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International Journal of Soft Computing and EngineeringInternational Journal of Soft Computing and EngineeringInternational Journal of Soft Computing and Engineering
n E d n g i na e g e n i r t i n u g p m o C t f o S I n f t eo l r n a a n r t i u o o n J a l
IJSCEIJSCE
Exploring Innovation
www.ijsce.org
EXPLORING INNOVA
TION
ISSN : 2231 - 2307Website: www.ijsce.org
Volume-4 Issue-2, May 2014Volume-4 Issue-2, May 2014
Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
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
Volume-4 Issue-2, May 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.
Page
No.
1.
Authors: Nihar Ranjan, Neha Thombare, Pallavi Deshmukh, Simantini Patil, Shailesh Jain
Paper Title: Personalized Image Search
Abstract: Personalized Search is a feature in which when a user is logged into a account, all of his or her searches
on Personal Search are recorded into Web History. Then, when a user performs a search, the search results are not
only based on the relevancy of each web page to the search term, but the service also takes into account what
websites the user previously visited through search results to determine which search results to determine for future
searches, to provide a more personalized experience. The feature only takes effect after the user has performed
several searches, so that it can be calibrated to the user's tastes. Social sharing websites like facebook, twitter,
YouTube they are allowing user to comment, tag, like and unlike the shared documents or images. Rapid Increase
in the search services for social websites has been developed.
Keywords: Personalized Search, Tagging, Topic Model
References: 1. Learn to Personalized Image Search from the Photo Sharing Websites Jitao Sang, Changsheng Xu, Senior Member, IEEE, Dongyuan Lu
2. B. Smyth, “A community-based approach to personalizing web search,”Computer, vol. 40, no. 8, pp. 42–50, 2007. 3. Personalized Search on Flickr based on Searcher’s Preference Prediction Dongyuan Lu, Qiudan Li
1-3
2.
Authors: Bipin Pandey, Rituraj Jain
Paper Title: Soft Computing Based Approaches for Software Testing: A Survey
Abstract: Software testing is the process of validation and verification of the software product which in turn
deliver the reliable and quality oriented software product to users with lower maintenance cost, and more accurate
and reliable results. Software testing effectiveness always depends on issues like generated test cases, prioritization
of test cases etc. These issues demands on effort, time and cost of the testing. Many academicians and researchers
are using soft computing based approached for better accuracy in testing. The aim of this research paper is to
evaluate and compare soft computing approaches to do software testing and determine their usability and
effectiveness.
Keywords: Black Box Testing, Fuzzy Logic, Genetic Algorithms, Neural Network, Soft Computing, Software
Testing, Tabu Search, White Box testing
References: 1. Dr. Velur Rajappa, Arun Biradar, Satanik Panda "Efficient software test case generation Using Genetic algorithm based Graph theory"
International conference on emerging trends in Engineering and Technology, pp. 298-303, IEEE (2008). 2. Praveen Ranjan Srivastava and Tai-hoon Kim "Application of Genetic algorithm in software testing", International Journal of software
Engineering and its Applications, vol.3, No.4, pp. 87-96 (2009).
3. André Baresel , Hartmut Pohlheim , Sadegh Sadeghipour, Structural and functional sequence test of dynamic and state-based software with evolutionary algorithms, Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII, July 12-
16, 2003, Chicago, IL, USA
4. O. Buehler and J. Wegener. Evolutionary functional testing of an automated parking system. In International Conference on Computer, Communication and Control Technologies and The 9th International Conference on Information Systems Analysis and Synthesis,
Orlando, Florida, USA, 2003.
5. H. P. Schwefel and R. Manner, editors, Parallel Problem Solving From Nature, pages 176–185. Springer-Verlag, October 1990
6. D E Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley, Reading, 1989.
7. J Holland, “Adaptation in Natural and Artificial Systems”, MIT Press, Cabmridge, MA, 1975.
8. D Koza, “Genetic Programming, On the Programming of Computers by Means of Natural Selection”, MIT Press, Cambridge, MA, 1992. 9. P von Laarhoven and E Aarts, “Simmulatd Annealing: Theory and Applications, Mathematics and its Applications” Kluwer, Dordrecht,
1987.
10. http://www.softcomputing.net.in/ (Access on 15th Feb, 2014) 11. Chattopadhyay S (2006), “Soft Computing Techniques in combating the complexity of the atmosphere-a review”, Arxiv preprint
nlin/0608052.
12. David E. Goldberg (1989)” Genetic Algorithm in Search, Optimization and Machine Learning”, Pearson Education-India. 13. M. Melanie, “An Introduction to Genetic Algorithms, Massachusetts”, MIT Press, 1999.
14. K. F. Man , K. S. Tang and S. Kwong "Genetic algorithms: Concepts and applications", IEEE Trans. on Industrial Electronics, vol. 43,
no. 5, pp.519 -534 1996 15. S. Sabharwal, R. Sibal and C. Sharma, "Applying genetic algorithm for prioritization of test case scenarios derived from UML diagrams",
International Journal of Computer Science Issues (IJCSI), vol. 8, No. 2, pp. 433-444, May 2011.
16. Radim Belohlavek and George J. Klir. “Concepts and fuzzy logic”, Cambridge Mass. London: MIT Press, 2011 17. http://www.seattlerobotics.org/encoder/mar98/fuz/flindex.html, (Access on 18th Feb, 2014)
18. [18] Maureen Caudill, “Neural networks primer, part I”, AI Expert, v.2 n.12, p.46-52, Dec. 1987
19. Zupan, J. (1994) “Introduction to artificial neural network (ANN) methods: what they are and how to use them” Acta Chim. Slov. 41, 327–352.
20. Naresh Chauhan, “Software Testing: Principles and Practices”, Oxford University Press, 2010. 21. Paul C. Jogersen, “Software testing: A craftsman approach” 3rd edition, CRC presses, 2008.
22. http://khannur.com/stb6.2.htm (access date: 19th Feb, 2014)
23. Z. Bo and W. Chen, "Automatic generation of test data for path testing by adaptive genetic simulated annealing algorithm," in Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on, 2011, pp. 38-42.
24. Praveen Ranjan Srivastava et. al., “Generation of test data using Meta heuristic approach” IEEE, 2008, pp.19 - 21.
25. Ribeiro, J. C. B., Zenha-Rela, M. A., and de Vega, F. F. (2008a), “A Strategy for Evaluating Feasible and Unfeasible Test Cases for the Evolutionary Testing of Object-Oriented Software” In Proceedings of the 3rd international workshop on Automation of Software Test
(AST ’08) , pages 85–92, Leipzig, Germany. ACM.
26. Girgis, “Automatic test generation for data flow testing using a genetic algorithm”, Journal of computer science, 11 (6), 2005, pp. 898 –
4-8
915. 27. Yeresime Suresh et. al, “A Genetic Algorithm based Approach for Test Data Generation in Basis Path Testing” The International Journal
of Soft Computing and Software Engineering, Vol. 3, No. 3, Special Issue [SCSE’13], March 2013
28. Premal B. Nirpal and Kale K.V.(2010), “Comparison of Software Test Data for Automatic Path Coverage Using Genetic Algorithm”, Internal Journal of Computer Science and Engineering Technology, Vol. 1, Issue 1.
29. Jasmine Minj Lekhraj Belchanden, “Path Oriented Test Case Generation for UML State Diagram using Genetic Algorithm” International
Journal of Computer Applications (0975 – 8887) Volume 82 – No 7, November 2013 30. Eugenia Díaz , Javier Tuya , Raquel Blanco , José Javier Dolado, “A tabu search algorithm for structural software testing”, Computers and
Operations Research, v.35 n.10, p.3052-3072, October, 2008
31. Francisca Emanuelle et. al., “Using Genetic algorithms for test plans for functional testing”, 44th ACM SE proceeding, 2006, pp. 140 - 145.
32. Mark Last et. al., “Effective black-box testing with genetic algorithms”, Lecture notes in computer science, Springer, 2006, pp. 134 -148.
33. Chartchai Doungsaard, Keshav Dahal, Alamgir Hossain, and Taratip Suwannasart, 2007, “An Automatic Test Data Generation from UML State Diagram using Genetic Algorithm”, The proceedings of the Second International Conference on Software Engineering Advances.
34. H. Bhasin, S. Gupta, M. Kathuria, “Regression testing using fuzzy logic”, International Journal of Computer Science and Information
Technology (IJCSIT), 4(2), pp. 378-380, 2013. 35. Abhas Kumar, “Dynamic Test Case Generation using Neural Networks”,
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.3110&rep=rep1&type=pdf (Access on 20th Feb, 2014)
3.
Authors: Manisha P. Khorgade, Shweta Hajare, P.K.Dakhole
Paper Title: Structural level designing of Processing Elements using VHDL
Abstract: This paper involves structural design and development of processing elements using Hardware
Description Language (HDL) using Altera or Xilinx softwares and implements them on Field Programmable Gate
Arrays (FPGAs). In this paper, we will simulate and synthesize the various parameters of processing elements by
using VHDL on Xilinx ISE 13.1 and target it for SPARTAN 6 FPGA board. The output is displayed by means of
Liquid Crystal Display (LCD) interface. The state of each output bit is shown by using Light Emitting Diodes
(LED). The processor can perform 2n number of operations where n is the control bit. More number of designs can
be implemented on FPGA as per user’s needs.
Keywords: FPGA, XILINX ISE 13.1, SPARTAN 6.
References: 1. V. Khorasani, B. V. Vahdat, and M. Mortazavi, “Design and implementation of floating point ALU on a FPGA processor”, IEEE
International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 772-776, 2012. 2. Suchita Kamble, Prof .N. N. Mhala, “VHDL Implementation of 8-Bit ALU”, IOSR Journal of Electronics and Communication
Engineering (IOSRJECE), ISSN : 2278-2834 Volume 1, Issue 1 (May-June 2012), PP 07-1.
3. Prof. S. Kaliamurthy & Ms. U. Sownmiya, "VHDL design of arithmetic processor", Global Journals Inc. (U.S.A), November 2011. 4. S.Kaliamurthy, R.Muralidharan, “VHDL Design of FPGA Arithmetic Processor” International Conference on Engineering and ICT,
2007.
5. Charles H. Roth, Jr., "Digital system design using VHDL", PWS publishing company, 2006. 6. B.Stephen Brown, V. Zvonko, “Fundamentals Of digital logic with VHDL Design”, 2nd Edition, McGraw Hill International Edition,
2005.
7. Bryan H. Fletcher, “FPGA Embedded Processors”, 8. Embedded Systems Conference San Francisco 2005
9. ETP-367.
10. J. Bhaskar, “VHDL Primer”, Pearson Education, 3rd edition, 2000. 11. Douglas L.Perry, “VHDL”, tata mc grawhill, international edition 1999.
12. Module 4: Design of Embedded Processor, Lesson 20: Field Programmable Gate Arrays and Applications, Version 2, EE IIT
Kharagpur.
9-13
4.
Authors: Maitham Ali Naji
Paper Title: Implementation of Encryption Data Table byUsing Multi-Keys
Abstract: The paper presents an encryption application that is able to work with data access table. In this paper
the Caesar method is developed to generate one key to each record. The length of the key is computed from first
word of record. The record after encryption will be stored in separate line in text file that separate each field by
semicolon, this process will continue until the end of table.
Keywords: Caesar Cipher, Database Encryption, Text File,and Visual Basic.
References: 1. Lee, K, H., "Basic Encryption and Decryption", on line document
2. http://www.apprendre-en-ligne.net/crypto/bibliotheque/PDF/Kwang.pdf 3. Freeman J., Neely R., and Megalo L., "Developing Secure Systems", IEEE Journal of Computer and Communication, Vol. 89, PP. 36-45,
1998.
4. "The Basics of Cryptography", on line documents ftp://ftp.pgpi.org/pub/pgp/6.5/docs/english/IntroToCrypto.pdf 5. Fernandez EB, Summers RC, Wood C, "Database Security and Integrity", Addison-Wesley, Massachusetts, 127, 1980.
6. M. Bellare, A. Desai, E. Jokipii, P. Rogaway, "A Concrete Security Treatment of Symmetric Encryption", In Proceedings of the 38th
Symposium on Foundations of Computer Science, IEEE, 1997. 7. O. Goldreich, "Foundations of Cryptography", Cambridge University, Press, 2003.
8. Stalling W., "Cryptography and Network Security Principles and Practices", Printice Hill publishing, PP. 36, 2005.
14-17
5.
Authors: Sunil Kumar Mishra, Vishakha Nandanwar, Eskinder Anteneh Ayele, S.B. Dhok
Paper Title: FPGA Implementation of Single Precision Floating Point Multiplier using High Speed Compressors
Abstract: Floating point multiplier is one of the vital concerns in every digital system. In this paper, the concepts
of High speed compressors are used for the implementation of a High speed single precision binary Floating point
multiplier by using IEEE 754 standard. Since compressors are special kind of adder which is capable to add more
number of bits at a time, the use of these compressors makes the multiplier faster as compared to the conventional
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multiplier. For Mantissa calculation, a 24×24 bit multiplier has been developed by using these compressors. Owing
to these high speed compressors, the proposed multiplier obtains a maximum frequency of 1467.136MHz. It is
implemented using Verilog HDL and it is targeted for Xilinx Virtex-5 FPGA.
Keywords: Compressors, Floating point multiplier, Mantissa, IEEE754 standard, Verilog HDL.
References: 1. Dandapat, S. Ghosal, P. Sarkar, D. Mukhopadhyay, “A 1.2-ns16×16-Bit Binary Multiplier Using High Speed Compressors”,
International Journal of Electrical and Electronics Engineering, 2010.
2. Shubhajit Roy Chowdhury, Aritra Banerjee, Aniruddha Roy, Hiranmay Saha,”Design, Simulation and Testing of a High Speed Low
Power 15-4 Compressor for High Speed Multiplication Applications”, First International Conference on Emerging Trends in Engineering and Technology, 2008.
3. Jeevan, S. Narender, Dr C.V. Krishna Reddy, Dr K. Sivani,”A High Speed Binary Floating Point Multiplier Using Dadda Algorithm”,IEEE,2013.
4. Loucas Louca, Todd A. Cook, William H. Johnson, “Implementation of IEEE Single Precision Floating Point Addition and
Multiplication on FPGAs”, IEEE,1996. 5. Shaifali, Sakshi, “ FPGA Design of Pipelined 32-bit Floating Point Multiplier”, International Journal of Computational Engineering &
Management, Vol. 16, 5th September 2013.
6. IEEE 754-2008, IEEE Standard for Floating-Point Arithmetic, 2008. 7. Mohamed Al-Ashrafy, Ashraf Salem, Wagdy Anis, “An Efficient Implementation of Floating Point Multiplier”, IEEE, 2008.
8. Guy Even, Silvia M. Mueller, Peter-Michael Seidel,” A dual precision IEEE floating-point multiplier”, INTEGRATION the VLSI
journal, pp167-180, 2000. 9. M. Morris Mano, “Digital Design”,3rd edition, Prentice Hall,2002
6.
Authors: Wojciech Wodo, Lucjan Hanzlik, Konrad Zawada
Paper Title: USB Keyboard Security Unit
Abstract: Every user has its specific rhythm of typing which could be used as a biometrics in order to build some
kind of "footprint" – unique profile. If somebody gets to know this profile, legitimate user is endangered by tracking
and being impersonated. That is the way typing rhythm must be protected. We designed a hardware-based device in
order to protect the identity of the individual during usage of keyboard (typing). The unit is plugged between the
keyboard and the personal computer and works as an interface modifying data on the fly in the model "man in the
middle". Thanks to these modifications, an adversary who eavesdrops communication between a legitimate user and
workstation gets practically no information about the "keystroking identity" of user. The security unit is based on
two microprocessors: AVR AT90USB1287 working as USB Host - simulating workstation and AVR Atmega88
working as USB Device - simulating virtual keyboard. In the paper we present technical details of the security unit
including electronic schemes and PCB referring to previously designed protection algorithms and results of
performed efficiency tests as well.
Keywords: biometrics, security and privacy protection, microprocessors and microcomputers, user interfaces,
human factors in software design.
References: 1. Giot, R., El-Abed, M., Hemery, B., and Rosenberger, C. (2011). Unconstrained Keystroke Dynamics Authentication with Shared Secret.
In Computers & Security 30(6-7), pages 427-445.
2. Fridman, A.; Stolerman, A.; Acharya, S.; Brennan, P.; Juola, P.; Greenstadt, R., and Kam, M., (2013). Decision Fusion for Multi-Modal
Active Authentication. In IT Professional 15(4), pages 29-33. 3. Zhong, Y., Deng, Y., and Jain, A. K. (2012). Keystroke dynamics for user authentication. In CVPR Workshops, pages 117–123.
4. Klonowski, M., Syga, P., and Wodo, W. (2012). Some remarks on keystroke dynamics - global surveillance, retrieving information and simple countermeasures. In SECRYPT, pages 296–301.
5. Hanzlik, L., and Wodo, W., (2013). Identity Security in Biometric Systems Based on Keystroking. In SECRYPT, pages 524–530.
6. Universal Serial Bus (USB), Device Class Definition for Human Interface Devices (HID), 2001, [Online] 7. http://www.usb.org/developers/devclass_docs/HID1_11.pdf
8. Documentation for 8-bit Atmel Microcontroller with 64/128 Kbytes of ISP Flash and USB Controller
9. AT90USB646,AT90USB647,AT90USB1286,AT90USB1287, 2012, [Online] http://www.atmel.com/Images/doc7593.pdf 10. Documentation for 8-bit Atmel Microcontroller with 4/8/16K Bytes In-System Programmable Flash ATmega48/V, ATmega88/V,
ATmega168/V, 2011, [Online]
11. http://www.atmel.com/images/doc2545.pdf 12. LUFA Library Documentation, 2013, [Online]
13. http://www.fourwalledcubicle.com/files/LUFA/Doc/130303/html
14. V-USB, A Firmware-Only USB Driver for the AVR, [Online] 15. http://vusb.wikidot.com
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7.
Authors: V.Sivaranjani, J.Umamaheswari
Paper Title: Comparison of Repeating Pattern Extraction Techniques for Audio Pitch Detection
Abstract: Music separation methods are more demanding and complex, demanding system “training,” user
designation of special music features, and audio processing time to support their complicated frameworks. Pattern
extraction from music strings is an complex problem. The repeated sequence extracted from music strings can be
used as features for music extracted or compared. various works on music pattern extraction only focus on exact
repeating patterns. However, music segments with minor differences may sound similar. Present the REpeating
Pattern Extraction Technique (REPET), a novel and simple approach for separating the repeating “background”
from the non-repeating “foreground” in a mixture. The basic idea is to identify the periodically repeating segments
in the audio, compare them to a repeating segment model derived from them, and extract the repeating patterns via
time-frequency masking. But in proposed system doesn’t support the Small rhythmic patterns, but rhythmic
patterns are essential for the balance of the music, and can be a way to identify a song. And enhanced a method to
extract a monophonic rhythmic signature from a symbolic polyphonic score. To go beyond the simple extraction of
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all time intervals between onsets we select notes according to their length (short and long extractions) or their
intensities (intensity+/− extractions). Once the frequency is calculated, now use dynamic programming to compare
several sequences of audio.
Keywords: Pitch extraction, musical information retrieval, audio mining, pitch tracking, pattern extraction,
audio segments.
References: 1. Jonathan T. Foote, “An Overview of Audio Information Retrieval”, In Multimedia Systems, vol. 7 no. 1, pp. 2-11, ACM Press/Springer-
Verlag, January 1999..
2. Kirthika, P. ; Chattamvelli, R. ,“A review of raga based music classification and music information retrieval (MIR)”, Engineering Education: Innovative Practices and Future Trends (AICERA), 2012 ,Digital Object Identifier: 10.1109/AICERA.2012.6306752
Publication Year: 2012 , Page(s): 1 – 5
3. Krishnaswamy, A, “Application of pitch tracking to South Indian classical music”, Applications of Signal Processing to Audio and Acoustics, 2003 IEEE Workshop on 19-22 Oct. 2003.
4. Lew, Michael S.; Sebe, Nicu; Djeraba, Chabane; Jain, Ramesh , “Content- based multimedia information retrieval: State of the art and
challenges” , ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 2, No. 1, February 2006, Pages 1–19..
5. Parag Chordia, “Automatic raag classification of pitch tracked performances using pitch-class and pitch-class dyad distributions”, In
Proceedings of International Computer Music Conference, 2006.
6. https://ccrma.stanford.edu/~pdelac/research/MyPublishedPapers/icmc_2001-pitch_best.pdf
7. http://www.musipedia.org
8. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2629434/ 9. http://www.riaa.org
10. http://www.shazam.com
11. http://en.wikipedia.org/wiki/Audio_mining and shazam
8.
Authors: Rama Shanker, Shambhu Sharma, Uma Shanker, Ravi Shanker, Tekie Asehun Leonida
Paper Title: The Discrete Poisson-Janardan Distribution with Applications
Abstract: In the present paper a discrete Poisson-Janardan distribution (PJD), of which the Sankaran’s (1970)
discrete Poisson-Lindley distribution (PLD) is a particular case, has been obtained by compounding Poisson
distribution with the Janardan distribution of Shanker et al (2013). The first four moments of this distribution have
been obtained and the estimation of its parameters using the method of maximum likelihood and the method of
moments has been discussed. The distribution has been fitted to some data-sets to test its goodness of fit and its
fitting of two data sets has been presented.
Keywords: Poisson-Lindley distribution, Janardan distribution, compounding, moments, estimation of
parameters, goodness of fit.
References: 1. Beall, G. (1940): The fit and significance of contagious distributions when applied to observations on larval insects, Ecology, Vol. 21,
460-474
2. Cochran, W. G. (1952): The test of goodness of fit, Annals of Mathematical Statistics, Vol. 23, pp. 315- 345.
3. Cochran, W.G. (1954): Some methods for strengthening the common tests, Biometrics, Vol. 10, pp. 417 - 451. 4. Ghitany, M. E., and Al-Mutairi, D.K. (2009): Estimation Methods for the discrete Poisson-Lindley distribution, Journal of Statistical
Computation and Simulation, Vol.79 (1), 1 – 9.
5. Kemp, C.D. and Kemp, A.W. (1965): Some properties of the Hermite distribution, Biometrika, Vol. 52, 381-394. 6. Lindley, D. V. (1958): Fiducial distributions and Bayes theorem, Journal of Royal Statistical Society, Ser. B, Vol.20, 102-107
7. Sankaran, M. (1970): The discrete Poisson-Lindley distribution, Biometrics, Vol. 26, 145-149. 8. Shanker, R., Sharma, S., Shanker, U., and Shanker, R. (2013): Janardan distribution and its Applications to Waiting times data, Indian
Journal of Applied Research, Vol. 3, Issue 8, pp. 500 – 502.
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9.
Authors: Le Ngoc Son, Daji Ergu, Pham Xuan Kien
Paper Title: A New Approach for Dealing with Uncertain Degree in Group Judgment Aggregation using
Triangular Intuitionistic Fuzzy Numbers
Abstract: The goal of this paper is to propose a new approach for aggregating group judgment using the
triangular intuitionistic fuzzy number (IFN). The original group decision making (GDM) problems are converted to
a triangular intuitionistic fuzzy decision making model by adding one simple conversion step which generates
triangular IFNs from the mean and deviation of group judgment values to the process of GDM methods and inherits
existing techniques. Using of triangular IFNs to express group judgment aggregation values keeps completely the
information after aggregating and reflects evaluation more truthfully. Consequently, the application of the proposed
model helps improve the efficiency and accuracy of GDM methods. In addition, an illustrative example is also
presented in order to put this process in detail and comparing with conventional methods.
Keywords: Group decision making, triangular intuitionistic fuzzy set, group judgment, group aggregation.
References: 1. Morais, Danielle C., and Adiel Teixeira de Almeida. "Group decision making on water resources based on analysis of individual
rankings." Omega 40.1 (2012): 42-52. 2. Ju, Yanbing, and Aihua Wang. "Emergency alternative evaluation under group decision makers: A method of incorporating DS/AHP with
extended TOPSIS." Expert Systems with Applications 39.1 (2012): 1315-1323.
3. Chaudhuri, Atanu, Bhaba Krishna Mohanty, and Kashi Naresh Singh. "Supply chain risk assessment during new product development: a group decision making approach using numeric and linguistic data." International Journal of Production Research 51.10 (2013): 2790-
2804. 4. Sanayei, Amir, S. Farid Mousavi, and A. Yazdankhah. "Group decision making process for supplier selection with VIKOR under fuzzy
environment." Expert Systems with Applications 37.1 (2010): 24-30.
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5. Zadeh, Lotfi A. "Fuzzy sets." Information and control 8.3 (1965): 338-353. 6. Atanassov, Krassimir T. "Intuitionistic fuzzy sets." Fuzzy sets and Systems 20.1 (1986): 87-96.
7. Xu, Zeshui, and Ronald R. Yager. "Some geometric aggregation operators based on intuitionistic fuzzy sets." International journal of
general systems 35.4 (2006): 417-433. 8. Xu, Zeshui. "Intuitionistic fuzzy aggregation operators." Fuzzy Systems, IEEE Transactions on 15.6 (2007): 1179-1187.
9. Wei, Guiwu. "Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision
making." Applied Soft Computing 10.2 (2010): 423-431. 10. Zhao, Hua, et al. "Generalized aggregation operators for intuitionistic fuzzy sets." International Journal of Intelligent Systems 25.1 (2010):
1-30.
11. Yu, Xiaohan, and Zeshui Xu. "Prioritized intuitionistic fuzzy aggregation operators." Information Fusion 14.1 (2013): 108-116. 12. Sadiq, Rehan, and Solomon Tesfamariam. "Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy
process (IF-AHP)." Stochastic Environmental Research and Risk Assessment 23.1 (2009): 75-91.
13. Zhao, Hua, et al. "Generalized aggregation operators for intuitionistic fuzzy sets." International Journal of Intelligent Systems 25.1 (2010): 1-30.
14. Li, Deng-Feng. "TOPSIS-based nonlinear-programming methodology for multiattribute decision making with interval-valued
intuitionistic fuzzy sets." Fuzzy Systems, IEEE Transactions on 18.2 (2010): 299-311. 15. Li, Jinquan, et al. "The relationship between similarity measure and entropy of intuitionistic fuzzy sets." Information Sciences 188 (2012):
314-321.
16. Boran, F. E., K. Boran, and T. Menlik. "The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS." Energy Sources, Part B: Economics, Planning, and Policy 7.1 (2012): 81-90.
17. Wan, Shu-Ping, Qiang-Ying Wang, and Jiu-Ying Dong. "The extended VIKOR method for multi-attribute group decision making with
triangular intuitionistic fuzzy numbers." Knowledge-Based Systems 52 (2013): 65-77.
18. Atanassov, Krassimir T. Intuitionistic fuzzy sets. Physica-Verlag HD, 1999.
19. Xu, Zeshui. "Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain
linguistic environment." Information Sciences 168.1 (2004): 171-184. 20. Mendel, Jerry M. "Fuzzy sets for words: why type-2 fuzzy sets should be used and how they can be used." presented as two-hour tutorial
at IEEE FUZZ, Budapest, Hongrie (2004).
21. Chan, Felix TS, and Hing Kai Chan. "An AHP model for selection of suppliers in the fast changing fashion market." The International Journal of Advanced Manufacturing Technology 51.9-12 (2010): 1195-1207.
22. Sanayei, Amir, S. Farid Mousavi, and A. Yazdankhah. "Group decision making process for supplier selection with VIKOR under fuzzy
environment." Expert Systems with Applications 37.1 (2010): 24-30. 23. Devi, Kavita. "Extension of VIKOR method in intuitionistic fuzzy environment for robot selection." Expert Systems with Applications
38.11 (2011): 14163-14168.
10.
Authors: Yousif I. Al Mashhadany, Eman Huassan, Aseel Najeeb
Paper Title: Design and Implementation of GUI Package for the Muscle Diseases Recognition Based on EMG
Signals
Abstract: An artificial neural network (ANN) provides a comprehensive and specialized service for the
diagnosis and care of muscle diseases. Medical consultations are offered at the neuromuscular clinics, which are
staffed by neurologists with special expertise in muscle diseases. This work presents the design and implementation
of muscle diseases detection based on real electromyography (EMG) signals. This paper consists of three main
parts. The first part presents the measurement of the signals of real human arm muscles (EMG signal). The data are
then rearranged and recorded using EMGLab software. Surface electrodes are used to measure the real EMG
signals. The suitable features of signal are extracted for classification. The second part applies signal requirements,
such as filtering amplification and normalization, using MATLAB or any software. Muscle diseases were classified
using an ANN package based on the features of EMG signals, amplitude of signals, and period of signals to identify
the diseases. The third part explains the design of the graphical user interface based on MATLAB to implement the
classification on real EMG signals. Satisfactory results are obtained from numerous executions with different cases
of human arm muscles, thus ensuring the feasibility of this design for practical implement in hospitals or private
clinics.
Keywords: Electromyography (EMG) signals; Graphical User Interface (GUI); EMGLab software.
References: 1. K. G. Murphy, “Effective Information Display and Interface Design for Decomposition-based Quantitative Electromyography” , M.Sc
thesis, University of Waterloo, Canada, 2002.
2. N. BU, “ EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network ”, Journal of Intelligent Information Systems,
21:2, 113–126, 2003. 3. Toshio Tsuji1 " Pattern classi"cation of time-series EMG signals using neural networks", international journal of adaptive control and
signal processing , 2000.
4. Madeleine M. Lowery, " A Multiple-Layer Finite-Element Model of the Surface EMG Signal", MAY 2002. 5. O. Bida, “ Influence of Electromyogram (EMG) Amplitude Processing in EMG-Torque Estimation ”, M.Sc Thesis , worcester
polytechnic institute ,Electrical Engineering , January 2005.
6. M. B. I. Reaz, " Techniques of EMG signal analysis detection, processing, classification and applications", March 23, 2006. 7. Anne K. G. Murphy, " Effective Information Display and Interface esign for Decomposition-based Quantitative Electromyography,
thesis, 2002.
8. Luca Mesin and Dario Farina, " Simulation of Surface EMG Signals Generated by Muscle Tissues With Inhomogeneity Due to Fiber Pinnation", Sep. 2004.
9. Andrew Hamilton, " Physiologically Based Simulation of Clinical EMG Signals", FEBRUARY 2005.
10. M. Z. Al-Faiz, Yousif. I. Al-Mashhadany, “Human Arm Movements Recognition Based on EMG Signal”, MASAUM Journal Of Basic and Applied Sciences (MJBAS) Volume 1 Issue 2, PP 164-171, (September 2009).
11. Yousif. I. Al-Mashhadany, "Measurement of human leg joint angle through motion based on electromyography (EMG) signal", The
Engineering Conference of Control, Computers and Mechatronics, ECCCM2011, university of Technology, 30-31, January, 2011 12. Yousif I. Al-Mashhadany, “Design and Analysis of Virtual Human Arm Driven by EMG Signal”, BOOK , ISBN: 978-3-8433-7973-1,
2011, LAP LAMBERT Academic Publishing GmbH & Co. KG, 2011
13. Yousif Al Mashhadany, Nasrudin Abd Rahim, “Real-Time Controller for Foot-Drop Correction by Using SEMG Sensor”, Proc IMechE Part H: J Engineering in Medicine, 227(4) 373–383. Jan, 2013,( Q2 ISI Journal)(I.F.=1.208)
14. N. A. Shrirao, N. arender, P, Reddy, “ Neural network committees for finger joint a ngle estimation from surface EMG signals”,
BioMedica l Engineeri ng OnLine 8 :2, 2009. 15. V. R . Mankar, A. A. Ghatol, “Design of Adaptive Filter Using Jordan/Elman Neural Network in a Typical EMG Signal Noise Removal
40-44
”,Hindawi Publishing Corporation Advances in Artificial Neural Systems Volume 2009, Article ID 942697, 9 pages doi:10.1155, 2009. 16. O. A. Alsayegh, “ EMG Based Human Machine Interface System” , IEEE Transaction of Biomedical Engineering, 0-7803-6535-4/00/ pp
925-928, 2000.
17. M. M. Lowery, N. S. Taflove, “ A Multiple Layer Finite-Element Model of the Surface EMG Signal ” , IEEE Transaction of Biomedical Engineering, Vol. 49, no.5, PP 446-454, May 2002.
18. L. Mesin, D. Farina, “ Simulation of Surface EMG Signals Generated by Muscle Tissues with Inhomogeneity Due to Fiber Pinnation ”,
IEEE Transaction on Biomedical Engineering Vol. 51, no. 9, PP 1521-1529, September 2004. 19. Motion Lab Systems, Inc, “A software user guide for EMG Graphing and EMG Analysis EMG Analysis”, Updated Thursday, February
26, 2009.
20. EMGLAB software Version 0.9 User’s Guide, “The MathWorks, at www.mathworks.com, May 2008.
11.
Authors: I A. Kamani C Samarasinghe, Saluka Kodituwakku, Roshan D. Yapa
Paper Title: Understanding the Internet Usage Habits of the Students of University of the Visual & Performing
Arts through Data Mining
Abstract: This Data mining has been a commonly used tool in the telecommunication sector. This is due to the
useful insights that it can provide to assess the user preferences and optimize the service offerings consistent with
user expectations. Data mining provides the required insights to the Internet and data usage habits of the students of
the University of the Visual & Performing Arts (UVPA). It is clear that the data usage demand has been
accelerating over the past few years and mobile data usage has been in the forefront of growth. Reduced prices as
well as increased Internet usage options along with benefits achieved from the attributes of increased speed had
augmented the usage of the data services. The study was a quantitative study and the information was collected
from a random set of students who had registered with the University for their Degree program. The information
collected had been processed consistent with the requirements to gain insight on the Internet usage habits of the
students. The study represents data of 133 students who are from 17 districts of the country. The findings indicate
that 83% of the households are Internet ready and Close to 50% of the households possess desktops followed by
over 40% of households employing smartphones, which facilitate Internet access. Meanwhile, several students use
dongles for Internet connectivity. The Internet is utilized for various purposes; where the purposes of online
education and social networking are the two prominent areas.
Keywords: Data mining, Telecommunication, University of the Visual & Performing Arts.
References: 1. Baxter, G., (2003). Challenge and Change in the Information Society. Journal of Documentation. Vol. 59, Iss: 6, pp.731 – 734.
2. Elragal, A. and El-Gendy, N., (2013). Trajectory data mining: integrating semantics. Journal of Enterprise Information Management. Vol.
26, Iss: 5, pp.516 – 535.
3. Gargano, M.L. and Raggad, B. G., (1999). Data mining - a powerful information creating tool. OCLC Systems & Services. Vol. 15, Iss: 2,
pp.81 – 90.
4. Jareevongpiboon, W. and Janecek, P., (2013). Ontological approach to enhance results of business process mining and analysis. Business Process Management Journal. Vol. 19, Iss: 3, pp.459 – 476.
5. Lee, S.J. and Siau, K., (2001). A review of data mining techniques. Industrial Management & Data Systems. Vol. 101, Iss: 1, pp.41 – 46.
6. Mutula, S. M., (2002). Current developments in the Internet industry in Botswana. Electronic Library. The, Vol. 20, Iss: 6, pp.504 – 511. 7. Nemati, H. R. and Barko, C.D., (2003). Key factors for achieving organizational data-mining success. Industrial Management & Data
Systems. Vol. 103, Iss: 4, pp.282 – 292.
8. Ozgulbas, N. and Koyuncugil, A.S. (2006). Application of Data Mining Method for Financial Profiling. Social Responsibility Journal. Vol. 2, Iss: 3/4, pp.328 – 334.
9. Sharma, S. Goyal, D. P. and Mittal, R. K., (2007). Evaluation model for data mining software: an empirical investigation of ICICI bank.
Journal of Advances in Management Research. Vol. 4, Iss: 2, pp.63 – 68. 10. Telecommunication Regulations Commission (TRC), (2013). Statistics. [Online] Available
11. at:<http://www.trc.gov.lk/index.php/information/statistics.html> (Accessed March, 2014).
12. Viktor, H. L. and Arndt, H., (2006). Combining data mining and human expertise for making decisions, sense and policies. Journal of Systems and Information Technology. Vol. 4, Iss: 2, pp.33 - 56
45-48
12.
Authors: Rohini.A.Maind, Alka Khade, D.K.Chitre
Paper Title: Image Copy Move Forgery Detection using Block Representing Method
Abstract: As one of the most successful applications of image analysis and understanding, digital image forgery
detection has recently received significant attention, especially during the past few years. At least two trend account
for this: the first accepting digital image as official document has become a common practice, and the second the
availability of low cost technology in which the image could be easily manipulated. Even though there are many
systems to detect the digital image forgery, their success is limited by the conditions imposed by many applications.
Most existing techniques to detect such tampering are mainly at the cost of higher computational complexity. In this
paper, we present an efficient and robust approach to detect such specific artifact. Firstly, the original image is
divided into fixed-size blocks, and discrete cosine transform (DCT) is applied to each block, thus, the DCT
coefficients represent each block. Secondly, each cosine transformed block is represented by a circle block and four
features are extracted to reduce the dimension of each block. Finally, the feature vectors are lexicographically
sorted, and duplicated image blocks will be matched by a preset threshold value. In order to make the algorithm
more robust, some parameters are proposed to remove the wrong similar blocks. Experiment results show that our
proposed scheme is not only robust to multiple copy-move forgery, but also to blurring or nosing adding and with
low computational complexity.
Keywords: Didgital forencics copy-move forgery circle block duplicated region
References: 1. A. Fridrich, et al., Detection of Copy-move Forgery in Digital Images, 2003.
2. Y. Huang, et al., Improved DCT-based detection of copy-move forgery in images,Forensic Science International 206 (1–3) (2011) 178–184. 3. Popescu and H. Farid, Exposing digital forgeries by detecting duplicate image regions, Dept. Computer. Sci. Dartmouth College, Tech.Rep.
49-53
TR2004 515, 2004. 4. Mahdian, S. Saic, Detection of copy-move forgery using a method based on blur moment invariants, Forensic Science International 171
(2007) 180–189.
5. Li Jing, and Chao Shao,” Image Copy-Move Forgery Detecting Based on Local Invariant Feature Journal Of Multimedia,Vol.7,No.1, February 2012.
6. Vincent Christlein,” An Evaluation of Popular Copy-Move ForgeryDetection Approaches”, IEEE Transactions On Information Forensics
And Security, 2011. 7. S. Bayram, H.T. Sencar, N. Memon,” An efficient and robust method for detecting copy-move forgery”, in: IEEE International Conference
on Acoustics, Speech and Signal Processing, IEEE Press, New York, 2009.
8. X. Pan, S. Lyu,” Detecting image region duplication using SIFTfeatures”, in: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010, 2010, 1706–1709.
9. Frank Y. Shih and Yuan Yuan,”A Comparison Study on Copy-Cover Image Forgery Detection”,The Open Artificial Intelligence Journal,
2010, 4, 49-54. 10. Preeti Yadav, YogeshRathore, Aarti Yadav,” DWT Based Copy-Move Image Forgery Detection”, International Journal of Advanced
Research in Computer Science an Electronics Engineering Volume 1, Issue 5, July 2012
11. Hwel-Jen Lin, Chun-We Wang,” Fast Copy-Move Forgery Detection”, WSEASTransactions on SIGNAL PROCESSING, May 2009. 12. B.L.Shivakumar1 and Lt. Dr. S.SanthoshBaboo,” Detection of Region Duplication Forgery in Digital Images Using SURF”, IJCSI
International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011.
13. Sevinc Bayram, Taha Sencar, and Nasir Memon, “An efficient and robust method for detecting copy-move forgery,” in Proceedings of 14. Yanjun Cao a,*, TiegangGao,” A robust detection algorithm for copy-move forgery in digital images”,Forensic Science International 214
2012.
13.
Authors: Rahul Ravindran, Riya Suchdev, Yash Tanna
Paper Title: Heterogeneous Parallel Programming
Abstract: This paper presents Heterogeneous Parallel Computing (HPC), which is a well-orchestrated and co-
ordinated effective use of a suite of diverse high performance machines to provide super-speed processing for
computationally demanding tasks with diverse computational needs. GPUs are accoutered with a much more
throughput oriented design as compared to that of the CPUs thus making them a powerful alternative to boast
overall performance. It is now used all the way from mobile computing to supercomputing, like in Blue Star Super
Computers. Upcoming Exascale and Petascale systems have embraced even heterogeneity in order to overcome
power limitations. This paper also illustrates programming example using CUDA C to demonstrate the efficiency
achieved in problems like matrix multiplication using a more heterogeneous approach as compared to that of
sequential approach. It also explains how Heterogeneous Parallel Programming is a plausible, novel technique
which allows to exploit inherent capabilities of a wide range of computational machines to solve computationally
intensive problems that have several types of embedded parallelism by breaking it into separate modules. This paper
also puts light on the challenges and concerns which exist when programming in HPC environment and some
techniques to alleviate them.
Keywords: About four key words or phrases in alphabetical order, separated by commas.
References: 1. Jacques A. Piennar,Srimat Chakradar and Anand Ragunathan “automatic generation of software peipeline for heterogeneous parallel
systems” 2. Ashfaq A. Khokar “Heterogeneous Computing:Challenges and opportunities”
3. T. Berg and H.J. Siegel, “Instruction Execution Trade-offs for SIMD vs. MIMD vs. Mixed-Mode Parallelism,’’ Proc. Int’l Parallel
Processing Symposium (IPPS), IEEE CS Press. Los Alamitos. Calif., Order NO. 2167. 1991, pp. 301-308. 4. Khokhar et al.. “Heterogeneous Supercomputing: Problems and Issues,” Proc. Workshop on Heterogeneous Processing, IEEE CS Press,
Los Alamitos. California Order No. 2702. 1992. pp. 3-12.
5. R. Freund. “Optimal Selection Theory for Superconcurrency.” Proc. 89 Super- computing, IEEE CS Press, Los Alamitos, Calif., Order No. M2021 (microfiche), 1989. pp. 13-17.
6. The Multi-core Dilemma white paper by CITO Research
7. Heterogeneous supercomputing: Problems and issues Ashfaq Khokhar, Viktor Prasanna, Muhammad Shaaban, Cho-Li Wang
8. Instruction set innovations for the convey HC-1 by TM Brewer
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14.
Authors: Daulat Singh, Rakesh Shrivastava, Dinesh Varshney
Paper Title: Identification and statistical analysis of the problems associated with Edusat based distance learning
with special reference to Madhya Pradesh Bhoj (Open) University, Bhopal
Abstract: This Paper is an attempt to understand the problems associated with education imparted in distance
learning mode by Bhoj Open University, Bhopal, Madhya Pradesh. The paper also deals with the tentative possible
solutions to minimise the problems. A study was conducted on 80 students of 2centres of different regions and data
was collected related to hub and 40 SIT’s (i.e. BER (Bit Error Rate), Bandwidth, number of operation days, number
of recorded and live lectures telecasted) to understand the nature of the various problems. A detailed analysis was
performed using SPSS 22.0 on the primary data collected. On the basis of observations and interpretation of the
analysis the present study attempts to categories the problems and suggests possible solutions to make the education
imparted though edusat satellite more effectively.
Keywords: Satellite dedicated for education, SIT – Satellite. Interactive terminals, BER – Bit Error Rate,
Bandwidth – the amount of data that can be carried from one point to another in given time period. (Usually a
second).
References: 1. Keegan, D. Foundations of Distance Education. Rutledge Taylor and Francis Group, New York, 10-50, 1996. 2. Carter, A. Interactive distance education: Implications for the adult learner. International Journal of Instructional media , 28 (3), 249-261,
2001. 3. Teaster, P., & Blieszner, R. Promises and pitfalls of the interactive television approach to teaching adult development and aging.
Educational Gerontology, 25 (8), 741-754, 1999.
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4. Daulat Singh, Shiv Kumar, Rakesh Shrivastava, Dinesh Varshney, Edusat Satellite Based Education: Study of Scope for Enhancement of Audio-Video Quality- A Case Study of Madhya Pradesh Bhoj (Open) University, International Journal of Soft Computing and
Engineering (IJSCE) 2, 11, 2012
15.
Authors: Sreelatha P, P Pradeep Kumar, S V Mohankumar
Paper Title: A Lab VIEW Based Extended (10, 5) Binary Hamming Code Generator for Telecommanding
Applications
Abstract: The paper presents the design details of an extended binary Hamming code generator for generation
of codewords suitable for remote applications needing telecommands. It is required that these telecommand codes
maintain a minimum Hamming distance of three. For the present application, a suitable (10, 5) Extended Hamming
code generator is designed for 5 data bits, which generates a corresponding 10 bit codeword for each data word. The
design implemented in LabVIEW is detailed here along with a distance table showing the Hamming distance
between the generated codes.
Keywords: Extended Hamming code, Hamming distance, error correction, SEC-DED, LabVIEW.
References: 1. Proakis J.G., Digital Communications, 4th Edition, McGraw Hill Co., 2001.
2. Forouzan, Behrouz A., Data Communications and Networking, 4th Edition, McGraw Hill Higher Education, 2007.
3. Hall J.I., “Notes on Coding Theory”, http://www.mth.msu.edu/~jhall/classes/codenotes/coding-notes.html
4. Bhattacharya D. K. and S Nandi, “Theory and design of SEC-DED-AUED codes”, IEE Proceedings on Computers and digital
techniques, Vol. 145, Issue 2, pp 121-126, 1998. 5. Bhattacharya D. K. and S Nandi , “An Efficient Class of SEC-DED-AUED Codes”, Proceedings of the Third International Symposium I-
SPAN97, pp 410-416, 1997.
6. www.ni.com
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16.
Authors: Shama Kousar Jabeen.A, B.Arthi
Paper Title: Software Effort Estimation for Size Proxy Metric Framework Modelling using Software Estimation
Models and Neuro Fuzzy Logic Approach
Abstract: As software grew in size and requirements it also successively grew in complexity and cost. Evaluating
size estimates accurately at an initial stage in the software conglomeration is of high priority. Conventional
techniques have the problems of uncertainty and precision during the evaluation of size estimates. Software
engineering cost models and estimation techniques are used for a number of purposes. In our work we have
compared the results using three function point based effort estimation models. We have also compared MMRE,
MMER, MRE, MER values by training the dataset using neuro-fuzzy logic based machine learning aapproach
which overcomes the problems present in the traditional methods. In this paper effort estimates has been obtained
by modeling and training the size metric framework. The dataset trained in our work is for 100 projects .
Keywords: size metric, fuzzy logic software effort ,software engineering, cost estimation models, MMRE
,MER,MRE, MMER.
References: 1. B. Boehm, Software Engineering Economics, Prentice-Hall,City:Englewood Cliffs,State:N.J., 1981.
2. B.Boehm,C.Abts,S.Chulani,”Software development Cost Estimation approaches-a survey”,in J.C.Baltzer, Annals Of Software
Engineering,Vol.10,Science Publishers,pp.177-205,Issue ¼. 3. International Function Point Users Group (IFPUG) <http://www.ifpug.org/>
4. J. Wong, D. Ho, L.E. Capretz, An investigation of using Neuro-Fuzzy with software size estimation, in: ICSE Workshop on Software
Quality, WOSQ’09, 2009, pp. 51–58. 5. K. Srinivasan and D. Fisher, “Machine Learning Approaches to Estimating Software Development Effort”, IEEE Transactions on
Software Engineering, 21 (2), 1995, pp. 126-136.
6. K.K. Shukla, Neuro-genetic prediction of software development effort,Information & Software Technology 42 (10) (2000) 701–713. 7. L.Putnam and W.Myers,”Measures For Excellence”,Yordon Press,1992
8. M.W.Nisar,W.Yong-Ji,M.Elahi,“Software Effort Effort Estimation Using Fuzzy Logic –A survey”, In Fifth International Conference on
Fuzzy Systsms and Knowledge Discovery,FKSD’08, pp-421-427, 2008 9. Moataz A.Ahamed, Irfan A.Ahmad, Jarallah S.Alghamdi,”Probabilistic Size Proxy For Software Effort prediction: A framework”, King
Fahd University Of Petroluem and Minerals,Dhahran 31261,Saudi Arabia, pp 241-251, 2013.
10. Moataz A.Ahmad, Zeeshan Muzaffar, ”Handling Imprecision and uncertanty in software development effort prediction : A Type-2 fuzzy logic based framework”, Journal Of Information and Software Technology(IST),Vol.51,No.3,pp.92-
109,http://dx.doi.org/10.1016/j.infsof.2008.09.004.2009
11. R.S.Pressman, Software Engineering,”A Practioner’s Approach”, Mcgraw Hill, ,pp-674-702,2001. 12. S.H.Kan,Metrics and Models in Software Quality Engineering,Second Edition,Pearson Education Inc,2002.
13. Sandeep Kad,Vinay Chopra,“Software development Effort Estimation using Soft Computing”,International Journal Of Machine Learning and Computing (IJMLC),V2.186,Vol.2, No.5,pp.548-551, ISSN: 2010-3700,DOI:10.7763,October 2012.
14. Shama Kousar Jabeen.A and Mrs.B.Arthi,”A Proposal On Size Metric Framework Modelling For Software Effort Estimation Models
Using Neuro Fuzzy Logic Approach”Proceedings of fourth International Conference On ”Advance Computing,Control Systems,Machnies and Embedded Technology”,(ICACT),pp-1372-1376,ISBN:978-93-80757-74-2.
15. Shama Kousar Jabeen.A and Mrs.B.Arthi,”Size Proxy Metric Framework Modeling Of Software Effort Estimation in Soft Computing”,
Journal Of Emerging Technolgies,Vol:8,pp:1-5,Special Issue-III,Feb 2014. 16. Visual Use Case Tool, <http://www.technosolutions.com/topteam-use-case.html>.
74-79
17.
Authors: Neha Khanduja, Simmi Sharma
Paper Title: Performance Analysis of CSTR using Adaptive Control
Abstract: In industry nowadays the control of chemical process is important task. Mostly all the chemical
processes are highly nonlinear in nature and this causes instability of process. This paper presents the performance
evaluation on the application of model reference adaptive control with various types of command inputs in a
process plant. In the design of model reference adaptive control (MRAC) scheme, adaption law have been
developed based on MIT and Lyapunov rule. This paper deals with basic simulation studies of the Continuous
80-84
Stirred Tank Reactor (CSTR). The mathematical model is developed from material balances. Numerical
mathematics is used for steady-state analysis and dynamic analysis which is usually represented by a set of
differential equations.A simulation is carried out using Mat Lab and Simulink to control the process system
using the adaptive control algorithm. It is also concluded that the adaptive controller will be superior to the
conventional controller even without parameters change in the process. In a real world situation, these parameters
could be estimated by using simulations or real execution of the system. It may be possible to improve the
performance of the adaptive controller by further modifying the adaptation law or by incorporating parameter
identification into the control.
Keywords: Process control – CSTR ; Adaptive controller; MIT rule,Lyapunov Rule.
References: 1. Rahul Upadhyay, Rajesh Singla,”application of adaptive control in a process control”,2nd international conference on education
technology and computer(ICETE),2010.(IEEE).
2. R.Aruna,M.Senthil Kumar, “Adaptive Control for interactive thermal process “proceedings of ICTECT,2011.(IEEE)
3. KarlJ.Astrom and Bjorn Witten mark,Adaptivecontrol,secondedition,Pearson Education, 2001. 4. K. S. Narendra, L. S. Valavani, Stable Adaptive Controller Design Direct Control. IEEE Trans. Auto. Control, vol.23, pp.
570-583, Aug. 1978.
5. R.Aruna,M.SenthilKumarD.Babiyola,”Intelligence based and model based controller to the interactive thermal process “international
conference on VLSIcommunicationand instrumentation(ICVCI),2011.
6. K.Prabhu,Dr.V.MuraliBhaskaran,”optimization of control loop using adaptive method”, International Journal Of Engineering and
Innovative Technology,Volume1,Issue3,March 2012. 7. S.Lakshminarayanan, RaoRaghurajK.S.Balaji, “CONSIM-MS Excelbasedstudentfriendly simulatorforteachingProcess control theory”,
Proceedings ofthe11thAPCCHEcongress,August27-30, 2006
8. Jiri vojtesec,petrdostal,”simulation analysis of continuous stirred tank reactor”, proceeding 22nd Europeanconference on modeling and simulation(ECMS),2006.
9. ComanAdrian,AxenteCorneliu,BoscoianuMircea,”the simulation of adaptive system using MIT rule”,10thinternational conference on
mathematical methods and computational technique in electrical engineering(MMACTEE),2008. 10. Dr. M.J.Willis,”continuous stirred tank reactor models”,.Deptt. ofChemical and Process Engineering, University of
Newcastle,March2010.
11. K.Prabhu,Dr.V.MuraliBhaskaran,”optimization of control loop using adaptive method”, International Journal Of Engineering and Innovative Technology,Volume1,Issue3,March 2012.
12. S.Jegan,K.Prabhu,”Temperature control of CSTR process using adaptive control”, International Conference on Computing and Control
Engineering(ICCCE),2012.
18.
Authors: J. Hussain, Lalthlamuana
Paper Title: Unicode Mizo Character Recognition System Using Multilayer Neural Network Model
Abstract: The current investigation presents an algorithm and software to detect and recognize pre-printed mizo
character symbol images. Four types of mizo fonts were under investigation namely – Arial, Tohoma, Cambria, and
New Times Roman. The approach involves scanning the document, preprocessing, segmentation, feature extraction,
classification & recognition and post processing. The multilayer perceptron neural network is used for classification
and recognition algorithm which is simple and easy to implement for better results. In this work, Unicode encoding
technique is applied for recognition of mizo characters as the ASCII code cannot represent all the mizo characters
especially the characters with circumflex and dot at the bottom. The experimental results are quite satisfactory for
implementation of mizo character recognition system.
Keywords: Character Recognition, Neural Network, Multi-Layer Perceptron, and Unicode.
References: 1. Vivek Shrivastava and Navdeep Sharma, “Artificial Neural Network based Optical Character Recognition”, Signal & Image Processing:
An International Journal (SIPIJ), vol.3, No.5, October, 2012.
2. Mohanad Alata and Mohammad Al-Shabi, “Text Detection and Character Recognition using Fuzzy Image Processing”, Journal of Electrical Engineering, vol.57, No.5, 2006, p258-267.
3. Pritpal Singh and Sumit Budhiraja, “Feature Extraction and Classification Techniques in OCR Systems for Handwritten Gurmukhi
Script-A Survey”, International Journal of Engineering Research and Applications (IJERA), vo.1, Issue 4, 2011, pp.1736-9622. 4. Seethalakshmi R, Sreeranjani T.R., and Balachandar T., “Optical Character Recognition for Printed Tamil Text using Unicode”, Journal
of Zhejiang University Science, 2005 6A(11):1297-1305, September, 2005.
5. Pramod J Simha and Suraj K V, “Unicode Optical Character Recognition and Translation Using Artificial Neural Network”, International Conference on Software Technology and Computer Engineering (STACE-2012), 22nd July 2012, Vijayawada, Andhra
Pradesh, India.
6. Kauleshwar Prasad, Devrat C. Nigam, Ashmika Lakhotiya, Dheeren Umre, “Character Recognition using Matlab’s Neural Network Toolbox”, International Journal of u- and e- Service, Science and Technology, Vol. 6, No. 1, February, 2013
7. Md. Mahbub Alam, Dr. M. Abul Kashem, “A complete Bangla OCR System for Printed Characters”, International Journal of Computer
and Information Technology, Vol. 01, Issue 01, July, 2010. 8. Madhup Shrivastava, Monika Sahu, and Dr. M.A. Rizvi, “Artificial Neural Network Based Character Recognition using
Backpropagation” International Journal of Computers & Technology, vol. 3, No. 1, Aug, 2012
9. Om Prakash Sharma, M.K.Ghose, Krihna Bikram Shah and Benoy Kumar Thakur, “Recent Trends and Tools for Feature Extraction in
OCR Technology”, International Journal of Soft Computing and Engineering (IJSCE), volume-2, Issue-6, January, 2013.
10. Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, The Neural Network ToolboxTM 7 User’s Guide. 3 Apple Hill Drive,
Natick, MA: The Mathwork Inc., 2010 11. S.N. Sivanandam, S. Sumathi, S.N. Deepa, Introduction to Neural Networks using Matlab 6.0, Tata McGraw-Hill, 2006
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19.
Authors: Khushwant Kaur, Swimpy Pahuja
Paper Title: A Brief Study of Data Mining
Abstract: Data mining plays a significant role on human activities and has become an essential component in
various fields of human life. It is the knowledge discovery process which analyzes the large volumes of data from
various perspectives and summarizes it into useful information. Data mining is greatly inspired by advancements in
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Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities. In this
paper, we have discussed the concept of data mining, its tools and techniques, its applications and
advantages/disadvantages from beginning of the term to present scenario.
Keywords: Data Mining, Tools, Techniques, Applications
References: 1. Heikki, Mannila. 1996. Data mining: machine learning, statistics, and databases, IEEE. 2. Piatetsky-Shapiro, Gregory. 2000. The Data-Mining Industry Coming of Age. IEEE Intelligent Systems.
3. Salmin, Sultana et al. 2009. Ubiquitous Secretary: A Ubiquitous Computing Application Based on Web Services Architecture ,
International Journal of Multimedia and Ubiquitous Engineering Vol. 4, No. 4, October, 2009. 4. Hsu, J. 2002. Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century, The
Proceedings of the 19th Annual Conference for Information Systems Educators (ISECON 2002), ISSN: 1542-7382. 5. Z. K. Baker and V. K.Prasanna. 2005. Efficient Parallel Data Mining with the Apriori Algorithm on FPGAs. In Submitted to the IEEE
International Parallel and Distributed Processing Symposium (IPDPS ’05).
6. Jing He.2009. Advances in Data Mining: History and Future, Third international Symposium on Information Technology Application, 978-0-7695-3859-4/09 IEEE 2009 DOI 10.1109/IITA.2009.204.
7. Han, J., & Kamber, M. 2001. Data mining: Concepts and techniques .Morgan-Kaufman Series of Data Management Systems. San Diego:
Academic Press. 8. http://en.wikipedia.org/wiki/Data_mining
9. http://www.statsoft.com/textbook/stdatmin.html
20.
Authors: R. Vasundhara Devi, S. Siva Sathya, Mohane Selvaraj Coumar
Paper Title: Multi- Objective Genetic Algorithm for De Novo Drug Design
Abstract: Genetic algorithms, can be used to solve NP-hard problems in various domains, including computer-
aided drug design (CADD). As design & development of a drug molecule takes a number of man years and is also
an expensive process, use of computer-aided techniques could help to reduce the time required and the cost of
developing drugs. De novo drug design (DNDD) is one of the CADD technique used to design drug-like molecules
virtually from smaller fragments/building blocks. This paper proposes a multi-objective genetic algorithm for the de
novo design of novel molecules similar to a known reference molecule, possessing drug-like properties from a
given set of input fragments and reference molecules. It could be used to design a variety of other virtual drug-like
molecules by varying the input fragments and reference molecules based on the user requirement.
Keywords: computer-aided drug design, de novo drug design, multi-objective genetic algorithm.
References: 1. R. Ng. Drugs: From disocovery to approval. 2nd ed. New Jersey: John Wiley & Sons, Inc., 2009, pp. 1-52.
2. T.T. Talele, S.A. Khedkar, A.C. Rigby, Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr. Top. Med. Chem., 10, 2010, 127-141.
3. D.E. Clark, What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov., 1, 2006, pp. 103-110.
4. G. Schneider, U. Fechner. Computer-based de novo design of drug-like molecules. Nature Rev. Drug Discov., 4, 2005, pp. 649-663. 5. K. Loving, I. Alberts, W. Sherman, Computational approaches for fragment-based and de novo design. Curr. Top. Med. Chem., 10, 2010,
pp. 14-32.
6. C.A. Nicolaou, C. Kannas, E. Loizidou, Multi-objective optimization methods in de novo drug design. Mini Rev. Med. Chem., 12, 2012, pp. 979-987.
7. D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison-Wesley Longman Publishing Co.,
Inc, 1989. 8. K. Deb, Multi-objective optimization using Evolutionary algorithms. London: Wiley, 2001.
9. C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 23, 1997, pp. 3-25.
10. C. A. Lipinski. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today Technol., 1, 2004, pp. 337-341.
11. N. Brown, Chemoinformatics - An introduction for Computer Scientists. ACM Computing Surveys, 41, 2009, 8. 12. T. Tanimoto. An Elementary Mathematical theory of Classification and Prediction. “IBM Internal Report,” IBM technical report series,
1957.
13. E. Pihan, L. Colliandre, J.F. Guichou and D. Douguet. e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design, Bioinformatics, 28, 2012, pp. 1540-1541.
14. C. Steinbeck, Y. Han, S. Kuhn, O. Horlacher, E. Luttmann, E. Willighagen. The Chemistry Development Kit (CDK): an open-source
Java library for Chemo- and Bioinformatics. J. Chem. Inf. Comput. Sci., 43, 2003, pp. 493-500. 15. C. Steinbeck, C. Hoppe, S. Kuhn, M. Floris, R. Guha, E. L. Willighagen. Recent developments of the chemistry development kit (CDK)
— an open-source java library for chemo- and bioinformatics. Curr. Pharm. Des., 12, 2006, pp. 2111-2120.
16. https://www.chemaxon.com/products/marvin/ 17. http://www.drugbank.ca/
18. M.S. Coumar, C.Y. Chu, C.W. Lin, H.Y. Shiao, Y.L. Ho, R. Reddy, et al. Fast-forwarding hit to lead: aurora and epidermal growth factor
receptor kinase inhibitor lead identification. J. Med. Chem., 53, 2010,
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21.
Authors: Hadi Razmi
Paper Title: Investigation of the AVR Output Voltage Limits in Power System Voltage Stability Assessment
Abstract: Voltage stability problems have been one of the major concerns for electric power utilities due to
increased interconnections and loading of the present day power systems. The accurate representation of the voltage
instability phenomena requires a detailed model of power system components (generators, transformers, loads, etc.).
On the other hand, reactive power generation limits have a significant effect on voltage collapse. In general, the
system equations change non-smoothly when these limits are encountered. This paper presents a continuation-based
method to steady-state voltage stability analysis that considered complete model of power system and the automatic
voltage regulator (AVR) output voltage limits that indirectly control the reactive power generation limits. Results
are provided for the New England 39-bus power system model. By comparing results obtained through this method
and the continuation power flow (CPF) method, it is concluded that for design and developing the power systems,
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using proposed method seems a better approach due to its higher accuracy.
Keywords: steady-state voltage stability, automatic voltage regulator (AVR) output voltage limit, power system
differential-algebraic equation (DAE) model.
References: 1. G. M. Huang, N. C. Nair, “Voltage stability constrained load curtailment procedure to evaluate power system reliability measures,” In:
Proceedings of IEEE/PES Winter Meeting, New York, 2002.
2. Tiranuchit, R. J. Thomas, “A posturing strategy against voltage instability in electric power systems,” IEEE Trans. Power Syst., vol. 3,
1998, pp. 87–93. 3. P. Kessel, H. Glavitch, “Estimating the voltage stability of a power system,” IEEE Trans. Power Deliver., vol. 1, 1986, pp. 346–354.
4. N. Amjady, “Dynamic voltage security assessment by a neural network based method,” Electr. Power Syst. Res., vol. 66, 2003, pp. 215–
226. 5. F.M. Echavarren, E. Lobato, L. Rouco, “Steady-state analysis of the effect of reactive generation limits in voltage stability,” Electric
Power Systems Research, vol. 79, 2009, pp. 1292–1299.
6. Y. Kataoka, Y. Shinoda, “Voltage stability limit of electric power systems with generator reactive power constraints considered,” IEEE Trans. Power Syst., vol. 20, 2005, pp. 951–962.
7. V. Ajjarapu, Computational techniques for voltage stability assessment and control, Springer, 2007.
8. P. Kundur, Power system stability and control, McGraw-Hill, 1994. 9. C. Z. De Souza, C. A. Canizares, V. H. Quintana, “New techniques to speed up voltage collapse computations using tangent vectors,”
IEEE Trans. Power Syst., vol. 12, 1997, pp. 1380–1387.
10. A. Gharaveisi, M. Rashidinejad, A. Mousavi, “Voltage security evaluation based on perturbation method,” Electr. Power Energy Syst., vol. 31, 2009, pp. 227–235.
11. W. C. Rheinbolds, Numerical analysis of parameterized nonlinear equations, New York: John Wiley & Sons Interscience, 1986.
12. W. C. Rheinbolds, J. V. Burkradt, “A locally parameterized continuation process,” ACM Trans. Math. Software, vol. 9, 1983, pp. 215–235.
13. R. Seydel, From equilibrium to chaos, Elsevier, New York, 1988.
22.
Authors: Alireza Asadi
Paper Title: Inducing Stepping- Like Movement by Controlling Movement Primitive Blocks Using Intraspinal
Microstimulation
Abstract: Recently, intraspinal microstimulation (ISMS) has been developed as a potential technique for restoring
the motor function in paralyzed limbs. It has been shown that that there are functional units in the spinal cord (i.e.,
motor pools, motor primitives) that generates a specific motor output by selecting a specific pattern of muscle
activation. Dynamics identification of these spinal primitives is a critical issue in rehabilitation the motor function
using spinal microstimulation. In this paper, we have triggered the motor primitives by electrical microstimulation
of the interneuron networks within the spinal cord. The major challenge in generating Walking cycles is finding
suitable patterns to stimulate each primitive. By using EMG of normal walking we have tuned patterns of each
primitive but this procedure is too time-consuming, thus we have applied closed-loop control using neuro-adaptive
fuzzy sliding mode control. The results show both procedures can reconstruct walking, But in closed-loop
procedure we tune little controller parameters once. Whereas in open loop procedure for each animal different
pattern must be find.
Keywords: functional electrical stimulation, intraspinal Microstimulation, movement primitives, neuro-Fuzzy
sliding mode.
References: 1. C. Tai, C. J. Robinson. “Isometric torque about the knee joint generated by microstimulation of the cat L6 spinal cord,” IEEE Trans.
Rehabil. Eng, vol. 7, no. 1, March 1999, pp. 46-55. 2. Lau, L. Guevremont, V. K. Mushahwar “Strategies for Generating Prolonged Functional Standing Using Intramuscular Stimulation or
Intraspinal Microstimulation,” IEEE Trans. Neural Syst. Rehabil. Eng, vol. 15, no. 2, JUNE 2007, pp. 273 - 285.
3. V. K. Mushahwar, K.W. Horch, “Selective activation of muscle groups in the feline hindlimb through electrical microstimulation of the ventral lumbo-sacral spinal cord,” IEEE Trans. Rehabil. Eng, vol. 8, no. 1, March 2000, pp. 11 - 21.
4. V. K. Mushahwar, K.W. Horch, “Intraspinal Microstimulation Generates Locomotor-Like and Feedback-Controlled Movements,” IEEE
Trans. Neural Syst. Rehabil. Eng, vol. 10, no. 1, March 2002, pp. 68 - 81. 5. S.F. Giszter, F.A. Mussa-Ivaldi, E. Bizzi ,” Convergent force fields organized in the frog's spinal cord,” J. Neurosci, vol. 13,1993, pp.
467–491.
6. F.A. Mussa-Ivaldi, S.F. Giszter, E. Bizzi, “Linear combinations of Primitives in vertebrate motor control,” Proc. Natl. Acad. Sci. vol. 91 , 1994, pp. 7534–7538.
7. M.C. Tresch, P.Saltiel, E. Bizzi, “The construction of movement by the spinal cord,” Nat. Neurosci, vol. 2, no. 2, 1999, pp. 162–167. 8. M.C. Tresch, V.C.K. Cheung, A.d'Avella, “Matrix factorization algorithms for the identification of muscle synergies: evaluation on
simulated and experimental data sets,” J. Neurophysiol, vol.95, 2006, pp. 2199–2212.
9. A.R. Asadi, A.Erfanian, “adaptive neuro-Fuzzy sliding mode control of multi-joint movement using Intraspinal microstimulation,” J neural sys and rehab eng, vol. 20, no.4, July 2012, pp. 499 – 509
10. A. K. Thota, S. CarlsonWatson, E. J. Knapp, B. T. Thompson, and R. Jung, “Neuromechanical control of locomotion in the rat,” J.
Neurotrauma, vol. 22, no. 4, Apr. 2005, pp. 442–465.
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23.
Authors: M.Vamsi Krishna Reddy, Md Ali Hussain
Paper Title: Content Based Filtering In Social Networking Sites Using Web Apllication
Abstract: In consideration to the today’s globalized world everybody in the society are being addicted in using
the Social Networks Sites.The basic problem that we are gonna be seen in using these sites is “Lack Of Privacy”.
Till today, Social Networks Sites provide little support to this requirement. To sort out this problem, in this project
we are proposing a system which will provide the indirect control to the users of these sites .This proposed model
can be achieved through a modern rule-based system, that allows administrators to customize the filtering criteria to
be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in support of
content-based filtering.
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Keywords: Social Networks Sites, Content-based filtering, Machine Learning, Rule-based system
References: 1. Marco Vanetti, Elisabetta Binaghi, Elena Ferrari, Barbara Carminati and Moreno Carullo, “a system to filter unwanted messages from osn
userwalls,” IEEE Trans. Knowledge and Data Eng., vol. 25, no. 2, february 2013. 2. M. Chau and H. Chen, “A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis,” Decision Support
Systems, vol. 44, no. 2, pp. 482-494, 2008.
3. M. Vanetti, E. Binaghi, B. Carminati, M. Carullo, and E. Ferrari, “Content-Based Filtering in On-Line Social Networks,” Proc. ECML/PKDD Workshop Privacy and Security Issues in Data Mining and Machine Learning (PSDML ’10), 2010.
4. S. Pollock, “A Rule-Based Message Filtering System,” ACM Trans. Office Information Systems, vol. 6, no. 3, pp. 232-254, 1988.
5. R. J. Mooney and L. Roy, “Content-based book recommending using learning for text categorization,” in Proceedings of the Fifth ACM Conference on Digital Libraries. New York: ACM Press, 2000, pp. 195–204.
6. Nicholas J.Belkin and W.Bruce Croft ” Information filtering and information retrieval: Two sides of the same coin?” in Communications
of the ACM, DEC1992 V35 N12 P29(10). 7. BrainWhitworth notes on” The Social Requirements of Technical Systems”Massey University-Auckland,NZ.
24.
Authors: S. Adebayo Daramola, Ademola Abdulkareem, K. Joshua Adinfona
Paper Title: Efficient Item Image Retrieval System
Abstract: Content based image retrieval system is a very effective means of searching and retrieving similar
images from large database. This method is faster and easy to implement compare to text based image retrieval
method. Ability to extract discriminative low level feature from these images and use them with appropriate
classifier is factor in determining retrieval result. In this work efficient item image retrieval system is proposed. The
system utilizes Haar wavelet transform, Phase Congruency and Support Vector Machine. Haar wavelet transform
acted on image to form four sub-images. Texture feature is extracted from smaller image blocks from detailed bands
and it was combined with shape feature from approximation band to form feature vector. Feature distance margin is
achieved between query image and images in the database using Support Vector Machine (SVM). The effectiveness
of the system is confirmed from output retrieval results.
Keywords: Content, Texture, shape, Support Vector Machine, Phase Congruency
References: 1. Neha Jain, Sumit Sharma, Ravi Mohan Sairam, “Content Base Image Retrieval using Combination of Color, Shape and Texture Features”,
International Journal of Advanced Computer Research Volume-3 Number-1 Issue-8, 2013, pp. 70 -77.
2. P.S Hiremath and Jagadeesh Pujari, “Content Based Image Retrieval based on Color, Texture and Shape features using Image and its
complement”, International Journal of Computer Science and Security, Volume (1): Issue (4), pp.25 – 35.
3. Monika Jain, S.K.Singh, “ A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets”, International Journal of Managing Information Technology (IJMIT) Vol.3, No.4, 2011, pp.23-39.
4. Manimala Singha and K.Hemachandran, “ Content Based Image Retrieval using Color and Texture”, Signal & Image Processing : An
International Journal (SIPIJ) Vol.3, No.1, 2012, pp.39-56. 5. A.Komali , R.Veera Babu, “An Efficient Content Based Image Retrieval System for Color and Shape Using Optimized K-Means
Algorithm”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 2, Issue 8, 2013, pp.
203-207. 6. Arun K.S, Hema P Menon, “Content Based Medical Image Retrieval by Combining Rotation Invariant Contourlet Features and Fourier
Descriptors”, International Journal of Recent Trends in Engineering, Vol 2, No. 2, 2009, pp.35-39.
7. Ch.Srinivasa Rao , S. Srinivas Kumar , B.N.Chatterji , “Content Based Image Retrieval using Contourlet Transform”, ICGST-GVIP Journal, Volume 7, Issue 3, 2007, pp.9 – 15.
8. Swapna Borde, Udhav Bhosle, “Image Retrieval using Contourlet Transform”, International Journal of Computer Applications , Volume
34, No.5, November 2011 pp.37-43. 9. Sujata T Bhairnallykar, V.B.Gaikwad, “Content based Medical Image Retrieval with SVM Classification and Relevance Feedback”,
International Conference & workshop on Advanced Computing 2013 (ICWAC 2013) pp.25 – 29.
10. T. Dharani, I. Laurence Aroquiaraj, “Content Based Image Retrieval System Using Feature Classification with Modified KNN Algorithm,
International Journal of Computer Treds and Technology, Vol.4, Issue 7, 2013, pp.2008 – 2013.
11. Anju Maria, Dhanya S, “Amalgamation of Contour, Texture, Color, Edge, and Spatial features for Efficacious Image Retrieval”, International Journal of Research in Engineering and Technology, Volume: 03 Issue: 02, 2014, pp. 674- 680.
12. M. S. Shirdhonkar and Manesh B. Kokare, “Handwritten Document Image Retrieval”, International Journal of Modeling and
Optimization, Vol. 2, No. 6, 2012, pp.693-696 .
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25.
Authors: Heena Sharma, Navdeep Kaur Kaler
Paper Title: A Synthesized Approach for Comparison and Enhancement of Clustering Algorithms in Data
Mining for Improving Feature Quality
Abstract: K-Means and Kohonen SOM clustering are two major analytical tools for unsupervised forest
datasets. However, both have their innate disadvantages. Clustering is currently one of the most crucial techniques
for dealing with massive amount of heterogeneous information on the databases, which is beyond human being’s
capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm,
the K-means algorithm, is more suitable for large datasets. Also, as clusters grow in size, the actual expression
patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses
initial centroids randomly; in addition, it is sensitive to outliers. SOM We present an improved approach to
combined merits of the two and discard disadvantages.
Keywords: Clustering, K-means, Kohonen SOM, Data Mining
References: 1. Balaji,S., .Srivatsa, S.K. (2012)” Decision Tree induction based classification for mining Life Insurance Databases” IRACST -
International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 2, No.3, June
2012 pp 699-704. 2. Dan,Ji, Jianlin,Qiu, Xiang, Gu,Li,Chen, Peng, He (2010)” A Synthesized Data Mining Algorithm Based on Clustering and Decision
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Tree” 2010 10th IEEE International Conference on Computer and Information Technology (CIT 2010) 978-0-7695-4108-2/10 © 2010 IEEE ,pp 2722-2728
3. Fayyad, Piatetsky-Shapiro, Smyth, "From Data Mining to Knowledge Discovery: An Overview" in Fayyad, Piatetsky-Shapiro, Smyth,
Uthurusamy, Advances in Knowledge Discovery and Data Mining ,AAAI Press / The MIT Press, Menlo Park, CA, 1996, pp.1-34. 4. Hatamlo, Abdolreza, Abdullah, Salwani (2011) ”Two stage algorithm for clustering” in Data Mining and Optimisation Research Group,
Center for Artificial Intelligence Technology Int' Conf. Data Mining DMIN 2011, pp 135-139.
5. Huo, Jianbing, Wang, Xizhao, Lu, Mingzhu, Chen, Junfen (2006) ” Induction of Multi-stage decision tree” 2006 IEEE International Conference on Systems, Man, and Cybernetics October 8-11, 2006, Taipei, Taiwan pp 835-839
6. .Kanellopoulos,Y. , Antonellis, P., Tjortjis,C., Makris,C., Tsirakis, N. (2011) ” k-attactors a partitional clustering algorithm for numeric
data analysis”Applied Artificial Intelligence, 25:97–115, 2011 Copyright 2011 Taylor & Francis Group, LLC pp 97-115. 7. Kristensen, Terje, Jakobsen, Vemund (2011)” Three Different Paradigms for Interactive Data Clustering” Int' Conf. Data Mining DMIN
2011 pp-3-9.
8. Li, Xiangyang, Ye, Nong (2006) ”A Supervised Clustering and Classification Algorithm for Mining Data With Mixed Variables” IEEE Tranactionon systems, man and Cybernetics-part systems and humans, VOL. 36, NO. 2, MARCH 2006 pp 396-406
9. Lin, Zetao, Ge, Yaozheng, Tao, Guoliang (2005) “Algorithm for Clustering Analysis of ECG Data” Proceedings of the 2005 IEEE
Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005 pp-3857-3860 10. Mao,Guojun ,Yang, Yi (2011)” A micro-Cluster based Ensemble Approach for Classifying Distributed Data Streams” 2011 23rd IEEE
International Conference on Tools with Artificial Intelligence pp-753-759.
11. Shi, Yong, Meisner, Jerry (2011) ”An Approach to Selecting Proper Dimensions for Noisy Data” Int' Conf. Data Mining DMIN 2011 pp 172-175.
12. Silwattananusarn, Tipawan , Tuamsuk, Kulthida (2012) ” Data Mining and Its Applications for Knowledge Management : A Literature
Review from 2007 to 2012” International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.5, September
2012 pp 13-24.
13. Ramamohan, Y., Vasantharao, K., Chakravarti, C. Kalyana, Ratnam, A.S.K. (2012)” A Study of Data Mining Tools in Knowledge
Discovery Process” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012 pp 191-194.
26.
Authors: AhmadHamza Al Cheikha
Paper Title: Matrix Representation of Groups In the Finite Fields GF(2n)
Abstract: The representation of mathematical fields can be accomplished by binary rows (or columns) of a binary
triangular matrix as the Hamming’s matrices, but this representation don’t show the basic product properties of the
fields, that is the nonzero elements of the fields forms a cyclic multiplicative group.In this paper we show that the
elements of the fields GF(2n), and their subgroups, can represent as square matrices by m – sequences, which
satisfies the product properties as a cyclic group.
Keywords: Galois fields, m-sequences,bcyclic groups, Orthogonal sequences.
References: 1. Yang K , Kg Kim y Kumar l. d ,“Quasi – orthogonal Sequences for code - Division Multiple Access Systems ,“ IEEE Trans
.information theory ,
2. Vol. 46 NO3, 200, PP 982-993
3. Jong-Seon No, Solomon W& Golomb, “Binary Pseudorandom Sequences For period 2n-1 with Ideal Autocorrelation,” IEEE Trans. Information Theory,Vol. 44 No 2, 1998, PP 814-817
4. Lee J.S &Miller L.E, ” CDMA System Engineering Hand Book, ” Artech House. Boston, London,1998.
5. Yang S.C,”CDMA RF System Engineering,” Artech House.Boston-London,1998. 6. LIDL,R.& PILZ,G., ”Applied Abstract Algebra,” Springer – Verlage New York, 1984.
7. Lidl, R. & Niderrreiter, H., “ Introduction to Finite Fields and Their Application,” Cambridge university U SA, 1994.
8. Thomson W. Judson , “Abstract Algebra: Theory and Applications ,” Free Software Foundation,2013. 9. FRALEIGH,J.B., “A First course In Abstract Algebra, Fourth printing. Addison-Wesley publishing company USA,1971.
10. Mac WILIAMS,F.G& SLOANE,N.G.A., “The Theory Of Error- Correcting Codes,” North-Holland, Amsterdam, 2006.
11. KACAMI,T.&TOKORA, H., “Teoria Kodirovania,” Mir(MOSCOW), 1978. 12. David, J., “Introductory Modern Algebra,” Clark University USA, 2008.SLOANE,N.J.A., “An Analysis Of The Stricture And
Complexity Of Nonlinear Binary Sequence Generators,” IEEE Trans. Information Theory Vol. It 22 No 6,1976, PP 732-736.
118-125
27.
Authors: Gul Ahmad, Tariq Rahim Soomro, Mohammad Nawaz Brohi
Paper Title: XSR: Novel Hybrid Software Development Model (Integrating XP, Scrum & RUP)
Abstract: Software industries are progressively adopting the agile development practices of customized models
such as Extreme Programming (XP) or Scrum or Rational Unified Process (RUP). Scrum and Extreme
Programming (XP) are frequently used agile models, whereas Rational Unified Process (RUP) is one popular
classic plan driven software development methodology. Both agile and plan driven models have their own merits &
demerits such as XP has good engineering practices, team collaboration and on the other hand weak documentation,
poor performance in medium & large scale projects. Scrum is based on project management practices. RUP model
has some limitations such as impractical for small and fast paced projects, tendency to be over budgeted, condemn
rapid changes in requirements. This research paper based on propose novel hybrid framework XSR by combining
strengths of Scrum, XP and RUP by suppressing their limitations to produce high quality software.
Keywords: eXtreme Programming (XP), Scrum, Rational Unified Process (RUP), XP Scrum RUP (XSR)
References: 1. M. Salman Bashir, M. Rizwan Jameel Qureshi, HYBRID SOFTWARE DEVELOPMENT APPROACH FOR SMALL TO MEDIUM
SCALE PROJECTS: RUP, XP & SCRUM, 2012, Sci.Int.(Lahore), 24(4),381-384, 2012
2. Lamia Nassif, Jessy, Nadine Ghanem, & Pedro Maroun Eid, Extreme Programming, March 2002, Software Engineering CSC 423 B - MWF 11-12
3. Zaigham Mushtaq, M. Rizwan Jameel Qureshi, Novel Hybrid Model: Integrating Scrum and XP, I.J. Information Technology and
Computer Science, 2012, 6, 39-44 4. M. Grant, “Introduction to Extreme Programming”. http://www.xprogramming.com
5. Sillitti and G. Succi, “The Role of Plan-Based Approaches in Organizing Agile Companies,” Cutter IT Journal, Vol. 19, No. 2,
2006, pp. 14-19.
126-130
6. “Extreme Programming Official Website”. http://www.extremeprogramming.org/map/project.html 7. Schwaber K, Beedle M. Agile Software Development with Scrum. Prentice Hall, USA, 2001.
8. Abrahamson P, Salo O, Ron K. Agile Software Development Methods: Reviews and Analysis. VTT Electronics, 2002.
9. Maria P. Sadra D., Casper L. Distributed Agile Development: Using Scrum in Large Projects. In: Proceedings of IEEE International Conference on Global Software Engineering, Bangalore, India, August 2008, 87-95.
10. Brent Barton, Evan Campbel, Ken. Implementing a Professional Services Organization Using Type C Scrum. In: Proceedings of the 40th
Annual Hawaii International Conference on System Sciences, Hawaii, 2007, 275 a-275 a. 11. P. Kruchten, “The Rational Unified Process—An Introduction,” 2nd Edition, Addison-Wesley, 2000.
12. P. Kroll and P. Kruchten, “Rational Unified Process Made Easy: A Practitioner’s Guide to the RUP,” Addison Wesley, Boston,
2003. 13. Sadaf Un Nisa, M. Rizwan Jameel Qureshi, Empirical Estimation of Hybrid Model: A Controlled Case Study, I.J. Information
Technology and Computer Science, 2012, 8, 43-50 Published Online July 2012 in MECS (http://www.mecs-press.org/) DOI:
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Direction, European Academic Research , Feb 2014
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Conference on Agile processes in software engineering and extreme programming, Como, Italy,2007.240–244. 17. Kamlesh V, Ahmad S. Evaluating Evolutionary Prototyping for Customizable Generic Products in Industry. M.S. Thesis, School of
Engg. Blekinge Inst. Tech. (Ronneby, Sweden), 2008
18. Scott W. Ambler, Disciplined Agile Delivery: An introduction,, April 2011,
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19. J.Grey, The development of a hybrid agile project management methodology, June 2011, Potchefstroom Campus of the North-West
University 20. Juyun Cho, A HYBRID SOFTWARE DEVELOPMENT METHOD FOR LARGE-SCALE PROJECTS: RATIONAL UNIFIED
PROCESS WITH SCRUM, Issues in Information Systems,2-2009
21. Ghulam Rasool, Shabib Aftab, Shafiq Hussain, Detlef Streitferdt, eXRUP: A Hybrid Software Development Model for Small to Medium Scale Projects, Sept 2013, Journal of Software Engineering and Applications.
22. Jiang, Armin Eberlein. Towards a framework for understanding the relationships between classical software engineering and agile
methodologies. In: Proceedings of the 2008 international workshop on Scrutinizing agile practices or shoot-out at the agile corral, Germany, May 2008, 9-14.
23. Jeff S, Anton V, Jack B, Nikolai P. Distributed Scrum: Agile Project Management with Outsourced Development Teams. In:
Proceedings of the 40th Annual Hawaii International Conference on System Sciences, Hawaii, 2007, 274a-274a, 2007. 24. Y. Dubinskyl, O. Hazzanz and A. Keren, “Introducing Extreme Programming into a Software Project at the Israeli Air
Force,” Proceedings of the 6th International Conference on Extreme Programming and Agile Processes in Software
Engineering, Sheffield, 18-23 June 25. ScrumUP A Visual Blog on IT Improvement using Scrum, XP & RUP, May 20, 2011 2011,
http://blog.scrumup.com/2011/05/comparing-methods.html
28.
Authors: Kirti B. Satale, Anagha P. Khedkar
Paper Title: Analysis of Different Speed Controllers and Implementation of Novel Speed Controller using FPGA
for BLDC Motor
Abstract: BLDC motor controller has significant importance because of inherent properties of motor like high
efficiency, low noise operation, maintenance free, etc. Variety of speed controllers like PID, fuzzy PID and adaptive
fuzzy PID are reviewed. In adaptive fuzzy PID controller, to have online control, gains of fuzzy PID controller are
changed with change in error. After simulating and comparing speed characteristic of different speed controllers, a
novel speed controller having less rise time and overshoot will be implemented using FPGA.
Keywords: BLDC motor, Fuzzy logic, PID controller.
References: 1. M.V. Ramesh, J. Amarnath, S. Kamakshaiah, G. S. Rao, “ Speed Control of Brushless DC Motor by Using Fuzzy Logic PI Controller”,
ARPN journal of engineering and applied science, vol. 6, No. 09, pp. 55-62, Sept. 2011.
2. Y. Wu, H. Jiang, M. Zou, “The Research on Fuzzy PID Control of the Permanent Magnet Linear Synchronous Motor”, Elsevier,
International conference on applied science and industrial engineering, pp. 1311-1318, 2012.
3. B. Park, T. Kim, D. Hyun, “Fuzzy Back EMF Observer for improving performance of Sensorless Brushless DC Motor Drive”, IEEE, pp. 674-978,2006.
4. J. Wang, C. Wang, Y. Chang, C. teng, “Intelligent Control of High-speed Sensorless Brushless DC Motor for Intelligent Automobiles”,
IEEE, International conference on systems, man and cybernetics, pp. 3394-3398, 2008. 5. T. Siong, B. Ismail, S. Siraj, M. Mohmmed, “Fuzzy Logic Controller for BLDC Permanent Magnet Motor Drives”, IEEE, International
journal of electrical and computer sciences, vol. 11, No. 02, pp. 12-17, April 2011.
6. N. Parhizkar, M. Shafiei, , M. Kauhshahi, “Direct Torque Control of Brushless DC Motor Drive with Reduced Starting Current using Fuzzy Logic Controller”, IEEE, International conference on uncertainty reasoning and knowledge engineering, pp. 129-132, 2011.
7. Rajan, R. Raj, S. Vasantharathna, “Fuzzy Based Reconfigurable Controller for BLDC Motor”, IEEE, International conference on
computing, communication and networking technologies, 2010. 8. P. Shreekala, A. Sivasubramanian, “Speed Control of Brushless DC Motor with PI and Fuzzy Logic Controller using Resonantpole
Inverter”, IEEE PES innovative smart grid technologies, 2011.
9. W. Yuanxi, Yu Yali, Z. Guosheng, S. Xiaoliang, “Fuzzy Auto-adjust PID Controller Design of Brushless DC Motor”, Elsevier, International conference on medical physics and biomedical engineering, pp. 1553-1559, 2012.
10. R. Arulmozhiyal, “Design and Implementation of Fuzzy PID Controller for BLDC Motor using FPGA”, IEEE, International conference
on power electronics, drives and systems, Dec. 2012. 11. R. Kandiban, R. Arulmozhiyal, “Speed Control of BLDC Motor using Adaptive Fuzzy PID Controller”, Elsevier, International
conference on modelling, optimisation and computing, pp.306-313, 2012.
12. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller- Part I”, IEEE Transaction on systems, man and cybernetics, vol. 20, No. 02, pp. 404-418, March 1990.
13. C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controller- Part II”, IEEE Transaction on systems, man and cybernetics, vol. 20,
No. 02, pp. 419-435, March 1990. 14. J. E. Miller, “Brushless Permanent Magnet and Reluctance Motor Drives”, Oxford university press.
131-134
29.
Authors: Manpreet Kaur, Chirag Sharma
Paper Title: Improved Method for Segmentation of Real-time Image of Printed Documents
Abstract: The investment possibility of making a vast database of archive picture has left an enormous need for
vigorous approaches to get to the data. Up to date engineering has made it conceivable to handle, process, transmit
and store computerized pictures productively. Thusly, the measure of visual data is expanding at a quickening rate
in numerous different provision zones. To completely misuse this picture recovery methods are needed. Archive
picture recovery frameworks could be used in numerous associations which are utilizing record picture databases
widely. The paper proposes a strategy to concentrate and recover the pictures from a printed archive.
Keywords: Document image, retrieval, segmentation, image extraction
References: 1. Hyung Il Koo, Segmentation and Rectification of Pictures in the Camera-Captured Images of Printed Documents, IEEE
2. AndrásBarta and IstvánVajk, Document Image Analysis by Probabilistic Network and Circuit Diagram Extraction, Informatica29 3. (2005) 291–301
4. MohammadrezaKeyvanpour and Reza Tavoli, Document Image Retrieval: Algorithms, Analysis and Promising Directions, International
Journal of Software Engineering and Its Applications, Vol.7,No.1,January, 2013 5. M. N. S. S. K. Pavan Kumar and C. V. Jawahar, Information Processing from Document Images
6. KonstantinosZagoris, KavallieratouErgina , Nikos Papamarkos, A Document Image Retrieval System
7. Manesh B. Kokare, M.S.Shirdhonkar, Document Image Retrieval: An Overview, 2010 International Journal of Computer Applications (0975– 8887) Volume 1–No. 7
8. Jilin Li, Zhi-Gang Fan, Yadong Wu and Ning Le, Document Image Retrieval with Local Feature Sequences, 2009 10th International
Conference on Document Analysis and Recognition 9. REZA TAVOLI, Classification and Evaluation of Document Image Retrieval System, WSEAS TRANSACTIONS on COMPUTERS,
Issue 10, Volume 11, October 2012, E-ISSN:2224-2872
10. Mrs.Waykule J.M, Ms. Patil V.A, Region Filling and Object Removal by Exemplar-Based Image In painting, International Journal of Scientific & Engineering Research Volume 3, Issue 1, January-2012 ISS N 2229-5518
11. Muhammad Waseem Khan, A Survey: Image Segmentation Techniques, International Journal of Future Computer and Communication,
Vol. 3, No. 2, April 2014 12. Rajwantkaur, Sukhpreetkaur (2013), “Object Extraction and Boundary Tracing Algorithms for Digital Image Processing: Comparative
Analysis: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering 3(5), May - 2013, pp.
263-268, ISSN: 2277 128X 13. Manjusha Singh, AbhishekMisal (2013),”A Survey Paper on Various Visual Image Segmentation Techniques”, International Journal of
Computer Science and Management Research Vol 2 Issue 1 January 2013 ISSN 2278-733X
14. Toru Tamakia, Tsuyoshi Yamamurab and Noboru Ohnishi, “Image segmentation and object extraction based on geometric features of regions”, IS&T/SPIE Conf. on VCIP'99, SPIE Vol.3653, Part Two, pp.937
15. Atsushi Yamashita, Toru Kaneko, and Hajime Asama, Precise Extraction of Visual Information from Imagesby Image Processing
Techniques, SUPEN 16. 2013
135-138
30.
Authors: A.G. Ovskiy
Paper Title: Instrumented System for the Solution of Static Problems on the Theory of Elasticity for a Multilayer
Elastic Foundation
Abstract: The article presents an instrumented system developed by the author on the basis of analytical
methods. The essence of analytical methods is given in the text. The compute kernel of the instrumented system is
represented by Maxima computer mathematics. Examples of instrumented system operation constitute the fully
automated development of analytical solutions of static problems on the theory of elasticity for a multilayer elastic
foundation in two-dimensional and three-dimensional setting.
Keywords: computer mathematics system, instrumented system, preprocessor, theory of elasticity.
References: 1. Vlasov V.Z. Beams, plates and covers on elastic foundation. / V. Vlasov, N. Leontyev – Moscow: FIZMATGIZ, 1960. – 491 p.
2. Gorshkov A.G. Theory of elasticity and plasticity / Gorshkov A.G., Starovoytov E.I., Talakovskiy D.V.; Textbook for higher educational
establishments. – M.: FIZMATLIT, 2002. – 416 p. 3. Polianin A.D. Reference book on linear equations of mathematical physics / Polianin A.D. – M. FIZMATLIT, 2001. – 576 p.
4. Ovskiy A.G. Application of Maple system in the implementation of Vlasov’s initial functions method / Ye.Ye. Galan, Ovskiy A.G., V.A.
Tolok // Journal of Zaporozhye National University: Collection of Scientific Articles. Physics and Mathematics Sciences. – Zaporozhye: ZNU. – 2008. – No. 1. – P. 16-26.
5. Ovskiy A.G. Application of Maple computer mathematics system for substantiation of orthogonality law for direct and inversion
matrices built by Vlasov V.Z. / A.G. Ovskiy, V.A. Tolok // Journal “Radioelectronics, Computer Science. Management”. – Zaporozhye. ZNTU. – 2008. – No. 1. – P. 78-85.
6. Ovskiy A.G. Modeling a scheme of solution of elasticity theory three-dimensional problem within the Maple system / A.G. Ovskiy, V.O.
Tolok // Hydroacoustic journal. – 2008. No. 3. – P. 88-97. 7. Ovskiy A.G. Preprocessor for solution of static two-dimensional and three-dimensional problems on the theory of elasticity. / A.G.
Ovskiy, V.A. Tolok // Information technologies of modeling and management. – Voronezh. – 2014. – No. 85. – P. 47-58.
139-143
31.
Authors: Edmore Chikohora, Obeten O. Ekabua
Paper Title: A Genetic Approach to Parameterization of Feature Extraction Algorithms in Remote Sensing
Images
Abstract: Genetic Algorithms (GA) are an adaptive heuristic search algorithm found on the evolutionary ideas
of natural selection. In this paper, we propose an adaptive heuristic based on the Gabor Filter (GF) to generate
useful solutions to optimization of parameter selection strategies for Feature Extraction Algorithms (FEA) in
Remote Sensing Images. Experiments were done using computer simulations and a critical analysis on performance
of the heuristic algorithm is done in a comparative manner with the rest of the algorithms.
Keywords: Average Ranking, Square Error, Local Extrema, Phenotype, Genotype.
References:
144-149
1. M. Henrique and G. Easson, Feature Extraction from High-Resolution Remotely Sensed Imagery using Evolutionary Computation. Mississippi, USA: Prof. Eisuke Kita, 2011.
2. E. Chikohora and O. O. Ekabua, "Feature Extraction Techniques in Remote Sensing Images: A survey on Algorithms, Parameterization
and Perfomance," International Journal of Soft Computing and Engineering, vol. 4, no. 1, pp. 140-144, March 2014. 3. P. Moreno, A. Benardino, and J. Santos-Victor, "Gabor Parameter Selection for Local Feature Detection," in IBPRIA 2nd Iberian
Conference on Pattern Recogniton and Image Image Analysis, Portugal, 2005.
4. J. Yang, L. Liu, T. Jiang, and Y. Fan, "A modified Gabor Filter Design Method for Fingerprint Image Enhancement," Pattern Recognition Letters, no. 24, pp. 1805 - 1817, January 2003.
5. H. N. Al-Duwaish, "Parameterization and Compensation of Friction Forces Using Genetic Algorithms," in Industry Applications
Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the 1999 IEEE, Phoenix, AZ , 1999, pp. 653-655. 6. C. M. Keet. (2002, May) Homepage of Maria (Marijke) Keet : Genetic Algorithms - An Overview. [Online].
Http://www.meteck.org/gaover.html
7. P. B. Brazdil and C. Soares, "A Comparison of Ranking Methods for Classification Algorithm Selection," in Machine Learning: ECML 2000, R. L. Mántaras, Ed. Porto, Portugal: Springer Berlin Heidelberg, 2000, pp. 63-75.
8. W. M. Spears and V. Anand, "A Study Of Crossover Operators In Genetic Operators," in Methodologies for Intelligent Systems, W. Z.
Ras and M. Zemankova, Eds. Charlotte, N. C, USA: Springer Berlin Heidelberg, 1991, pp. 409-418. 9. M. E. Famer, S. Bapna, and A. K. Jain, "Large Scale Feature Selection Using Modified Random Mutation Hill Climbing," in Pattern
Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Volume:2 ) , 2004, pp. 287-290.
32.
Authors: Abdulaziz S. Al-Aboodi
Paper Title: Monte Carlo Simulation on Estimation of Contact Pressure at Tubular Exchanger
Abstract: The strength of tube-to-tubesheet joints is influenced by many factors such as method of attachment,
details of construction, and material properties. The strength of tube-tubesheet joints is measured in terms of the
force required to pull or push the tube out of the hole in which it was expanded or by the radial interfacial residual
contact pressure. In this paper, Monte Carlo method was conducted to estimate the tube/tubesheet mean interfacial
pressure and its standard deviation using experimental sample data Sampling repetition were conducting to estimate
the mean and standard deviation. Finally, a linear relation between force and contact pressure were introduced with
the coefficient of determination, R2 of 0.9895..
Keywords: Monte Carlo, Simulation, Contact pressure, Tubular Exchanger.
References: 1. Grimison, E. D., Lee, G. H., “Experimental Investigation of Tube Expansion”, Transaction of The ASME, July 1943, pp. 497-505.
2. Scott, D. A., Wolgemuth, G. A., Aikin, J. A., “Hydraulically Expanded Tube-to-Tubesheet Joints", Journal of Pressure Vessel Technology, Vol. 106, Feb. 1984, pp. 104-109.
3. Jawad, M. H., Clarkin, E. J., Schuessler, R. E., “Evaluation of Tube-to-Tubesheet Junctions” Journal of Pressure Vessel Technology, Vol.
109, Feb. 1987, pp. 19-26. 4. Sherburne, P. A., Hornbach, D. J., Ackeman, R. A., Mcllree, A. R., “Residual Stresses in OTSG Tube Expansion Transitions” 8th Water
Reactors, Proceeding, Aug 10, 1997, Amelia Island, Florida, NACE, TMS.
5. Shuaib, A. N., Merah, N., Khraisheh, M. K., Allam, I. M., Al-Anizi, S. S., “Experimental Investigation of Heat Exchanger Tubesheet Hole Enlargement” Journal of Pressure Vessel Technology, Vol. 125, Feb. 2003, pp. 19-25.
6. Lee, Jae Bong; Park, Jai HakView Profile; Kim, Hong-Deok; Chung, Han-Sub; Kim,Tae Ryong, "Statistical assessment of integrity in
steam generator tubes considering uncertainty of nde", Key Engineering Materials 326-328 (2006): 545-548. 7. Wu, Gary,"A probabilistic-mechanistic approach to modeling stress corrosion cracking propagation in Alloy 600 components with
applications", University of Maryland, College Park, ProQuest, UMI Dissertations Publishing, 2011. 1501253.
8. Mao, Dan, "Bayesian modeling of pitting corrosion in steam generators", University of Waterloo (Canada), ProQuest, UMI Dissertations Publishing, 2007. MR35285.
9. Vincent, Brady, "A probabilistic assessment technique for shell-and-tube heat exchanger inspection", University of New Brunswick
(Canada), ProQuest, UMI Dissertations Publishing, 2007. MR56487.
150-152
33.
Authors: Ipsa Das, Md Imran Alam, Jayanti Dansana
Paper Title: A Survey on Location Based Services in Data Mining
Abstract: Data privacy has been the primary concern since the distributed database came into the picture. More
than two parties have to compile their data for data mining process without revealing to the other parties.
Continuous advancement in mobile networks and positioning technologies have created a strong challenge for
location-based applications. Challenges resembling location-aware emergency response, location-based
advertisement, and location-based entertainment. Privacy protection in pervasive environments has attracted great
interests in recent years. Two kinds of privacy issues, location privacy and query privacy, are threatening the
security of the users. The novel combined clustering algorithm for protecting location privacy and query privacy,
namely ECC, is discussed. ECC applies an iterative K-means clustering method to group the user requests into
clusters for providing location safety while utilizing a hierarchical clustering method for preserving the query
privacy.
Keywords: Location Based Services (LBSs), K-Anonymity, Location K-Anonymity, Clustering, Clustering Cloak
References: 1. B. Gedik and L. Liu, “Protecting location privacy with personalized k-anonymity: Architecture and algorithms,” Mobile Computing,
IEEE Transactions on, vol. 7, no. 1, pp. 1–18, 2008. 2. L. Yao, C. Lin, X. Kong, F. Xia, and G. Wu, “A clustering based location privacy protection scheme for pervasive computing”, in
Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical
and Social Computing. IEEE Computer Society, 2010, pp. 719–726. 3. Chi Lin, Guowei Wu, Lin Yao, Zuosong Liu"A Combined Clustering Scheme for Protecting Location Privacy and Query Privacy in
Pervasive Environments", 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
4. M. Gruteser and D. Grunwald, “Anonymous Usage of Location- Based Services through Spatial and Temporal Cloaking”, Proc. ACM Int’l Conf. Mobile Systems, Applications, and Services (MobiSys ’03), 2003.
5. L. Liu, “From Data Privacy to Location Privacy”, VLDB '07: Proceedings of the 33rd international conference on Very large data bases,
ACM Press, Sep. 2007, pp. 1429-1430.
153-158
34.
Authors: Vaibhav Ingle, Nilesh Swami, Mahesh Shelke, Saurabh Kataria, Chhaya Varade
Paper Title: Web Services and Security
Abstract: The vision of a landscape of heterogeneous web services deployed as encapsulated business software
assets in the Internet is currently becoming a reality as part of the Semantic Web. When pro-active agents handle the
context-aware discovery, acquisition, composition, management of applications services and data, ensuring the
security if customers data become a principle task. In this paper we propose neoteric way web services and security.
A methodology based on type-based information flow to control the security of dynamically computed data and
their proliferation to other web services. The approach is based on the following trine guidelines: (1)The business
and security concern of integrated web services are separated and building them independently.(2)Runtime
modification of integrated web services.(3)Providing compartmentalization so that one service can not affect
another. We are developing flight system to demonstrate the feasibility of our approach.
Keywords: The business and security concern of integrated web services are separated and building them
independently, Runtime modification of integrated web services, Providing compartmentalization so that one
service can not affect another.
References: 1. Benslimane, D.; Dustdar, S.; Sheth, A. (2008). "Services Mashups: The New Generation of Web Applications". IEEE Internet Computing
10 (5): 13–15.doi:10.1109/MIC.2008.110
2. Maler, Eve. "Minutes of 9 January 2001 Security Services TC telecon". security-services at oasis-open mailing list. Retrieved 7 April 2011.
3. Bob Atkinson, et. al.: Web Services Security (WS-Security) http://www.oasis-open.org/committees/tc home.php?wg abbrev=wss.
4. Anne Anderson, “WS-XACML:Authorization and Privacy Policies for Web Services” https://www.oasis-open.org 5. F. Paci, E. Bertino, and J. Crampton, “An Access-Control Framework for WS-BPEL,” International Journal of Web Services Research,
vol. 5, no. 3, pp. 20–43, 2008.
159-161
35.
Authors: Swati D. Nikam, Sachin D. Ruikar
Paper Title: A Method of Color Image Denoised and Enhanced Using Wavelet Transform
Abstract: The objective of the image enhancement is to remove the noise. Real color images are images with
noise. In traditional image enhancement algorithms color images are firstly converted to gray images. These
algorithms enhanced noise while they enhanced image. In this paper wavelet transform is used for color image
enhancement. Wavelet transform is an efficient tool to represent a multi resolution analysis of an image. A novel
method of color image enhancement based on Hue invariability in HIS color pattern is presented here.
Keywords: Image enhancement, HIS color space, Wavelet transforms.
References: 1. Wang Ping, Cheng Hao, Lou Yingxin. “Color Image Enhancement Based on Hue Invariability”. Journal. Journal of Image and
Graphics.2007 IEEE. 2. SHI Meihong, LI Yonggang, ZHANG Junying. “Novel method of color image enhancement” Journal. Computer Application, 2004
IEEE.
3. Zhang Yujun, Image Engineering. Tsinghua University Press Beijing.1999 IEEE. 4. Kartik Sau, Amitabha Chanda, Milan Pal, “Color Image Enhancement based on Wavelet Transform and Human Visual System”. IEEE
2011.
5. Zhang Yanhong, Hou Dewen. “An Image Enhancement Algorithm Based on Wavelet Frequency Division and Bi-histogram Equalization”, Journal. Computer Application and Software 2007 IEEE.
6. S. Daly, “The visible differences predictor: an algorithm for the assessment of image fidelity,” in Digital Images and Human Vision, A.
B.Watson, Ed., chapter 14, pp. 179–206, MIT Press, Cambridge, Mass, USA, 1993.
7. L .Meylan, Tone mapping for high dynamic range images, Ph.D. thesis, EPFL, Lausanne, Switzerland, July 2006.
8. Meylan and S. S¨usstrunki, “Bio-inspired color image enhancement,” in Human Vision and Electronic Imaging Conference, vol. 5292 of
Proceedings of SPIE, pp. 46–56, San Jose, Calif, USA, 2004. 9. Ruan Qiuqi. Digital Image Processing, Publishing House of Electronics Industry, Beijing 2001.
10. Jinyong Cheng, Caixia Liu, - “Novel Method of Color Image Enhancement Based on Wavelet Analysis”, 2008 IEEE.
11. Shaohua Chen and Azeddine Beghdadi, - “Natural Enhancement of Color Image”, 2010 IEEE. 12. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing, Prentice Hall, Upper Saddle River, NJ, USA, 2004 IEEE.
13. Han Xiaowei. “The research on Color image processing key technology”. North Eastern University, 2005. 1.1.
14. Zhang Yanhong, Hou Dewen. “An Image Enhancement Algorithm Based on Wavelet Frequency Division a Bi-histogram Equalization”, Journal. Computer Application and Software. 2007.11, 24(11), pp.159-161
15. Jobson D J, Rahman Z U, Woodell G A. The statistics of visual representation [C] // Processing of SPIE Visual Information
processing XI. Washington: SPIE Press, 2002:25-35 438 16. V. Buzuloiu, M. Ciuc, M. R. Rangayyn & C. Vartan. Adaptive neighborhood histogram equalization of color images. International
journal of electron Image. 10(2), 2001, 445-459.
17. P. E. Trahanias, & A. N. Venetsanopoulos, Color image enhancement through 3-D histogram equalization Proc. 11th IAPR conference on pattern recognition, The Hague, Netherlands, 1992, 545-548.
18. B. A. Thomas. R. N. Strickland, & J. J. Rodriguez, Color image enhancement using spatially adaptive saturation feedback. Proc. 4th
IEEE conf. on image processing, Santa Barbara, CA, USA, 1997, 30-33 19. Gupta, & B. Chanda, A hue preserving enhancement scheme for a class of color images, Pattern recognition Letters, 17(2), 1996, 109-
114
20. Kartik Sau, Amitabha Chanda, - “Color Image Enhancement Based on Wavelet Transform and Human Visual System”, IEEE2011. 21. S. Chen and A. Beghdadi, “Natural rendering of color image based on retinex” in Proceedings of the IEEE International Conference on
Image Processing (ICIP ’09), Cairo Egypt, November 2009.
162-165
36.
Authors: Pallavi Grover, Sonal Chawla
Paper Title: Evaluation of Ontology Creation Tools
Abstract: Representation of distributed information, with a well defined meaning understandable for 166-169
different parties, is the major challenge of Semantic Web. Several solutions have been built up. Use of Ontologies is
one of the solutions to challenges faced by semantic web. This paper highlights importance of ontologies. This
paper has three fold objectives. Firstly the paper throws light on how a semantic web based tool helps producing
information using ontologies. Secondly, paper highlights the importance of ontology. Lastly a comparison of
various tools for ontology development has been presented on various parameter
Keywords: Ontology Ontology Tools, RDF, Semantic Web
References: 1. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6245779
2. http://en.wikipedia.org/wiki/Semantic_Web 3. http://www.rdfabout.com/intro/#Introducing RDF
4. Ontology Development Tools for ontology-based knowledge management, S.Youn, D.McLeod, University of Southern California, 2006
5. http://www2012.wwwconference.org/proceedings/ companion/p595.pdf 6. Kapoor, B., Sharma, S., “A Comparative Study Ontology Building Tools for Semantic Web Applications”, International Journal 1, July
(2010), 1-13.
7. Kalyanpur, A., Parsia, B., Sirin, E., Grau, B.C., Hendler, J., “Swoop: A „Web‟ Ontology Editing Browser”, Mind, July 2005, 1-20. 8. Gruber, T. R. “A translation approach to portable ontology specification. Knowledge Acquisition” 5(2), 199-220, 1993.
9. Snae. C and Brueckner. M. Ontology-Driven E-Learning System Based on Roles and Activities for Thai Learning Environment.
Interdisciplinary Journal of Knowledge
37.
Authors: Amlan Raychaudhuri, Arkadev Roy, Ashesh Das, Gourav Kumar Shaw, Pratik Kumar Mitra
Paper Title: Moving Object Detection using Differential Evolution
Abstract: Moving Object detection is the process of detecting a change in position of an object relative to its
surroundings or the change in the surroundings relative to an object. Different complex algorithms are employed to
detect a moving object in a video. It has large number of applications in video surveillance and other security
systems that are used to process video information. We have achieved it using Differential Evolution (DE). The
proposed method is successfully tested over two video sequences.
Keywords: Clustering, Differential Evolution, Moving Object Detection, Temporal video segmentation.
References: 1. K. Skifstad, R. Jain and C. Ramesh, “Illumination independent change detection for real world image sequences”, Computer Vision,
Graphics and Image Processing 46(3), pp. 387–399, (1989).
2. Y. Z. Hsu, H. H. Nagel and G. Rekers, “New likelihood test methods for change detection in image sequences”, Computer Vision,
Graphics and Image Processing, 26(1), pp. 73–106, (1984).
3. T. Aach, A. Kaup and R. Mester, “Statistical model-based change detection in moving video”, Signal Processing 31, pp. 165–180, (1993).
4. K. McKoen, R. Navarro-Prieto, B. Duc, E. Durucan, F. Ziliani and T. Ebrahimi, “Evaluation of video segmentation methods for
surveillance applications”, EUSIPCO 2000, Tampere, Finland, (Sept. 2000). 5. P. Villegas, X. Marichal and A. Salcedo, “Objective evaluation of segmentation masks in video sequences”, WIAMIS’99 Workshop,
Berlin, Germany, (May 1999).
6. M. Wollborn and R. Mech, “Procedure for objective evaluation of VOP generation algorithms”, Fribourg, ISO/IEC JTC1/SC29/WG MPEG97/2704, (1997).
7. E. Durucan and T. Ebrahimi, “Change Detection and Background Extraction by Linear Algebra”, Proceedings of the IEEE 89(10),
(October 2001). 8. K. V. Price, “An Introduction to Differential Evolution”, New Ideas in Optimization, McGraw-Hill, London, pp. 79-108, (1999).
9. R. Storn and K. Price, “Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”,
Journal of Global Optimization 11, pp. 341–359, (1997). 10. S. Z. Li, “Markov Random Field Modeling in Image Analysis”, New York: Springer, (2001).
11. E. Y. Kim and K. Jung, “Genetic Algorithms for video Segmentation”, Pattern Recognition 38(1), pp. 59-73, (2005).
12. E. Y. Kim and S. H. Park, “Automatic video Segmentation using genetic algorithms”, Pattern Recognition Letters 27(11), pp. 1252-1265, (Aug. 2006).
13. S. W. Hwang, E. Y. Kim, S. H. Park and H.J. Kim, “Object Extraction and Tracking using Genetic Algorithms”, Proceedings of
International Conference on Image Processing 2, pp. 383-386, (2001). 14. A. Raychaudhuri and M. De,”A Novel Approach of Detection of Moving Objects in a Video”, International Journal of Advanced
Research in Computer and Communication Engineering 2(11), pp. 4485 – 4488, (2013).
170-173
38.
Authors: Safaa ERRIHANI, Said ELFEZAZI, Khalid BENHIDA
Paper Title: IT Project Management According To the PMBoK Adaptation and Application in a Set of
Computing Projects in a Moroccan Public Body
Abstract: With the growth of the computing projects and the limited visibility by the administrators regarding
the use of the resources as well as the technologies which lead to the abundance of projects, along with the trend to
the subcontracting in external suppliers, the project management becomes the key function (office) for the success
of all the technical projects. So the Project Management Body Of Knowledge (Guide PMBOK)) of PMI (Project
Management Institute) is in phase to become an essential tool for the practitioners in all organizations and business
sectors.The main axis of this article is the PMBoK’s analysis and modeling in order to reach a uniform model of
project management. The obtained model will be projected on a set of projects within a Moroccan public
department. Let's recall that the PMBoK defines the project management in terms of: integration, scope, time, cost,
quality, human resources, communication, risk, procurement and the stakeholders of the project.
Keywords: IT project, Modeling, PMBOK, Project Management..
References: 1. foad.refer.org/IMG/pdf/INTRODUCTION_module_6.pdf
2. John M. Nicholas ET Herman Steyn “Project Management for Business, Engineering, and Technology “, principals and practices, 3rd
174-182
edition, Elsevier 2008 3. Le guide de gestion de projets- Introduction Cadre amélioré pour la gestion des projets de technologie de l'information. Bureau de
dirigeant principal de l'information Secrétariat du Conseil du Trésor du Canada. Février 2002
4. A guide to the project management body of knowledge (PMBOK GUIDE) - Fourth edition ANSI/PMI 99-001-2008 5. http://www.piloter.org/
6. A guide to the project management body of knowledge 3rd Ed ANSI/PMI 99-001-2004
7. The principals of project management by Meri Williams 2008 SitePoint 8. Project management for Dummies 3rd edition by Stanley E. Portny 2010 Wiley Publishing
9. C. Dumont ‘’ITIL pour un service informatique optimal’’, Eyrolles, 2007
10. Introduction à ITIL V3 et au cycle de vie des services, Pascal Delbrayelle, (www.itilfrance.com), juillet 2011 11. http://www.best-management-practice.com
12. the official introduction to the ITIL service lifecycle. Office of Government Commerce (OCG) 2007
13. E. Delbaldo, ‘’CMMI light’’, éditions AFNOR, 2008 14. M.Lamnabhi, ‘’Evaluer avec CMMI’’, éditions AFNOR, 2008
15. CMMISM for Systems Engineering/Software Engineering, Version 1.02 (CMMI-SE/SW, V1.02, CMMI Product Development Team,
SEI Joint Program Office 16. D. Moisand & F. Garnier de Labareyre ‘’CobiT : Pour une meilleure gouvernance des systèmes d'information’’ Paris, Eyrolles, 2009
17. http://itil.fr/COBIT/
18. Cobit 1.4, IT Governance Institute, 2007 19. C.v. Wangenheim, D. A. da Silva, L. Buglione, R. Scheidt, R.Prikladnicki ‘’Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM)
and PMBOK ’’ Information and Software Technology, (52), 7, 749-757, 2010
20. Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM) and PMBOK 2008, Information and Software Technology 52 (2010) 749–
757, 2010 Elsevier
21. Successful Project Management Third Edition, Larry Richman, 2011 American Management Association
22. The Complete Project Management Methodology and Toolkit Gerard M. Hill ISBN: 978-1-4398-0154-3
49.
Authors: Safaa ERRIHANI, Said ELFEZAZI, Khalid BENHIDA
Paper Title: IT Project Management According To the PMBoK Adaptation and Application in a Set of
Computing Projects in a Moroccan Public Body
Abstract: With the growth of the computing projects and the limited visibility by the administrators regarding
the use of the resources as well as the technologies which lead to the abundance of projects, along with the trend to
the subcontracting in external suppliers, the project management becomes the key function (office) for the success
of all the technical projects. So the Project Management Body Of Knowledge (Guide PMBOK)) of PMI (Project
Management Institute) is in phase to become an essential tool for the practitioners in all organizations and business
sectors.The main axis of this article is the PMBoK’s analysis and modeling in order to reach a uniform model of
project management. The obtained model will be projected on a set of projects within a Moroccan public
department. Let's recall that the PMBoK defines the project management in terms of: integration, scope, time, cost,
quality, human resources, communication, risk, procurement and the stakeholders of the project.
Keywords: IT project, Modeling, PMBOK, Project Management..
References: 23. foad.refer.org/IMG/pdf/INTRODUCTION_module_6.pdf
24. John M. Nicholas ET Herman Steyn “Project Management for Business, Engineering, and Technology “, principals and practices, 3rd edition, Elsevier 2008
25. Le guide de gestion de projets- Introduction Cadre amélioré pour la gestion des projets de technologie de l'information. Bureau de
dirigeant principal de l'information Secrétariat du Conseil du Trésor du Canada. Février 2002 26. A guide to the project management body of knowledge (PMBOK GUIDE) - Fourth edition ANSI/PMI 99-001-2008
27. http://www.piloter.org/
28. A guide to the project management body of knowledge 3rd Ed ANSI/PMI 99-001-2004 29. The principals of project management by Meri Williams 2008 SitePoint
30. Project management for Dummies 3rd edition by Stanley E. Portny 2010 Wiley Publishing
31. C. Dumont ‘’ITIL pour un service informatique optimal’’, Eyrolles, 2007
32. Introduction à ITIL V3 et au cycle de vie des services, Pascal Delbrayelle, (www.itilfrance.com), juillet 2011
33. http://www.best-management-practice.com
34. the official introduction to the ITIL service lifecycle. Office of Government Commerce (OCG) 2007 35. E. Delbaldo, ‘’CMMI light’’, éditions AFNOR, 2008
36. M.Lamnabhi, ‘’Evaluer avec CMMI’’, éditions AFNOR, 2008
37. CMMISM for Systems Engineering/Software Engineering, Version 1.02 (CMMI-SE/SW, V1.02, CMMI Product Development Team, SEI Joint Program Office
38. D. Moisand & F. Garnier de Labareyre ‘’CobiT : Pour une meilleure gouvernance des systèmes d'information’’ Paris, Eyrolles, 2009
39. http://itil.fr/COBIT/ 40. Cobit 1.4, IT Governance Institute, 2007
41. C.v. Wangenheim, D. A. da Silva, L. Buglione, R. Scheidt, R.Prikladnicki ‘’Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM)
and PMBOK ’’ Information and Software Technology, (52), 7, 749-757, 2010 42. Best practice fusion of CMMI-DEV v1.2 (PP, PMC, SAM) and PMBOK 2008, Information and Software Technology 52 (2010) 749–
757, 2010 Elsevier
43. Successful Project Management Third Edition, Larry Richman, 2011 American Management Association 44. The Complete Project Management Methodology and Toolkit Gerard M. Hill ISBN: 978-1-4398-0154-3
183-187
40.
Authors: Adhar Vashishth, Bipan Kaushal, Abhishek Srivastava
Paper Title: Caries Detection Technique for Radiographic and Intra Oral Camera Images
Abstract: In the modern times, caries is one of the most prevelent disease of the teeth in the whole world. A
large percentage of population is affected by them. Dentists try their level best to identify the problem at an earlier
stage, but, with poor dentist to patient ratio , the problem becomes compounded. To provide them a helping hand
various machines and techniques are developed. Prominent among them is the DIFOTI(digital imaging fiber-optic
transillumination) technique, but it requires very expensive machinery to work with which can not be afforded by
most of the dentists. We are proposing a method that can provide the needed diagnostic help without requiring the
kind of machinery currently in use. We have used image processing technique to identify the caries that provide the
188-190
dentists with the precise results about caries and the area affected. This method can detect caries in radiographic
images as well as in intra oral camera images. This will not only help in countering the low man power problem but
will also provide an accurate and cost effective method in identifying and treating caries.
Keywords: binarization, caries, mask, RGB plane, MATLAB.
References: 1. Stefan Oprea, Costin Marinescu, Ioan Lita, Mariana Jurianu, Daniel Alexandru Vişan, Ion Bogdan Cioc “Image Processing Techniques
used for Dental X-Ray Image Analysis “ , IEEExplore , E-ISBN: 978-1-4244-3974-4.
2. Grace F. Olsen, Susan S. Brilliant, David Primeaux, and Kayvan Najarian “An Image-Processing Enabled Dental Caries Detection
System” , Published in Complex Medical Engineering, 2009. CME, E-ISBN : 978-1-4244-3316-2 3. Supaporn Kiattisin, Adisorn Leelasantitham, Kosin Chamnongthai and Kohji Higuchi “ A Match of X-ray Teeth Films Using Image
Processing Based on Special Features of Teeth”, Published in SICE Annual Conference 2008, E-ISBN : 978-4-907764-29-6. 4. “Dental Caries: The Disease and Its Clinical Management”, 2nd Edition by Ole Fejerskov and Edwina Kidd.
5. Paul Fotek, DMD, Florida Institute for Periodontics & Dental Implants Published in Medical Plus, A service of the U.S. National Library
of Medicine National Institutes of Health. 6. “Digital Image Processing Using MATLAB” by Rafael C. Gonzalez.
7. Programming in MATLAB ®: A problem-solving approach by Ram N Patel and Ankush Mittal.
8. World statistics published by WHO in 2013 9. By Brett Shoelson, PhD,Email: [email protected]
41.
Authors: Ameya V. Mane, Yogesh Ankurkar, Pratik K. Bajaria
Paper Title: Cooperative Mobile Robotics
Abstract: “Cooperative control” is a term which is used to capture those problem areas in which some type of
repetition of identical or non-identical subsystems, which are interconnected together, occurs. Such systems are
often found in nature, i.e. in the motion of clusters of birds, fish, insects, etc. moving together, in the cell structure
of mammals and life-forms, and also in the man-made systems such as in transportation systems. In such systems, a
decentralized control configuration is often applied to control the overall system, so that some common objective is
achieved. In this paper two non-linear models in multi-agent systems are proposed. These models operate on the
principles of distributed control and cascade control.
Keywords: About four key words or phrases in alphabetical order, separated by commas.
References: 1. David Payton, Mike Daily, Regina Estowski, Mike Howard And Craig Lee, “Pheromone Robotics”, Autonomous Robots 11, 319–324,
2001.
2. Y. Uny Cao, Alex S. Fukunaga, Andrew B. Kahng, “Cooperative Mobile Robotics: Antecedents and Directions”, Autonomous Robots 4, 7–27 (1997)
3. Maziar E. Khatir, Edward J. Davison, “Cooperative Control of Large Systems”, Block Island Workshop, Post workshop volume 2003.
4. Marios M. Polycarpou, Yanli Yang, Kevin M. Passino, “Cooperative Control of Distributed Multi-Agent Systems”, IEEE Control Systems Magazine (June 2001).
5. Po Wu, Panos J. Antsaklis, “Distributed Cooperative Control System Algorithms – Simulations and Enhancements”, ISIS-2009-001,
April 2009
191-194
42.
Authors: Sarah Bal, Anmol Kalra, Rishi Kumar
Paper Title: Motioned Facial Recognition from Live Feed for Surveillance Solutions
Abstract: The paper focuses on how face recognition can be done on live video stream (using a webcam-inbuilt
or USB attached).The live video is checked for any human face. If a human face is detected, a rectangular box is
formed around the face. If nothing is found for the face detection method, a text box showing the error is presented
in front of the user. If the face is detected this face is then matched with the already saved database which was
priorly created having images of different faces. This is the training database which is then matched with the face
image extracted from the live video stream. Initially the project shows the process of face detection and matching
procedure from images and then proceeds to face recognition and matching through a live video streaming. The live
video here considered is the webcam, the face is detected through the webcam and if any match is found from the
train database previously stored in the computer or the device is found then both the detected image and the current
image are displayed on the graphical user interface. The GUI being made consists of three axes windows, one
showing the continuous live streaming of video, the second shows the screenshot or singular frame of the face
detected in the live stream and the third has the image got from the database that somewhat matches to the current
image being displayed. The two databases are there, one the train database where the images of different faces of
people are stored which would then be used for matching from live video stream for the purpose of security and
authentication. The test database consists of the images that are being received from the live video stream, the video
stream as soon as it detects the face of human, takes the snapshot of the frame and saves it to the test database, these
images are then checked for authentication by matching them with the images in the train database.
Keywords: The test database consists of the images that are being received from the live video stream, the video
stream as soon as it detects the face of human, takes the snapshot of the frame and saves it to the test database, these
images are then checked for
References: 1. Study of Moving Object Detection and Tracking for Video Surveillance, International Journal of Advanced Research in Computer
Science and Software Engineering
2. Real Time Motion Detection in Surveillance Camera Using MATLAB, International Journal of Advanced Research in Computer Science
and Software Engineering, Iraqi National Cancer Research Center ,Baghdad University, Iraq
195-201
3. A Video-based Face Detection and Recognition System using Cascade Face Verification Modules, Ping Zhang, Department of Mathematics and Computer Science, Alcorn State University, USA
4. A Surveillance System based on Audio and Video Sensory Agent cooperating with a Mobile Robot, The University of Padua, Italy
5. Face Recognition using Eigenfaces, Mathew.A.Turk and Alex.P.Pentland, Vision and Modeling Group, The Media Laboratory, Massachusetts Institute of Technology
6. Performance evaluation of object detection algorithms for video surveillance, Jacinto Nascimento⋆, Member, IEEE andJorge Marques
7. Face recognition using multiple eigenface subspaces, P.Aishwarya and Karnan Marcus, Journal of Engineering and Technology
Research Vol. 2(8), pp. 139-143, August 2010
8. Development of a real-time face recognition system for access control, Desmond E. van Wyk, James Connan, Department of Computer Science, University of Western Cape, South Africa
9. Face Recognition and Retrieval in Video, Caifeng Shan
10. Image-based Face Detection and Recognition: “State of the Art”, Faizan Ahmad , Aaima Najam and Zeeshan Ahmed 11. OBJCUT for Face Detection, Jonathan Rihan, Pushmeet Kohli, and Philip H.S. Torr, Oxford Brookes University, UK
12. Design Of Efficient Face Recognition Based On Principle Component Analysis Using Eigenfaces Method, Mr.A.R.Sejani 13. Eigenfaces for Recognition, Alex Pentland and Mathew Turk, MIT
14. An Improved Face Detection Method in Low-resolution Video, Chih-Chung Hsu and Hsuan T. Chang∗ Photonics and Information
Laboratory Department of Electrical Engineering National Yunlin University of Science & Technology Douliu Yunlin, 64045 Taiwan
ROC
15. Biometrics and Face Recognition Techniques, International Journal of Advanced Research in Computer Science and Software Engineering, Renu Bhatia
16. Biometrics- Fingerprint Recognition, International Journal of Information & Computation Technology, Sarah Bal and Anmol Kalra
17. Face Detection and Tracking in a Video by Propagating Detection Probabilities, IEEE TRANSACTIONS ON PATTERN ANALYSIS
AND MACHINE INTELLIGENCE, VOL. 25, NO. 10, OCTOBER 2003, Ragini Choudhury Verma, Cordelia Schmid, and Krystian
Mikolajczyk
43.
Authors: S.Y.S Hussien, H.I Jaafar, N.A Selamat, F.S Daud, A.F.Z Abidin
Paper Title: PID Control Tuning VIA Particle Swarm Optimization for Coupled Tank System
Abstract: This paper presents the use of meta-heuristic technique to obtain three parameters (KP, KI and KD)
of PID controller for Coupled Tank System (CTS). Particle Swarm Optimization (PSO) is chosen and Sum Squared
Error is selected as objective function. This PSO is implemented for controlling desired liquid level of CTS. Then,
the performances of the system are compared to various conventional techniques which are Trial and Error, Auto-
Tuning, Ziegler-Nichols (Z-N) and Cohen-Coon (C-C) method. Simulation is conducted within Matlab environment
to verify the transient response specifications in terms of Rise Time (TR), Settling Time (TS), Steady State Error
(SSE) and Overshoot (OS). Result obtained shows that performance of CTS can be improved via PSO as PID
tuning methods.
Keywords: Coupled Tank System (CTS), Particle Swarm Optimization (PSO), PID Controller, PID Tuning
Method.
References: 1. M. Abid, “Fuzzy Logic Control of Coupled Liquid Tank System”, International Conference on Information and Communication
Technologies, 27-28 August 2005, Karachi, Pakistan, pp. 144-147.
2. M. F. Rahmat and S.M. Rozali, “Modelling and Controller Design for a Coupled-Tank Liquid Level System: Analysis & Comparison”,
Journal of Technology, vol. 48 (D), June. 2008, pp. 113-141. 3. H. Abbas, S. Asghar, S. Qamar, “Sliding Mode Control for Coupled-Tank Liquid Level Control System”, International Conference on
Frontiers of Information Technology, 17-19 Dec. 2012, Islamabad, Pakistan, pp. 325-330.
4. K. O. Owa, S. K. Sharma, R. Sutton, “Optimised Multivariable Nonlinear Predictive Control for Coupled Tank Applications”, IET Conference on Control and Automation, 4-5 June 2013, Birmingham, England, pp. 1-6.
5. N. A. Selamat, N. A. Wahab, and S. Sahlan, “Particle Swarm Optimization for Multivariable PID Controller Tuning”, 2013 IEEE 9th
International Colloquium on Signal Processing and its Applications, 8-10 March 2013, Kuala Lumpur, Malaysia, pp. 170-175. 6. H. I. Jaafar, Z. Mohamed, A. F. Z. Abidin and Z. A. Ghani, “PSO-Tuned PID Controller for a Nonlinear Gantry Crane System”, 2012
IEEE International Conference on Control System, Computing and Engineering, 23-25 Nov. 2012, Penang, Malaysia, pp. 515-519.
7. H. I. Jaafar, S. Y. S. Hussien, N. A. Selamat, M. S. M. Aras and M. Z. A. Rashid, “Development of PID Controller for Controlling
Desired Level of Coupled Tank System”, International Journal of Innovative Technology and Exploring Engineering, vol. 3 (9), Feb.
2014, pp. 32-36. 8. M. Khairuddin, A. S. A. Dahalan, A. F. Z. Abidin, Y. Y. Lai, N. A. Nordin, S. F. Sulaiman, H. I. Jaafar, S. H. Mohamad, N. H. Amer,
“Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning of PID Controller in Industrial Couple Tank
System”, Advanced Materials Research, vol. 903, Feb. 2014, pp. 321-326. 9. Coupled-Tank Liquid Level Computer-Controlled Laboratory Teaching Package: Experimental and Operation (Service) Manual,
Augmented Innovation Sdn. Bhd., Kuala Lumpur, Malaysia.
10. J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, WA, 27 Nov. - 1 Dec. 1995, pp. 1942-1948.
11. Q. Bai, “Analysis of Particle Swarm Optimization Algorithm”, Computer and Information Science, vol 3 (1), February 2010, pp. 180-184.
202-206
44.
Authors: D.Ashok Kumar, P.Samundiswary
Paper Title: Design and Study of Enhanced Parallel FIR Filter Using Various Adders for 16 Bit Length
Abstract: Now a day’s parallel Finite Impulse Response (FIR) filter plays very important role in the Digital
Signal Processing (DSP) based applications. FIR filters are one of the most widely used fundamental filters in the
DSP systems. The parallel FIR filters are derived from FIR digital filter. In this paper, design and study of enhanced
parallel FIR filter with various adders using the structure of Fast FIR Algorithm (FFA) based FIR filter and
symmetric convolution based FIR filter structures considering 2-parallel and 3-parallel filters is done. These entire
filter structures are also designed using Ripple Carry Adder (RCA), Carry save Adder (CSA) and Carry Increment
Adder (CIA) by replacing the existing adders with the input bit length and coefficient length of 16-bits. Then the
performance metrics of the above two structures is done by designing using Verilog HDL. Further, they are
simulated and synthesized using Xilinx ISE 13.2 for Vertex family device of speed -12.
Keywords: Parallel FIR filter, FFA, symmetric convolution, Ripple Carry Adder (RCA), Carry Save Adder
207-213
(CSA), Carry Increment Adder (CIA).
References: 1. S.Balasubramaniam, R.Bharathi, “Performance analysis of parallel FIR digital filter using VHDL,” International Journal of Computer
Applications, vol.39, no.9, pp.1-6, February 2012.
2. Yu-Chi Tsao and Ken Choi, “Area Efficient of parallel FIR digital filter structures for Symmetric Convolutions based on Fast FIR algorithm,” IEEE Transactions on VLSI systems, vol.20, no.2, pp.366-371, February 2012.
3. Yu-Chi Tsao and Ken Choi, “Area Efficient VLSI Implementation for parallel Linear-Phase FIR digital filters of odd length based on Fast
FIR algorithm,” IEEE Transactions on Circuits and Systems, vol.59, no.6, pp.371-375, June 2012. 4. D.A.Parker, K.K. Parhi, “Low-area/power parallel FIR digital filter implementations,” Journal of VLSI Signal Processing Systems, vol.
17, no. 1, pp. 75– 92, Sep 1997.
5. J. G. Chung, K. K. Parhi, “Frequency-spectrum-based low-area low power parallel FIR filter design,” Journal of European Association for Signal Processing Application Signal Processing, doi:10.1155/S1110865702205077, pp. 944–953, Jan 2002.
6. K.K.Parhi, VLSI Digital Signal Processing Systems: Design and Implementation. New York: Wiley, 1999. 7. C. Cheng and K. K. Parhi, “Hardware efficient fast parallel FIR filter structures based on iterated short Convolution,” IEEE Transactions
on Circuits Systems. I, Regular Papers, vol. 51, no. 8, pp. 1492–1500, Aug. 2004.
8. C. Cheng, K. K. Parhi, “Further complexity reduction of parallel FIR filters,” Proceedings of IEEE International Symposium on Circuits and Systems, USA, vol. 2, pp. 1835–1838, May 2005.
9. C.Cheng , K.K.Parhi, “Low cost parallel FIR structure with two stage parallelism”, IEEE Transactions on Circuits and Systems.I, Regular
Papers, vol.54, no: 2, pp.280-290, Feb 2007. 10. Xilinx13.4, “Synthesis and Simulation Design Guide”, UG626 (v13.4) January 19, 2012.
11. Xilinx 13.1, “RTL and Technology Schematic Viewers Tutorial”, UG685 (v13.1), March 1, 2011.
12. Xilinx, “7 Series FPGAs Configurable Logic Block”, UG 474 (v 1.5), August 6, 2013. 13. Xilinx 12.4, “ISim User Guide”, UG660 (v 12.4), December 14, 2010.
14. Ashok kumar, Maroju Saikumar and Dr. P.Samundisary, “ Design and study of modified parallel FIR filter using Fast FIR algorithm and
Symmetric convolution”, Proceedings of IEEE International Conference on Information Communications and Embedded Systems, Chennai, 27-28, Feb-2014.
45.
Authors: Sasikumar Gurumurthy, T. Niranjan Babu, G. Siva Shankar
Paper Title: An Approach for Security and Privacy Enhancing by Making Use of Distinct Clouds
Abstract: Security challenges are the major concern when we considering the acceptence of cloud service. A
lot of research activities regarding to cloud security resulting in an amount of application and targeting the cloud
security threats. The cloud concept comes with a new set of unique features, techniques and architectures. This
paper is related to security and privacy enhancing by making use of multiple distinct clouds. Based on the different
cloud architecture, the security and privacy capabilities can be approximated. Cloud computing refers to
applications and services that run on a distributed network using virtualized resources and accessed by common
internet protocols. In this paper, we are introduced different clouds for encryption, decryption and storage process.
Keywords: Security, Privacy, Multicloud, data partitioning.
References: 1. P. Mell and T. Grance, “The NIST Definition of Cloud Computing, Version 15,” Nat’l Inst. of Standards and Technology, Information
Technology Laboratory, vol. 53, p. 50, http://csrc.nist.gov/groups/ SNS/cloud-computing/, 2010.
2. F. Gens, “IT Cloud Services User Survey, pt.2: Top Benefits & Challenges,” blog, http://blogs.idc.com/ie/?p=210, 2008. 3. Gartner, “Gartner Says Cloud Adoption in Europe Will Trail U.S. by at Least Two Years,” http://www.gartner.com/it/page.
jsp?id=2032215, May 2012.
4. J.-M. Bohli, M. Jensen, N. Gruschka, J. Schwenk, and L.L.L. Iacono, “Security Prospects through Cloud Computing by Adopting Multiple Clouds,” Proc. IEEE Fourth Int’l Conf. Cloud Computing (CLOUD), 2011.
5. D. Hubbard and M. Sutton, “Top Threats to Cloud Computing V1.0,” Cloud Security Alliance, http://www.
cloudsecurityalliance.org/topthreats, 2010. 6. M. Jensen, J. Schwenk, N. Gruschka, and L. Lo Iacono, “On Technical Security Issues in Cloud Computing,” Proc. IEEE Int’l Conf.
Cloud Computing (CLOUD-II), 2009.
7. T. Ristenpart, E. Tromer, H. Shacham, and S. Savage, “Hey, You, Get Off of My Cloud: Exploring Information Leakage in Third- Party Compute Clouds,” Proc. 16th ACM Conf. Computer and Comm. Security (CCS ’09), pp. 199-212, 2009.
8. N. Gruschka and L. Lo Iacono, “Vulnerable Cloud: SOAP Message Security Validation Revisited,” Proc. IEEE Int’l Conf. Web Services
(ICWS ’09), 2009. 9. M. McIntosh and P. Austel, “XML Signature Element Wrapping Attacks and Countermeasures,” Proc. Workshop Secure Web Services,
pp. 20-27, 2005.
10. J. Kincaid, “Google Privacy Blunder Shares Your Docs without Permission,” TechCrunch, http://techcrunch.com/2009/03/07/ huge-google-privacy-blunder-shares-your-docs-withoutpermission/, 2009.
214-217
46.
Authors: Sasi Kumar Gurumurthy, T. Siva Shankar, Niranjan Babu
Paper Title: Monitoring Company Status on Single dashboard by using GRC
Abstract: In this paper we are going to describe how to organize a company based records securely. The
company contains several module such as audit, asset, policy and so on. Nowadays every company maintains their
records using XL Sheet, So we need to enter our each and every data manually. This contains several setbacks such
as litter of time, entering and recoup time also gets high, high manpower required, not secured and even we may
also enter our data in wrong fields. Here this system provides full security of maintaining company relevant records
and creating an application for maintaining company records by using fully role based access manner. In which only
authorized user can access respective action and suppose unauthorized user trying to access someone's data at that
point of time it sends an alert message to respective authorized user. Overall company status can be seen in an
single dashboard and based upon that status we can act accordingly. Means each and every module such as asset,
audit, policy and so on status can be monitored in a single dashboard at a same time.
Keywords: GRC, Dash board, Asset ,policy, Risk business continuity management, audit ,standards.
218-220
References: 1. Racz, N. ; Tech. Univ. Vienna, Vienna, Austria ; Weippl, E. ; Seufert, A., Governance, Risk & Compliance (GRC) Software - An
Exploratory Study of Software Vendor and Market Research Perspectives, 284 (5) (2011) 1–10.
2. Racz, N. ; Inst. of Software Technol. & Interactive Syst., Tech. Univ. Vienna, Vienna, Austria ; Weippl, E. ; Bonazzi, R, IT Governance,
Risk & Compliance (GRC) Status Quo and Integration: An Explorative Industry Case Study, Services (SERVICES), 2011 IEEE World
Congress on 4-9 July 2011.
3. Nissen, V. ; Dept. of Service Inf. Syst. Eng., Univ. of Technol. Ilmenau, Ilmenau, Germany ; Marekfia, W. Towards a Research Agenda
for Strategic Governance, Risk and Compliance (GRC) Management - Vol. 2, No.1 pp.1 - 6 4. N. Racz, E. Weippl, and A. Seufert, "A process model for integrated IT governance, risk, and compliance management," Databases and
Information Systems, Proc. of the Ninth International Baltic Conference (DB&IS 2010), Riga University Press, Jul. 2010, pp. 15570.
5. IT Policy Compliance Group, "2008 Annual Report. IT Governance, Risk, and Compliance," Retrieved 10 November, 2010, from http://www.itpolicycompliance.eom/pdfs/ITPCGAnnualReport2008.p df, 2008.
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