The objective of this course is to facilitate a profound comprehension of computations in both the time domain and frequency domain, in addition to exploring various other computation schemes. Throughout this course, students will gain hands-on experience in analyzing systems and designing filters, equipping them with practical skills to manipulate and process digital signals effectively.
MATLAB with Signal Processing Toolbox (https://www.mathworks.com/products/signal.html)
Upon successful completion of this course, students will be able to:
(1) Recall the principles and significance of the sampling theorem in digital signal processing,
(2) Explain resolution changes of time-domain sampled signals and their implications of signal quantization,
(3) Apply Discrete Fourier Transform (DFT) to convert time-domain signals into frequency-domain representation,
(4) Analyze the Fast Fourier Transform (FFT) algorithm and its efficiency in computing the DFT of large data sets,
(5) Evaluate statistical properties of random signals, forecasting future samples using Wiener prediction techniques,
(6) Design Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters considering frequency response, stability, and computational complexity.
Proakis, J. G., & Manolakis, D. G. (1996). Digital Signal Processing: Principles, Algorithms, and Applications. Pearson International Edition, 3rd Edition, Pearson.
Test/Exam (70%), Quiz (20%), Performance Project (Written, Oral) (10%)
| Workload | Hrs |
|---|---|
| Lectures | 42 |
| Course Readings | 18 |
| Debate | 5 |
| Observation | 5 |
| Hands-on Work | 28 |
| Exams/Quizzes | 42 |
| Report on a Topic | 5 |
| Demonstration | 5 |