Inhalt

[ 489WSIVESPK19 ] KV Efficient Signal Processing Algorithms

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M2 - Master's programme 2. year Computer Science Michael Lunglmayr 2 hpw Johannes Kepler University Linz
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
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,…)
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
On-site course
Maximum number of participants 35
Assignment procedure Assignment according to sequence