[ 489WSSIESPK22 ] KV Efficient Signal Processing Algorithms

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M2 - Master's programme 2. year (*)Informationselektronik Michael Lunglmayr 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Electronics and Information Technology (ELIT) 2023W
Objectives Students know and understand advanced signal processing algorithms. They know how to efficiently implement these algorithms in digital hardware. They gained a deep understanding on possible implementation challenges and know how to optimize the algorithms for efficient implementation.
  • 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,…)
Criteria for evaluation Oral or written exam (75%), grading of homework (25%)
Methods Lecture, Matlab/VHDL/bit-true demos, solving of selected homework examples, video recording of lecture (screen capture and audio recording)
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.
Changing subject? No
Earlier variants They also cover the requirements of the curriculum (from - to)
489WSIVESPK19: KV Efficient Signal Processing Algorithms (2019W-2022S)
On-site course
Maximum number of participants -
Assignment procedure Assignment according to sequence