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Understanding The Fourier Transform

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Re: Understanding The Fourier Transform

#41

The Fourier Transform can also be thought of as part of Linear Algebra, because it's actually funding a representation of a given function in the basis consisting of sin and cos functions (or complex exponentials). See, the collection of non-pathological functions is a vector space. We add elements by adding the functions pointwise, we multiply by a constant in the obvious way, and the other requirements can be check…

> And vector spaces have bases. One basis for the vector space of functions is the collection of sin and cos functions. Thus we can see that finding the Fourier Transform is just finding how much of each basis vector we need to make the function.

You have indicated quite explicitly that you're being informal (by using non-technical terms like 'non-pathological functions'), but I think it's worth making the small observation here that sine and cosine functions do not form a basis (implicitly: "a Hamel basis") for, say, the space of continuous functions on R/Z in the usual sense of linear algebra, since not every function can be written as a finite linear combination of them.

Rather, they are a topological basis, in the sense that every function can be written as an infinite linear combination of them. Why is it worth making this point, which probably seemed too obvious to say? The reason is that, unlike finite sums, which are unambiguous algebraic constructs, infinite sums require topology to compute; and being a topological basis in, say, the L^2 topology is quite different to being a topological basis in, say, the topology of pointwise convergence. The study of the different kinds of summability of Fourier series is the subject of some very, very deep work.

Re: Understanding The Fourier Transform

#42

Earlier quoted context omitted.

This was Fourier's big theorem, that every periodic, non-pathological function is "simply" the sum of phase-shifted sine waves of all necessary frequencies. You can remove the "periodic" if you allow infinitely many frequencies, and so on. The phase-shifting is taken care of by having a sine and a cosine at the same frequency, but (possibly) different amplitudes. So start with a periodic wave and look at how much sin…

ah thanks for answering the other questions I hadn't gotten around to asking (the reason why it's an orthonormal basis). I think I need to go home and have a play with this now!

For an orthonormal set, one also gets another answer to your question: how can you see how much such a set spans?

Theorem: If S is an orthonormal (or even orthogonal) set in a Hilbert space V (here, L^2 functions on R/Z), then put S^\perp = {v in V : v is orthogonal to every s in S}. Then the span of S is (S^\perp)^\perp. (The containment of the span in the double-orthocomplement is formal; the other direction requires a supplemental theorem on the geometry of Hilbert spaces.)

With this in mind, we see that S is a topological basis (spans everything) when S^\perp = 0; so, to show that the sine and cosine functions span, it suffices to show that nothing (non-0) is orthogonal to all of them. This still requires computation, but at least it's easier to imagine doing this than somehow finding a Fourier series for any L^2 function.

Re: Understanding The Fourier Transform

#43

The Fourier Transform can also be thought of as part of Linear Algebra, because it's actually funding a representation of a given function in the basis consisting of sin and cos functions (or complex exponentials). See, the collection of non-pathological functions is a vector space. We add elements by adding the functions pointwise, we multiply by a constant in the obvious way, and the other requirements can be check…

Yup, that's how I usually picture it, change the values to a collection of sin and cos functions. When I started explaining it to some computer science students, it helped by giving a particular example of its usefulness: Sound is composed of waves so, when you want to send a music to a friend it's all a bunch of values like [0, 1, 2, 1, 0, -1 , -2, -1, 0, ...]. If you know they're going to look like waves (sinusoida…

> Bonus: you can add the phase values, 2 sin(x + phase), to get the beats just right.

Fortunately, you don't need to do so; if the complex number z = A + iB has magnitude r and argument theta, then Acos(t) + Bsin(t) is the same as r*cos(t - theta). That is, combining cosines and sines of the same frequency already accounts for the phase shift.

Re: Understanding The Fourier Transform

#44
post #41

The Fourier Transform can also be thought of as part of Linear Algebra, because it's actually funding a representation of a given function in the basis consisting of sin and cos functions (or complex exponentials). See, the collection of non-pathological functions is a vector space. We add elements by adding the functions pointwise, we multiply by a constant in the obvious way, and the other requirements can be check…

> And vector spaces have bases. One basis for the vector space of functions is the collection of sin and cos functions. Thus we can see that finding the Fourier Transform is just finding how much of each basis vector we need to make the function. You have indicated quite explicitly that you're being informal (by using non-technical terms like 'non-pathological functions'), but I think it's worth making the small obse…

All true - the explanation should be littered with "here be dragons ..." - but for the sake of grasping the concepts without drowning in detail, I thought it was worth leaving out such tricky (but important!) Issues.

But you're right, there is interesting and deep material here.

Re: Understanding The Fourier Transform

#45
post #37
post #23

You can also think about the Fourier Transform in terms of its physical properties. For example, the Fourier transform is behind quantum uncertainty (dp.dx>h). Think of it this way: the inverse Fourier transform of a frequency impulse (zero extent) is a sine wave of infinite duration. Truncate the infinite sine wave and its spectrum ceases being an impulse, broadening into the shape of the windowing function used to…

"That is, an attempt to constrain/define time leads to a broadening in frequency, and vice versa." That is also a really well phrased one line point of commonality to talk to a telecom / RF / EE type person about communications bandwidth theory. If you just wedge in signal to noise ratio / bit error rate, and look at what you phrase "time definition" and "frequency broadening" in the right way, then you pretty much h…

That would be a nice topic for a website: to encapsulate and present the duality present in any number of fields.

A major barrier to understanding any new field is being able to strip away the jargon and recast the ideas into a familiar form. One can envisage website, where you tick the field(s) "A" that you want to learn about, tick the field(s) "B" that you already know about, and it recasts field(s) "A" in terms of the terminology of field(s) "B".

Make it have a "plug-in" structure, so supporting a new field is a matter of writing a mapping of field specific concepts to a set of "design patterns". Make it open source, so experts can jump in and contribute mappings for a wide variety of fields. Getting fancy, mappings could be written in terms of any two fields (so the expert does not have to learn a set of web-site specific design patterns), and the software could construct a graph to allow conversions between arbitrary fields.

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