MCIT - 101 MATHEMATICAL FOUNDATIONS OF INFORMATION TECH

Unit 1

Uncertainty, Information and Entropy Information Measures Characteristics on

information measure, Shannon's concept of information, Shannon's measure of

information, Model for source coding theorem communication system: Source coding ad

line / channel coding, channel mutual information capacity (Bandwidth).

Unit 2

Channel coding, Theorem for discrete memory less channel, Information Capacity

theorem: Error detecting & error correcting codes, types of codes: Block codes, Tree

codes, Hamming and Lee Metrics, Description of linear block codes by matrices,

Description of linear tree codes by matrices, Parity check codes, and Parity check

polynomials.

Unit 3

Introduction to Fuzzy Sets – Basic Definition and Terminology – Set-theoretic

operations – Member Function Formulation and parameterization – Fuzzy Rules and

Fuzzy Reasoning - Extension principle and Fuzzy Relations – Fuzzy If-Then Rules –

Fuzzy Reasoning.

Unit 4

Discrete Fourier transform, Fast Fourier transform, Wavelet Transform, Numerical

Solutions of Boundary Value Problems.

Unit 5

Finite probability - Probability distributions - Conditional Probability – Independence -

Bayes’ theorem - Mathematical expectation.

Reference Books :

1. Judith L.Gersting, Mathematical Structures for Computer Science, Freeman Co.

2. J.P. Tremblay and R. Manohar, “Discrete Mathematical Structures with Applications

to Computer Science”, TMH

3. Kenneth H. Rosen, “Discrete Mathematics and its Applications”, Fifth Edition, TMH

4. R.P. Grimaldi, “Discrete and Combinatorial Mathematics”, Pearson Edition, New Dlhi

5. M.K Venkataraman, Sridharan, Chandrasekaran, Discrete Mathematics, National Pub

6. Scheinerman, Mathematics: A discrete Intoduction, Cengaga Learn (Thomson)