2021 2022 Student Forum Sathyabama Institute of Science and Technology M.E. - Applied Electronics SECA5101 Transforms and Random Process for Electronics Engineering Syllabus

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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 variables-Discrete, 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 process-Linear 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 – Non-Markovian Queues– Pollaczek-Khinchine formula – Machine interference model –
steady state analysis – self service queue – Priority Queues – Open and Closed Networks – queuing applications-Open
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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