The Scientist and Engineer's Guide to
Digital Signal Processing
By Steven W. Smith, Ph.D.

More than 500 figures, graphs and illustrations!

Here are a few examples:

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FIGURE 12-8
Execution times for calculating the DFT. The
correlation method refers to the algorithm
described in Table 12-2. This method can be
made faster by precalculating the sine and
cosine values and storing them in a look-up
table (LUT). The FFT (Table 12-3) is the
fastest algorithm when the DFT is greater
than 16 points long. The times shown are for
a Pentium processor at 100 MHz.

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FIGURE 25-13
A simple CT system passes a narrow
beam of x- rays through the body from
source to detector. The source and
detector are then translated to obtain a
complete view. The remaining views
are obtained by rotating the source and
detector in about 1 degree increments,
and repeating the translation process.

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FIGURE 25-14
Computed tomography image. This is a CT slice of
a human abdomen, at the level of the navel. Many
organs are visible, such as the (L) Liver, (K) Kidney,
(A) Aorta, (S) Spine, and (C) Cyst covering the right
kidney. CT can visualize internal anatomy far better
than conventional medical x-rays.

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FIGURE 22-6
Compact disc playback block diagram. The digital information is retrieved from the disc with an optical sensor, corrected for EFM and Reed-Solomon encoding, and converted to stereo analog signals.

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FIGURE 17-6
Deconvolution of old phonograph recordings. The frequency spectrum produced by the original singer is illustrated in (a). Resonance peaks in the primitive equipment, (b), produce distortion in the recorded frequency spectrum, (c). The frequency response of the deconvolution filter, (d), is designed to counteracts the undesired convolution, restoring the original spectrum, (e). These graphs are for illustrative purposes only; they are not actual signals.

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FIGURE 20-2
Chebyshev frequency responses. Figures (a) and (b) show the frequency responses of low-pass Chebyshev filters with 0.5% ripple, while (c) and (d) show the corresponding high-pass filter responses.

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FIGURE 23-5
The human retina. The retina contains three principle layers: (1) the rod and cone light receptors, (2) an intermediate layer for data reduction and image processing, and (3) the optic nerve fibers that lead to the brain. The structure of these layers is seemingly backward, requiring light to pass through the other layers before reaching the light receptors.

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FIGURE 22-8
Human speech model. Over a short segment of time, about 2 to 40 milliseconds, speech can be modeled by three parameters: (1) the selection of either a periodic or a noise excitation, (2) the pitch of the periodic excitation, and (3) the coefficients of a recursive linear filter mimicking the vocal tract response.

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FIGURE 25-11
Binary skeletonization. The binary image of a fingerprint, (a), contains ridges that are many pixels wide. The skeletonized version, (b), contains ridges only a single pixel wide.

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FIGURE 19-2
Single pole low-pass filter. Digital recursive filters can mimic analog filters composed of resistors and capacitors. As shown in this example, a single pole low-pass recursive filter smoothes the edge of a step input, just as an electronic RC filter.

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FIGURE 24-3
Common point spread functions. The pillbox, Gaussian, and square, shown in (a), (b), & (c), are common smoothing (low-pass) filters. Edge enhancement (high-pass) filters are formed by subtracting a low-pass kernel from an impulse, as shown in (d). The sinc function, (e), is used very little in image processing because images have their information encoded in the spatial domain, not the frequency domain.

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FIGURE 24-4
3x3 edge modification. The original image, (a), was acquired on an airport x-ray baggage scanner. The shift and subtract operation, shown in (b), results in a pseudo three-dimensional effect.