Estimation and Linear Prediction - Part 2

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Related lectures (32)

Page 1 of 4

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

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.

Signals & Systems II: Second Order Statistics

Explores second order statistics in signal processing, stationarity in random signals, and the distinction between ergodic and non-ergodic processes.

Neural Signals and Signal Processing

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

Signal Processing: Basics and Spectral Analysis

Covers the basics of signal processing, linear estimation, and digital filters.

Harmonic Signals and Spectrum Estimation

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

Discrete Fourier Transform: Unlimited Duration Signals

Explores the Discrete Fourier Transform applied to signals of unlimited duration using various windows for improved accuracy.

Continuous-Time Stochastic Processes: Spectral Density

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

Neural Signals and Signal Processing

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