book

Digital Signal Processing with Examples in MATLAB, 2nd Edition

by Samuel D. Stearns, Donald R. Hush

April 2016

Intermediate to advanced

516 pages

12h 44m

English

CRC Press

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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
1.1 Digital Signal Processing1.2 How to Read This Text1.3 Introduction to MATLAB®1.4 Signals, Vectors, and Arrays1.5 Review of Vector and Matrix Algebra Using MATLAB® Notation1.6 Geometric Series and Other Formulas1.7 MATLAB® Functions in DSP1.8 The Chapters AheadReferencesFurther Reading
2. Least Squares, Orthogonality, and the Fourier Series
2.1 Introduction2.2 Least Squares2.3 Orthogonality2.4 The Discrete Fourier SeriesExercisesReferences
3. Correlation, Fourier Spectra, and the Sampling Theorem
3.1 Introduction3.2 Correlation3.3 The Discrete Fourier Transform (DFT)3.4 Redundancy in the DFT3.5 The FFT Algorithm3.6 Amplitude and Phase Spectra3.7 The Inverse DFT3.8 Properties of the DFT3.9 Continuous Transforms, Linear Systems, and Convolution3.10 The Sampling Theorem3.11 Waveform Reconstruction and Aliasing3.12 Resampling3.13 Nonuniform and Log-Spaced SamplingExercisesReferencesFurther Reading
4. Linear Systems and Transfer Functions
4.1 Continuous and Discrete Linear Systems4.2 Properties of Discrete Linear Systems4.3 Discrete Convolution4.4 The z-Transform and Linear Transfer Functions4.5 The Complex z-Plane and the Chirp z-Transform4.6 Poles and Zeros4.7 Transient Response and Stability4.8 System Response via the Inverse z-Transform4.9 Cascade, Parallel, and Feedback Structures4.10 Direct Algorithms4.11 State-Space Algorithms4.12 Lattice Algorithms and Structures4.13 FFT Algorithms4.14 Discrete Linear Systems and Digital Filters4.15 Functions Used in This ChapterExercisesReferencesFurther Reading
5. FIR Filter Design
5.1 Introduction5.2 An Ideal Lowpass Filter5.3 The Realizable Version5.4 Improving an FIR Filter with Window Functions5.5 Highpass, Bandpass, and Bandstop Filters5.6 A Complete FIR Filtering Example5.7 Other Types of FIR Filters5.8 Digital Differentiation5.9 A Hilbert TransformerExercisesReferencesFurther Reading
6. IIR Filter Design
6.1 Introduction6.2 Linear Phase6.3 Butterworth Filters6.4 Chebyshev Filters6.5 Frequency Translations6.6 The Bilinear Transformation6.7 IIR Digital Filters6.8 Digital Resonators and the Spectrogram6.9 The All-Pass Filter6.10 Digital Integration and AveragingExercisesReferencesFurther Reading
7. Random Signals and Spectral Estimation
7.1 Introduction7.2 Amplitude Distributions7.3 Uniform, Gaussian, and Other Distributions7.4 Power and Power Density Spectra7.5 Properties of the Power Spectrum7.6 Power Spectral Estimation7.7 Data Windows in Spectral Estimation7.8 The Cross-Power Spectrum7.9 AlgorithmsExercisesReferencesFurther Reading
8. Least-Squares System Design
8.1 Introduction8.2 Applications of Least-Squares Design8.3 System Design via the Mean-Squared Error8.4 A Design Example8.5 Least-Squares Design with Finite Signal Vectors8.6 Correlation and Covariance Computation8.7 Channel Equalization8.8 System Identification8.9 Interference Canceling8.10 Linear Prediction and Recovery8.11 Effects of Independent Broadband NoiseExercisesReferencesFurther Reading
9. Adaptive Signal Processing
9.1 Introduction9.2 The Mean-Squared Error Performance Surface9.3 Searching the Performance Surface9.4 Steepest Descent and the LMS Algorithm9.5 LMS Examples9.6 Direct Descent and the RLS Algorithm9.7 Measures of Adaptive System Performance9.8 Other Adaptive Structures and AlgorithmsExercisesReferencesFurther Reading
10. Signal Information, Coding, and Compression
10.1 Introduction10.2 Measuring Information10.3 Two Ways to Compress Signals10.4 Adaptive Predictive Coding10.5 Entropy Coding10.6 Transform Coding and the Discrete Cosine Transform10.7 The Discrete Sine Transform10.8 Multirate Signal Decomposition and Subband Coding10.9 Time–Frequency Analysis and Wavelet TransformsExercisesReferences
11. Models of Analog Systems
11.1 Introduction11.2 Impulse-Invariant Approximation11.3 Final Value Theorem11.4 Pole–Zero Comparisons11.5 Approaches to Modeling11.6 Input-Invariant Models11.7 Other Linear Models11.8 Comparison of Linear Models11.9 Models of Multiple and Nonlinear Systems11.10 Concluding RemarksExercisesReferencesFurther Reading
12. Pattern Recognition with Support Vector Machines
12.1 Introduction12.2 Pattern Recognition Principles12.3 Learning12.3.1 The Independent and Identically Distributed Sample Plan12.3.2 Learning Methods12.4 Support Vector Machines12.4.1 The Support Vector Machine Function Class12.4.2 The Support Vector Machine Learning Strategy12.4.3 The Core Support Vector Machine Algorithm12.4.3.1 Constructing the Primal, Dual, and Dual-to-Primal Map
12.4.3.2 Margin, Support Vectors, and the Sparsity of Exact Solutions
12.4.3.3 Decomposition Algorithms for the Dual Quadratic Programming Problem12.4.3.4 Rate Certifying Decomposition Algorithms12.5 Multi-Class Classification12.6 MATLAB® ExamplesExercisesReferences
Appendix: Table of Laplace and z Transforms

Content preview from Digital Signal Processing with Examples in MATLAB, 2nd Edition

7 Random Signals and Spectral Estimation

7.1 Introduction

Random signals are signals with elements that cannot be predicted or derived exactly from other elements. In our previous examples of discrete signals, we used finite data vectors or analytic signals that are either periodic or transient; that is, we used signals that can be described completely in simple terms.

When a signal has random components and cannot be described analytically, we may be able to describe it in terms of its statistical properties, which we discuss in this chapter. There are two basic sources of the most commonly used statistical properties: (1) the amplitude distribution of the random signal and (2) the autocorrelation function or, equivalently, the ...

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Digital Signal Processing with Examples in MATLAB, 2nd Edition

by Samuel D. Stearns, Donald R. Hush

7

Random Signals and Spectral Estimation

7.1 Introduction

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