How do I know "what Fourier transform" to use?

In summary: Fourier transforms because there are so many conventions that he thinks he got more confused each time he thinks about it.See an example, "Find the Fourier transform of $$V(t) = Ve^{iwt} \text{ if } nT \leq t \leq n(T + \tau) \text{ for } n = 0,1,...,N-1$$$$V(t) = 0 \text{ otherwise }$$Jason doesn't know what Fourier transform to apply!There is the convention ##F(w) = \int V(t) e^{iwt} dt##, but there is also ##
  • #1
LCSphysicist
646
162
Homework Statement:: .
Relevant Equations:: .

I am having a hard time thinking about Fourier transform, because there are so many conventions that i think i got more confused each time i think about it.

See an example, "Find the Fourier transform of $$V(t) = Ve^{iwt} \text{ if } nT \leq t \leq n(T + \tau) \text{ for } n = 0,1,...,N-1$$$$V(t) = 0 \text{ otherwise }$$

I don't know what Fourier transform to apply!

There is the convention ##F(w) = \int V(t) e^{iwt} dt##, but there is also ##F(w) = \int V(t) e^{-iwt} dt##.

Of course the second one would be more properly to this problem, but shouldn't both types of FT gives the same answer? Shouldn't they be equivalent?

Now, to let the things get even worst, is to talk about FT from Position to momentum. Everytime i tried to remember the expression, one new arose.

\begin{align*}
F(k) &= (2\pi)^{n/2} \int e^{-ikr} F(r) d^{n}(r) \\
f(k) &= \int d^3 x e^{-kx} f(x)
\end{align*}

I am not sure of this, but i think that all these expression are equivalent, and OK. THe problem is when the problem ask for the FT, as the one above. How the heck i know what convention i should use?

[Moderator's note: moved from homework to Calculus due to its general nature.]
 
Physics news on Phys.org
  • #3
mathman said:
Basic definition: https://en.wikipedia.org/wiki/Fourier_transform

##T(t)=\int_R f(x)e^{-2i\pi xt}dx##.
Which is the one everyone actually uses, I think. There is some ambiguity in how people deal with the ##\frac{1}{2\pi}## term in the inverse transform. Some put ##\frac{1}{\sqrt{2\pi}}## in front of both transform and inverse.

In any case, there is a burden on people to tell you which way they like to do these things. If you have to guess, I'd always guess the version above. If it's your own work, choose what works for you and tell everyone what you did (life will be easier if you choose the same thing they like too).
 
  • #4
mathman said:
Basic definition: https://en.wikipedia.org/wiki/Fourier_transform

##T(t)=\int_R f(x)e^{-2i\pi xt}dx##.
I use Fourier transforms constantly, but never that convention. The only time I would use it would be when helping answer a question here on Physics forums where the OP used that convention.

The lack of a standard is kind of a pain. My advice is to use the convention most used in whatever field you are working in.

jason
 
  • Like
Likes DaveE and FactChecker

FAQ: How do I know "what Fourier transform" to use?

How do I know which type of Fourier transform to use?

The type of Fourier transform you should use depends on the type of data you are analyzing. If your data is continuous and has a defined frequency range, you should use a continuous Fourier transform (CFT). If your data is discrete and has a finite number of data points, you should use a discrete Fourier transform (DFT). If your data is both continuous and discrete, you may need to use a combination of both transforms.

What is the difference between a Fourier transform and a Fourier series?

A Fourier transform is used to analyze continuous data in the frequency domain, while a Fourier series is used to analyze periodic data in the time domain. A Fourier series is a special case of a Fourier transform, where the data is assumed to repeat itself infinitely in time.

Can I use a Fourier transform on non-periodic data?

Yes, a Fourier transform can be used on non-periodic data. However, the results may not be as meaningful as with periodic data, as the transform assumes the data repeats itself infinitely in time. In this case, a windowing technique can be used to limit the data to a specific time interval.

How do I choose the appropriate window size for my Fourier transform?

The window size should be chosen based on the frequency resolution you require in your analysis. A larger window size will provide better frequency resolution, but at the cost of losing time resolution. It is important to balance these trade-offs based on the specific needs of your analysis.

Can I use a Fourier transform on non-numerical data?

No, a Fourier transform is only applicable to numerical data. If your data is non-numerical, you may need to convert it to a numerical format before using a Fourier transform. Additionally, the data should be evenly spaced in time for accurate results.

Similar threads

Back
Top