EEEN40130

Building on the core principles established in the precursor module, EEEN30050 (Signal Processing), this module delves further into the domain of statistical signal processing, with a particular emphasis on random signals. Whereas EEEN30050 focuses on deterministic signals and their transformations, this module introduces students to properties of random signals and some fundamental statistical signal processing techniques such as power spectrum estimation, linear adaptive filtering, and some basic operations on image signals, such as template matching.

The module structure is as follows:

1. Random Signal Characterization: The module begins with the fundamentals of random signal characterization. This foundation lays the groundwork for understanding the nature of random signals and the statistical tools used to analyze them.

2. Estimation Theory Introduction: Students will be introduced to the basics of estimation theory. This portion provides the necessary concepts for predicting and understanding the behavior of random signals.

3. Filtering of Random Signals and Power Spectral Density Estimation: The next phase dives into the practical aspects of processing random signals. Students will explore different techniques for filtering random signals and estimating power spectral density. This section also includes optimal linear prediction and linear adaptive filtering.

4. Applications of Linear Filters: Once a firm understanding of the techniques is established, the module extends to the theory and applications of linear filters in various fields. This real-world application allows students to see the practicality of the theories learned.

5. Image Processing: The final segment of the module focuses on image processing. Students will learn how to apply signal processing techniques and domain-specific knowledge to derive efficient approaches for processing this unique type of signals.

Teaching methods are designed to be interactive, combining in-class lectures and tutorials with computer-based demonstrations. Students are encouraged to bring their laptops to each class, enabling them to engage actively with the numerical experiments conducted. All module materials will be made available at the beginning of the trimester, providing a comprehensive set of resources. Although a reading list is provided, the lecture notes are designed to be self-sufficient. Python is the programming language for this module.

The module is a blend of theory and practice, with real-world applications examined in depth. As part of the course requirements, students will be asked to confirm theoretical results by writing their own Python code. This will involve three comprehensive signal processing assignments. The deadlines for these tasks will be communicated well in advance, with a clear grading scheme outlined in the assignment handouts. A dedicated tutorial will be scheduled for each assignment, providing interactive, hands-on instruction. Each student will receive detailed, individual feedback for each task. In addition to the assignments, the assessment includes an 2 hour end-of-trimester exam.