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Baldwin explains the underlying signal processing concepts that make the Fast Fourier Transform (FFT) algorithm possible.
Revised: Mon Oct 19 13:36:25 CDT 2015
This page is included in the following book:
Digital Signal Processing - DSP
Table of contents
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 module wastaken from a series that concentrates on having fun while programming in
Java.
Viewing tip
I recommend that you open another copy of this module in a separate
browser window and use the following links to easily find and view the Figuresand Listings while you are reading about them.
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Figure 1. Transform of pulse with negative slope.
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Figure 2. Transform of pulse with positive slope.
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Figure 3. Transform of the sum of two pulses.
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Figure 4. Transform of an impulse with no shift.
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Figure 5. Transform of an impulse with a shift equal to one sample interval and a negative value.
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Figure 6. Transform of an impulse with a shift equal to two sample intervals and a positive value.
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Figure 7. Transform of an impulse with a shift equal to four sample intervals and a positive value.
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Figure 8. Transform of a complex impulse with a shift equal to two sample intervals.
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Figure 9. Case A. Transform of a real sample with two non-zero values.
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Figure 10. The numeric output for Case A.
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Figure 11. Case B in graphical form.
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Figure 12. Case B output in numeric form.
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Figure 13. The graphic form of Case C.
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Figure 14. Case C output in numeric form.
Listings
General discussion
The purpose of this module is to help you to understand how the
Fast Fourier Transform (FFT) algorithm works. In order to understand theFFT, you must first understand the Discrete Fourier Transform (DFT). I
explained how the DFT works in an earlier module titled
Fun with Java, How and Why Spectral Analysis Works .
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Source:
OpenStax, Digital signal processing - dsp. OpenStax CNX. Jan 06, 2016 Download for free at https://legacy.cnx.org/content/col11642/1.38
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