Fourier Transforms: Evaluating Periodic Triangle Pulses

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The discussion focuses on evaluating the Fourier transform of a periodic train of triangle-shaped pulses and its power spectral density. It highlights the challenge of transitioning from a single triangle pulse to a periodic signal, suggesting the use of Fourier series to find the magnitude of the Fourier transform. The conversation also touches on the relationship between convolution in the time domain and multiplication in the frequency domain, emphasizing that convolution leads to autocorrelation. Additionally, it notes that a triangle pulse can be represented as a convolution of box functions, leading to a sinc-squared function in the Fourier domain. Overall, the participants explore various methods to approach the problem, affirming that convolution and multiplication are key concepts in Fourier analysis.
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I am having a little trouble. I am asked to evaluate the Fourier transform of a periodic train of triangle shaped pulses. Then I have to evaluate the power spectral density. Now it is very easy to find the Fourier transform of one of the triangles, but what do I do when it is periodic? Do I have evaluate the signal with the Fourier series and using the relationships between the magnitude of Dn to find the mag. of F(w) and then get the spectral density?
 
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A train of triangles could also be written as a single triangle convolved with an impulse train. Do you know what convolution in the time domain becomes in the frequency domain?
 
Ya in the frequency domain it becomes autocorrelation. But is that the only way?
 
If autocorrelation means multiplication, then yes, that is correct. Convolution in one domain goes to multiplication in the other.

Maybe there's an easier way to do it, but I don't think this way is that hard.

A triangle is a box convolved with another box. A box is a sinc(x) function, so a triangle would be sinc(x)^2.

The Fourier transform of an impulse train is another impulse train, but with different spacing and weights.

Multiply the two together, and you get a sampled sinc(x)^2 function. Yes?
 
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