Fourier Transform: best window to represent function

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Discussion Overview

The discussion revolves around the selection of window functions for representing functions in frequency spectra analysis, particularly in the context of Fourier transforms. Participants explore the characteristics of different window functions and their implications for data analysis.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant presents a specific function, f(t) = cos(ω₀t), and its Fourier transform, discussing the implications of using different window functions such as top-hat and ramp functions.
  • Another participant shares a resource from the PicoScope USB Oscilloscope Manual, suggesting that it provides a summary of various windowing functions relevant to frequency domain analysis.
  • It is noted by one participant that different data analysis types favor different window functions, indicating that there is no universally best option, and that the choice depends on the specific features of the dataset being analyzed.
  • A later reply acknowledges the complexity of the choice of window functions, suggesting that there are nuances that may not have been initially considered.

Areas of Agreement / Disagreement

Participants generally agree that the choice of window function is nuanced and context-dependent, with multiple competing views on which window function may be preferable for specific analyses. The discussion remains unresolved regarding which window function is definitively better.

Contextual Notes

Limitations include the lack of consensus on the best window function and the dependence on specific characteristics of the datasets being analyzed. Participants have not fully explored the mathematical implications of their choices.

Who May Find This Useful

Individuals interested in frequency domain analysis, particularly those working with Fourier transforms and windowing functions in data analysis contexts.

Master1022
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Homework Statement
We have a time-continuous signal [itex] f(t) [/itex]. A new signal [itex] g(t) [/itex] is created by either by multiplying [itex] f(t) [/itex] with a top-hat function (half-width [itex] \frac{T}{2} [/itex]) or a ramp function (half-width [itex] T [/itex]), both with amplitude 1. Which window should we, using qualitative judgement, choose to have a better representation of [itex] F(\omega ) [/itex]
Relevant Equations
Fourier transform
Hi,

I was hoping to gain more insight into these window questions when looking at frequency spectra questions. I don't really know what makes windows better than one another.

My attempt:
In the question, we have f(t) = cos(\omega_0 t) and therefore its F.T is F(\omega ) = \pi \left( \delta(\omega - \omega_0 ) + \delta(\omega + \omega_0) \right). For the window functions, we have a top-hat function with a transform of:
\frac{ T sin(\omega T / 2)}{\omega T / 2} and a ramp function with transform:
\frac{ 4 sin^2 (\omega T / 2)}{\omega^2 T}

To find the effect of multiplying the time signals, we can carry out convolution in the time domain and utilize the sifting property of the delta function.

I can see that we basically have the choice of sinc or sinc^2. Perhaps the ramp function will be better as it has smaller peripheral pulses (due to the sinc function being squared). Also, I notice that the ramp function is the result of convolving the top-hat function with itself.

I am not sure what other aspects I should be looking out for.

Any help is greatly appreciated.
 
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Master1022 said:
I was hoping to gain more insight into these window questions when looking at frequency spectra questions. I don't really know what makes windows better than one another.
Here is some information on various Windowing Functions from the PicoScope USB Oscilloscope Manual (I'm using one right now in one of my test setups at work to do FFTs and frequency domain analysis of powerline communication network waveforms). It's a good brief summary of Windowing functions, and should give you some good search terms for further searching/reading:

https://www.picotech.com/download/manuals/picoscope6-oscilloscope-software-users-guide.pdf

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Data analysis types each have their favorites, which means that none of them are the best. They each have their own pros and cons. Ideally, if you care, you will need to analyze the effects of each and pick the best for your particular analysis. The answer lies in being clear about what features of your data set you really care about most.
 
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@berkeman and @DaveE - thank you for your replies! There was more nuance to the choice than I previously thought.
 
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