Digital Signal Processing with Examples in MATLAB

Samuel D. Stearns, Donald R. Hush

• Publisher: CRC Press
• Year: 2016
• Print ISBN: 9781439837825
• eBook ISBN: 9781000755633
• Topic: Computer Science
• Subtopic: Electrical Engineering & Telecommunications
• Index: Computer Science

Based on fundamental principles from mathematics, linear systems, and signal analysis, digital signal processing (DSP) algorithms are useful for extracting information from signals collected all around us. Combined with today's powerful computing capabilities, they can be used in a wide range of application areas, including engineering, communicati

1

Introduction

1.1 Digital Signal Processing

Digital signal processing (DSP) has become an established area of electrical and computer engineering in relatively recent times. In fact, because all types of signals, when they are processed, are now most often processed in digital form, scientists and engineers in all disciplines have come to at least a nodding acquaintance with the subject in order to understand what their instruments and displays are trying to tell them.

Compared with other areas of electrical engineering that share their analytic tools with DSP, such as fields and waves, communication theory, circuits, and control theory, DSP has a relatively short history, a relatively rapid growth, and astonishing diversification into nearly every branch of technology. This is primarily due to the remarkable growth and change in DSP electronic hardware, two aspects of which are illustrated in Figure 1.1.

The left plot* of the figure illustrates the exponential increase in the number of transistors on a processor chip over the years 1970–2010. Since the area of a chip has remained relatively constant, the plot also implies an exponential decrease in transistor size and spacing between transistors. The increase in number of transistors is called exponential because, when the ordinate scale is logarithmic as it is in Figure 1.1, the growth curve is a straight line. Processing speeds, in terms of operations per unit time, have also increased in proportion, due to faster switching rates as well as shorter delays caused by the decreased distance between transistors.

The right plot^† of the figure illustrates a similar exponential increase in the number of computations per unit of energy, or in other words, an exponential decrease in the power and energy requirements of a chip designed to perform a given processing task. This has enabled the wide diversity in lightweight mobile and handheld DSP devices, as well as processors that run for long periods on low power in space and other remote locations.

FIGURE 1.1
The remarkable growth and diversity in digital signal processing applications, especially since 1970, has been due, first, to the exponential increase in the number of transistors on a processor chip, and second, to the exponential increase in the number of computations possible per unit of energy.

Al...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Foreword to the Second Edition
• Foreword to the First Edition
• Preface to the Second Edition
• Preface to the First Edition
• Authors
• 1. Introduction
• 2. Least Squares, Orthogonality, and the Fourier Series
• 3. Correlation, Fourier Spectra, and the Sampling Theorem
• 4. Linear Systems and Transfer Functions
• 5. FIR Filter Design
• 6. IIR Filter Design
• 7. Random Signals and Spectral Estimation
• 8. Least-Squares System Design
• 9. Adaptive Signal Processing
• 10. Signal Information, Coding, and Compression
• 11. Models of Analog Systems
• 12. Pattern Recognition with Support Vector Machines
• Appendix: Table of Laplace and z Transforms