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DSP exam - Coggle Diagram
DSP exam
- Random Processes and Prediction
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- Spectral Analysis and Sampling
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Upsampling, downsampling, and filtering
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- System Analysis and Design
- System Functions and Properties
- Z-transform and Region of Convergence (ROC)
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- System Function and Filter Types
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- Draw the unit circle, zeros and poles in the z-plane.
- The system is stable iff the ROC includes the unit circle (\(|z|=1\)).
- The system is casual if the ROC is everything outside the radius of the outermost pole, and it is anti-casual if the ROC is everything inside the radius of the innermost pole.
- The system is minimum phase if stable, casual, all zeros inside the unit circle and no zeros or poles in right half-plane.
Magnitude response
Evaluate \(\mathcal{H}\) at \(z=e^{j\omega}\), and take the magnitude \(|\mathcal{H}(e^{j\omega})|\).
Phase response
Evaluate \(\mathcal{H}\) at \(z=e^{j\omega}\), and take the argument \(\angle\mathcal{H}(e^{j\omega})\).
Remember that frequencies in digital signals are from \(-\pi\) to \(\pi\) unless sampling rate is specified then it is from \(-\frac{f_s}2\) to \(\frac{f_s}2\) because of \(f=\frac{\omega}{2\pi}f_s\).
- Fundamental system properties (linearity, time-invariance)
A system \(\mathcal{H}\) is linear iff \(\mathcal{H}\{ax_1[n]+bx_2[n]\}=a\mathcal{H}\{x_1[n]\}+b\mathcal{H}\{x_2[n]\}\)
A system \(\mathcal{H}\) is time-invariant iff \(\mathcal{H}\{x[n]\})=y[n]\iff \mathcal{H}\{x[n-k]\})=y[n-k]\)
A system \(\mathcal{H}\) is static if it only depends on current input, while a dynamic depends on future or past inputs/outputs.
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- Impulse response derivation and verification
Given time domain, set \(x[n]=\delta[n]\) which gives you \(y[n]=h[n]\), and solve for \(h[n]\text{ for }n=0,1,\ldots\) if possible
Given a z-transform, just do the inverse z-transform, use partial decomposition if hard. This is because \(\mathcal{Z}^{-1}\{H(z)\}=h[n]\)
- Implementations and Structures
- Direct Form I and II implementations
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Quantization noise in direct, cascade, and parallel forms
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- Filtering Structures with Noise
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