Stochastic Models for Communications: Continuous-Time Stochastic Processes

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

Explores control of dynamic systems, impulse response, Laplace transform, and Fourier transform for solving differential equations.

Covers the concept of stationarity in continuous-time stochastic processes and its applications in communication systems.

Covers the estimation and prediction of random signals in linear systems.

Explores time series models, emphasizing autoregressive processes, including white noise, AR(1), and MA(1), among others.