Linear Estimation and Prediction

Related lectures (32)

Page 2 of 4

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

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

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

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

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

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

Explores adaptive denoising techniques, emphasizing echo cancellation and the need for real-time adaptive filters.

Explores adaptive signal processing, gradient descent, and the LMS algorithm for efficient filtering and neural network training.

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Covers the basics of signal processing, including auto covariance function, power spectral density, and filter design.