Lectures related to 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.

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.

Covers the properties of Linear Time-Invariant (LTI) systems and their implications.

Explores system analysis in the time domain, covering mathematical modeling, transfer functions, responses, and system identification.

Explores distributions, linear systems, impulse response, convolution operators, and causal systems in signals and systems.

Discusses oscillations, energy transfer, and the Green function in physics.

Explores the frequency response of stable LTI systems in both continuous-time and discrete-time domains.

Covers the analysis of LTI systems using impulse response and convolution.

Covers distributions, linear systems, impulse response, convolution, and examples of linear systems.

Explores stability and solutions of linear systems in continuous and discrete time.

Covers spectral densities, signal correlations, and white noise processes in stochastic systems.

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