Why can a smooth function be described with fewer terms in a Fourier series?

In summary, the nastier/less smooth a function is, the more “spread out” its Fourier transform is. This is because the sines and cosines summed by the fourier-inverse integral get weighed increasingly equally regardless of their frequencies, as the function becomes less periodic. However, if you have a slowly changing function that isspread out and slowly changing, the Fourier transform will be narrow around 0, meaning only the low-frequency sines and cosines in the fourier-inverse integral will dominate.
  • #1
Nikitin
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Hi! I am taking a second look on Fourier transforms. While I am specifically asking about the shape of the Fourier transform, I'd appreciate if you guys could also proof-read the question below as well, as I've written down allot of assumptions that I've gained, which might be wrong.

OK.

As far as I am aware, the nastier/"less smooth" (ie sharp, large and discontinious derivative and so on) a function ##f(x)## is (for ex. ##f(x) = \delta (x)##, the more "spread out" its Fourier transform is. That is, the sines and cosines summed by the fourier-inverse integral get weighed increasingly equally regardless of their frequencies, as ##f(x)## becomes less periodic.

However if you got a very spread out and slowly changing ##f(x)## (for ex. ##f(x)=1##), the Fourier transform will be narrow around 0, meaning only the low-frequency sines and cosines in the fourier-inverse integral will dominate.

Why is this so? This applies to periodic functions as well, so let me rephrase the question in case you don't get me: Why can a "smooth and slow" function be described adequately with less terms in a Fourier series, than a nasty one? Is it perhaps because the further you go out in a Fourier series, the bigger the derivatives will be and thus these violent sines and cosines can adequately describe a swiftly changing function?
 
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  • #2
And can somebody tell me how to evaluate the infinite integral of the complex exponential function so I can get something representing the dirac delta? (I didn't want to open a new thread for this question alone as it's related to the OP, so don't murder me moderators).

I read that you can multiply it a converging term as a trick, like this, ##\lim_{\epsilon \to 0} \int_{-\infty}^{\infty} e^{-\epsilon x^2} \cdot e^{isx} dx##, but I don't remember how to evaluate a gaussian integral multiplied by another function.
 
  • #3
Nikitin said:
And can somebody tell me how to evaluate the infinite integral of the complex exponential function so I can get something representing the dirac delta? (I didn't want to open a new thread for this question alone as it's related to the OP, so don't murder me moderators).

I read that you can multiply it a converging term as a trick, like this, ##\lim_{\epsilon \to 0} \int_{-\infty}^{\infty} e^{-\epsilon x^2} \cdot e^{isx} dx##, but I don't remember how to evaluate a gaussian integral multiplied by another function.

##\int_{-\infty}^{\infty} e^{-\epsilon x^2} \cdot cos(sx) dx=\sqrt{{\pi}/{\epsilon}}\cdot e^{{-s^2}/{4\epsilon}}##, from Gradshteyn and Ryzhik. Note that the imaginary part = 0.
 
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  • #4
Nikitin said:
Why can a "smooth and slow" function be described adequately with less terms in a Fourier series, than a nasty one? Is it perhaps because the further you go out in a Fourier series, the bigger the derivatives will be and thus these violent sines and cosines can adequately describe a swiftly changing function?
You confuse two things: number (amount) of non-zero terms, and their frequencies (i.e. this [itex]n[/itex] in your [itex]\cos nx[/itex] and [itex]\sin nx[/itex] or, in the standard presentation, [itex]\exp(inx)[/itex]).

Also,
  • Do not use such terms as “nasty function”. Not clear.
  • Make distinction between Fourier transform of functions on ℝ and Fourier series on the circle (a.k.a. for periodic functions).
  • Learn to think in exponents, not ugly real trigonometry.
 

1. What is the shape of the Fourier transform?

The shape of the Fourier transform depends on the function being transformed. In general, the Fourier transform is a complex-valued function with both real and imaginary components. The real part represents the amplitude of each frequency component, while the imaginary part represents the phase shift.

2. How does the shape of the Fourier transform change with different input signals?

The shape of the Fourier transform will vary depending on the input signal. For example, a simple sinusoidal input signal will result in a single peak in the Fourier transform, while a more complex signal with multiple frequencies will result in a more complex shape with multiple peaks.

3. What does the shape of the Fourier transform tell us about the input signal?

The shape of the Fourier transform provides information about the frequency components present in the input signal. The location and amplitude of the peaks in the transform correspond to the frequency and amplitude of the corresponding components in the signal. This allows us to analyze and manipulate the signal in the frequency domain.

4. Can the shape of the Fourier transform be modified?

Yes, the shape of the Fourier transform can be modified by applying different transformation techniques such as filtering or windowing. These techniques can alter the amplitude and phase of the frequency components in the signal, resulting in a different shape in the Fourier transform.

5. Is the shape of the Fourier transform unique to each input signal?

Yes, the shape of the Fourier transform is unique to each input signal. This is because each signal has a unique combination of frequency components, resulting in a unique shape in the Fourier transform. However, similar signals may have similar shapes in the transform, allowing for pattern recognition and analysis.

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