Wavelets and Wavelet Transform Systems and Their Applications - A Digital Signal Processing Approach

✍ Scribed by Cajetan M. Akujuobi

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 657
• Edition: 1
• Category: Library

✦ Synopsis

This textbook is unique because of its in-depth treatment of the applications of wavelets and wavelet transforms to many areas, across many disciplines. The book is written to serve the needs of a one or two semester course at either the undergraduate or graduate level. The author uses a very simplified, accessible approach that de-emphasizes mathematical rigor. The presentation includes many diagrams to illustrate points being discussed and uses MATLAB for all of application code. The author reinforces concepts introduced in the book with easy to grasp review questions and problems, tailored to each specific chapter for better mastery of the subject matter. This book enables students to understand the fundamental concepts of wavelets and wavelet transforms, as well as how to use them for problem solutions in digital signal and image processing, mixed-signal testing, space applications, aerospace applications, biomedical, cyber security, homeland security and many other application areas.

Provides textbook coverage of Wavelets and applications, suitable for one and two semester courses, either at the undergraduate or graduate level;
Discusses many types of wavelets and their applications across many disciplines;
Includes MATLAB code illustrations to simplify the understanding of the various applications;
Uses many illustrations, figures, tables, and visual comparisons to simplify and clarify the various concepts of wavelets, wavelet transforms and the various application areas;
Ends each chapter with review questions/answers, as well as exercises to reinforce and test concepts introduced;
Solutions manual and PowerPoint slides for each chapter available for instructors.

Cajetan M. Akujuobi received his O.N.D. from Institute of Management and Technology Enugu, Nigeria in 1974, the B.S. degree from Southern University, Baton Rouge, Louisiana, in 1980, the M.S. degree from Tuskegee University, Alabama, in 1982, all in electrical & electronics engineering. He received the M.B.A. degree from Hampton University, Hampton, Virginia, in 1987. In 1995, he received the Ph.D. degree from George Mason University, Fairfax, Virginia, in electrical engineering with specialization in signal/image/video processing & communication systems.

He is a full Professor in the Department of Electrical & Computer Engineering and the former Vice President for Research, Innovation and Sponsored Programs at Prairie View A&M University (PVAMU). He served as Dean in two different Universities - the Dean for Graduate Studies at PVAMU and founding STEM Dean at Alabama State University (ASU). He is the founder and the Executive Director of the Center of Excellence for Communication Systems Technology Research (CECSTR), a Texas A&M Board of Regents approved center where he has been able to attract research-funding exceeding over $25 Million. He is the founder and the Principal Investigator for the SECURE Cybersecurity Center of Excellence at PVAMU where he has received over $7 Million research award. Under his leadership as the Vice President for Research, Innovation and Sponsored Programs at Prairie View A&M University (PVAMU), he was instrumental in bringing to PVAMU five new Chancellors’ Research Initiative (CRI) research centers worth about $35 Million. He has worked in such corporations as Texas Instruments, Advanced Hardware Architecture, Schlumberger, Data Race Corporation, Spectrum Engineering, Intelsat and Bell Laboratories.

        Prof. Akujuobi developed and taught the Wavelets and Their Applications course at PVAMU for over 20 years. His research interests are in Wavelets and Wavelet transform Analysis and Applications, Cybersecurity, Smart & Connected Cities and DSP Solutions. In addition, his research interests include Communication Systems, Compressive Sensing, Signal/Image/Video Processing, Broadband Communication Systems, and Mixed Signal Systems. He was a participant and collaborative member of the ANSI TIEI.4 Working Group that had the technical responsibility of developing the T1.413, Issue 2 ADSL standard. He has received several professional and community related honors in teaching, research and service and has published extensively including writing books and book Chapters.  Two of the books he published with Dr. M. N. O. Sadiku, are “Introduction to Broadband Communication Systems”, and “Solutions Manual for Introduction to Broadband Communication Systems”, both published by Chapman & Hall/CRC and Sci-Tech  Publication, Boca Raton, Florida.

Prof. Akujuobi is the current Chair of the IEEE Houston Section Life Members Group. He is also a Life Senior Member of the Institute of Electrical and Electronic Engineers (IEEE), Senior Member of Instrument Society of America (ISA), Member of American Society for Engineering Education (ASEE), Sigma XI, the Scientific Research Society, and the Texas Society for Biomedical Research (TSBR) Board of Directors. He is a licensed Professional Engineer in the State of Texas, USA.

✦ Table of Contents

Preface
Overview
Acknowledgments
Contents
About the Author
Abbreviations
Chapter 1: Fundamental Concepts
1.1 Introduction
1.2 Fourier Transform and Analysis
1.3 Short-Time Fourier Transform
1.4 The Wavelet Transform Idea
1.5 Types of Wavelet Transforms
1.5.1 The Continuous Wavelet Transform (CWT)
1.5.2 The Discrete Wavelet Transform (DWT)
1.6 Wavelet Frames and Bases
1.7 Constructing Orthonormal Wavelet Bases with Compact Support
1.8 Similarities Between Wavelets and Fourier Transforms
1.9 Dissimilarities Between Wavelets and Fourier Transforms
References
Part I: Wavelets, Wavelet Transforms and Generations of Wavelets
Chapter 2: Wavelets
2.1 Introduction
2.2 What Are Wavelets and Why Do We Look at Wavelets?
2.3 Types of Wavelets
2.3.1 The Haar Wavelet
2.3.2 The Daubechies Wavelet
2.3.3 The Morlet Wavelet
2.3.4 The Meyer Wavelet
2.3.5 The Mexican Hat Wavelet
2.3.6 The Berlage Wavelet
2.3.7 The Biorthogonal Wavelets
2.3.8 The Shannon Wavelet
2.3.9 The Symlet Wavelet
2.3.10 The Coiflet Wavelet
2.3.11 The Spline Wavelet
2.3.12 The Gabor Wavelet
2.3.13 The Lemarie-Battle Wavelet
2.3.14 The Mallat Wavelet Multiresolution Analysis
2.3.15 The Poisson Wavelet
2.3.16 Mathieu Wavelets
2.3.17 Strömberg Wavelet
2.3.18 Legendre Wavelet
2.3.19 Beta Wavelet
References
Chapter 3: Generations of Wavelets
3.1 Introduction
3.2 Brief History of Wavelets
3.3 First-Generation Wavelets
3.4 Second-Generation Wavelets
3.5 The Shortcomings with the First and Second Generations of the Wavelet Models
3.6 Third Generation of Wavelets
3.7 Next-Generation Wavelets
3.8 Comparisons of the Different Generations of Wavelets
References
Chapter 4: Wavelet Transforms
4.1 Introduction
4.2 The Multiscale Wavelet Transform
4.3 One-Dimensional Wavelet Transform
4.3.1 Relationship Between the High-Pass and the Low-Pass Coefficients
4.3.2 Multiple Stage Decomposition and Reconstruction Idea
4.3.3 Determination of the Number of Stages for Decomposition and Reconstruction
4.4 Two-Dimensional Wavelet Transform
References
Chapter 5: Similarities Between Wavelets and Fractals
5.1 Introduction
5.2 The Self-Similarity Idea in Wavelets and Fractals
5.3 Fractal Dimension Idea
5.4 Iterated Function System Code
5.5 Types of Fractals
5.5.1 Random Fractals
5.5.2 Scaling Fractals
5.5.3 The Koch Fractal
5.5.4 The Sierpinski Fractal
5.5.5 The Cantor Fractal
5.6 Areas of Similarities Between Wavelets and Fractals Based on Their Properties
5.6.1 Self-Similarity
5.6.2 Scaling Function
5.6.3 Affine Transforms
5.7 Areas of Similarities Between Wavelets and Fractals Based on Their Application Areas
5.7.1 Application to Modeling and Seismic Studies
5.7.2 Construction and Reconstruction of Images
5.7.3 Graphical, Storage, and Communication Applications
5.7.4 Image Compression
5.7.5 Texture Segmentation
5.7.6 Edge Detection of Images
5.7.7 Geometrical Objects Representation
5.7.8 The Multiscale Analysis and Representation of Signals
References
Part II: Wavelet and Wavelet Transform Applications to Mixed Signal Systems
Chapter 6: Test Point Selection Using Wavelet Transforms for Digital-to-Analog Converters
6.1 Introduction
6.2 The Stenbakken and Souders Algorithm
6.3 Wavelet Transform Test Point Selection Algorithm
6.3.1 Circuit Model
6.3.2 Three Basic Model Types
6.3.3 Circuit Model by Wavelet Transforms
6.3.4 Choosing Test Points by QR Factorization
6.4 Implementation of the Test Method in Programming
6.4.1 Measured INL Data of 8-Bit DAC
6.4.2 Selection of a Set of Maximally Independent INL
6.4.3 Multiresolution Decomposition
6.4.4 Selection of the Independent Signatures (Coefficients)
6.4.5 Generation of a Reduced Matrix Ar from Q and R Matrices
6.4.6 Estimation of the Matrix Parameters of the Device
6.4.7 Generation of the Predicted INL ypk for all M Candidate Test Points
6.4.8 Getting the Root-Mean-Square for the Device k
6.4.9 Plotting the Measured INL, Predicted INL, and RMS Figures
References
Chapter 7: Wavelet-Based Dynamic Test of ADCS
7.1 Introduction
7.2 Measuring ENOB Using the Conventional Method
7.2.1 Measuring the ENOB Using FFT Method
7.2.2 Computing SNR Through FFT
7.2.3 Computing ENOB Through SNR
7.3 Measuring DNL Using Sinusoidal Histogram
7.3.1 Differential and Integral Nonlinearity
7.3.2 Sinusoidal Histogram Measurement
7.3.3 Measuring ENOB and DNL of ADC Using Discrete Wavelet Transform
7.3.4 Measuring the Instantaneous ENOB and DNL Using Haar Wavelet Transform
7.3.5 Measuring the Instantaneous ENOB
7.3.6 Measuring the Instantaneous DNL
7.4 Measuring ENOB and DNL of ADC Using Daubechies-4 Wavelet Transform
7.4.1 Daubechies-4 Wavelet Transform Using MATLAB
7.4.2 Measuring the ADC Instantaneous ENOB and DNL Using Daubechies-4 Wavelets
7.5 Extensions of the Wavelet-Based ADC Dynamic Test Algorithms and Key Observations
7.5.1 Hilbert Transform Implementation
7.5.2 The Range of |z[n]|
7.5.3 Using Different Formulations of the Haar Wavelet Coefficients in the Algorithm
7.6 Comparative Analysis of the Measurements for ADC
7.6.1 ENOB Measurements
7.6.2 DNL Measurements
7.7 The MATLAB Program for the ENOB and DNL Measurements
7.8 Differences Between Wavelet Transform Techniques and the Conventional Techniques in a Tabular Format
References
Chapter 8: Wavelet-Based Static Test of ADCs
8.1 Introduction
8.2 Static Testing of ADCs by Transfer Curve
8.2.1 ADC Transfer Curve
8.2.2 Testing for ADC Errors Using the DNL
8.3 Wavelet Transform-Based Static Testing of an ADC
8.3.1 The ADC Static Testing Method and Choice of Wavelet Transforms
8.3.2 Simulation of the ADC Testing Using Wavelet Transform
8.3.3 MATLAB Program for Measuring and Plotting some of the Wavelet-Based Static Testing of ADCs
References
Chapter 9: Mixed Signal Systems Testing Automation Using Discrete Wavelet Transform-Based Techniques
9.1 Introduction
9.2 Noise and Quantization Error
9.3 Worst-Case Effective Number of Bits (ENOB)
9.4 Instantaneous Differential Nonlinearity (DNL)
9.5 Instantaneous Integral Nonlinearity (INL)
9.6 Automation Testing Setup with LabVIEW and DWT
9.7 Testing Automation Programming Process
9.7.1 NI PXI-1042 Chassis
9.7.2 Power Supplies
9.7.3 HSDIO Card PXI-6552: Arbitrary Digital Waveform Generator
9.7.4 Scope Card PXI-5922
9.8 ADC Testing Setup and LabVIEW VIs
9.8.1 HSDIO Card PXI-6552
9.8.2 Waveform Generator NI PXI-5421
9.9 The GUI (Graphic User Interface)
9.10 Implementation of an Automated DWT-Based Algorithm for the Testing of ADCS
9.11 A Comparative Tabular Summary of the Automated ADC Testing Using DWTs
9.12 Implementation of an Automated DWT-Based Algorithm for the Testing of DACs
9.13 Cost Analysis in Terms of Test Duration Reduction
References
Part III: Wavelets and Wavelet Transform Application to Compression
Chapter 10: Wavelet-Based Compression Using Nonorthogonal and Orthogonally Compensated W-Matrices
10.1 Introduction
10.2 Orthogonality Condition
10.3 W-Transform and W-Matrix
10.4 The Orthogonality Compensation Process
10.5 Compression Algorithms for the Nonorthogonal and Orthogonally Compensated Cases
10.6 Simulation Examples
10.7 Performance Evaluation for the Simulation Examples
10.8 The Simulation Example Results and Discussions
References
Chapter 11: Wavelet Application to Image and Data Compression
11.1 Introduction
11.2 Compression Ideas
11.2.1 Irrelevant Information Redundancy
11.2.2 Spatial and Temporal Redundancy
11.2.3 Coding Redundancy
11.3 Justification for Compression
11.4 The Different Modes of Compression
11.4.1 Lossless Compression Mode
11.4.2 Lossy Compression Mode
11.4.3 Predictive Compression Mode
11.4.4 Transform Coding Compression Mode
11.5 The Different Compression Techniques
11.5.1 JPEG/DCT Compression Technique
11.5.2 Vector Quantization (VQ) Compression Technique
11.5.3 Fractal Image Compression Technique
11.5.4 Wavelet Image Compression Technique
11.6 The Compression and Decompression of an Image/Data Using Wavelet Transform
11.7 The EZWT Algorithm
11.8 The SPIHT Algorithm
11.9 The EBCOT Algorithm
11.10 The WDR Algorithm
11.11 The ASWDR Algorithm
11.12 Usefulness of Wavelet-Based Compression
References
Chapter 12: Application of Wavelets to Video Compression
12.1 Introduction
12.2 Video Compression Quality and the Metrics
12.3 Video Compression Errors
12.3.1 Blocking Artifacts
12.3.2 Blurriness
12.3.3 Motion Estimation Errors
12.4 Transmission Errors: Packet Loss
12.5 Justification for Wavelet-Based Video Compression
12.6 The Basic Principles of the Wavelet-Based Technique to SVC
12.7 Wavelet-Based Three-Dimensional Video Compression
12.8 The Basic Image and Video Compression Standards
12.8.1 The Joint Pictures Experts Group (JPEG)
12.8.2 The Joint Pictures Experts Group (JPEG) 2000
12.8.3 The H.261 Video Compression Standard
12.8.4 The H.263 Video Compression Standard
12.8.5 The H.264 Video Compression Standard
12.8.6 The MPEG-1 Video Compression Standard
12.8.7 The MPEG-2 Video Compression Standard
12.8.8 The MPEG-4 Video Compression Standard
12.8.9 The MPEG-7 Video Compression Standard
References
Part IV: Wavelets and Wavelet Transforms to Medical Application
Chapter 13: Wavelet Application to an Electrocardiogram (ECG) Medical Signal
13.1 Introduction
13.2 Description of an ECG Signal
13.3 Discrete Wavelet Transform Application to ECG Signals
13.3.1 One-Level DWT Decomposition and Reconstruction of the ECG Process
13.3.2 Extension to Five Levels of Decomposition and Reconstruction of the ECG Signals
13.3.3 The DWT Noise Removal Technique Using ECG Signals
13.4 Metrics for Performance Evaluation
13.4.1 Signal-to-Noise Ratio (SNR)
13.4.2 Peak Signal-to-Noise Ratio (PSNR)
13.4.3 Mean Squared Error (MSE)
13.4.4 Maximum Squared Error (MAXERR)
13.5 Evaluations of the Performance Measures
References
Part V: Wavelet and Wavelet Transform Application to Segmentation
Chapter 14: Application of Wavelets to Image Segmentation
14.1 Introduction
14.2 Image Segmentation Idea
14.3 Thresholding Technique
14.3.1 Local Thresholding
14.3.2 Adaptive Thresholding
14.3.3 Global Thresholding
14.4 Implementation of the Region-Based Technique
14.4.1 Growing the Region
14.4.2 Region Splitting and Merging
14.5 Watershed Segmentation Technique
14.5.1 Watershed Segmentation Algorithm
14.5.2 Gradient of the Image
14.6 K-Means Clustering
14.7 Template Matching Segmentation Technique
14.7.1 The Definition and Method Template Matching
14.7.2 The bi-Level Image Template Matching
14.7.3 Gray-Level Image Template Matching
14.8 Contour-Based Segmentation Technique
14.8.1 Internal Energy
14.8.2 External Energy
14.9 Wavelet-Based Segmentation Technique
14.9.1 Image Feature Extraction
14.9.2 Pixel Differences
14.9.3 Circular Averaging Filtering
14.9.4 Thresholding
References
Chapter 15: Hybrid Wavelet- and Fractal-Based Segmentation
15.1 Introduction
15.2 The Hybrid Wavelet- and Fractal-Based Segmentation Model
15.3 Computation of Wavelet-Based Analysis Image Data for the Hybrid Segmentation Process
15.3.1 Wavelet-Based Analysis Image Data Computation Process for Segmentation
15.3.2 Algorithm for the Computation of the Wavelet-Based Analysis Image Data for Segmentation
15.4 Computation of Fractal-Based Analysis Image Data for the Hybrid Segmentation Process
15.4.1 The Fractal-Based Analysis Image Data Computation Process
15.4.2 Algorithm for the Computation of the Fractal-Based Analysis Image Data for Segmentation
15.5 Formalizing the Notion of Segmentation
15.6 The Segmentation Model Process
15.6.1 Classification Theory Formulation
15.6.2 The Segmentation (Classification) Model Algorithm
15.7 Example of Simulation Results and Discussions
15.8 Performance Complexity Evaluation
References
Part VI: Wavelet and Wavelet Transform Application to Cybersecurity Systems
Chapter 16: Wavelet-Based Application to Information Security
16.1 Introduction
16.2 Information Security Schemes
16.2.1 Confidentiality
16.2.2 Integrity
16.2.3 Availability
16.3 Wavelet Application Analysis to Information Security
16.4 Detection Methods
16.5 Detection Schemes
16.6 Information Network Security Data Analysis Using Wavelet Transforms
16.6.1 Data Collection and Information Network Security Database (INSD) Segment
16.6.2 The Wavelet Transform Application Including the Denoised Segment
16.7 MATLAB Implementation of the Wavelet Transform-Based Analysis Algorithms
16.8 Wavelet Transforms and Cryptography in Information Security
16.8.1 AES Algorithm
16.8.2 Description of the Ciphers
16.8.3 Non-Uniform Block Adaptive Segmentation on Information (NUBASI)
16.8.4 Randomized Secret Sharing (RSS)
16.9 Cryptographic Symmetric and Asymmetric Systems
16.9.1 Substitution Permutation Cipher
References
Chapter 17: Application of Wavelets to Biometrics
17.1 Introduction
17.2 Biometric Characteristics
17.3 Biometric System
17.3.1 Enrollment Mode
17.3.2 Verification Mode
17.3.3 Identification Mode
17.4 Basics of Fingerprint Recognition
17.5 Classification of Fingerprints
17.5.1 Loops
17.5.2 Whorls
17.5.3 Arches
17.6 Fingerprint Matching Techniques
17.6.1 Ridge Feature-Based Matching
17.6.2 Correlation-Based Matching
17.6.3 Minutiae-Based Matching
17.7 The Fingerprinting Minutiae Matching System Algorithm Using Wavelet Transform
17.7.1 Partial Image Enhancement
17.7.2 Minutiae Extraction
17.7.3 Fingerprint Image Post-Processing
17.7.4 Procedure for Validating a Candidate Ridge Ending Point
17.7.5 Procedure for Validating a Candidate Bifurcation Point
17.8 Methodology
17.8.1 Performance Metrics
17.9 MATLAB Simulation Examples
17.10 Matching Indices for Different Wavelets
References
Chapter 18: Wavelet Application to Blockchain Technology Systems
18.1 Introduction
18.2 Capabilities and Limitations of Blockchain
18.2.1 Capabilities of Blockchain Technology
18.2.2 Limitations of Blockchain Technology
18.3 Blockchain-Based Strategies
18.4 How Blockchain Powers Applications Such as Bitcoin and Other Token-Based Initiatives
18.5 Different Types of Blockchain Models
18.5.1 Hyperledger
18.5.2 Fabric
18.5.3 Ethereum
18.6 Wavelet Transform Analysis
18.7 Wavelet Transform Analysis of Blockchain Systems
18.8 Wavelets and Bitcoin
18.9 Main Drivers of the Bitcoin Price as Evidenced from Wavelet Coherence Analysis
18.10 Advantages of Using Wavelets in Blockchain Systems
18.11 Disadvantages of Using Wavelets in Blockchain Systems
References
Part VII: Wavelet and Wavelet Transform Application to Detection, Discrimination and Estimation
Chapter 19: Wavelet-Based Signal Detection, Identification, Discrimination, and Estimation
19.1 Introduction
19.2 Wavelet Transform
19.2.1 Haar Wavelet Characteristics
19.2.2 Morlet Wavelet Characteristics
19.3 Overview of the Wavelet-Based Signal Detection
19.3.1 Chirp Signal
19.3.2 Frequency-Shift Keying (FSK) Signals
19.3.3 Phase-Shift Keying (PSK) Signals
19.3.4 Quadrature Amplitude Modulation (QAM) Signals
19.4 Overall Detection System Model
19.5 Chirp Signal Detection
19.6 PSK, FSK, and QAM Interclass Detection
19.7 PSK and FSK Intraclass Detection, Estimation, and Identification
19.7.1 M-ary PSK Identification, Estimation, and Detection
19.7.2 M-ary FSK Identification, Estimation, and Detection
19.8 Overview of the GUI-Based Simulation Interface
References
Chapter 20: Wavelet-Based Identification, Discrimination, Detection, and Parameter Estimation of Radar Signals
20.1 Introduction
20.2 AMRTDS
20.3 Wavelet-Based Detection of Signals Using Pattern Recognition
20.3.1 Wavelet Transform Algorithm
20.3.2 Discrimination of Signals Using Signal Pattern Recognition
20.4 Wavelet-Based Detection of Signals Using Bayes´ Theorem
20.4.1 Signal Detection Using Bayes´ Theorem and Wavelet Transform
20.5 Calculation Examples
20.6 Receiver Operating Characteristic (ROC) Curve
20.7 Estimation Parameter and Theory
20.7.1 Frequency and Power Estimation
20.8 Frequency Modulation
20.9 Channel-to-Channel Phase Estimation
20.10 Important Issues to Note
References
Chapter 21: Application of Wavelets to Vibration Detection in an Aeroelastic System
21.1 Introduction
21.2 Overview of Wavelet Analysis for the Aeroelastic Systems
21.2.1 The Wavelet Transform
21.2.2 The Vibration Signal Analysis Techniques
21.3 Development of a Vibration Model: An Example
21.4 Wavelet Families Used for Vibration Detection
21.5 Development of the Vibration Detection Algorithm
21.5.1 Initial Consideration: Decomposition Level
21.5.2 Initial Consideration: Threshold
21.5.3 Vibration Detection Algorithm: An Example of the Procedure
21.6 Simulation Examples of the Vibration Model
21.7 Threshold Experimentation
21.7.1 Global Thresholding
21.7.2 Per-Level Thresholding
21.8 Application of the PLT Algorithm on an Aeroelastic Vibration Data for Verification
21.9 Remarks on the Wavelets Used for the Vibration Signal Detection
References
Appendices
Appendix A: Wavelet Coefficients
Appendix B: Riesz Basis
Appendix C: The QR Factorization (QRF)
Appendix D: Signal Power: Parseval´s Relation to Fast Fourier Transform
Appendix E: Automation Process Testing GUIs Operation Manual for Mixed Signal Systems Using DWT
Appendix F: 12-Bit and 14-Bit ADCs DWT Testing Results (Figs. F1, F2, F3, and F4)
Appendix G: TLC876, ADS5410, and ADS5423 Data Sheet Manual
Appendix H: 12-Bit and 14-Bit DWT DACs Testing (Figs. H1, H2, H3, H4, and H5)
Appendix I: DAC2900, DAC2902, and DAC2904 Data Sheet Manual
Appendix J: Samples of Fingerprint Images
Appendix K: MATLAB Program Listings for Fingerprint Minutiae Processing Using Six Different Types of Wavelets
Appendix K1: Program to Create Database of Statistical Parameters for 80 Fingerprint Images
Appendix K2: Program to Verify a Test Image
Appendix L: Instruction for MATLAB Programs Execution for Fingerprint Minutiae Processing of Appendix K
Appendix M: MATLAB Programs for Chap. 19
Appendix N: MATLAB Programs for Chap. 20
Appendix O: MATLAB Programs for Chap. 21
Appendix O_1 of N Using Haar Wavelet
Appendix O_2 of O Using Daubechies-4 Wavelet
Appendix O_3 of O Using Morlet Wavelet
Appendix O_4 of O
Programs Using the Vibration Model and PLT
Appendix O_4 of O Using Daubechies-14 Wavelet
Appendix O_5 of O
Programs Using Flight Research Data
Index