Digital signal processing with examples in MATLAB 2nd Edition by Don R Hush, Samuel D Stearns ISBN 1466500050 9781466500051

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ISBN 10: 1466500050 
ISBN 13: 9781466500051
Author: Don R Hush, Samuel D Stearns

Digital signal processing with examples in MATLAB 2nd Table of contents:

1. Introduction

• 1.1 Digital Signal Processing

• 1.2 How to Read This Text

• 1.3 Introduction to MATLAB®

• 1.4 Signals, Vectors, and Arrays

• 1.5 Review of Vector and Matrix Algebra Using MATLAB® Notation

• 1.6 Geometric Series and Other Formulas

• 1.7 MATLAB® Functions in DSP

• 1.8 The Chapters Ahead

• References

• Further Reading

2. Least Squares, Orthogonality, and the Fourier Series

• 2.1 Introduction

• 2.2 Least Squares

• 2.3 Orthogonality

• 2.4 The Discrete Fourier Series

• Exercises

• References

3. Correlation, Fourier Spectra, and the Sampling Theorem

• 3.1 Introduction

• 3.2 Correlation

• 3.3 The Discrete Fourier Transform (DFT)

• 3.4 Redundancy in the DFT

• 3.5 The FFT Algorithm

• 3.6 Amplitude and Phase Spectra

• 3.7 The Inverse DFT

• 3.8 Properties of the DFT

• 3.9 Continuous Transforms, Linear Systems, and Convolution

• 3.10 The Sampling Theorem

• 3.11 Waveform Reconstruction and Aliasing

• 3.12 Resampling

• 3.13 Nonuniform and Log-Spaced Sampling

• Exercises

• References

• Further Reading

4. Linear Systems and Transfer Functions

• 4.1 Continuous and Discrete Linear Systems

• 4.2 Properties of Discrete Linear Systems

• 4.3 Discrete Convolution

• 4.4 The z-Transform and Linear Transfer Functions

• 4.5 The Complex z-Plane and the Chirp z-Transform

• 4.6 Poles and Zeros

• 4.7 Transient Response and Stability

• 4.8 System Response via the Inverse z-Transform

• 4.9 Cascade, Parallel, and Feedback Structures

• 4.10 Direct Algorithms

• 4.11 State-Space Algorithms

• 4.12 Lattice Algorithms and Structures

• 4.13 FFT Algorithms

• 4.14 Discrete Linear Systems and Digital Filters

• 4.15 Functions Used in This Chapter

• Exercises

• References

• Further Reading

5. FIR Filter Design

• 5.1 Introduction

• 5.2 An Ideal Lowpass Filter

• 5.3 The Realizable Version

• 5.4 Improving an FIR Filter with Window Functions

• 5.5 Highpass, Bandpass, and Bandstop Filters

• 5.6 A Complete FIR Filtering Example

• 5.7 Other Types of FIR Filters

• 5.8 Digital Differentiation

• 5.9 A Hilbert Transformer

• Exercises

• References

• Further Reading

6. IIR Filter Design

• 6.1 Introduction

• 6.2 Linear Phase

• 6.3 Butterworth Filters

• 6.4 Chebyshev Filters

• 6.5 Frequency Translations

• 6.6 The Bilinear Transformation

• 6.7 IIR Digital Filters

• 6.8 Digital Resonators and the Spectrogram

• 6.9 The All-Pass Filter

• 6.10 Digital Integration and Averaging

• Exercises

• References

• Further Reading

7. Random Signals and Spectral Estimation

• 7.1 Introduction

• 7.2 Amplitude Distributions

• 7.3 Uniform, Gaussian, and Other Distributions

• 7.4 Power and Power Density Spectra

• 7.5 Properties of the Power Spectrum

• 7.6 Power Spectral Estimation

• 7.7 Data Windows in Spectral Estimation

• 7.8 The Cross-Power Spectrum

• 7.9 Algorithms

• Exercises

• References

• Further Reading

8. Least-Squares System Design

• 8.1 Introduction

• 8.2 Applications of Least-Squares Design

• 8.3 System Design via the Mean-Squared Error

• 8.4 A Design Example

• 8.5 Least-Squares Design with Finite Signal Vectors

• 8.6 Correlation and Covariance Computation

• 8.7 Channel Equalization

• 8.8 System Identification

• 8.9 Interference Canceling

• 8.10 Linear Prediction and Recovery

• 8.11 Effects of Independent Broadband Noise

• Exercises

• References

• Further Reading

9. Adaptive Signal Processing

• 9.1 Introduction

• 9.2 The Mean-Squared Error Performance Surface

• 9.3 Searching the Performance Surface

• 9.4 Steepest Descent and the LMS Algorithm

• 9.5 LMS Examples

• 9.6 Direct Descent and the RLS Algorithm

• 9.7 Measures of Adaptive System Performance

• 9.8 Other Adaptive Structures and Algorithms

• Exercises

• References

• Further Reading

10. Signal Information, Coding, and Compression

• 10.1 Introduction

• 10.2 Measuring Information

• 10.3 Two Ways to Compress Signals

• 10.4 Adaptive Predictive Coding

• 10.5 Entropy Coding

• 10.6 Transform Coding and the Discrete Cosine Transform

• 10.7 The Discrete Sine Transform

• 10.8 Multirate Signal Decomposition and Subband Coding

• 10.9 Time–Frequency Analysis and Wavelet Transforms

• Exercises

• References

11. Models of Analog Systems

• 11.1 Introduction

• 11.2 Impulse-Invariant Approximation

• 11.3 Final Value Theorem

• 11.4 Pole–Zero Comparisons

• 11.5 Approaches to Modeling

• 11.6 Input-Invariant Models

• 11.7 Other Linear Models

• 11.8 Comparison of Linear Models

• 11.9 Models of Multiple and Nonlinear Systems

• 11.10 Concluding Remarks

• Exercises

• References

• Further Reading

12. Pattern Recognition with Support Vector Machines

• 12.1 Introduction

• 12.2 Pattern Recognition Principles

• 12.3 Learning

  • 12.3.1 The Independent and Identically Distributed Sample Plan

  • 12.3.2 Learning Methods

• 12.4 Support Vector Machines

  • 12.4.1 The Support Vector Machine Function Class

  • 12.4.2 The Support Vector Machine Learning Strategy

  • 12.4.3 The Core Support Vector Machine Algorithm

    • 12.4.3.1 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 Problem

    • 12.4.3.4 Rate Certifying Decomposition Algorithms

• 12.5 Multi-Class Classification

• 12.6 MATLAB® Examples

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Tags: Don R Hush, Samuel D Stearns, Digital signal