6.011 Introduction to Communication, Control, and Signal Processing

As taught in: Spring 2010

An illustration of spectral shaping of a white-noise signal.

Spectral shaping of a white-noise signal. (Image by MIT OpenCourseWare. Courtesy of Prof. Alan Oppenheim and Prof. George Verghese.)




Prof. Alan V. Oppenheim

Prof. George Verghese

Course Features

Course Highlights

This course features a complete set of course notes, Signals, Systems and Inference.

Course Description

This course examines signals, systems and inference as unifying themes in communication, control and signal processing. Topics include input-output and state-space models of linear systems driven by deterministic and random signals; time- and transform-domain representations in discrete and continuous time; group delay; state feedback and observers; probabilistic models; stochastic processes, correlation functions, power spectra, spectral factorization; least-mean square error estimation; Wiener filtering; hypothesis testing; detection; matched filters.