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Biomedical Signal and Image Processing
Conditional Remix & Share Permitted
CC BY-NC-SA
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This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.

Subject:
Applied Science
Career and Technical Education
Electronic Technology
Engineering
Health, Medicine and Nursing
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Clifford, Gari
Fisher, John
Greenberg, Julie
Wells, William
Date Added:
02/01/2007
Signals, Systems and Information for Media Technology
Conditional Remix & Share Permitted
CC BY-NC-SA
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0.0 stars

This class teaches the fundamentals of signals and information theory with emphasis on modeling audio/visual messages and physiologically derived signals, and the human source or recipient. Topics include linear systems, difference equations, Z-transforms, sampling and sampling rate conversion, convolution, filtering, modulation, Fourier analysis, entropy, noise, and Shannon's fundamental theorems. Additional topics may include data compression, filter design, and feature detection. The undergraduate subject MAS.160 meets with the two half-semester graduate subjects MAS.510 and MAS.511, but assignments differ.

Subject:
Applied Science
Arts and Humanities
Career and Technical Education
Electronic Technology
Engineering
Graphic Arts
Mathematics
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Bove, V.
Picard, Rosalind
Smithwick, Quinn
Date Added:
09/01/2007
Think DSP: Digital Signal Processing in Python
Conditional Remix & Share Permitted
CC BY-NC
Rating
0.0 stars

The examples and supporting code for this book are in Python. You should know core Python and you should be familiar with object-oriented features, at least using objects if not defining your own. If you are not already familiar with Python, you might want to start with my other book, Think Python, which is an introduction to Python for people who have never programmed, or Mark Lutz’s Learning Python, which might be better for people with programming experience.

Subject:
Applied Science
Computer Science
Material Type:
Textbook
Provider:
Green Tea Press
Author:
Allen B. Downey
Date Added:
01/01/2012