Properties of the Fourier transform

In summary: The Fourier series transform is a sum over all k, not the value for a single k.TL;DR Summary: Properties of the Fourier transform of two functions.
  • #1
redtree
285
13
TL;DR Summary
Properties of the Fourier transform of two functions
I was wondering if the following is true and if not, why?

$$
\begin{split}
\hat{f}_1(\vec{k}) \hat{f}_2(\vec{k}) &= \hat{f}_1(\vec{k}) \int_{\mathbb{R}^n} f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \int_{\mathbb{R}^n} \hat{f}_1(\vec{k}) f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \mathscr{F}\left[\hat{f}_1(\vec{k}) f_2(\vec{x}) \right]
\end{split}
$$
where
$$
\mathscr{F} \left[ f_n(\vec{x}) \right] = \hat{f}_n(\vec{k})
$$
 
Last edited by a moderator:
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  • #2
Your LaTex isn't rendering.
 
  • #3
jbergman said:
Your LaTex isn't rendering.
Fixed!
 
  • #4
It looks like an identity. Am I missing something?
 
  • #6
It’s not the convolution theorem in that only $$\hat{f}_2$$ is Fourier transformed.

I was told by that $$\hat{f}_1$$ cannot be moved into the integral $$\int_{-\infty}^{+\infty} dx$$ and so the equation is not accurate. I disagreed and so posted the question. It seems an identity to me too.
 
  • #7
The Fourier series transform is a sum over all k, not the value for a single k.
 
  • #8
redtree said:
TL;DR Summary: Properties of the Fourier transform of two functions

I was wondering if the following is true and if not, why?

$$
\begin{split}
\hat{f}_1(\vec{k}) \hat{f}_2(\vec{k}) &= \hat{f}_1(\vec{k}) \int_{\mathbb{R}^n} f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \int_{\mathbb{R}^n} \hat{f}_1(\vec{k}) f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \mathscr{F}\left[\hat{f}_1(\vec{k}) f_2(\vec{x}) \right]
\end{split}
$$
where
$$
\mathscr{F} \left[ f_n(\vec{x}) \right] = \hat{f}_n(\vec{k})
$$
From a certain perspective it's only true point-wise in "##\vec k##" space, so it might be misleading. I can't think of any setting off the top of my head where that equation (i.e. ##\mathscr{F}\left[\hat f_1(\vec k) f_2(\vec x)\right] = \hat f_1(\vec k)\hat f_2(\vec k)##) specifically would be useful. The identity ##\mathscr{F}\left[ f\right](\vec k) \equiv \hat f(\vec k)## can be helpful, however, when introducing Fourier analysis to the uninitiated, or improving the flow of a paper/derivation where Fourier analysis is used extensively and intermittently. In general, ##\hat f(\vec k) \hat g(\vec k) = \mathscr{F}\left[f * g\right](\vec k)##, where ##*## is the convolution operator (i.e. ##f * g(x) \equiv \int_y f(y) g(x - y)##, which can be checked with the heuristic "identity" ##\int \frac{dk}{2\pi}e^{ik\cdot x} = \delta(x)##.)
 
Last edited:
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1. What is the Fourier transform?

The Fourier transform is a mathematical operation that decomposes a function into its constituent frequencies. It converts a function from its original domain (usually time or space) to a representation in the frequency domain.

2. What are the properties of the Fourier transform?

The properties of the Fourier transform include linearity, time shifting, frequency shifting, scaling, convolution, and symmetry. These properties allow for easier manipulation and analysis of signals in the frequency domain.

3. How is the Fourier transform used in signal processing?

The Fourier transform is a powerful tool in signal processing as it allows for the analysis and manipulation of signals in the frequency domain. It is used in applications such as filtering, compression, and spectral analysis.

4. What is the difference between the Fourier transform and the inverse Fourier transform?

The Fourier transform converts a signal from the time domain to the frequency domain, while the inverse Fourier transform converts a signal from the frequency domain back to the time domain. They are essentially inverse operations of each other.

5. Can the Fourier transform be applied to any signal?

Yes, the Fourier transform can be applied to any signal as long as it is a finite, continuous, and periodic function. It is commonly used in applications such as audio and image processing, but can also be applied to non-physical signals such as financial data.

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