Introduction to Random Signals and Noise

1 credits · Undergraduate

Introduction to probability and random processes with example applications to processing data with uncertainty. Probability, conditional probability, discrete & continuous random variables, expected values, characteristic functions, multiple random variables. Intro to random processes, random discrete-time signals, power spectrum, autocorrelation, analysis of linear systems driven by wide-sense stationary random processes. Gaussian random processes, band limited white Gaussian noise. Intro to binary hypothesis testing, Bayesian signal detection, applications involving uncertain & random signals & noise. Prerequisite: ECE 280L, Math 212 or 219.