bullet Advances in Signal Processing: Reviews, Vol. 1

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  Title: Advances in Signal Processing: Reviews, Vol. 1, Book Series

  Editor: Sergey Y. Yurish

  Publisher: International Frequency Sensor Association (IFSA) Publishing

  Formats: paperback (print book) and printable pdf Acrobat (e-book) 548 pages

  Price: 125.00 EUR (shipping cost by a standard mail without a tracking code is included)

  Delivery time for print book: 7-17 days dependent on country of destination. Please contact us for priority (5-9 days), ground (3-8 days) and express (3-5 days) delivery options by e-mail

  Pubdate: 25 August 2018

  ISBN: 978-84-09-04329-3

  e-ISBN: 978-84-09-04328-6


  Creative Commons License



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 Book Description



The principles of signal processing are using widely in telecommunications, control systems, sensors, smartphones, tablets, TV, video- and photo-cameras, computers, audio systems, etc. Taking into account the growing tendency of analog and digital signal processing applications, it was decided by IFSA Publishing to start a new multivolume Open Access Book Series on ‘Advances in Signal Processing: Reviews’ in 2018.


Written by 43 experienced and well-respected experts from universities, research centres and industry from 14 countries: Argentina, Australia, Brazil, China, Ecuador, France, Japan, Poland, Portugal, Spain, Switzerland, UK, Ukraine and USA the ‘Advances is Signal Processing: Reviews’, Vol. 1, Book Series, contains 13 chapters from the signals and systems theory to real-world applications. The authors discuss existing issues and ways to overcome these problems as well as the new challenges arising in the field. The book concludes with methods for the efficient implementation of algorithms in hardware and software. The advantages and disadvantages of different approaches are presented in the context of practical examples.


In order to offer a fast and easy reading of each topic, every chapter in this volume is independent and self-contained. All chapters have the same structure: first, an introduction to specific topic under study; second, particular field description including sensing or/and measuring applications. Each of chapter is ending by well selected list of references with books, journals, conference proceedings and web sites.


‘Advances in Signal Processing: Reviews’ is a valuable resource for graduate, post-graduate students, researchers, practicing engineers and scientists in electronics, communications, and computer engineering, including artificial intelligence.







1. Artificial Intelligence Based Approach for Robust Radar Detection in Maritime Scenarios

1.1. Introduction
1.2. Case Study
    1.2.1. Target Model
    1.2.2. Interference Model
1.3. Reference CGLR Detectors
1.4. Intelligent Detectors
    1.4.1. Intelligent Agent Architectures
    1.4.2. Error Function of the Training Process
1.5. Experiments and Results
    1.5.1. Detectors Design
    1.5.2. Real Scenario
1.6. Conclusions


2. Robust Hierarchical Bayesian Modeling  for Automatic Model Order Determination

2.1. Introduction
    2.1.1. Non-Negative Data Representation
    2.1.2. Sparseness in Data Representation
    2.1.3. Non-Negative Matrix Factorization and Model Order Determination Non-Negative Matrix Factorization Model Order Determination
2.2. Hierarchical Bayesian Modeling
2.3. Data Model
    2.3.1. Gaussian Distribution
    2.3.2. Poisson Distribution
    2.3.3. Gamma Distribution
2.4. Parameter Model 47
    2.4.1. Half Normal Distribution
    2.4.2. Exponential Distribution
    2.4.3. Tweedie Distribution
2.5. Hyper-parameter Model
    2.5.1. Informative Prior Gamma Distribution Tweedie Distribution Variational Bayesian Distribution
    2.5.2. Non-Informative Prior Stirling’s Algorithm Expectation Maximization (EM) Algorithm
2.6. Result from Different Methods
    2.6.1. Fence Dataset
    2.6.2. Swimmer Dataset
    2.6.3. Face Dataset
    2.6.4. Music Dataset
2.7. Conclusion


3. Complex Wavelet Additive Synthesis of Audio Signals: Introduction and Applications

3.1. Introduction
3.2. Audio, Time-Frequency Distributions and Wavelets
    3.2.1. Basic Definitions
    3.2.2. The Complex Continuous Wavelet Transform General Considerations Extracting Information from the Complex Continuous Wavelet Transform
3.3. Gaussian Complex Filtering
    3.3.1. The Morlet Wavelet Revised
    3.3.2. Mathematical Considerations The Simplest Case: A Pure Cosine with Constant Amplitude Pure Cosine with Variable Amplitude The Sum of n Pure Cosines The Quadratic Phase FM Signal
    3.3.3. A General Monocomponent Signal: The Linear Chirp Approximation
    3.3.4. The Additive Synthesis Method
3.4. CWAS: Discretization
    3.4.1. Constant Resolution Filter Bank
    3.4.2. Variable Resolution Filter Bank
3.5. The Complex Wavelet Additive Synthesis algorithm
    3.5.1. Calculation of the Wavelet Coefficients
    3.5.2. Wavelet Spectrogram and Scalogram
    3.5.3. Partials and Additive Synthesis Reconstruction
    3.5.4. Partials and Tracking of Partials
3.6. Resynthesis and Extraction of Time-Frequency Characteristics of the Audio Signal
    3.6.1. Audio Model: Time-Frequency Characteristics
    3.6.2. Plotting the Characteristics of the Signal 2D Representation 3D Representation
3.7. CWAS Applications
    3.7.1. F0 Estimation General Considerations NHRF0 Polyphony Inference: Accumulated Energy HRF0 Discrete Cepstrum Estimation Results
    3.7.2. Blind Audio Source Separation General Considerations BASS using CWAS
3.8. Conclusions and Future Work


4. A Review of the Recent Cepstrum-based Methods for Acoustic Feedback Cancellation

4.1. Introduction
4.2. Cesptral Analysis of the SR System
4.3. Cesptral Analysis of the AFC System
    4.3.1. Cesptral Analysis of the Microphone Signal
    4.3.2. Cesptral Analysis of the Error Signal
4.4. AFC Methods Based on Cepstral Analysis
    4.4.1. AFC-CM
    4.4.2. AFC-CE
4.5. Simulation Configuration
    4.5.1. Simulated Environment Feedback Path Forward Path Delay Filter
    4.5.2. Evaluation Metrics Maximum Stable Gain Misalignment Frequency-weighted Log-spectral Signal Distortion W-PESQ
    4.5.3. Speech Database
4.6. Simulation Results
    4.6.1. Results for Long Acoustic Feedback Path
    4.6.2. Results for Short Acoustic Feedback Path
4.7. Conclusions


5. Covariance Analysis of Periodically Correlated Random Processes for Unknown Non-stationarity Period

5.1. Introduction
5.2. Period Estimation as a Problem of Searching for Hidden Periodicities
5.3. PCRP-model of Hidden Periodicities
5.4. Coherent Covariance Analysis
    5.4.1. The Estimation of the Mean Function Period
    5.4.2. Estimation of Mean Function and Its Fourier Coefficients
    5.4.3. Estimation of Covariance and Correlation Function Period
    5.4.4. Estimation of Covariance Function and Its Fourier Coefficients
    5.4.5. The Verification of the Developed Approach The Analysis of Simulated Data The Covariance Analysis of Vibration of the Fast Moving Spindle
5.5. Component Covariance Analysis
    5.5.1. Period Estimation of Deterministic Component Cosine and Sine Transforms of Realization Component Period Estimation
    5.5.2. Covariance Functionals Cosine and Sine Covariance Functionals Component Covariance Functional
    5.5.3. Correlation Functionals Cosine and Sine Correlation Functionals Component Correlation Functional
    5.5.4. Moment Functions Estimators Mean Function Estimator The Covariance Function Estimator
    5.5.5. Period Estimation for Simulated and Real Data Simulated Data Analysis Application to Vibration Signal Analysis
5.6. Comparison with Other Results
5.7. Conclusions

6. Learning Colours from Textures by Effective Representation of Images

6.1. Introduction
6.2. Early Learning-based Colourisation Methods
6.3. Learning Colours from Texture on Non-linear Manifolds
    6.3.1. Colourisation Problem
    6.3.2. Manifold of Patch Populations
    6.3.3. Sparsity Constraints
    6.3.4. Semantic Groups
    6.3.5. Prediction Algorithm
    6.3.6. Implementation Details
    6.3.7. Experiment Results
6.4. Transferring Colours by Deep Neural Networks
    6.4.1. Fully Convolutional and Diluting Neural Networks
    6.4.2. Colouration by FCN
6.5. End Notes


7. The Physical Layer of Low Power Wide Area Networks: Strategies, Information Theory’s Limit and Existing Solutions

7.1. Introduction
7.2. Constraints of the Physical Layer
    7.2.1. Power Amplifier and Constant Envelope Modulations
    7.2.2. Achieving Long Range
    7.2.3. Sensitivity of the Receiver
7.3. Strategies
    7.3.1. Reduce the Bandwidth
    7.3.2. Reduce the Required Signal-to-Noise Ratio Reduce the Spectral Efficiency Reduce the Spectral Efficiency Along with the Required E_b/N_0 Reduce the Level of Required E_b/N_0
    7.3.3. Comparison to the Information Theory’s Limit
7.4. Existing Solutions
    7.4.1. Proprietary Solutions Sigfox LoRa
    7.4.2. Standardized Technologies 802.15.4k Narrow-Band IoT
    7.4.3. Turbo-FSK Transmitter Receiver
    7.4.4. Performance
7.5. Conclusion


8. Deep Belief Networks and Its Applications  in Evaluation of Vehicle Interior Noise

8.1. Introduction
8.2. Related Works
    8.2.1. Psychoacoustics-based Interior Sound Quality Prediction
    8.2.2. Machine-learning-based Interior Sound Quality Prediction
8.3. Deep Belief Networks
    8.3.1. Restricted Boltzmann Machine Architecture
    8.3.2. Deep Belief Network Architecture
    8.3.3. Development of Deep Belief Networks to Model Continuous Data
8.4. Recording and Evaluating the Vehicle Interior Noise
    8.4.1. Development of the Vehicle Interior Noise Database
    8.4.2. Subjective Evaluation of the Vehicle Interior Noise
8.5. Interior Noise Feature Extraction
    8.5.1. Psychoacoustic-based Feature Extraction
    8.5.2. Energy-based Feature Extraction
    8.5.3. Feature Comparison and Selection
8.6. Development of the CRBM-DBN-based Sound Quality  Prediction Model
    8.6.1. Performance Measurements
    8.6.2. Designing the Architecture of CRBM-DBN
    8.6.3. Model Verification and Comparison
8.7. Conclusion


9. Selection of Diagnostic Symptoms  for Technical Condition Assessment  and Prognosis

9.1. Introduction
9.2. Singular Value Decomposition (SVD) Method
    9.2.1. Theoretical Outline
    9.2.2. Examples
9.3. Information Content Measure (ICM) Method
    9.3.1. Background
    9.3.2. The Question of Stationarity
    9.3.3. The Question of Abrupt Changes
    9.3.4. Representativeness Factor
9.4. Example
9.5. Conclusions


10. Nonlinear Acoustic Echo Cancellation  Using a Dynamic Micro-speaker Model

10.1. Introduction
10.2. Loudspeaker Nonlinearities
10.3. Quasi-linear Model of the Micro-speaker
10.4. Model Identification of a System Characterised by Nonlinear Damping
10.5. Experimental Identification of a Micro-speaker
10.6. Conclusions


11. A New Damage Detection Method for Bridge Condition Assessment in Structural Health Monitoring (SHM)

11.1. Introduction
    11.1.1. An Idea of the System State Representation Methodology (SRM)
    11.1.2. Advances of SRM Compare with the Traditional Methods and Research Scopes
    11.1.3. Data Management
    11.1.4. Nonlinear Simulation and Feature Extracting
    11.1.5. Condition Assessment
11.2. SRM Concept and Algorithms
    11.2.1. Definition of the State of a System
    11.2.2. System State Defining
    11.2.3. System State Approximating
    11.2.4. Optimal State Representation Based on a Kernel Function
11.3. SRM Assessment Method
    11.3.1. Probability-based Assessment Methods Statistic Test Methods System State Indices Hypothesis Probability with Two Divided-levels Approximate Probability with Two Divided-levels Approximate Probability with Multi-Levels Assessment Based on State Probability Assessment Based on Information Entropy
    11.3.2. A Simple Simulation Experimental Result
    11.3.3. Summary
11.4. SRM Tool 1: Compatible Gradient Algorithm for Large Scale Linear Problem
    11.4.1. A Simple Gradient Algorithm for Linear Problem
    11.4.2. Compatible Gradient Algorithm
    11.4.3. A Simple Example
    11.4.4. Summary
    11.4.5. Monotonic Descent Conjugates Gradient (MDCG) Method
11.5. SRM Tool 2: Frequency Slice Wavelet Transform Theory  and Methods
    11.5.1. Introduction
    11.5.2. Frequency Slice Wavelet Transform Analysis Frequency Slice Wavelet Transform Bandwidth-to-Frequency Ratio Property Scale Selection Reconstructive Procedure Independency Property FSF Design Property Filter Property Discrete FSF Discrete FSWT
    11.5.3. FSWT Signal Process Flow
    11.5.4. Comparing with Classical Transforms
    11.5.5. Applications Signal Filter in Time and Frequency Domain Separation of Close Modals Resolution and Symmetry Energy Leakage Problem Scale Optimization Dynamic Scale A Real Life Signal
    11.5.6. Summary

11.6. SRM Feature Extracting Methods and Applications
    11.6.1. Basic Idea of System Feature
    11.6.2. Approximate Feature Vectors Problem
    11.6.3. Extracting Damping Feature
    11.6.4. Feature Extract Algorithm
    11.6.5. Overall Logics of SRM Method
    11.6.6. Application to a Laboratory Bridge Monitoring System and Discussions Outline of SRM-based Damage Detection Overview of Bridge Model Girder Experiment Experimental Results and Discussions
11.7. Concluding Remarks


12. On Line Diagnosis in Induction Motors and Load

12.1. Introduction
12.2. Internal Fault Detection in Induction Motors
    12.2.1. Motor Current Signature Analysis
    12.2.2. Extended Park’s Vector Approach
    12.2.3. Towards an Integrated Fault Diagnostic System
    12.2.4. Experimental Results The Experimental Prototype Case Study 1 Case Study 2 Case Study 3 General Comments
12.3. Mechanical Faults Detection beyond the Motor
    12.3.1. General Comments
    12.3.2. Misaligned Drives Model
    12.3.3. Fault Detection Method
    12.3.4. Experimental Results
12.4. Gear Box Fault Detection
12.5. Conclusions


13. Vocal Folds Dynamics by Means of Optical Flow Techniques: A Review of the Methods

13.1. Introduction
    13.1.1. Voice Assessment and Endoscopy
    13.1.2. Scientific and Clinical Applications of Laryngeal High-Speed Videoendoscopy
    13.1.3. From a Big Amount of Data to a Synthesized View
13.2. Optical-Flow Computation
    13.2.1. Theoretical Background
    13.2.2. Optical Flow Evaluation
    13.2.3. Optical Flow in LHSV
13.3. Application of OF Computation to LHSV Image Analysis
    13.3.1. Database
    13.3.2. Image Processing Implementation
    13.3.3. Optical Flow Facilitative Playbacks Optical Flow Kymogram (OFKG) Optical Flow Glottovibrogram (OFGVG) Glottal Optical Flow Waveform (GOFW) Vocal Folds Displacements Trajectories
    13.3.4. Reliability Assessment of Optical Flow Playbacks
13.4. Assessment of OF-Based Playbacks
    13.4.1. Displacements Trajectories Comparison
    13.4.2. Comparison between OFGVG, GVG and |dxGVG|
    13.4.3. Glottal Velocity: Derivative of Glottal Area Waveform and Glottal Optical FlowWaveform
    13.4.4. Optical Flow Using Local Dynamics along One Line: Digital Kymogram and Optical Flow Kymogram
13.5. Conclusions





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