Sawtooth function Fourier transform

In summary: I mean, the Fourier transform of the uniform function is just the delta function.In summary, you need to find the Fourier transform of y_2(t) to calculate the Fourier transform of f(t).
  • #1
roam
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Homework Statement


For a periodic sawtooth function ##f_p (t) = t## of period ##T## defined over the interval ##[0, T]##, calculate the Fourier transform of a function made up of only a single period of ##f_p (t),## i.e.

$$f(t)=\left\{\begin{matrix}f_p (t) \ \ 0<t<T\\0 \ \ elsewhere \end{matrix}\right.$$

Use the result that

$$FT \Big[ f(t) = t \Big] = \frac{j \delta' (\nu)}{2 \pi}$$

Homework Equations

The Attempt at a Solution



I am not sure how to approach this problem. I think somehow we need to evaluate ##f(\nu) = \frac{j \delta' (\nu)}{2 \pi}## over the interval ##[0, T]##, but it's not possible since we are in Fourier space. If this is the case, do we need to write the interval in terms of the corresponding frequency (##[\nu = 0, \ 1/T ]##)?

Any help would be greatly appreciated.
 
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  • #2
Note that ##f(t)## can be obtained by multiplying a function ##y_1(t)=t## and a top hat function
$$
y_2(t)=\left\{\begin{matrix}1 \ \ 0<t<T\\0 \ \ elsewhere \end{matrix}\right.
$$
i.e. ##f(t)=y_1(t)y_2(t)##. The Fourier transform of ##f(t)## is then the convolution between ##\tilde{y}_1(\nu)## and ##\tilde{y}_2(\nu)##. Now, you have been given ##\tilde{y}_1(\nu)##, which is equal to ##\frac{j \delta' (\nu)}{2 \pi}##, and your task now is to find ##\tilde{y}_2(\nu)##.
 
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  • #3
Thank you very much for this hint. So if I write the answer as:

$$F(\nu)=\left\{\begin{matrix}\frac{j \delta'(\nu)}{2 \pi} \ \ 0<t<T\\0 \ \ elsewhere \end{matrix}\right.$$

is that alright?
 
  • #4
As I said, the Fourier transform of ##f(t)## will be a convolution between the transform of the individual functions in the time domain. The one you proposed above doesn't seem to be one. Have you calculated the transform of ##y_1## and ##y_2##?
 
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  • #5
I see. So for the case ##0<t<T## the convolution would be:

$$\tilde{y_1} * \tilde{y_2} = \int^T_0 \tilde{y_1} (\tau) \tilde{y_2} (t- \tau) \ d\tau = \frac{j \delta' (\nu)}{2 \pi} \int^T_0 1 \ d \tau = \frac{j \delta' (\nu)}{2 \pi} T$$

So, we have

$$F(\nu)=\left\{\begin{matrix}\frac{j \delta'(\nu)}{2 \pi} T \ \ 0<t<T\\0 \ \ elsewhere \end{matrix}\right.$$

Is this correct now?
 
  • #6
No, it's not correct. The Fourier transforms are functions of frequency, not time.
 
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  • #7
roam said:
I see. So for the case ##0<t<T## the convolution would be:

$$\tilde{y_1} * \tilde{y_2} = \int^T_0 \tilde{y_1} (\tau) \tilde{y_2} (t- \tau) \ d\tau = \frac{j \delta' (\nu)}{2 \pi} \int^T_0 1 \ d \tau = \frac{j \delta' (\nu)}{2 \pi} T$$

So, we have

$$F(\nu)=\left\{\begin{matrix}\frac{j \delta'(\nu)}{2 \pi} T \ \ 0<t<T\\0 \ \ elsewhere \end{matrix}\right.$$

Is this correct now?
No, that's wrong. What the convolution theorem states is that when you have two functions ##y_1(t)## and ##y_2(t)## multiplied in the time domain ##f(t)=y_1(t)y_2(t)##, then Fourier transform of the product function in frequency domain will be a convolution of the form
$$
\tilde{f}(\nu) = FT[f(t)] = \int_{-\infty}^\infty \tilde{y}_1(\nu')\tilde{y}_2(\nu-\nu') d\nu'
$$
 
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  • #8
Since ##\tilde{y}_2## is equal to ##1## on (0, T) the convolution would become

$$\int_{-\infty}^\infty \tilde{y}_1(\nu')\tilde{y}_2(\nu-\nu') d\nu' = \frac{j}{2 \pi} \int_{-\infty}^\infty \delta' (\nu') . 1 \ d \nu'=- \frac{j}{2 \pi} \Big[ \delta(\nu') \Big]^{\infty}_{-\infty}$$

But the last expression is equal to zero since the Dirac delta is zero everywhere other than on ##0## (or alternatively it equals to ##1## if 0 is contained within the integration limits).

So ##\tilde{f} (\nu) = 0## in this case? Is that right?
 
  • #9
roam said:
Since ~y2y~2\tilde{y}_2 is equal to 111 on (0, T) the convolution would become
No. It's ##y_2(t)## which is unity in the interval [0,T], not ##\tilde{y}_2(\nu)## which is the Fourier transform of ##y_2(t)##.
 
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  • #10
blue_leaf77 said:
No. It's ##y_2(t)## which is unity in the interval [0,T], not ##\tilde{y}_2(\nu)## which is the Fourier transform of ##y_2(t)##.

Thanks, so I know that the Fourier transform of the uniform function is equal to ##\delta(\nu)##. So the convolution integral becomes:

$$\frac{j}{2 \pi} \int^\infty_{-\infty} \delta ' (\nu ') \ \delta (\nu - \nu') d \nu '$$

I know that there is a property that says: ##\int^\infty_{-\infty} \delta' (\nu) \phi (\nu ') d \nu' = - \phi' (0)##. Is it possible to use this? Or do I need to proceed with integration by parts?
 
  • #11
roam said:
so I know that the Fourier transform of the uniform function is equal to δ(ν)
##y_2(t)## is not a constant function, it has top hat form and hence its Fourier transform is not equal to a delta function.
 
  • #12
blue_leaf77 said:
##y_2(t)## is not a constant function, it has top hat form and hence its Fourier transform is not equal to a delta function.

What do you mean? Because you said earlier that it is equal to unity in the interval [0, T], so the Fourier transform of 1 is just a delta function.
 
  • #13
Look at the form of ##y_2(t)## in post#2. Does that look like a constant function over the entire time axis?
 
  • #14
In post #2 you have ##y_2(t) =1## which is a constant over the interval under consideration (##0<t<T##). So I looked the FT only in this region.

But if we consider the time axis outside this region, the function looks like a rectangular pulse function of duration ##T## (in our course it is denoted by ##\Pi##). In this case it would be ##\Pi(\frac{t}{T})## whose FT is ##T sinc(T \nu)##. Is this what you meant?
 
  • #15
roam said:
Is this what you meant?
Yes. However, there is a small difference. The rectangular pulse we have in this problem is not centered with respect to the origin. If you know the shifting theorem of Fourier transform, this can help you find the corresponding transform.
 
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  • #16
blue_leaf77 said:
Yes. However, there is a small difference. The rectangular pulse we have in this problem is not centered with respect to the origin. If you know the shifting theorem of Fourier transform, this can help you find the corresponding transform.

Okay so if we rewrite the rectangular function as centered at T/2:

$$\Pi \left( \frac{t- (\frac{T}{2})}{T} \right)$$

Using the shifting property (##f(t-t_0) \iff e^{-j 2 \pi \nu t_0} F(\nu)##) the FT becomes: ##T \ sinc (T \nu) e^{-j 2 \pi (T/2)}.## So

$$\tilde{f} = \frac{jT}{2 \pi} \int^{\infty}_{- \infty} \delta ' (\nu ') \ sinc (T \nu) e^{-j 2 \pi \nu (T/2)} \ d \nu'$$

I am not sure how to proceed from here. The delta function still becomes zero over this integration limit. Are there any special properties we could use?
 
  • #17
It should be
$$
\tilde{f} = \frac{jT}{2 \pi} \int^{\infty}_{- \infty} \delta ' (\nu ') \ sinc (T (\nu-\nu')) e^{-j 2 \pi (\nu-\nu') (T/2)} \ d \nu'
$$
writing ##\delta ' (\nu ')d \nu' = d\delta(\nu ')##, that integral becomes
$$
\tilde{f} = \frac{jT}{2 \pi} \int^{\infty}_{- \infty} \ sinc (T (\nu-\nu')) e^{-j 2 \pi (\nu-\nu') (T/2)} \ d\delta(\nu ')
$$
At this point, consider using integration by parts.
 
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  • #18
Thank you so much for the help.
 

What is a Sawtooth function Fourier transform?

A Sawtooth function Fourier transform is a mathematical technique used to decompose a sawtooth wave into its constituent sinusoidal components. It is a type of Fourier transform that is specifically used to analyze waves with sharp changes or discontinuities, such as the sawtooth wave.

How is a Sawtooth function Fourier transform calculated?

To calculate a Sawtooth function Fourier transform, the sawtooth wave is first broken down into its constituent sinusoidal components using the Fourier series. This involves finding the amplitude and frequency of each sinusoidal component. The Fourier transform is then used to convert the signal from the time domain to the frequency domain.

What is the purpose of a Sawtooth function Fourier transform?

The purpose of a Sawtooth function Fourier transform is to analyze and understand the frequency components of a sawtooth wave. It allows for the identification of the individual sinusoidal components that make up the wave, which can be useful in fields such as signal processing, communications, and physics.

What are the advantages of using a Sawtooth function Fourier transform?

One advantage of using a Sawtooth function Fourier transform is that it can be used to analyze and understand waves with sharp changes or discontinuities, which cannot be analyzed using other types of Fourier transforms. Furthermore, it allows for the identification of individual frequency components, providing valuable information about the signal.

Are there any limitations to using a Sawtooth function Fourier transform?

One limitation of using a Sawtooth function Fourier transform is that it assumes the signal is periodic and has a repeating pattern. This may not always be the case in real-world applications. Additionally, the accuracy of the transform may be affected by noise or non-periodic components in the signal.

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