Fourier series - by Ray Betz: Difference between revisions

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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>
<math> X(f) = \mathcal{F}^{-1}[x(t)] = \int_{-\infty}^\infty x(t) e^ {j 2 \pi f t} dt</math>

We can take the derivitive of <math> x(t) </math> and then put in terms of the reverse fourier transform.

<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)]

</math>

Revision as of 13:35, 20 October 2005

Fourier Series

If

  1. 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.