The main objective of this course is to enable students to understand the principles of probability theory and statistics as they apply to signal processing. The course also aims to provide students with the opportunity to develop their proficiency in analyzing and modeling random signals and noise by exploring methods for signal detection, estimation, and hypothesis testing.
MATLAB
Upon successful completion of this course, students will be able to:
(1) Recognize the basic principles of probability theory, random variables, expected value, variance, and moments,
(2) Explain the concepts of stationarity and ergodicity in the context of signals and systems,
(3) Utilize statistics for noise-corrupted signal analysis and linear filters for noise reduction and signal enhancement,
(4) Analyze noise effects on signal quality and estimation methods in bias, variance, and error,
(5) Assess signal model assumptions for real-world relevance and algorithms for detection and classification effectiveness,
(6) Create denoising algorithms with statistical filters and methods for parameter estimation of noise-affected signals.
(1) Hayes, M. H. (1996). Statistical Signal Processing and Modeling. Wiley.
(2) Papoulis, A. (1991). Probability, Random Variables, and Stochastic Processes (3rd ed.). McGraw Hill.
Test/Exam (60%), Case Studies / Homework (40%)
| Workload | Hrs |
|---|---|
| Lectures | 42 |
| Course Readings | 70 |
| Exams/Quizzes | 70 |
| Case Study Analysis | 43 |