Stochastic Models for Communications: Continuous-Time Linear Systems

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Linear Prediction and Estimation

Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.

Signals & Systems II: Stationary Signals and Spectral Density

Explores stationary signals, spectral density, cross power, and Gaussian processes.

Continuous-Time Stochastic Processes: Spectral Density

Covers the spectral density of continuous-time stochastic processes and its estimation.

Stochastic Processes: Power Spectral Density

Explores power spectral density in continuous-time stochastic processes and its significance in signal processing applications.