Spring 2016 Course Descriptions

COMP 150-07 Theory of Compressive Sensing and Its Applications

P. Chin
TR 6:00p-7:15p, Miner Hall 112
N+ Block

Sparsity has become a very important concept in recent years in applied mathematics, especially in mathematical signal and image processing, as in inverse problems. The key idea is that many classes of natural signals can be described by only a small number of significant degrees of freedom. This course offers complete coverage of the recently emerged field of compressed sensing, which asserts that, if the true signal is sparse to begin with, accurate, robust, and even perfect signal recovery can be achieved from just a few randomized measurements. The focus is on describing the novel ideas that have emerged in sparse recovery with emphasis on theoretical foundations, practical numerical algorithms, image recognitions, and various related signal processing applications. Students from diverse backgrounds (math, CS, ECE, BME, ME, Medical School, etc.) who are either interested in the subject or want to apply this new theory in their research are encouraged to attend.

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