Convolution theorem; integration

In summary, the conversation involved Fourier transforming various equations and using the results to prove the convolution theorem and find total displacement. The expert summarizer noted some mistakes and suggested using ω = 0 to simplify part (e).
  • #1
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Homework Statement



Part(a)/(b)/(c): Fourier transform the following:

Part(d): Prove the convolution theorem

Part (e): Find total displacement

convolution1.png

Homework Equations


The Attempt at a Solution



Part(a)
[tex]f = \frac {1}{sqrt{2\pi}} \int_{-\infty}{\infty} F e^{-iωt} dω[/tex]

[tex]\frac {1}{\sqrt{2\pi}} \int_{-T}{T}[/tex]
[tex]= \frac{1}{iω\sqrt{2\pi}} [e^{iωT} - e^{-iωT}] [/tex]
[tex] = \sqrt{\frac{2}{\pi}} \frac {sin (ωt)}{ω}[/tex]

Part (b)
[tex]F_2 = \frac{1}{\sqrt{2\pi}} \int_{-\infty}^{0}e^{-at}e^{iωt} dt + \frac{1}{\sqrt{2\pi}} \int_{0}^{\infty}e^{-at}e^{iωt} dt [/tex]
[tex] = -\frac{2a}{\sqrt{2\pi}(a^2 + ω^2)} [/tex]

Part(c)

Using result from (b):
[tex] F_2 = -\frac{2a}{\sqrt{2\pi}(a^2 + ω^2)} [/tex]

Let a = 1,

[tex]e^{-|t|} = -\frac{1}{2\pi} \int_{-\infty}^{\infty} \frac {2}{1+ω^2} e^{-iωt} dω [/tex]

[tex] t→-t, [/tex]
[tex]e^{-|-t|} = \frac {1}{\pi} \int_{-\infty}^{\infty} \frac {1}{1+ω^2} e^{iωt} dω [/tex]

[tex] t→ω,[/tex]
[tex] e^{-|ω|} = \frac {1}{\pi} \int_{-\infty}^{\infty} \frac {1}{1+t^2} e^{iωt} dt [/tex]

Part(e)

[tex] V_{(ω)} = \sqrt{2\pi} F_{(ω)}G_{(ω)} = \frac {2}{\pi} \frac {a sin (ωT)}{a^2 + ω^2}[/tex]

[tex] sin (ωT) = \frac {1}{2i} (e^{iωT} - e^{-iωT}) ,[/tex]

Taking inverse Fourier transform of Vω:

[tex] V_{(t)} = \frac {1}{i\pi}(\frac{a}{\sqrt{2\pi}}) \int_{-\infty}^{\infty} \frac {1}{a^2 + ω^2} e^{iω(T-t)} dω + \frac {1}{i\pi}(\frac{a}{\sqrt{2\pi}}) \int_{-\infty}^{\infty} \frac {1}{a^2 + ω^2} e^{-iω(t+T)} dω [/tex]

My velocity is complex! Strange...

This is related to inverse Fourier transform of F2, f2 but as a function of (t-T) and (t+T):

[tex]i(\frac{1}{\sqrt{2\pi}}) f_2(t-T) - i(\frac{1}{\sqrt{2\pi}}) f_2(t+T) [/tex]
[tex]= i\frac{1}{\sqrt{2\pi}}[e^{-a|t-T|} - e^{-a|t+T|}] [/tex]

To find the displacement, simply integrate the above with respect to t from -∞ to ∞.

[tex] s = i\frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} e^{-a|t-T|} dt - i\frac{1}{\sqrt{2\pi}} \int_{-\infty}^{\infty} e^{-a|t+T|} dt [/tex]
[tex] = i\sqrt{\frac{2}{\pi a}}(e^{-aT} - e^{aT}) [/tex]

Again complex displacement.. (and negative)
 
Last edited:
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  • #2
That's some nice work on parts a to c. at the end of part a, it should be ##\tau## (not ##t##), but I'm guessing that is just a miss-type. For part e, there is a mistake in this equation:
[tex]V_{(ω)} = \sqrt{2\pi} F_{(ω)}G_{(ω)} = \frac {2}{\pi} \frac {a sin (ωT)}{a^2 + ω^2}[/tex]
there should be another ##\omega## from ##f_1## (which you have got in part a). And it looks like you've missed out the ##\sqrt{2\pi}## which is on the left hand side of the equation.

Also, there is a simpler way to do part e. You don't need to do the inverse Fourier transform. hint: you have got ##\tilde{V}(\omega )## and you want
[tex]\int_{-\infty}^{\infty}V(t) dt [/tex]
So, thinking about the definition of the Fourier transform, what is the easiest way to get this?
 
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  • #3
BruceW said:
That's some nice work on parts a to c. at the end of part a, it should be ##\tau## (not ##t##), but I'm guessing that is just a miss-type. For part e, there is a mistake in this equation:
[tex]V_{(ω)} = \sqrt{2\pi} F_{(ω)}G_{(ω)} = \frac {2}{\pi} \frac {a sin (ωT)}{a^2 + ω^2}[/tex]
there should be another ##\omega## from ##f_1## (which you have got in part a). And it looks like you've missed out the ##\sqrt{2\pi}## which is on the left hand side of the equation.

Also, there is a simpler way to do part e. You don't need to do the inverse Fourier transform. hint: you have got ##\tilde{V}(\omega )## and you want
[tex]\int_{-\infty}^{\infty}V(t) dt [/tex]
So, thinking about the definition of the Fourier transform, what is the easiest way to get this?

[tex]\tilde{V}(ω) = \int_{-\infty}^{\infty} V_{(t)} e^{iωt} dt [/tex]

I've been thinking of a way to 'remove' the exp[iωt] from the integral and everything would be perfect. The only way is if ω = 0. But if ω = 0, LHS = 0..
 
  • #4
yes, use ##\omega=0##. The left hand side is not zero, it is ##\tilde{V}(0)## this isn't zero. Even if it was zero, that would still be the correct answer, since the right hand side is exactly what you are looking for.
 
  • #5
BruceW said:
yes, use ##\omega=0##. The left hand side is not zero, it is ##\tilde{V}(0)## this isn't zero. Even if it was zero, that would still be the correct answer, since the right hand side is exactly what you are looking for.

The 0 comes about from the sin (ωT) function and part (a) looks right to me..
 
  • #6
you have got the correct answer to part (a), but part (a) evaluated at ##\omega=0## is not equal to zero.

edit: you have
[tex]\sqrt{\frac{2}{\pi}} \frac {sin (ω\tau)}{ω}[/tex]
but this is not zero for ##\omega=0##
 
  • #7
BruceW said:
you have got the correct answer to part (a), but part (a) evaluated at ##\omega=0## is not equal to zero.

edit: you have
[tex]\sqrt{\frac{2}{\pi}} \frac {sin (ω\tau)}{ω}[/tex]
but this is not zero for ##\omega=0##

Ah ok, I get it, thanks so much!
 
  • #8
hehe, no problem!
 

1. What is the convolution theorem?

The convolution theorem is a mathematical concept that states that the convolution of two functions in the time domain is equivalent to the multiplication of their respective Fourier transforms in the frequency domain.

2. How is the convolution theorem used in signal processing?

The convolution theorem is used in signal processing to simplify the computation of convolutions, which are frequently used in operations such as filtering and deconvolution. It allows for faster calculations by transforming the operation into a multiplication in the frequency domain.

3. What is the relationship between the convolution theorem and integration?

The convolution theorem is closely related to integration, as the convolution can be thought of as a continuous version of multiplication. In fact, the convolution of two functions can be expressed as the integral of their product.

4. What are the advantages of using the convolution theorem in data analysis?

The convolution theorem allows for faster and more efficient calculations, making it a useful tool in data analysis. It also simplifies complex mathematical operations and can help in identifying patterns and relationships in data.

5. Can the convolution theorem be applied to functions in higher dimensions?

Yes, the convolution theorem can be applied to functions in multiple dimensions, such as in image processing. In this case, the convolution operation becomes a multidimensional integral, but the same principles still apply.

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