EEEN40130

Invalid Option The module builds on the foundation module in EEEN30050 (Signal Processing). While EEEN30050 focuses on deterministic signals and their transforms, this module will expose students to random signals and some fundamental statistical signal processing techniques, including adaptive filtering and its applications. Basic operations (e.g. template matching) on image signals are also covered.

The module starts with fundamentals in relation to the characterization of a random signal, followed by a brief introduction to estimation theory. In the following part, filtering of random signals, methods for power spectral density estimation, optimal linear prediction, and linear adaptive filtering are covered. Applications of linear filter in wireless communications are also discussed. The last part of the module deals with image processing. The aim is to demonstrate how signal processing techniques and the domain knowledge can be exploited to derive efficient signal processing approaches for this special class of signals.

It is expected that the delivery of the module blends pre-recorded videos, synchronous online and in-class lectures and tutorials. All module materials are available from the beginning of the trimester. Though a reading list is provided, the lecture notes are meant to be self-contained.

The treatment is a blend of the theoretical and the practical with real world applications being dealt with in some depth. Students are required to confirm some theoretical results by writing their own Matlab code. Three comprehensive signal processing tasks will be assigned. The deadlines will be communicated to students well in advance. The grading scheme of each assignment will be made clear with the handout. Each student will get individual detailed feedback for each assignment. In addition to the assignments, the assessment includes an 2 hour end-of-trimester exam.