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| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2022W |
| Ziele |
(*)Students know and understand the basics of analog as well as discrete-time signals and systems qualitatively and mathematically and can apply them to fundamental problems. They are able to
- plot signals in time- and frequency domain,
- explain the sampling theorem,
- apply the convolution operation,
- derive the spectrum of a signal numerically with the help of the DFT and the FFT,
- describe the behavior of discrete-time LTI systems in time- and frequency domain,
- analyze and design digital FIR and IIR filters, and
- apply the correlation operation.
Students know and understand the basics of adaptive filters, and the basics of time-frequency analysis.
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| Lehrinhalte |
(*)- Basics of Analog Systems Theory
- Discrete Time Signals Fundamentals
- Discrete Time LTI Systems Fundamentals
- Fourier Transform of Discrete Time Signals (DTFT)
- Sampling and Reconstruction
- Correlation
- Spectral Analysis of Discrete Time Signals (DFT, FFT, Short Time Fourier Transformation, Applications)
- z Transform
- Digital Filters (FIR, IIR)
- Vector Matrix Representations of Discrete Time Signals and LTI Systems
- Adaptive Filters
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| Beurteilungskriterien |
(*)Written Exam
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| Lehrmethoden |
(*)Lecture using slides and blackboard, Matlab based presentations
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)- Lecture Slides
- Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing: Pearson New International Edition, Pearson Education Limited 2014.
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| Lehrinhalte wechselnd? |
Nein |
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