Conceptual Problem with Convolution Theorem

In summary, the convolution theorem can be applied to solve the second equation in the equation for the Fourier Transform of p(x)u(x) but the first equation is different and requires a different convention for the Fourier Transform.
  • #1
thrillhouse86
80
0
Hi - I'm trying to work out the following convolution problem:

I have the following integral:
[tex]
\int^{\infty}_{-\infty}p(x)U(x)e^{-i \omega x}dx
[/tex]

Where p(x) is any real function which is always positive and U(x) is the step function

Obviously this can easily be solved using the convolution theorem because I have
[tex]
\mathcal{F}[p(x)U(x)] = P(\omega)*U(\omega)
[/tex]

The problem I having is with the very similar integral but the exponential is now positive:
[tex]
\int^{\infty}_{-infty}p(x)U(x)e^{+i \omega x}dx
[/tex]

I don't know how to deal with this integral - even though I suspect I can use the convolution theorem on it.

I've tried to derive the convolution theorem for both exponentials but I get stuck at the stage:

[tex]
\int^{\infty}_{-\infty} \int^{\infty}_{-\infty}P(\omega')U(\omega-\omega')d\omega'e^{-i\omega x}d\omega = p(x)u(x)
[/tex]

And:

[tex]
\int^{\infty}_{-\infty} \int^{\infty}_{-\infty}P(\omega')U(\omega-\omega')d\omega'e^{+i\omega x}d\omega = p(x)u(x)
[/tex]

My problem is this:
If I define the [tex] \int^{\infty}_{-\infty} f(x) e^{-i\omega x}[/tex] integral as the Fourier Transform - then I can write the second equation as:
[tex]
\mathcal{F}^{-1}[P(\omega)*U(\omega)] =p(x)u(x)
[/tex]
And thus applying the inverse Fourier operator to both sides I get:
[tex]
[P(\omega)*U(\omega)] =\mathcal{F}[p(x)u(x)]
[/tex]

But If I set up this convention for my Fourier Transform how do I deal with the first equation:
[tex]
\int^{\infty}_{-\infty} \int^{\infty}_{-\infty}P(\omega')U(\omega-\omega')d\omega'e^{-i\omega x}d\omega = p(x)u(x)
[/tex]

This isn't a Fourier Transform operation anymore - its slightly different. Is there anything I can do from here to show what:
[tex]
\int^{\infty}_{-\infty} p(x)u(x)e^{i\omega x} dx
[/tex]

is in terms of convolutions ?
 
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  • #2
Think about real and imaginary parts of the integral separately.

p(x) and U(x) are real functions.

[itex]e^{i\omega x} = \cos \omega x + i \sin \omega x[/itex]

[itex]e^{-i\omega x} = \cos \omega x - i \sin \omega x[/itex]

That's all you need to answer the question.
 
Last edited:
  • #3
Thanks AlpehZero - I guess it always helps to go back to the fundamental definitions ...
 

1. What is the Convolution Theorem?

The Convolution Theorem is a mathematical concept that relates to the process of convolution. It states that the Fourier transform of the convolution of two functions is equal to the product of their individual Fourier transforms. In simpler terms, it allows us to analyze the frequency components of a convolution in terms of the frequency components of the individual functions.

2. What is the significance of the Convolution Theorem in science?

The Convolution Theorem is a fundamental concept in many fields of science, especially in signal processing and image analysis. It allows us to simplify and speed up calculations involving convolutions, which are common in these areas. It also helps in understanding the frequency content of signals and images, which is crucial in many applications.

3. What are the potential problems with the Convolution Theorem?

One potential problem with the Convolution Theorem is that it assumes the functions involved are continuous and infinite. In real-world applications, this is not always the case, and the theorem may not hold. Additionally, the Convolution Theorem may be limited in its applicability to non-linear systems or non-stationary signals.

4. Can the Convolution Theorem be applied to any type of function?

No, the Convolution Theorem can only be applied to functions that are integrable and have a well-defined Fourier transform. This means that the functions must have finite energy and cannot have any discontinuities or infinite spikes. In some cases, modifications or extensions of the theorem may be used to overcome these limitations.

5. How is the Convolution Theorem used in practice?

The Convolution Theorem is used extensively in various scientific fields, including signal and image processing, communication systems, and physics. It is also used for solving differential equations, filtering and deconvolution of signals, and analyzing the frequency response of systems. In practice, the theorem is often implemented through fast Fourier transform algorithms, making it a powerful and widely used tool in scientific research and applications.

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