , “Building a secured wireless LAN”, International Journal of Recent Trends in Engineering, Vol 2, No. 4,
November 2009.
3. Peisong Ye and Guangxue Yue, “Security Research on WEP of WLAN”, Proceedings of the Second International Symposium on
Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010.
4. Emilio J.M. Arruda Filho, Paulo N. L. Fonseca Jr Mairio J. S. Leitdo and Paulo S. F. de Barros , “Security versus Bandwidth: The Support of Mechanisms WEP e WPA in 802.11g Network” , IEEE 2007.
5. Vincent Guyot ,”Using WEP in Ad-Hoc Networks”, IEEE 2007.
6. Shadi R. Masadeh and Nidal Turab, “A Formal Evaluation of the Security Schemes for Wireless Networks”, Research Journal of Applied
Sciences, engineering and Technology 3(9): 910-913, September 2011.
7.
C. SAJEEV and G. JAI ARUL JOSE , “Elliptic Curve Cryptography Enabled Security for Wireless Communication , International Journal on Computer Science and Engineering Vol. 02, No. 06, 2010.
8. Md. Rafiqul Islam, Md. Sajjadul Hasan, Ikhtear Sharif and Muhammad Asaduzzaman, “A New Point Multiplication Method for Elliptic
186-190

38.
Curve Cryptography Using Modified Base Representation” International Journal of The Computer, the Internet and Management Vol.16.
N.o.2 (May-August, 2008).
9.
Olufade, F. W. Onifade, Adenike, O. Osofisan and Chukwuzitere, U. OBODO, “A Dual Layered Encryption Algorithm For WIreless
Equivalent Privacy (WEP) Algorithm”, IJCSNS, VOL.8 No.5, May 2008.
10.
Allam Mousa and Ahmad Hamad ,”Evaluation of the RC4 Algorithm for Data Encryption”, International journal of computer and
Application,Vol 3, No 2, June 2006.
11. Lazar Stosic, Milena Bogdanovic,” RC4 stream cipher and possible attacks on WEP, (IJACSA) International Journal of Advanced
Computer Science and Applications, Vol. 3, No. 3, 2012.
12.
Vinay Bhatia, Dushyant Gupta AND SINHA H.P, “Analysis OF Dictionary Attack On Wireless LAN FOR Different Nodes” , Journal of
Information Systems and Communication ,ISSN: 0976-8742 & E-ISSN: 0976-8750, Volume 3, Issue 1, 2012.
13. Vinay Bhatia, Dushyant Gupta AND SINHA H.P. “Throughput and Vulnerability Analysis of an IEEE 802.11b Wireless LAN”, IJCA
(0975 – 8887) Volume 52– No.3, August 2012.
14.
Marisa W. Paryasto, Kuspriyanto, Sarwono Sutikno and Arif Sasongko , “Issues in Elliptic Curve Cryptography Implementation”,
Internetworking Indonesia Journal , Vol 1 / No 1 2009.
15. Elliptic Curve Cryptography – An Implementation Tutorial by Anoop MS.
Authors: Kandla Arora
Paper Title: Real Time Application of Face Recognition Concept
Abstract: Face Recognition concept is one of the successful and important applications of image analysis. It’s a holistic approach towards the technology and have potential applications in various areas such as Biometrics,
Information society, Law enforcement and Surveillance, Smart cards, Access control etc. This paper provides an overview of real time application of Face Recognition concept by generating a matlab code using image acquisition tool box. The basic approach used is Principal Component Analysis using Eigen faces, popularized by the seminal work of Turk and Pentland.
Keywords: Eigen faces, Eigenvectors, Face recognition, Principal component analysis (PCA).
References:
1.
M.A. Turk and A.P. Pentland. “Face recognition using Eigenfaces”. In Proc. of Computer Vision and Pattern Recognition, pages 586-591.
IEEE, June 1991b.
2.
M.Turk and A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive Neuroscience, March 1991.
3.
L.I. Smith. “A tutorial on principal components analysis”
4.
Delac K., Grgic M., Grgic S., “Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set”, International Journal of
Imaging Systems and Technology, Vol. 15, Issue 5, 2006, pp. 252-260.
5.
H. Moon, P.J. Phillips, “Computational and Performance aspects of PCA-based Face Recognition Algorithms”, Perception, Vol. 30, 2001, pp. 303-321.
6.
Matlab tutorials and learning resources
7.
Matlab image acquisition tool box. http://www.mathworks.in/products/imaq/
Authors: B.Srinivas, P.Satheesh, G.Gowthami
191-196
39.
Paper Title: A Novel Approach for Secure Keying Protocol for Sensor Networks
Abstract: A sensor network is composed of large number of sensor nodes. Each sensor needs to securely communicate with the adjacent sensors in the network. To provide security between every pair of adjacent sensor each sensor in a network needs to store ‘n -1’symmetric keys But The storage requirement of this keying protocol is very complex. In this paper, we propose a new keying protocol which reduces the number of keys to be stored in each sensor. we present a fully secure keying protocol here each sensor needs to store (n+1)/2 keys, which is less than the n-1 keys and symmetric keys are used to encrypt and decrypt the exchanged data for secure communication.
At least (n-1)/2 keys are required to store in each sensor to secure an entire keying protocol. This new secure keying protocol is very secure against impersonation, eavesdropping and collision attacks.
Keywords: keying protocol, secure communication, sensor network, symmetric keys
References:
1.
Dorothy Denning. Cryptography and Data Security. Addison-Wesley, 1982
2.
G. Gaubatz, J.-P. Kaps, and B. Sunar. Public key cryptography in sensor networks - revisited. In ESAS, pages 2{18, 2004.
3.
A. Howard, M. J. Mataric, and G. S. Sukhatme, “Mobilesensor network deployment using potential fields: Adistributed, scalable solution to the area coverage problem,”in Proceedings of the International Symposium onDistributed Autonomous Robotic Systems (DARS), 2002, pp.
299–308.
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S. Sana and M. Matsumoto, “Proceedings of a wirelesssensor network protocol for disaster management,” inInformation, Decision and
Control (IDC), 2007, pp. 209–213.
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S. Hynes and N. C. Rowe, “A multi-agent simulationfor assessing massive sensor deployment,” Journal ofBattlefield Technology, vol. 7, pp.
23–36, 2004.
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B. Dahill et al., “A Secure Protocol for Ad Hoc Networks,“ IEEE ICNP, 2002X.
7.
Du, H. Chen, "Security in Wireless Sensor Networks", IEEE Wireless Communications, 2008.
8.
L. Eschenauer and V. D. Gligor, “A key-managementscheme for distributed sensor networks,” in Proceedingsof the 9th ACM Conference on
Computer and communicationssecurity (CCS), 2002, pp. 41–47.
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L. Gong and D. J. Wheeler, “A matrix key-distributionscheme,” Journal of Cryptology, vol. 2, pp. 51–59,January 1990.
10.
S. S. Kulkarni, M. G. Gouda, and A. Arora, “Secret instantiationin ad-hoc networks,” Special Issue of ElsevierJournal of Computer
Communications on DependableWireless Sensor Networks, vol. 29, pp. 200–215, 2005.
11.
A. Aiyer, L. Alvisi, and M. Gouda, “Key grids: Aprotocol family for assigning symmetric keys,” in Proceedingsof IEEE International
Conference on NetworkProtocols (ICNP), 2006, pp. 178–186.
12.
E. S. Elmallah, M. G. Gouda, and S. S. Kulkarni,“Logarithmic keying,” ACM Transactions on AutonomicSystems, vol. 3, pp. 18:1–18:18,
December 2008.
13.
J. Granjal, R. Silva, J. Silva, "Security in Wireless Sensor Networks", CISUC UC, 2008
14.
A. Perrig, R. Szewczyk, V.Wen, D. Culler, and J. Tygar, "SPINS: Security protocols for sensor networks," in Proceedings of
MobileNetworking and Computing 2001, 2001.
197-200
40. Authors:
Herve Kabamba Mbikayi

41.
An Evolution Strategy Approach toward Rule-Set Generation for Network Intrusion Detection
Paper Title:
Systems (IDS)
Abstract: With the increasing number of intrusions in systems’ and networks’ infrastructures, Intrusion Detection
Systems (IDS) have become an active area of research to develop reliable and effective solutions to detect and counter them. The use of Evolutionary Algorithms in IDS has proved its maturity over the times. Although most of the research works have been based on the use of genetic algorithms in IDS, this paper presents an approach toward the generation of rules for the identification of anomalous connections using evolution Strategies . The emphasis is given on how the problem can be modeled into ES primitives and how the fitness of the population can be evaluated in order to find the local optima, therefore resulting in an optimal rules that can be used for detecting intrusions in intrusion detection systems.
Keywords: intrusion detection systems, evolution strategy, evolutionary algorithms.
References:
1.
Seyed Habib, A. Rahmati, “Proposing a Pareto-Based Multi-Objective Evolutionary Algorithm to Flexible Job Shop Scheduling,” World
Academy of Science , Engineering and Technology 61, 2012
2. Anup Goyal, Chetan Kumar, “GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System” [Online]. Available: http://www.cs.northwestern.edu/~ago210/ganids/GANIDS.pdf
3.
Ch.Satya Keerthi.N.V.L, P.Lakshmi prasanna, B.Minny Priscilla, “Intrusion Detection System Using Genetic Algorithm,” International
Journal of P2P Network Trends and Technology, Volume1 Issue 2, 2011
4. Mohammad Sazzadul Hoque, Md. Abdul Mukit, Md. Abu Naser Bikas, “An Implementation Of Intrusion Detection System using Genetic
Algorithms,” International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.2, March 2012.
5. Wei li, “Using Genetic Algorithm for Network Intrusion Detection Systems,”, [Online]. Available:
6. http://www.security.cse.msstate.edu/docs/Publications/wli/DOECSG2004.pdf
7. Hans-Georg Beyer, Hans-Paul Schwefel, “Evolution strategies a Comprehensive Introduction,” Natural Computing, pp 3-52, 2002.
8. Rechenberg,I., “Evolutionsstrategie: Optimierung technischer Systeme und Prinzipien der biologische,” Frommann-Holzboog, Stuttgart,
1973
9. Schwefel, H.-P. Numerical Optimization of Computer Models, Chichester: Wiley, 1981.
10.
Trung Hau Tran, Cédric Sanza, Yves Duthen, “Evolving Prediction Weights Using Evolution Strategy,” Proceedings of GECCO conference companion on Genetic and evolutionary computation, pp 2009-2016, 2008
11. K. Scarfone, P. Mell, “Guide to Intrusion Detection and Prevention Systems (IDPS),” Computer Security Resource Center (National
Institute of Standards and Technology). February 2007.
12. Kevin P. Anchor, Jesse B. Zydallis, Gregg H. Gunsch, Gary B. Lamont, "Extending the Computer Defense Immune System: Network
Intrusion Detection with a Multiobjective Evolutionary Programming Approach," [Online].Available:
13. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.6943&rep=rep1&type=pdf
14. W. Lu, I. Traore, “Detecting New Forms of Network Intrusion Using Genetic Programming,” Computational Intelligence, vol. 20, pp. 3,
Blackwell Publishing, Malden, pp. 475-494, 2004.
201-205
Authors: Pankesh Bamotra, Prashant Dwivedi
Paper Title: Secure Transmission Of Grayscale Images Using Discrete Fourier Transform
Abstract: The paper presented here deals with image encryption using the well-known algorithm of discrete
Fourier transform (DFT). The DFT of real values has a special property called the conjugate symmetry property X
(N-m) = X*(m) | m = 0, 1,..N-1 which is the basis for this paper. The grayscale pixel values of the image to be encrypted are read into a matrix. Using this property we need to find the 1D -DFT for only the first ⌈ N/2 ⌉ terms. Then we make two images of size N/2*N which are termed as ‘real image’ and ’imaginary image’ using suitable modifications. The two images are appended after encrypting their pixel values using two key values which serve as the shared secret between two parties. On the receiver side the two images are separately read and their pixel values are retrieved and decrypted and the image can be regenerated by finding the inverse 1D-DFT of the obtained pixel values.
Keywords: Complex conjugate symmetry, DFT, Grayscale images, IDFT, Image encryption.
References:
1. Vilardy, J.M.., “Digital image phase encryption using fractional Fourier transform”, Electronics, Robotics and Automotive Mechanics
Conference, 2006.
2.
John A. Stuller, ‘Introduction to signals and systems’, Cengage learning, 2010
3. Cornell University library archives, http://arxiv.org/list/cs/recent.
Authors: V.Vijayadeepa, P. Anitha
206-209
42.
Paper Title: A Mobile Target Tracking and Data-Centric Sensor Using Wireless Network
Abstract: In data-centric sensor networks, sensor data is not necessarily forwarded to a central sink for storage; instead, the nodes themselves serve as a distributed in-network storage, collectively storing sensor data and waiting to answer user queries. A key problem in designing such a network is how to map data and queries to their corresponding rendezvous nodes so that a query can find its matching data quickly and efficiently. Existing techniques are mostly aimed to address a certain type of queries. Both resource allocation and reactive resource allocation problems in multi- server data-centric sensor(DCS) to attack Poisson process. A queuing network, where multi servers at each service station are allocated, and also each activity of a project is operated at a devoted service station with only one server located at a node of the network. The problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. The objective functions are the project direct cost (to be minimized), the mean of project completion time (min), the variance of project completion time (min), and the probability that the project completion time does not exceed a certain threshold (max). It is impossible to solve this problem, optimally. Therefore, we apply a genetic algorithm for numerical optimizations of constrained problems to solve this multi-objective problem.
210-212

Keywords: key predistribution, mobile sink, security, unattended wireless sensor network.
References:
1.
S. RatNasamy, B. karp, L. Yin, F. Yu, D. Estrin, R. Govindan, and S. Shenker, “GHT: A Geographic Hash Table for Data-Centric Storage,”
ACM International Workshop on Wireless Sensor Networks and Applications, September 2002.
2.
S. Shenker, S. Ratnasamy, B. Karp, R. Govindan, and D. Estrin, “Datacentric storage in sensornets,” ACM SIGCOMM Computer
Communication Review archive, vol. 33, no. 1, pp. 137–142, 2003.
3.
D. Ganesan, B. Greenstein, D. Perelyubskiy, D. Estrin and J. Heidemann, “Multi-resolution Storage and Search in Sensor Networks,” ACM
Transactions on Storage, August 2005.
4.
W. Zhang, G. Cao, and T. La Porta, “Data Dissemination with Ring-Based Index for Wireless Sensor Networks,” IEEE International
Conference on Network Protocols (ICNP), pp. 305–314, November 2003.
5.
B. Karp and H. Kung, “GPSR: Greedy Perimeter Stateless Routing for Wireless Networks,” ACM Mobicom, 2000.
6.
A. Perrig, J. Stan kovic, and D. Wagner, “Security in Wireless Sensor Networks,” Communications of the ACM, vol. 47, no. 6, June 2004.
7.
J. Deng, C. Hartung, R. Han, and S. Mishra, “A practical study of transitory master key establishment for wireless sensor networks,” in
IEEE/Create Net Conference on Security and Privacy for Emerging Areas in Communication Networks (Secure Comm), 2005, pp. 289–299.
8.
A. Perrig, R. Szewczyk, V. Wen, D. Culler, and J. Tygar, “Spins: security protocols for sensor netowrks,” in ACM Mobicom, 2001.
9.
W. Du, J. Deng, Y. Han, and P. Varshney, “A pairwise key pre-distribution scheme for wireless sensor networks,” in Proceedings of the 10th
ACM Conference on Computer and Communications Security (CCS’03), 2003, pp. 42–51.
10.
D. Liu and P. Ning, “Establishing pairwise keys in distributed sensor networks,” in ACM CCS, 2003.
11.
S. Zhu, S. Setia, and S. Jajodia, “Leap: Efficient security mechanisms for large-scale distributed sensor networks,” in ACM CCS, 2003.
12.
Y. Zhang,W. Liu, W. Lou, and Y. Fang, “Location-based compromisetolerant security mechanisms for wireless sensor networks,” IEEE
Journal on Selected Areas in Communications, Feb. 2006.
Authors: K.Parimala, V.Palanisamy
43.
Paper Title: Implementation of Fuzzy K-Means In Multi-Type Feature Coselection For Clustering
Abstract: Feature Selection is a preprocessing technique in supervised learning for improving predictive accuracy while reducing dimension in clustering and categorization. Multitype Feature Coselection for Clustering (MFCC) with hard k means is the algorithm which uses intermediate results in one type of feature space enhancing feature selection in other spaces, better feature set is co selected by heterogeneous features to produce better cluster in each space. Soft Clustering is an optimization technique of data analysis and pattern recognition which allocates a set of observations to cluster in a fuzzy way, constructing a membership-function matrix whose (i, j)th element represents the “the degree of belonging” of the ith observations to the jth cluster. This paper presents the empirical results of the
MFCC algorithm with soft clustering and also gives the comparison results of MFCC with hard and soft k means.
Fuzzy k-means clustering is proposed for getting the robustness against the outliers.
Keywords: Feature Selection, MFCC, Fuzzy k-means.
References:
1.
M. Dash and H. Liu, “Feature Selection for Classification,” Int’l J. Intelligent Data Analysis, vol. 1, no. 3, pp. 131-156, 1997.
2.
Y. Yang and J.O. Pedersen, “A Comparative Study on Feature Selection in Text Categorization,” Proc. Int’l Conf. Machine Learning (ICML
’97), pp. 412-420, 1997.
3.
Shen Huang, Zheng Chen, Yong YU & Wei_Ying Ma, “Multi type Features Coselection for Web document Clusering”, IEEE Transactions on Knowledge and Data Engineering; vol-18,no.4,April 2006.
4.
M. Dash and H. Liu, “Feature Selection for Clustering,” Proc. 2000 Pacific-Asia Conf. Knowledge Discovery and Data Mining, pp. 110
121,2000.
5.
H.C.L. Martin, A.T.F. Mario, and A.K. Jain, “Feature Saliency in Unsupervised Learning,” Technical Report, Michigan State Univ., 2002
6.
T. Liu, S. Liu, Z. Chen, and W.-Y. Ma, “An Evaluation on Feature Selection for Text Clustering,” Proc. Int’l Conf. Machine Learning
(ICML’03), pp. 488-495, 2003.
7.
R. Weiss, B. Velez, M.A. Sheldon, C. Namprempre, P. Szilagyi, A. Duda, and D.K. Gifford, “HyPursuit: A Hierarchical Network Search
Engine that Exploits Content-Link Hypertext Clustering,”Proc. Seventh ACM Conf. Hypertext, pp. 180-193, 1996.
8.
K. Nigam and R. Ghani, “Analyzing the Effectiveness and Applicability of Co-Training,” Proc. Information and Knowledge Management, pp. 86-93, 2000.
9.
A. Blum and T. Mitchell, “Combining Labeled and Unlabeled Data with Co-Training,” Proc. Conf. Computational Learning Theory, pp. 92-
100, 1998.
10.
M. Montague, “Metasearch: Data Fusion for Document Retrieval,” PhD Thesis, Dartmouth College, 2002.
Authors: Shelly Chawla, Jagtar Singh, Paras Chawla
213-217
44.
Paper Title: Single to Multiband Frequency Technique for Wireless and Telecomm Microstrip Antenna Design
Abstract: In this article, the variations of changing the number of rings, diameter and patch material are studied for circular ring-shape planar antenna and explore the possibility of converting a single band antenna to multiband.
While doing so retain the compactness of multiband antenna. Such type of planar structure has wide potential with multiband support which is to be sight for different aerospace and telecommunication applications. A circular conductor is placed centered in the narrow annular ring having inner radius (Ri) and outer radius (Ro). These patterns are printed on polytetraflouroethlene (PTFE) substrate having dielectric constant 4.4, a thickness of 1.59mm, and a loss tangent of 0.020. The design laminate antenna structure consists of, a ring-shape radiating conducting material and a 50 ohm matching coaxial type feed line which work well for common resonating frequency 7.5 GHz bands. In single band the return loss value is -12.96dB and in case of dual ring the return loss value is -20.32dB at 7.5 GHz.
The other band achieved in two ring is 6.5 GHz having return loss value is -21.45dB.
Keywords: Single to multiband; RF device; Microstrip antenna; Ring-shape, PTFE.
References:
1. L. Shih-Chieh, L. Yi-Fang, C. Hua-Ming, C. F. Yang, “A novel design of circularly polarized annular-ring patch antenna”, IEEE
International Symposium on Antennas and Propagation Society, pp. 3924–3927, 2007.
2. H. F. Abu Tarboush, R. Nilavalan, K. M. Nasr, H. S. Al-Raweshidy, D. Budimir, “Widely Tunable Multiband Reconfigurable Patch
Antenna for Wireless Applications”, Proceedings of the Fourth European Conference on Antennas and Propagation (EuCAP), pp. 1-3,
218-220

45.
2010.
3. R. Garg, P. Bhartia, I. Bahl and A. Ittipiboon, “Microstrip Antenna Design Handbook,” Artech House Antennas and Propagation, 2001.
4. A.K. Bhattacharyya & R. Garg, "Input Impedance of Annular Ring Microstrip Antennas using Circuit Theory Approach", IEEE Trans.
Antennas and Prop., Vol. 33, pp 369-374, 1986.
5. L. Shun-Yun and W. Kin-Lu, “Enhanced performances of a compact conical-pattern annular-ring patch antenna using a slotted ground plane,” IEEE conference on Asia-Pacific Microwave (ASMC), Vol. 3, pp. 1036-1039, 2001.
6. Z. Fan & K. F. Lee, "Input Impedance of Annular-Ring Microstrip Antennas with a Dielectric Cover", IEEE Trans. Antennas and Prop.,
Vol. 40, No. 8, pp. 992-994, 1992.
7. J. C. Batchelor, K. Voudouris and R. J. Langley, "Dual Mode and Stacked Concentric Ring Patch Antenna Arrays", Electronics Letters,
Vol. 29, No. 15, 22 July 1993.
8. Xiaoye, Z. Zhijun, F. Zhenghe, “ Dual-Band Circularly Polarized Stacked Annular-Ring Patch Antenna for GPS Application,” IEEE
Antennas and Wireless Propagation Letters, Vol. No. 10, pp. 49-52, 2011.
9.
C. A. Balanis, “Antenna Theory, Antenna Analysis & Design”, 2nd edition, John Wiley & Sons, Inc. 1993.
10. S.E. El-Khamy, R.M. El-Awadi and E.-B. A. El-Sharrawy, "Simple Analysis and Design of angular Ring Microstrip Antennas", IEEE
Proceedings Vol. 133, No. 3, pp. 198-202, 1986.
11. X. L. Bao and M. J. Ammann, “Compact annular-ring embedded circular patch antenna with cross-slot ground plane for circular polarisation”, Electronics Letters, IET Journals & Magazines, Vol. 42, No. 3, pp. 192-193, 2006
12. A. Al-Zoubi, Y. Fan and A. Kishk, “An efficient center-fed circular patch antenna loaded with an annular ring”, IEEE International
Symposium on Antennas and Propagation Society, pp. 1 - 4, 2008.
13.
K. P. Ray and S. S. Thakur, “Printed Annular Ring with Circular Patch Monopole UWB Antenna”, IEEE International conference on
Advances in Computing and Communications (ICACC), pp. 270 - 273, 2012.
14. C. Jin-Sen and C. Horng-dean, “Dual-band characteristics of annular-ring slot antenna with circular back-patch”, Electronics Letters, IET
Journals & Magazines, Vol. 39, No. 6, pp. 487-488, 2003
15. C. F. L. Vasconcelos, S. G. Silva and A. G. D'Assuncao, “Study and development of microstrip antenna withannular ring patch on ferrimagnetic substrate”, IEEE International Symposium on Antennas and Propagation (APSURSI), pp. 1266-1269 , 2011.
Authors: N.Sivakumar, K.Vivekanandan
Paper Title: Exploring the Need for Specialized Testing Technique for an Agent-Based Software
Abstract: The field of Software Engineering has been complimented with a number of research works that helps in developing software products that performs well within ever-changing organizational environments. Functional efficiency of a product greatly depends on the software development approach used to build it and the testing techniques involved. Most widely used software development approaches are conventional approach, object-oriented approach and agent-oriented approach. Among the Software Development Life Cycle (SDLC) phases, software testing is an important activity aimed at evaluating an attribute or capability of a program or system and determining that it meets the functionality of the system like the actual code or the non-functional requirements of the system like amicable user interface. There are various testing strategies used in the conventional approach like unit testing, integration testing, validation testing and system testing. As all the basic entities are viewed as objects and classes in the object-oriented software development, conventional testing approaches are not suitable and thereby specialized object-oriented software testing evolves. Recent literature study claims that none of the existing Agent Oriented
Software Engineering (AOSE) methodologies possesses testing phase in their SDLC stating that the software developed using agent paradigm were been tested using either conventional or object-oriented testing mechanism.
Since the agent characteristics such as autonomy, pro-activity, reactivity, social ability, intelligence etc., differs with object characteristics, object-oriented testing mechanisms are not sufficient and also inadequate to test the agent oriented software. Thus this paper compares various approaches of building and testing the software with the help of a case study (online book store application) and thereby the need for a specialized agent-oriented software testing mechanism is justified for the better functional outcome of the software product developed using agent oriented approach.
Keywords: Software Engineering, Agent-Oriented Software Testing, Software Development Life Cycle.
References:
1.
Roger S.Pressman, “Software Engineering – A Practitioner’s Approach”, Tata Mc Graw Hill, 6th edition, 2005.
2. Nicholas R.Jennings, Michael Woolridge, “Agent oriented software engineering”,Queen Mary and Westfield college, University of
London.
3. Federico Bergenti, Marie Pierre Gleizes, Franco Zambonelli, “Methodologies and Software Engineering for Agent Systems-book”, Kluwer
Academic Publishers.
4. Chia-En Lin, Krishna M.Kavi, Frederick T. Sheldon and Thomas E. Potok, “A Methodology to Evaluate Agent Oriented Software
Engineering Techniques”, Computer Science and Engineering department, University of North Texas.
5. Bray, Mike. "Object-Oriented Design", Carnegie Mellon Software Engineering Institute, Feb 2008.
6. James.A.Whittaker, "What is software testing? And why is it so hard?", Florida Instittute of Technology,IEEE 2000.
7.
Shalini Gambhir, “ Testing strategies for Object-Oriented Systems” , International Journal of computer Science and Information
Technology & Security, Vol.2, No.2, April 2012.
8. Zhe (Jessie) Li and Tom Maibaum, ”An Approach to Integration Testing of Object-Oriented Programs”, Department of Computing and
Software McMaster University, Hamilton, ON, Canada, IEEE 2007.
9. Praveen Ranjan Srivastava, Karthik Anand V, Mayuri Rastogi, Vikrant Yadav, G Raghurama, ”Extension of object oriented software testing techniques to agent oriented software testing”, Journal Of Object Technology, Birla Institute of technology and science, Pilani,
India.
10. Mailyn Moreno, Juan Pavon, Alejandro Rosete. Testing in Agent Oriented Methodologies. IWANN 2009, Part II, LNCS 5518, pp. 138–
145, 2009.© Springer-Verlag Berlin Heidelberg 2009
11. Levine, David, “Relationship between Agent and Object Technologies”, Agent technology green paper, OMG Agent work group, 1999.
12. Shoham, Y. Agent oriented programming (Technical Report STAN-CS-90-1335) Stanford University: Computer science department, 1994.
13.
L. Padgham, J. Thangarajah, and M. Winikoff: “The Prometheus Design Tool – A Conference
Management System Case Study.”
In: M. Luck and L. Padgham (Eds.): AOSE 2007, LNCS 4951, pp. 197–211, Springer-Verlag Berlin Heidelberg 2008.
Authors: S.Kezia, I.Shanti Prabha, V.Vijaya Kumar
221-228
46. Paper Title: Innovative Segmentation Approach Based on LRTM
Abstract: Texture refers to the variation of gray level tones in a local neighbourhood. The “local” texture
229-233

information for a given pixel and its neighbourhood is characterized by the corresponding texture unit. This paper describes new statistical approaches for texture segmentation, based on minimum and maximum of fuzzy left and right texture unit matrix. In these methods, the “local” texture information for a given pixel and its neighbourhood is characterized by the corresponding fuzzy texture unit. The proposed Minimum Fuzzy Left and Right Texture Unit
Matrix (MFLRTU) and Maximum Fuzzy Left and Right Texture Unit Matrix (MXFLRTU) segmentation methods overcome the computational complexity of Fuzzy Texture Unit (FTU) by reducing the texture unit from 2020 to 79.
The proposed schemes are compared with the Wavelet Transform with Image Fusion (WTIF) in [20]. The results demonstrate the efficacy of the proposed methods.
Keywords: Fuzzy Texture unit, Left Right Texture Unit Matrix, Texture Spectrum, Texture.
References:
1. Robert M. Haralick en Linda G. Shapiro. Computer and Robot Vision Addison-Wesley Publishing Company, Inc. 1992.
2. Todd R. Reed and J.M. Hans du Buf. ,”A Review of Recent Texture Segmentation and Feature Extraction Techniques “. CVGIP: Image
Understanding, Vol. 57, No. 3, May, PP. 359-372, 1993.
3. Rafael C. Gonzalez and Paul Wintz. Digital Image Processing, Second Edition. Addison-Wesley Publishing Company, Inc. 1987.
4. C.H. Chen, L.F. Pau & P.S.P Wang. Handbook of Pattern Recognition & Computer Vision. World Scientific Publishing Co. Pte. Ltd. 1993.
5. Jenq-Neng Hwang and Eric Tsung-Yen Chen. Textured Image Synthesis and Segmentation via Neural Network Probabilistic Modeling.
Information processing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, 1994.
6. Andreas Kellner, Holger Magnussen and Josef A. Nossek. Texture Classification, Texture segmentation and Texture Segmentation with
Discrete-Time Cellular Neural Networks. CNNA-94 Third IEEE International Workshop on Cellular Neural Networks and their
Applications, Rome, Italy, 1994.
7. Abdulrahman Al-Janobi, “Performance evaluation of cross-diagonal texture matrix method of texture analysis,” Pattern Recognition, Vol.
34, pp.171-180, 2001.
8. B. Sujatha, Dr. V. VijayaKumar, M. Chandra Mohan, “ Rotationally Invariant Texture Classification using LRTM based on Fuzzy
Approach ,” IJCA,Vol 33,No.4,Nov 2011,pp.1-5.
9. Image processing Principles and Applications, Ajoy K. Ray, Tinku Acharya, A John Wiley & Sons, MC., Publication.
10. G.Wiselin Jiji, L.Ganesan, “A new approach for unsupervised segmentation,” Applied Soft Computing, Vol.10, pp.689-693, 2010.
11. A. Said and W. A. Peralman, “A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Trans. on
Circuitsand Systems for Video Technology, 6(3), June 1996, 243-250.
12.
A. Said and W. A. Peralman, “An Image Multiresolution Representationfor Lossless and Lossy Compression,” IEEE Trans. on Image
Processing,5, September 1996, 1303-1310.
13. P. Chen, T. Acharya, and H. Jafarkhani, “A Pipelined VLSI Architecture for Adaptive Image Compression,” International Journal of
Robotics andAutomation, 14(3), 1999, 115-123.
14. G.cross,A.Jain,1983,” markov random fields feature models,”IEEE Trans. Pattern Anal. Mach.Intell,5,pp.25-39.
15.
J.M. Keller , S.chen, R.M.Crownover,1989.”texture description and segmentation through fractal geometry,”Comput.Vision Graphics
Image process,45,pp.150-166.
16. A.Bovik,M.Clark and W.Geisler,1990.”Multichannel texture analysis using localized spatial filters,” IEEE Trans. Pattern Anal.
Mach.Intell,12(1),pp.55-73.
17. L.S.Davis,1981,Polarograms: “a new tool for texture analysis ,”Pattern Recognition,13,pp.19-223.
18.
B.Kartikeyan,A.Sarkar,1991.”An identification approach for 2D AR models for describing textures,”CVGIP: Graphical models Image
Process,53,121-131.
19. M. Tuceryan and A.K. Jain, Texture Analysis in The handbook of pattern recognition and computer vision, second ed.: World Scientific
Publishing Co., 1998.
20.
M.Joseph Prakash, S.Kezia, B.V. Ramana Reddy, “An approach for texture segmentation based on random field model and wavelet fusion”, IJSIP, Vol. 1, Iss.3-2010,pp.183-189.
21.
B.V.Ramana Reddy, M.Radhika Mani, V.VijayaKumar, “ A random set view of texture segmentation”, IJSIP, vol.1,iss.1,pp.24-30.
22. Vakulabharanam Vijaya Kumar, B.Eswara Reddy, A.Nagaraja Rao, U.S.N. Raju ,”Texture Segmentation Methods Based on Combinatorial of Morphological and Statistical Operations,” Journal of Multimedia, Vol 3, No 1 (2008), 36-40, May 2008.
23.
V. Vijaya Kumar, U.S.N.Raju, M. Radhika Mani , A.L.Narasimha Rao ,”Wavelet based Texture Segmentation methods based on
Combinatorial of Morphological and Statistical Operations,” IJCSNS, VOL.8 No.8, August 2008.
24.
Dr. V. Vijaya Kumar , A. Nagaraja Rao, V.V.Krishna , Dr. A. Damodaram ,”A new unsupervised algorithm for texture segmentation, “Map
Asia 2003.
25. Dr.V.VijayaKumar, Prof.M.Joseph Prakash, Dr.B.Eswara Reddy, S.Kezia, “A novel approach for texture segmentation based on mathematical morphology, “ICRATEMS 2011, pp. 270-275.
26.
V.Vijaya Kumar, A. Nagaraja Rao, U.S.N.Raju, and B.Eswara Reddy, “Pipeline Implementation of New Segmentation Based on Cognate
Neighborhood Approach,”IJCS, Vol.35, Iss.1,Feb 2008.
Authors: Sanjeev Kumar Sharma, Randhir Singh, Parveen Lehana
47.
Paper Title: To Investigate the Electrical Impedance of the Aloe Barbadensis Miller Leaves
Abstract: The growth and health status of plants can be accessed from the electrical properties of its limbs such as leaves and stem. Present investigations were carried out to study the electrical impedance of Aloe Barbadensis Miller
(Aloe-vera) leaves for investigating the effect of external ac current on its tissues Impedance was estimated over the frequency ranging from 500 Hz to 20 kHz. Aloe-vera plant has been chosen because of its extensive applications in dermatology. It is also known as “Lilly of Desert”. The leaves of Aloe-vera contain a soothing thick sap that is useful for treatment and curing of wounds and diseases. The estimated impedance of the leaves was found to be a function of applied ac frequency across the leaves. The impedance of the leaf tissue decreases with the increase in frequencies of input signal. This may be due to change of dielectric properties of Aloe-vera tissues indicating a mechanism of evaluating the health status of Aloe-vera by estimating its electrical impedance
Keywords: Aloe-vera, Thigmomophogenisis, Agri-wave technology, AC signal impedance, CAM.
References:
1. Fred and F. Holtzclaw, (2010) “ Plant Responses to Internal and External Signal”,AP Biology Reading Guide Chapter No. 39.
2.
E. W. Chehab, Eich, E and , J . Braam (2008), “Thigmomorphogenesis a complex plant response to mechano-stimulation”, Journal of experimental Botany, Vol. 60, No. 1, pp. 43–56
3. T. Z. Hou, and R. E. Mooneyham, . (1999) “Effect of Agri-Wave Technology on Yield and Quality of Tomato”, An International Journal of Comparative Medicine East and West, Vol.27, No.01 pp27.
234-238

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49.
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Vol.51, pp1851-1856.
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V. D .Yannick, (2000). “ Influence of variable sound frequencies on the growth and development of plants” Hogeschool Gent. Belgium. 22
June.
6.
P. J. Aphalo, (2006) “Light signals and the growth and development of plants a gentle introduction”, Department of Biological and
Environmental Sciences Plant Biology University of Helsinki, Finland Biology University of Helsinki, Finland.
7. A. Uchida, and K.T Yamamoto,. (2002) “Effects of mechanical vibration on seed germination of Arabidopsis thaliana(L.) Heynh.”, Plant cell physil. 43(6), pp647-651.
8.
S. Lautner, T. E. Edgar Grams, R. Matyssek, and J. Fromm, “Characteristics of Electrical Signals in Poplar and Responses in
Photosynthesis,” Plant Physiology, vol. 138, pp. 2200-2209, 2005.
9. S. Lautner, R. Matyssek & Jorg Fromm, “Distinct roles of electric and hydraulic signals on the reaction of leaf gas exchange upon reirrigation in Zea mays,” Plant Cell and Environment., vol. 30, pp. 79-84, 2007.
10. Taiz & Zeiger. 2002. Plant Physiology, Third Ed. Sinauer Associates, Inc. Chapter 10, pages 33-46.
11.
R Rodr´ıguez, D.Jasso de Rodr´ıguez, J.A.Gil-Margın, Angulo-S´anchez, and Lira-Saldivar, J.L “Growth, stomatal resistance, and transpiration of Aloe vera under different soil water potentials”, sciencedirect, vol 25 (2007) pp123–128.
12. D. Grindlay, T. Reynolds “The Aloe vera phenomenon: a review of theproperties and modern uses of the leaf parenchyma gel. J
Ethnopharmacol. (1986) vol 16 pp117-51.
13.
I. Danho, “Potential reversal of chronological and photo-aging of the skin by topical application of natural substances. Phytotherapy
Research. 1993; 7:S53-S56.
14. A.D Klein, N.S Penneys. Aloe vera. J Am Acad Dermatol .1988; vol 18 pp714-720.
15. R. Henry. An updated review of aloe vera. Cosmetics and toiletries. 1979; vol 94 pp42-50.
Authors: Anupam Choudhary, Ravi Kshirsagar
Paper Title: Process Speech Recognition System using Artificial Intelligence Technique
Abstract: This paper describes the detail process of speech recognition using artificial intelligence technique. It includes coustic model, Language model,Trigram model, Class model ,Source channel model .Speech recognition or natural language processing referred to artificial intelligence methods of communicating with a computer in natural language like English. The objective of NLP Program is to understand the IP and initiate the action. Method gives theoretical conceptual view to process the speech recognition, a acoustic model need sto be able to interface with telephony system because there is no GUI it needs to manage a spoken dialogue with user.
Keywords: NLP, GUI, IP, channel model.
References:
1. “Hidden Markov models for speech recognition; X.D. Huang, Y. Ariki, M.A. Jack. Recognition”, The Complete Practical Reference Guide;
T. Schalk, P. J. Foster: Telecom Library Inc, New York; ISBN O-9366648-39-2.
2.
“Automatic speech recognition: the development of the SPHINX system”,Kai-Fu Lee; Boston; London: Kluwer Academic, c1989
3. “An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition”, S. E.
Levinson, L. R. Rabiner and M. M. Sondhi; in Bell Syst. Tech. Jnl. v62(4), pp1035--1074, April 1983
4. “Review of Neural Networks for Speech Recognition, R. P. Lippmann; in Neural Computation”, v1(1), pp 1-38, 1989.
5.
“Automatic Speech and Speaker Recognition: Advanced Topics”, C.H. Lee, F.K. Soong and K.K. Paliwal (Eds.), Kluwer, Boston, 1996.
6.
“Fiction database for emotion detection in abnormal situations.” Clavel, C., Vasilescu, I., Devillers, L., Ehrette, T.,In: Proc. Int. Conf.
Spoken Language Process.(ICSLP ’04). Korea, pp. 2277–2280, 2005.
7. Modifications of phonetic labial targets in emotive speech: effects of the co-production of speech and emotions. Speech Communication,
Caldognetto, E. M., Cosi, P., Drioli, C., Tisato, G., Cavicchio, pp173–185, 2003.
8.
“You stupid tin box- children interacting with the AIBO robot: A crosslinguistic emotional speech “ Batliner, A., Hacker, C., Steidl, S.,
N¨oth, E., D’ Archy,S., Russell, M., Wong, M., In: Proc. Language Resources and Evaluation (LREC ’04). Lisbon, 2004.
Authors: Arpan Shah, Ankit Kapil, Rekha Agarwal, Sandhya Sharma
239-242
Analysis and Comparative Study of Effect of Feedback on BJT and CMOS Amplifier using Tanner
Paper Title:
Tools
Abstract: Feedback is a method in which a portion of the output returned to the input in order to modify the characteristics of the device. Feedback can applied to transistor amplifier circuits to modify their performance characteristics such as gain, bandwidth, input and output impedance etc.. There are number of ways by which a signal can be derived from output and can be returned to input.
1. In this paper I will analyze and compare the different characteristics of Feedback and without feedback amplifier circuit of BJT.
2. After that I will compare the feedback amplifier of BJT with feedback amplifier of CMOS.
3. Different waveforms will be finding out by manipulating different parameters in the circuit design and mathematical calculation and comparison will be done.
Keywords: Feedback, Gain, Slew Rate, Amplifier, Power consumption.
References:
1.
K. Wang, P. C. Huang and C. Y. Huang, “A Fully Differential CMOS Transconductance-Transimpedance Wideband Amplifier,” IEEE
Trans. Circ. and Syst. II, vol. 42, no. 11, pp. 745-748, November 1995.
2.
TSENG S.-H., HUNG Y.-J., JUANG Y.-Z., LU M. S.-C., A 5.8-GHz VCO with CMOS- compatible MEMS inductors, Sensors and
Actuators A: Physical, Vol. 139, pp. 187-193,2007.
3.
Sam Ben-Yaakov “A Unified Approach to Teaching Feedback in Electronics Circuits Courses” IEEE Transaction on education VOL. 34 no.
4, NOV. 1991
4.
NAGEL, L.W.: ‘SPICE 2: A computer program to simulate semiconductor circuits’, ERL Memo ERL-M520, Electronics Research
Laboratory, University of California, Berkeley, May 1975
5.
VLADIMIRESCU, A., NEWTON, A.R., and PEDERSON, D.O.:‘SPICE version 2G.0, user’s guide’, University of California, Berkeley,
Sept. 1980.
6.
Gianna De Rubertis, Dept. of Chem. Eng. & Appl. Chem., Univ. of Toronto, Ont., Canada Davies, S.W. NanoBioscience, IEEE Transactions on Dec. 2003
7.
A.P. Malvino, Electronic Principles (2nd Ed. 1979. ISBN 0-07-039867-4)
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Jacob Millman, Microelectronics: Digital and Analog Circuits and Systems, McGraw-Hill, 1979, ISBN 0-07-042327-X,
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9.
Horowitz, Paul; Hill, Winfield (1989). The Art of Electronics. Cambridge, UK: Cambridge University Press.
Authors: Gursewak Singh
Paper Title: Robust Stability Analysis with Time Delay Using PI Controller
Abstract: In this paper, real time implementation of PI and PID controllers is Investigated for stabilizing an arbitrary-order plant or system. Simple closed loop feedback ,PI controller can overcome all the problems of uncertainties like time delay in LTI (Linear Time Invariant) system.This paper will give us an implortant alogorithm to be imlemented in MATLAB for achieving the robust stability in LTI plant in presence of any type of perturbations or external disturbances. In this paper, a single input and single output (LTI) linear time invariant plant under perturbed conditions with additive uncertainity weight senstivity is considered. In this paper especially, I have found all set of PI gains Kp,Ki which will promise us for the stability in a given disturbed (SISO) (LTI) system by achieving the robust stability constraint using frequency domain analysis .
<=1 can be found by obtaining the PI controller gains Kp,Ki
Keywords: PI Controller, Time delay, Robust Stability, Uncertainity weight, frequency domain.
248-252
References:
1.
Ho., K.W., Datta, A., and S.P. Bhattacharya, “Generalizations of the Hermite-Biehler theorem,” Linear Algebra and its Applications, Vol.
302-303, 1999, pp. 135-153.
2.
Ho., K.W., Datta, A., and S.P. Bhattacharya, “,” Proc. 42nd IEEE Conf. on Decision and Control, Maui, Hawaii, 2003.
3.
Bhattacharyya, S.P., Chapellat, H., and L.H. Keel, “Robust Control: The Parametric Approach”, Prentice Hall, N.J., 1995.
4.
Manoj Gogoi , “Robust and Nominal stability of LTI plant using PID controller ,Dissertation 2010,pp 44.
5.
B.S Manke,.” linear control systems with matlab applications, ”Khana publishers,pp.202-203,2005.
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K. Zhou, J.C. Doyle, K. Glover, “Robust and Optimal Control”, Prentice-Hall, London, 1996.
7.
Ho., K.W., Datta, A., and S.P. Bhattacharya, “PID stabilization of LTI plants with time-delay,” Proc. 42nd IEEE Conf. on Decision and
Control, Maui, Hawaii, 2003.
8.
Bhattacharyya, S.P. and L.H. Keel, “PID controller synthesis free of analytical methods,” Proc. of IFAC 16th Triennial World Congress,
Prague, Czech Republic, 2005.
9.
Linlin Ou,Peidong Zhou,weidong Zhang and Li Yu, “H∞ Robust Design of PID controller for Arbitrart order LTI system with time delay”,2011 50th IEEE conference on decision and control and Eueropean control conference (CDC-ECC) orlando,FL,USA< December 12-
15,2011.
Authors: Ashish Dubey
51.
Paper Title: Quality of Service (QoS) in Wireless Network and NS-3, VOIP Simulation Environment
Abstract: Quality of Service (QoS) is what determines if a wireless technology can successfully deliver high value services such as voice and video. The UGS service flow handles the traffic generated by VOIP calls in the most optimum way. Simulation is a powerful tool for analysis and improvement of networking technologies, and many simulation packages are available. One that is growing in popularity is NS-3, the successor to the popular NS-2. It is a significant departure from NS-2.
Keywords: QoS, VOIP, IEEE 802.16 Standards, NS2, NS3.
References:
1.
Gustavo Carneiro, “NS-3: Network Simulator 3.” UTM Lab Meeting April 20,2010. http://www.nsnam.org/tutorials/NS-3-LABMEETING-
1.pdf
2.
Kevin Fall (Ed), Kannan Varadhan (Ed), “The NS-2 Manual,” 2010. http://www.isi.edu/nsnam/ns/ns-documentation.html.
3. “The NS-3 Manual,” The NS-3 Project, 2010.
4. http://www.nsnam.org/docs/release/3.10/manual/singlehtml/index.html
5. “The NS-3 Tutorial,” The NS-3 Project, 2010. http://www.nsnam.org/ docs/release/3.10/tutorial/singlehtml/index.html
6. Chakchai So-In, Raj Jain, Abdel-Karim Al Tamimi OCSA: An algorithm for Burst Mapping in IEEE 802.16e Mobile WiMAX Networks,
Proceedings 15th Asia-Pacific Conference on Communications (APCC 2009), 8th-10th Oct, 2009, SanghaiChina.
7. Chakchai So-In, Raj Jain, Abdel Karim Al Tamimi, "eOCSA: An Algorithm for Burst Mapping with Strict QoS Requirements in IEEE
802.16e Mobile WiMAX Networks," Proceedings of the Second IFIP Wireless Days Conference, Paris, France, 14-16 December 2009.
8.
Wlia Weingärtner, Hendrik Vom Lehn, Klaus Wehrle, “A Performance Comparison of Recent Network Simulators”, Proceedings of the
2009 IEEE International Conference on Communications.
9. Joe Kopena, “NS-3 Overview,” March 19, 2008.
10. http://www.nsnam.org/docs/ns-3-overview.pdf
11. Raj Jain (Ed), “WiMAX System Evaluation Methodology, V2.1”, WiMAX Forum, July 7, 2008.
12. Yi-Ting Mai, Chun-Chuan Yang, and Yu-Hsuan Lin, "Cross-Layer QoS Framework in the IEEE 802.16 Network" Advanced
Communication Technology, The 9th International Conference on Volume 3, 12-14 Feb. 2007 Page(s):2090 – 2095.
13.
Xin Wang; Giannakis, G.B.; Marques, A.G, “A Unified Approach to QoSGuaranteed Scheduling for Channel-Adaptive Wireless
Networks”, Proceedings of the IEEE, Volume 95, Issue 12, Dec. 2007 Page(s):2410 –2431
14. IEEE Std 802.16e-2005, "IEEE Standard for Local and metropolitan area networks--Part 16: Air Interface for Fixed Broadband Wireless
Access Systems--Amendment 2: Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed
Bands," Feb. 2006.
15.
“Mobile WiMAX – Part I: A Technical Overview and Performance Evaluation”, Prepared on Behalf of the WiMAX Forum, Rev 1.2:
February 9, 2006.
16. Cicconetti, C., Lenzini, L., Mingozzi, E., Eklund, C., "Quality of service support in IEEE 802.16 networks", IEEE Network, Volume 20,
Issue 2, March-April 2006 Page(s):50 – 55
17.
“Mobile WiMAX – Part II: A Comparative Analysis”, Prepared on Behalf of the WiMAX Forum, May 2006.
18. Alexander Sayenko, Olli Alanen, Juha Karhula, Timo Hämäläinen, "Ensuring the QoS requirements in 802.16 scheduling”, MSWiM '06:
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems, October 2006.
19. Jenhui Chen, Chih-Chieh Wang, Frank Chee-Da Tsai, Chiang-Wei Chang, Syao- Syuan Liu, Jhenjhong Guo, Wei-Jen Lien, Jui-Hsiang
Sum, Chih-Hsin Hung, "The design and implementation of WiMAX module for ns-2 simulator", WNS2 '06: Proceeding from the 2006 workshop on ns-2: the IP network simulator, October 2006
20. Zhang, Q.; Zhu, W., Zhang, Y, “End-to-End QoS for Video Delivery Over Wireless Internet”, Proceedings of the IEEE, Volume 93, Issue
1, Jan. 2005 Page(s):123 – 134
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52.
21. J. Chen, W. Jiao, and Q. Guo, "Providing integrated QoS control for IEEE 802.16 broadband wireless access systems," Proceedings of the
IEEE 62nd Vehicular Technology Conference (VTC 2005-Fall), vol. 2, pp. 1254-1258, Sept. 2005.
22.
H.S. Alavi, M. Mojdeh, and N. Yazdani, “A Quality of Service Architecture for IEEE 802.16 Standards”, Proceedings of 2005 Asia-Pacific
Conference on Communications, pp.249-253, Oct. 2005.
23.
“Can WiMAX address your application”, Prepared on Behalf of the WiMAX Forum, October 2005.
24. “WiMAX End-to-End Network Systems Architecture - Stage 2: Architecture Tenets, Reference Model and Reference Points,” WiMAX
Forum, December, 2005.
25. G. Nair, J. Chou, T. Madejski, K. Perycz, D. Putzolu and J. Sydir, “IEEE 802.16 Medium Access Control and Service Provisioning”, Intel
Technology Journal, Volume: 08, Issue: 03, August 2004, PP. 213-28
26. IEEE 802.16-2004, “IEEE standard for Local and Metropolitan Area Networks — Part 16: Air Interface for Fixed Broadband Wireless
Access Systems,” Oct. 2004.
27. GuoSong Chu, Deng Wang, and Shunliang Mei. “A QoS architecture for the MAC protocol of IEEE 802.16 BWA system”. IEEE
International Conference on Communications, Circuits and Systems and West Sino Expositions, 1:435– 439, June 2002
28. D. Wu, Y. T. Hou, and Y.-Q. Zhang, “Transporting real-time video over the Internet: Challenges and approaches,” Proc. IEEE, vol. 88, no.
12, pp. 1855– 1877, Dec. 2000.
29. R. Guerin and V. Peris, “Quality of Service in packet networks: Basic mechanisms and directions”, Computer Networks, Vol. 31, No. 3, pp.169 – 189, February 1999
30. Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z. and W. Weiss, "An Architecture for Differentiated Services", RFC 2475, December
1998.
Authors: Aman Kumar Sharma, Arvind Kalia, Hardeep Singh
Paper Title: Metrics Identification for Measuring Object Oriented Software Quality
Abstract: In the modern era many object-oriented quality suites exists for assessing software quality against features of object-oriented design and as well as against the factors of evaluating quality. This paper presents a review of quality metrics suites namely, MOOD, CK and Lorenz & Kidd, and then selects some metrics and discards other metrics based on the definition and capability of the metrics.
Keywords: CK Suite, Lorenz and Kidd Suite, Metrics, MOOD Suite, Software Quality.
References:
1.
Roger S. Pressman, “Software Engineering: A Practitioner’s Approach”, 6th ed., McGraw Hill International, 2005.
2.
N. Fenton and S. Lawrence Pfleeger, “Software Metrics: A Rigorous Approach”, 2nd ed., International Thomson Press, London, 1996.
3.
H. Zuse, “Software Complexity: Measures and Methods”, Walter de Gruyer, New York, 1991.
4. F. Brito e Abreu, and R. Carapuca, “Candidate Metrics for Object-Oriented Software within a Taxonomy Framework”, Proceedings of
AQUIS’93 (Achieving Quality In Software), Italy, 1993.
5. Fernando Brito e Abreu, and Walcelio Melo, “Evaluating the Impact of Object-Oriented Design on Software Quality”, Proceedings of the third international Software Metrics Symposium (Metrics’96), IEEE, Germany 1996.
6.
F. B. Abreu, “The MOOD Metrics Set”, In Proceedings of ECOOP’95, Workshop on Metrics, 1995.
7. Aman Kumar Sharma, Arvind Kalia, and Hardeep Singh, “An Analysis of Optimum Software Quality Factors”, IOSR Journal of
Engineering, vol. 2 issue 4, 2012.
8.
Aman Kumar Sharma, Arvind Kalia, and Hardeep Singh, “Empirical Analysis of Object Oriented Quality Suites”, International Journal of
Engineering and Advanced Technology (IJEAT), vol. 1 issue 4, 2012.
9.
B. A. Kitchenham, N. Fenton, and S. Lawrence Pfleeger, “Towards a Framework for Software Measurement Validation”, IEEE Transaction on Software Engineering, vol. 21, no. 12, 1995.
10. F. Brito e Abreu, M. Goulao, and R. Estevers, “Towards the Design Quality Evaluation of OO Software Systems”, Proceedings in Fifth
International Conference on Software Quality, 1995.
11.
Rachel Harrison, Steve J. Counsell, and Reuben V. Nithi, “An Evaluation of the MOOD Set of Object-Oriented Software Metrics”, IEEE
Transactions on Software Engineering, vol. 24, no. 6, 1998.
12. Aline Lucia Baroni, and Fernando Brito e Abreu, “A Formal Library for Aiding Metrics Extraction”, 4th International Wokshop on OO
Rengineering, 2003.
13. M. Subramanyam, and R. Krishnan, “Empirical Analysis of CK Metrics for OOD Complexity: Implication for Software defect”, IEEE transactions on software engineering, 2003.
14.
Jagdish Bansiya, and Carl G. Davis, 2002, “A Hierarchical Model for Object-Oriented Design Quality Assessment”, IEEE Transactions on
Software Engineering, vol. 28 no. 1, 2002.
15. Aman Kumar Sharma, Arvind Kalia, and Hardeep Singh, “Taxonomy of Metrics for Assessing Software Quality”, International Journal of
Engineering Research and Technology (IJERT), vol. 1 Issue 06, 2012.
16.
Gurdev Singh, Dilbag Singh, and Vikram Singh, “A Study of Software Metrics”, International Journal of Computational Engineering and
Management (IJCEM), vol. 11, 2011.
17. V.L. Basili, L. Briand, and W.L. Melo, “A Validation of Object-Oriented Metrics as Quality Indicators,” IEEE Transactions Software
Engineering, vol. 22 no. 10, 1996.
18.
L.C. Briand, J. Wust, J.W. Daly, and D.V. Porter, “Exploring the Relationship Between design Measures and Software Quality in Object-
Oriented Systems”, Journal Systems and Software, vol. 51 no. 3, 2000.
19.
R. Shatnawi, “An Investigation of CK Metrics Thresholds”, ISSRE Supplementary Conference Proceedings, 2006.
20. Wei Li, “Another Metric Suite For Object-Oriented Programming”, Journal of Systems and Software, vol. 44, no. 2, 1998.
21. Alexander Chatzigeorgiou, “Mathematical Assessment of Object-Oriented Design Quality”, IEEE Transactions on Software Engineering, vol. 29 no. 11, 2003.
22.
Shyam R. Chidamber, and Chris F. Kemerer, “A Metrics Suite for Object Oriented Design”, IEEE Transactions on Software Engineering, vol. 20, no. 6, 1994.
23.
R. Harrison, S. Counsell, and R. Nithi, “An Overview of Object-Oriented Design Metrics”, Proceedings of the 8th International Workshop on Software Technology and Engineering Practice (STEP’97), 1997.
24. Khaled EL Emam, Saida Beniarbi, Nishith Goel, and Shesh Rai, “A Validation of Object-Oriented Metrics”, National Research Council
Canada Internal Report No. 43607.
25.
Fernando Brito e Abreu, and Rogeria Carapuca, “Object-Oriented Software Engineering: Measuring and Controlling the Development
Process”, 4th International Conference on Software Quality, USA, 1994.
26. L.H. Rosenberg, and L. Hyatt, “Applying ant Interpreting Object Oriented Metrics”, Proceedings of Software Technology Conference, Utah
1998.
Authors: Shayesteh Tabatabaei, Mohamad Teshnehlab
53.
Paper Title: Power-Efficient Reliable Routing Protocol to Increase Throughput in Ad hoc Networks
Abstract: In mobile ad hoc networks, each node acts as both host and router and performs all the routing and state maintenance. Due to the unpredictable movement of mobile nodes, the network topology of a mobile ad hoc network
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55. changes frequently. It will directly cause the more power-efficient and reliable routing protocols are needed. In this paper we propose a novel routing protocol, PEAODV (Power Efficient Ad-hoc On-demand Distance Vector for mobile Ad-hoc networks), that providing power- efficient and reliable packet transmission. PEAODV uses a new cost function to select the optimum path based on considering the minimum residual energy of the nodes on a path, and the path’s stability in accordance with the rate mobility of node and the available bandwidth and the radio frequency. Our study also compares the performance of the PEAODV protocol with the well-known Ad hoc On-
Demand Distance Vector (AODV) protocol, Results obtained by a simulation campaign show that PEAODV increases the throughput and decreases the delay, route discovery time, data dropped and number of hops per route.
Keywords: PEAODV, AODV.
References:
1.
G. Forman and J. Zahorjan, “The challenges of mobile computing,” Computer, vol.27, no.4, pp.38–47, April 1994.
2.
Kyoung-Jin KIM, Nonmember and Sang-Jo YOO, “Power-E.cient Reliable Routing Protocol for Mobile Ad-Hoc Networks”, IEICE
TRANS. COMMUN., VOL.E88–B, NO.12 DECEMBER 2005.
3.
Hamed EL_afandi . An Intellig ent Wireless Ad hoc Routing Protocol. In University of Wisconsin_Milwaukee, 2006.
4.
H. Badis, et al., “QoS for Ad hoc Networking Based on Multiple Metrics: Bandwidth and Delay”, MWCN’03, October 2003.
5.
OPNET Modeler, Available from: http://www.opnet.com
6.
J. Broch, D. A.Maltz, D. B. Johnson, Y. Hu, and J. Jetcheva. A performance comparison of multi-hop wireless ad hoc network routing protocols. In MobiCom ’98: Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking, pages
85–97, New York, NY, USA, 1998. ACM Press.
7.
P. Michiardi and R. Molva. Simulation-based analysis of security exposures in mobile ad hoc networks. In Proceedings of European
Wireless Conference, 2002.
8.
Nen-Chung Wang and Yung-Fa Huang and Jhu-Chan Chen. A stable weight-based on-demand routing protocol for mobile ad hoc networks.
In Elsevier Inc., 2007.
Authors: J.Senthil Kumar, S.K.Srivatsa
Component Based Approach for Technically Feasible and Economically Viable E-Content Design and
Paper Title:
Development
Abstract: The world has undergone a transition from the Industrial Age to the Information Age and to the present
Knowledge Age in a rapid way. In this era, wherein the economy is knowledge-based; continuous learning will decide the success or failure of every organization and individual. E-learning, it is believed, would mark the zenith of the evolution of learning. Socio-economic changes in the world have been causing drastic changes in the way people look at education and training as we have progressed from agriculturist mode of economy to the information age. Elearning has had a broadly positive pedagogic impact. The “learning object” model is perhaps the most prominent
“revolutionary” approach. This paper aims to address the issues associated with different types of pedagogical approaches and the key benefits of content development using learning object.
Keywords: E-learning, learning object, pedagogic, ict.
References:
1. Lams Foundation.org
2. Wiki.Laptop.org
3. Informal description of Laurillard's Model
4. E-moderating: The Key to Teaching and Learning Online – Gilly Salmon, Kogan Page,2000,ISBN 0-7494-4085-6
5. Bloom, B. S., and D. R. Krathwohl. (1956). Taxonomy of Educational Objectives: Handbook
6. Baath, J. A. (1982) "Distance Students' Learning – Empirical Findings and Theoretical Deliberations"
7. Areskog, N-H. (1995) The Tutorial Process – the Roles of Student Teacher and Tutor in a Long Term Perspective.
8. The Dublin Core Meta-Data Initiative. http://purl.oclc.org/dc/groups/index.htm
9. IMS (Instructional Management Systems) Project from Educause. http://www.imsproject.org/
10. IEEE Learning Technology Standards Committee (LTSC). http://ltsc.ieee.org/
11. ADL Sharable Courseware Object Reference Model, SCORM http://www.adlnet.org/
12. Frakes, W. & Terry, C. (1996). Software Reuse and Reusability Metrics and Models, ACM Computing Survey, 28(2), 415-435.
13. Washizaki, H., Yamamoto, Y. & Fukazawa, Y. (2003). A Metrics Suite for Measuring Reusability of Software Components.
14. Hafefh, M., Mili, A., Yacoub, S. &Addy, E. (2002). Reuse-Based Software Engineering. Canada: John Wiley.
15. Poulin, J., Caruso, J., & Hancock, D. (1993). The business case for software reuse. IBM Systems Journal, 32(4), 567-594.
16. Learning Technology Standards Committee (2002), Draft Standard for Learning Object Metadata. IEEE Standard 1484.12.1, New York: nstitute of Electrical and Electronics Engineers, retrieved 2008-0429.
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Authors: M.Vinay Kumar, Sharad Kulkarini
Paper Title: Tumors Classification using PNN Methods
Abstract: In this paper, pnn with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance images contain a noise caused by Operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Hence, in this paper the Probabilistic Neural Network was applied for the purposes. Decision making was performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN). The performance of the PNN classifier was evaluated in terms of training performance and classification accuracies. Probabilistic Neural Network gives fast and accurate classification and is a promising tool for classification of the tumors.
Keywords: PNN, Neural Network, PCA, MRI, Classification
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56.
References:
1.
N. Kwak, and C. H. Choi, “Input Feature Selection for Classification Problems”, IEEE Transactions on Neural Networks, 13(1),
143–159, 2002.
2.
E. D. Ubeyli and I. Guler, “Feature Extraction from Doppler Ultrasound Signals for Automated Diagnostic Systems”, Computers in
Biology and Medicine, 35(9), 735–764, 2005.
3.
D.F. Specht, “Probabilistic Neural Networks for Classification, mapping, or associative memory”, Proceedings of IEEE International
Conference on Neural Networks, Vol.1, IEEE Press, New York, pp. 525-532, June 1988.
4.
D.F. Specht, “Probabilistic Neural Networks” Neural Networks, vol. 3, No.1, pp. 109-118, 1990.
5.
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6.
N. Kwak, and C. H. Choi, “Input Feature Selection for Classification Problems”, IEEE Transactions on Neural Networks, 13(1),
143–159, 2002.
7.
E. D. Ubeyli and I. Guler, “Feature Extraction from Doppler Ultrasound Signals for Automated Diagnostic Systems”, Computers in
Biology and Medicine, 35(9), 735–764, 2005.
8.
D.F. Specht, “Probabilistic Neural Networks for Classification, mapping, or associative memory”, Proceedings of IEEE International
Conference on Neural Networks, Vol.1, IEEE Press, New York, pp. 525-532, June 1988.
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Authors: Raju, Nitin Sikka, Sanjeev Kumar, Rahul Gupta
Paper Title: Artificial Intelligence in Games
Abstract: Based on the recent surge in interest in the both academic and games industry in character- based artificial intelligence. Although the games are mainly related with entertainments, but with this there are other serious applications of gaming, including military training, educational games, driving training, medical training and games that reflect social consciousness or advocate for a cause. Artificial intelligence in games is a concept of taking game applications beyond the limits of interactive gaming. Such system learn about the player’s behaviours during game play and beyond the pre-programmed set provides and interactively develop and provide a best experience to the players. General Terms The main aim of our research is to develop such artificial intelligence techniques that can have a substantial impact in the game industry. In the following research paper we are going to study about Case Based Reasoning (CBR), automatic behaviour adjustment for believing characters, drama management and user modelling for interactive stories and strategic behaviour planning for real time strategy games. We include problems in adopting artificial intelligence in games and some algorithms for respective games.
Future aspect of artificial intelligence in games is also mentioned in the paper.
Keywords:
1. Need of Artificial Intelligence
2. Problem in Computer Games Artificial Intelligence
3. Behavior Adaption for Believing Characters Artificial Intelligence
4. Action transformation System
5. Case Base Planing For Strategy Games
6. Drama Management In Intractive Stories
7. Integrate user modelling In with drama management
References:
1.
Cognitive Computing Lab (CCL) College of Computing, Georgia Institute of Technology Atlanta, GA 30332/0280 fsanti
,abhishek, mehtama1, ashwing@cc.gatech.edu
2.
depot.com/GameAI/Design.html
3.
Aamodt, A., and Plaza, E. 1994. Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial
Intelligence Communications 7(1):39–59.
4.
http://www.stat.columbia.edu/~jakuli n/FT/
5.
http://www.codeproject.com/KB/arc hitecture/aigame.aspx
6.
B. Magerko, J. Laird, M. Assanie, A. Kerfoot, and D. Stokes. AI characters and directors for interactive computer games. In Proceedings of the 2004 Innovative Applications of Arti_cial Intelligence Confercence, 2004.
7.
Isbister, K., and Doyle, P. 2003.Web guide agents: Narrative context with character.
Authors: O.Harikrishna, A.Maheshwari
269-273
57.
Paper Title: Satellite Image Resolution Enhancement using DWT Technique
Abstract: Satellite images are used in many applications such as geosciences studies, astronomy, and geographical information systems. One of the most important quality factors in images comes from its resolution. Interpolation in image processing is a well-known method to increase the resolution of a digital image. Interpolation has been widely used in many image-processing applications such as facial reconstruction, multiple-description coding, and resolution enhancement. In this project, we propose a new satellite image resolution enhancement technique based on the interpolation of the high-frequency sub bands obtained by discrete wavelet transform (DWT) and the input image.
The proposed resolution enhancement technique uses DWT to decompose the input image into different subbands.
Then, the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution-enhanced image by using inverse DWT. In order to achieve a sharper image, an intermediate stage for estimating the high-frequency sub bands has been proposed. The proposed technique has been tested on satellite benchmark images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.
Keywords: Discrete wavelet transform (DWT), interpolation, satellite image resolution enhancement, wavelet zero
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58. padding.(keywords)
References:
1.
L. Yi-bo, X. Hong, and Z. Sen-yue, “The wrinkle generation method for facial reconstruction based on extraction of partition wrinkle line features and fractal interpolation”.
2.
T. Celik, C. Direkoglu, H. Ozkaramanli, H. Demirel, and M. Uyguroglu, “Region-based super-resolution aided facial feature extraction from low-resolution video sequences”.
3.
H. Demirel and G. Anbarjafari, “Satellite image resolution enhancement using complex wavelet transform”.
4.
H. Demirel, G. Anbarjafari, and S. Izadpanahi, “Improved motion-based localized super resolution technique using discrete wavelet transform for low resolution video enhancement”.
5.
Y. Rener, J. Wei, and C. Ken, “Downsample-base multiple description coding and post-processing of decoding”.
6.
X. Li and M. T. Orchard, “New edge-directed interpolation”.
7.
Y. Piao, L. Shin, and H. W. Park, “Image resolution enhancement using inter-subband correlation in wavelet domain”.
8.
G. Anbarjafari and H. Demirel, “Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image”.
9.
M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, “Wavelet-based statistical signal processing using hidden Markov models”.
10.
C. B. Atkins, C. A. Bouman, and J. P. Allebach, “Optimal image scaling using pixel classification”.
11.
K. Kinebuchi, D. D. Muresan, and T. W. Parks, “Image interpolation using wavelet based hidden Markov trees”.
12.
W. K. Carey, D. B. Chuang, and S. S. Hemami, “Regularity-preserving image interpolation”.
Authors: Ruchika Jain, Deepak Bhatnagar, Kirti Vyas
Paper Title: Design of a Rectangular Fractal Patch Geometry for Modern Communication Systems
Abstract: In modern wireless communication systems, compact multiband antennas with wider bandwidth and low profile configuration are in great demand. This has initiated antenna research in many directions. One of such antennas is a fractal shaped antenna element. The fractal structures may be designed by stacking of numerous small units together in such a way that the final geometry has the same shape as that of the unit structure. In addition to their larger bandwidths, fractal antennas are compact in size relative to the conventional antennas because of their efficient volume filling nature. In the present paper, design of new compact fractal multiband antenna geometry is presented. This antenna is targeted for application in Wi-MAX communication systems which operates in three bands namely lower band (2.3 to 2.9 GHz), medium band (3.4 to 3.96 GHz) and upper band (5.25 to 5.85 GHz). Two iterations of the rectangular fractal multiband antenna, with probe feeding in 2-6 GHz band are examined. The proposed antenna resonates at three frequencies namely 2.8 GHz, 3.95 GHz and 5.8 GHz after 2nd iteration. These three frequencies lie in the three considered bands. From the return loss variations with frequency; it is realized that proposed antenna may also be used for Wi-Fi (5.1-5.825 GHz) applications. Finally this antenna is fabricated and its return loss, VSWR and input impedance variations with frequency are tested.
Keywords: Wi-MAX, VSWR
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14. Chakrabarty S. B., Klefenz F., Dreher A., Sharma S. B. and Schroth A. (2003) “Dual polarized stacked SSFIP array antenna in C-band for beam-pointing” International Journal of Electronics, 2003, VOL. 90, NO. 8, 533–541.
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Authors: Manoj Vishnoi, Arun Kumar, Minakshi Sanadhya
Paper Title: Design of Improved Built-In-Self-Test Algorithm (8n) for Single Port Memory
Abstract: This paper presents a modified March (8n) test algorithm to the Built-In Self-Test (BIST) for Single Port
Memory. In this algorithm, test patterns are complemented to generate state-transitions that are needed for the detection of frequently occurring as well as newer occurring faults with the shrinking of channel length. The test pattern will be generated for the single port memory. The use of 8n pattern to generate state transition allow to reducing both of time and energy for detection of faults. As a result, the number of test patterns required is very less than of the traditional method, while the extra hardware is negligible.
Keywords: March Algorithm, BIST (Built-In-Self-Test), Channel Length, Faults, Test-Pattern.
References:
1.
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281-285
Authors: Sanjay Sharma, Amarjeet Kaur, Shaweta Narula, Meenu Jangir, Ashish Sharma, Hemchand Vashist
Design of High Breakdown Voltage and Power Dissipation of 6H-SIC DIMOSFET Using Uniformly
Paper Title:
Doped Profile of Drift Region.
Abstract: In this paper a novel approach for designing of High Breakdown Voltage and Power Dissipation of 6H-
SiC DIMOSFET Using Uniformly Doped Profile of Drift Region has been presented. All the calculations& graphs for Forward Voltage, Power Dissipation, On Resistance and Drain to Source Voltage at different levels of doping for different values of Current Density have been observed using MATLAB 7.0 .
Keywords: DIMOSFET, Forward Voltage, Power Dissipation, On Resistance, Drain to Source Voltage, Current
Density, Doping, MATLAB
References:
1.
MUNISH VASHISHATH1 AND ASHOKE K. CHATTERJEE “ANALYTICAL ESTIMATION OF BREAKDOWN VOLTAGE AND
POWER DISSIPATION OF DOUBLE IMPLANTED MOSFET ON 6H SILICON CARBIDE WAFER WITH LINEARLY GRADED
PROFILE” JOURNAL OF ELECTRON DEVICES, VOL. 7, PP. 214-224, 2010.
2.
RAJNEESH TALWAR AND A.K.CHATTERJEE, “ESTIMATION OF POWER DISSIPATION OF A 4H-SIC SCHOTTKY BARRIER
DIODE WITH A LINEARLY GRADED DOPING PROFILE IN THE DRIFT REGION”, MAEJO INTERNATIONAL JOURNAL OF
SCIENCE AND TECHNOLOGY, VOL.03, ISSUE 02, PP. 352-36, 2009.
3.
M. HASANUZZAMAN, S. K. ISLAM, L. M. TOLBERT AND BURAK OZPINECI, “MODEL SIMULATION AND VERIFICATION OF
VERTICAL DOUBLE IMPLANTED (DIMOSFET) TRANSISTOR IN 6H-SIC”, INT. J. MODELING AND SIMULATION, VOL.4, PP.
1-4, 2003.
4.
J. A. COOPER, M. R. MELLOCH, R. SINGH, A. AGGARWAL, AND J. W. PALMOUR, “STATUS AND PROSPECT FOR SIC
MOSFET”, IEEE TRANS. ELECTRON DEVICES, VOL. 49, PP. 658-664, APRIL, 2002.
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S. RYU, K. T. KORNEGAY, J. A. COOPER, JR., AND M. R. MELLOCH, " DIGITAL CMOS ICS IN 6H-SIC OPERATING ON A 5V
POWER SUPPLY," TO APPEAR IN IEEE TRANS. ON ELECTRON DEVICES, 1998.
8.
SPITZ, M. R. MELLOCH, J. A. COOPER, JR., AND M. A. CAPANO,"HIGH-VOLTAGE (2.6 KV) LATERAL DMOSFETS IN 4H-SIC,"
IEEE ELECTRON DEVICE LETT., 19, 100, 1998.
9.
J. N. SHENOY, J. A. COOPER, JR., AND M. R. MELLOCH,"HIGH-VOLTAGE DOUBLE-IMPLANTED POWER MOSFETS IN 6H-
SIC," IEEE ELECTRON DEVICE LETT., 18, 93, 1997.
286-291
Authors: Kapil Kumar Bansal, Rakesh Kumar Yadav
61.
Paper Title: Analysis of Sliding Window Protocol for Connected Node
Abstract: Abstract- In conventional networks lack of reliable and efficient transmission of data over unreliable channels that can lose, reorder and duplicate messages due to router or receiver limited buffer space, are retransmitted by the source. These scenarios usually data blocks contain error detection codes to detect whether an
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62.
63. error has occurred during data transmission or not. If an error is detected, the receiver can try to fix the error if the received data has enough redundant bits or request a retransmission of data, for fulfill this requirement sliding window protocol is use. This paper shows calculated link utilization is better than observed link utilization for sliding window protocol using Deft Netz2.0 network simulation.
Keywords: Network, Congestion, Buffer, Simulation, Bandwidth, Reliable, Efficient, Netz2.0.
References:
1.
Wang et al., Forward Error-Correction Coding Crosslink - The Aerospace Corporation magazine of advances in aerospace technology. The
Aerospace Corporation. Vol-3,Winter 2002.
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Verification of a Sliding Window Protocol in LOTOS. FORTE '91, Sydney, Australia, , pages 495-510 November 1991,.
3.
Mark A. Smith and Nils Klarlund. Verification of a Sliding Window Protocol Using IOA and MONA. FORTE/PSTV 2000, Pisa, Italy, pages
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4.
Dmitri Chkliaev, Jozef Hooman and Erik de Vink. Formal Verification of an Improved Sliding Window Protocol. In proceeding of the
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Authors: Govind Kumar Rahul, Madhu Khurana
Paper Title: A Comparative Study Review of Soft Computing Approach in Weather Forecasting
Abstract: In a developing country, like India where the agriculture & industries are base for the national economy, the weather conditions play leading role for their proper development and smooth running. Therefore having accurate weather forecasting information may allow farmers or industry managers to make better decisions on managing their farms. Soft computing using ANN is an innovative approach to construct a computationally intelligent system that is able to process non-linear weather conditions within a specific domain, and make prediction. A number of researches have been done or being done using Soft Computing Approach for forecasting. In this paper the presentation is all about to present the comparative study of several researches and some key findings that are initials for better start any soft computing model for prediction.
Keywords: Soft Computing, Artificial Neural Network (ANN), Back Propagation Algorithms, Multilayer Feed
Forward Neural network (MLFFNN), Mean Square Error (MSE) etc.
References:
1. Mohsen Hayati & Zahra Mohebi, Temperature Forecasting Based on Neural Network Approch World Applied Science Journal 2(6) 613-
620, 2007,ISSN 818-4952 ©IDOSI Publications 2007
2. Dr.S.Santhosh Baboo & I.Kadar Shereef,An efficient weather forecasting system using Artificial Neural network International Journal of
Environment Science & Development, W1.1, No.4, October-2010,ISSN: 2010-0264
3. S.S. De, Artificial Neural Network Based Prediction of Max. & Min. Temperature in the Summer-Monsoon month over India.University of
Kolkata,Applied Physics Research,Vol.1,No.2,Nov-2009
4. Ch.Jyosthna Devi #1, B.Syam Prasad Reddy#2, K.Vagdhan Kumar#3,B.Musala Reddy#4,N.Raja Nayak#5,ANN Approach for Weather
Prediction using Back propagation,International Journal of Engineering Trends and Technology- Volume3Issue1- 2012,ISSN: 2231-5381
5. Arvind Sharma,PG Research Group (M.Tech. CSE), SATI, A Weather Forecasting System using concept of Soft Computing: A new approach, Vidisha (MP) India,1-4244-0716-8/06/$20.00 ©2006 IEEE. 353-356
6. C.N. Schizas'BJ S. Michaelides' C.S. Pattichis3 R.R. Livesay, Artificial Neural Network in Forecasting Minimum Temperature, University of Indianapolis, U.S.A * Meteorological Service, Cyprus,The Cyprus lnstiute of Neurology and Genetics, Cyprus, IEEE Paper
7. Y.Radhika & M.Shashi, Atmospheric Temperature prediction using Support Vector Machine. International Journal of Computer Theory &
Engineering, Vol.1.No.1.April 2009 1793-8209
8. 1P.P. Kadu, 2Prof. K.P. Wagh, 3Dr. P.N. Chatur3,A Review on Efficient Temperature Prediction System Using Back Propagation Neural
Network, International Journal of Emerging Technology and Advanced Engineering (ISSN 2250-2459, Volume 2, Issue 1, January 2012).
9. Fatemeh Mosalman1*, Alireza Mosalman1, H. Mosalman Yazdi and M. Mosalman Yazdi2,One day-ahead load forecasting by artificial neural network,Scientific Research and Essays Vol. 6 (13), pp. 2795-2799, 4 July,2011,www.academicjournals.org/SRE,ISSN 1992-2248
©2011
10. Smith, B.A., R. W. McClendon, and G. Hoogenboom, Air Temperature Prediction Using Artificial Neural Networks,. Published in
International Journal of Computational Intelligence.
295-299
Authors: S.Ramesh, Ravi Ranjan, Ranjeet Mukherjee, Swarnali Chaudhuri
Paper Title: Vehicle Collision Avoidance System Using Wireless Sensor Networks
Abstract: Collision Avoidance systems, as a subsequent step to collision mitigation, are one of the Great challenges in the area of active safety for road vehicles. In India the total annual deaths due to road accidents has crossed 1.18 lakh, according to the latest report of National Crime Records Bureau (NCRB). If these deficiencies are not controlled at early stages they might cause huge economical problems affecting the road side networks. The main part of the work was to carry out a feasibility study on vehicle collision avoidance system using wireless sensor networks. The collision avoidance can be done by Laser sensor. Vehicle collision avoidance system can be identified by using Laser rays with the laser transmitter and laser receiver. Laser transmitter is connected to the laser sensor.
Can controller is connected to the all sides of the nodes and send the information via Zigbee and transmit the message to the LCD output on the driver side. Laser receiver is connected to the can controller.
Keywords: Can controller, Collision, Laser, Zigbee, LCD
References:
1.
FadiBasma research assistant ,Hazem H. Refai associate professor,Collision Avoidance System at Intersections, FINAL REPORT - FHWA-
OK-09-06,ODOT SPR ITEM NUMBER 2216
2.
Jonathan W. Steed and Jerry L. Atwood (2009). Supramolecular Chemistry (2nd ed.). John Wiley and Sons.p. 844.ISBN 978-0-470-51234-0.
3.
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Sohraby, K., Minoli, D., Znati, T. "Wireless sensor networks: technology, protocols, and applications, John Wiley and Sons", 2007 ISBN
978-0-471-74300-2, pp. 203–209
7.
Renjun Li, Chu Liu and FengLuo, A Design for Automotive CAN Bus Monitoring System, IEEE Vehicle Power and Propulsion
Conference(VPPC), September 3-5, 2008, Harbin, China.
8.
Fang Li, Lifang Wang, Chenglin Liao, CAN (Controller Area Network) Bus Communication System Based on Matlab/Simulink. Available: http://www.ieeexplore.ieee.org