4:40 5:45 Tuesday & Thursday (LL 45 -SLN: 38165 3 credit hours)
D. Cochran, A. Gelb, N. Kovvali, A. Papandreou, R. Platte, R. Renaut, and S. Roudenko
This course is designed to teach cutting edge techniques in data acquisition and signal processing to graduate students in mathematics and engineering.
While various applications will be used to motivate the course, there will be some emphasis on MRI. Scientists from Barrows Neurological Institute will provide
initial motivating lectures. The course will use a seminar participative style of learning. Assignments will focus on using real data and high level numerical algorithms.
Topics include, but are not limited to, Sampling theory, Fourier methods, Edge detection, Radial Basis functions, Prolate Spheroidal Wave functions, Hidden Markov models
, Time-varying signals, Wavelets, and Inverse problems. The course will be team taught by mathematics and engineering faculty and is cross-listed.
Prerequisites: Instructor approval.
Click here for full Syllabus