Fourier transform question (pretty simple, i think)

In summary, you normalized and solved for b to get pi/2, then calculated the a0, an, and bn series. You integrate these series to get the Fourier transform of F.
  • #1
holden
30
0
ok, i have a wave packet which is defined between (-pi/(2b)) and (pi/(2b)) as cos(bx), and it's zero everywhere else. here's what I've done so far:

i normalized and solved for b, getting pi/2. so now I'm thinking i should calculate the a0, an, and bn series, and add, right? and here is where I'm stuck. how do i go about doing this? what are the limits of integration? and do i just add the results together to get the final Fourier transform? any and all attempts at help is much appreciated. thanks a lot!
 
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  • #2
huh? I understand you want to write your wave packet as a Fourier integral, like so,

[tex]F(x)=\int_{-\infty}^{+\infty}G(\omega)e^{i\omega x}d\omega[/tex]

??

All you need to do is calculate [itex]G(\omega)[/itex] by doing the Fourier transform of F:

[tex]G(\omega)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}F(x)e^{-i\omega x}dx=\frac{1}{2\pi}\int_{-\frac{\pi}{2b}}^{+\frac{\pi}{2b}}cos(bx)e^{-i\omega x}dx[/tex]
 
  • #3
i'm sorry, i guess i should've been more clear. i need to write it in k space.. as in, i need an expression for phi(k) (sorry, can't get LaTeX to display it)
 
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  • #4
soo.. i don't need to normalize and solve for b?
 
  • #5
Ok, from the top. This is how it goes... You have a wave packet F(x,t) propagating in the positive x direction at speed v. In the spirit of Fourier series, you're hoping to write your wave packet as a "sum" of progressive waves of carefully chosen amplitudes: [itex]\phi(k)e^{k(x-vt)}[/itex] ([itex]\phi(k)[/itex] represents the amplitude of the wave of wave number k. It is the analogue in Fourier integrals of the coefficients [itex]c_n[/itex] in Fourier series):

[tex]F(x,t)=\int_{-\infty}^{+\infty}\phi(k)e^{ik(x-vt)}dk[/tex]

But what is [itex]\phi(k)[/itex]? You know that at t=0, the wave has the form [itex]F(x,0)=\cos(bx)[/itex] for [itex]-\pi/2b<x<\pi/2b[/itex] and 0 elsewhere. In this case, your wave packet becomes

[tex]F(x,0)=F(x)=\int_{-\infty}^{+\infty}\phi(k)e^{ikx}dk[/tex]

This is a form that is suitable for a Fourier transform. According to Fourier's inversion theorem then,

[tex]\phi(k)=\frac{1}{2\pi}\int_{-\infty}^{+\infty}F(x)e^{-ikx}dx[/tex]

[tex]\phi(k)=\frac{1}{2\pi}\int_{-\frac{\pi}{2b}}^{+\frac{\pi}{2b}}cos(bx)e^{-ikx}dx[/tex]
 
  • #6
In post #4, you're talking about normalization, this suggest that F(x,t) is intended as a wave function in the quantum mechanical sense? In that case, yes, b cannot have just about any value. For F to be a wave function satisfying the probabilistic interpretation of QM, [itex]FF^*[/itex] needs to have the properties of a probability density function. One of these is that the "sum" of the probabilities be 1. In this case,

[tex]1=\int_{-\infty}^{+\infty}FF^*dx[/tex]

[tex]1=\int_{-\frac{\pi}{2b}}^{+\frac{\pi}{2b}}cos^2(bx)dx[/tex]

What condition does that set on b?
 
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  • #7
Think I got it :) I got b as [tex]\frac{1}{\pi}[/tex] and my final equation in k space as [tex]\phi(k) = \frac {\pi}{\frac{\pi^2}{4}+k^2}e^{2ik}[/tex] .Thanks a lot for the help!
 
  • #8
I got b=pi/2, but I could be wrong. Still it's worth double checking your work I think.
 
  • #9
er, you're right. That's what I got too. I was looking at something else. Thanks so much for the help, it's truly appreciated.
 

1. What is the Fourier transform?

The Fourier transform is a mathematical operation that takes a function in the time domain and expresses it as a combination of sinusoidal functions in the frequency domain. It is commonly used in signal processing and image analysis to decompose a complex signal into its individual frequency components.

2. How is the Fourier transform calculated?

The Fourier transform is calculated using an integral equation that involves multiplying the original function by a complex exponential function and integrating over all possible frequencies. This process results in a complex-valued function in the frequency domain, which can then be graphed as a frequency spectrum.

3. What are the practical applications of the Fourier transform?

The Fourier transform has many practical applications in fields such as engineering, physics, and mathematics. It is used in signal processing to analyze and filter signals, in image analysis to enhance and manipulate images, and in solving differential equations in physics and engineering problems.

4. Is the Fourier transform reversible?

Yes, the Fourier transform is reversible. The inverse Fourier transform can be applied to the frequency domain function to retrieve the original function in the time domain. This makes it a useful tool for analyzing and manipulating signals and images.

5. Are there any limitations to the Fourier transform?

Yes, there are some limitations to the Fourier transform. It assumes that the function being transformed is periodic and that the frequency components are continuous. It also has difficulty representing functions with sharp edges or discontinuities. In addition, the Fourier transform is not well-suited for analyzing non-linear systems or functions with a wide range of frequencies.

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