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5 Methods for Discrete Processing and Analysis of Biosignals

Discrete Processing of Signals

1.

In what other case can the alias effect occur besides A/D conversion? How can its

occurrence be prevented? What losses do you have to accept?

2.

Explain the amplitude spectrum of a discrete time series. Which aspects have to be

considered in the interpretation? How do you calculate the frequency axis, what

is the significance of the individual lines?

3.

The amplitudes in the DFT spectrum from Matlab do not correspond to the amp-

litudes of the signal components in the time domain. How do you explain this?

Which operation must always be performed for the adjustment?

4.

What is meant by frequency resolution? How can the frequency resolution of a

spectrum be improved?

5.

What is the highest frequency that can be represented in the spectrum?

6.

In the short-time Fourier transform, a signal window with a width of 100 ms is

spectrally investigated. What frequency resolution can be achieved?

7.

What is the advantage of the wavelet transform over the short-time Fourier trans-

form?

8.

A windowing is a multiplication in the time domain, which operation with what

has to be done in the frequency domain to get the same result?

9.

Explain the term impulse response. What is the Fourier-transform of the impulse

response called?

10. When is it best to represent a signal in the time-frequency domain?

11. Is it advantageous to represent a sinusoidal signal with constant frequency and

amplitude in the time-frequency domain?

12. What is the most important difference between the Short-Time Fourier Transform

(SFTF) and the Wavelet-Transform?

13. What is the advantage of the discrete wavelet transform (DWT) over the continu-

ous wavelet transform (CWT)?

14. Why do you use window functions for the SFTF, the DWT and the CWT?

15. Which averaging methods do you know and what can they be used for?

LTI systems and Digital Filters

1.

What is a filter, what are its ideal characteristics?

2.

Which filter characteristics do you know? How can they be represented in the fre-

quency domain?

3.

Why do we work mainly with digital filters? Where are analogue components in-

dispensable?

4.

What do the terms stable, causal, linear, time-invariant mean, and why are they

so important when creating digital filters?

5.

What is an IIR filter and what is an FIR filter? What is the difference?

6.

How does a moving average filter affect signals? What extreme cases of the win-

dow can you imagine, and what would the result of the filtering be in each case?