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
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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
$$
\mathscr{F} \left[ f_n(\vec{x}) \right] = \hat{f}_n(\vec{k})
$$
 
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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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FAQ: Properties of the Fourier transform

What is the Fourier transform?

The Fourier transform is a mathematical operation that transforms a time-domain signal into its frequency-domain representation. It decomposes a function or signal into its constituent frequencies, allowing analysis of the frequency components and their amplitudes.

What are the key properties of the Fourier transform?

The key properties of the Fourier transform include linearity, time shifting, frequency shifting, scaling, and convolution. These properties facilitate the analysis and manipulation of signals in both time and frequency domains.

How does the Fourier transform relate to convolution?

The Fourier transform of the convolution of two signals is equal to the product of their individual Fourier transforms. This property, known as the Convolution Theorem, simplifies the process of analyzing linear systems and signals in the frequency domain.

What is the significance of the duality property in the Fourier transform?

The duality property states that if a function has a Fourier transform, then its Fourier transform also has a Fourier transform that is related to the original function. This property highlights the symmetric relationship between the time and frequency domains.

How does the Fourier transform handle non-periodic signals?

The Fourier transform can be applied to non-periodic signals by representing them as a superposition of sinusoidal functions over an infinite interval. This results in a continuous spectrum of frequencies, allowing for the analysis of aperiodic signals in the frequency domain.

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