Signals & Systems II: Second Order Statistics

This lecture covers the analysis of second order statistics in signal processing, focusing on complex random vectors and the duality of energies in Fourier transformation. It explains the modeling of physical noises with Gaussian processes, the concept of stationarity in random signals, and the distinction between ergodic and non-ergodic processes. The instructor discusses the properties of stationary signals, the importance of separate notations, and the relationship between time averages and statistical means. Examples are provided to illustrate the concepts of stationarity and ergodicity.