**Fourier Transforms**

**Definition of Fourier Transform**

The frequencies associated with coefficients of the Fourier Series, c

_{n}, is w_{0}. Since w_{0}=2p/T, the spacing between consecutive coefficients decreases as T increases. As T goes to infinity(i.e.the function becomes aperiodic), the spacing between coefficients goes to zero and we get a continuous "spectrum" rather than distinct values. I used this information to derive the Fourier Transform from the Fourier Series. Start with the Fourier Series for c_{n}:X(w) is the Fourier Transform of the aperiodic function x(t). We can also derive the inverse Fourier Transform by starting with the Fourier Series definition of x(t)

Fourier TransformSynthesis EquationAnalysis Equation

**Relationship between Fourier Series and Fourier Transform**

Consider a function x

_{p}(t) that is the periodic extension, with period=T, of the aperiodic function x(t).The Fourier Transform (X(w)) of the function x(t) is related to the Fourier Series coefficients (c

_{n}) of the function x_{p}(t) by:

**Convergence of Fourier Transform**

Without proof I stated that the Fourier Transform exists if three criteria are met

There are a finite number of discontinuities in a finite interval.

There are a finite number of maxima and minima in a finite interval.

Only the first one of these criteria will matter to us

**Fourier Transform Properties**

You might want to print out Fourier Transform Pairs and Fourier Transform Properties. With just a few pairs and properties I showed that you could find the Fourier Xform of many different functions that were not in the table. With a little cleverness and consideration you can often find the Fourier Xform of a function without having to resort to integration by consulting the table.

**Fourier Transform of a Periodic Function**

It seems that a periodic function would not have a Fourier Transform because it violates the first of the convergence criteria. However, if we allow for impulse functions, we can get around this restriction (this will allow us to use Fourier Transforms for both periodic and aperiodic functions).

Consider the a frequency domain function that is a simple impulse scaled by 2p (the scaling factor will be convenient a bit later).

We can find the corresponding time domain function by calculating the inverse Fourier Transform,

(The last step was performed using the sifting property of the impulse function.) Note that the time domain function, x(t), is periodic. So if we allow impulses in the Fourier domain we can have periodic functions in the time domain. This was a special case, but we can represent

any(subject to convergence criteria like those for the Fourier Series) periodic function with a Fourier Transform. First consider a Fourier Transform that is an infinite sum of impulses (this is contrived, but it simplifies to something useful).(This derivation also uses the sifting property.) So, to find the Fourier Transform of a periodic signal, x(t), first find the Fourier Series coefficients, c

_{n}, then

In class I gave you a handout that relates Fourier Xforms of aperiodic
functions (x(t)), the Fourier Series of the periodic extension of aperiodic
functions (x_{p}(t)) and the Fourier Xform of the periodic extension.
The handout is here.