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| Detailed information |
| Original study plan |
Master's programme Electronics and Information Technology 2019W |
| Objectives |
- Gain knowledge on important signal processing algorithms as well as their implementation in digital hardware
- Understand implementation issues and gain knowledge on the optimization of algorithms for efficient implementation
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| Subject |
- Repetition of digital signal processing concepts and implementation aspects of signal processing in digital hardware.
- Implementation of efficient algorithms for solving least squares problems and equation systems (e.g.: Kaczmarz algorithms, Dichotomous Coordinate Descent,…); use cases for these algorithms
- Implementation of efficient algorithms for sparse estimation and reconstruction problems
- Implementations of adaptive Filters (e.g. LMS, Sparse LMS,…)
- Signal decomposition (e.g. FFT and related approaches)
- Implementation aspects of selected machine learning and data science algorithms
- Efficient hardware design (memory management, arithmetic simplifications, approximations,…)
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| Criteria for evaluation |
Oral or written exam (75%), grading of homework (25%)
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| Methods |
Lecture, Matlab/VHDL/bit-true demos, solving of selected homework examples, video recording of lecture (screen capture and audio recording)
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| Language |
Upon agreement with participants – English or German |
| Study material |
- Lecture slides
- U. Meyer-Baese, Digital Signal Processing with Field Programmable Gate Arrays, *E. Chong, S. Zak, An Introduction to Optimization, Wiley, 2001.
- J. H. Friedman, R. Tibshirani und T. Hastie, The Elements of Statistical Learning, Springer, 2001.
- U. Spagnolini, Statistical Signal Processing in Engineering, Wiley 2018.
- H. Bauschke et. al, Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Springer, 2011.
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| Changing subject? |
No |
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