Introduction to Compressed Sensing
Instructor: Prof. Heung-No Lee
This course was offered in the Spring semester 2011 at GIST.
|Here is the lecture note Book_CS.pdf for this course.
Here is the presentation of professor Heung-No Lee presented at PSIVT 2011 Overview of Compressed Sensing
|1||Introduction to Compressive Sensing, Shannon Nyquist Sampling Theorem|
|2||Comparison of L0, L1, L2 solutions, application of sparse representation theory in filter array based spectrometers||HW#1|
|3||Compressive Sensing Theory: L0 and L1 equivalence, The Spark = Dmin of parity check matrix, The Singleton bound, Givens Rotation based Matrix Design|
|4||Compressive Sensing Mathematics: Generalized Uncertainty Principle, Sparse Representation, conditions for the unique ell-0 solution, and the unique ell-1 solution, the Donoho approach
|5||Compressive Sensing Mathematics: conditions for the ell-0 solution, and the unique ell-1 solution, the Candes-Tao approach.
|6||Compressive Sensing Mathematics: Sensing matrices and oversampling factors||HW#3|
|9||Recovery Algorithm I : Homotopy, LASSO, LARs , OMP.||HW#4|
|10||Recovery Algorithm II : ell-1 minimization , SOCP, Message Passing Algorithm s|
|11||Class Presentations #1/#2/#3||HW#5|
|12||Connection to the Shannon TheoryClass Presentations #4/#5/#6|
|13||The Rate Distortion TheoryClass Presentations #7/8/9||HW#6|