JavaFun with Java, Understanding the Fast Fourier Transform (FFT) Algorithm

# Fun with Java, Understanding the Fast Fourier Transform (FFT) Algorithm

Developer.com content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Java Programming, Notes # 1486

## Preface

Programming in Java doesn’t have to be dull and boring.  In fact,
it’s possible to have a lot of fun while programming in Java.  This
lesson is one in a series that concentrates on having fun while programming
in Java.

Viewing tip

You may find it useful to open another copy of this lesson in a separate
browser window.  That will make it easier for you to scroll back and
forth among the different figures and listings while you are reading about
them.

Supplementary material

I recommend that you also study the other lessons in my extensive collection
of online Java tutorials.  You will find those lessons published at
Gamelan.com.  However,
as of the date of this writing, Gamelan doesn’t maintain a consolidated index
of my Java tutorial lessons, and sometimes they are difficult to locate there.
You will find a consolidated index at www.DickBaldwin.com.

## General Discussion

The purpose of this lesson is to help you to understand how the Fast Fourier
Transform (FFT) algorithm works.  In order to understand the FFT, you must
first understand the Discrete Fourier Transform (DFT).  I explained how the
DFT works in an earlier lesson entitled
Fun with Java,
How and Why Spectral Analysis Works
.

There are several different FFT algorithms in common use.  In addition,
there are many sites on the web where you can find explanations of the
mechanics of FFT algorithm.  I won’t replicate those
explanations.  Rather, I will explain the underlying concepts that make the
FFT possible and
illustrate those concepts using a simple program.  Hopefully, once you
understand the underlying concepts, one or more of the explanations of the
mechanics that you find on other sites will make sense to you.

A general-purpose transform

The Fourier transform is most commonly associated with its use in
transforming time-domain data into frequency-domain data.  However, it is
important to understand that there is nothing inherent in the Fourier transform
regarding either the time domain or the frequency domain.  Rather, the
Fourier transform is a general-purpose transform that is used to transform a set
of complex data in one domain into a different set of complex data in another
domain.  It is purely happenstance that it happens to be so valuable in
describing the relationship between the time domain and the frequency domain.

Transforming from space domain to wave number domain

For example, my first job after earning a BSEE degree in 1962 was in the
Seismic Research Department of Texas Instruments.  That is where I had my
first encounter with Digital Signal Processing.  In that job, I did a lot
of work with Fourier transforms involving the time domain and the frequency
domain.  I also did a lot of work with Fourier transforms involving the
space domain and the wave-number domain.

Wave number is the name given to the reciprocal of wavelength for
compression and shear waves propagating through a medium such as an iron bar, earth, water,
or air, and also for electromagnetic waves such as radio and radar propagating
through space.

(Those familiar with the subject will know that while compression
waves will propagate through water and air, those media won’t support shear
waves.)

Two-dimensional Fourier transforms

For example, one of the things that we did was to compute two-dimensional
Fourier transforms on diagrams representing weighted points in two-dimensional
space.  We would transform the weighted points in the space domain into
points in the wave-number domain.

The weighted points in the space domain represented the locations and
amplifications of seismometers in a two-dimensional array on the surface of the
earth.  Each seismometer was amplified by a different gain factor and
polarity.  The amplified outputs of the seismometers were added together in
various and complex ways intended to enhance signals and suppress noise.

Wave-number response to seismic waves

In this case, the wave number was the reciprocal of the wave length of
seismic waves propagating across the array.  By plotting the results of the
transformation in the wave-number domain, we could estimate which seismic waves
would be enhanced and which seismic waves would be suppressed by the processing
being applied to the seismometer outputs.

We could also perform experiments on the computer where we caused the weights
to vary with frequency, thus, allowing us to design and place digital filters on
the seismometers to optimize the response of the array to earthquake signals
while suppressing seismic noise associated with nearby cities and other sources
of seismic noise.

A general purpose mathematical transform

I mention all of this simply to illustrate the general nature of the Fourier
transform.  Once again, the Fourier transform is simply a mathematical
process that can be used to transform a set of complex values in one domain into
a set of complex values in a different domain.

Before getting into the details of this discussion, I want to refer you to a couple of
excellent references on the FFT.  Of course, you can find many more by
performing a Google search for the keyword
FFT.

Fourier transform images

Many of the images that you will see in this lesson were produced using an

http://sepwww.stanford.edu/oldsep/hale/FftLab.html
.  I changed the name
of the applet class to cause it to fit into my file-naming scheme.  I also
made a couple of minor modifications to force its output to fit into this narrow
publication format.  Otherwise, I used the applet in its original form.
This applet is extremely useful in performing FFT experiments very quickly and
easily.  I strongly recommend that you become familiar with it.

Information on the FFT algorithm

I also want to refer you to Chapter 12 of the excellent online book entitled
The Scientist and Engineer’s Guide to Digital Signal Processing by Steven
W. Smith, Ph.D.  You will find the book at

http://www.dspguide.com/pdfbook.htm
.  This book contains a wealth of
information, including Smith’s explanation of the mechanics of the FFT algorithm.

Will discuss underlying concepts

As mentioned earlier, the FFT algorithm is very complicated.  I won’t
discuss the mechanics of the algorithm in this lesson.  Rather, I will
explain the underlying concepts that make the FFT algorithm possible.

Hopefully after reading my explanation of the basic concepts, you will be
able to understand the explanation of the mechanics of the algorithm provided by
Smith and others.

A linear transform

The FFT algorithm is an algorithm that takes advantage of several reasonably
well-know facts along with some less well-known facts.

One of those facts is that the Fourier transform is a linear transform.  By this, I mean that the
transform of the sum of two or more input series is equal to the sum of the
transforms of the individual input series.  I will attempt to illustrate
this in Figure 1, Figure 2, and Figure 3.

Images produced by the FFT applet

The images in these figures were produced using the FFT applet mentioned
earlier. Figure 1 Transform of pulse with negative slope.

An examination of Figure 1 shows that the display produced by the applet contains
two sections.  One section is labeled f(x) and the other section is labeled
F(k).

This is an interactive applet with the ability to transform the
complex samples represented by f(x) into complex samples represented by F(k).
Alternatively, the applet can be used to transform complex samples represented
by F(k) into complex samples represented by f(x).

Real and imaginary sections

Each section contains two boxes, one labeled Real and the other labeled
Imaginary.  One box contains a visual representation of a set of real
samples and the other box contains a visual representation of a set of imaginary
samples.

With one exception, each sample is represented by a black circle.  In
each box, one of the samples is represented by an empty circle.  The empty
circle represents an index value of zero.  Samples to the right of the
sample with the empty circle are samples at positive indices, and samples to the
left of the sample with the empty circle are samples at negative indices.

A complex sample

A pair of values, one taken from the Real box and one taken from the
Imaginary box, represents a complex sample.  Any of the circles can be
interactively moved up or down with the mouse.    The value of
each sample is represented by the distance of the corresponding circle from the
horizontal line.

When a change is made to the value of any sample belonging to either f(x) for
F(k), the transformation is recomputed and the display of the other function is
modified accordingly.  If you modify the value of a sample in f(x), the
values in F(k) are automatically modified to show the Fourier transform of f(x).
If you modify the value of a sample in F(k), the values in f(x) are
automatically modified to show the inverse Fourier transform of F(k).

This is an extremely powerful interactive tool.

Powers of two

Most FFT algorithms require the input series to contain a number of complex
samples that is a power of two such as 2, 4, 8, 16, 32, etc.  Most FFT
algorithms also produce the same number of complex samples in the output as are
provided in the input.  The FFT algorithm used in this applet is no
exception to those rules.

A pull-down list at the bottom of the applet lets the user specify 16, 32, or
64 complex samples for both the input and the output.  All of the examples
in this lesson use 16 complex samples for input and output.

Location of the origin

The applet also provides a check box that allows the user to cause the origin
(the empty circle at index value zero) to either be centered or placed at
the left end.  The display in Figure 1 has the origin centered.
Other displays that I will use later have the origin at the left end.

Other applet controls

The other pull-down list and the button at the bottom of the applet provide
other control features that don’t need to be discussed here.  I strongly
urge you to download this applet and experiment with it.  The results can
be very enlightening.

Back to the concept of the linear transform

Having discussed the features of the interactive FFT tool that I used to
produce many of the images in this lesson, it is time to get back to the
discussion of the Fourier transform as a linear transform.  The fact that
the Fourier transform is a linear transform is illustrated in Figure 1, Figure 2, and
Figure 3.

In these three figures, the input series is shown in the real area in the
upper left.    For simplification, the values of the imaginary part of the
input
series shown in the upper right are all zero.

Also, for simplification, the
zero origin is shown in the center by the value with the empty circle.

The real and imaginary parts of the transform output are shown in the bottom of each
figure.

Figure 1 shows an input series consisting of a pulse that starts with a high
value at the origin and extends down and to the right for five samples, ending in a
large negative value.

This input series produces a rather complicated transform output series, as
can be seen in the bottom two boxes in Figure 1.  I will come back to a
discussion of the transform output later.

A mirror-image pulse

Figure 2 shown an input series consisting of a pulse that begins with a large
negative value four samples to the left of the origin and extends up and to the right
ending with a large positive value at the origin.  The input series in
Figure 2 is the mirror image of the input series in Figure 1 relative to the
origin. Figure 2 Transform of pulse with positive slope.

The transform output

Once again, the output from the transform of the input series is shown in the
bottom two boxes of Figure 2.

A comparison of the real part of each of the transforms for Figure 1 and
Figure 2 shows that the real
parts are the same, at least insofar as I was able to control the input by
interactively adjusting the locations of the circles using the mouse.

A comparison of the imaginary part of each of the transforms shows that the
imaginary parts are the same except for the algebraic sign of each of the values
in the imaginary part.  The algebraic sign of each of the values in Figure
2 is the reverse of the algebraic sign of each of the values in Figure 1.

Now sum the two input series

To demonstrate that the Fourier transform is a linear transform, I will
create a new input series that is the sum of the input series from Figure 1 and
Figure 2.  I will show that the transform of the sum is the sum of the
transforms.

This is illustrated in Figure 3. Figure 3 Transform of the sum of two pulses.

The transform of the sum equals the sum of the
transforms

Figure 3 shows an input series that is the sum of the individual input series
from Figure 1 and Figure 2.  This produces a pulse that is symmetric
around the origin indicated by the value with the empty circle.

Normalized output

Note that the display of the transform values produced by this applet is
normalized so as to keep them in a reasonable range for plotting.  As a
result, absolute values don’t have much meaning.  Only relative
values have meaning.

The real part of the transform of the input series in Figure 3 has the same shape as the real parts of the transforms of the input series in
Figure 1 and Figure 2.  This is what would be produced by adding the real
parts of the transforms of the pulses in Figure 1 and Figure 2, and then
normalizing the result.

The imaginary part sums to zero

The imaginary part of the transform of the input series in Figure 3 is zero at
all sample values.  This is what would be produced by adding the imaginary parts
of the transforms of the input series in Figure 1 and Figure 2.

(Recall that the values in the imaginary parts of the two earlier
transforms had the same magnitude but opposite signs).

Thus, Figure 1, Figure 2, and Figure 3 demonstrate that the
transform of the sum of two or more input series is equal to the sum of the
transforms of the individual input series.  The Fourier transform is a linear
transform.

Single sample real pulse with a delay

The real part of the transform of a single real sample with a shift relative
to the origin has
the shape of a cosine curve with a period that is proportional to the reciprocal
of the shift.  Negative sample values produce cosine curves with
negative amplitudes.

The imaginary part of the transform of a single real sample with a shift
relative to the origin
has the shape of a sine curve with a period that is proportional to the
reciprocal of the shift.  Negative sample values produce sine curves
with negative amplitudes.

The magnitude of the transform is the square root of the sum of the squares
of the real and imaginary parts at each output sample point.  For the case
of a single input sample with a shift, that magnitude is
constant for all output sample points and is proportional to the absolute value of the
sample.

The above facts are illustrated in Figure 4, Figure 5, Figure 6, and Figure 7. Figure 4 Transform of a real single sample with no shift.

A shift of zero

Figure 4 shows the transform of a single real pulse with a shift of zero
relative to the origin.

(Note that in this series of figures, the origin was moved from
the center to the left end.  Once again, the sample with the empty
circle represents the origin.)

Although it isn’t obvious, the real part of the transform in Figure 4 has
the shape of a cosine curve with a period that is the reciprocal of the shift.  Because the
shift is zero, the period of the cosine curve is infinite, producing
real values that are constant at all output sample values.

Similarly, the imaginary part of the transform in Figure 4 has a shape that
is a sine curve with an infinite period.  Thus, it is zero at all
output sample values.

A shift of one sample interval

Figure 5 shows the transform of a single real sample with a negative value
and a shift of one sample interval relative to the origin. Figure 5 Transform of a real single sample with a shift equal to one sample interval and a negative value.

A cosine curve and a sine curve

The shape of the real part of the transform output is an upside down cosine curve.
It is upside down because it has a negative amplitude.  This is caused by
the fact that the input sample has a negative value.

The shape of the
imaginary part of the transform is an upside down sine curve.

Number of output samples equals number of input
samples

This transform program computes real and imaginary values from zero to an
output index that is one output sample interval less than the sampling
frequency.  The number of output values is equal to the number of
samples in the input series.  This is very typical of FFT algorithms.

In this case, I set the applet up to accept sixteen input samples and to
produce sixteen output samples.

Representing time and frequency

For the moment, lets think in terms of time and frequency.  Assume that
the input series f(x) is a time series and the output series F(k) is a frequency
spectrum.

To make the arithmetic easy, let’s assume that the sampling interval for the
input time series in the upper left box of Figure 5 is one second.  This gives a
sampling frequency of one sample per second, and a total elapsed time of sixteen
seconds.

The sine and cosine curves in
Figure 5 each go through one complete period between a frequency of zero and
the sampling frequency, which one sample per second.  Thus, the period of the sine and
cosine curves along the frequency axis is one sample per second.  This is the reciprocal of the
time shift
of one sample interval at a sampling frequency of one sample per second.

Stated differently, the number of periods of the sine and cosine curves in
the real and imaginary parts of the transform between a frequency of zero and a
frequency equal to the sampling frequency is equal to the shift in sample
intervals.  A shift of one sample interval produces sine and cosine
curves having one period in the frequency range from zero to the sampling
frequency.  A shift of two sample intervals produces sine and cosine curves
having two periods in the frequency range from zero to the sampling frequency,
etc.  This is illustrated by Figure 6.

A shift of two sample intervals

Figure 6 shows the transform of a real single sample with a shift equal
to two sample intervals and a positive value. Figure 6 Transform of a real single sample with a shift equal to two sample intervals and a positive value.

The real part of the transform has the shape of a cosine curve with two
complete periods between zero and an output index equal to the
sampling frequency.

The imaginary part of the transform has the shape of a sine curve with two
complete periods within the same output interval.  This agrees with the
conclusions stated in the previous section.

A shift of four sample intervals

Finally, Figure 7 shows the transform of a real single sample with a shift equal to four sample intervals. Figure 7 Transform of a real single sample with a shift equal to four sample intervals and a positive value.

The cosine and sine curves that represent the real and imaginary parts of the
transform each have four complete periods between zero and an output index equal to the sampling frequency.

Equations to describe the real and imaginary parts
of the transform

The main point is that if you know the value of a single real sample and you
know its position in the series relative to the origin, you can write equations
that describe the real and imaginary parts of the transform of that single
sample without any requirement to actually perform a Fourier transform.

Those
equations are simple sine and cosine equations as a function of the units of the
output domain.  This is an important concept that contributes greatly to
the implementation of the FFT algorithm.

Transformation of a complex series

The FFT algorithm is an algorithm that transforms a series of complex values
in one domain into a series of complex values in another domain.  The images in the figures discussed so far indicate a
transformation of a complex function given by f(x) into another complex function
given by F(k).  There is nothing in these images to indicate anything about
time and frequency.

If the complex part of the input series f(x) is not zero, things get somewhat
more complicated.  For example, the real and imaginary parts of the
transform of a single delayed sample having both real and imaginary parts
are not necessarily cosine and sine curves.  This is illustrated in Figure
8. Figure 8  Transform of a complex single sample with a
shift equal to two sample intervals.

Figure 8 shows the results of transforming a single sample having both real
and imaginary parts and a shift of two sample intervals.

Although both the real and imaginary parts of the transformed result
have the shape of a sinusoid, neither is a cosine curve and neither is a sine
curve.  Both of the curves are sinusoidal curves that have been
shifted along the horizontal output axis moving their peaks and zero crossings
away from the origin.

Linearity still applies

Because the Fourier transform is a linear transform, you can transform the
real and imaginary parts of the input separately and add the two resulting
transforms.  The sum of the two transforms represents the transform of the
entire input series including both real and imaginary parts.  The program
that I will discuss later takes advantage of this fact.

Even for a complex input series, if you know the values
of the real and imaginary parts of a sample and you know the value of the
shift associated with that sample, you can write equations that describe the real part and the imaginary
part of the transform results.

Can produce the transform of a time series by the
adding transforms of the individual samples

That brings us to the crux of the matter.  Given an input series
consisting of a set of sequential samples taken at uniform sampling intervals,
we know how to write equations for the real and imaginary parts that would be
produced by performing a Fourier transform on each of those samples
individually.

The input series is the sum of the individual
samples

We know that we can consider the input series to consist of the sum of the
individual samples, each having a specified value and a different shift.
We know that the Fourier transform is a linear transform.  Therefore, the
Fourier transform of an input series is the sum of the transforms of the
individual samples.

If we are clever enough, we can use these facts to develop a computational
algorithm that can compute the Fourier transform of a time series much faster
than can be obtained using a brute force DFT algorithm.  Fortunately, some very
clever
people have already developed that algorithm.  It goes by the name of the
Fast Fourier Transform, or FFT algorithm.

Steps in the FFT algorithm

In truth, there are several different forms of the FFT algorithm,
and the mechanics of each may be slightly different.  At least one, and
probably many of the algorithms operate by performing the following steps:

• Decompose an N-point complex series into N individual complex series,
each consisting of a single complex sample. The order of the decomposition
in an FFT algorithm is rather complicated. It is this order of
decomposition, and the order of the subsequent recombination of transform
results that causes the FFT algorithm to be so fast. It is also that order
that makes the algorithm somewhat difficult to understand. Note that the program that
I will discuss later does not implement that special order of decomposition
and recombination.
• Calculate the transform of each of the N complex series, each consisting
of a single complex sample.  This treats each complex sample as if it
is located at the origin of a complex series. This step is trivial. The real
part of the transform of a single complex sample located at the origin of
the series is a complex constant whose values are proportional to the real
and imaginary values that make up the complex sample.  Since the
complex input series consists of only one complex sample, there is only one
complex value in the complex transform.
• Correct each of the N transform results to reflect the original position
of the complex sample in the input series. This involves the application of
sine and cosine curves to the real and imaginary parts of the transform.
This step is usually combined with the recombination step that follows.
• Recombine the N transform results into a single transform result that
represents the transform of the original complex series. This is a very
complicated operation in a real FFT algorithm. It must reverse the order of
decomposition in the first step described earlier. As mentioned earlier, it
is the order of the decomposition and subsequent recombination that
minimizes the arithmetic operations required and gives the FFT its
tremendous speed. The program that I will discuss later does not implement
the special order of decomposition and recombination used in an actual FFT
algorithm.

## A Sample Program

I want to emphasize at the outset that this program DOES NOT implement an FFT algorithm.  Rather, this program illustrates the underlying
signal processing concepts that make the FFT possible in a form that is more easily understood than is normally the case with an actual FFT algorithm.

Separate processes in an FFT algorithm

In summary, a typical FFT algorithm performs the following processes:

• Decompose an N-point complex series into N individual complex series,
each consisting of a single complex sample.
• Recognize that the complex transform of a single complex sample is equal
to the value of the complex sample.
• Correct the transform for each complex sample to reflect the original
position of the complex sample in the input series.
• Recombine the N transform results into a single transform result that
represents the transform of the original complex series.

This program performs each of the processes listed above.  However, it
does not perform those processes in the special order used by an FFT algorithm
that causes the FFT algorithm to be able to perform those processes at very high
speed.

How the processes are implemented

The decomposition process in this program takes the complex samples in the
order that they appear in the input complex series.

The transform of each complex sample is simply the sample itself. This is the
result that would be obtained by actually computing the transform of the complex
sample if the sample were the first sample in the series.

The transform result for each complex sample (the sample itself) is then
corrected for position by applying sine and cosine curves to reflect the actual
position of the complex sample within the original complex series.

In order to accomplish the recombination of the corrected transform results, the
real and imaginary parts of the corrected transform are added to accumulators.
These accumulators are used to accumulate the corrected real and imaginary parts
from the corrected transforms for all of the individual complex samples.

Once the real and imaginary parts have been accumulated for all of the complex
samples, the real part of the accumulator represents the real part of the
transform of the original complex series. The imaginary part of the accumulator
represents the imaginary part of the transform of the original complex series.
However, an actual transform was never performed on the original complex series.

Three cases are examined

This program creates three separate complex series, applies the processes listed
above to each of those series, and displays the results on the screen.

No attempt is made to manage the decomposition and the subsequent
recombination in the manner of a true FFT algorithm. Therefore, this program is
designed to illustrate the processes involved, and is not designed to provide
the speed of a true FFT algorithm.

This program was tested using SDK 1.4.2 under WinXP.

Will discuss in fragments

As is my usual approach, I will discuss and explain this program in
fragments.  A complete listing of the program is provided in Listing 9 near
the end of the lesson.

The program begins in Listing 1, which shows the beginning of the controlling
class named Fft02 and the beginning of the main method.

 ```class Fft02{ public static void main(String[] args){ Transform transform = new Transform(); Listing 1```

Instantiate a Transform object

The first statement in the main method instantiates an object of the
Transform class.  This object implements the processes used in an FFT, but
does not implement those processes in the special order required by an FFT algorithm.

The purpose of an object of the Transform class is to illustrate the processes
commonly used in an FFT in a manner that is more easily understood than is often the case with an actual FFT algorithm.

I will put the main method on the back burner for the moment and
explain the class named Transform.

The class named Transform

Listing 2 presents the beginning of the class named Transform
Listing 2 also presents the beginning of an instance method of that class named
doIt.  The doIt method computes and returns the complex
transform (via output parameters) of an incoming complex series.

 ```class Transform{ void doIt(double[] realIn, double[] imagIn, double scale, double[] realOut, double[] imagOut){ Listing 2```

The method parameters

The doIt method receives five incoming parameters.  The first two
parameters are references to two array objects of type double containing
the real and imaginary parts of the input series.

The third parameter is a scale factor that is applied to the transform output
in an attempt to keep the values in a range suitable for plotting if desired.

The last two parameters are references to array objects of type double
The results of performing the transform are used to populate these two arrays.
This is the mechanism by which the object returns the transform results to the
calling program.  It is assumed that all of the elements in these two array
objects contain values of zero upon entry to the doIt method.

Performing the transform

The body of the doIt method is presented in Listing 3.  The code
in Listing 3 iterates on the input arrays, passing each complex sample contained
in those two arrays to a method named correctAndRecombine.

 ``` for(int cnt = 0;cnt < realIn.length;cnt++){ correctAndRecombine(realIn[cnt], imagIn[cnt], cnt, realIn.length, scale, realOut, imagOut); }//end for loop }//end doIt Listing 3```

The transforms of the complex input samples

Each complex value in the incoming arrays represents both a complex sample and the transform of that complex sample under the assumption that the complex sample appears at the
origin of the input series.

Correct for actual position and recombine

The method named correctAndRecombine corrects the transform result for each of the complex samples in the series
so as to reflect the actual position of the complex sample in the original input series.

Then the method named correctAndRecombine adds the corrected transform result into
a pair of accumulators, one for the real part and one for the imaginary part.
This accomplishes the recombination of the corrected transforms of the input
samples in order to produce the transform of the entire original complex input series.

The correctAndRecombine method

The correctAndRecombine method is shown in Listing 4.  Listing 4
also signals the end of the Transform class.

 ``` void correctAndRecombine(double realSample, double imagSample, int position, int length, double scale, double[] realOut, double[] imagOut){ //Calculate the complex transform values for // each sample in the complex output series. for(int cnt = 0; cnt < length; cnt++){ double angle = (2.0*Math.PI*cnt/length)*position; //Calculate output based on real input realOut[cnt] += realSample*Math.cos(angle)/scale; imagOut[cnt] += realSample*Math.sin(angle)/scale; //Calculate output based on imag input realOut[cnt] -= imagSample*Math.sin(angle)/scale; imagOut[cnt] += imagSample*Math.cos(angle)/scale; }//end for loop }//end correctAndRecombine }//end class transform Listing 4```

This method accepts an incoming complex sample value and the position in the series associated with that sample. The method
corrects the real and imaginary transform values for that complex sample to
reflect the specified position in the input series.

After correcting the transform values for the sample on the basis of
position, the method updates the corresponding real and imaginary values contained in array objects
that are used to accumulate the real and imaginary values for all of the samples.

References to the array objects are received as input parameters. Outgoing results are scaled by an incoming parameter in an attempt to cause the output values to fall within a reasonable range in case someone wants to plot them.

The incoming parameter named length specifies the number of output
samples that are to be produced.

Hopefully this explanation will make it possible for you to understand the
code in Listing 4.

Note in particular the use of the Math.cos and Math.sin methods
to apply the cosine and sine curves in the correction of the transforms of the
individual complex samples.  This is used to produce results similar to
those shown in Figure 5 through Figure 7.

Note the use of the position and length parameters in the
computation of the angle that is passed as an argument to the Math.cos
and Math.sin methods.

Also note how the correction is made separately on the real and imaginary
parts of the input.  This produces results similar to those shown in Figure
7 after those results are added in the accumulators.

Back to the main method

Returning now to the main method, the code in Listing 5 prepares the input data and the output arrays for
the first case that we will look at.  This case is labeled as Case A.

 ``` System.out.println("Case A"); double[] realInA = {0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1}; double[] imagInA = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}; double[] realOutA = new double; double[] imagOutA = new double; //Perform the transform and display the // transformed results for the original // complex series. transform.doIt(realInA,imagInA,2.0,realOutA, imagOutA); display(realOutA,imagOutA); Listing 5```

Note that for Case A, the input complex series contains non-zero values only in the real part.  Also, most of the values in the real part are zero.

The graphic form

Case A is shown in graphic form in Figure 9.  As you can see, the input
series consists of two non-zero values in the real part.  All the values in
the imaginary part are zero. Figure 9  Case A.  Transform of a real sample with
two non-zero values.

The real part of the transform of the complex input series looks like one
cycle of a cosine curve.  All of the values in the imaginary part of the
transform result are zero.

The numeric output

As you saw in Listing 5, the code in the main method invokes a method
named display to display the complex transform output in numeric form on
the screen.  The output produced by Listing 5 is shown in Figure 10.
(Note that I manually inserted line breaks to force the material to fit in
this narrow publication format.)

 ```Case A Real: 1.0 0.923 0.707 0.382 0.0 -0.382 -0.707 -0.923 -1.0 -0.923 -0.707 -0.382 0.0 0.382 0.707 0.923 imag: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Figure 10```

If you plot the real and imaginary values in Figure 10, you will see that
they match the transform output shown in graphic form in Figure 9.

Case B code

The code from the main method for Case B is shown in Listing 6.  Note that the input complex series contains non-zero values in both the real and imaginary parts.

 ``` System.out.println("nCase B"); double[] realInB = {0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1}; double[] imagInB = {0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1}; double[] realOutB = new double; double[] imagOutB = new double; transform.doIt(realInB,imagInB,2.0,realOutB, imagOutB); display(realOutB,imagOutB); Listing 6```

Case B in graphical form

Case B is shown in graphical form in Figure 11. Figure 11  Case B.  Transform of a simple complex series.

Case B in numeric form

The output from the code in Listing 6 is shown in Figure 12.

 ```Case B Real: 1.0 0.923 0.707 0.382 0.0 -0.382 -0.707 -0.923 -0.999 -0.923 -0.707 -0.382 0.0 0.382 0.707 0.923 imag: -1.0 -0.923 -0.707 -0.382 0.0 0.382 0.707 0.923 1.0 0.923 0.707 0.382 0.0 -0.382 -0.707 -0.923 Figure 12```

If you plot the values for the real and imaginary parts from Figure 12, you
will see that they match the real and imaginary output shown in Figure 11.

Case C code

The code extracted from the main method for Case C is shown in Listing
7.

 ``` System.out.println("nCase C"); double[] realInC = {1.0,0.923,0.707,0.382,0.0,-0.382,-0.707, -0.923,-1.0,-0.923,-0.707,-0.382,0.0, 0.382,0.707,0.923}; double[] imagInC = {0.0,-0.382,-0.707,-0.923,-1.0,-0.923, -0.707,-0.382,0.0,0.382,0.707,0.923, 1.0,0.923,0.707,0.382}; double[] realOutC = new double; double[] imagOutC = new double; transform.doIt(realInC,imagInC,16.0,realOutC, imagOutC); display(realOutC,imagOutC); Listing 7```

The complex input series for Case C is a little more complicated than that
for either of the previous two cases.  Note in particular that the input complex series contains non-zero values in both the real and imaginary parts.  In addition, very few of the values in the complex series have a value of zero.

(The values of the complex samples actually describe a cosine curve and a
negative sine curve as shown in Figure 13.)

The graphic form of Case C

Case C is shown in graphic form in Figure 13. Figure 13  Case C.  Transform of a more complicated complex series.

The Fourier transform is reversible

One of the interesting things to note about Figure 13 is the similarity of
Figure 13 and Figure 5.  These two figures illustrate the reversible nature
of the Fourier transform.

If I had used a positive input real value instead of a negative input real
value in Figure 5, the input of Figure 5 would look exactly like the output in
Figure 13, and the output of Figure 5 would look exactly like the input of
Figure 13.

With that as a hint, you should now be able to figure out how I used a mouse
and drew the perfect sine and cosine curves in Figure 13.  In fact, I
didn’t draw them at all.  Rather, I used my mouse and drew the output, and
the applet gave me the corresponding input automatically.

Case C in numeric form

The output produced by the code in Listing 7 is shown in Figure 14.

 ```Case C Real: 0.0 0.999 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 imag: 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Figure 14```

If you plot the real and imaginary input values from Listing 7, you will see
that they match the input values in Figure 13.  If you plot the real and
imaginary output values in Figure 14, you will see that they match the output
values shown in Figure 13.

Listing 7 signals the end of the main method.

The display method

Listing 8 shows the code for a simple method named display.  The purpose of
the display method is to display a real series and an imaginary series, each contained in an incoming array object of type
double.  The double values are truncated to no more than four digits before displaying them.  Then they are displayed on a single line.

 ``` static void display(double[] real, double[] imag){ System.out.println("Real: "); for(int cnt=0;cnt < real.length;cnt++){ System.out.print(((int)(1000.0*real[cnt])) /1000.0 + " "); }//end for loop System.out.println(); System.out.println("imag: "); for(int cnt=0;cnt < imag.length;cnt++){ System.out.print(((int)(1000.0*imag[cnt])) /1000.0 + " "); }//end for loop System.out.println(); }//end display }//end class Fft02 Listing 8```

Listing 8 also signals the end of the controlling class named Fft02.

## Run the Program

I encourage you to copy and compile the program that you will find in Listing
9.  Experiment with different complex input series.

http://sepwww.stanford.edu/oldsep/hale/FftLab.html
and experiment with it as
well.  Compare the numeric output produced by this program with the graphic
output produced by the applet.

I also encourage you to read what Steven Smith has to say about the FFT
algorithm at
http://www.dspguide.com/ch12.htm
.  Pay particular attention to his
explanation of the order in which the input series is decomposed and the order
in which the individual transform outputs are recombined.

Finally, I encourage you to examine the source code for the applet.  You
will find that source code at http://sepwww.stanford.edu/oldsep/hale/FftLab.java
Concentrate on that portion of the source code that performs the FFT.
Hopefully, what you have learned in this lesson in addition to what you learn
from Steven Smith’s book will make it easier for you to understand the source
code for the FFT.

## Summary

In this lesson, I have explained some of the underlying signal processing
concepts that make the FFT possible.  I illustrated those concepts in a
program designed specifically to be as simple as possible while still
illustrating the concepts.

Now that you understand those concepts, you should
be able to better understand explanations of the mechanics of the FFT algorithm
that appear on various websites.

## Complete Program Listing

A complete listing of the program is provided in Listing 9 below.

 ```/*File Fft02.java Copyright 2004, R.G.Baldwin Rev 4/30/04 This program DOES NOT implement an FFT algorithm. Rather, this program illustrates the underlying FFT concepts in a form that is much more easily understood than is normally the case with an actual FFT algorithm. The steps in the implementation of a typical FFT algorithm are as follows: 1. Decompose an N-point complex series into N individual complex values, each consisting of a single complex sample. The order of the decomposition in an FFT algorithm is rather complicated. It is this order of decomposition, and the order of the subsequent recombination of transform results that causes the FFT to be so fast. It is also that order that makes the algorithm somewhat difficult to understand. This program does not implement that order of decomposition and recombination. 2. Calculate the transform of each of the N complex samples, treating each as if it were located at the beginning of the complex series. This step is trivial. The real part of the transform of a single complex sample located at the beginning of the series is a complex constant whose values are proportional to the real and imaginary values that make up the complex sample. 3. Correct each of the N transform results to reflect the actual position of the complex sample in the series. This involves the application of sine and cosine curves to the real and imaginary parts of the transform. This step is usually combined with the recombination step that follows. 4. Recombine the N transform results into a single transform result that represents the transform of the original complex series. This is a very complicated operation in a real FFT algorithm. It must reverse the order of decomposition in the first step described earlier. As mentioned earlier, it is the order of the decomposition and subsequent recombination that minimizes the arithmetic operations required and gives the FFT its tremendous speed. This program does not implement the special order of decomposition and recombination used in an actual FFT algorithm. This program creates three separate complex series, applies the processes listed above to each of those series, and displays the results on the screen. No attempt is made to manage the decomposition and the subsequent recombination in the manner of a true FFT algorithm. Therefore, this program is designed to illustrate the processes involved, and is not designed to provide the speed of a true FFT algorithm. The decomposition process in this program takes the complex samples in the order that they appear in the input complex series. The transform of each complex sample is simply the sample itself. This is the result that would be obtained by actually computing the transform of the complex sample if the sample were the first sample in the series. The transform result for each complex sample is then corrected by applying sine and cosine curves to reflect the actual position of the complex sample within the complex series. The real and imaginary parts of the corrected transform results are then added to accumulators that are used to accumulate the corrected real and imaginary parts from the corrected transforms for all of the individual complex samples. Once the real and imaginary parts have been accumulated for all of the complex samples, the real part of the accumulator represents the real part of the transform of the original complex series. The imaginary part of the accumulator represents the imaginary part of the transform of the original complex series. Tested using SDK 1.4.2 under WinXP ************************************************/ class Fft02{ public static void main(String[] args){ //Instantiate an object that will implement // the processes used in an FFT, but not in // the order required by an FFT algorithm. Transform transform = new Transform(); //Prepare the input data and the output // arrays for Case A. Note that for this // case, the input complex series contains // non-zero values only in the real part. // Also, most of the values in the real part // are zero. System.out.println("Case A"); double[] realInA = {0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1}; double[] imagInA = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0}; double[] realOutA = new double; double[] imagOutA = new double; //Perform the transform and display the // transformed results for the original // complex series. transform.doIt(realInA,imagInA,2.0,realOutA, imagOutA); display(realOutA,imagOutA); //Process and display the results for Case B. // Note that the input complex series // contains non-zero values in both the real // and imaginary parts. However, most of the // values in the real and imaginary parts are // zero. System.out.println("nCase B"); double[] realInB = {0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1}; double[] imagInB = {0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0,-1}; double[] realOutB = new double; double[] imagOutB = new double; transform.doIt(realInB,imagInB,2.0,realOutB, imagOutB); display(realOutB,imagOutB); //Process and display the results for Case C. // Note that the input complex series // contains non-zero values in both the real // and imaginary parts. In addition, very // few of the values in the complex series // have a value of zero. (The values of the // complex samples actually describe a cosine // curve and a sine curve.) System.out.println("nCase C"); double[] realInC = {1.0,0.923,0.707,0.382,0.0,-0.382,-0.707, -0.923,-1.0,-0.923,-0.707,-0.382,0.0, 0.382,0.707,0.923}; double[] imagInC = {0.0,-0.382,-0.707,-0.923,-1.0,-0.923, -0.707,-0.382,0.0,0.382,0.707,0.923, 1.0,0.923,0.707,0.382}; double[] realOutC = new double; double[] imagOutC = new double; transform.doIt(realInC,imagInC,16.0,realOutC, imagOutC); display(realOutC,imagOutC); }//end main //===========================================// //The purpose of this method is to display // a real series and an imaginary series, // each contained in an incoming array object // of type double. The double values are // truncated to no more than four digits // before displaying them. Then they are // displayed on a single line. static void display(double[] real, double[] imag){ System.out.println("Real: "); for(int cnt=0;cnt < real.length;cnt++){ System.out.print(((int)(1000.0*real[cnt])) /1000.0 + " "); }//end for loop System.out.println(); System.out.println("imag: "); for(int cnt=0;cnt < imag.length;cnt++){ System.out.print(((int)(1000.0*imag[cnt])) /1000.0 + " "); }//end for loop System.out.println(); }//end display }//end class Fft02 //=============================================// //This class applies the processes normally used // in an FFT algorithm. However, this class does // not apply those processes in the special order // required of an FFT algorithm. It is that // special order that minimizes the arithmetic // requirements of an FFT algorithm and causes it // to be very fast. The purpose of an object of // this class is to illustrate the processes in a // more easily understood fashion that is often // the case with an actual FFT algorithm. class Transform{ void doIt(double[] realIn,double[] imagIn, double scale,double[] realOut, double[] imagOut){ //Each complex value in the incoming arrays // represents both a complex sample and the // transform of that complex sample under the // assumption that the complex sample appears // at the beginning of the series. //Correct the transform result for each of // the complex samples in the series to // reflect the actual position of the complex // sample in the series. Add the corrected // transform result into accumulators in // order to produce the transform of the // original complex series. for(int cnt = 0;cnt < realIn.length;cnt++){ correctAndRecombine(realIn[cnt], imagIn[cnt], cnt, realIn.length, scale, realOut, imagOut); }//end for loop }//end doIt //===========================================// //This method accepts an incoming complex // sample value and the position in the series // associated with that sample. The method // calculates the real and imaginary transform // values associated with that complex sample // when it is located at the specified // position. Then it updates the corresponding // real and imaginary values contained in array // objects used to accumulate the real and // imaginary values for all of the samples. // References to the array objects are received // as input parameters. Outgoing results are // scaled by an incoming parameter in an // attempt to cause the output values to fall // within a reasonable range in case someone // wants to plot them. void correctAndRecombine( double realSample,double imagSample, int position,int length,double scale, double[] realOut,double[] imagOut){ //Calculate the complex transform values for // each sample in the complex output series. for(int cnt = 0; cnt < length; cnt++){ double angle = (2.0*Math.PI*cnt/length)*position; //Calculate output based on real input realOut[cnt] += realSample*Math.cos(angle)/scale; imagOut[cnt] += realSample*Math.sin(angle)/scale; //Calculate output based on imag input realOut[cnt] -= imagSample*Math.sin(angle)/scale; imagOut[cnt] += imagSample*Math.cos(angle)/scale; }//end for loop }//end correctAndRecombine }//end class transform //=============================================// Listing 9```

Copyright 2004, Richard G. Baldwin.  Reproduction in whole or in
part in any form or medium without express written permission from Richard
Baldwin is prohibited.

Richard Baldwin
is a college professor (at Austin Community College in Austin, TX) and
private consultant whose primary focus is a combination of Java, C#, and
XML. In addition to the many platform and/or language independent benefits
of Java and C# applications, he believes that a combination of Java, C#,
and XML will become the primary driving force in the delivery of structured
information on the Web.

Richard has participated in numerous consulting projects, and he frequently
provides onsite training at the high-tech companies located in and around
Austin, Texas.  He is the author of Baldwin’s Programming Tutorials,
which has gained a worldwide following among experienced and aspiring programmers.
He has also published articles in JavaPro magazine.

In addition to his programming expertise, Richard has many years of
practical experience in Digital Signal Processing (DSP).  His first
job after he earned his Bachelor’s degree was doing DSP in the Seismic Research
Department of Texas Instruments.  (TI is still a world leader in DSP.)
In the following years, he applied his programming and DSP expertise to other
interesting areas including sonar and underwater acoustics.

Richard holds an MSEE degree from Southern Methodist University and
has many years of experience in the application of computer technology
to real-world problems.

-end-

Subscribe to Developer Insider for top news, trends & analysis