From Fourier Series to Fourier Transforms

In summary: I would recommend you check out Reed & Simon for a more advanced treatment of Fourier analysis. They also have a book on Fourier series, but I haven't read it.In summary, the conversation discusses the logical steps involved in proving that the Fourier Series of a function with an infinite period leads to the Fourier transform. The participants discuss the conversion from a discrete sum to a continuous integral and its relation to the Fourier coefficients. A more rigorous approach is presented, using Riemann sums and density results, and a recommendation is made to consult a book by Reed & Simon for a deeper understanding of Fourier analysis.
  • #1
mnb96
715
5
Hello,

I am trying to formalize the logical steps to prove that the Fourier Series of a function with period[itex]\rightarrow \infty[/itex] leads to the Fourier transform. So let's have the Fourier series:

[tex]f(x)=\sum_{n=-\infty}^{+\infty}c_n e^{i\cdot \frac{2\pi n}{L}x}[/tex]

where L is the period of the function f.
Many texts simply say that when L tends to infinity, cn becomes a continuous function [itex]c(n)[/itex] and the summation becomes an integral.

[tex]f(x) = \int_{-\infty}^{+\infty}c(k) e^{i\cdot k x}dk[/tex]

Unfortunately they do not explain why, and they do not mention what is the logical step that allows one to switch from the discrete cn to the continuous c(k), and from the summation to an integral with dk.

Any hint?
 
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  • #3
Uhm...so far, no replies.
I made the following attempt:

the Fourier coefficients cn are given by:

[tex]c_n = \frac{1}{T}\int_{-T/2}^{T/2}f(x)e^{-i\frac{2\pi}{T}nx}dx[/tex]

Plugging the cn's into the first equation in my first post one gets:

[tex]f(x) = \frac{1}{T} \sum_{n=-\infty}^{+\infty} \left( \int_{-T/2}^{T/2}f(x)e^{-i\frac{2\pi}{T}nx}dx \right) e^{i\frac{2\pi}{T}nx}[/tex]

Now we introduce the quantity [itex]k_n=n/T[/itex], and observe that [itex]\Delta k_n=k_{n+1}-k_n = 1/T[/itex].

Clearly, when [itex]T\rightarrow +\infty[/itex], we have [itex]\Delta k_n \rightarrow 0[/itex], and we replace the discrete quantity kn with a continuous variable u (this step is not rigorous!). As a consequence we must also replace the Fourier coefficients [itex]\{c_n\}[/itex] with a continuous variable c(u) (this step is not rigorous!)

In doing so we obtain:

[tex]f(x) = \Delta k_n \sum_{n=-\infty}^{+\infty} \left( \int_{-\infty}^{+\infty}f(x)e^{-i\ 2\pi u x}dx \right) e^{i 2\pi k_n x} = \Delta k_n \sum_{n=-\infty}^{+\infty} c(k_n) e^{i 2\pi k_n x}[/tex]

The rightmost term is a Riemann sum, so we write:

[tex]f(x) = \int_{-\infty}^{+\infty} c(u) e^{i 2\pi u x}du[/tex]

Which is the continuous inverse Fourier transform of c(u)
I hope someone else will put this into a more rigorous form and spot possible mistakes.
 
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  • #4
Your argument has the right idea. Let [itex]f\in C^\infty_c (\mathbf{R})[/itex] and choose [itex]\epsilon>0[/itex] sufficiently small so that [itex]\mathrm{supp}(f) \subset (-1/\epsilon, 1/\epsilon)[/itex]. Then [itex]f[/itex] has the convergent Fourier series [tex] f(x) = \sum_{n\in \mathbf{Z}} c_n e^{\mathrm{i} \pi \epsilon n x} [/tex]where
[tex] c_n = \frac{\epsilon}{2} \int e^{-\mathrm{i} \pi \epsilon n x} f(x)\, \mathrm{d}x. [/tex]
Set [itex]K_\epsilon = \{ k\in \mathbf{R} : k/\pi \epsilon \in \mathbf{Z} \}[/itex]. Then this can be written
[tex] f(x) = \sum_{k \in K_\epsilon} \frac{\hat{f}(k)e^{\mathrm{i} kx}}{2\pi } (\pi \epsilon) \qquad (*) [/tex]
where
[tex] \hat{f}(k) = \int e^{-\mathrm{i} kx} f(x)\, \mathrm{d}x. [/tex]
Note that [itex]\mathbf{R}[/itex] is the disjoint union of intervals of length [itex](\pi \epsilon)[/itex] centered about the points in [itex]K_\epsilon[/itex], it follows that [itex](*)[/itex] is the Riemann sum for the integral
[tex] \frac{1}{2\pi} \int e^{\mathrm{i} kx} \hat{f}(k)\, \mathrm{d} k. [/tex]
Since [itex]f\in C^\infty_c (\mathbf{R})[/itex] it follows that [itex]\hat{f}(k)e^{\mathrm{i} kx}[/itex] is integrable, so the Riemann sums converge to the integral as [itex]\epsilon\rightarrow 0[/itex], so you are done. The result extends to larger function spaces using standard density results.

Reed & Simon [Methods of Mathematical Physics, Vol II] use this approach to prove the Fourier inversion theorem. It allows you to prove many of the "usual" results for Fourier transforms using the corresponding results from Fourier series.
 
  • #5
Hi Anthony!

Thanks a lot for providing a rigorous approach to this problem!
The explanation is now much clearer and easier to follow.
 
  • #6
No problem - glad to be of service.
 

What is the difference between Fourier series and Fourier transforms?

Fourier series and Fourier transforms are both mathematical techniques used to analyze and describe periodic functions. Fourier series decomposes a periodic function into a sum of sinusoidal functions, while Fourier transforms decompose a non-periodic function into a continuous spectrum of sinusoidal functions. In simpler terms, Fourier series is used for periodic functions while Fourier transforms are used for non-periodic functions.

Why are Fourier transforms useful in scientific research?

Fourier transforms are useful because they allow us to analyze complex signals and systems in terms of simpler sinusoidal components. This can help us understand the underlying patterns and behavior of a system, and also allows us to manipulate and modify signals in various ways. Fourier transforms are used in many fields of science and engineering, including signal processing, image processing, and quantum mechanics.

How do Fourier transforms relate to the concept of frequency?

Fourier transforms involve decomposing a function into a continuous spectrum of sinusoidal functions, each with a specific frequency. This means that the frequency content of a function can be represented in terms of the amplitudes and phases of these sinusoidal components. In other words, Fourier transforms allow us to look at a function in the frequency domain rather than the time domain.

Can Fourier transforms be applied to non-continuous functions?

Yes, Fourier transforms can be applied to both continuous and discrete functions. In the case of discrete functions, we use a discrete Fourier transform (DFT) instead of a continuous Fourier transform. The principles and concepts of Fourier transforms still apply, but instead of a continuous spectrum, the DFT produces a discrete spectrum of sinusoidal components.

How are Fourier transforms used in data compression?

Fourier transforms are used in data compression by identifying and eliminating redundant or unnecessary information in a signal. By analyzing the frequency content of a signal, we can determine which components are most important and preserve those, while removing or reducing the importance of other components. This allows us to represent the signal with fewer data points, resulting in compressed data without significant loss of information.

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