Gibbs Phenomenon: Impact on kth Harmonics for Bandwidth Selection

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    Gibbs Phenomenon
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Discussion Overview

The discussion centers around the Gibbs Phenomenon and its impact on selecting the kth harmonics for bandwidth selection in the context of Fourier series analysis of square waves. Participants explore how the number of harmonics affects the representation of the signal and the implications for bandwidth efficiency.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant questions how the Gibbs Phenomenon affects the choice of the kth harmonics for accurately representing a square wave.
  • Another participant explains that increasing the number of harmonics improves the mean square difference to the original square wave but highlights that the maximum error near discontinuities does not decrease with more harmonics due to the Gibbs Phenomenon.
  • A method involving tapering or windowing the Fourier series is proposed as a way to reduce ringing artifacts associated with the Gibbs Phenomenon.
  • A participant presents a comparison of Fourier series representations with and without windowing, showing the effects on the waveform.
  • There is a discussion about the trade-off between using more harmonics for better signal representation versus the bandwidth implications, questioning whether this decision is subjective.

Areas of Agreement / Disagreement

Participants express differing views on the balance between the number of harmonics used and the resulting bandwidth, indicating that no consensus has been reached on the optimal approach for bandwidth selection in relation to the Gibbs Phenomenon.

Contextual Notes

Participants do not fully resolve the implications of using different numbers of harmonics on bandwidth and signal accuracy, nor do they clarify the specific conditions under which tapering methods are most effective.

goldfronts1
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How does the Gibbs Phenomenon effect the choice of the kth harmonics for bandwidth selection?

Basically, I have plotted a square wave using the Fourier series analysis for choosing the kth harmonics. As, I increase the kth harmonics the oscillations increase. What is the smallest harmonic i can go to that is still an accurate representation of the signal. Or does it not matter.

Confused

Thanks
 
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As the number of harmonics (N) increases the resultant waveform becomes progressively closer (in the sense of mean square difference) to the original square wave. Gibbs phenomenon refers to the fact that, despite getting ever closer in a mean squared sense, the convergence is very poor near the discontinuity (the edge) and in fact the maximum error at the "ringing overshoot" doesn't actually get any smaller as you increase N.

Tapering (instead of abruptly truncating) the truncated Fourier series (also called "windowing") is a very useful method for reducing these ringing artefacts. For example you could taper off the series with a "Hamming" type of window using something like : w(k) = 0.54 + 0.46 \cos( (k-1)\pi/(N-1) )

In other words, instead of using \sin(2 \pi (2k-1) t)/(2k-1) for the k-th term in the series you would use w(k) \sin(2 \pi (2k-1) t)/(2k-1) instead. Give it a try, you'll be surprised what a big improvement it makes.
 
The attachment shows a 20 term (sine) Fourier series for a square wave, both with and without windowing being used. The window I used was w(k) as defined in the previous post.
 

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  • windowed_fourier.png
    windowed_fourier.png
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Ok, so what's the difference if I use 60 terms, which gives me about 99% of the power in the signal versus using 20 terms which gives me about 95% of the power in the signal? When the ultimate goal is to have the smallest bandwidth. I guess the gibbs phenomenon is still present in either case, but the signal is closer to the original when you use more k-terms, but that uses up more bandwidth. Is this just a judgment call? I guess which one is best?
 
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