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19th April 2021, 10:28 AM
 
 
Sathyabama Institute of Science and Technology M.E.  Applied Electronics SECA5101 Transforms and Random Process for Electronics Engineering Syllabus Sathyabama Institute of Science and Technology M.E.  Applied Electronics SECA5101 Transforms and Random Process for Electronics Engineering Syllabus SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF ELECTRICAL AND ELECTRONICS ENGINEERING SECA5101 TRANSFORMS AND RANDOM PROCESS FOR ELECTRONICS ENGINEERING (For VLSI & AE) L T P Credits Total Marks 3 0 0 3 100 UNIT 1 2D TRANSFORMS 9 Hrs. Need for transform – Review of 1D Transform – 2D DFT – IDFT – properties – Image transforms–2D Orthogonal and Unitary transform and its properties–Separable transforms– Walsh, Hadamard, Haar, DST, DCT,Slant, SVD and KL transforms. UNIT 2 PROBABILITY CONCEPTS 9 Hrs. Probability – The axioms of probability – marginal, conditional, joint probability – Baye‘s theorem – Moments – Moment generating functions – Binomial, Poisson, Geometric, Uniform, Exponential and Normal distributions. UNIT 3 INTRODUCTION TO 2D RANDOM VARIABLES 9 Hrs. Transformation of random variables – 2D random variablesDiscrete, Continuous and Mixed Random Variables –– Expected Value of a Random Variable Correlation – Regression – Central Limit Theorem. UNIT 4 RANDOMPROCESS 9 Hrs. Notion of Stochastic processes – Stationary and Independence; WSS & Ergodicity – Correlation Functions; Auto Correlation, Cross Correlation & its properties – expectations – variance, co variance – Power Spectral Density – properties – energy spectral density – Parseval’s theorem – Wiener Khintchine relation –Renewal processLinear systems with Randominputs–responseoflinearsystemstowhitenoise –simulationofwhitenoise–Noise Bandwidth – low pass filtering of whitenoise. UNIT 5 QUEUING THEORY 9 Hrs. Introduction to queuing theory – Characteristics of Queuing Systems – Little’s Law – Markovian Queues –Single server models – Multiple server models – NonMarkovian Queues– PollaczekKhinchine formula – Machine interference model – steady state analysis – self service queue – Priority Queues – Open and Closed Networks – queuing applicationsOpen Jackson Networks. Max. 45 Hrs. COURSE OUTCOMES On completion of the course, student will be able to CO1  Understand the axiomatic formulation of modern Probability Theory and think of random variables as an intrinsic need for the analysis of random phenomena CO2  Have a well founded knowledge of standard distributions which can describe real life phenomena and to understand and characterize phenomena which evolve with respect to time in a probabilistic manner. CO3  Apply the regression model in practical applications CO4  Understand the concept of random processes and determine covariance and spectral density of stationary random processes. CO5  Demonstrate the specific applications to Poisson and Gaussian processes. CO6  Understand basic characteristic features of a queuing system and acquire skills in analyzing queuing models TEXT/REFERENCE BOOKS 1. Rafael C.Gonzalez & Richard E Woods, "Digital Image Processing", 3rd Edition, Pearson Prentice Hall, 2009. 2. Peyton Z.Peebles, "Probability, Random Variables and random signal principles", 4th Edition, TMH Publication, 2001. 3. Anil K Jain, "Fundamentals of Digital Image Processing", Prentice Hall, 1989. 4. Raghuveer M. Rao & Ajit S. Bopardikar, "Wavelet Transform: Introduction to Theory & Applications", Pearson Education, 1998. 5. Donald Gross, John F. Shortle, James M. Thompson and Carl W. Harris, "Fundamentals of Queuing Theory", 4th Edition, Wiley 2008. END SEMESTER EXAMINATION QUESTION PAPER PATTERN Max. Marks: 100 Exam Duration: 3 Hrs. PART A: 5 Questions of 6 marks each  No choice 30 Marks PART B: 2 Questions from each unit of internal choice, each carrying 14 marks 70 Marks 
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