Numerical Modeling and Signal Processing

What it is about

You learn methods of signal processing and numerical modeling, in particular the representation of time series in the time/space and frequency domain, filter and approximation techniques, as well as solution methods for ordinary and elliptic partial differential equations. In the end you will be able to analyze signals, design filters, apply numerical procedures and assess results.

What you will be able to do

• Represent and analyze signals in the time/space and frequency/spectrum domains
• Understand and apply autocorrelation and power spectral density
• Assess the effects of discretization (aliasing, windowing, leakage)
• Design convolution and filtering in the space/time domain as well as digital frequency filters
• Apply approximation techniques and parameter estimation
• Numerically solve ordinary and elliptic partial differential equations as well as initial value problems
• Select and apply appropriate mathematically-statistical methods for concrete problems

What the module consists of

• VorlesungDelivery of the theoretical foundations of signal processing, approximation and numerical methods
• Übungen / Praktikum (Labs)Application of the content in practical tasks and Matlab-supported laboratory experiments

Teaching method

• Vorlesung (Frontalunterricht, Tafel/Präsentation)Explains the theoretical foundations and demonstrates examples
• Laborübungen und Übungsblätter, teils GruppenarbeitDeepens understanding through practical application and problem solving, use of Matlab