**Instructor: **

**Shayan G. Srinivasa,**

[Monday, Friday 3:30 pm – 5:00 pm, Old Conference Hall]

**Pre-requisities: **

Digital signal processing at the undergrad level.

**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 (FT and variations, KLT), other transform methods.
- Wavelets: Characterization of wavelets, wavelet transform, multi-resolution analysis.
- 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 **

- Homework #1 – Solutions
- Homework #2 – Solutions
- Homework #3 – Solutions

- Homework #4 – Solutions
- Homework #5 – Solutions

**Exams**

**Project**

**Course announcements**

- Exam #2 on 2nd Nov. 2016 (7pm to 10 pm).
- Mini project topic and abstract submission due on 14th Oct. 2016.
- Exam #1 on 30th Sep. 2016.