Instructor:
Shayan G. Srinivasa,
[Tuesday, Thursday 11:30 am – 1 pm, DESE auditorium]
Pre-requisities:
Undergraduate signals and systems/DSP course.
Course Syllabus:
- Review of basic signals, systems and signal space: Review of 1-D signals and systems, review of random signals, multi-dimensional signals, review of vector spaces, inner product spaces, orthogonal projections and related concepts.
- Basics of multi-rate signal processing: sampling, decimation and interpolation, sampling rate conversion (integer and rational sampling rates), oversampled processing (A/D and D/A conversion), and introduction to filter banks.
- Signal representation: Transform theory and methods (FFT and variations, KLT), other transform methods.
- Statistical signal modeling: The least squares method, Pade’s approximation, Prony’s method, Shanks’ method, iterative pre-filtering, all-pole modeling and linear prediction, autocorrelation and covariance methods, FIR least squares inverse filter design, applications and examples.
- Inverse problems (signal reconstruction): underdetermined least squares, pseudo-inverse (SVD), min-norm solutions, regularized methods, reconstruction from projections, iterative methods such as projection onto convex sets, expectation-maximization and simulated annealing.
Reference Books:
- Moon & Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000. (required)
- Monson Hayes, Statistical Digital Signal Processing and Modeling, John Wiley and Sons, 1996. (optional)
- Class notes
Grading Policy:
| Policy #1 | Policy #2 |
| Homeworks : 15%
Exam #1 : 15% Exam #2 : 20% Project : 15% Final Exam : 35% |
Homeworks : 15%
Exam #1 : 15% Exam #2 : 20% Project : 20% Final Exam : 30% |
The final grade is max (Policy#1,Policy#2) which ever works best for the student.
Homeworks
Exams
Project