Learning objectives
Signal processing processes in digital form require a new form of abstraction for the analytical description of signals and processes. The students will be introduced to this type of abstraction, and will achieve the competence to formulate problems in the field of signal processing self-reliantly and to describe solution methods. They will be able to master the basics such as sampling, spectral analysis and filtering for the following lecture sequence, as well as the conversion of practical problems of signal processing to suitable algorithms.
Content
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| Mathematical Representation of Sampling: Model network of ADC and DAC, frequency folding and aliasing, sampling of lowpass and bandpass signals, reconstructing the analog signal from the digital output, Hilbert transform |
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| FIR-Filters, IIR-Filters: Difference equation, frequency response, FIR filters with linear phase, bilinear transformation, Z-transform, allpass filters, pole-zero pattern, mapping from the s plane to the z plane, stability conditions, direct form and canonical form, Applications: CD-player with oversampling, Hilbert transformer |
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| Quantization Effects: Signal quantization, oversampling and decimation, interpolation, noise shaping, truncation noise, limit cycle oscillation, Applications: Sigma delta ADC, bit stream DAC |
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| Signal Analysis: Spectrum analysis using the DFT, FFT, DCT, algorithms, short-time Fourier analysis, convolution of sequences, Wavelet analysis, waveform encoding (LPC, RELP, CELP o. a.) |
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