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Machine Learning in Signal Processing
Applications, Challenges, and Road Ahead



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ISBN 9780367618902
December 10, 2021 Forthcoming by Chapman and Hall/CRC
368 Pages 211 B/W Illustrations

 
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Book Description

Machine Learning in Signal Processing: Applications, Challenges and Road Ahead offers a comprehensive approach towards research orientation for familiarising ‘signal processing (SP)’ concepts to machine learning (ML).

Machine Learning (ML), as the driving force of the wave of Artificial Intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for Machine Learning (ML).

The focus is on understanding the contributions of signal processing and ML and its aim to solve some of the Artificial Intelligence (AI) and Machine Learning (ML) challenges.

 

●       Fully focused on addressing the missing connection between signal processing and ML

●       Provides one-stop guide reference for the readers

●       Oriented towards the material and flow with regard to general introduction, technical aspects

●       Comprehensively elaborates on the material with examples and diagrams

 

This book is complete outlet and designed exclusively for advanced undergraduate, post graduate students, research scholars, faculties and academicians of Computer Science and Engineering, Computer Science and Applications as well as Electronics & Telecommunication Engineering.

Table of Contents

Machine Learning Espousal in Signal Processing: Applications, Challenges and Road Ahead

Table of Contents

 

Chapter-1 Introduction to Signal Processing and Machine Learning

Kavita Somraj, Higher Colleges of Technology, Dubai, United Arab Emirates

Abstract

1.1   Introduction

1.2   Basic Terminologies

1.2.1          Signal Processing

1.2.1.1. Continuous and Discrete Signals

1.2.1.2 Sampling and Quantization

1.2.1.3 Change of Basis

1.2.1.4    Importance of time domain and frequency domain analysis

1.2.2          Machine Learning

1.3             Distance ased signal classification, Nearest Neighbor Classifier and Hilbert Space

1.3.1          Distance based Signal Classification

1.3.1.1 Metric Space

1.3.1.2 Normal Linear Space

1.3.1.3 Inner Product Space

1.3.2          Nearest Neighbor Classification

1.3.3          Hilbert Space

1.4             Fusion of Machine Learning in Signal Processing

1.5             Benefits of Machine Learning in Signal Processing

1.6             Conclusion

References

 

Chapter-2 Learning Theory (Supervised/Unsupervised) for Signal Processing

Ruby Jain, Assistant Professor, Symbiosis Skills and Professional University, Pune

Bhuvan Jain, Assistant Professor, DPU IDL, Pune

Manimala Puri, Director Academics, Symbiosis Open Education Society, Pune

 

Abstract

2.1 Introduction

2.2 Machine Learning

2.3 Machine Learning Algorithms

2.4 Supervised Learning

2.5 Unsupervised Learning

2.6 Semi-Supervised Learning

2.7 Reinforcement Learning

2.8 Use case of Signal Processing using Supervised and Unsupervised Learning

2.9 Deep Learning for Signal Data

2.10 Conclusion

References

 

Chapter-3 Supervised and Unsupervised Learning Theory for Signal Processing

Sowmya K B, Assistant Professor, Department of ECE, RV College of Engineering, Begaluru, India

 

Abstract

3.1 Introduction

3.2 Supervised Learning Method

3.2.1 Classification Problems

3.2.2 Regression Problems

3.2.3 Examples of Supervised Learning

3.3 Unsupervised Learning Method

            3.3.1 Illustrations of Unsupervised Learning

3.4 Semi-Supervised Learning Method

3.5 Binary Classification

3.5.1 Different Classes

3.5.2 Classification in Preparation

3.6 Conclusion

References

 

Chapter-4 Applications of Signal Processing

Anuj Kumar Singh, Amity University, Gurugram

Ankit Garg, Amity University, Gurugram

Abstract

4.1 Introduction

4.2 Audio Signal Processing

            4.2.1 Machine Learning in Audio Signal Processing

                        4.2.1.1 Spectrum and Cepstrum

                        4.2.1.2 Mel Frequency Cepstral Coefficients (MFCC)

                        4.2.1.3 Gammatone Frequency Cepstral Coefficients (GFCC)

                        4.2.1.4 Building the classifier

4.3 Audio Compression

            4.3.1 Modeling and Coding

            4.3.2 Lossless Compression

            4.3.3 Lossy Compression

            4.3.4 Compressed Audio with Machine Learning Algorithms

4.4 Digital Image Processing

4.4.1 Fields Overlapping with Image Processing

4.4.2 Digital Image Signal Processing

4.4.3 Machine Learning with Digital Image Processing

            4.4.3.1 Image Classification

            4.4.3.2 Data Labelling

            4.4.3.3 Location Detection

4.5 Video Compression

            4.5.1 Video Compression Model

            4.5.2 Machine Learning in Video Compression

                        4.5.2.1 Development Savings

                        4.5.2.2 Improving Encoder Density

4.6 Digital Communications

            4.6.1 Machine Learning in Digital Communications

                        4.6.1.1 Communication Networks

                        4.6.1.2 Wireless Communication

                        4.6.1.3 Smart Infrastructure and IoT

                        4.6.1.4 Security and Privacy

                        4.6.1.5 Multimedia Communication

            4.6.2 Healthcare

                        4.6.2.1 Personalized Medical Treatment

                        4.6.2.2 Clinical Research and Trial

                        4.6.2.3 Diagnosis of Disease

                        4.6.2.4 Smart Health Records

                        4.6.2.5 Medical Imaging

                        4.6.2.6 Drug Discovery

                        4.6.2.7 Outbreak Protection

            4.6.3 Seismology

                        4.6.3.1 Interpreting Seismic Observations

                        4.6.3.2 Machine Learning in Seismology

            4.6.4 Speech Recognition

            4.6.5 Computer Vision

            4.6.6 Economic Forecasting

4.7 Conclusion

References

 

Chapter-5 Deep Dive in Deep Learning: Computer Vision, Natural Language Processing and Signal Processing

V.Ajantha Devi, Research Head, AP3 Solutions, Chennai, India

Mohd Naved, Assistant Professor, Jagannath University, Delhi, India

 

Abstract

5.1 Deep Learning: Introduction

5.2 Past, Present and Future on Deep Learning

5.3 Natural Language Processing

            5.3.1 Word Embeddings

            5.3.2 Global Vectors for Word Representation

            5.3.3 Convolutional Neural Networks

            5.3.4 Feature Selection and Pre-processing

            5.3.5 Named Entity Recognition

5.4 Image Processing

            5.4.1 Introduction to Image Processing and Computer Vision

            5.4.2 Localization

            5.4.3 Smart Cities and Surveillance

            5.4.4 Medical Imaging

            5.4.5 Object Representation

            5.4.6 Object Detection

5.5 Audio Processing and Deep Learning

            5.5.1 Audio Data Handling Using Python

            5.5.2 Spectrogram

            5.5.3 Wavelet-Based Feature Extraction

            5.5.4 Current Methods

5.6 Conclusion

References

 

Chapter-6 Brain Computer Interface

Dr. Paras Nath Singh, CMRIT, Bangalore, India

 

6.1 Introduction to BCI and its Components

6.2 Framework/Architecture of BCI

6.3 Functions of BCI

            6.3.1 Correspondence and Control

            6.3.2 Client state checking

6.4 Applications of BCI

            6.4.1 Healthcare

            6.4.2 Neuro-ergonomic and Smart Environment

            6.4.3 Neuro-Marketing and Advertisement

            6.4.4. Pedagogical and Self-Regulating Oneself

            6.4.5 Games and Entertainment

            6.4.6 Security and Authentication

6.5 Signal Acquisition

            6.5.1 Invasive Techniques

                        6.5.1.1 Intra-Cortical

                                6.5.1.2 ECoG (Electrocorticography) & Cortical Surface

            6.5.2 Non-Invasive Techniques

                        6.5.2.1 Magneto-encephalography(MEG)

            6.5.2.2 fMRI (functional Magnetic Resonance Imaging)

            6.5.2.3 fNIRS (functional Near-Infrared Spectroscopy)

            6.5.2.4 EEG (Electroencephalogram)

6.6 Electrical Signal of BCI

            6.6.1 Evoked Potential or Evoked Response (EP)

            6.6.2 Event Related Desynchronization and Synchronization

6.7 Challenges of BCI and Proposed Solutions

            6.7.1 Challenges of Usability

            6.7.2 Technical Issues

            6.7.3 Proposed Solutions

                        6.7.3.1 Noise Removal

                        6.7.3.2 Disconnectedness of Multiple Classes

6.8 Conclusion

References

 

Chapter-7 Adaptive Filters

Sowmya K B, Chandana, Anjana Mahaveer Daigond

ECE Department, RV College of Engineering Bengaluru, India

 

Abstract

7.1 Introduction

            7.1.1 Adaptive Filtering Problem

7.2 Linear Adaptive Filter Implementation

            7.2.1 Stochastic Gradient Approach

            7.2.2 Least Square Estimation

7.3 Non-Linear Adaptive Filters

            7.3.1 Volterra Based Non-Linear Adaptive Filter

7.4 Applications of Adaptive Filter

            7.4.1 Biomedical Applications

                        7.4.1.1 ECG Power-Line Interference Removal

                        7.4.1.2 Maternal-Foetal ECG Separation

            7.4.2 Speech Processing

                        7.4.2.1 Noise Cancellation

            7.4.3 Communication Systems

                        7.4.3.1 Channel Equalization in Data Transmission Systems

                        7.4.3.2 Multiple Access Interference Mitigation in CDMA

            7.4.4 Adaptive Feedback Cancellation in Hearing Aids

7.5 Neural Network

            7.5.1 Learning Techniques in ANN

7.6 Single and Multi-Layer Neural Net

            7.6.1 Single Layer Neural Networks

            7.6.2 Multi-Layer Neural Net

7.7 Applications of Neural Networks

            7.7.1 ECG Classification

            7.7.2 Speech Recognition

            7.7.3 Communication Systems

                        7.7.3.1 Mobile Station Location Identification using ANN

                        7.7.3.2 ANN Based Call Handoff Management Scheme for Mobile Cellular Network

                        7.7.3.3 A Hybrid Path Loss Prediction Model based on Artificial Neural Networks

                        7.7.3.4 Classification of Primary Radio Signals

                        7.7.3.5 Channel Capacity Estimation using ANN

References

 

Chapter-8 Adaptive Decision Feedback Equalizer Based on Wavelet Neural Network

 

Abstract

Saikat Majumder,

National Institute of Technology, Raipur, Chhattisgarh, India.

8.1 Introduction

8.2 System Model

            8.2.1 Channel Equalization

            8.2.3 Decision Feedback Equalization

8.3 Wavelet Neural Network

            8.3.1 Wavelet Analysis

            8.3.2 Wavelet Neural Network

8.4 Multidimensional Wavelet Neural Network

8.5 Proposed WNN DFE Architecture

            8.5.1 Equalizer Architecture

            8.5.2 Cuckoo Search Optimization

            8.5.3 CSO-based Training of WNN DFE

            8.5.4 Simulation Results

                        8.5.4.1 MSE Performance

                        8.5.4.2 Effect of EVR

                        8.5.4.3 Effect of Time-varying channel

                        8.5.4.4 BER Performance Evaluation

8.6 Conclusion

References

 

 

 

 

 

 

 

 

Chapter-9 Intelligent Video Surveillance Systems using Deep Learning Methods

Anjanadevi Bondalapati, Department of Information Technology, MVGR College of Engineering, Vizianagaram, India

Manjaiah D H, Department of Computer Science, Mangalore University, India

 

Abstract

9.1 Introduction

            9.1.1 Deep Learning

            9.1.2 Deep Learning- Past, Present and Future

            9.1.3 Recent Methodologies

            9.1.4 Concepts used in Deep Learning

                        9.1.4.1 Convolutional Neural Network

9.2 Natural Language Processing Using Deep Learning

            9.2.1 Introduction to Natural Language Processing (NLP)

9.2.2 Word-Vector Representations (Simple Word, Multi Word Prototypes and Global Contexts)

            9.2.2.1 Word Vector Representation

            9.2.2.2 Simple Word2VectorRepresentation

            9.2.2.3 Learning Representation Through Backpropagation

            9.2.2.4 Natural Language Tasks for Text Classification

            9.2.2.5 Natural Language Tasks for Image Description Generation

9.3 Machine Translation Using Gated Recurrent Neural Networks (GRNN) and Long Short Time Memory (LSTM)

9.3.1 Gated Recurrent Units (GRUs)

9.3.2 Long Short Term Memory (LSTM)

9.3.3 Results Analysis

9.4 Image Processing Using Deep Learning Algorithms

            9.4.1 Introduction to Image Processing and Computer Vision

            9.4.2 Data preparation for Image processing tasks

            9.4.3 Classification Algorithms with Applications

 

9.5 Light Weight Deep Convolution Neural Network Architecture (LW-DCNN)

            9.5.1 Introduction

            9.5.2 Architecture

            9.5.3 Results

9.6 Improved Unified Model for Moving Object Detection

            9.6.1 Introduction

            9.6.2 Object Detection Architecture

            9.6.3 Results

            9.6.4 Comparison Analysis

            9.6.5 Applications to Human Action Recognition

 

9.7 Wavelet Based feature extraction methods and application to audio signals

            9.7.1 Introduction to Discrete Wavelet Transform Techniques

            9.7.2 Wavelet based selection methods

            9.7.3 Hybrid feature extraction method for classification

            9.7.4 Results

            9.7.5 Various applications of Audio Signals

Conclusion

References

 

Chapter-10 Stationary Signal, Autocorrelation, Linear and Discriminant Analysis

Dr. Bandana Mahapatra, Symbiosis Skills and Professional University, Pune, India

Kumar Sanjay Bhorekar, Symbiosis Skills and Professional University, Pune, India

 

Abstract

10.1 Introduction

10.2 Fundamentals of Linear Algebra and Probability Theory

            10.2.1 What is Linear Algebra?

            10.2.2 Probability Theory

10.3 Basic Concepts of Machine Learning

10.4 Supervised and Unsupervised ML Techniques for Digital Signal Processing

10.5 Applications of Signal Based Identification using Machine Learning Approach

            10.5.1 ML for Audio Classification

            10.5.2 Audio Signals Classification

            10.5.3 ML for Image Processing

10.6 Applications of ML methods in Optical Communications

Conclusion

References

 

 

Chapter-11 Intelligent System for Fault Detection in Rotating Electromechanical Machines

Saad Chakkor

LabTIC, ENSA of Tangier, University of AbdelmalekEssaâdi, Route de Ziaten Km 10,

Tanger Principale, B.P : 1818 – Tangier, Morocco

Pascal Doré

LabTIC, ENSA of Tangier, University of AbdelmalekEssaâdi, Route de Ziaten Km 10,

Tanger Principale, B.P : 1818 – Tangier, Morocco

Ahmed El Oualkad

LabTIC, ENSA of Tangier, University of AbdelmalekEssaâdi, Route de Ziaten Km 10,

Tanger Principale, B.P : 1818 – Tangier, Morocco

 

Abstract

11.1 Introduction

11.2 Related works

11.3 Asynchronous Machines

11.4 Electromechanical Defects

            11.4.1 Bearing Faults

            11.4.2 Broken rotar bar faults

            11.4.3 Eccentricity defects

            11.4.4 Misalignment defects

11.5 Methods for detecting anomalies

            11.5.1 Definition

            11.5.2 Importance of anomaly detection

            11.5.3 Some Techniques for anomaly detection

11.6 Frequency Signatures

11.7 The MCSA Measurement Method

            11.7.1 Modeling of the stator current of the asynchronous machine

11.8 Variants of the ESPRIT method    

11.9 MOS (Order Selection Model)

            11.9.1 Principle

            11.9.2 Mathematical expressions

                11.9.3 Results obtained by each of the MOS algorithms

11.10 Intelligent defect classification algorithms

            11.10.1 Artificial Neuronal Networks and Genetic Algorithms (ANN-AG)

                        11.10.1.1 Artificial neural networks      

                        11.10.1.2 Genetic Algorithms (GA)

                                    11.10.1.2.1 Chromosome coding

11.10.1.2.2   Generation of the initial population

11.10.1.2.3   Calculation of the evaluation function (fitness)

            11.10.1.2.4 Selection of individuals for reproduction

                                          11.10.1.2.4.1 Mutation

                                                                      11.10.1.2.4.2 Crossover

            11.10.2 Fusion ANN et AG

            11.10.3Association of two architectures

            11.10.4 Support Vectors Machine (SVM)

                        11.10.4.1 How it works

            11.10.5 K-Nearest Neighbors (K-NN)

            11.10.6 Extrem Learning Machines

                        11.10.6.1 Principle or algorithm

11.11 Simulation and analysis of results

            11.11.1 High-resolution estimation methods

                        11.11.1.1 Preparation of simulation data

                        11.11.1.2 Frequency error analysis

                        11.11.1.3 Amplitude error analysis

                        11.11.1.4 Interpretations on frequency analysis

                        11.11.1.5 Interpretations on amplitude analysis

                        11.11.1.6 Interpretations on frequency and amplitude analysis

                        11.11.1.7 Interpretation of algorithm execution times

            11.11.2 Fault Classification Algorithms

                        11.11.2.1 Artificial Neural Networks and Genetic Algorithms

                                    11.11.2.1.1 Preparation of the simulation data

                                    11.11.2.1.2 Simulation in the time domain        

                                    11.11.2.1.3 Simulation in the frequency domain

                                    11.11.2.1.4 Simulation in the frequency domain

            11.11.3 Vector machine supports

                        11.11.3.1 Simulation in the time domain

                        11.11.3.2 Simulation in the frequency domain

            11.11.4 K-Nearest neighbors

                        11.11.4.1Simulation in the time domain

                        11.11.4.2 Simulation in the frequency domain

            11.11.5 Exterm Learning Machine

                        11.11.5.1 Simulation in the time domain

                        11.11.5.2 Simulation in the frequency domain

            11.11.6 Comparative table of the different algorithms developed in time and frequency

                        11.11.6.1 Comparison of intelligent fault classification algorithms in the time and frequency domain

                                    11.11.6.1.1 In the time domain

                                    11.11.6.1.2 In the frequency domain

Conclusion

References

 

Chapter-12 Wavelet Transformation and Machine Learning Techniques for Digital Signal Analysis in IoT Systems

Rajalakshmi Krishnamurthy, Jaypee Institute of Information Technology, Noida, India

Dhanalekshmi Gopinathan, Jaypee Institute of Information Technology, Noida, India

Abstract

12.1 Introduction

12.2 Digital Signal processing techniques for IoT Devices

            12.2.1 Fourier Transform

            12.2.2 Wavelet Transform

                        12.2.2.1 Continuous Wavelet Transform

                        12.2.2.2. Discrete Wavelet Transformation DWT

                        12.2.2.3. Computation of Discrete wavelet transform (DWT)

12.3. Machine learning and Deep Learning techniques for time series analysis in IoT

            12.3.1. Time series classification algorithms

            12.3.2. Time series classification using deep learning

12.4 Comparison for Morlet, Mexican Hat, Frequency B-spline wavelet towards the classification of ECG signal.

                12.4.1 Mexican wavelet transform

                12.4.2 Morlet Wavelet transform

            12.4.3 Frequency B-spline wavelet transform

Conclusion

References

 

 

 

 

 

 

 

 

 

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Editor(s)

Biography

Sudeep Tanwar (M’15, SM’21) is currently working as a Professor of the Computer Science and Engineering Department at the Institute of Technology, Nirma University, India. Dr Tanwar was a visiting Professor at Jan Wyzykowski University in Polkowice, Poland and the University of Pitesti in Pitesti, Romania. Dr Tanwar’s research interests include Blockchain Technology, Wireless Sensor Networks, Fog Computing, Smart Grid, and IoT. He has authored 02 books and edited 13 books, more than 200 technical papers, including top journals and top conferences, such as IEEE TNSE, TVT, TII, WCM, Networks, ICC, GLOBECOM, and INFOCOM. He is a Senior Member of IEEE, CSI, IAENG, ISTE, CSTA, and the member of the Technical Committee on Tactile Internet of IEEE Communication Society. He is leading the ST research lab where group members are working on the latest cutting-edge technologies.

Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks and Swarm Intelligence. He is currently working in Graduate School, Duy Tan University, Da Nang, Vietnam. A Certified Professional with 75+ Professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published 100+ Research Papers in various National International Journals (Scopus/SCI/SCIE/SSCI Indexed) with High Impact Factor. Member of more than 50+ Associations as Senior and Life Member. He is acting as Editor-in-Chief of IGI-Global, USA Journal titled “International Journal of Smart Vehicles and Smart Transportation (IJSVST)”.

Rudra Rameshwar (Ph.D. – IIT Roorkee, M.Tech. – IIT Roorkee, D.B.E. – EDII Ahmedabad, B.Tech. (Elect. Engg.) – DEI Agra, B.Sc. – DEI Agra) is full-time management faculty working in LMTSOM, Thapar Institute of Engineering & Technology (Deemed-to-be-University) Patiala (Punjab State), India. He is associated with core MBA specializations working in the area of “Operations, Energy & Sustainability, and Analytics”. Additionally, he is working in the area of Industry 4.0, Education 4.0, Business Analytics, HR Analytics, CSR, Service Operations Management, Sustainable Development, Warehouse Management, Sustainable Business Strategies, Industrial Marketing, Technology & Innovation, Research Methodology, Data Analytics, International Management, Business Statistics, Research Design and Statistical Tools – Techniques, - Data Analysis, Interpretation – SPSS/EViews/Minitab Training, Meta-Analysis, Advanced Regression Analysis, Qualitative & Quantitative Research, Academic Publishing and Integrity. He is a Life member of Thomason Alumni Association (IIT Roorkee), Indian Science Congress Association (ISCA) Kolkata, Confederation of Indian Industry (CII) Chandigarh.