*Course Description* 

This course covers advanced topics in digital signal processing (DSP), including time-frequency analysis, adaptive filtering, multi rate systems, and applications in communications and machine learning. 

 

*Learning Objectives* 

By the end of this course, students will: 

1. Design and analyse multi rate DSP systems (e.g., filter banks). 

2. Apply adaptive filtering techniques (LMS, RLS algorithms) to real-world problems. 

3. Implement wavelet transforms and time-frequency analysis methods. 

4. Integrate DSP concepts with machine learning applications. 

 

*Required Materials* 

- Textbook: Advanced Digital Signal Processing by Proakis & Manolakis (2nd ed.). 

- Software: MATLAB/Python (labs). 

 

*Course Schedule* 

| Week | Topics                                      | Assessments | 

|------|---------------------------------------------|-------------| 

| 1    | Review of DFT, FFT, Z-transform             | Homework 1  | 

| 2–3  | Multirate Systems: Decimation, Interpolation| Lab 1       | 

| 4–5  | Adaptive Filters (LMS/RLS Algorithms)       | Midterm 1   | 

| 6–7  | Wavelet Transforms & Time-Frequency Analysis| Project Proposal | 

| 8–10 | DSP in Machine Learning (Feature Extraction)| Final Project | 

 

*Assessment* 

- Homework (20%) 

- Labs (30%) 

- Midterm Exam (20%) 

- Final Project (30%) 

 

 *Policies* 

- Late work: 10% penalty per day. 

- Collaboration: Allowed on labs, but individual reports. 

- Academic integrity: Zero tolerance for plagiarism. 

 

*Graduate Program Curriculum Overview* 

A typical M.S./Ph.D. in Electrical Engineering includes *core courses, **electives*, and a thesis/research project. 

 

*Core Courses* 

1. *Advanced Electromagnetic Theory* 

   - Maxwell’s equations, wave propagation, antennas. 

   - Computational EM (FDTD, FEM). 

 

2. *Power Systems Analysis* 

   - Grid stability, renewable integration, smart grids. 

 

3. *Modern Control Systems* 

   - Nonlinear control, robust control, optimization. 

 

4. *VLSI Design* 

   - CMOS circuits, FPGA prototyping, low-power design. 

 

*Electives* 

- *Machine Learning for Engineers* 

- *Optical Communications* 

- *Renewable Energy Systems* 

- *Embedded Systems Design* 

 

*Research/Thesis* 

- Original research project (2–4 semesters). 

- Defence and publication required for Ph.D. 

 

*Program Policies* 

- *Credits:* 30–36 credits (M.S.), 60+ credits (Ph.D.). 

- *Advising:* Mandatory faculty advisor meetings each term. 

- *Comprehensive Exams:* Required for Ph.D. candidacy.