Signal Processing Fundamentals

This lecture covers the fundamentals of signal processing, focusing on discrete time signals, random variables, probability density functions, and spectral factorization. It also delves into the concepts of minimum phase systems, zero-phase systems, and spectral factorization in the context of causal systems and transforms. The instructor explains the correlation, covariance, and stationarity of stochastic processes, emphasizing the power spectral density and the projection theorem in Hilbert spaces. Practical exercises involve generating and analyzing AutoRegressive (AR) processes.