Digital Signal Processing

Digital signal processing (DSP) plays a major role in the fast development of new methods within areas such as telecommunication, multimedia, medicine, seismic, remote sensing, monitoring and control of industrial processes, and so on. DSP may be defined as the mathematical tools that are used to analyze, model, or transform physical (real-world) signals/processes and sources. This includes basic operations like filtering and frequency analysis but also methods/systems for modeling, estimation, and classification. The following topics are taught: time, frequency, and z-domain description of discrete signals and linear time-invariant systems; analysis and design of filters; multirate systems, correlation, and energy spectrum; statistical properties, modeling, and estimation of discrete stochastic processes.

Knowledge: Student has a) an understanding of why digital signal processing is used. b) has detailed knowledge of the basic methods for processing discrete signals and systems. c) a basic understanding of analysis, modeling, and estimation of real-world stochastic signals. Skills: Students can 1) solve basic problems within filtering, frequency analysis, and signal modeling by combining earlier acquired knowledge and skills within mathematics, statistics, and basic signal processing. 2) use Matlab/Python to solve the above basic problems

Lectures, compulsory and optionally homework problems and computer exercises in Matlab.