Probelm getting started with Fouriertransforms

  • Thread starter toofle
  • Start date
In summary, the formulas for the Fourier transform are off-putting and the second one will not be equivalent to the first even though they are using the same linearity.
  • #1
toofle
20
0
I have trouble getting started with Fouriertransforms. I have access to Mathematica but don't obtain the same results(see below).
Isn't my reasoning correct?

Example:
Fouriertransform 1/(t^2+1):
F19: e^(-a|t|),a>0 <-> 2a/(a^2+w^2)

1/(t^2+1) = 1/2 * 2/(t^2+1)
Linearity => f(t) = 1/2*g(t), g(t) = 2/(t^2+1)

Symmetry(F10)+ f19 => gtop(w) = e^-|w| * 2*Pi

=> ftop(w) = 1/2*2*Pi*e^(-|w|)
<=> ftop(w) = Pi*e^(-|w|)

Answer: ftop(w) = Pi*e^(-|w|)

But Mathematica:
FourierTransform[1/(1 + t^2), t, \[Omega]]
E^-Abs[\[Omega]] Sqrt[\[Pi]/2]

Where does the root come from?
 
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  • #2
Welcome to PF, toofle! :smile:

The Fourier transform and the inverse Fourier transform have normalization constants ([itex]1 \over \sqrt{2\pi}[/itex]).
There are different conventions where to put these constants.

If you look in Mathematica you'll see that the FourierTransform function has optional FourierParameters specifying what to do with these normalization constants.
 
  • #3
Thanks that clears some things up.

But is:
-(1/6) E^(-3 \[Omega]) Sqrt[\[Pi]/2] (E^(
5 \[Omega]) (-3 + 2 E^\[Omega]) HeavisideTheta[-\[Omega]] + (2 -
3 E^\[Omega]) HeavisideTheta[\[Omega]])

Equivalent to:
1/2 E^(-2 Abs[\[Omega]]) \[Pi] + -(1/3) E^(-3 Abs[\[Omega]]) \[Pi]


?
Because if I split into partial fractions and use linearity I get the second one without norming.
So it seems so but it is kind of difficult to see.
 
  • #4
Let's first see if I can make some sense of your formulas.
As they are, they are rather off-putting to analyze.
Let me (just this once) set them in LaTeX first.


toofle said:
Thanks that clears some things up.

But is:
[tex]-{1 \over 6} e^{-3 \omega} \sqrt{\pi \over 2} \left(e^{
5 \omega} (-3 + 2 e^\omega) \Theta(-\omega) + (2 -
3 e^\omega) \Theta(\omega) \right)
[/tex]

Equivalent to:
[tex]{1 \over 2} e^{-2 |\omega|} \pi + -{1 \over 3} e^{-3 |\omega|} \pi[/tex]


?
Because if I split into partial fractions and use linearity I get the second one without norming.
So it seems so but it is kind of difficult to see.

Let me know if I have your formulas right...?
 
  • #5
Looking at the formulas I can see immediately that they will not be equivalent.

I can't see straight away if the e-powers match up, but I can see that the factor of pi cannot be equivalent.
The first one has a square root of pi, while the second has a factor of pi.
So it seems likely there is a factor of about [itex]\sqrt{2\pi}[/itex] between them, which is a normalization constant.
 

What is a Fourier transform?

A Fourier transform is a mathematical tool used to decompose a complex signal into its individual frequency components. It converts a signal from the time or spatial domain to the frequency domain, allowing for analysis and manipulation of the signal's frequency content.

Why are Fourier transforms important?

Fourier transforms are important because they allow us to analyze and understand complex signals in terms of their individual frequency components. They have a wide range of applications in various fields such as signal processing, image analysis, and data compression.

How do I get started with Fourier transforms?

To get started with Fourier transforms, it is important to have a basic understanding of mathematical concepts such as trigonometry, complex numbers, and calculus. You can then learn about the theory and applications of Fourier transforms through textbooks, online resources, or courses.

What are some common challenges when using Fourier transforms?

Some common challenges when using Fourier transforms include understanding the underlying mathematical concepts, choosing the appropriate type of Fourier transform for a specific problem, and dealing with issues such as aliasing and noise in the data.

Are there any software tools available for performing Fourier transforms?

Yes, there are several software tools available for performing Fourier transforms, such as MATLAB, Python's NumPy library, and Mathematica. These tools have built-in functions for performing Fourier transforms and also provide visualization capabilities for better understanding of the results.

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