If you want to sign up for this laboratory, you need to register with the following information in the registration form

• surname, first name,
• (stu) e-mail address,
• matriculation number,

Please note, that the registration period starts the 14.09.2026 at 00:01 am and ends the 18.10.2026 at 23:59 am. All applications before and after this registration period will not be taken into account.

Registration will be possible within the before mentioned time by sending an e-mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it.

The registration is binding. A deregistration is possible by sending an e-mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it. until Friday, 23.10.2026 at 12:59 am. All later cancellations of registration will be considered as having failed the lab.

Requirements, rules and commitments will be announced per mail before the first experiment (attendance is mandatory).

Will be announced on the 19.10.2026.

The contents of the lab is the following:

• Introduction to Matlab (Octave)
• Time-continuous periodic and non-periodic signals
  • Continuous Fourier transform and Fourier series
  • Spectrum of selected signals
  • Band-limited step function
  • Gibb's phenomenon
• Time-discrete periodic and non-periodic signals
  • Spectrum of time-discrete signals
  • Sampling and reconstruction of signals
  • Sampling theorem
• Discrete Fourier transform (DFT)
  • Time-limited and band-limited signals
  • Non-limited signals
  • Aliasing
  • DFT and periodic signals
  • Leakage effect
  • Windowing
• Linear, time invariant systems
  • Pole-zero plot
  • Impulse response
  • Time-domain behaviour
  • Linear phase
  • Minimum phase
  • All-pass filter
• Time-discrete simulation of time-continuous systems
  • Excitation-invariant transforms
  • Bilinear transform
  • Spectral overlaps
  • Reactions to broadband and narrowband signals
• Stochastic signals
  • Probability density function
  • Stationary and ergodic stochastic processes
  • Autocorrelation
  • Power density spectrum
• Influence of a linear system on a stochastic process
  • Influence on mean, variance, autocorrelation, and power density spectra of stochastic processes
  • Identification of systems with random signals
• An Introduction to adaptive filters
  • NLMS algorithm
  • System identification