Take equation (234) from http://www.math.ohio-state.edu/~gerlach/math/BVtypset/node31.html#eq:completeness_with_function and there, replace [tex]\hat{f}(k)[/tex] by equation (233).
Then you almost have equation (235), except that you have to change
the order of integration.
pivoxa15 said:
Right, but why is the f(hat) function in terms of t?
f is in terms of x and k only.
The function [tex]\hat{f}(k)[/tex] is indeed a function of k, but it is not the function f, whose variable is x. Maybe you are a little confused about the meaning of [tex]\hat{f}(k)[/tex].
Think of the Fourier expansion as sort of linear combination of basis-vectors.
Let me try to explain:
Take the vectors in [tex]R^3[/tex].
Every vector can be represented as a linear combination of the basis-vectors [tex](1,0,0)[/tex], [tex](0,1,0)[/tex] and [tex](0,0,1)[/tex].
For example:
Take the vector [tex]v=(9,8,4)[/tex]. This vector can be written as a linear combination of the basisvectors vectors
[tex]u_1=(1,0,0)[/tex]
[tex]u_2=(0,1,0)[/tex]
[tex]u_3=(0,0,1)[/tex]
in the following way:
[tex](9,8,4) = 9 \cdot (1,0,0) + 8 \cdot (0,1,0) + 4 \cdot (0,0,1)[/tex]
[tex](9,8,4) = 9 \cdot u_1 + 8 \cdot u_2 + 4 \cdot u_3[/tex]
Let's call the numbers 9,8,4 in front of the basis-vectors coefficients.
[tex](9,8,4) = c_1 \cdot u_1 + c_2 \cdot u_2 + c_3 \cdot u_3[/tex]
where [tex]c_1 = 9[/tex], [tex]c_2=8[/tex] and [tex]c_3=4[/tex]
What if I choose different basis vectors, for example:
[tex]b_1=(3,0,0)[/tex]
[tex]b_2=(0,2,0)[/tex]
[tex]b_3=(0,0,2)[/tex]
Then our vector [tex]v=(9,8,4)[/tex] can be written as:
[tex](9,8,4) = 3 \cdot b_1 + 4 \cdot b_2 + 2 \cdot b_3[/tex]
Thus, the coefficients are 3,4,2 for our new basis-vectors
[tex]b_1[/tex],[tex]b_2[/tex] and [tex]b_3[/tex].
In general, you can write a vector [tex]v[/tex] as a linear combination of
basis-vectors [tex]b_k[/tex], where in front of the basis-vectors you have the coefficients [tex]c_k[/tex].
[tex]v = \sum_{k=1}^{3} c_k b_k[/tex]
In our last example we had
[tex]b_1=(3,0,0)[/tex]
[tex]b_2=(0,2,0)[/tex]
[tex]b_3=(0,0,2)[/tex]
together with the coefficients:
[tex]c_1=3[/tex]
[tex]c_2=4[/tex]
[tex]c_3=2[/tex]
Just check the formula
[tex]v = \sum_{k=1}^{3} c_k b_k[/tex]
by plugging in the above values:
[tex]v = c_1 \cdot b_1 + c_2 \cdot b_2 + c_3 \cdot b3[/tex]
[tex]v = 3 \cdot b_1 + 4 \cdot b_2 + 2 \cdot b_3[/tex]
[tex]=3 \cdot (3,0,0) + 4 \cdot (0,2,0) + 2 \cdot (0,0,2)[/tex]
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So, every vector can be represented by some basis vectors [tex]b_k[/tex]
as
[tex]v = \sum_{k=1}^{3} c_k b_k[/tex]More general, if we have a vector with n entries instead of 3, we write
[tex]v = \sum_{k=1}^{n} c_k b_k = c_1 b_1 + c_2 b_2 + ... + c_n b_n[/tex]
Now, let's make a step from the discrete to the continuous case.
Say we have a function [tex]f(x)[/tex] and we also want to ask, whether it's possible to represent [tex]f(x)[/tex] as a linear combination of ''basis-vectors''.
Question: Is it possible to write
[tex]f(x) = \sum_{k=1}^n c_k b_k[/tex]
Let us be more specific and ask: Can I write [tex]f(x)[/tex] as a sum
of [tex]b_k = e^{ikx}[/tex]? So my new basis-vectors are [tex]b_k=e^{ikx}[/tex].
The question then becomes:
Question: Is it possible to write
[tex]f(x) = \sum_{k=1}^n c_k e^{ikx}[/tex]
Indeed, it is possible, with a correction for the values of k.
Instead of going from k=1 to n, we use infinitely many basis-vectors, and
write [tex]k=- \infty[/tex] to [tex]k=+ \infty[/tex].
Corrected version:
[tex]f(x) = \sum_{k=-\infty}^{+\infty} c_k e^{ikx}[/tex]
The only question is, how do the coefficients
[tex]c_k[/tex] look like?
Have a look at
Wikipedia
or
here on page 2 of the pdf. It shows how the coefficients can be calculated.
To finally get to your Fourier integral, we replace the sum by an integral
[tex]f(x) = \int_{-\infty}^{+\infty} c(k) e^{ikx} dk[/tex]
Now, on your ohio-website, [tex]c(k)[/tex] is [tex]\hat{f}(k)[/tex], see equation (234) on the ohio-website.
Thus, [tex]\hat{f}(k)[/tex] plays the role of the coefficients.
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Note 1 (on how to get from the Fourier series to the Fourier integral):
A Fourier series can sometimes be used to represent a function over an interval. If a function is defined over the entire real line, it may still have a Fourier series representation if it is periodic. If it is not periodic, then it cannot be represented by a Fourier series for all x. In such case we may still be able to represent the function in terms of sines and cosines, except that now the Fourier series becomes a Fourier integral.
The motivation comes from formally considering Fourier series for functions of period 2T and letting T tend to infinity.
The quote is taken from
http://209.85.135.104/search?q=cach...mes+fourier+integral&hl=de&ct=clnk&cd=4&gl=de.
Also see http://phy.syr.edu/~trodden/courses/mathmethods/chapter4.pdf
for the transition from the Fourier series to the Fourier integral.
Note 2: Summary:
In summary, consider the Fourier integral as a linear combination of basis vectors [tex]e^{ikx}[/tex] with the
coefficients [tex]c(k)[/tex] (or [tex]\hat{f}(k)[/tex]).
Note 3 (on applications of the Fourier transform:
Have a look at
What is a Fourier Transform and what is it used for?.
http://grus.berkeley.edu/~jrg/ngst/fft/applicns.html
in Communications, Astronomy, Geology and Optics
Note 4 (functions as basis-vectors?)
You might ask "Why can I consider the functions [tex]e^{ikx}[/tex] as
basis-vectors?"
The functions [tex]e^{ikx}[/tex] fulfill some properties similar to those of
basis vectors (1,0,0),(0,1,0),(0,0,1)from [tex]R^3[/tex].
The functions are orthonormal, that is they are
orthogonal to each other and they are normalized to 1 (delta-function?).
See
Fourier Analysis on page 3.
http://web.mit.edu/8.05/handouts/SupplementarynotesonDiracNotation,QuantumStatesEtc.pdf on page 8.
Orthonormal functions: Definition on Wolfram mathworld
http://mathworld.wolfram.com/OrthonormalBasis.html
Note 5: Some examples of coefficients
Examples of Fourier transforms can be found http://www.mpipks-dresden.mpg.de/~jochen/methoden/topics/ft_ex.html
Note 6: Java Applets
Approximation of a function by a Fourier transform
This applet shows you how you can approximate a function by a Fourier transform. The more
coefficients you use, the better the approximation becomes.
Applet: Rectangular pulse approximation by Fourier Transform
This applet shows how you approximate a rectangular shaped pulse. If you don't use enough
"basis-vectors" (bandwidth is limited), then the pulse will not look rectangular anymore. This has applications in
electronics where you want to transmit a signal but the bandwidth is limited.