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Information Theory & Coding (2710502)

Teaching Scheme (in Hours)

Theory Tutorial Practical Total
4 2 2 2

Subject Credit :  6

Examination Scheme (in marks)

Theory
ESE (E)
Theory
PA (M)
Practical
ESE Viva (V)
Practical
PA (I)
Total
0

Syllabus Content    Download

Unit-1:  Introduction

Basic concept of coding, Unique decodable codes and instantaneous decodable codes (IDC) Construction of IDC, Krafts inequality and MC Millan’s theorem, Huffman and shannon - fano code.

Unit-2:  Fundamentals of entropy

Entropy, Entropy of sources and their extension. Loss less image compression

Unit-3:  Arithmetic Coding

Basic of channel coding and Hamming distances, channel capacity and shannon’s fundamental theorem

Unit-4:  Linear block codes

Systematic linear codes and optimum decoding for the binary symmetric channel; Generator and parity Check Matrices Syndrome decoding on symmetric channels; Hamming codes

Unit-5:  Other codes

Cyclic code, Burst errors, BCH Code, Reedsolomon Codes

Unit-6:  Convolution code

Convolution codes, Viterbi decoding algorithm

Unit-7:  Decoding algorithms

Wozencraft’s sequential decoding algorithm, Fann’s algorithm and other sequential decoding algorithms

Reference Books

Sr. Title Author Publication Amazon Link
1 Foundation of coding Jiri Adamek John Wiley and sons
2 Principal of Digital Communication and Coding A.J. Viterbi and J.K.Ormura McGraw Hill
3 Digital communication fundamental and Application Bernard Sklar PE India
4 Information and Coding N. Abramson McGraw Hill,1963
5 Information Theory R.B.Ash Prentice Hall,1970

Course Outcome

Course Outcome:

1. To obtain an understanding of the theoretical principles of source coding.

2. To focus on the application of Information Theory to communications in general and on channel coding and capacity in particular.

3. To analyze various error correcting codes.

4. To compare coded Vs. uncoded system.

5. To Use MATLAB for analysis of various source coding and channel coding techniques.

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