Signals and Systems

1st year/2nd year
Programme main editor:
Onsite in:
ECTS range:



  • Advanced Mathematics

Pedagogical objectives:

The concepts of signals and systems are powerful tools for any engineer dealing with information bearing, measurable physical quantities. Areas of applications include, among others, communications engineering, signal processing, control engineering, and systems engineering. The students will be able to classify, interpret, and compare signals and systems with respect to their characteristic properties. They can explain and apply analytical and numerical methods to analyse and synthesize signals and systems in time and frequency domain. Suitable signal transformations can be chosen and calculated with the help of transformation tables. The students can recognize stochastic signals and analyse them based on their characteristic properties. They can calculate and interpret the influence of linear time-invariant systems on stochastic signals.

Evaluation modalities:

Oral exam.


Topics include:

  • Basic properties of discrete-time and continuous-time systems
  • Z-transform – Basic properties of discrete-time and continuous-time systems
  • Linear time-invariant systems, convolution integral
  • Fourier transform, discrete Fourier transform, Fourier series
  • Sampling theorem
  • Probability theory, random variables, and stochastic processes
  • Stochastic signals and linear time-invariant systems

Required teaching material

Literature: • Alan V. Oppenheim and Alan S. Willsky: Signals and Systems, Prentice Hall 1996 • Mrinal Mandal and Amir Asif: Continuous and Discrete Time Signals and Systems, Cambridge University Press, 2007 • Athanasios Papoulis and S. Unnikrishna Pillai: Probability, Random Variables, and Stochastic Processes, McGraw-Hill, 2002 • Thomas Frey und Martin Bossert: Signal- und Systemtheorie, B.G. Teubner Verlag, 2004 • Jens Ohm und Hans Dieter Lüke: Signalübertragung, Springer Verlag 2010 • Moodle Course at - Account needed at SAPS of UUlm

Teaching volume:
30 hours
6 hours
Supervised lab:


  • Laboratory-Based Course Structure
  • Open-Source Software Requirements