tags:-rgpv ece 7th sem syllabus rgpv ece random signal theory syllabus, rgpv ece 7th sem elective 2 subject syllabus, ece 7th sem complete syllabus, rgpv ece 7th sem randon signal theory syllabus
RGPV 7TH SEMESTER SYLLABUS FOR RANDOM SIGNAL THEORY (EC-7203)
RAJIV GANDHI PROUDYOGIKI VISHWAVIDYALAYA, BHOPAL
PROGRAMME: ELECTRONICS AND COMMUNICATION
SEMESTER-VII
COURSE: EC-7203 RANDOM SIGNAL THEORY
COURSE: EC-7203 RANDOM SIGNAL THEORY
Unit-I
Introduction: Random input signals, random disturbances, random system characteristics, random experiments and events.
Random variables: Concept of random variable, distribution functions, density functions, mean values and moments, the Guassian random variable, density functions related to guassian- Rayleigh distribution, Maxwell distribution, Chi-square distribution, log normal distribution. Other distribution functions-uniform distribution, exponential distribution, delta distribution. Conditional probability distribution and density functions.
Unit-II
Several random variables: Two random variables, joint conditional probability, statistical independence, correlation between random variables, density function of the sum of two random variables, probability density function of a function of two random variables, the characteristic function.
Elements of statistics: Sampling theory- the sample mean, the sample variance, sampling distributions and confidence interval,hypothesis testing, curve fitting and linear regression, correlation between two sets of data.
Unit-III
Random Processes: Continuous and discrete, deterministic and nondeterministic, stationary and nonstationary, ergodic and nonergodic.
Correlation functions: Introduction, autocorrelation function of a binary process, properties of auto correlation functions, examples of autocorrelation functions, crosscorrelation functions, properties of crosscorrelation functions, examples and applications of crosscorrelation functions, correlation matrices for sampled functions.
Unit-IV
Spectral Density: Introduction, relation of spectral density to the fourier transform, properties of spectral density, spectral density and the complex frequency plane, mean square values from spectral density, relation of spectral density to the autocorrelation function,white noise, cross spectral density, examples and applications of spectral density,
Unit-V
Response of linear systems to random input: Analysis in the time domain, mean and mean square value of system output,autocorrelation function of system output, crosscorrelation between input and output, spectral density at the system output.Optimum linear systems: Criteria of optimality, restrictions on the optimum system, optimization by parameter adjustment, systems that maximize signal-to-noise ratio, systems that minimize mean square error.
References:
1. G. R. Cooper and C. D. Mcgillem: Probabilistic Methods of Signal and System Analysis, Third Edition, Oxford University Press. Introduction: Random input signals, random disturbances, random system characteristics, random experiments and events.
Random variables: Concept of random variable, distribution functions, density functions, mean values and moments, the Guassian random variable, density functions related to guassian- Rayleigh distribution, Maxwell distribution, Chi-square distribution, log normal distribution. Other distribution functions-uniform distribution, exponential distribution, delta distribution. Conditional probability distribution and density functions.
Unit-II
Several random variables: Two random variables, joint conditional probability, statistical independence, correlation between random variables, density function of the sum of two random variables, probability density function of a function of two random variables, the characteristic function.
Elements of statistics: Sampling theory- the sample mean, the sample variance, sampling distributions and confidence interval,hypothesis testing, curve fitting and linear regression, correlation between two sets of data.
Unit-III
Random Processes: Continuous and discrete, deterministic and nondeterministic, stationary and nonstationary, ergodic and nonergodic.
Correlation functions: Introduction, autocorrelation function of a binary process, properties of auto correlation functions, examples of autocorrelation functions, crosscorrelation functions, properties of crosscorrelation functions, examples and applications of crosscorrelation functions, correlation matrices for sampled functions.
Unit-IV
Spectral Density: Introduction, relation of spectral density to the fourier transform, properties of spectral density, spectral density and the complex frequency plane, mean square values from spectral density, relation of spectral density to the autocorrelation function,white noise, cross spectral density, examples and applications of spectral density,
Unit-V
Response of linear systems to random input: Analysis in the time domain, mean and mean square value of system output,autocorrelation function of system output, crosscorrelation between input and output, spectral density at the system output.Optimum linear systems: Criteria of optimality, restrictions on the optimum system, optimization by parameter adjustment, systems that maximize signal-to-noise ratio, systems that minimize mean square error.
References:
2. M. Lefebvre: Applied Probability and Statistics, Springer, Macmillan India Limited.
3. A. Papoulis, S. U. Pillai: Probability, Random Variable and Stochastic Processes, TMH.
0 comments