Advanced DSP

More Basic than Basic DSP

In Brief: Linearity and Time Invariance are universally applicable approximations. How can we analyze LTI systems? How can we negate their effects? How can we control LTI systems?

Linearity and Time Invariance are highly useful rules of thumb. How can we design LTI systems? What can they achieve? What is the best way to implement them?

This course tackles design and analysis of various LTI systems for various purposes, with various kinds of signals passing through them.

Target Audience: A course for future (and present) computer and electronics engineers and scientists. A must for those who find DSP interesting or useful. Also useful as an introduction to matrix theory as well as probability and random processes. Useful for people interested in control systems, process prediction, multimedia compression, or other advanced subjects related to DSP.

Teacher's Introduction: Udayan Kanade did his MS in CS with the specialization "Optimization and Signal Processing" from Stanford. He led the DSP team at Codito for three years. Udayan has taught Advanced DSP thrice, and also basic DSP twice.

Course Topics: Convolution, deconvolution, least squares problems and algorithms, system models and identification, the Fourier transform, resampling, filterbanks. Random processes, their spectra, whitening, spectrum estimation.

Prerequisites: A basic course in DSP is assumed. If not, you should be ready to work a little harder.