Normally written exam:
In the written exam students shall demonstrate that they are able to solve problems dealing with the design, analysis and implementation of DSP systems in soft and hardware considering computational complexity and hardware resource limitation, by using their thorough understanding of the theoretical concepts, especially frequency domain analysis, and insights gained from the practical implementation of DSP systems in software using Python and on microprocessors, such that they are able to design, select, use and apply actual and future DSP systems for various signal processing application in commercial products.


Alternatively these skills can also be demonstrated in an oral exam.

The follwowing subjects can be presented quickly assuming students have had prior exposure to discrete-time systems:
Signals, Systems and Digital Signal Processing
Discrete-Time Linear Time-Invariant Systems
Ideal Sampling and Reconstruction
Fourier-Transform of Discrete-Time Signals
The z-Transform

The follwoing subjects should be presented in depth:
Discrete Fourier-Transform
Design of Digital Filters
Random Signals
Advanced Sampling Techniques
The course should be complemented with selected topics from the following advanced subjects:
Optimum Linear Filters
Spectrum Estimation
Baseband Representation of Bandpass Signals
Wifi Sensing
The theory should be illustrated and put into practise by Python code of the presented methods and algorithms.

Semesterbegleitende Tests in Form von Aufgaben, die den bis zum jeweiligen Zeitpunkt in der Vorlesung/Übung behandelten Stoff aufgreifen und so bei Bestehen sicherstellen, dass die Grundlagen zur erfolgreichen Teilnahme an den entsprechenden Praktikumsversuchen oder Projekten gegeben ist.

Python based lab exercises and/or implementation on mircoprocessors.

Dedicated lab experiments or small microprocessor projects.