Stochastic Models for Communications

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Page 3 of 4

Introduces the fundamentals of noise in electronics, covering origins, signal types, power characteristics, and noise sources.

Linear Estimation & Prediction: Models & Methods

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Continuous-Time Stochastic Processes: Ergodicity Examples

Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.

Linear Prediction and Estimation

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

Noise in Electronics: Fundamentals and Applications

Explores noise in electronics, covering average power, signal-to-noise ratio, deterministic and random signals, and noise amplification.

Linear Time Invariant Filters: Time Series

Explores estimation, ergodicity, sampling, and linear time invariant filters in time series analysis.

Spectral Analysis: Integrated Spectrum and Autocovariance

Explores spectral analysis, integrated spectrum, autocovariance, estimation, and convergence in time series models.

Time Series: Linear Filtering and Spectral Estimation

Explores linear filtering, spectral estimation, and second-order stationarity in time series analysis.

Harmonic Signals and Spectrum Estimation

Explores harmonic signals, spectrum estimation, and signal analysis methods using MATLAB tools.