Fourier Transform of Logarithmic Functions

In summary, the conversation is about finding the Fourier transforms of two distributions - log|x| and d/dx log|x|. The solution for the second distribution involves using the property of moving the Fourier transform to the test function and then using complex residuals. The person is also stuck on the first distribution as they cannot find any information on Fourier transforming a logarithm.
  • #1
tom_rylex
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Homework Statement


Find the Fourier transforms of the following distributions: log|x|, d/dx log|x|.

The Attempt at a Solution


I'm starting with the second distribution:
[tex] \langle F(pf(\frac{1}{x})),\phi \rangle = \langle pf(\frac{1}{x}),F(\phi) \rangle [/tex]
where F() is the Fourier transform, and pf is a pseudo function. I'm applying the property that let's me move the Fourier transform to the test function.
[tex] = \lim_{\substack {\varepsilon \rightarrow 0}} \left[ \int^\infty _\varepsilon \frac{1}{x} \int^\infty _{-\infty} \phi(y) e^{ixy} dy dx + \int^{-\varepsilon} _{-\infty} \frac{1}{x} \int^\infty _{-\infty} \phi(y) e^{ixy} dy dx\right][/tex]

I think the direction I'm supposed to go is to put the exp and 1/x terms together and determine the answer via complex residuals. If that's correct, I could use some help getting there.
 
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  • #2
I'm also stuck on the first problem, since I can't find any information on Fourier transforming a logarithm.
 

FAQ: Fourier Transform of Logarithmic Functions

What is the Fourier transform (log)?

The Fourier transform (log) is a mathematical operation used to transform a signal from the time domain to the frequency domain. It is a version of the Fourier transform that uses a logarithmic scale for the frequency axis, which can be useful for analyzing signals with a wide range of frequencies.

How is the Fourier transform (log) different from the regular Fourier transform?

The main difference between the Fourier transform (log) and the regular Fourier transform is the use of a logarithmic scale for the frequency axis. This allows for a more efficient representation of signals with a wide range of frequencies, as the logarithmic scale compresses the higher frequencies and expands the lower frequencies.

What are the applications of the Fourier transform (log)?

The Fourier transform (log) has many applications in fields such as signal processing, image processing, and data analysis. It is commonly used for analyzing signals with a wide range of frequencies, such as audio signals, and for filtering out unwanted frequencies from a signal.

How is the Fourier transform (log) calculated?

The Fourier transform (log) is calculated by taking the complex Fourier transform of the signal and then plotting the magnitude of the result on a logarithmic scale for the frequency axis. This can be done using mathematical formulas or with the help of software tools such as MATLAB or Python libraries.

Are there any limitations to using the Fourier transform (log)?

One limitation of the Fourier transform (log) is that it assumes the signal is periodic, meaning it repeats itself infinitely. This may not always be the case in real-world signals, and in such cases, other methods may need to be used. Additionally, the Fourier transform (log) may not be suitable for analyzing signals with sharp discontinuities or sudden changes, as it may result in inaccurate frequency representations.

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