Laws and methods of probability are introduced. Concepts such as random variables, probability distributions for discrete-time and continuous-time signals, and averages are developed. Random processes and random signals are defined and examined through temporal correlation functions and Fourier spectral characteristics. The techniques of linear system analysis, filtering and optimization with random signal and noise inputs are developed using power spectral density functions. Practical applications, using computational methods such as FFT, are explored.
Prerequisite(s)/Course Notes: Prerequisite or corequisite: ENGG 177 or MATH 144.