This course aims to equip students with the ability to express continuous and discrete-time signals mathematically. Students will gain an understanding of input-output relationships in linear time-invariant systems, perform convolution, and apply Fourier series to represent periodic signals. Students will also analyze Fourier transforms for continuous and discrete-time signals, recognizing their frequency characteristics. Finally students will learn to formulate conditions for proper signal sampling to prevent aliasing.
MATLAB with Signal Processing Toolbox (https://www.mathworks.com/products/signal.html)
Upon successful completion of the course, students will be able to:
(1) Express the mathematical representations of continuous and discrete-time signals,
(2) Explain the input-output relationships in linear time-invariant systems and the steps needed to perform convolution,
(3) Apply the concepts of Fourier series representation to represent continuous-time and discrete-time periodic signals,
(4) Analyze the Fourier transform of a continuous-time signal, understanding its frequency content and spectral characteristics,
(5) Evaluate the Fourier transform for discrete-time signals, identifying their frequency components and spectral properties,
(6) Formulate the necessary conditions for properly sampling a continuous-time signal to avoid aliasing (sampling theorem).
(1) McClellan, J. H., Schafer, R. W., & Yoder, M. A. (2003). Signal Processing First. 2nd Edition, Pearson/Prentice Hall.
(2) Tervo, R. J. (2013). Practical Signals Theory with MATLAB Applications. 1st Edition, Wiley.
Oppenheim, A. V., Willsky, A. S., & Nawab, S. H. (2013). Signals and Systems: Pearson New International Edition. 2nd Edition, Pearson.
Test/Exam (85%), Active Learning Exercises (15%)
| Workload | Hrs |
|---|---|
| Lectures | 42 |
| Course Readings | 42 |
| Exams/Quizzes | 42 |
| Active Learning Exercises | 24 |