Fourier Transforms by Looking at it

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Discussion Overview

The discussion revolves around techniques for performing Fourier transforms without relying on traditional integral calculations. Participants explore various methods, tricks, and the role of experience in simplifying the process of obtaining Fourier transforms, with a focus on convolution and specific examples involving shapes like square waves and Gaussians.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • Some participants suggest that convolution is a helpful method for approximating Fourier transforms, as illustrated by the example of convolving square waves to produce a triangle wave.
  • Others recount anecdotal experiences of individuals who can perform Fourier transforms quickly, implying that this ability may stem from extensive practice and familiarity.
  • One participant compares the process of learning Fourier transforms to mastering basic arithmetic, suggesting that experience allows for faster mental calculations.
  • A participant introduces a specific technique involving the convolution of a Gaussian with delta functions to derive the Fourier transform of multiple Gaussian shapes, noting that this can generalize to periodic signals.

Areas of Agreement / Disagreement

Participants express a range of views on the methods for performing Fourier transforms, with no consensus on a single best approach. The discussion includes both anecdotal evidence and technical strategies, indicating a variety of perspectives on the topic.

Contextual Notes

Some claims rely on assumptions about the participants' experiences and the applicability of certain techniques, which may not be universally valid. The discussion does not resolve the effectiveness of different methods or the extent to which experience influences ability.

Mindscrape
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Some people are able to do Fourier transforms without doing a single integral (i.e. just looking at a function). After thinking about it for a while I discovered that convolution is really helpful. For example, because two square waves are convolved to make a triangle wave, then the Fourier transform will be the Fourier transform of the square multiplied by the Fourier transform of the square. I am sure there are other methods though, does anybody know of anything better or any good tutorials of approximating the Fourier transform of functions?
 
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i've heard of a former prof at my old university who could do stuff like that. people always seemed to just think he was some sort of calculating freak because when talking to someone calculating a Fourier transform never seemed to slow him down. if there's a trick to it i think it would be cool to know.
 
It may just be experience. Like the way most of us can do integrals, without having to go through pen/paper or even acknowledge intermediary steps.
 
How many Fourier transforms have you done in your lifetime? A couple dozen? How many Fourier transforms do you think he has done in his lifetime? :smile:

In some sense, it's like ordinary arithmetic. If you don't even know your addition tables, it's hard to add things. When you learn your addition tables, you can add things a lot faster, and sometimes in your head. And if you do lots of addition (but not mindlessly), or go looking for them, you can pick up tricks that can let you add faster.
 
hmm working at it... that's a good trick :wink: :-p
 
That reminds me of a Simpsons episode.

Bart Simpson was once compelled to find a way to distract himself from a disturbing scene, and the only option was to repeatedly read off the names of the planets off of a nearby poster.

He later got an A on an astronomy test. He remarked that the answers were stuck in his brain; it was a whole new kind of cheating!
 
Another useful trick is when you have more than one copy of a single shape. For example, consider two gaussians side by side. You can obtain this shape by convolving a single gaussian with two delta functions centered at, say, -T and +T. The Fourier transform of these is just eiwT+e-iwT=2cos(wT), and so the transform of two gaussians is just the transform of a single gaussian modulated by a cosine function. This readily generalizes to more than two copies of the shape (and you can even take the infinite limit to recover the transform of a periodic signal), or copies with different amplitudes.
 

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