The students are able to record signals and data in the laboratory with sensors and measurement devices. They can capture and save measured values from sensors with the MATLAB software.
They know characteristic signal parameters in the time and frequency range and are able to determine them by means of calculation or with software support (amplitude parameters, energy and power, periodicity, etc.).
For the calculation of spectra, the students use software (e.g. MATLAB) to apply the FFT and apply window functions to reduce the leakage effect. The students understand the concept of autocorrelation, which they apply to calculate the power density spectrum for non-deterministic signals and to recognize bit patterns in noisy signals.
Students are able to present data graphically in different ways and interpret it.
They understand the terms variance, covariance and correlation.
Students can create bivariate and multivariate regression models with the help of suitable software and evaluate them statistically for their quality.

None

The module covers the following topics/contents:

• Acquisition of measured values and data
• Analysis of continuous-time signals (characteristics in the time domain, spectral analysis by Fourier series and Fourier transform)
• A/D conversion (sampling and aliasing, quantization noise)
• Analysis of discrete-time signals (characteristics in the time domain, Spectral analysis by discrete Fourier transform, leakage effect, windowing)
• Analysis of random signals (stochastic processes, noise, autocorrelation function, power spectral density, SNR)
• Use of software (e.g. MATLAB) as a simulation and calculation tool
• Exploratory data analysis
• Variance and covariance, correlation
• Bivariate and multivariate regression analysis
• Model building and evaluation of the quality of the model

Recommended literature:

• M. N. O Sadiku, W. H. Ali, Signals and Systems: A Primer with MATLAB®, CRC Press, 2020
• D. Sundararajan, Digital Signal Processing, Springer, 2022
• A. V. Oppenheim, R. W. Schafer, Digital Signal Processing, 3rd ed., Pearson, 2021
• D. Sundararajan, A Practical Approach to Signals and Systems, Wiley, 2008
• A. D. Poularikas, Discrete Random Signal Processing and Filtering Primer with MATLAB, CRC Oress, 2019

Lecture with integrated calculation exercises.
Programming exercises with MATLAB (in groups of max. 20 students).
Guided exercises in the lab (in groups of max. 20 students).
Individual assignments (information & submission on Moodle platform): Calculation tasks, protocols, answering questions on the self-learning material provided.

Integrated module examination
Immanent examination character: Participation and elaborated protocols for MATLAB and laboratory exercises, elaborations for self-study tasks, written examination