MATH 465 - Signal Processing

This course leverages theory and computation to explore how transforming data from one domain into a different domain often makes it easier to analyze, compress, communicate, or find structure in the data. Topics include: how signals such as audio clips and images can be broken down into combinations of basic building blocks (analysis); how those fundamental building blocks can be combined into more complicated signals (synthesis); the theory and applications of Fourier, discrete cosine, wavelet, and time-frequency transforms; the Nyquist-Shannon sampling theorem; the Heisenberg uncertainty principle; and sparse representations. Applications will be drawn from audio and speech processing, graph signal processing, medical imaging, physics, geology, biology, finance, and other disciplines. Prerequisite(s): MATH 236 and COMP 123. MATH 365 recommended. Alternate fall semesters. (4 Credits)