Stochastic Models for Communications

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

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Covers spectral density, cross power signals, stationarity, and Gaussian processes.

Explores stochastic processes, stationarity, ergodicity, and Wiener filtering for image restoration.

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

Covers the analysis of second-order stochastic processes in communication systems and image restoration using the Wiener filter.

Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.

Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.

Explores complex random vectors, stationarity, ergodicity, and the analysis of signals.

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.