Adaptive Signal Processing: LMS Filter

Related lectures (30)

Page 2 of 3

Explores digital filters, their implementation, characteristics, and realizations, with examples for illustration.

Covers signal representations using concepts such as Haar wavelets and FIR filters.

Explores linear least squares, normal equations, and the importance of linear regression in minimizing errors.

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.

Explains estimation through mean-squared error and Fisher information in the context of adaptive filters and exponentiated distributions.

Covers the frequency response of linear systems and filter design principles.

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

Covers Direct Sequence Spread Spectrum Modulation (DSSS) to improve signal quality through wider bandwidth distribution and interference reduction.

Explores adaptive equalization in digital communication systems to compensate for channel distortion and track time-varying conditions.

Covers Ordinary Least Squares Regression analysis and regression model restrictions.