Courses Detail Information
ECE7607J – Compressed Sensing Theory and Its Applications
Instructors:
Credits: 3 Credits
Pre-requisites: Graduate Standing
Description:
This is a graduate-level introduction to the fundamentals of compressed sensing and their applications. In the theory part, the basic ideas behind random dimension reduction and ill-posed inverse problems will be covered. In particular, the concept of random measure concentration and some basic tools from high-dimensional probability will be introduced to help understand the use of random sampling. Some common structural regularizations and algorithms will be discussed along with some simplified analyses. In the application part, this course will cover the most important practical scenarios including computational imaging, wireless communication, radar, and data analysis. This course will present the widely used algorithms and their variants in those applications.
Course Topics:
Linear Algebra and Approximation Theory
Bases, Frames and Sampling Theory
High-Dimensional Probability and Concentration of Measure
Compressed Sensing Algorithms and Theory
Low-Rank Matrix Completion
PCA, Subspace Estimation, Model Order Selection
Compressive and Super-Resolution Imaging
Communications and Radar
Sparse FFT, Sparse SVM and other Advanced Algorithms