Discrete Fourier transform in k and 1/k

Click For Summary

Discussion Overview

The discussion revolves around the concept of obtaining a frequency spectrum using the discrete Fourier transform (DFT) in terms of the variable 1/k, as opposed to the standard k. Participants explore the implications of this transformation, particularly in the context of periodic functions and specific applications such as the De Haas-van Alphen effect.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant inquires about the possibility of obtaining a frequency spectrum in 1/k, expressing uncertainty about the terminology and seeking guidance.
  • Another participant questions whether the term "frequency spectrum" refers to integer frequencies and introduces the concept of fractional frequencies based on fractional derivatives.
  • A participant clarifies their interest in mapping the DFT in k to a function that is periodic in 1/k, indicating that this might be complex.
  • One contributor expresses confusion about the meaning of "periodic in 1/k" and suggests providing equations for clarity, while also asserting that the DFT is essentially a Discrete Fourier Series.
  • A participant mentions the De Haas-van Alphen effect as a relevant example, where the magnetic moment oscillates with a period related to 1/B, suggesting a parallel to the original inquiry.
  • Another participant asks about the sampling method for the magnetic moment function and the values used in the DFT.
  • One suggestion is made to plot results against 1/k on a nonlinear scale, noting that this is a common practice in optical spectra.
  • A participant explains the process of sampling a function and computing the DFT, emphasizing the normalization of the domain and the relationship between the discrete and continuous representations.

Areas of Agreement / Disagreement

Participants express varying levels of understanding and approaches to the topic, with no clear consensus on how to effectively transform the DFT from k to 1/k. The discussion includes both supportive suggestions and requests for clarification, indicating ongoing uncertainty and exploration.

Contextual Notes

Some participants highlight the complexity of mapping the DFT in k to a function periodic in 1/k, and there are unresolved questions about the specific sampling methods and the implications of such transformations.

gnulinger
Messages
29
Reaction score
0
Say you have some function that is periodic in a parameter k. The discrete Fourier transform from a sampling may be found in the usual way, giving the frequency spectrum in k. But what if I want to find the frequency spectrum in 1/k ?

I'm not really sure what this is called, and so I've had a hard time Google searching for it. Any links or help would be appreciated. Thanks.
 
Physics news on Phys.org
Hey gnulinger.

When you say frequency spectrum are you talking about integer frequencies?

I do know that there are ways to get fractional frequencies that are based on fractional derivatives and subseqent integrals:

http://mathworld.wolfram.com/FractionalDerivative.html

But if you are talking about just having a transfer function to get something in F(1/k) instead of F(k), then I think this is going to be a bit more involved and you should probably outline the reason why you want the function in terms of 1/k as opposed to the linear transform space k.
 
chiro said:
But if you are talking about just having a transfer function to get something in F(1/k) instead of F(k), then I think this is going to be a bit more involved and you should probably outline the reason why you want the function in terms of 1/k as opposed to the linear transform space k.

I am talking about the latter, and yes, I think it will be fairly involved. I have a function that is periodic in 1/k, and I am wondering if there is some way of mapping the DFT in k to that in 1/k.
 
i know a lot about the DFT, it's definition, the theorems, how it is related to the continuous Fourier transform. but i cannot decode at all what you're talking about. what do you mean that it is "periodic in 1/k" ? try tossing up equations to be clear.

BTW, even though i get in fights about this on comp.dsp, i maintain that the DFT is nothing other than the Discrete Fourier Series. the DFT maps one discrete and periodic sequence of length N to another discrete and periodic sequence of the same length. and the inverse DFT maps it back. don't know if that answers your question.
 
Part of the problem is that I too am unclear on this subject, so it is hard for me to ask the right questions. I was hoping that someone may have heard of something related to what I was asking about, and could have pointed me in the right direction.

In the De Haas-van Alphen effect, wikipedia link, the magnetic moment of a crystal oscillates with period related to 1/B, where B is the magnetic field. The DFT would ostensibly give you a frequency spectrum in 1/B.

This is similar to what I want to do.
 
so, are you sampling the magnetic moment function of time somehow? where do the numbers that go into the DFT get set to some value?
 
The simplest way is to plot the results against 1/k on a nonlinear scale. You often see optical spectra plotted this way--the calculation is done for frequency but the plot is done against lambda.
 
marcusl said:
The simplest way is to plot the results against 1/k on a nonlinear scale. You often see optical spectra plotted this way--the calculation is done for frequency but the plot is done against lambda.

Do plot your data or the DFT against 1/k?
 
You have a function f(t) that has a Fourier transform, F(ω), that is null or almost null for |ω| > Ω. f(t) is sampled at every multiple of a given interval h to obtain a discrete signal f(n) = f(nh), where h should be < π/Ω. When you compute the DFT, F(μ), you are working on a normalized domain 0≤μ<π, but you can express it in "real" ω by multiplying by Ω or by 2π/h.
It's indifferent.
 

Similar threads

  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 11 ·
Replies
11
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 8 ·
Replies
8
Views
5K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 4 ·
Replies
4
Views
2K