Digital Signal Processing with Examples in MATLAB, 2nd Edition

Digital Signal Processing with Examples in MATLAB, 2nd Edition

by Samuel D. Stearns, Donald R. Hush
Released April 2016
Publisher(s): CRC Press
ISBN: 9781000755633

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

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

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Table of Contents
  7. Foreword to the Second Edition
  8. Foreword to the First Edition
  9. Preface to the Second Edition
  10. Preface to the First Edition
  11. Authors
  12. 1. Introduction
    1. 1.1 Digital Signal Processing
    2. 1.2 How to Read This Text
    3. 1.3 Introduction to MATLAB®
    4. 1.4 Signals, Vectors, and Arrays
    5. 1.5 Review of Vector and Matrix Algebra Using MATLAB® Notation
    6. 1.6 Geometric Series and Other Formulas
    7. 1.7 MATLAB® Functions in DSP
    8. 1.8 The Chapters Ahead
    9. References
    10. Further Reading
  13. 2. Least Squares, Orthogonality, and the Fourier Series
    1. 2.1 Introduction
    2. 2.2 Least Squares
    3. 2.3 Orthogonality
    4. 2.4 The Discrete Fourier Series
    5. Exercises
    6. References
  14. 3. Correlation, Fourier Spectra, and the Sampling Theorem
    1. 3.1 Introduction
    2. 3.2 Correlation
    3. 3.3 The Discrete Fourier Transform (DFT)
    4. 3.4 Redundancy in the DFT
    5. 3.5 The FFT Algorithm
    6. 3.6 Amplitude and Phase Spectra
    7. 3.7 The Inverse DFT
    8. 3.8 Properties of the DFT
    9. 3.9 Continuous Transforms, Linear Systems, and Convolution
    10. 3.10 The Sampling Theorem
    11. 3.11 Waveform Reconstruction and Aliasing
    12. 3.12 Resampling
    13. 3.13 Nonuniform and Log-Spaced Sampling
    14. Exercises
    15. References
    16. Further Reading
  15. 4. Linear Systems and Transfer Functions
    1. 4.1 Continuous and Discrete Linear Systems
    2. 4.2 Properties of Discrete Linear Systems
    3. 4.3 Discrete Convolution
    4. 4.4 The z-Transform and Linear Transfer Functions
    5. 4.5 The Complex z-Plane and the Chirp z-Transform
    6. 4.6 Poles and Zeros
    7. 4.7 Transient Response and Stability
    8. 4.8 System Response via the Inverse z-Transform
    9. 4.9 Cascade, Parallel, and Feedback Structures
    10. 4.10 Direct Algorithms
    11. 4.11 State-Space Algorithms
    12. 4.12 Lattice Algorithms and Structures
    13. 4.13 FFT Algorithms
    14. 4.14 Discrete Linear Systems and Digital Filters
    15. 4.15 Functions Used in This Chapter
    16. Exercises
    17. References
    18. Further Reading
  16. 5. FIR Filter Design
    1. 5.1 Introduction
    2. 5.2 An Ideal Lowpass Filter
    3. 5.3 The Realizable Version
    4. 5.4 Improving an FIR Filter with Window Functions
    5. 5.5 Highpass, Bandpass, and Bandstop Filters
    6. 5.6 A Complete FIR Filtering Example
    7. 5.7 Other Types of FIR Filters
    8. 5.8 Digital Differentiation
    9. 5.9 A Hilbert Transformer
    10. Exercises
    11. References
    12. Further Reading
  17. 6. IIR Filter Design
    1. 6.1 Introduction
    2. 6.2 Linear Phase
    3. 6.3 Butterworth Filters
    4. 6.4 Chebyshev Filters
    5. 6.5 Frequency Translations
    6. 6.6 The Bilinear Transformation
    7. 6.7 IIR Digital Filters
    8. 6.8 Digital Resonators and the Spectrogram
    9. 6.9 The All-Pass Filter
    10. 6.10 Digital Integration and Averaging
    11. Exercises
    12. References
    13. Further Reading
  18. 7. Random Signals and Spectral Estimation
    1. 7.1 Introduction
    2. 7.2 Amplitude Distributions
    3. 7.3 Uniform, Gaussian, and Other Distributions
    4. 7.4 Power and Power Density Spectra
    5. 7.5 Properties of the Power Spectrum
    6. 7.6 Power Spectral Estimation
    7. 7.7 Data Windows in Spectral Estimation
    8. 7.8 The Cross-Power Spectrum
    9. 7.9 Algorithms
    10. Exercises
    11. References
    12. Further Reading
  19. 8. Least-Squares System Design
    1. 8.1 Introduction
    2. 8.2 Applications of Least-Squares Design
    3. 8.3 System Design via the Mean-Squared Error
    4. 8.4 A Design Example
    5. 8.5 Least-Squares Design with Finite Signal Vectors
    6. 8.6 Correlation and Covariance Computation
    7. 8.7 Channel Equalization
    8. 8.8 System Identification
    9. 8.9 Interference Canceling
    10. 8.10 Linear Prediction and Recovery
    11. 8.11 Effects of Independent Broadband Noise
    12. Exercises
    13. References
    14. Further Reading
  20. 9. Adaptive Signal Processing
    1. 9.1 Introduction
    2. 9.2 The Mean-Squared Error Performance Surface
    3. 9.3 Searching the Performance Surface
    4. 9.4 Steepest Descent and the LMS Algorithm
    5. 9.5 LMS Examples
    6. 9.6 Direct Descent and the RLS Algorithm
    7. 9.7 Measures of Adaptive System Performance
    8. 9.8 Other Adaptive Structures and Algorithms
    9. Exercises
    10. References
    11. Further Reading
  21. 10. Signal Information, Coding, and Compression
    1. 10.1 Introduction
    2. 10.2 Measuring Information
    3. 10.3 Two Ways to Compress Signals
    4. 10.4 Adaptive Predictive Coding
    5. 10.5 Entropy Coding
    6. 10.6 Transform Coding and the Discrete Cosine Transform
    7. 10.7 The Discrete Sine Transform
    8. 10.8 Multirate Signal Decomposition and Subband Coding
    9. 10.9 Time–Frequency Analysis and Wavelet Transforms
    10. Exercises
    11. References
  22. 11. Models of Analog Systems
    1. 11.1 Introduction
    2. 11.2 Impulse-Invariant Approximation
    3. 11.3 Final Value Theorem
    4. 11.4 Pole–Zero Comparisons
    5. 11.5 Approaches to Modeling
    6. 11.6 Input-Invariant Models
    7. 11.7 Other Linear Models
    8. 11.8 Comparison of Linear Models
    9. 11.9 Models of Multiple and Nonlinear Systems
    10. 11.10 Concluding Remarks
    11. Exercises
    12. References
    13. Further Reading
  23. 12. Pattern Recognition with Support Vector Machines
    1. 12.1 Introduction
    2. 12.2 Pattern Recognition Principles
    3. 12.3 Learning
      1. 12.3.1 The Independent and Identically Distributed Sample Plan
      2. 12.3.2 Learning Methods
    4. 12.4 Support Vector Machines
      1. 12.4.1 The Support Vector Machine Function Class
      2. 12.4.2 The Support Vector Machine Learning Strategy
      3. 12.4.3 The Core Support Vector Machine Algorithm
        1. 12.4.3.1 Constructing the Primal, Dual, and Dual-to-Primal Map
        2. 12.4.3.2 Margin, Support Vectors, and the Sparsity of Exact Solutions
        3. 12.4.3.3 Decomposition Algorithms for the Dual Quadratic Programming Problem
        4. 12.4.3.4 Rate Certifying Decomposition Algorithms
    5. 12.5 Multi-Class Classification
    6. 12.6 MATLAB® Examples
    7. Exercises
    8. References
  24. Appendix: Table of Laplace and z Transforms

Product information

  • Title: Digital Signal Processing with Examples in MATLAB, 2nd Edition
  • Author(s): Samuel D. Stearns, Donald R. Hush
  • Release date: April 2016
  • Publisher(s): CRC Press
  • ISBN: 9781000755633