Digital Signal Processing

Fundamentals, Applications, and Deep Learning

4th Edition - February 5, 2025
Imprint: Academic Press
Authors: Li Tan, Jean Jiang
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 7 3 3 5 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 7 3 3 6 - 0

Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of digital signal processing (DSP) whi… Read more

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Digital Signal Processing: Fundamentals, Applications, and Deep Learning, Fourth Edition introduces students to the fundamental principles of digital signal processing (DSP) while also providing a working knowledge that they take with them into their engineering careers. Many instructive, worked examples are used to illustrate the material, and the use of mathematics is minimized for an easier grasp of concepts. As such, this title is also useful as a reference for non-engineering students and practicing engineers.

This book goes beyond DSP theory, showing the implementation of algorithms in hardware and software. Additional topics covered include DSP for artificial intelligence, adaptive filtering with noise reduction and echo cancellations, speech compression, signal sampling, digital filter realizations, filter design, multimedia applications, over-sampling, etc. More advanced topics are also covered, such as adaptive filters, speech compression such as pulse-code modulation, µ-law, adaptive differential pulse-code modulation, multi-rate DSP, oversampling analog-to-digital conversion, sub-band coding, wavelet transform, and neural networks.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Preface
CHAPTER 1. Introduction to Digital Signal Processing
1.1 Basic Concepts of Digital Signal Processing
1.2 Basic Digital Signal Processing Examples in Block Diagrams
1.2.1 Digital Filtering
1.2.2 Signal Frequency (Spectrum) Analysis
1.3 Overview of Typical Digital Signal Processing in Real-World Applications
1.3.1 Digital Crossover Audio System
1.3.2 Interference Cancellation in Electrocardiography
1.3.3 Speech Coding and Compression
1.3.4 Vibration Signature Analysis for Defected Gear Teeth
1.3.5 Digital Image Enhancement
1.3.6 Artificial Intelligence Examples
1.3.6.1 Machine Fault Detection
1.3.6.2 Speech Feature Extraction
1.3.6.3 Electroencephalogram Classification
1.4 Digital Signal Processing Applications
1.5 Summary
CHAPTER 2. Signal Sampling and Quantization
2.1 Sampling of Continuous Signals
2.2 Signal Reconstruction
2.2.1 Practical Considerations for Signal Sampling: Anti-Aliasing Filtering
2.2.2 Practical Considerations for Signal Reconstruction: Anti-Image Filter and Equalizer
2.3 Analog-to-Digital Conversion, Digital-to-Analog Conversion, and Quantization
2.4 Summary
2.5 MATLAB® Programs
2.6 Problems
MATLAB® Projects
Advanced Problems
CHAPTER 3. Digital Signals and Systems
3.1 Digital Signals
3.1.1 Common Digital Sequences
3.1.2 Generation of Digital Signals
3.2 Linear Time-Invariant, Causal, and Bounded-Input and Bounded-Output Systems
3.2.1 Linearity
3.2.2 Time Invariance
3.2.3 Causality
3.2.4 Bounded-Input and Bounded-Output Stability
3.3 Difference Equations and Impulse Responses
3.3.1 Format of Difference Equations
3.3.2 System Representation Using Its Impulse Response
3.4 Digital Convolution
3.5 Properties of Linear Time-Invariant System
3.5.1 Causality of Linear Time-Invariant System
3.5.2 Bounded-Input and Bounded-Output Stability
3.6 Block Diagrams
3.7 Summary
3.8 Problems
Advanced Problems
CHAPTER 4. The Z-Transform
4.1 Definition
4.2 Properties of the Z-Transform
4.3 Inverse Z-Transform
4.3.1 Partial Fraction Expansion and Look-Up Table
4.3.2 Partial Fraction Expansion Using MATLAB®
4.3.3 Power Series Method
4.3.4 Inversion Formula Method
4.4 Solution of Difference Equations Using the Z-Transform
4.5 Two-Sided Z-Transform
4.6 Summary
4.7 Problems
Advanced Problems
CHAPTER 5. Discrete Fourier Transform and Signal Spectrum
5.1 Discrete Fourier Transform
5.1.1 Fourier Series Coefficients of Periodic Digital Signals
5.1.2 Discrete Fourier Transform Formulas
5.2 Amplitude Spectrum and Power Spectrum
5.3 Spectral Estimation Using Window Functions
5.4 Application to Signal Spectral Estimation
5.5 Fast Fourier Transform
5.5.1 Method of Decimation-in-Frequency
5.5.2 Method of Decimation-in-Time
5.6 Summary
5.7 Problems
Computer Problems With MATLAB®
MATLAB® Projects
Advanced Problems
Chapter 6. DIGITAL SIGNAL PROCESSING SYSTEMS, BASIC FILTERING TYPES, AND DIGITAL FILTER REALIZATIONS
6.1 Difference Equation and Digital Filtering
6.2 Difference Equation and Transfer Function
6.2.1 Impulse, Step, and System Responses
6.3 The Z-Plane Pole-Zero Plot and Stability
6.4 Digital Filter Frequency Response
6.5 Basic Types of Filtering
6.6 Realization of Digital Filters
6.6.1 Direct-Form I Realization
6.6.2 Direct-Form II Realization
6.6.3 Cascade (Series) Realization
6.6.4 Parallel Realization
6.7 Application: Signal Enhancement and Filtering
6.7.1 Preemphasis of Speech
6.7.2 Bandpass Filtering of Speech
6.7.3 Enhancement of Electrocardiogram Signals Using Notch Filtering
6.8 Summary
6.9 Problems
MATLAB® Problems
MATLAB® Projects
Advanced Problems
Chapter 7. FINITE IMPULSE RESPONSE FILTER DESIGN
7.1 Finite Impulse Response Filter Format
7.2 Fourier Transform Design
7.3 Window Method
7.4 Applications: Noise Reduction and Two-Band Digital Crossover
7.4.1 Noise Reduction
7.4.2 Speech Noise Reduction
7.4.3 Noise Reduction in Vibration Signals
7.4.4 Two-Band Digital Crossover
7.5 Frequency Sampling Design Method
7.6 Optimal Design Method
7.7 Design of the Finite Impulse Response Differentiator and Hilbert Transformer
7.8 Realization Structures of Finite Impulse Response Filters
7.8.1 Transversal Form
7.8.2 Linear Phase Form
7.9 Coefficient Accuracy Effects on Finite Impulse Response Filters
7.10 Summary of Finite Impulse Response Design Procedures and Selection of the FIR Filter Design Methods in Practice
7.11 Summary
7.12 MATLAB® Programs
7.13 Problems
Computer Problems with MATLAB®
MATLAB® Projects
Advanced Problems:
Chapter 8. INFINITE IMPULSE RESPONSE FILTER DESIGN
8.1 Infinite Impulse Response Filter Format
8.2 Bilinear Transformation Design Method
8.2.1 Analog Filters Using Lowpass Prototype Transformation
8.2.2 Bilinear Transformation and Frequency Warping
8.2.3 Bilinear Transformation Design Procedure
8.3 Digital Butterworth and Chebyshev Filter Designs
8.3.1 Lowpass Prototype Function and Its Order
8.3.2 Lowpass and Highpass Filter Design Examples
8.3.3 Bandpass and Bandstop Filter Design Examples
8.4 Higher-Order Infinite Impulse Response Filter Design Using the Cascade Method
8.5 Application: Digital Audio Equalizer
8.6 Impulse Invariant Design Method
8.7 Pole-Zero Placement Method for Simple Infinite Impulse Response Filters
8.7.1 Second-order Bandpass Filter Design
8.7.2 Second-Order Bandstop (Notch) Filter Design
8.7.3 First-Order Lowpass Filter Design
8.7.4 First-Order Highpass Filter Design
8.8 Realization Structures of Infinite Impulse Response Filters
8.8.1 Realization of Infinite Impulse Response Filters in Direct-Form I and Direct-Form II
8.8.2 Realization of Higher-Order Infinite Impulse Response Filters Via the Cascade Form
8.9 Application: 60-Hz Hum Eliminator and Heart Rate Detection Using Electrocardiography
8.10 Coefficient Accuracy Effects on Infinite Impulse Response Filters
8.11 Application: Generation and Detection of DTMF Tones Using the Goertzel Algorithm
8.11.1 Single-Tone Generator
8.11.2 Dual-Tone Multifrequency Tone Generator
8.11.3 Goertzel Algorithm
8.11.4 Dual-Tone Multifrequency Tone Detection Using the Modified Goertzel Algorithm
8.12 Summary of Infinite Impulse Response (IIR) Design Procedures and Selection of the IIR Filter Design Methods in Practice
8.13 Summary
8.14 Problems
MATLAB® Projects
Advanced Problems
Chapter 9. ADAPTIVE FILTERS AND APPLICATIONS
9.1 Introduction to Least Mean Squares Adaptive Finite Impulse Response Filters
9.2 Basic Wiener Filter Theory and Adaptive Algorithms
9.2.1 Wiener Filter Theory and Linear Prediction
9.2.1.1 Basic Wiener Filter Theory
9.2.1.2 Forward Linear Prediction
9.2.2 Steepest Descent Algorithm
9.2.3 Least Mean Squares Algorithm
9.2.4 Recursive Least Squares Algorithm
9.3 Applications: Noise Cancellation, System Modeling, and Line Enhancement
9.3.1 Noise Cancellation
9.3.2 System Modeling
9.3.3 Line Enhancement Using Linear Prediction
9.3.4 Channel Equalization in Communication Systems
9.4 Other Application Examples
9.4.1 Canceling Periodic Interferences Using Linear Prediction
9.4.2 Electrocardiography Interference Cancellation
9.4.3 Echo Cancellation in Long-Distance Telephone Circuits
9.5 Summary
9.6 Problems
Computer Problems With MATLAB®
MATLAB® Projects
Advanced Programs
Chapter 10. WAVEFORM QUANTIZATION AND COMPRESSION
10.1 Linear Midtread Quantization
10.2 μ-Law Companding
10.2.1 Analog μ-Law Companding
10.2.2 Digital μ-Law Companding
10.3 Examples of Differential Pulse Code Modulation, Delta Modulation, and Adaptive Differential Pulse Code Modulation G.721
10.3.1 Examples of Differential Pulse Code Modulation and Delta Modulation
10.3.2 Adaptive Differential Pulse Code Modulation G.721
Simulation Example
10.4 Discrete Cosine Transform, Modified Discrete Cosine Transform, and Transform Coding in MPEG Audio
10.4.1 Discrete Cosine Transform
10.4.2 Modified Discrete Cosine Transform
10.4.3 Transform Coding in MPEG Audio
10.5 Summary
10.6 MATLAB® Programs
10.7 Problems
Computer Problems with MATLAB®
Advanced Problems
Chapter 11. MULTIRATE DIGITAL SIGNAL PROCESSING, OVERSAMPLING OF ANALOG-TO-DIGITAL CONVERSION, AND UNDERSAMPLING OF BANDPASS SIGNALS
11.1 Multirate Digital Signal Processing Basics
11.1.1 Sampling Rate Reduction by an Integer Factor
11.1.2 Sampling Rate Increase by an Integer Factor
11.1.3 Changing the Sampling Rate by a Noninteger Factor L/M
11.1.4 Application: CD Audio Player
11.1.5 Multistage Decimation
11.2 Polyphase Filter Structure and Implementation
11.3 Oversampling of Analog-to-Digital Conversion
11.3.1 Oversampling and Analog-to-Digital Conversion Resolution
11.3.2 Sigma-Delta Modulation Analog-to-Digital Conversion
11.4 Application Example: CD Player
11.5 Undersampling of Bandpass Signals
11.6 Summary
11.7 Problems
MATLAB® Problems
MATLAB® Project
Chapter 12. SUBBAND AND WAVELET-BASED CODING
12.1 Subband Coding Basics
12.2 Subband Decomposition and Two-Channel Perfect Reconstruction-Quadrature Mirror Filter Bank
12.3 Subband Coding of Signals
12.4 Wavelet Basics and Families of Wavelets
12.5 Multiresolution Equations
12.6 Discrete Wavelet Transform
12.7 Wavelet Transform Coding of Signals
12.8 MATLAB® Programs
12.9 Summary
12.10 Problems
MATLAB® Problems
MATLAB® Projects
Chapter 13. IMAGE PROCESSING BASICS
13.1 Image Processing Notation and Data Formats
13.1.1 Eight-Bit Gray-Level Images
13.1.2 Twenty-Four-Bit Color Images
13.1.3 Eight-Bit Color Images
13.1.4 Intensity Images
13.1.5 Red, Green, and Blue Components and Grayscale Conversion
13.1.6 MATLAB® Functions for Format Conversion
13.2 Image Histogram and Equalization
13.2.1 Grayscale Histogram and Equalization
13.2.2 Twenty-Four-Bit Color Image Equalization
13.2.3 Eight-Bit Indexed Color Image Equalization
13.2.4 MATLAB® Functions for Equalization
13.3 Image Level Adjustment and Contrast
13.3.1 Linear Level Adjustment
13.3.2 Adjusting the Level for Display
13.3.3 MATLAB® Functions for Image Level Adjustment
13.4 Image Filtering Enhancement
13.4.1 Lowpass Noise Filtering
13.4.2 Median Filtering
13.4.3 Edge Detection
13.4.4 MATLAB® Functions for Image Filtering
13.5 Image Pseudo–Color Generation and Detection
13.6 Image Spectra
13.7 Image Compression by Discrete Cosine Transform
13.7.1 Two-Dimensional Discrete Cosine Transform
13.7.2 Two-Dimensional JPEG Grayscale Image Compression Example
13.7.3 JPEG Color Image Compression
RGB-to-YIQ transformation
DCT on image blocks
Quantization
Differential pulse code modulation on direct-current coefficients:
Run-length coding on alternating-current coefficients
Lossless entropy coding
Coding DC coefficients
Coding AC coefficients
13.7.4 Image Compression Using Wavelet Transform Coding
13.8 Creating a Video Sequence by Mixing Two Images
13.9 Video Signal Basics
13.9.1 Analog Video
Phase alternative line video
Séquentiel couleur à mémoire video
13.9.2 Digital Video
13.10 Motion Estimation in Video
13.11 Summary
13.12 Problems
MATLAB® Problems
Chapter 14. SIGNAL PROCESSING FOR ARTIFICIAL INTELLIGENCE
14.1 Artificial Neural Network
14.1.1 Single-Layer Perceptron
14.1.2 Multilayer Perceptron
14.1.3 Backpropagation Algorithm for Artificial Neural Network
14.2 Convolutional Neural Networks
14.2.1 Structures of Convolutional Neural Networks
14.2.2 Backpropagation Algorithm for Convolutional Neural Network
14.3 Activation and Cost Functions
14.3.1 Activation Functions
14.3.2 Softmax Layer and Cost Function
14.3.3 Adam Optimizer–Based Backpropagation Algorithm
14.4 Applications
14.4.1 Signal Processing for Feature Extraction
14.4.2 Intelligent Systems Using Vibration, Speech, and Electroencephalogram Signals
14.4.2.1 Machine Fault Detection
14.4.2.2 Speech Recognition
14.4.2.3 Electroencephalogram Classification
14.5 Summary
14.6 Problems
Projects
14.7 MATLAB® Programs
Chapter 15. HARDWARE AND SOFTWARE FOR DIGITAL SIGNAL PROCESSORS
15.1 Digital Signal Processor Architecture
15.2 Digital Signal Processor Hardware Units
15.2.1 Multiplier and Accumulator
15.2.2 Shifters
15.2.3 Address Generators
15.3 Digital Signal Processors and Manufacturers
15.4 Fixed- and Floating-Point Formats
15.4.1 Fixed-Point Format
15.4.2 Floating-Point Format
15.4.3 IEEE Floating Point Formats
15.4.4 Fixed-Point Digital Signal Processors
15.4.5 Floating-Point Digital Signal Processors
15.5 Finite Impulse Response and Infinite Impulse Response Filter Implementations in Fixed-Point Systems
15.6 Digital Signal Processing Programming Examples
15.6.1 Overview of TMS320C67X DSK
15.6.2 Concept of Real-Time Processing
15.6.3 Linear Buffering
15.6.4 Sample C Programs
15.7 Additional Real-Time Digital Signal Processing Examples
15.7.1 Adaptive Filtering Using the TMS320C6713 DSK
15.7.2 Signal Quantization Using the TMS320C6713 DSK
15.7.3 Sampling Rate Conversion Using the TMS320C6713 DSK
15.8 Summary
15.9 Problems
Appendix A. INTRODUCTION TO THE MATLAB® ENVIRONMENT
A.1 Basic Commands and Syntax
A.2 MATLAB® Array and Indexing
A.3 Plot Utilities: Subplot, Plot, Stem, and Stair
A.4 MATLAB® Script Files
A.5 MATLAB® Functions
Appendix B. REVIEW OF ANALOG SIGNAL PROCESSING BASICS
B.1 Fourier Series and Fourier Transform
B.1.1 Sine-Cosine Form
B.1.2 Amplitude-Phase Form
B.1.3 Complex Exponential Form
Solution:
B.1.4 Spectral Plots
Solution:
Solution:
Solution:
B.1.5 Fourier Transform
Solution:
Solution:
Solution:
Solution:
B.2.1 Laplace Transform and its Table
Solution:
Solution:
B.2.2 Solving Linear Differential Equations Using Laplace Transform
Solution:
B.2.3 Transfer Function
Solution:
B.3.1 Poles, Zeros, and Stability
Solution:
B.3.2 Convolution
Solution:
B.3.3 Sinusoidal Steady-State Response
Solution:
Appendix C. NORMALIZED BUTTERWORTH AND CHEBYSHEV FUNCTIONS
C.1 Normalized Butterworth Function
Solution:
Solution:
Solution:
Solution:
Solution:
Appendix D. SINUSOIDAL STEADY-STATE RESPONSE OF DIGITAL FILTERS
D.1 Sinusoidal Steady-State Response
D.2 Properties of the Sinusoidal Steady-State Response
Appendix E. FINITE IMPULSE RESPONSE FILTER DESIGN EQUATIONS BY FREQUENCY SAMPLING DESIGN METHOD
Appendix F. WAVELET ANALYSIS AND SYNTHESIS EQUATIONS
F.1 Basic Properties
F.2 Analysis Equations
F.3 Wavelet Synthesis Equations
Appendix G. REVIEW OF DISCRETE-TIME RANDOM SIGNALS
G.1 Random Variable Statistical Properties
G.2 Random Signal Statistical Properties
G.3 Wide-Sense Stationary Random Signals
G.4 Ergodic Signals
G.5 Statistical Properties of the Linear System Output Signal
G.6 Z-Transform Domain Representation of Statistical Properties
Appendix H. SOME USEFUL MATHEMATICAL FORMULAS
L’Hospital’s rule:
Answers to Selected Problems
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 15
Appendix B
Index

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Health

Digital Signal Processing

Fundamentals, Applications, and Deep Learning

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