The Scientist and Engineer's Guide to Digital Signal Processing146
Each of the four Fourier Transforms can be subdivided into real and
complex versions. The real version is the simplest, using ordinary numbers
and algebra for the synthesis and decomposition. For instance, Fig. 8-1 is
an example of the real DFT. The complex versions of the four Fourier
transforms are immensely more complicated, requiring the use of complex
numbers. These are numbers such as: , where j is equal to 3%4j
&1
(electrical engineers use the variable j, while mathematicians use the
variable, i). Complex mathematics can quickly become overwhelming, even
to those that specialize in DSP. In fact, a primary goal of this book is to
present the fundamentals of DSP without the use of complex math, allowing
the material to be understood by a wider range of scientists and engineers.
The complex Fourier transforms are the realm of those that specialize in
DSP, and are willing to sink to their necks in the swamp of mathematics.
If you are so inclined, Chapters 30-33 will take you there.
The mathematical term: transform, is extensively used in Digital Signal
Processing, such as: Fourier transform, Laplace transform, Z transform,
Hilbert transform, Discrete Cosine transform, etc. Just what is a transform?
To answer this question, remember what a function is. A function is an
algorithm or procedure that changes one value into another value. For
example, is a function. You pick some value for x, plug it into they ' 2x%1
equation, and out pops a value for y. Functions can also change several
values into a single value, such as: , where and c arey ' 2a % 3b % 4c a, b,
changed into y.
Transforms are a direct extension of this, allowing both the input and output to
have multiple values. Suppose you have a signal composed of 100 samples.
If you devise some equation, algorithm, or procedure for changing these 100
samples into another 100 samples, you have yourself a transform. If you think
it is useful enough, you have the perfect right to attach your last name to it and
expound its merits to your colleagues. (This works best if you are an eminent
18th century French mathematician). Transforms are not limited to any specific
type or number of data. For example, you might have 100 samples of discrete
data for the input and 200 samples of discrete data for the output. Likewise,
you might have a continuous signal for the input and a continuous signal for the
output. Mixed signals are also allowed, discrete in and continuous out, and
vice versa. In short, a transform is any fixed procedure that changes one chunk
of data into another chunk of data. Let's see how this applies to the topic at
hand: the Discrete Fourier transform.
Notation and Format of the Real DFT
As shown in Fig. 8-3, the discrete Fourier transform changes an N point input
signal into two point output signals. The input signal contains theN/2%1
signal being decomposed, while the two output signals contain the amplitudes
of the component sine and cosine waves (scaled in a way we will discuss
shortly). The input signal is said to be in the time domain. This is because
the most common type of signal entering the DFT is composed of