— Random Signals & Noise

Prerequisites & course information

Course Description: Random processes as probabilistic models for signals and noise. Probability and random variables, Convergence and limit theorems, Specification of random processes, Markov Chains, Wiener process and white Gaussian noise, Poisson process and shot noise, Processing and frequency analysis of random signals. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter. Learning Activities: Lecture 3 hour(s), Discussion 1 hour(s). Grade Mode: Letter.