Signal Processing: Linear Filters and Estimation

Related lectures (32)

Page 3 of 4

Explores the theory and design of linear phase filters, emphasizing the importance of achieving optimal filter performance.

Explores neural signals, EMG processing, muscle synergies, and prosthetic control using advanced signal processing techniques.

Discusses the sampling theorem and its applications in signal processing.

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

Explores neural signal processing methods, fMRI designs, GLM analysis, and quality control.

Covers the leaky integrator, denoising signals, and its impulse response.

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

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

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Discusses signal representation, focusing on mathematical expressions and inequalities in signal processing.