Supported By IEEE UP SECTION A CONFERENCE On SIGNAL PROCESSING and REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27, 2011 Organized by Department of Electronics Engineering Harcourt Butler Technological Institute, Kanpur, (U.P)208 002 Editor Dr. Krishna Raj About the Conference: The program will intend to promote high standards in Technical Education by way of extending opportunitiestotheacademiciansandworkingprofessionalsbyprovidingaforumforsharing theirknowledge,experiences,innovationsandinventionsinvariousfieldsofSignalProcessing and Real Time Operating System The conference will cover the following fields: Signal collection/Storage/representation Data communication Data (signal) processing Monitoring of Data (signal) Multimedia signal processing and coding. Audio-Video Processing Techniques, Compression & Coding Standards Signal Processing in Network Analysis and VLSI architectures Speaker identificationComputer Network Analysis and Design Signal detection and Spectrum estimation Application of DSP in Remote Sensors Digital and Multirate Signal Processing Digital Signal Processors Multidimensional and Multimodal signal processing. Sensor array and multi-channel signal processing Integrated Services Digital Networks (ISDN) Architecture, implementation, and applications of digital filters Signal Processing in Encrypted DomainReal-Time Signal/ Image/Video Processing Embedded systemsSignal & Image Processing in Neural Networks and Artificial Intelligence Application of VxWorks About H.B.T.I. Kanpur The Harcourt Butler Technological Institute, Kanpur, one of the premier institutes of the country in the heart of THE MANCHESTER CITY OF NORTHINDIAKANPUR, founded on 25th November1921,hasretaineditsnameandreputationeversince.Ithasfulfilledthe responsibilities of lead institute in Technical Education Quality Improvement Program (TEQIP) scheme of Word Bank project. It has significantly contributed towards manpower development, research and training. The Institute runs 13 under graduate and 6 post graduate level courses and doctoral program in all emerging disciplines of science and technology. About the Department Department of Electronics Engineering offers B. Tech in Electronics engineering and M. tech in Electronics&CommunicationEngineering.Thedepartmentisguidedbyateamofqualified, dedicatedandcompetentfacultymembers.Ahighlyexperiencedanddedicatedfacultyimparts quality training to student, with great emphasis being laid on understanding the fundamentals and applyingittosolvethechallengingproblems.Thedepartmenthasexcellentinfrastructureto explore technological advancements in the relevant fields. All the labs in the department are well equippedwiththestateoftheartequipmentenablingtoundertakeresearch/industryoriented projects both at under-graduate and post graduate level. SPONSORS TheconferenceissupportedbyIEEEUPSection,SponsoredbyWindRiverInc.andalso supported by IE, &QUARBZ and other organizations. Advisory Committee 1.Prof. K.P. Singh, Director, IT BHU, Varanasi,U.P. 2.Prof. Y.N.Singh, IIT, Kanpur,U.P. 3.Prof. S.N.Singh, IIT, Kanpur,U.P. 4.Prof. B.P.Singh,MIST Laxmangarh, Seekar, (Raj.) 5.Prof. J.P.Saini, Principal MMMEC, Gorakhpur,U.P. 6.Prof. D.R.Bhaskar, JMI, Jamianagar, New Delhi 7.Prof. U.S. Triar, Dean, NIT Patna, Bihar8.Prof. M.M.Sharma, Deptt. of E&C, NIT Jaipur, (Raj.) 9.Dr. Nishchal Verma, IIT, Kanpur, U.P. 10.Prof. Ajit Kumar Panda, NIST, Berhampur,Odisha 11. Prof. J.P. Pandey, KNIT, Sultanpur, U.P. 12.Prof. G.S.Tomar, Principal, M.I.T.M. Gwalior, M.P. 13.Prof. Sanjeev Jain, Director M.I.T.S. Gwalior, M.P. 14.Mr. Venkatesh Kumaran, Country Manager(India), Wind River Inc. Organizing Committee:Patron: Prof. R. K. Khitoliya, Director, H.B.T.I. Kanpur Convener: Dr. Krishna Raj,H.B.T.I. Kanpur Members: 1.Sri G.P.Bagaria,H.B.T.I. Kanpur 2.Dr. Rachna Asthana,H.B.T.I. Kanpur 3.Smt. Rajani Bisht,H.B.T.I. Kanpur 4.Sri M.K.Shukla,H.B.T.I. Kanpur 5.Sri A.K.Shankhwar,H.B.T.I. Kanpur 6.Sri Ashutosh Singh,H.B.T.I. Kanpur 7.Sri R.C.S. Chauhan,H.B.T.I. Kanpur Address for Communication Dr. Krishna Raj Head of the Department, Department of Electronics Engineering, Harcourt Butler Technological Institute, Kanpur, (U.P.) 208002 e-mail: kraj_biet@yahoo.com; sprtos@rediffmail.com Ph.05122534001-5 Ext 208, Fax.05122533812 Mob.9721456087, 9450607840 CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 1 of 15 COMMUNICATION ENGINEERING RELATED PAPERS S.NO PAPERNO. PAPER TITLE AUTHOR(S) NAME EMAIL-ID 1 COM0101Comparison of LFSR and CA as Pseudorandom Number Generator Ashutosh Mishra*, Dr. Harsh Vikram Singh**, S.P. Gangwar** *Student (M.Tech), ** Astt. Prof. Dept. of Electronics Engineering, KNIT SULTANPUR ashutosh.suryasurya@gmail.com 2 COM0102Cost-effective markov based segmentation S.RobinsonSobitha Raj, Mrs.V.S.Bindhu robins.singh@yahoo.co.in bindhuvs2003@gmail.com 3 COM0104Performance Comparison of VANET Routing Protocols. NITIN SHARMA Department of Computer Science and Engineering MNIT, Allahabad, India nsharma283@gmail.com Mobile: +91 8861 6365 11 POOJA RANI Department of Information Technology ITM, Sector-23A, Gurgaon, India me_pooja@msn.com Mobile: +91 9899 7470 33 nsharma283@gmail.com me_pooja@msn.com 4 COM0105Greedy Grid Scheduling Algorithm in Static Environment. . POOJA RANI Department of Information Technology ITM, Sector-23 A, Gurgaon, India E-mail: me_pooja@msn.com Mobile: +91 9899 7470 33 NITIN SHARMA Department of Computer Science & Engineering MNNIT, Allahbad, India E-mail: cs0802@mnnit.ac.in Mobile: +91 8861 6365 11 me_pooja@msn.com nsharma283@gmail.com 5COM0106A comparative study of ip and mpls based network Sandeep singhM.Altamash Sheikh School of Information and Communication Technology, Gautam Buddha University er.sandeep_vhdl@yahoo.com altsheikh@gmail.com 6COM0107INTEGRATED SERVICES DIGITAL NETWORK AJAY BHATIA 7 COM0108PERFORMANCE ANALYSIS OF WIRED NETWORKS UNDER VARYING NETWORK ATTRIBUTES Vaibhav Nijhawan Department of Electronics & Communication Engineering Allenhouse Institute of Technology, Kanpur vaibhav.nijhawan@gmail.com 8 COM0109OFDM PAPR reduction based on nonlinear functions without BER Degradation and out of band emission NeelamSuchita neelamdewangan@csitdurg.in CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 2 of 15 9 COM0110Analysis on Placement of Wavelength Converters in WDM p-Cycle Network Rupali Agarwal Deptt. Of EC, BBD, Lucknow Dr. Rachna Asthana Asso. Professor(HBTI Kanpur) roopali.ipec@gmail.com 10 COM0201COMPARATIVE ANALYSIS OF WIRELESS NETWORKS USING OPNET Yaduvir Singh1, Vikas Gupta2, Rahul Malhotra3 rlyadava@yahoo.com 11 COM0202Comparative Analysis of Core Migration Techniques in Wireless Ad Hoc Networks Rahul malhotra (ECE Bhai Maha Singh College of Engineering, Sri Muktsar Sahib by Er. Rahul Malhotra HOD ,ECE .) Reena aggarwal rlyadava@rediffmail.com 12 COM0203Performance comparision ofRI & TBI based system infading envoirement usingMRC scheme M. Shukla Department of Electronics Engineering Harcourt Butler Technological Institute Kanpur, India manojs@hbti.ac.inAasheesh Shukla, Rohit, Sankalp Department of Electronics & Communication EngineeringGLA University Mathura, India aasheeshshukla@gmail.com 13 COM0205Security Constraints in Peer-to-Peer Network on Overlay Networks Raj Kumar Gaur Divya Gupta rajkumargaur@gmail.com guptadivya28@gmail.com 14 COM0206ENERGY CONSERVATION THEORY IN CONTEXT AWARE COMPUTING Umesh Chaudhary , Chandrika Prasad Department of Computer Science, Banaras Hindu University Varanasi-221005 umesh9794@gmail.com, chandrika.bhumca08@gmail.com 15 COM0207AUTHENTICATE SECURE ROUTING PROTOCOL ABHAY SHUKLA (M.Tech., persuing P.hd.) Asociate t. Prof. Department of Computer Science and Technology SSAIET, Kanpur* 9235651921 abhay002@sify.com 16 COM0209Improvement of Hata Propagation Prediction Model for Suburban Area Himani Kaur*, Er. Ravneet Singh ** sapna.katiyar@abes.in 17 COM0210 Design of Stepped-Impedance Microstrip Line Low Pass Filter for Wireless Communication Navita Singh(Sr. Lecturer(EC Deptt.) KIET, Ghaziabad), Saurabh Dhiman, Prerna Jain, Shrestha, Tanmay Bhardwaj singh.navita@rediffmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 3 of 15 18 COM0211End Coupled Band Pass Filter for Wireless Communication Avnish Kumar ,Manohar Kumar, Ashish Kumar Verma, Navita Singh Final Year (ECE) KIET, Ghaziabad avi.k.prajapati@gmail.com 19COM0212Security ofelectronic money (RFID CREDIT CARD) PERFORMANCE MR. Pankaj singh Mr. Sanyam agarwal Mr. Rohit sharma Singhpankaj76@gmail.com20COM0213High temperature mems based transmitter for wireless sensor and communication network. Ruchi gupta Asst. profJPIET ruchi.siet@gmail.com 21COM0214An overview on load balancing routing protocols for mobile adhoc networks Bhawana bharadwaj Sanjay kumar bhavana.bhardwaj@gmail.com 22COM0215Localization of Wireless Sensor Network using Geographical routing Saurabh Dixit1 and Arun Kumar Singh2 1.Member IEEE, IETE Department of Electronics and Communication Northern India Engineering College, Lucknow saurabh2911@ieee.org aksingh_uptu@rediffmail.com 23COM0216Timestamp based loadbalancing in aodev in mobile adhoc networks Bhawana bharadwaj Sanjay kumar bhavana.bhardwaj@gmail.com 24COM0302Performance of Cross-Shaped Junction Microstrip Antenna using IE3D Jyoti Gupta, Babita Singh, D. C. Dhubkarya Department of Electronics & Communication Engineering, B.I.E.T., Jhansi (U.P.)INDIA gold.jyoti@gmail.com, babitasngh20@gmail.com,dcd3580@yahoo.com 25COM0303Broadband Slot Loaded Microstrip Patch Antenna A. Ansaari , kamakshi, Ram Brij Ram, A.Singh, Anurag Mishra, N.P.Yadav Department of Electronics and Communication University of Allahabad kamakshi_iert_el@yahoo.co.in, mishraanurag31@gmail.com 26COM0304Microstrip bandpass filter at 6 ghz for wimax Vanish kumar, Navita singh Manohar kumar Ashutosh kr. Varun avi.k.prajapati@gmail.com 27COM0305Antenna selection algorithm for MIMO BC channel using partial side information Swadhin kumar mishra Prabina pattanayak A.k. panda swadhin.mishra@gmail.com 28COM0306HILBERT ANTENNAS IN MOBILE , RADAR AND LIGHT COMBAT AIR CRAFT TECHNOLOGY KUMAR ASHOK[1], D.SUNITA[2],SINGH PUSHPENDRA[3] akprasad212@yahoo.co.in sunitadeviiet@yahoo.co.in CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 4 of 15 29COM0401PERFORMANCE of SOFT DECODING VERSES HARD DECODING Archana Sharma, Mohd Murtaja, SCRIET, CCS.University Campus, Meerut Prof. (Dr.) Rajeev Kapoor, H.O.D. of EC, Delhi Technical University, Delhi Archna.mtech@gmail.com ,murtaza_amu@yahoo.com, rajeevkapoor@dce.ac.in 30COM0402PERFORMANCE EVALUATION OF VARIOUS ERROR CORRECTING CODES ON PLC SYSTEM Priyank Sharma Ravikant Saini sh_priyank@yahoo.com 31COM0421Design and Analysis of Microstrip Antennas for W-LAN and Ku-band Applications 1Ravindra Kumar Yadav and 2Ram Lal Yadava 1Department of Electronics and Communication Engineering, I.T.S. Engineering College, Greater Noida, Uttar Pradesh, India 2Department of Electronics and Communication Engineering, Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh ravipusad@gmail.com 32COM0422A Proposed Design of One Dimensional Optical Orthogonal Coding Scheme and Analysis with Others 1Ratnesh Kumar, 2R.C.S. Chauhan, 3G.P.Bagaria 1Department of Electronics Engineering, H.B.T.I., Kanpur, U.P., India iam_ratnesh@yahoo.co.in 33COM0431Error modelling of BFSK over Rayleigh Fading Channel A Statistical Frame Work Vicky Singh1, Amit Sehgal2, Rajeev Agrawal3 1,2,3ECE Department, G.L. Bajaj Institute of Technology and Management, Gr. Noida vicky.glbitm@gmail.com, amitsehgal09@gmail.com, rajkecd@gmail.com COMPUTER RELATED PAPERS Sr.NoPaperCode Paper TitleAuthor(s) Name Email ID 1COP0101Critical Analysis on Prosthetic Arm Anil kumar, Dr. Monika Jain anil00699@gmail.com monika_jain24@rediffmail.com 2COP0102Analysis of Bio-inspired Algorithms BFO, PSO and FFA Vipul Singhal Vipul.vanshi@gmail.com 3COP0104Biometric authentication using image processing tools Neha mittal Ruchi gupta Pratibha singh Nehamittal2008@gmail.com4COP0105Medical image enhancement in spatial domain :statical analysis Sanjeev kumar gupta ,Sonika singh ,Rahul singh s.gupta@alumni.iitg.ernet.in CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 5 of 15 5COP0301AN INTELLIGENT ALGORITHM FOR TASK ALLOCATION IN DISTRIBUTED NETWORK USING STATIC APPROACH 1Kapil 1Kapil Govil, 2Dr. Avanish Kumar and 3Rakesh Kumar Dwivedi drkapilgovil@gmail.comdravanishkumar@yahoo.com,r_dwivedi2000@yahoo.com6COP0302Improvement in Dijkstra algorithm Ashutosh Singh, Sandeep Rathour singhash@iitk.ac.inersandeep.hbti@gmail.com 7COP0303 Executed Wrist Movement Classification For Brain Computer Interface Mariam Siddiqui, Ayman Nidal Khalidi, Omar Farooq,YusufU Khan ankhalidi@gmail.com 8COP0304Color Images Enhancement Based on Piecewise Linear Mapping Anubhav Kumar, Awanish Kumar Kaushik,R.L Yadav,Divya Sexena rajput.anubhav@gmail.com9COP0305 Information Fusion Based on Multiplicity of Data Preprocessing Improves AdaBoost Classification Efficiency Divya Somvanshi and R. D. S. Yadava divya.jrf@gmail.com10COP0306Real-time Hand Gesture Recognition Techniques for Human-Computer Interaction ANSHUL SHARMA getanshulsharma@gmail.com 11COP0307Forensic Research by Face Reading Systems Pramod Kumar Palpramod_pal123@rediffmail.com 12COP0401An Automatic Machine Learning and Particle Filtering Based Approach to Real Time Human Tracking in Videos Chandra Mani Sharma1, Alok Kumar Singh Kushwaha1, Ashish Khare2 and Sanjay Tanwani1 chandramani.sharma@yahoo.com, alokkumarsingh.jk@gmail.com, akhare@allduniv.ac.in, sanjay_tanwani@hotmail.com 13COP0402An Efficient Image Segmentation Method Based on Normalized Cut Vaibhav Sharmavaibhavshar@gmail.com 14COP0501OPTIMIZATION OF WIRED NETWORKS USING LOAD BALANCERS Yaduvir Singh1, Vikas Gupta2, Rahul Malhotra3 rlyadava@yahoo.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 6 of 15 COMPUTATION & COMPUTER OPERATING SYSTEM RELATED PAPERS S.NOPAPER NUMBER PAPER TITLEAUTHOR(S) NAMEEMAIL-ID 1COS0101TEMPORAL DIFFERENCES IN PROCESSING OF SYMMETRIC OBJECTS IN HUMAN BRAIN Taslima Ahmed, Umesh Kumar 3Prasant kumar "ahmed.taslima@gmail.com" 2COS0102Modelling Neuron for Biomedical Applications: A Review Taslima Ahmed , Dr Jiten Ch Dutta jitend@tezu.ernet.in 3COS0103Organ Failure Assessment in Malarial Patients Using Artificial neural networks Mr. Jay Kumar Pandey Shri Ramswaroop Memorial College of Engineering and Management , Lucknow , U.P er.jay11@gmail.com 4COS0201AN INTELLIGENT CONTROL METHOD TO REDUCE BRAKE NOISE Pramod Kumar Pandey daddapramodpandey@gmail.com Department of Electrical Instrumentation Engineering Thapar University Patiala Punjab -147004 Yaduvir Singh

dryaduvirsingh@gmail.com Department of Electrical Instrumentation Engineering Thapar University Patiala Punjab -147004 daddapramodpandey@gmail.com 5COS0202APPLICATION OF FUZZY LOGIC IN AUTOMATIC BOTTLE FILLING SYSTEM Nikita Agarwal Ritika Srivastava Preeti Dhiman 6COS0203Real Time Control of Rotary Flexible Joint with LQR and Fuzzy Controller Mr. Jay Kumar Pandey Shri Ramswaroop Memorial College of Engineering and Management , Lucknow , U.P er.jay11@gmail.com 7COS0204Fuzzy Logic Control for a Speed Control of Induction Motor using Pulse Width Modulation Mr. Jay Kumar Pandey Shri Ramswaroop Memorial College of Engineering and Management , Lucknow , U.P er.jay11@gmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 7 of 15 8COS0205Optimal Placement techniques of Facts Controllers in multi machine power system environments: A literature Suvey Bindeshwar Singh N.K.Sharma,A.N.Tiwari Department of Electrical EngineeringKamla Nehru Institute of Technology Email:bindeshwar.singh2025@gmail.com

bindeshwar_singh2006@rediffmail.com 9COS0206Fuzzy logic and ruled based system: research issues and challanges Praveen kumar shukla Surya prakash tripathi farhad_aei@yahoo.com 10COS0301A comparative analysis of controllers controllinguncertain factors affectingthe robust position control of DC motor Farhad AslamGagandeep Kaur Deptt. ofElectrical& InstrumentationEngg. Thapar University,Patiala. farhad_aei@yahoo.com 11COS0302A SURVEY ON CURRENT CONVEYOR:NOVEL UNIVERSAL ACTIVE BLOCK Indu Prabha Singhand 2 Dr. Kalyan Singh 1Deptt. of Electronics and Comm.Engg. SITM, UNNAO-209859, India 2Dept. of Physics and Electronics Engg. , Dr. RML AVADH UNIVERSITY, FAIZABAD, India induprabhasingh@gmail.com, ksshrinet@yahoo.co.in 12COS0401A comparative study of genetic algorithm and the particle swarm optimization Sapna Katiyar, Deepika Pandey, Sakshi Chhabra, Vaishali Gupta Sapna.katiyar@abes.in SIGNAL PROCESSING & RELATED PAPERS S.NO PAPER NUMBER PAPER TITLEAUTHOR(S) NAMEEMAIL-ID 1 SIP0101 MEMS Based MicroResonator Design & Simulation Based On Comb-Drive Structure Mr. Prashant Gupta Ideal Institute of Technology, Ghaziabadprashant_iit@ieee.org 2SIP0102Different Look-Ahead Algorithm for Pipelined Implementation of Recursive Digital Filters VivekanandYadav Krishna Raj vivekanand.hbti@gmail.com 3SIP0103Study of MC-EZBC and H.264 Video Codec Agha Asim1Husain and Agha Imran2 Husain 1Deptt of Electronics & Comm. Engg, ITS Engg College, 201301, India 2Deptt of Computer Science & Engg, MRCE, 121004, India aghahusain@gmail.com, aghaimrank2k@yahoo.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 8 of 15 4SIP0105FPGA BASAED IMPLEMENTATION OF IIR FILTERS Anup Saha, Saikat Karak, Surajit Kangsabanik, and Joyita RoyChowdhury anupsaha.0733@gmail.com,anupsaha.0733@gmail.com karaksaikat@yahoo.com,surajitkangsabanik@gmail.com, joyitaroychowdhory@yahoo.in 5 SIP0106Enhanced Clocking Rule for A5/1 Encryption Algorithm Rosepreet Kaur Bhogal, ECE Dept.,Nikesh Bajaj, Asst. Prof., ECE Dept. Lovely Professional University -India rosepreetbhogal155-2006@lpu.innikesh.14730@lpu.co.in 6SIP0107An Application of Kalman Filter in State Estimation of a Dynamic System Vishal Awasthi1 Krishna Raj2 awasthiv@rediffmail.com 7SIP0108Wideband Direction of Arrival Estimation by using Minimum Variance and Robust Maximum Likelihood SteeredBeamformers: A Review SANDEEP SANTOSH1, O.P.SAHU2, MONIKA AGGARWAL3 Astt. Prof., Department of Electronics and Communication Engineering1 ,Associate Prof., Department of Electronics and Communication Engineering2 ,National Institute of Technology , Kurukshetra, Associate Prof., Centre For Applied Research in Electronics (CARE)3 , Indian Institute of Technology, New Delhi.3 INDIA profsandeepkkr@gmail.com8SIP0109Electricity Generation by People Walk through Piezoelectric Shoe: An Analysis 1.Dr. Monika Jain,2.Ms. Usha Tiwari, 3.Mohit Gupta, 4.Magandeep singh Bedi1.Member IEEE, IETE,Professor-Dept of Electronics & Instrumentation Engg,Galgotias College of Engineering & Technology,Greater Noida,UP, INDIA 2. Assistant Professor-Dept of Electronics & Instrumentation Galgotias College of Engineering & Technology,Greater Noida,UP, INDIA .3&4.B.Tech, 4th year student Dept of Electronics & Instrumentation Galgotias College of Engineering & Technology,Greater Noida,UP, INDIA 1monika_jain24@rediffmail.com 2 usha.pant@rediffmail.com. 3mohit_luvkush@yahoo.co.in4. magandeep.bedi@gmail.com 9SIP0110Spectral and Cepstral analysis Using Modified Bartlett Hanning Window RohitPandey1,Rohit KumarAgrawal1,Sneha Shree1 rohitpandey2108@gmail.com, rohitagrawal07162@gmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 9 of 15 10SIP0111A 3d approach to face expression recognition Akshay guptaGarima chandel akshaygupta.abes@gmail.com 11SIP0112Performance evaluation of signal selective DOA tracking for wideband cyclostationary sources Sandeep Santosh, O P Sahu, Monika Aggarwal profsandeepkkr@gmail.com12 SIP0113BARTLETT WINDOWED FAST COMPUTATION OF DISCRETE TRIGNOMETRIC TRANSFORMS FOR REAL TIME DATA PROCESSING ABHIJIT KHARE SHUBHAM VARSHNEY VIKRAM KARWAL khareabhijit14@gmail.com shubham7502909dece@gmail.com vikram.karwal@jiit.ac.in 13SIP0114 Lossless compression scheme based on prediction for bayer color filter Anita patil Sudhirkumar D. sawarkar Nareshkumar harale patilanitau@gmail.com 14SIP0201OPTIMAL RECEIVER FILTER DESIGN Vivek kumar Dr. k. raj Vivekhbti07@gmail.com kraj_biet@yahoo.com 15SIP0202Signal Acquisition and Analysis System Using LabVIEW Subhransu Padhee, Yaduvir Singh subhransu_padhee@yahoo.com, dryaduvirsingh@gmail.com 16SIP0203METHODSOF INTERCARRIER INTERFERENCE CANCELLATIONFORORTHOGONAL FREQUNCY DIVISIONMULTIPLEXING Dr.R.L.Yadav Mrs.Dipti Sharma rlyadava@rediffmail.comdipti_sharma510@yahoo.co.in 17SIP0204OBJECT DETECTION BASED ON CROSS-CORRELATIONUSING PARTICLE SWARM OPTIMIZATION Sudhakar SinghYaduvir Singh sudhakarsingh86@gmail.com 18SIP0205Multisegmentation through wavelets: Comparing the efficacy of Daubechies vs Coiflets Madhur Member, IEEE, Yashwant Yashu, Member, IETE, Satish K. Member, IEEE madhur.manas@gmail.com yashwantyashu.jiet@gmail.com satish432002@gmail.com 19 SIP0206Analysis of Signals in Fractional Fourier Domain Ajmer Singh, Student of Lovely Professional University(LPU)-India, Nikesh Bajaj, Asst. Prof., ECE Dept.(LPU ajmersingh155-2006@lpu.in , nikesh.14730@lpu.co.in 20SIP0207Parzen-Cos6 (t) combinational window family based QMF bank Narendra Singh (*) and Rajiv Saxena, Jaypee University of Engineering and Technology, Raghogarh, Guna (MP) narendra_biet@rediffmail.com ; narendra.singh@jiet.ac.in CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 10 of 15 21 SIP0208

Performance Analysis of Sub Carrier Spacing Offset in Orthogonal Frequency Division Multiplexing System Shivaji Sinha, Member IETE, Rachna Bhati, Dinesh Chandra, Member IEEE & IETE Department of Electronics & Communication Engineering, JSSATE Noida shivaji2006@gmail.com,rachna.bhati1988@gmail.com 22SIP0301A Comparative Analysis of ECG Data Compression Techniques Sugandha Agarwal Amity School of Engineering and Technology Amity University Uttar Pradesh, Lucknow Sugandhaa7@gmail.com 23SIP0303Biologically inspired Cryptanalysis- A Review Ashutosh Mishra*, Dr. Harsh Vikram Singh**, S.P. Gangwar** *Student (M.Tech), ** Astt. Prof. Dept. of Electronics Engineering, KNIT SULTANPUR ashutosh.suryasurya@gmail.com 24SIP0304Eye Based Cursor movement using EEG in brain computer interface. Tariq S khan, Mudassir Ali, Omar Farook,Yusuf U Khan tariq252@gmail.com 25SIP 0333 AN INTERNET BASEDINTELLIGENT TELEDIAGNOSIS SYSTEM FOR ARRHYTHMIA K.A.Sunitha N.Senthil kumar K.Prema3 Sandeep Kotikalapudi sunithasrm@yahoo.in nsk_vnr@yahoo.com premsb4u@gmail.com sandeep_yours18@yahoo.com 26 SIP0401 Projected View & Novel Application of Context Based Image Retrieval Techniques Shivam agarwal Rajeev Singh Chauhan*,Vivek Vyas shivam.agrawal@live.com , 101.vivek@gmail.comrajeev0507@hotmail.com 27SIP0402Recursive Algorithm and Systolic Architecture for the Discrete Sine Transform M.N. Murty,Department of Physics NIST, Berhampur-761008, Orissa, India sudhansusekharnayak@yahoo.commnarayanamurty@rediffmail.com 28SIP0403Multiscale Edge Detection Based on Wavelet Transform Divesh Kumar, Dr. Yaduvir Singh kambojdivesh@gmail.com, dryaduvirsingh@gmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 11 of 15 29SIP0404Color Image Enhancement by Scaling Luminance and Chromatic Components 1Satyabrata Das, 2Sukanti Pal and 3Ajit Kumar Panda satyabratadas.m@gmail.com,sukanti.nist@gmail.com, akpanda62@hotmail.com 30SIP0405A Tutorial on Image Compression Techniques 1Vedvrat, 2Krishna Raj 1Department of Electronics & Communication Engineering A.I.T, Kanpur, U.P., India 2Department of Electronics Engineering H.B.T.I., Kanpur, U.P., India 1r.ved.hbti@gmail.com,2kraj_biet@yahoo.com 31 SIP0406Comparative Study of Lifting based Discrete Wavelet transform Architectures

VidyadharGupta,Krishna Raj

Departmentofelectronics engineering

HarcourtButler TechnologicalInstitute, Kanpur

vidyadhar.gupta@gmail.com 32SIP0407 A Novel Approach in Image De-noising for Salt & Pepper Noise J S Bhat B N Jagadale Lakshminarayan H K jsbhat@kud.ac.in basujagadale@gmail.com; lakkysharmahk@yahoo.co.in 33SIP0427Content Based Image Retrieval System for Medical Images Prof.K.Narayanan1, Shaista Khan2 1 Asst.Professor, Fr.Agnel College of Engg., University of Mumbai, India unniknarayan@gmail.com khan_shaista26@yahoo.co.in 34SIP0501AUDIO +Abhay Kumar Research Scholar at Associated Electronics Research Foundation, Phase-II Noida (U.P.) abhay.2t@gmail.com 35SIP0502Speaker Identification Prerana & Aditi Choudhary M.tech (DC) DIT DEHRADUN prerana6289@yahoo.com 36SIP0503Modeling of FBAR Resonator and Simulation using APLAC Deepak kumar, Navaid Z.Rizvi,Rajesh Mishra Gautam Buddha University,Greater Noida dkumar.gbu@gmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 12 of 15 37SIP0506Role of Speech Scrambling and Encryption in Secure Voice Communication Himanshu Gupta Prof. (Dr.) Vinod Kumar Sharma

himanshu_gupta4@yahoo.co.in vks_sun@ymail.com VLSI & RELATED PAPERS S.No.Paper NumberPaper TitleAuthor(s) NameE-mail ID 1.VLP0101Low power On-Chip Amplifier for CCD Array Er. Rahul Malhotra*, Er. Amit Kumar, Bhai Maha Simgh College of Engineering, Sri Muktsar Sahib, India blessurahul@gmail.com, amitkumar_sgnr@yahoo.co.in 2.VLP0102Programmable Input Output Resistances of FET Amplifier Mrs. Meena Singh , Dr. B. P. Singh , Mr. Arun Kumar Singh, Madan Mohan Malaviya Engg. College, Gorakhpur meena71_singh@rediffmail.com, singh16.arun@gmail.com,bpsingh@ieee.org 3.VLP0103RELIABILITY PREDICTION FOR IGBT BASED INVERTERS UNDER DIFFERENT SWITCHING PATTERNS Fuzail Ahmad, S.K.Singh, Amit Kumar Verma, CENTRE GORAKHPUR,INDIA, DOEACC CENTRE GORAKHPUR,INDIA IBM GURGAON,INDIA 1er.fuzailahmad@hotmail.com 3amitkverma@in.ibm.com sksingh@doeaccgkp.edu.in 4.VLP0104Carry Ripple Adder based on Charge Recyclingfor Lower Energy MTCMOS Arvind Kumar, Member, IEEE , Sanjeev Rai, Sarad Shrestha, ECED, MNNIT,Allahabad sarad693@gmail.com arvindk@mnnit.ac.in 5.VLP0105Forthcoming CMOS Technology in Nanoscale Era Shashank Mishra, Kshitij Bhargava, Rohit Tripathi, Piyush Jain Jaypee Institute of Information Technology,Noida 1shashankmishra05EI44@gmail.com 2kbhargava3@gmail.com 3rohit.tri2008@gmail.com piyush.oct@rediffmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 13 of 15 6.VLP0106On Demand Simulation of Input and Output Resistances of MOSFET Amplifier Mrs. Meena Singh , Dr. B. P. Singh , Mr. Arun Kumar Singh, Madan Mohan Malaviya Engg. College, Gorakhpur meena71_singh@rediffmail.com, singh16.arun@gmail.com,bpsingh@ieee.org 7.VLP0107Performance Analysis and Comparison of PFSCL and MCML Kirti Gupta , Ranjana Sridhar, Jaya Chaudhary DTU ranjanasridhar@gmail.com 8.VLP0108Comparative Study ofFast Addersusing VHDL andFPGA Nishi ChandraRajani Bisht, H.B.T.I.,Kanpur lognishi@gmail.com 9.VLP0109Organic Thin Film Transistor: Materials,Structures and Operational Parameters Poornima Mittal1, Brijesh Kumar2, B. K. Kaushik3, Y. S. Negi4 and Krishna Raj5 1Electronics and Communication Engineering, Graphic Era University, Dehradun, INDIA 3Electronics and Computer Engineering, Indian Institute of Technology, Roorkee, INDIA 2,4Polymer Science and Technology Group, DPT, Indian Institute of Technology, Roorkee, INDIA 5Department of Electronics Engineering, H.B.T.I., Kanpur, INDIA poornima2822@gmail.com, brijesh2228@gmail.com, bkk23fec@iitr.ernet.in, ynegifpt@iitr.ernet.in, kraj_biet@yahoo.com 10.VLP0110CHARACTERIZATION OF 4T SRAM CELL Setu Garg ,GCET, Greater Noida Prof.S.N.Sharan, GNIT, Greater Noida , Garima Chandel Member IEEE, Hridesh Verma ABES IT Ghaziabad, India gargsetu06@gmail.com, snathsharan@yahoo.com, garimachandel@rediffmail.com, hridesh.verma@gmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 14 of 15 11.VLP0111Quantitative Analysis and Optimization Techniquesfor On-Chip Cache Leakage Power Vikas TiwariM.Tech (VLSI Design ITM, Gwalior, India Shyam Akashe ,Associate Professor. ITM, Gwalior Rajkumar Rajoriya Assist. Professor ITM, Gwalior vikas.esvd@gmail.comvlsi.shyam@gmail.com 12.VLP0112Fault Tolerant Design for analog and digital circuits Dr. Anand Mohan,Akhilesh Pathak, Tarang Agarwal,Trailokya nath sasama(IIT Bhu) Pathak.akhilesh@gmail.com Sasamal.trailokyanath@gmail.com 13.VLP0113Floating Point Arithmetic Operations Using VHDL S.C. Yadav, S. S. Chauhan1, A. R. Khan2 Electronics & Communication Engg.,1,2 Graphic Era University 566/6 Bell Road, Dehradun (India) subhash.yadav775@gmail.com, sudakarnith@gmail.com1, abdulamu@gmail.com2 14. a VLP0114A Complete CMOS Based Low Power Supply Bandgap Voltage Reference CircuitImplemented On TSMC 0.35-m Process Kshitij Bhargava#1, Kirmender Singh*2 ECE Department (Microelectronics And Embedded Technology) Jaypee Institute Of Information Technology University, Noida India 1kbhargava3@gmail.com

1kshitijbhargava.09305160@gmail.com 2kirmender.singh@jiit.ac.in 15.VLP0115Performance analysis of carbon nanotube FET Harish kumar mishra S.P gangwar Dr. Harsh v.singh Knit sultanpur rblharish@gmail.com harshvikram@gmail.com 16. VLP0201POWER AWARE PHYSICAL MODEL FOR EMBEDDED SYSTEMS Asstt Prof Yasmeen Hasan Mtech(Electronic Circuits &Systems (VLSI)) DEPT OF ECE, INTEGRAL UNIVERSITY, LUCKNOW yasmeen.hasan9@gmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 LIST OF PAPERS Page 15 of 15 17.VLP0202Efficient Power Utilisation By ControllingIndustrial And Home Appliances Using GSM and Microcontroller Raj Singh Yadav and Nidhi Mishra Krishna Institute of Engineering and Technology Electronics and Communication Department Ghaziabad India yadav.raj31@gmail.com nidhi.mishra9@gmail.com 18.VLP0301 Design and Implementation of Radix-2 & Radix-4 Booth Multipliers Using VHDL S. S. Chauhan, S.C. Yadav, A. R. Khan Graphic Era University sudakarnith@gmail.com, subhash.yadav775@gmail.com, abdulamu@gmail.com

19.VLP0302 A Novel Approach to Design of a Multiplier Using Reversible Logic Gates S. S. Chauhan, S.C. Yadav, A. R. Khan Graphic Era University sudakarnith@gmail.com, subhash.yadav775@gmail.com, abdulamu@gmail.com

20.VLP0401OTRA based Grounded Inductor and its application Rajeshwari Pandey(member IEEE),Neeta Pandey(member IEEE),Ajay Singh, B.Sriram, Kaushalendra Trivedi Delhi Technological University, Delhi rajeshwaripandey@gmail.com 21.VLP0402OTRA based Precision Full Wave Rectifier Rajeshwari Pandey (member IEEE) , Ajay Singh, B.Sriram, Kaushalendra Trivedi rajeshwaripandey@gmail.com 22.VLP0403GaN-based HEMTs for Communication Circuits T R Lenka1 and A K Panda2 National Institute of Science and Technology Palur Hills, Berhampur, Odisha trlenka@gmail.com, akpanda62@hotmail.com 23.VLP0405 Ternary logic in digital communication for high speed and performance vinay.ic01@gmail.com, vinay.ic1@rediffmail.com CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O1-1 Comparison of LFSR and CA as Pseudorandom Number Generator Ashutosh Mishra*, Dr. Harsh Vikram Singh**, S.P. Gangwar** *Student (M.Tech), ** Astt. Prof. Dept. of Electronics Engineering, KNIT SULTANPUR Abstract Pseudorandom number generator (PRNG) is aKeyelementinstreamcipher.Thispaper comparestwotechniques:LinearFeedback ShiftRegister(LFSR)andCellular Automata(CA),usedforpseudorandom numbergeneration.BothLFSRandCAare analyzedbasedontheirconstructionand characteristics.AcomparisonofLFSRand CAispresentedtodemonstratetheir shortfallsandsuitabilitytocertain applications. Introduction Incryptography,astreamciphercombines theplaintextbitswithapseudorandom cipherbitstream(keystream),typicallyby anexclusive-or(XOR)operation.Ina streamciphertheplaintextdigitsare encryptedoneatatime,andthe transformationofsuccessivedigitsvaries during theencryption.In practice, the digits are typically single bits or bytes. A stream cipher makes use of much smaller andmoreconvenientkey128bits,for example.Basedonthiskey,itgeneratesa pseudorandomkeystreamwhichcanbe combinedwiththeplaintextdigitsina similarfashiontotheone-timepad. However,thiscomesatacost:becausethe keystreamisnowpseudorandom,andnot trulyrandom,theproofofsecurity associatedwiththeone-timepadnolonger holds: it is quite possible for a stream cipher tobecompletelyinsecure.Binarystream ciphersareoftenconstructedusingPRNG such as linear feedback shift register (LFSR) oradditivecellularautomat(ACA)in todays cryptographic scenario.LFSRscanbeimplementedinhardware, andthismakesthemusefulinapplications that require very fast generation of a pseudo-randomsequence,suchasdirect-sequence spreadspectrumradio.LFSRshavealso beenusedforgeneratinganapproximation ofwhitenoiseinvariousprogrammable sound generators. An ACA is a cellular automaton whose rule iscompatiblewithanadditionofstates. Typically,thisadditionisderivedfrom modular arithmetic. Additive rules allow the evolutionfordifferentinitialconditionsto becomputedindependently,thentheresults combinedbysimplyadding.Theresultsfor arbitrary starting conditions can therefore be computed very efficiently by convolving the evolution of a single cell with an appropriate convolutionkernel(which,inthecaseof two-color automata, would correspond to the set of initially "active" cells). Linear feedback shift register A linear feedback shift register (LFSR) is a shiftregisterwhoseinputbitisalinear function of its previous state. Theonlylinearfunctionofsinglebitsis XOR,thusitisashiftregisterwhoseinput CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O1-2 bitisdrivenbytheexclusive-or(XOR)of some bits of the overall shift register value. TheinitialvalueoftheLFSRiscalledthe seed,andbecausetheoperationofthe register is deterministic, the stream of values producedbytheregisteriscompletely determined by its current (or previous) state. Likewise,becausetheregisterhasafinite number of possible states, it must eventually enterarepeatingcycle.However,anLFSR withawell-chosenfeedbackfunctioncan produceasequenceofbitswhichappears random and which has a very long cycle. Additive cellular automata Cellularautomataevolveinstepandthe valueofnodedependsonthevalueof neighbors.AnadditiveAutomata(ACA) consistsofacollectionofcells/nodes formedbyflip-flopswhicharelogically relatedtotheirnearestneighborsusing XOR/XNORgates[5].Whenthevalueofa nodeisdeter-minedonlybyneighboring cells theACA is knownas one-dimensional linear CA. The logical relations which relate anodetoitsneighborsareknownasrules andtheydefinethecharacteristicsofan ACA.Therearemanyruleswhichcanbe used to constructan ACA register, the most popular being rules 90 and 150 illustrated in figure . =(Rule 90)

(Rule 150) Thenextstate(t+1)ofthenodeXiis determinedby thecurrentstateX(t)of neighboringnodesXi-1andX i+1forrule90 andnodesXi,Xi-1andXi+1forrule150.All the nodes of a CA register do not have to be implementedwiththesamerule,different nodescanemploydifferentrules.Thefirst and the last nodes of a CA register have only oneneighborunlikeallothernodeswhich havetwo,hencenormalrulescannotbe appliedhere[5].Onesolutionistoassume that the missing neighbor is fixed at logic 0 (nullboundarycondition).Theother solutionassumesthelastandfirstnodesto be neighbors and is connected using normal rules(cycliccondition).Connection between the end nodes (first and last nodes) introducesafeedbackloopinthecyclic boundarycondition;thismakesnull boundary condition a better choice. Comparison Building on the results of Serra, et.al [3] the consequencesofthesimilarity transformationbetweencellularautomata and LFSRs has been explored. By definition theLFSRobtainedbyasimilarity transformationofthetransitionmatrixofa CAhasthesamecharacteristicpolynomial astheoriginalCA.Ithasbeenshownthat thecharacteristicequationdeterminesthe recursionrelationamongthebitsinthe D Q Xi-1 D Q Xi D Q Xi+1 D Q Xi-1 D Q Xi D Q Xi+1 CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O1-3 outputbitsequenceofaCA.Thisimplies thatthesamelineardependenciesexistin theoutputbitstreamofaCAasinthe output of the similar LFSR. LFSRandCAarecharacterizedbytheir transitionmatrices,theanalysisofthese matricesalongwithsimulationsgivethe measureoftherandomnessinthepatterns generated;thesemeasuresshowthehigher randomnessofpatternsproducedbyCAs. Parallel patternsgenerated byLFSRs(using outputsfromdifferentnodesofanLFSR) have a strong correlation between each other duetotheshiftingofdata.Pattern generationinCAsdoesnotinvolveshifting of data. Thereisgreaterprobabilityofanerrorin LFSRbyaliasingcomparedtoACAdueto shiftingofdatainLFSR[1].IncaseofCA eachnodevalueisafunctionofthe neighboringnodesresultinginalower probabilityofanerror.Thepresenceof XORgatesinthefeedbackpathofan ExternalFeedbackLFSRandlackofa feedbackpathinanullboundarycondition results in higher operating speed for CAs. LFSR have a feedback from their end nodes; this means a redesign of the LFSR is needed if the pattern length has to be changed. This isnotthecasewithACA.ACAislogically connectedtotheironlytotheirneighbors andthereisnofeed-backforanACA employingthenullboundarycondition[5]. Therefore,thepatternlengthgeneratedby CAs can be easily changed by cascading the nodes. The regular structure of the nodes for CAmakesthemidealforCADtoolsby providingthemuchneededflexibilityin design. However, it is difficult to construct a maximum length sequence CAas compared toanLFSRwhichcanbeconstructedusing theprimitivepolynomialswhicharevery well documented.AnLFSRcanbeimplementedusingonlya fewXORgateswhereasaCArequiresat least one XOR gate for each node. This fact bringsupanobviousdraw-backofCA: Higherareaoverheadinvolvedin implementationofCAcomparedtoan LFSR. So, the designer has to pay a penalty ontheareaover-headbychoosingCAover LFSR. InACAthecommunicationisgenerally local,beingrestrictedtothenearest neighboringcellsandcellsareregularand topologicallyequivalenttooneanotherbut in the case of LFSR these property does not exist [2]. Followingtableshowsthesummaryof comparison CharacteristicsLFSRACA Performance Verygood incaseof internal feedback, poorfor external feedback Good-no feedback pathand maximum onXOR gate between nodeRandomnessof generated pattern Low- shiftingof bitcauses correlation between pattern Highno shiftingof bit CAD FriendlyNo- requires redesignfor changein pattern length Yes-node canbe cascaded easily Speed Lowerthan CA Higher than LFSR Error Probability Greater-by aliasingdue toshifting Lower-no shiftingof bit CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O1-4 of bit Conclusion LFSR has been well researched and provide a compact and simpler circuit for application using polynomial division and generation of patterns.Theirpopularityisowedtosimple andcompactdesignforcryptographic applicationtheyfinduseinpseudorandom numbergeneration.Butresearchhasshown someoftheshortfallsoftheLFSRcanbe overcomebytheuseofCellularAutomata. ACAuselargernodesascomparedtothe LFSRbutprovidemuchmorerandom patternsandcanbeeasilycascadedfor design flexibility.

References [1] M. Serra, T. Slater, J. C. Muzio & D. M. Miller,TheAnalysisofOneDimensional LinearCellularAutomataandTheir AliasingProperties,IEEEtrans.OnCAD, pp. 767-778, 1990. [2] Ph. Tsalides, T.A. York, A. Thanailakis, Pseudorandom number generators for VLSI systemsbasedonlinearcellularautomata, IEEProceedings-e,Vol.138,no.4,July 1991. [3]M.Serra,D.M.Miller,J.C.Muzio, LinearcellularautomataandLFSRsare isomorphic,Proc.Thirdtech.Workshop on new dir. For IC Testing, Halifax N.S. Pp. 213-223, oct. 1988. [4]PinoCaballero-Gil,AmparoFster-Sabater,OscarDelgado-Mohatar,Linear CellularAutomataasDiscreteModelsfor GeneratingCryptographicSequences, JournalOfResearchAndPracticeIn InformationTechnology,Vol.40,No.4, November 2008. [5]S.Nandi,B.K.Kar,andP.Pal Chaudhuri,TheoryandApplicationsof CellularAutomatainCryptography,IEEE Transactions on Computers, vol. 43, no. 12, December 1994. [6]S.Zhangt,R.Byrnel,J.C.Mutiot,D.M. Miller,WhyCellularAutomataAreBetter ThanLFSRsasBuilt-InSelf-Test GeneratorsforSequential-TypeFaults, IEEEInternationalSymposiumonCircuits and Systems, Vol. 1, 1994. [7]N.M.Thamrin,G.Witjaksono,A. Nuruddin,M.S.Abdullah,AnEnhanced Hardware-basedHybridRandomNumber GeneratorforCryptosystem,International ConferenceonInformationManagement and Engineering, 2009. [8]PaulH.Bardell,AnalysisofCellular AutomataUsedasPseudorandomPattern Generators,IEEEInternationalTest Conference 1990. CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O2-1

COST-EFFECTIVE MARKOV BASED SEGMENTATION S.Robinson Sobitha Raj Mrs.V.S.Bindhu M.E., Applied Electronics, Asst. Professor, EEE dept Noorul Islam University, Kumaracoil.Noorul Islam University, Kumaracoilrobins.singh@yahoo.co.inbindhuvs2003@gmail.com. AbstractImagesegmentationisanimportant preprocessingstepinasophisticatedandcomplex imageprocessing algorithm. In thispaper we are going tointroduceaproceduretominimizethe misclassificationcostwithclass-dependentcost.This procedureassumesthehiddenMarkovmodel(HMM) whichhasbeenpopularlyusedforimagesegmentation inrecentyears.WerepresentallfeasibleHMM-based segmenters(orclassifiers)asasetofpointsinthe receiveroperatingcharacteristic(ROC)space.Then, theoptimalsegmenter(orclassifier)isfoundby computing the tangential point between the iso-cost line withgivenslopeandtheconvexhullofthefeasibleset intheROCspace.Weillustratetheprocedureby segmentingaerialimageswithdifferentselectionof misclassification costs. Indexterms---Convexhull,hiddenMarkovmodels, image segmentation, iso-cost line, ROC convex analysis, ROC curve. I.INTRODUCTION Imagesegmentationextractsexplicitinformation about content, and it allows human observers to understand imagesclearly byfocusing specific regions of interest. For thisreason,itisoftenusedasaninitialprocedureto simplifyasophisticatedandcompleximageprocessing system.Segmentationoftenrequiressmoothboundary betweenregionsfordifferentclasses,andhiddenMarkov model(HMM)is possiblyoneofthemostpopularmodels for it [4], [8], [11]. The HMM assumes that the true hidden classMarkoviandependencyand,thus,hassmooth boundarybetweensegmentedregions.Thepopularlyused Markovmodelshavetwoparameters,whichwedenoteby and , where indicates the popularity of each class over imagesandtheotherindicatesthestrengthofspatial coherence.Theparametersareestimatedinsegmentation procedures or pre-decided by an expert. In real examples, the cost of misclassification can dependontheclasses.Forexample,incancerdiagnosis, misclassifyingtumorcellspaysmuchhighercostthan doingnormalcellstotumorcells;or,segmentingand detectingmilitarytargets,mistakenlydetectingtargetsto nontargetsmaycostmorethantheothertypeof misclassification.However,allexistingsegmentation proceduresdonottakeintoaccountthecostof misclassification, particularly the unequal cost that depends on the classes. Themainthemeofthispaperistodesigna classifier to minimize the expected cost of misclassification withclass-dependentmisclassificationcost.Wedenoteit asacost-effectiveclassifier.Thecost-effectiveclassifiers studiedmuchwiththenameROCconvexanalysisin machine learning literature [2], [3], [5]-[7] [9], [10], where targets to be classified are often independent to each other. However,ithasrarelybeenstudiedintheframeworkof imagesegmentation,inwhichtargetsaredependentfrom the Markovian model. In this paper, we introduce the ROC convexanalysisprocedureforHMM-basedimage segmentationtoconsiderclass-dependentmisclassification cost. TheROCconvexanalysisdrawsgreatattention inthemachinelearningsociety.Inatwo-classproblem (positive and negative class), the ROC curve (or set) is the plotoftheprobabilityoffalsepositivedecision(false positiverate,FPR)andthatoftruepositivedecision(true positive rate, TPR). The ROC convex hull analysis finds an optimumpointinanROCspacetominimizethe misclassification cost of classifier , which is defined as (1) Where impliesfalsenegativeratewhichisequal to.Oncemisclassificationcostisgiven,we have a family of iso-cost lines with slope On which costs of classifiers arethe same. Then, the ROC convex analysis finds the optimal classifier as the classifier whose(FPR,TPR)pairisthetangentialpointbetween these iso - cost line and the convex hull of the ROC curve. The ROC convex analysis requires the entire set of feasible classifiers. In the HMM based segmentation, the model has twounknownparameters whichareestimatedin segmentingtheimage.Fixingthesetwoparameters,not estimating, provides the set of classifier C (, ) s which are indexedby .TheROCset isthesetoferrorrates sofclassifier .Wedenoteitsconvex hull as . Theset shouldbecharacterizedtofindthe mostcosteffectiveclassifier,butitsnotcomputationally feasible. In this paper, we provide an alternative way which doesnotcomputetheentireset ,butweapproximateits boundary. We find that the tangential point between the iso costlinewithgivenslopeandexistsontheboundary of ,whichwedenoteas .Also,wefindthatisthe convex hull of the boundary of. Hence, we suggest tocomputeorapproximate .Toapproximatethe boundaryof ,wefixtheparameterbutestimatein segmentingthetargetimages.Wecompute the of classifiers ins. CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O2-2 Weapplytheproposedproceduretosegment aerialimageswithtwoclasses,man-maderegionand natural region, which could be used for target recognition andtracking.Here,thecostofmisclassifyingtargets(or man-maderegions)ishigherthanthatofmisclassifying non targets (or natural regions) to targets. The results show that,asthemisclassificationcostofman-maderegions increases, the HMM based segmenter tends to classify both man-made and natural regions to man-made ones. Thus, the rate of falsely classifying into man-made regions increases. Theremainderofthispaperisorganizedasfollows.We introduceROCconvexanalysisinsectionII.Westudyits applicationtoHMMbasedsegmentationprocedurein sectionIII.InsectionIV,Weapplytheproposed procedure to segment an aerial image. Section V concludes this paper. II. ROC CONVEX ANALYSIS Inabinaryclassifier,theROCcurve(orspace) plots two accuracymeasures of a classifier, FPRand TPR. SupposeweuseacontinuousclassificationscoreXto diagnose a certain disease, and we classifya subject into a disease(positive)groupifhis/herscoreXishigherthana givencriticalpoint;otherwise,weclassifyhim/herinto non disease group (negative) group. The ROC curve plots a series of (FPR, TPR) pairs produced from different choices of . TheoptimalROCcurveistheoneproducedby theclassifierswhichhasthemaximumTPRgivenFPR. The optimal ROC curve has several geometrical properties includingconvexity.Supposeitisnotconvexonan interval , where a and b correspond to critical values andinthewaythattheFPRatandisa andb,respectively.Then,forthediagnosiswithcritical values ,wefindabetterdiagnosticsystem (classifier)whichhasthesameFPRbuthigherTPRby randomlychoosingbetweentwodiagnoseswithcritical values and.Thus,theconvexhulloftheobserved ROCcurvesrepresentstheROCcurveofthesetof potentiallyoptimalclassifiers.WeletinROCspacebe thesetof sofallclassifiersweconsider,and bethesetoftheirrandommixtures.Then,isthe smallest convex region which contains .Thegoalofthispaperistofindtheclassifier whichminimizeshecost(1)amongclassifiersin . Since , (2) Werepresentthecostfunctions sin the ROCspace.Then,theinterceptoftheiso-costline(2)is and,asinFig.2.1,thepointtominimizethe cost is the tangential one between the line with slopeand the convex area . In summary, proposed procedure to find the cost-effective classifier has the following steps: S1)wedefineasetofclassifiers,sayC,tobeconsidered. In case of HMM-based segmentation, this becomes a set of classifierscorrespondingtoeachoftwoparametersIsing modelprior.WewillseedetailsonHMM-based segmentation in the next section. S2) we compute FPRs and TPRs of classifiers in C and plot them. This defines the region in the ROC space. Further, we get the ROC convex hull region of. S3) we find the tangential point between the line with slope in(2)andtheROCconvexhullregion .Theclassifier correspondingtothefoundtangentialpointisthemost cost-effective classifier. Fig.2.1. Illustration of ROC convex hull analysis III. COST MINIMIZATION IN HMM-BASED IMAGE SEGMENTATION In this section, we find the HMM-based classifier tominimizethe(expected)costofmisclassification(1) (ECM).Togetbetterunderstandingoftheproblem,we beginwithHMMwithoutspatialcoherencesuchasGMM [1]. The model assumes that the testing image is composed ofmanyindependentsub-blocks,sayXsfork=1,2,, K. The model has unknown parameter which indicates the prevalenceofeachclass.Weletbetheratioofprior probabilityofpositiveclass(classP)tothatofnegative class(classN).Then,given,theoptimalclassifierto minimize the ECM among all classifiers is the maximum a posteriori(MAP)classifierthatassignsxtoclassN,for each k, if (3)

Wherefn(x)andfp(x)istheprobabilitydensity function of class N and class P, respectively. In GMM, both fn(x)andfp(x)aredensityfunctionsoftheGMM. Suppose we denote the classifier in (3) given as C (), and theircollectionasF.Wefurther findthatthiscollectionD isinvarianttothecost.Inotherwords,wealwayshave same collection regardless of what we choose. Inpractice,tofindtheoptimalclassifier,wesettobean arbitraryfixednumber,andweconsiderthesetof conditionally optimal classifiers which minimize the cost of misclassificationgiven>0.WeletDbethesetoftheir FPRsandTPRsintheROCspace.Followingthestepsin theprevioussection,wegetDandfindthetangentpoint and between the line (2) and D. Now,wemovetotheHMM-basedsegmenter. TheHMMbasedsegmenterassumesthatthehidden processisfromtheMarkovrandomfieldhavingtwo CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O2-3 unknownparametersand;theparameteristhe parameterthatrepresentsthemagnitudeofmagnetization of the random field which implies the dominance of class P againstclassNincommonwords;itisalsorelatedtothe ratio of the prior probability of P to N; is the parameter to measure the strength of spatial coherence. For example, the HMGMMmodel[11]usesthegeneralizedbond percolation (BP) model. Let Z = {} with Z = 1 or 1: Z = 1 andZ=1impliestheclassPandN,respectively.The generalized BP assumes that the probability of Z is (4) Where (Z) ( (Z)) is the number of concordant (dis- concordant)adjacentpairswhichareneighborstoeach other. Here,) is the partition function that is Fig.3.1. ROC convex Hull for HMGMM Classifiers Asinthespatiallyuncorrelatedmodel,weconsiderthe collection F of MAP classifier C , which, given and ,assignstheobservedimage i,j=1,2,,n}to .WeletCbetheMAP classifier,givenand,andFbethecollection of .WefurtherdefineDand similarly.Tofind theoptimalclassifier,again,wecomputethetangential point between the line (2) and , the convex hull of D in the ROC space. InHMM-basedsegmenter,evaluationofDis computationallyquiteintensiveinpractice.Here,we introduceasuboptimalbutfastalgorithmforit.Many algorithmsarestudiedfromdeterministicannealingto simulatedannealingtoMarkovchain MonteCarlomethod tofindtheMAP.Theyoftenassume=0andestimate, the parameter of spatial coherence in finding the MAP. For example,in[11],theparameterisestimatedusingthe maximum likelihoodestimate (MLE) along with the Gibbs sampler.TheMLEis(Z)|X}/totalnoof edges),andweapproximatetherighthandsideofthe equation using Monte Carlo method to get the estimate.Welet betheestimateofgiven,andwe approximate the boundary points of with FPRs and TPRs of bymoving.Asstatedbefore,knowing boundary issufficienttofindthetangentialpoint betweeniso-costlinesand .Theapproximatedboundary, denoted by ,isacurvefrom(0,0)to(1, 1). Finally,is approximated by the convex hull of, and the optimal classifier is found using the procedure in Section II. IV. EXPERIMENTAL RESULTS In this section, we apply the procedure in Section IIItosegmenttheaerialimagewithHMGMMwith generalized BP model in (4). The aerial image is composed ofmanysub-blockswhichareclassifiedintonatural regions or man-made regions. We call the natural sub-blockasnegativeandman-madeoneaspositive. TheHMGMMmodelhastwoparametersandwhich reflectstheoverallportionofman-madeandspatial coherencebetweenadjacentsub-blocks,respectively.Each introducesaclassifier,say ,andapointof (FPR,TPR)intheROCspace.WeletbeDthecollection ofalltheses.Theinputimagetakenis illustrated below. Fig.4.1. Input image Intheexperiment,we varyfrom0.35to 2.85 by 0.1, and we get the empirical FPRs and TPRs from the segmented results . We then compute the convex hull of ( s ofs to approximate the convex hullofD.Wedenotetheconvexenvelopeas .Fig.2 plotssandtheirconvexenvelopeintheROC space.Wefindtheoptimalclassifiertominimizethe misclassification cost (1) is the one that corresponds to the tangentialpointbetweenthe(2)and .Theis piecewise linear function with 11 supporting points (points thathavechangesinslope).Eachpointonisthe optimal classifier to minimize the cost for a specific choice of . Fig.4.2. Segmented image Now,wereportsomedetailsoftheanalysisfor=0.5,1, and 2. The noise mixed image is given by, segmented imageCONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O2-4 . Fig.4.3. Noised image Here,thehighvalueofimplieshighcostof misclassificationofmanmadesub-blocks(tonatural ones). Thus, as.increases, the segmenter tends to classify bothnaturalandman-madeblockstoman-made ones. The enhanced image is given by, Fig.4.4. Enhanced image The analysis graph is given below: Fig.4.5. Analysis graph Finally,theoriginalHMGMMin[11]istheclassifier with .ItsFPRandTPRis0.22and0.97, respectively, which is close to the (approximated) boundary of. Some computation shows that the HMGMM is close to the optimal forbetween 0.16 and 0.22. V. CONCLUSION Weproposeaproceduretofindtheoptimal classifiertominimizethemisclassificationcostbasedon HMM.Thecostfunctiontominimizeallowsunequalcost thatdependsonthetrueclass.Theoptimalclassifieris given as a classifier whose error rate pair (FPR, TPR) is the tangentialpointbetweentheiso-costline(2)andthe convexhulloffeasiblesetoferrorratesbyHMM-based segmenters.Weapplytheproceduretosegmentanaerial imagewithdifferentselectionofunequalmisclassification cost. The procedure in this paper does not depend on a specifictypeofimagebutisapplicabletoawiderclassof imagesandHMM-basedclassifiersforthem.Thesame procedurecanbeextendedtootherapplicationssuchas cancerdiagnosisbasedonMRIimagingor3-Dtemporal images.Apossibledifficultyintheseextensionisfrom obtaining , the boundary of the ROC convex space. The currentsuboptimalalgorithmcouldbemoreexhaustiveas thenumberofHMMparametersincreases,anditmight provide rough approximation if the size of training data sets is not enough. This would be our next step to move. VI.REFERENCES [1] A. Aiyer, K. Pyun, Y. Huang, D. B. OBrien, and R. M. Gray, Lloyd clustering of Gauss mixture models for image compressionandclassification,SignalProcess.:Image Commun., vol. 20, pp. 459485, Jun.2005. [2]H.BlockeelandJ.Struyf,Derivingbiasedclassifiers for improved ROC performance,Informatica, vol. 26, pp. 7784, 2002. [3]R.CaruanaandA.Niculesen-Mizil,Anempirical comparisonofsupervisedlearningalgorithms,inProc. 23rd Int. Conf. Machine Learning, 2006, pp. 161168. [4]P.A.Devijver,Segmentationofbinaryimagesusing thirdorderMarkovmeshimagemodels,inProc.8thInt. Conf. Pattern Recognition, 1986, pp. 259261. [6]P.A.FlachandS.Wu,Repairingconcavitiesinroc curves,inProc.19thInt.JointConf.Artificial Intelligence, 2005, pp. 702707. [7] D. M. Gavrila, J. Giebel, and S. Munder, Vision-based pedestrian detection: The protector system, inProc. IEEE Intelligent Vehicles Symp., 2004, vol. 1318. [8]J.Li,A.Najmi,andR.M.Gray,Imageclassification by a two dimensional hidden Markov model, inProc. Int. Conf.Acoustics,Speech,andSignalProcessing,1999,pp. 33133316. [9]R.C. Pratiand P.A.Flach,Roccer:Analgorithmfor rulelearningbasedonROCanalysis,inProc.19thInt. Joint Conf. Artificial Interlligence, 2005, pp. 823828. [10]F.ProvostandT.Fawcett,Robustclassificationfor impreciseenvironments,Mach.Learn.,vol.42,pp.203231, 2001. [11]K. Pyun,J.Lim,C.S.Won,andR. M.Gray,Image segmentationusinghiddenMarkovGaussmixture models,IEEETrans.ImageProcess.,vol.16,no.7,pp. 19021911, Jul. 2007. noised imageenhanced imagePerformance Comparison of VANET Routing ProtocolsNITIN SHARMADepartment of Computer Science and EngineeringMNIT, Allahabad, India nsharma283@gmail.comMobile:+91 8861 6365 11POOJA RANIDepartment of Information TechnologyITM, Sector-23A, Gurgaon, India me_pooja@msn.comMobile: +91 9899 7470 33ABSTRACTVehicular Ad-hoc Network (VANET) is a collectionof communicationvehicles, moving in different directions. The vehicles forma communication group to disseminate desired information. Various routing protocols are implemented in a VANET, each having benefitsand shortcoming in the domain ofimplementation. Inthispaperperformanceofthree routing protocols, namely Ad hoc On-Demand Distance Vector Routing (AODV),Destination Sequenced Distance Vector (DSDV)and Dynamic Source Routing (DSR) iscompared for different parameters. The protocols are simulated on Network Simulator-2(ns-2).Index TermsRouting protocols, Simulation, VANET, Vehicle-to-Vehicle Communication, WAVE. I - INTRODUCTIONVANETisanad-hocnetworkformedbetween vehicles as per their need of communication.In order todevelopaVANETeveryparticipating vehicle must be capable of transmitting and receivingwireless signals uptorangeof three hundred meters. VANET communication range is restricteduptoonethousandmetersinvarious implementation [1]. The performance of a VANETremains optimumwithinonethousand meters and beyond that it is not feasible to communicate among vehicles because of high packet loss rate [2],[3]. Finding optimum path is typical task for dynamic protocols as management of vehicle movement is quite complex. There is a need to update entries in the route finding node [4]. VANET is not restricted up to Vehicle-to-Vehicle communication, it takes benefits of road side infrastructure that can also participate in communication between vehicle [5], but in this paper the main focus is on Vehicle-to-Vehicle communication.There are various challenges for VANET suchas highspeedof vehicle, dynamic route finding, building, reflecting objects, roadside objects, other obstacles in path of radio communication, different direction of vehicles, concernabout privacy, authorizationofvehicle, security of data and sharing of multimedia services.High speedofvehiclerequiresregular updateof routingtablewhereas dynamicroute findingwouldresult intohightimelossbefore static communication [6].Various user groupamongVANETare getting popular, mostly it is used in traffic management agencies, highway safety agencies, law enforcement agencies and emergency services.The main service provided through VANETareGPSnavigationsystem, electronic payment of toll tax, authenticity of vehicle without human intervention, traffic message. Internet access, broadcastingof trafficscenario and multimedia streaming.As most of the properties of a Mobile Ad-hocNetwork(MANET) are commonwiththe VANET,various MANETroutingprotocolsare usedinVANET[7]. Basicdifferencebetween MANET and VANET is that under VANET PERFORMANCE COMPARISON OF VANET ROUTING PROTOCOLS1movement of vehicleisat highspeedandless random as compared to MANET, existing MANETrouting protocols are not compatible with VANET. VANET routing protocols are broadly divided into two categories Table Driven protocols and Source Initiated on Demand protocol.In TableDriven protocoleach vehicle maintains a table of neighborhood vehicles within its communication range and any change in vehicle position is updated regularly. In Source Initiated on Demand protocol, firstly source vehiclebroadcasts aquerytofindrouteupto node gets a route up to the destination node. The destinationrouteis repliedbacktothe source node via same path. In this paper, the performance of following three routing protocols are compared:Ad Hoc on-Demand Distance Vector Routing (AODV)It is a Source Initiated on Demand routing protocols used in VANET. In this protocol every vehiclemaintains arouteinformationof every vehicle. It uses sequence number concept to acknowledge the entry update time and time stampbasedconcept for tableentry. If atable entryisnot usedwithinacertaintimelimit, it will bedeletedfromtableandif thereis any breakage in linking with a vehicle toanother vehicle, route error (RERR) packet is forwarded so that vehicle route is effectively updated in the routing table.Destination Sequenced Distance Vector (DSDV)It is Table Driven routing protocol which is used inVANETandis basedonclassical Bellman-Ford algorithm. Initially every vehicle broadcasts its own route tables to its neighbor vehicles. The neighbor vehiclesupdateroutingtablewiththe help oftwotypeof packets- FullDumpPacket and Incremental Packet. Full Dump Packet contains information about every participating vehicle in the VANET. These packets are transmitted periodically after a long time interval. Incremental Packet contains updatedchangein vehicle position since last Full Dump Packet. These packets are transmitted periodically in short interval of time and are stored in additional table. Routes are selected with the latest entry in the table. DSDVis goodfor networks where location of nodes are less dynamic. If position of a vehicle changes very often, its performance goes down because more Full Dump Packets are needed to sent in the network, resulting into wastage of bandwidth.Dynamic Source Routing (DSR)It is Source Initiated on Demand routing protocol used in VANET and is based on link state routing algorithm. When a vehicle wants to communicate data to another vehicle, firstly it finds route up to that vehicle. For route discovery,source vehicle initiates arouterequest (RREQ) packet inthe networkandother nodes forward theRREQby changing their name as sender. Finally when RREQ packetreachestothe destinationvehicle or toavehicle havingpathtothe destination vehicle, a route reply (RREP) packet is unicasted tothe sendernode.Ifthe reply isnot received, thesourcevehiclerestartsaggressivediscovery of route up to the destination vehicle.The simulation setup is discussedinthe next sectionandresultsarepresentedinsectionIII. Section IV describes conclusion and future scope.II - SIMULATION SETUPTheProtocol stackof VANETconsists of two combination of standards Dedicated Short Range Communication (DSRC) and Wireless Access in Vehicular Environment (WAVE). DSRC contains three layer Physical Layer, Media Access Control (MAC) Layer, and Logical Link Control (LLC) Layer. The layers make communication possible in wireless environment. WAVE lies on top of the DSRC layer. WAVE is alsoknownas IEEEP1609andis usedas a standard for communication, which is further classified as follows: IEEEP1609.1:It is standard for resource manager, which defines the key resources for data-flow and other resources as well as the command messages.PERFORMANCE COMPARISON OF VANET ROUTING PROTOCOLS2 IEEEP1609.2:It is standard for security services in application and controlling messages, encoding and decoding of messages. IEEE P1609.3: It is the standard for network services and involves the key features of network and transport layer such as IP addressing,informationpassing andsecurity while switching services. IEEEP1609.4: It is thestandardfor MAC layer support andincreases communication capacity of node. Also it provides multichannel operation to maintain Quality of Service (QoS) with high speed.The ns-2 is used to evaluate performance of routing protocols in VANET. It is a network simulator andis alsowidelyusedfor VANET related simulation work [8]. Ns-2 provides a standard for IEEE 802.11p simulation in the form of tcl scripts. Also various types of communication patterns, traffic scenarios and resources are available with ns-2.The simulation grid is 500m x 500m. in which the initial position of every vehicle is specified at thestartingtimeof simulationbycallingC++ object and the destination of every vehicle is set after a certain time interval. The vehicle approach their destination with variable speed. In simulation, threeinput parametersareprovided namely, speed of vehicle vary between 10MPH to 100MPH, number of vehicles vary between 20 to 100 and distance between vehicles very between 20mto100m. forthetrafficpurpose, Constant Bit Rate (CBR) traffic with a fixed packet size is used. The vehicle antenna is omni directional and its transmission range is 150m. The channel data rate is 2Mbps. IEEE 802.11p standard is used for vehicle-to-vehicle communication, which is available is ns-2 are wireless support [9]. It follows most of the WAVE standard, Nakagami model is used for radio propagation, which is the best model for WAVE environment[10]. While implementationaroutingprotocol inns-2the behavior of queue and its adaptability criteria are carefully considered.In the simulation work, the available standard and data units are followed. Ns-2 produces output in the form of trace file which is furtherprocessedbyshell scriptingtocalculate thedesiredparameter. Shell scriptingandawk are widely used to process trace files. III - SIMULATION RESULTThe comparison of routing protocols are done on the basis of following parameters:Average Number of Hop Count:It is the number of vehicles running between source and destination and it signifies error in the network.Timeto Live(TTL) is decided onthe basis of Hop Count, which helps in avoiding the congestion in the network.Average Jitter Rate:It isthedelaybetweentwoconsecutivepacket delivery atanode.QualityofService (QoS)of the network is measured by Average Jitter Rate.Packet Delivery Ration:The fractional of total packets received at a node to the packets sent by the node. It is associated withtheQoSandbandwidthutilizationinthe network.Routing Overhead:ItistherationofnumberofMACpacketsand total number of packets sent, Routing Overhead increases with increases in vehicle density.Throughput:Throughput is the sum of data to all the nodes in the system during a period. In a time interval the throughput reflects the bandwidth utilization.For each of the above parameters, various traffic scenarios are simulated by changing the number of vehicles, distance between vehicles and vehicle speed. The effect of these changes in the routing protocols are analyzed andresults are shown below.PERFORMANCE COMPARISON OF VANET ROUTING PROTOCOLS3 Fig 1:Jitter Rate with various vehicle distances Fig 2:Packet Delivery Ratio with various vehicle distancesFigure 1 shows the Jitter Rate variation at different distances between vehicles. It is observed that DSRJitter Rate remains lowest throughput the interval of observation, while DSDVandAODVshowcomparativelyhigher Jitter rate.Figure 2 shows the Packet Delivery Ratio at various distances between vehicles. It is observed that DSRPacket DeliveryRatio remains high during entire duration of observation. AODV Packet Delivery Ratio in between other two and DSDVPacket DeliveryRatiois lowest among three.Fig 3:Routing Overhead with various vehicle distancesFig 4:Throughput with various vehicle distances Figure 3 shows the Routing Overhead at various distances betweenvehicles. It is observedthat DSRRoutingOverheadis lowest, AODVand DSDVRoutingOverheadremain similar from 20mto100mbut after 100mAODVRouting Overhead is going down.Figure 4 shows the Throughput with various vehicle distances is shown. Clear inference from graph DSR Throughput is highest. AODV Throughput remains inbetweenother twoand DSDV Throughput is lowest among all three.PERFORMANCE COMPARISON OF VANET ROUTING PROTOCOLS4In the simulation work, we compared the performance of AODV, DSDVand DSRon fifteen criteria as shown in Table I. It is observed that no protocol performs good in all the conditions. One protocol performs well in specific domain area and others in different domain area. DSR had better performance than other two protocols becauseof its dynamicroutefinding nature that decreases Hop Count as every time it increases portability of finding optimum route. It also increases Packet Delivery Ratio. Routing Overheadis alsodecreasebecause indynamic network it is found that most of the maintain any routingtable that benefits inHopCount, it is increase Throughput of network by reducing regular update packet.TABLE IPARAMETER COMPARISION OF ROUTING PROTCOLSParameters Variables Routing ProtocolsAODVDSDV DSRHOP COUNTDist. b/w Vehicles L M HHOP COUNTNo of Vehicles L H MHOP COUNTSpeed of Vehicles L M HJitter Rate Dist. b/w Vehicles M H LJitter Rate No of Vehicles M H LJitter Rate Speed of Vehicles M H LPacket Del RatioDist. b/w Vehicles M L HPacket Del RatioNo of Vehicles H L MPacket Del RatioSpeed of Vehicles H L MRouting Overhead Dist. b/w Vehicles H M LRouting Overhead No of Vehicles H M LRouting Overhead Speed of Vehicles H M LThroughput Dist. b/w Vehicles M L HThroughput No of Vehicles H L MThroughput Speed of Vehicles H L MAbbreviation:L- Low Value, M- Medium Value & H- High ValueAODV performs better than DSDV in most of the parameters because of its time limit in table entry usages. That benefits in Throughput, Packet Delivery Ration and some times that also benefits in Throughput Routing Overhead. DSDV Performanceisworst amongthreeprotocols, it uses updation of table and depends on table the unnecessary decreases Packet Delivery Ratio, increasesnumberofHopCountsanddecreases Throughput.IV - CONCLUSTION AND FUTURE WORKOnthebasis of simulationresults presentedin Table I for different traffic scenario and for three protocols, performance of DSR has been found to bebetter thanthat of AODVandDSDV. The performance results are summarized for Highway, Urban and Freeway traffic scenarios in Table II.Fromthe parameter values characterizing the three traffic scenarios. DSR is found suitable for Highway and Freeway traffics, whereas AODV is suitable for Urban traffic scenario.Thusit canbeconcludedthat asingleprotocol doesnt give best performance in all traffic scenarios. Since traffic scenarios change throughput the day, some part of hybrid adaptive protocol would give better performance.TABLE IIDIFFERENT TRAFFIC SCENARIOTraffic Scenario Freeway Highway UrbanDistance b/w VehiclesSmall Large smallDensity of VehiclesLow Low HighHigh Speed of Vehicles No Yes NoSuggested Protocol DSR DSR AODVPERFORMANCE COMPARISON OF VANET ROUTING PROTOCOLS5REFERENCES[1] Yi wang. Akram Ahmad, Bhaskar Krishnamachari and Konstantinos Posunis, IEEE 802.11p Performance Evaluation and Protocol Enhancement.IEEE International Conference on Vehicular Electronics and Safety, 2008. [2] Bo Xu, Aris Ouksel and Ouri Wolfson, Opportunistic Resouce Exchange in Inter-Vehicle Ad-hoc Networks,International Conference on Mobile Data Management, 2004.[3] Lin Yand,Jindua Guo and Ying Wu,Piggyback CooperativeRepetitionforReliableBroadcastingofSafety Message in VANETsIEEE Internatitional Conference on Consumer Communication and Networking, 2009, pp-1-5[4] Katrin Bilstrup, Elisabeth Uhlemann, Erik G.Strom, andUrbanBilstrup,OntheAbilityofthe 802.11pMACMethodandSTDMAtosupport Real-Time Vehicle-to-Vehicle Communication.EURASIP Journel onWirelessCommunicationandNetworking 2009.[5] RainerBaumann, SimonHeimilicherandMartin May, Towards Realistic Mobility Models for VehicularAd-hoc Networks.IEEEInternational Conference of Mobility Networking for Vehicular Environment, 2007.[6] ValeryNaumov, Rainder BaumannandThomas Gross,An Evaluation of Inter-Vehicle Ad-hoc Networking Based on Realistic Vehicular races,ACM International Symposium on Mobile AdHoc Networking and Computing, 2006.[7] D. Rajini Girinath and Dr. S. Selvan,Data Dissemination to regulate vehicular traffic using HVRPin urbanmobilitymodel,International Journel of Recent Trends in Engineering, 2009[8] Victor Cabrea, FranciscoJ. Ros andPedroM. Ruiz,SimulationbasedStudyofcommonIssueson VANET Routing Protocols,IEEE Vehicular Technology Conference, 2009.[9] Djamel Djenouri, WassimSoualhi and Elmalik Nekka,VANETs Mobility and Overtaking: An Overview,International Conference onInformation and Communication Technologies, 2008, pp1-6[10] Christoph Sommer, Isbel Dietrich and Falko Dressler, Realistic Simulation of Network Protocols in VANET Scenarios. International Conference on Mobile Networking for Vehicular Environments, 2007PERFORMANCE COMPARISON OF VANET ROUTING PROTOCOLS6Greedy Grid Scheduling Algorithm in Static EnvironmentPOOJA RANIDepartmentof Information TechnologyITM, Sector-23 A, Gurgaon, IndiaE-mail: me_pooja@msn.comMobile: +91 9899 7470 33NITIN SHARMADepartment of Computer Science & EngineeringMNNIT, Allahbad, IndiaE-mail: cs0802@mnnit.ac.inMobile: +91 8861 6365 11Abstract Gridschedulingisatechniquebywhichthe userdemands aremet andtheresources areefficiently utilized. The scheduling algorithms are used to minimize thejobswaitingtimeandcompletiontime. Most of the minimization algorithms are implemented in homogeneous resource environment. In this paper the presented algorithm minimize average turnaround time in heterogeneous resource environment. This algorithmis basedongreedy approachwhichis usedinstatic job submission environment where all the jobs are submitted at same time. Taken all jobs independent the turnaround time of eachjobis minimizedtominimizetheaverage turnaround time of all submitted jobs. Keywords- greedy, grid, heterogeneous, high performance computing, scheduling. I. INTRODUCTIONUsing the distributed resources to solve the applications involvinglargevolumeofdataisknownasgridcomputing [1], [2]. There exists many tools to submit jobs on the resources which have different computational power and are connected via Local Area Network (LAN) or Virtual Private Network(VPN). Themainchallengeingridcomputingis efficient resource utilization and minimization of turnaround time. Theexistingsystemmodel consistsofthewebbased gridnetworkplatformwithdifferent management policies, formingaheterogeneous systemwherethecomputingcost and computing performance become significant at each node [3], [4].In grid computing environment, applications are submitted for use of grid resources byusers fromtheir terminals. The resources include computingpower, communicationpower and storage. An application consists of number of jobs; users want to execute these jobs in an efficient manner [5]. There are two possibilities of submission of jobs/data on resources; in one of them, job is submitted on the resources where the input data is available and in the other, on the basis of specific criteria, resource is selected on which both job and input data are transferred. This paper uses second approach, wherein the job is submitted on a scheduler and data on a resource identified by the scheduler. A resource in existing algorithms is selected randomly, sequentially or according to its processing power [2], [6], [7]. In this paper the proposed algorithmchooses a resource on the basis of processing power, job requirement and time to start at that resource.The next section describes details about system model. Section 3 describes the proposed scheduling algorithmin static job submission environment. In section 4 the experimental details and the results ofexperiments are presentedwithcomparisonof someexistingalgorithms. In section 5 conclusions and suggestions for future improvements are proposed. II. SYSTEM MODELA grid is considered as the combination of multiple layers. In our model the whole systemis composedof three layers (Fig.1). The first layer is the user application layer in which the user authentication is done and jobs are submitted to the scheduler bytheuser. Thesecondlayer containsscheduler and GIS. The scheduler schedules jobs among various resources after taking resource statusinformationfrom GIS. The second layer is connected through a VPN to user. VPN provides additional securityandonlyauthorizedusers can access services. All the resources reside in third layer where user's jobs areexecutedwhicharealsoconnectedthrough VPN. Fig. 1: Layered architecture.III. PROPOSED ALGORITHM The existinggrid schedulingalgorithms are basedonthe speed of resources [6], [7]. Each resource of layer 3 (Figure.1) has different processingpower andall theresources of layer 3 are connected via homogeneous communication environment in which the communication delay between 1scheduler and resources is assumed constant, also the jobs are assumed to submitted on layer 1 having different job requirement.An algorithm is proposed in this paper which is suitable for static job submission in heterogeneous resource environment connected to the scheduler through homogeneous communication environment. Greedy approach is used to solvethejobschedulingproblem. Accordingtothegreedy approach A greedyalgorithm always makes the choice that looks best at that moment. That is, it makes a locally optimalchoiceinthehopethat this choicewill leadtoaglobally optimal solution" [8]. The proposed algorithm uses the similar approach; it takes every job as independent of each other and eachofthemisscheduledonaresourcetogiveminimum turnaround time for that job.The overall turnaround time of all the jobs is thus minimized. The parameters used in this algorithm are as follows: A set of resources, R = {R1, R2, R3,........, Rn}.Ji = The submitted ith job.Arr_timei = Arrival time of job Ji.Proc_powerj = Processing power of resource Rj.Strt_timej = Estimated time at which a job starts execution at resource Rj.Job_reqi = Length of job Ji.Schd_valueij=Expected turnaround time of ithjob at jth resource.Min = The minimum of Schd_valueij among all resources.Res_id= Current selected resource id having optimum turnaround time.The algorithm used to schedule a job is given as follows: GREEDY_SCHEDULE/*The users submit their jobs on the scheduler.*/For all resource Rj /*Initialize the start time at resources.*/ Strt_timej=0.0 End For /*The jobs are stored in a queue Q.*/ Insert all the jobs Ji in Q While Q is not empty do Delete the job Ji SUBMIT_NEW_JOB UPDATE_STATUS Advance the Q pointer End WhileEnd GREEDY_SCHEDULE The scheduler uses SUBMIT_NEW_JOB algorithm to find the best suited resource that minimizes the turnaround time. The turnaround time is calculated on the basis of expected completion time of a job. The detailed SUBMIT_NEW_JOB algorithm is as follows:SUBMIT_JOB Min = For every resource Rj/* Calculating the expected turnaround time*/Schd_valueij = Strt_timej + (Job_reqi/Proc_powerj)If Min is greater than Schd_valueij ThenMin = Schd_valueijRes_id = RjEnd IfEnd ForSubmit the job Ji to Res_id resourceSubmit the input data of Ji job to Res_id resourceEnd SUBMIT_NEW_JOBOnce the scheduler submits a job to a resource, the resource will remains for sometimeinprocessingof that job. The UPDATE_STATUSalgorithmisusedtofindout whenthe resource will be available to process a new job. The UPDATE_STATUS algorithm is given below: UPDATE_STATUS/* Res_id is the resource on which the job Ji is submitted. j is the index of resource on which the job Ji is submitted and Rj = Res_id*/Strt_timej = Schd_valueijEnd UPDATE_STATUS Theabovepresentedalgorithmhasthetimecomplexityof O(n) foreach job,where n isthe numberofresources. The above algorithm required additional space to store the resorces current status for availability.IV. EXPERIMENTAL RESULTSThe GridSim simulator [6] is used to simulate the algorithms. The GridSim toolkit is used to simulate heterogeneous resourceenvironment andthecommunicationenvironment. Theexperiments areperformedwiththreealgorithms. The algorithms are RandomResource SelectionandEqual Job Distribution and Proposed Algorithm. The input data is taken to be the same for all the three algorithms. The simulation is conducted with three resources which are shown in Table 1. TABLE 1: Resources with their architecture and processing power.Resource R0 R1 R2Architecture Sun Ultra Sun Ultra Sun UltraOS Unix AIX UnixProc_power(in MIPS)48000 43000 54000Theschedulersubmitsthesejobsonresourcesaccordingto thesealgorithms. Thealgorithms arepresentedonebyone with their simulation results. A. Random Resource SelectionInthis algorithmthescheduler contacts GIStoobtainthe resource information and then it chooses a resource randomly [7]. The job is submitted on this chosen resource. This algorithm is very simple to implement and has less overhead on the scheduler. The bar chart (Fig. 2) shows the turnaround time of different jobs. The completion time is a time at which 2the result of a job is available.After simulation the average turnaround time is found to be 20105.65seconds and all the jobs are completed at the 64420.25th second. Fig. 2: Jobs and turnaround time using Random Resource Selection.B. Equal Job DistributionIn Equal Job Distribution we firstly calculate the total length of all the jobs andthendistributetheselengthequallyoneveryresource. The main notations which are used in the formula are as follows:L = Total length of all the jobs taken together.Proc poweri = Processing power of resource Rj.tProc power = Total processing power of all resources.Loadi = Load assigned on resource Rj.Theformulausedtocalculatethejobdistributionisgiven bellow:Loadi = L* (Proc poweri /tProc power)The turnaround time of each job is shown by the bar chart in Fig. 3. Experimental results show that the average turnaround timeis17968.55secondsandthelast result isoutputedat 39000.22th second. Equal Job Distribution reduces the average turnaround time by10.62%and it takes less time in comparison to the Random Resource Selection to give all the results. Fig3: Jobs and turnaround time using Equal Job Distribution.C. Proposed Algorithm In Proposed Algorithm, the scheduler finds the resource information with the help of GIS and calculates the approximatecompletiontimeofthisjoboneveryresource. Using these values the scheduler chooses a resource which has the minimum of completion time and submits that job on this resource. Theturnaroundtimeofeachjobisshowninbar chart in Fig. 4. Through this algorithm the average turnaround timeofthesejobsis17208.77secondsandall thejobsare completed at41840.88thsecond. The Proposed Algorithm further reduces the average turnaroundtime by4.22%as compared with Equal JobDistribution. The completiontime of all jobs takes some more time than Equal Job Distribution algorithm. Fig.4: Jobs and turnaround time using Proposed Algorithm.V. CONCLUSION AND FUTURE WORKThe proposed scheduling algorithm reduces the average turnaround time of all submitted jobs. The considered environment executedthejobsondifferent resourceswhich are geographically distributed. It is observed that the Proposed Algorithmreduces the average turnaround by4.22%with Equal Job Distribution (as shown in Table 2). Thealgorithm uses meta-scheduler where resource failure is not considered. TABLE 2: Algorithms with their average turnaround time and completion time.Algorithms Average Turnaround Time(In Seconds)Completion Time(In Seconds)Random Resource Selection20105.65 64420.25Equal Job Distribution 17968.55 39000.22Proposed Algorithm17208.77 41840.88 3REFERENCES[1] Ammar H. Alhusaini, Viktor K. Prasanna,C.S. Raghavendra, UnifiedResourceSchedulingFrameworkfor Heterogeneous Computing Environments", in Proceedings of theEighthHeterogeneousComputingWorkshop, SanJuan, Puerto Rico, pp. 156-165, 1999.[2] N. Muthuvelu, J. Liu, N. L. Soe, S.r Venugopal, A. Sulistio and R. Buyya, ADynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids", Proceedings of the 3rd Australasian Workshop on Grid Computing and e-Research (AusGrid 2005), Newcastle, Australia, 41-48, January 30 - February 4, 2005. [3] I.Foster, C Kesselman, The Grid: Blueprint for a new computing infrastructure", Morgan Kaufmann Publishers, San Francisco, USA, 1999.[4] R. Buyya, D. Abramson, J.Giddy, Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computation Grid", International Conference on High Performance Computing in Asia-Pacific Region(HPCAsia2000), Beijing, China. IEEEComputer Society Press, USA, 2000.[5] Cong Liu, Sanjeev Baskiyar and Shuang Li, A General Distributed Scalable Peer to Peer for Mixed Tasks in Grids", High Performance Computing HiPC 2007,ISBN:978-3-540-77219-4, 320-330, 2007. [6] Rajkumar Buyya,Manzur Murshed, GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing",Technical Report, MonashUniversity, Nov. 2001. Toappear inthe Journal of Concurrency and Computation: Practice and Experience (CCPE), pp. 1-32, Wiley Press, May 2002.[7] Volker Hamscher, Uwe Schewiegelshohn, Achim Streit, Ramin Yahyapour,Evaluation of Job-SchedulingStrategies for Grid Computing", in 1st IEEE/ACM International WorkshoponGridComputing(Grid2000), Berlin, Lecture Notes in Computer Science (LNCS), Springer, Berlin, Heidelberg,NewYork,pp. 191-202,2000.[8]Cormen TH, LeisersonCE, Rivest RL, Introductiontoalgorithms 2nd edition", MIT and McGraw-Hill Book Company, Boston Massachusetts, cp. 16, 370-403, 2001.4CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O6-1 A Comparative Study of IP and MPLS Based Networks Sandeep singhSchool of Information and Communication Technology, Gautam Buddha University Greater Noida, U.P. er.sandeep_vhdl@yahoo.comM.Altamash Sheikh School of Information and Communication Technology, Gautam Buddha University Greater Noida, U.P. altsheikh@gmail.com

Abstract IntodaysworldofnetworkingIProuters buildup and uses the routing tables and these packets of data are routed on the IP prefixes which are already stored in routing tables of routers. These routers fallows the lookups to find out the next destination of packets. This processgivesseveraloverheadslikedelay, jitterandtrafficdrops.simpledatacan acceptmuchdelaybuttherealtimetraffic needs guarantied QoS with low jitter , delay andpacketdropetc.optimumperformance ofIP based network can not beachieved by justapplyingtheQoSbutMPLScan providebettersupporttoQoS.Herethe performancefactorofthenetworkcanbe improvedwithMPLSinabetterwayas compared to just IP routing. Keywords:QOS,TOS,MPLS,IPBased routing , Traffic Engineering. IntroductionWheneverthereistrafficto flownormalroutingfacesseveralissues relatedtocongestionanddelay.Routerneedstolookupthereroutingtableevery timesothismakesrouterverybusyduring theprocessing.Routersdecidesthepath basedonitsIPprefixesandthispathis alwaystheshortestpathbasedonshortest pathfirstalgorithm(SPF).Thisresultsthe unnecessaryloadonaparticularlinkand gives traffic drop. When we are dealing with therealtimetrafficitneedsguaranteed service[4] , [5]. IngeneralMPLScanprovidesacombined and better environment for the network with capabilityofsupportingQOSandtrafficengineering.Intheemerginganddevelopingtrendoftechnologysmall,mid sizedandlargecompaniesarefrequently movingforchangingthereinfrastructure. Thesecompaniesaremigratingtowards Traffic engineering[5] , [6] , [7]. Related work :BackgroundofQOS:Attheinitialstage whenIPheaderwascreatedtherewasthe spaceinthatheadercalledTypeOfService (TOS)bytewhichwasbasicallyforfuture perspective.Itwaswellknownthat technologiesweregrowingveryrapidlyas noonecouldthinkofit.ThisTypeof service(TOS)fieldwasreservedforfuture purpose of improving QOS[17].In the early development of internet the applicationsrunningoveritdidnottake muchcareaboutTOSbecauseduring forwardingofIPdatagramthedeviceslike CONFERENCE ON SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS) MARCH 26-27 2011 COMO1O6-2 routerdidnotinterpretthisfielduntilit gives any kind of service parameters. In this TOSbytethreefieldswerereservedforIP precedenceandotherthreefortypeof service[12], [13].HighertheIPprecedence datawouldtreatedmorepriorityandlower theIPprecedencedatawouldbedealtwith low priority . During the initial stage of internet it was notneededinthenetworkbecause commercialization was not so much but as it grownuptheneedofdifferenttypeof service were also grown up[16]. ClassificationoftrafficbasedonQOSClassificationofdifferentflowsoftraffic intoclassesmaydependonseveral parameters.Basicallysimilardatapackets areconsideredinthesameclass[18].The most common way to classify the flows may dependonheaderfieldssuchasIP precedence and DSCP fields[15]. One of the header of TCP is also used for classification of traffic by identifying the length of coming packets or by checking the MAC address of both senders and receivers address[14]. When the traffic is classified three main classes comes out (i) High priority for sensitive traffic : The trafficlikerealtimerequiressomespecial treatmentlikenodelayandlessjitter.This comesinHighPrioritytrafficandmost commonexampleofsuchtrafficis VoIP[10]. (ii)Bestefforttraffic:Whentrafficdoes notneedanydelayrelatedguarantees.I