Digital Signal Processing: Theory and Practice, 10th Edition

✍ Scribed by Maurice Bellanger

• Publisher: WILEY
• Year: 2024
• Tongue: English
• Leaves: 397
• Edition: 10
• Category: Library

✦ Synopsis

Understand the future of signal processing with the latest edition of this groundbreaking text Signal processing is a key aspect of virtually all engineering fields. Digital techniques enormously expand the possible applications of signal processing, forming a part of not only conventional engineering projects but also data analysis and artificial intelligence. There are considerable challenges raised by these techniques, however, as the gulf between theory and practice can be wide; the successful integration of digital signal processing techniques requires engineers capable of bridging this gulf. For years, Digital Signal Processing has met this need with a comprehensive guide that consistently connects abstract theory with practical applications. Now fully updated to reflect the most recent developments in this crucial field, the tenth edition of this seminal text promises to foster a broader understanding of signal processing among a new generation of engineers and researchers. Readers of the new edition of Digital Signal Processing will also find: Exercises at the end of each chapter to reinforce key concepts A new chapter covering digital signal processing for neural networks Handy structure beginning with undergraduate-level material before moving to more advanced concepts in the second half Digital Signal Processing is a must-own for students, researchers, and industry professionals in any of the hundreds of fields and subfields that make use of signal processing algorithms. This is the English language translation of the French original Traitement Numérique du Signal 10th edition by Maurice Bellanger © Dunod 2022 and is the 4th edition in English.

✦ Table of Contents

Cover
Title Page
Copyright
Contents
Foreword (Historical Perspective)
Preface
Introduction
Chapter 1 Signal Digitizing – Sampling and Coding
1.1 Fourier Analysis
1.1.1 Fourier Series Expansion of a Periodic Function
1.1.2 Fourier Transform of a Function
1.2 Distributions
1.2.1 Definition
1.2.2 Differentiation of Distributions
1.2.2.1 The Fourier Transform of a Distribution
1.3 Some Commonly Studied Signals
1.3.1 Deterministic Signals
1.3.2 Random Signals
1.3.3 Gaussian Signals
1.3.3.1 Peak Factor of a Random Signal
1.4 The Norms of a Function
1.5 Sampling
1.6 Frequency Sampling
1.7 The Sampling Theorem
1.8 Sampling of Sinusoidal and Random Signals
1.8.1 Sinusoidal Signals
1.8.2 Discrete Random Signals
1.8.3 Discrete Noise Generation
1.9 Quantization
1.10 The Coding Dynamic Range
1.11 Nonlinear Coding with the 13‐segment A‐law
1.12 Optimal Coding
1.13 Quantity of Information and Channel Capacity
1.14 Binary Representations
Exercises
References
Chapter 2 The Discrete Fourier Transform
2.1 Definition and Properties of the Discrete Fourier Transform
2.2 Fast Fourier Transform (FFT)
2.2.1 Decimation‐in‐time Fast Fourier Transform
2.2.2 Decimation‐in‐frequency Fast Fourier Transform
2.2.3 Radix‐4 FFT Algorithm
2.2.4 Split‐radix FFT Algorithm
2.3 Degradation Arising from Wordlength Limitation Effects
2.4 Calculation of a Spectrum Using the DFT
2.4.1 The Filtering Function of the DFT
2.4.2 Spectral Resolution
2.5 Fast Convolution
2.6 Calculations of a DFT Using Convolution
2.7 Implementation
Exercises
References
Chapter 3 Other Fast Algorithms for the FFT
3.1 Kronecker Product of Matrices
3.2 Factorizing the Matrix of a Decimation‐in‐Frequency Algorithm
3.3 Partial Transforms
3.3.1 Transform of Real Data and Odd DFT
3.3.2 The Odd‐time Odd‐frequency DFT
3.3.3 Sine and Cosine Transforms
3.3.4 The Two‐dimensional DCT
3.4 Lapped Transform
3.5 Other Fast Algorithms
3.6 Binary Fourier Transform – Hadamard
3.7 Number‐Theoretic Transforms
Exercises
References
Chapter 4 Time‐Invariant Discrete Linear Systems
4.1 Definition and Properties
4.2 The Z‐Transform
4.3 Energy and Power of Discrete Signals
4.4 Filtering of Random Signals
4.5 Systems Defined by Difference Equations
4.6 State Variable Analysis
Exercises
References
Chapter 5 Finite Impulse Response (FIR) Filters
5.1 FIR Filters
5.2 Practical Transfer Functions and Linear Phase Filters
5.3 Calculation of Coefficients by Fourier Series Expansion for Frequency Specifications
5.4 Calculation of Coefficients by the Least‐Squares Method
5.5 Calculation of Coefficient by Discrete Fourier Transform
5.6 Calculation of Coefficients by Chebyshev Approximation
5.7 Relationships Between the Number of Coefficients and the Filter Characteristic
5.8 Raised‐Cosine Transition Filter
5.9 Structures for Implementing FIR Filters
5.10 Limitation of the Number of Bits for Coefficients
5.11 Z–Transfer Function of an FIR Filter
5.12 Minimum‐Phase Filters
5.13 Design of Filters with a Large Number of Coefficients
5.14 Two‐Dimensional FIR Filters
5.15 Coefficients of Two‐Dimensional FIR Filters by the Least‐Squares Method
Exercises
References
Chapter 6 Infinite Impulse Response (IIR) Filter Sections
6.1 First‐Order Section
6.2 Purely Recursive Second‐Order Section
6.3 General Second‐Order Section
6.4 Structures for Implementation
6.5 Coefficient Wordlength Limitation
6.6 Internal Data Wordlength Limitation
6.7 Stability and Limit Cycles
Exercises
References
Chapter 7 Infinite Impulse Response Filters
7.1 General Expressions for the Properties of IIR Filters
7.2 Direct Calculations of the Coefficients Using Model Functions
7.2.1 Impulse Invariance
7.2.2 Bilinear Transform
7.2.2.1 Butterworth Filters
7.2.2.2 Elliptic Filters
7.2.2.3 Calculating any Filter by Transformation of a Low‐pass Filter
7.2.3 Iterative Techniques for Calculating IIR Filter with Frequency
7.2.3.1 Minimizing the Mean Square Error
7.2.3.2 Chebyshev Approximation
7.2.4 Filters Based on Spheroidal Sequences
7.2.5 Structures Representing the Transfer Function
7.2.6 Limiting the Coefficient Wordlength
7.2.7 Round‐Off Noise
7.2.8 Comparison of IIR and FIR Filters
Exercises
References
Chapter 8 Digital Ladder Filters
8.1 Properties of Two‐Port Circuits
8.2 Simulated Ladder Filters
8.3 Switched‐Capacitor Filters
8.4 Lattice Filters
8.5 Comparison Elements
Exercises
References
Chapter 9 Complex Signals – Quadrature Filters – Interpolators
9.1 The Fourier Transform of a Real and Causal Set
9.2 Analytic Signals
9.3 Calculating the Coefficients of an FIR Quadrature Filter
9.4 Recursive 90° Phase Shifters
9.5 Single Side‐Band Modulation
9.6 Minimum‐Phase Filters
9.7 Differentiator
9.8 Interpolation Using FIR Filters
9.9 Lagrange Interpolation
9.10 Interpolation by Blocks – Splines
9.11 Interpolations and Signal Restoration
9.12 Conclusion
Exercises
References
Chapter 10 Multirate Filtering
10.1 Decimation and Z‐Transform
10.2 Decomposition of a Low‐Pass FIR Filter
10.3 Half‐Band FIR Filters
10.4 Decomposition with Half‐Band Filters
10.5 Digital Filtering by Polyphase Network
10.6 Multirate Filtering with IIR Elements
10.7 Filter Banks Using Polyphase Networks and DFT
10.8 Conclusion
Exercises
References
Chapter 11 QMF Filters and Wavelets
11.1 Decomposition into Two Sub‐Bands and Reconstruction
11.2 QMF Filters
11.3 Perfect Decomposition and Reconstruction
11.4 Wavelets
11.5 Lattice Structures
Exercises
References
Chapter 12 Filter Banks
12.1 Decomposition and Reconstruction
12.2 Analyzing the Elements of the Polyphase Network
12.3 Determining the Inverse Functions
12.4 Banks of Pseudo‐QMF Filters
12.5 Determining the Coefficients of the Prototype Filter
12.6 Realizing a Bank of Real Filters
Exercises
References
Chapter 13 Signal Analysis and Modeling
13.1 Autocorrelation and Intercorrelation
13.2 Correlogram Spectral Analysis
13.3 Single‐Frequency Estimation
13.4 Correlation Matrix
13.5 Modeling
13.6 Linear Prediction
13.7 Predictor Structures
13.7.1 Sensor Networks – Antenna Processing
13.8 Multiple Sources – MIMO
13.9 Conclusion
Appendix: Estimation Bounds
Exercises
References
Chapter 14 Adaptive Filtering
14.1 Principle of Adaptive Filtering
14.2 Convergence Conditions
14.3 Time Constant
14.4 Residual Error
14.5 Complexity Parameters
14.6 Normalized Algorithms and Sign Algorithms
14.7 Adaptive FIR Filtering in Cascade Form
14.8 Adaptive IIR Filtering
14.9 Conclusion
Exercises
References
Chapter 15 Neural Networks
15.1 Classification
15.2 Multilayer Perceptron
15.3 The Backpropagation Algorithm
15.4 Examples of Application
15.5 Convolution Neural Networks
15.6 Recurrent/Recursive Neural Networks
15.7 Neural Network and Signal Processing
15.8 On Activation Functions
15.9 Conclusion
Exercises
References
Chapter 16 Error‐Correcting Codes
16.1 Reed–Solomon Codes
16.1.1 Predictable Signals
16.1.2 Reed–Solomon Codes in the Frequency Domain
16.1.3 Reed–Solomon Codes in the Time Domain
16.1.4 Computing in a Finite Field
16.1.5 Performance of Reed–Solomon Codes
16.2 Convolutional Codes
16.2.1 Channel Capacity
16.2.2 Approaching the Capacity Limit
16.2.3 A Simple Convolutional Code
16.2.4 Coding Gain and Error Probability
16.2.5 Decoding and Output Signals
16.2.6 Recursive Systematic Coding (RSC)
16.2.7 Principle of Turbo Codes
16.2.8 Trellis‐Coded Modulations
16.3 Conclusion
Exercises
References
Chapter 17 Applications
17.1 Frequency Detection
17.2 Phase‐locked Loop
17.3 Differential Coding of Speech
17.4 Coding of Sound
17.5 Echo Cancelation
17.5.1 Data Echo Canceller
17.5.1.1 Two‐wire Line
17.5.2 Acoustic Echo Canceler
17.6 Television Image Processing
17.7 Multicarrier Transmission – OFDM
17.8 Mobile Radiocommunications
References
Exercises: Solutions and Hints
Index
EULA