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<math> X(f) = \mathcal{F}^{-1}[x(t)] = \int_{-\infty}^\infty x(t) e^ {j 2 \pi f t} dt</math> |
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<math> X(f) = \mathcal{F}^{-1}[x(t)] = \int_{-\infty}^\infty x(t) e^ {j 2 \pi f t} dt</math> |
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We can take the derivitive of <math> x(t) </math> and then put in terms of the reverse fourier transform. |
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<math> \frac{dx}{dt} = \int_{-\infty}^\infty j 2 \pi f X(f) e^ {j 2 \pi f t} df = \mathcal{F}^{-1}[j 2 \pi f X(f)] |
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</math> |
Fourier Series
If
- Dirichlet conditions are satisfied
then we can write
The above equation is called the complex fourier series. Given , we may determine by taking the inner product of with .
Let us assume a solution for of the form . Now we take the inner product of with .
If then,
If then,
We can simplify the above two conclusion into one equation.
So, we may conclude
Orthogonal Functions
The function and are orthogonal on if and only if .
The set of functions are orthonormal if and only if .
Linear Systems
I may come back to this latter...
Fourier Series (indepth)
I would like to take a closer look at in the Fourier Series. Hopefully this will provide a better understanding of .
We will seperate x(t) into three parts; where is negative, zero, and positive.
Now, by substituting into the summation where is negative and substituting into the summation where is positive we get:
Recall that
If is real, then . Let us assume that is real.
Recall that Here is further clarification on this property
So, we may write:
Fourier Transform
Fourier transforms emerge because we want to be able to make Fourier expressions of non-periodic functions. We can take the limit of those non-periodic functions to get a fourier expression for the function.
Remember that:
So,
From the above limit we define and .
We can take the derivitive of and then put in terms of the reverse fourier transform.