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Chapter 13
Fast Fourier Transform (FFT)

13.1 Fourier Analysis
Suppose that you went to a Junior High band concert with a digital recorder and made a
recording of Mary Had a Little Lamb. Your ear told you that were a whole lot of different
frequencies all piled on top of each other, but perhaps you would like to know exactly what
they were. You could display the signal on an oscilloscope , but all you would see is a bunch
of wiggles. What you really want is the spectrum: a plot of sound amplitude vs. frequency.
If this is what you want, Matlab can give it to you with the fft command.

13.2 Matlab's FFT
Your recorder stores the data in digital form, with numerical signal values at equally spaced
time intervals. Matlab will read such data les in several formats, but the simplest to
would just be a text le with two columns, one for time t and one for the signal
f(t). Or perhaps it could just be the signal in one column and you know what the time
step is because you know the sampling rate.

If the 2-column le were called signal.txt and if it had two columns Matlab would
read it this way:

load signal.txt;
% the data is now stored in the variable signal as an Nx2 matrix
% unpack it into t and f(t) arrays


Or, if the le only contains one column of data and you know the interval between the data
points, then Matlab would read it this way

load signal.txt;
tau=.001; % tau is the time interval between the points in f

Now you have a series of data points (tj , fj) equally spaced in time by time interval
and there are N of them covering a total time from t = 0 to
The Matlab function fft will convert the array f(t) into an array g(ω) of amplitude vs.
frequency, like this

If you look at the array g you will find that it is full of complex numbers. It is complex
because it stores the phase relationship between frequency components as well as amplitude
information, just like the Fourier transform of mathematical analysis . If you wanted to
reconstruct the original time series by adding the frequency components back together, you
can use the Matlab function ifft (inverse fft)

similar to the inverse Fourier transform of mathematical analysis. When we don't care
about the phase information contained in the spectrum g(ω), we can work instead with the
so-called power spectrum P(ω), obtained this way:

To plot P or g vs. frequency, we need to associate a frequency with each element of
the array (just like we had to associate a time with each element of f). Before doing so,
however, it would probably be good to remind ourselves of the two types of frequencies:
"regular" frequency (measured in Hz, or cycles/second) which we'll denote by v, and angular
frequency (measured in radians/second). Both flavors of frequency are commonly
used, and you should make sure that you clearly understand which one you are using in a
given problem.

We can find the frequencies associated the elements of the array g by analysis of the
formulas Matlab uses to compute the fft and the ifft:

where tj gives the time for the jth element of f and !j give the frequency for the jth element
of g. Using the fft equation , the exponent becomes This form
allows us to identify the components in the frequency array as
The frequency interval from one point in g to the next is then given by

The lowest frequency is 0 and the highest frequency in the array is

The frequency array v (in cycles per second) and the ! array (in radians per second) would
be built this way:


% build v (regular frequency, cycles/sec)

% build w (angular frequency, radians/sec)

An important thing to notice from the definition (13.2) of the frequency step size is
that if you want to distinguish between two frequencies ω1 and ω2 in a spectrum, then you
must have You can refine this resolution in frequency by choosing a large
value for the length of the time series, In addition, from Eq. (13.3) we can see
that the maximum frequency you can detect isso if you want to see high
frequencies you need a small time step . Since the time step often needs to be tiny (so
that is big enough), and data the taking time often needs to be long (so that
is small enough) you usually just need lots and lots of points, maybe even more than your
computer memory will hold. So you need to design your data-taking carefully to capture
the features you want to see without breaking your computer.

You will find that all data sets are not created equal when fft is applied to them.
Sometimes fft will give you the answer really fast and sometimes it will be slow. You can
make it run at optimum speed if you always give it data sets with 64, 128, 1024, 16384,
etc. (powers of 2) data points in them. (Use help fft to see how to give fft a second
argument so powers of 2 are always used.)

Here is an example to show how this process works.

Example 13.2a (ch13ex2a.m)

% Example 13.2a (Physics 330)

% Build a time series made up of 5 different frequencies
% then use fft to display the spectrum

clear; close all;


% Make a signal consisting of angular frequencies
% w=1, 3, 3.5, 4, and 6
% the time plot is very busy and not very helpful
title('Time Series')

% now take the fft and display the power spectrum

title('Power Spectrum, including aliased points')

% Notice that the right side of this plot is a mirror
% image of the left side. This is called aliasing,
% and you can often ignore it by using the axis command
% to only look at half of the spectrum as follows:

title('Power Spectrum, without aliased points')
axis([0 max(w)/2,0 max(P)]);

Figure 13.1 Plot of the power spectrum from Example 13.2a

The power spectrum in this example (Fig. 13.1) has peaks at the ω's where they should
be, [1,3,3.5,4,6], but there are some extra peaks on the right side. These extra peaks are
due to a phenomenon called aliasing, and it comes up all the time in FFTs so we'll take a

Figure 13.2 Aliasing results from sampling a signal at discrete time intervals. The
ambiguity of the wheel rotation between (a) and (b) is due in part the the many
indistinguishable spokes. In (c) and (d) we can see that ambiguity still remains
with one spoke.

minute to discuss it in more detail.

13.3 Aliasing
If you have watched an old western movie, you may have noticed that stagecoach wheels
sometimes appear to be turning backwards. This phenomenon is due to the fact that movies
are made by taking a bunch of pictures separated by a defined time interval. We know the
wheel rotates between pictures because the spokes are oriented at different angles. But since
all the spokes look the same, there are many possible rotations that are consistent with the
new spoke angles, as depicted in Figs. 13.2(a) and (b). By looking at the figure, you should
be able to convince yourself that there are an infinite number of rotations (both positive and
negative) that are consistent with the spoke orientation in the two pictures. This ambiguity
of frequency is referred to as aliasing, and it comes up whenever you study the frequency
content of a signal that has been sampled at discrete times. Our brains generally resolve the
ambiguity by picking the rotation with the smallest magnitude (whether the sign is positive
or negative), which accounts for the stagecoach effect.

To get rid of complications due to the number of spokes in a wheel, imagine that we
painted one of the spokes bright red, and then sampled its position at t = 0 and t = as
shown in Figs. 13.2(c) and (d). The figure shows three of the possible angles that the wheel
may have rotated through during this time interval: and
These angles of rotation correspond to three possible frequencies:

With some careful study, we can write down a general expression for all frequencies that
are consistent with the wheel orientation at the two times:

Figure 13.3 Plot of the power spectrum from Example 13.2a showing the negative
frequencies that are aliased as positive frequencies.

Here n is an integer, with n ≥ 0 corresponding to positive frequencies and n < 0 corresponding
to negative frequencies. Notice that every frequency can be "aliased" by neighbor
frequencies located above or below the frequency of interest. If we recall from
Eq. (13.3) that is the width of the fft frequency window, we realize that any aliasing
we observe in the fft window comes from frequencies outside the frequency window defined
by Eq. (13.3).

We can understand the extra peaks in Fig. 13.1 by noting that the Fourier transform
of any real signal has an equal amount of positive and negative frequency content, i.e.
P(ω) = P(-ω). Since our original frequency window included only positive frequencies,
we didn't see the negative half of the spectrum. In Fig. 13.3 we've shown the negative
frequency content of the signal from Example 13.2a in dashed lines, and the fft power
spectrum in solid lines. Notice that each of the "extra" peaks on the right side of the
spectrum are a result of an aliased negative-frequency peak located below the peak.
In the time domain, the negative frequency and its positive alias produce signals that have
the same value at each sampling time, as shown in Fig. 13.4.

Since the aliased negative frequencies will always be present in the fft window (for real
signals), we can only reliably detect frequency components with an absolute value less than

This important limiting frequency is called the critical frequency or the Nyquist frequency.
If the signal contains frequency components outside this range, the aliased frequencies will
spill over into the positive frequencies and we will be unable to distinguish between actual
and aliased frequency peaks. Thus, the highest frequency that you can see without aliasing
trouble is vc (or ωc).

Figure 13.4 The cosine (left) and sine (right) of a negative frequency (dashed)
and its aliased positive frequency (solid). In our wagon wheel example, these would
be and . The dots indicate times at which the signal is sampled. There are
infinitely many other frequencies that also cross these sampled points, as specified
by Eq. (13.5).

13.4 Using the FFT to Compute Fourier Transforms
If you just want to know where the peaks in a spectrum occur, you can take the fft, lop o
the right half of the spectrum (after you have checked to make sure the aliased frequencies
aren't spilling over), and plot the remainder as was done at the end of Example 13.2a. There
are many other uses for the fft, however. For instance, it is often useful to numerically
calculate the Fourier transform of a signal, work on the spectrum in the frequency domain,
and then transform back into the time domain. To make this easier, we need to relate the
fft and ifft to the Fourier transform and its inverse:
Fourier transform :
Inverse Fourier transform :

We should warn you up front that these forms are not universally used. For instance,
you can put a factor of 1/2π on one transform rather than a factor of on both.
Physicists frequently use the form in Eq. (13.7) because it makes an energy conservation
theorem known as Parseval's theorem more transparent. Also, it is arbitrary which equation
is called the transform and which is the inverse transform (i.e., you can switch the minus
signs in the exponents). We prefer the convention shown, because the inverse transform
can then be used to cleanly represent a sum of traveling waves of the form The
other sign convention is also mathematically permissible, and often used. You should make
sure you clearly understand the conventions you are using, so you don't have factors of 2π
floating around or time running backward in your models!

The fft and ifft functions in Eq. (13.1) are related to the the forms in Eq. (13.7),
but there are several differences that need to be addressed: (1) The fft is a sum with
no normalization, so the height of your peaks scales with N (the number of points you
sample). (2) The fft has a negative exponent and (in our convention) the Fourier transform
has a positive exponent. (3) The fft aliases negative frequency components to positive
frequencies as discussed in the previous section. To address the first issue, we just need
some judiciously multiplication by factors of N and 2π . The second issue can be addressed
by using the ifft to calculate the Fourier transform and the fft to calculate the inverse.
Finally, to address the aliasing issue, we take the aliased positive frequencies, and put them
back where they belong as negative frequencies (Matlab uses the function fftshift to do
this). When you want to take the inverse Fourier transform, you have to put the negative
frequencies back where the Matlab functions expect them to be. When you put all of this
together, it looks like this:

Example 13.4a (ft.m)

% function to calculate the Fourier Transform of the time series St
function fourtran = ft(St,dt)

fourtran = length(St)*fftshift(ifft(ifftshift(St)))*dt/sqrt(2*pi);


Example 13.4b (ift.m)

% function to calculate the inverse Fourier Transform of the frequency series Sw
function invfourtran = ift(Sw,dw)

invfourtran = fftshift(fft(ifftshift(Sw)))*dw/sqrt(2*pi);

The nesting of functions in these these scripts is not particularly intuitive (so don't spend
a lot of time working out the details of the shifting functions), but they get the job done:
ft.m inputs a time series and a dt (i.e. ) and spits out a properly normalized (according
to our convention) Fourier transform with the negative frequencies at the beginning of the
array; ift.m inputs a dω and a properly normalized frequency spectrum with the negative
frequencies at the beginning of the array, and spits out its inverse Fourier transform.

Since we put the negative frequencies at the beginning of the series, the frequency series
that goes with the spectrum also needs to be fixed:

% Use this if N is even (usually the case since powers of 2 are even)
w = -(N/2)*dw:dw:dw*(N/2-1)

% Use this if N is odd (puts w=0 in the right place)
w = -((N-1)/2)*dw:dw:dw*((N-1)/2);

To illustrate how to use these functions, lets use them to analyze the same signal as in
Example 13.2a

Example 13.4c (ch13ex4c.m)

% Example 13.4c (Physics 330)
clear; close all;

% build a time series made up of 5 different frequencies
% then use ft.m to display the spectrum


% Notice that the w array is different than before
w = -(N/2)*dw:dw:dw*(N/2-1);

% Make a signal consisting of angular frequencies
% w=1, 3, 3.5, 4, and 6

% Use our new function to calculate the fourier transform
% which needs to be saved as ft.m
g = ft(f,tau);
P = abs(g).^2;

title('Power Spectrum with peaks at all the right frequencies')

% Now lets plot a normalized spectrum to compare the relative heights
% of the various peaks

ylabel('Normalized P(\omega)')
title('Normalized Power Spectrum to compare peak height')

Now we have frequency peaks at all the right frequencies. Also note that the peak heights
don't scale with N any more. The amplitude of peaks in a Fourier transform still tends
to be large, however, because instead of amplitude, it is amplitude density (amplitude per
unit frequency, or amplitude squared per unit frequency in the case of a power spectrum).
So if the signal is confined to a tiny range in ω, its density will be huge.

The relative peak heights are similar to what they should be, but zoom in closely
on the normalized power spectrum and you will see that they are not exactly the right
size. (Power is proportional to amplitude squared, so the peaks should be in the ratio
[1,0.25,0.16,0.49,0.04].) This is due to the discrete sampling nature of the data in an fft.

To understand why the relative sizes are o , lets take the Fourier transform of one of

Figure 13.5 Plot of the power spectrum from Example 13.4c

the frequency components in our signal analytically:

Yes, those are delta-functions located at ω = ±ω0, and they are infinite (but they have finite
area.) So when Matlab does the fft on periodic data, the result is a bunch of approximate
delta functions with very narrow widths and large amplitudes. However, since our frequency
array doesn't have, for example, a point exactly at ω = 3 where our signal should have a
delta function, the heights of the approximate delta function peaks are not exactly correct.
Another way of stating this is that only if the total sampling time happens to be an exact
multiple of the periods involved will you get correct amplitudes for periodic signals. You can
get around this problem using a technique called windowing, where you artificially confine
your signal in the time domain which makes your peaks broader in the frequency domain.
With broader peaks the height isn't as sensitive to where your data points fall in relation
to the exact center of the peaks.

We've only scratched the surface of what you can do with numerical Fourier transforms,
but its time to move on. For more details see online help or Mastering Matlab 6, Chapter 21.

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