# Information Theory & Coding - [2710502]

#### Teaching Scheme

 Theory Tutorial Practical Total 4 2 2 8

#### Examination Scheme

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

ESE = End Semester Examination, PA = Progressive Assessment

#### 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.

#### Fundamentals of entropy

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

#### Arithmetic Coding

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

#### 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

#### Other codes

Cyclic code, Burst errors, BCH Code, Reedsolomon Codes

#### Convolution code

Convolution codes, Viterbi decoding algorithm

#### Decoding algorithms

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

#### Reference Books

1Foundation of codingJiri AdamekJohn Wiley and sons
2Principal of Digital Communication and CodingA.J. Viterbi and J.K.OrmuraMcGraw Hill
3Digital communication fundamental and ApplicationBernard SklarPE India
4Information and CodingN. AbramsonMcGraw Hill,1963
5Information TheoryR.B.AshPrentice 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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