Question about fourier transform

In summary: If x(t) follows a normal distribution with parameters u and sigma^2, then the Fourier transform of x(t) is also a normal distribution. So, proving it is just a matter of doing the math.
  • #1
fanxilong
3
0
hi every one!

i want to know the Fourier transform of x(t)
x(t)=exp(-t/a)*sin(a*t), where a ,b is constant

and can it be work out by matlab?

another question is :
how to proof the Fourier transform of x(t) who follows normal distribution n(u, sigm^2 ) is also normal distribution ?

thanks !
 
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  • #2
Do you have your powers correct in the exponential? x(t) doesn't look like it would have a converging FT. Note, that as t -> -infinity, x(t) is unbounded.

BTW: sin(a*t) can be written in terms of complex exponentials.
 
Last edited:
  • #3
gammamcc said:
Do you have your powers correct in the exponential? x(t) doesn't look like it would have a converging FT. Note, that as t -> -infinity, x(t) is unbounded.

BTW: sin(a*t) can be writting in terms of complex exponentials.

yes, you are right , it should be like this:
x(t)=0 for t<0
x(t)=exp(-t/a)*sin(b*t) for t>=0

and then ?
 
Last edited:
  • #4
fanxilong said:
and can it be work out by matlab?

Hi! MATLAB calculates things numerically. If you plug in numbers corresponding values for that function, then I believe the FFT ("fast Fourier transform") algorithm will spit out numbers for the Fourier transform (can't recall if you have to work out the frequencies yourself).

This is NOT what you want though. You want an analytical expression for the Fourier transform of that function, right? So my question is, what is the hang up here? In other words, where are you having a problem. Start with the *definition* of the Fourier transform, and go ahead:

[tex] X(\omega) = \mathcal{F}\{x(t)\} = \int_{-\infty}^{\infty} x(t)e^{-i \omega t}\,dt = \int_{0}^{\infty} (e^{-t/a}\sin(bt))e^{-i \omega t}\,dt [/tex]

I think I got that right, but I'd double check it if I were you.

fanxilong said:
another question is :
how to proof the Fourier transform of x(t) who follows normal distribution n(u, sigm^2 ) is also normal distribution ?

Again, what is the issue? You know the definition of a Fourier transform, right?
 

1. What is a Fourier Transform?

A Fourier Transform is a mathematical method used to analyze and decompose complex signals or functions into simpler components, typically sinusoidal waves. It is a fundamental tool in signal processing and is widely used in fields such as physics, engineering, and mathematics.

2. How does a Fourier Transform work?

A Fourier Transform works by breaking down a signal or function into different frequency components. It does this by representing the signal as a sum of sinusoidal waves with different frequencies, amplitudes, and phases. The result is a frequency spectrum that shows the contributions of each frequency to the original signal.

3. What is the difference between a Fourier Transform and a Fourier Series?

A Fourier Series is a special case of a Fourier Transform, where the signal being analyzed is a periodic function. This means that the signal repeats itself over a certain interval. In contrast, a Fourier Transform can be applied to any signal, whether it is periodic or not.

4. What are the applications of Fourier Transform?

The applications of Fourier Transform are vast and diverse. It is used in fields such as audio and image processing, communications, medical imaging, and finance. It is also widely used in solving differential equations, analyzing data in frequency domain, and filtering out noise from signals.

5. Are there any limitations to using Fourier Transform?

While Fourier Transform is a powerful tool, it does have some limitations. It assumes that the signal is stationary, meaning it does not change over time. It also assumes that the signal is linear, which may not always be the case. In addition, Fourier Transform may not be suitable for analyzing non-periodic signals with sharp spikes or discontinuities, as these can cause inaccuracies and distortions in the frequency spectrum.

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