Convolution and space-time Fourier transform

In summary, the question is about finding the space-time Fourier transform of G⊗(∂nu/∂tn) and whether the relation G⊗(∂nu/∂tn) = ∂n(G⊗u)/∂tn holds true. The answer can be found by applying the properties of Fourier transforms and understanding the interchangeability of differentiation and integration. However, for more complex functions, generalized Fourier transforms may need to be used.
  • #1
shekharc
2
0
Hi,

I have a general function u(x,y,z,t). Then, (1) what would be the space-time Fourier transform of G⊗(∂nu/∂tn) and (2) would the relation G⊗(∂nu/∂tn) = ∂n(G⊗u)/∂tn hold true? Here, note that the symbol ⊗ represents convolution and G is a function of (x,y,z) only (i.e. it does not depend on time).

Any answer would appreciated. Thanks!

-Chandra
 
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  • #2
Chandra,

Most of your question is answered by applying the properties of Fourier transforms, all of which are, for exmaple, at:
http://fourier.eng.hmc.edu/e101/lectures/handout3/node2.html
including the convolution theorem (in link above and in this link: https://en.wikipedia.org/wiki/Convolution_theorem )
and a knowledge of the Fourier transform of a derivative.
shekharc said:
would the relation G⊗(∂nu/∂tn) = ∂n(G⊗u)/∂tn hold true?
Here you are essentially asking a question about interchanging differentiation and integration. As an engineer, I typically deal with "nice" functions for which this holds (indeed, I assume it holds!), but it doesn't always hold for any choice of functions. I cannot help you much more than that - sorry!
jason
 
  • #3
Dear Jason,

Thanks for your suggestions. In fact, I was a bit confused because of involvement of both space and time in the Fourier transform. Anyway, I did it (hopefully correctly) by taking Fourier transforms two times; first, I took the transform with respect to space, and then with respect to time. As for the 2nd question, "u" is not a very nice function--it does not converge to zero when x,y,z-->INFINITY. So, currently I am looking at whether I can use generalized Fourier Transforms to deal with it.
 

1. What is convolution?

Convolution is a mathematical operation that combines two functions to produce a third function. It is commonly used in signal and image processing to describe the relationship between an input signal and an output signal. In simpler terms, convolution is a way to blend or mix two functions together.

2. What is the purpose of a space-time Fourier transform?

A space-time Fourier transform is a mathematical tool used to analyze signals or functions in both space and time domains. It allows us to break down a signal into its individual frequency components and understand how they are distributed in both space and time. This can be useful in understanding the behavior and characteristics of complex signals.

3. How is convolution related to the space-time Fourier transform?

Convolution and space-time Fourier transform are closely related. In fact, the space-time Fourier transform of a convolution between two functions is equal to the product of the individual space-time Fourier transforms of those functions. This relationship is known as the convolution theorem and is a fundamental concept in signal processing.

4. What is the difference between 1D, 2D, and 3D convolution?

The number of dimensions in convolution refers to the number of independent variables in the functions being convolved. In 1D convolution, there is only one independent variable, typically representing time. In 2D convolution, there are two independent variables, representing both space and time. In 3D convolution, there are three independent variables, representing space, time, and another dimension (such as color in an image).

5. What are some real-world applications of convolution and space-time Fourier transform?

Convolution and space-time Fourier transform have many practical applications in various fields, including signal processing, image processing, and physics. Some examples include noise reduction in audio and image signals, image and video compression, and analysis of seismic data in geophysics. They are also essential tools in understanding and describing the behavior of physical systems and phenomena, such as electromagnetic waves and quantum mechanics.

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