Lectures related to Stochastic Processes and Spectral Densities

Signal Processing Fundamentals

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

Stochastic Processes: Basics & Stationarity

Covers signal generation, statistical relations, stationarity, white noise, and orthogonality in stochastic processes.

Harmonic Signals and Spectrum Estimation

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

Signal Processing: Correlation and Spectral Density

Delves into correlation and spectral density of signals, explaining their significance and applications.

Causal Systems & Transforms: Delay Operator Interpretation

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Ultrasonic Distance Sensor

Explores ultrasonic distance sensors, cross-correlation for error reduction, and Doppler sensors for flow measurement.

Frequency Search Principles

Explains the principles of frequency search and signal processing techniques for accurate results.

Parametric Signal Models: Matlab Practice

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

Yule Walker Equations: Efficient Implementation and Correlation Analysis

Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.

Properties of Time-Frequency Domain Signals

Covers the main properties of time-frequency domain signals and their limitations.

Time Series: Fundamentals and Models

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

Signals & Systems I: Cross-Correlation and Convolution

Explores cross-correlation, convolution, signal approximation, orthonormal functions, and orthogonal approximation theorem in signals and systems.

Noise in Devices and Circuits

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

Statistical Signal & Data Processing

Explores Statistical Signal & Data Processing fundamentals, tools, and applications in various fields.

Air Pollution: Correlation Analysis

Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.

Linear Prediction and Estimation

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

Spectral Estimation Methods

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

Signal Models and Methods: Parametric vs Nonparametric

Provides an overview of signal models and methods in statistical signal processing.

Estimation and Linear Prediction - Part 2

Explores power spectral density, Wiener-Khintchine theorem, ergodicity, and correlation estimation in random signals for signal processing.

Linear Estimation and Prediction

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.