Lectures related to Continuous-Time Stochastic Processes: Spectral Density

Linear Prediction and Estimation

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

Signal Processing Fundamentals

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

Estimation and Linear Prediction - Part 2

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

Stochastic Processes: Power Spectral Density

Explores power spectral density in continuous-time stochastic processes and its significance in signal processing applications.

Spectral Estimation Methods

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

Continuous-Time Stochastic Processes: Power Spectral Density

Covers the concept of power spectral density in continuous-time stochastic processes.

Harmonic Signals and Spectrum Estimation

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

Statistical Signal Processing for Wireless Communications

Covers statistical signal processing tools for wireless communications, focusing on signals like train of pulses, harmonic signals, and smooth spectrum signals.

Signal Processing: Correlation and Spectral Density

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

Stochastic Models for Communications: Continuous-Time Linear Systems

Covers continuous-time stochastic processes in linear systems, including signal analysis and filtering.

Neural Signals and Signal Processing

Explores neural signals, EMG control, muscle synergies, and HD-EMG for prosthetic devices.

Stochastic Processes and Spectral Densities

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

Sphere Packing

Covers the concept of sphere packing and its significance in time shifts and Nyquist criterion.

Statistical Signal Processing Tools

Explores statistical signal processing tools for wireless communications, including spectral estimation and frequency modeling.

Linear Estimation and Prediction: Part 2

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

Neural Signals and Signal Processing

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Signals & Systems I: Micro-Systems and Communication Systems

Introduces the fundamentals of signals and systems, communication systems, and signal processing.

Linear Estimation & Prediction: Models & Methods

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

Signals & Systems II: Stationary Signals and Spectral Density

Explores stationary signals, spectral density, cross power, and Gaussian processes.

Stochastic Models for Communications: Introduction

Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.