Machine Learning in Signal Processing : Applications, Challenges, and the Road Ahead (Hardcover)

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 AbdelmalekEssaadi, Route de Ziaten Km 10, Tanger Principale, B.P : 1818 ? Tangier, Morocco Pascal Dore LabTIC, ENSA of Tangier, University of AbdelmalekEssaadi, Route de Ziaten Km 10, Tanger Principale, B.P : 1818 ? Tangier, Morocco Ahmed El Oualkad LabTIC, ENSA of Tangier, University of AbdelmalekEssaadi, 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 ? ? ? ? ? ? ? ? ?