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
 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
 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 LogSpaced 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 zTransform and Linear Transfer Functions
 4.5 The Complex zPlane and the Chirp zTransform
 4.6 Poles and Zeros
 4.7 Transient Response and Stability
 4.8 System Response via the Inverse zTransform
 4.9 Cascade, Parallel, and Feedback Structures
 4.10 Direct Algorithms
 4.11 StateSpace 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 AllPass 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 CrossPower Spectrum
 7.9 Algorithms
 Exercises
 References
 Further Reading

8. LeastSquares System Design
 8.1 Introduction
 8.2 Applications of LeastSquares Design
 8.3 System Design via the MeanSquared Error
 8.4 A Design Example
 8.5 LeastSquares 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 MeanSquared 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 ImpulseInvariant Approximation
 11.3 Final Value Theorem
 11.4 Pole–Zero Comparisons
 11.5 Approaches to Modeling
 11.6 InputInvariant 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
 12.4 Support Vector Machines
 12.5 MultiClass Classification
 12.6 MATLAB® Examples
 Exercises
 References
 Appendix: Table of Laplace and z Transforms
Product information
 Title: Digital Signal Processing with Examples in MATLAB, 2nd Edition
 Author(s):
 Release date: April 2016
 Publisher(s): CRC Press
 ISBN: 9781000755633
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