Signal Processing: Continuous and Discrete

A blurry image of stars and its non-blurry counterpart, along with a block diagram of the system.

Data from instruments can be analyzed using filters to enhance different features; here, a deconvolution filter extracts a more detailed image from a blurry photo taken by the Hubble telescope. (Image by Prof. Derek Rowell.)


MIT Course Number


As Taught In

Fall 2008



Cite This Course

Course Features

Course Description

This course provides a solid theoretical foundation for the analysis and processing of experimental data, and real-time experimental control methods. Topics covered include spectral analysis, filter design, system identification, and simulation in continuous and discrete-time domains. The emphasis is on practical problems with laboratory exercises.

Derek Rowell. 2.161 Signal Processing: Continuous and Discrete, Fall 2008. (Massachusetts Institute of Technology: MIT OpenCourseWare), (Accessed). License: Creative Commons BY-NC-SA

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