[ 536DASCDSPV19 ] VL Digital Signal Processing

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B3 - Bachelor's programme 3. year (*)Artificial Intelligence Mario Huemer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2022W
Objectives Students know and understand the basics of analog as well as discrete-time signals and systems qualitatively and mathematically and can apply them to fundamental problems. They are able to

  • plot signals in time- and frequency domain,
  • explain the sampling theorem,
  • apply the convolution operation,
  • derive the spectrum of a signal numerically with the help of the DFT and the FFT,
  • describe the behavior of discrete-time LTI systems in time- and frequency domain,
  • analyze and design digital FIR and IIR filters, and
  • apply the correlation operation.

Students know and understand the basics of adaptive filters, and the basics of time-frequency analysis.

  • Basics of Analog Systems Theory
  • Discrete Time Signals Fundamentals
  • Discrete Time LTI Systems Fundamentals
  • Fourier Transform of Discrete Time Signals (DTFT)
  • Sampling and Reconstruction
  • Correlation
  • Spectral Analysis of Discrete Time Signals (DFT, FFT, Short Time Fourier Transformation, Applications)
  • z Transform
  • Digital Filters (FIR, IIR)
  • Vector Matrix Representations of Discrete Time Signals and LTI Systems
  • Adaptive Filters
Criteria for evaluation Written Exam
Methods Lecture using slides and blackboard, Matlab based presentations
Language English
Study material
  • Lecture Slides
  • Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing: Pearson New International Edition, Pearson Education Limited 2014.
Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment