Introduction to Communication, Control, and Signal Processing

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.)

Instructor(s)

MIT Course Number

6.011

As Taught In

Spring 2010

Level

Undergraduate

Cite This Course

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.

Other OCW Versions

OCW has published multiple versions of this subject. Question_OVT logo

Oppenheim, Alan, and George Verghese. 6.011 Introduction to Communication, Control, and Signal Processing, Spring 2010. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-011-introduction-to-communication-control-and-signal-processing-spring-2010 (Accessed). License: Creative Commons BY-NC-SA


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