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| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*)Digital signal processing nowadays is present in almost every consumer electronics device, vehicle, industrial machinery, et cetera. This lecture provides an introduction in the field of digital signal processing. Main topics are discrete-time signals and linear time-invariant systems, their representations and mathematical treatment in the time and frequency domain. Furthermore, important signal transforms like wavelet transforms and the discrete cosine transform are discussed. Combined with the exercise classes in which Matlab is frequently used the students should develop a good understanding of fundamental signal processing methods for further usage in their studies.
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| Lehrinhalte |
(*)- Analog Signals and Systems (Introduction incl. Fourier transform)
- Sampling, Sampling Theorem
- Discrete Time Signals
- Important Signals
- Correlation
- Spectral Representation of Discrete Time Signals: DTFT, DFT, FFT, Short time Fourier transform
- Digital Filters: Analysis and Design of FIR and IIR Filters
- Further Signal Transforms
- Wavelet Transform
- Discrete Cosine Transform
- Time-Frequency-Distributions
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| Beurteilungskriterien |
(*)Written Exam
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| Lehrmethoden |
(*)- Lecture style with powerpoint slides
- Matlab based demonstrations
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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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