Riassunto
In a field as rapidly expanding as digital signal processing, even the topics relevant to the basics change over time both in their nature and their relative importance. It is important, therefore, to have an up-to-date text that not only covers the fundamentals, but that also follows a logical development that leaves no gaps readers must somehow bridge by themselves. Digital Signal Processing with Examples in MATLAB® is just such a text. The presentation does not focus on DSP in isolation, but relates it to continuous signal processing and treats digital signals as samples of physical phenomena. The author also takes care to introduce important topics not usually addressed in signal processing texts, including the discrete cosine and wavelet transforms, multirate signal processing, signal coding and compression, least squares systems design, and adaptive signal processing. He also uses the industry-standard software MATLAB to provide examples of signal processing, system design, spectral analysis, filtering, coding and compression, and exercise solutions. All of the examples and functions used in the text are available online at www.crcpress.com. Designed for a one-semester upper-level course but also ideal for self-study and reference, Digital Signal Processing with Examples in MATLAB is complete, self-contained, and rigorous. For basic DSP, it is quite simply the only book you need.
Contenuti
PREFACE
INTRODUCTION
Digital Signal Processing
How to Read this Text
Introduction to MATLAB
Signals, Vectors, and Arrays
Review of Vector and Matrix Algebra Using Matlab Notation
Geometric Series and Other Formulas
Matlab Functions in DSP
The Chapters Ahead
References
LEAST SQUARES, ORTHOGONALITY, AND THE FOURIER SERIES
Introduction
Least Squares
Orthogonality
The Discrete Fourier Series
Exercises
References
CORRELATION, FOURIER SPECTRA, AND THE SAMPLING THEOREM
Introduction
Correlation
The Discrete Fourier Transform (DFT)
Redundancy in the DFT
The FFT algorithm
Amplitude and Phase Spectra
The Inverse DFT
Properties of the DFT
Continuous Transforms
The Sampling Theorem
Waveform Reconstruction and Aliasing
Exercises
References
LINEAR SYSTEMS AND TRANSFER FUNCTIONS
Continuous and Discrete Linear Systems
Properties of Discrete Linear Systems
Discrete Convolution
The z-Transform and Linear Transfer Functions
Poles and Zeros
Transient Response and Stability
System Response via the Inverse z-Transform
Cascade, Parallel, and Feedback Structures
Direct Algorithms
State-Space Algorithms
Lattice Algorithms and Structures
FFT Algorithms
Discrete Linear Systems and Digital Filters
Exercises
References
FIR FILTER DESIGN
Introduction
An Ideal Lowpass Filter
The Realizable Version
Improving an FIR Filter with Window Functions
Highpass, Bandpass, and Bandstop Filters
A Complete FIR Filtering Example
Other Types of FIR Filters
Exercises
References
IIR FILTER DESIGN
Introduction
Linear Phase
Butterworth Filters
Chebyshev Filters
Frequency Translations
The Bilinear Transformation
IIR Digital Filters
Other Types of IIR Filters
Exercises
References
RANDOM SIGNAL AND SPECTRAL ESTIMATION
Introduction
Amplitude Distributions
Uniform, Gaussian, and Other Distributions
Power and Power Density Spectra
Properties of the Power Spectrum
Power Spectral Estimation
Data Windows in Spectral Estimation
The Cross-Power Spectrum
Algorithms
Exercises
References
LEAST-SQUARES SYSTEM DESIGN
Introduction
Applications of Least-Squares Design
System Design via the Mean-Squared Error
A Design Example
Least-Squares Design with Finite Signal Vectors
Correlation and Covariance Computation
Channel Equalization
System Identification
Interference Canceling
Linear Prediction and Recovery
Effects of Independent Broadband Noise
Exercises
References
ADAPTIVE SIGNAL PROCESSING
Introduction
The Mean-Squared Error Performance Surface
Searching the Performance Surface
Steepest Descent and the LMS Algorithm
LMS Example
Direct Descent and the RLS Algorithm
Measures of Adaptive System Performance
Other Adaptive Structures and Algorithms
Exercises
References
SIGNAL INFORMATION, CODING AND COMPRESSION
Introduction
Measuring Information
Two Ways to Compress Signals
Entropy Coding
Transform Coding and the Discrete Cosine Transform
Multirate Signal Decomposition and Subband Coding
Time-Frequency Analysis and Wavelet Transforms
Exercises
References
INDEX
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