Basic question on the FFT in matlab

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Discussion Overview

The discussion revolves around the Fast Fourier Transform (FFT) in MATLAB, specifically addressing the interpretation of FFT outputs for a cosine function and the implications of finite sample durations on frequency representation. Participants explore the relationship between time-domain signals and their frequency-domain representations, as well as challenges related to sampling and windowing techniques.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Homework-related

Main Points Raised

  • One participant notes that the FFT output represents frequency contributions, questioning why a cosine function appears to have contributions from various frequencies despite Fourier theory suggesting only its own frequency should contribute.
  • Another participant points out that a regular sinusoid has infinitely many cycles, while the cosine function in question has a finite number of cycles, which may affect the FFT output.
  • A later reply suggests that increasing the range of the time variable improves the representation of the sinusoid, although the reasoning behind this is not fully understood by all participants.
  • One participant explains that the finite duration of the sinusoid leads to the need for multiple sinusoids to accurately represent the segment, and that a longer snippet increases frequency purity.
  • Another participant discusses how to relate the x-axis values to frequency by determining the period of the sinusoid and calculating frequency as the inverse of the period.
  • One participant mentions the importance of windowing the samples to avoid discontinuities that can introduce high-frequency artifacts in the FFT output.
  • Several posts request assistance with unrelated topics, such as numerical solutions for Bessel's equations and Fourier transforms in C programming, which diverges from the main FFT discussion.

Areas of Agreement / Disagreement

Participants express differing views on the implications of finite sample durations and the interpretation of FFT results. There is no consensus on the best approach to resolve the issues raised, particularly regarding the relationship between time-domain and frequency-domain representations.

Contextual Notes

Participants highlight limitations related to sample sizes, the effects of non-integer wavelengths on FFT outputs, and the necessity of windowing techniques to improve frequency resolution. These factors remain unresolved in the discussion.

O.J.
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Homework Statement



From what I understand, the array output of the FFT of a function in time represents the frequency contributions of different frequencies with the first output coming from 0 freqeuncy, 2nf from the 1st fundamental frequency and so on.. if so, then why does this code(which is for plotting the fft against frequency) give contributions from various frequencies to the cosine function when we learned in Fourier theory that the only frequency contributiong to a sinusoid are those of its own frequency:
t = -20:20;
x= cos (6*pi*t/13+pi/3);
y = fft(x);
tn = (-length(y)+1)/2 : (length(y)-1)/2;
stem (tn, y);


Homework Equations





The Attempt at a Solution

 
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O.J. said:

Homework Statement



From what I understand, the array output of the FFT of a function in time represents the frequency contributions of different frequencies with the first output coming from 0 freqeuncy, 2nf from the 1st fundamental frequency and so on.. if so, then why does this code(which is for plotting the fft against frequency) give contributions from various frequencies to the cosine function when we learned in Fourier theory that the only frequency contributiong to a sinusoid are those of its own frequency:
t = -20:20;
x= cos (6*pi*t/13+pi/3);
y = fft(x);
tn = (-length(y)+1)/2 : (length(y)-1)/2;
stem (tn, y);


Homework Equations





The Attempt at a Solution


Howdy!

How many cycles does a regular sinusoid have? In contrast, how long is yours?
 
sup!
well, a regular sinusoid has... infinitely many cycles. and mine has almost 3 cycles in that range of t.
 
Ok, after playing with it, I see what you're saying, the larger the range of t, the closer it is to the real thing (although I don't see why). but, how to relate the values on the x-axis to frequency?
 
O.J. said:
Ok, after playing with it, I see what you're saying, the larger the range of t, the closer it is to the real thing (although I don't see why). but, how to relate the values on the x-axis to frequency?

Precisely! When you're doing a Fourier Transform, you're breaking down your function and trying to represent it using sinusoids of infinite duration. Since you only have a small snippet of a sinusoid, you need a bunch of sinusoids to add together so that you get the sinusoid segment, and 0 everywhere else. And of course, the longer your snippet, the closer it is to an infinite-duration sinusoid, and the higher its frequency purity (i.e. the power of the signal comes almost entirely from its fundamental frequency, and none of the higher ones).

Regarding your second question, if you know the period T (which you can read off of the graph), then you just take 1/T as the frequency (this assumes that you're not working in degrees, or radian frequency):
http://en.wikipedia.org/wiki/Frequency

What blew my mind--this finite duration of sinusoid snippets and their spectral purity--is that this seemingly mathematical quirk actually has ramifications in physics, especially in regards to the coherence length of light, and why there's always some spectral width in any light source.
 
Any one could help me with , numerical Fourier transform of bessel's function using FFT in C language
 
also anyone could help me with,invistigate of bessel'e equation using runge-kutta
 
Welcome to Physicsforums, Nada!

In the future, please make a new thread for a topic unrelated to the one at hand. I'm not a computer modeling guy, but you might be able to find something in the book Numerical Recipes in C, probably available in your library (you can also probably find a cheap used copy):
http://www.nr.com/
 
OJ,

Making your sample size bigger increases your resolution. In other words, with more samples, you can resolve smaller details on the plot.

The real problem you're having is that your sample is a non-integer number of wavelengths. The FFT acts on your input as if it were copied end-to-end for all time, so its beginning and end better match up. If the ends do not match up, the discontinuity will appear as high-frequency garbage, which will ruin your plot.

What you need to do is window your samples, so that they go to zero at both ends. Then you can safely use the FFT. Look up the Hamming window, for example:

http://en.wikipedia.org/wiki/Hamming_window#Hamming_window_.5Bnote_1.5D]Hamming window

- Warren
 
  • #10
hello

--------------------------------------------------------------------------------

could you please help me with
numerical solution by runge-kutta for bessel's equation with C
Also
how i can write programme to compute the series(bessel function) Jn(x) for n=0.1,4 for range of x with C
also numerical Fourier transfom of bessel function using FFT with C too

I would be so grateful if you could if not how can I ask by this website
 

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