Nonlinear Signal Processing
These two courses combine lectures and problem classes in nonlinear signal processing theory and applications. The theoretical part lays foundations in the area of memoryless nonlinearities (limiters/quantizers/classifiers), nonlinear dynamical systems (nonlinear filters/oscillators/chaos theory), nonlinear statistics (higher-order statistics and information theory), as well as parallel distributed processing (neural networks). The application part is devoted to the modeling of natural signals with nonlinear systems, and algorithms for time series analysis and signal synthesis.
As a prerequisite, students should have prior knowledge in digital signal processing theory and, preferentially, in adaptive systems.
Please consult the TUG Online system for updated lecture dates.
The lecture materials are available to registered participants only via the corresponding Teachcenter course.
You have to take an oral exam where you have to answer two questions (whiteboard available). You can choose the first question yourself! For possible exam dates contact Professor Kubin via email and include Bianca Deutschmann in the CC list.
The theoretical course is accompanied by practical problem classes. Current information is provided in the course repository.