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[ 289SISYSIVV20 ] VL Signal Processing
Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
4,5 ECTS B2 - Bachelor's programme 2. year (*)Informationselektronik Mario Huemer 3 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Electronics and Information Technology 2025W
Learning Outcomes
Competences
Students know and understand the fundamentals of discrete-time signals and systems, both in qualitative and mathematical terms. They are able to utilize this knowledge to tackle fundamental signal processing problems. They are also capable of implementing and applying the fundamental signal processing algorithms using a numerical tool (such as Matlab).
Skills Knowledge
Students are able to

  • analyze discrete-time signals in time- and frequency-domain (k3, k4, k5)
  • analyze discrete-time LTI-systems (k3, k4, k5)
  • to apply the convolution operation (k3, k4, k5)
  • interpret the sampling theorem, and understand ideal and real signal reconstruction (k2, k5)
  • determine auto- and cross-correlation functions (k2)
  • apply the discrete Fourier transform and understand the FFT algorithm (k3, k6)
  • perform time-frequency signal analysis (k2, k3, k4, k5, k6)
  • apply the z-transform (k3, k6)
  • analyze and design digital filters (k3, k4, k6)
  • describe discrete-time LTI-systems in state space (k2)
  • describe discrete-time FIR-systems in vector-matrix-form (k3, k4)
  • interpolate and decimate discrete-time signals by integer and rational factors (k2)
  • Fundamentals of discrete-time signals
  • Discrete-time LTI-systems
  • Discrete-time Fourier-transform
  • Convolution, frequency response
  • Sampling, sampling theorem, and signal reconstruction
  • Correlation functions
  • Discrete Fourier-transform and FFT
  • Short-time Fourier transform
  • z-transform
  • Analysis and design of FIR- and IIR-filters
  • State space representations
  • Basics of multirate signal processing
Criteria for evaluation Written exam
Methods Lecture using slides and blackboard, Matlab based presentations
Language German
Study material
  • Lecture slides
  • A.V. Oppenheim, R.W. Schafer, J.R. Buck, Zeitdiskrete Signalverarbeitung, Pearson, München, 2004.
  • D. Ch. von Grünigen, Digitale Signalverarbeitung, Hanser, München, 2008.
  • K.D. Kammeyer, K. Kroschel, Digitale Signalverarbeitung, Vieweg + Teubner, Wiesbaden, 2009.
Changing subject? No
Further information This lecture and the accompanying exercise course form an inseparable didactic unit. The presented learning outcomes are achieved through the interaction of the lecture and the exercise course.
On-site course
Maximum number of participants -
Assignment procedure Assignment according to sequence