Stochastic Models for Communications: Continuous-Time Stochastic Processes

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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.

Introduces the z-transform, covering its basics, applications to systems, and practical examples.

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

Covers the analysis of continuous-time stochastic processes in the context of linear systems.

Covers the analysis of linear systems and their responses to different sources and configurations.

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