Digital Signal Processing (MA060255)
Digital signal processing (DSP) refers to various techniques for improving the accuracy and reliability of digital communications. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, statistical signal processing, estimation theory, signal sampling, re-sampling, and reconstruction free of errors. We will analyze signal distortions due to sampling, interpolation, and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. We will also deal with modulation and multipath propagation channel models. Hands-on examples and demonstrations will be routinely used to close the gap between theory and practice.
The extra topics covered in this course are:
- Spectrum analysis;
- Machine learning for signal processing;
- Fundamentals of random signal theory and analysis;
- Modeling communication signals as random processes;
- Baseband signal processing, signal synthesis, and filter design for communication;
- Statistical signal processing in communication;
It is hoped that through learning this course students will be equipped with a clear picture of DSP as well as a necessary foundation for further study of advanced DSP topics in the future.