Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey

Methods and Techniques in Deep Learning (eBook, ePUB)

Advancements in mmWave Radar Solutions

• Format: ePub

Produktbeschreibung

Methods and Techniques in Deep Learning Introduces multiple state-of-the-art deep learning architectures for mmWave radar in a variety of advanced applications Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions provides a timely and authoritative overview of the use of artificial intelligence (AI)-based processing for various mmWave radar applications. Focusing on practical deep learning techniques, this comprehensive volume explains the fundamentals of deep learning, reviews cutting-edge deep metric learning techniques, describes different typologies of reinforcement learning (RL) algorithms, highlights how domain adaptation (DA) can be used for improving the performance of machine learning (ML) algorithms, and more. Throughout the book, readers are exposed to product-ready deep learning solutions while learning skills that are relevant for building any industrial-grade, sensor-based deep learning solution. A team of authors with more than 70 filed patents and 100 published papers on AI and sensor processing illustrates how deep learning is enabling a range of advanced industrial, consumer, and automotive applications of mmWave radars. In-depth chapters cover topics including multi-modal deep learning approaches, the elemental blocks required to formulate Bayesian deep learning, how domain adaptation (DA) can be used for improving the performance of machine learning algorithms, and geometric deep learning are used for processing point clouds. In addition, the book: * Discusses various advanced applications and how their respective challenges have been addressed using different deep learning architectures and algorithms * Describes deep learning in the context of computer vision, natural language processing, sensor processing, and mmWave radar sensors * Demonstrates how deep parametric learning reduces the number of trainable parameters and improves the data flow * Presents several human-machine interface (HMI) applications such as gesture recognition, human activity classification, human localization and tracking, in-cabin automotive occupancy sensing Methods and Techniques in Deep Learning: Advancements in mmWave Radar Solutions is an invaluable resource for industry professionals, researchers, and graduate students working in systems engineering, signal processing, sensors, data science, and AI.

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Produktdetails

• Verlag: Wiley-Blackwell
• Seitenzahl: 336
• Erscheinungstermin: 21. November 2022
• Englisch
• ISBN-13: 9781119910671
• Artikelnr.: 66679592

Autorenporträt

Avik Santra is Head of Advanced Artificial Intelligence at Infineon Technologies, Munich, Germany. Souvik Hazra is a Senior Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany. Lorenzo Servadei is a Senior Staff Machine Learning Engineer at Infineon Technologies and a Lecturer at The Technical University of Munich (TU München), Germany. Thomas Stadelmayer is a Staff Machine Learning Engineer at Infineon Technologies, Munich, Germany. Michael Stephan is a PhD candidate at Infineon Technologies, Munich, Germany and Friedrich-Alexander-University of Erlangen-Nürnberg, Germany. Anand Dubey is a Staff Machine Learning Engineer at Infineon Technologies.

Inhaltsangabe

Preface

Acronyms

1 Introduction to Radar Processing & Deep Learning 1

1.1 Basics of Radar Systems 1

1.1.1 Fundamentals 2

1.1.2 Signal Modulation 2

1.2 FMCW Signal Processing 6

1.2.1 Frequency-Domain Analysis 7

1.3 Target Detection & Clustering 14

1.4 Target Tracking 19

1.4.1 Track Management 21

1.4.2 Track Filtering 22

1.5 Target Representation 28

1.5.1 Image Representation 30

1.5.2 Point-Cloud Maps 34

1.6 Target Recognition 36

1.6.1 Feedforward Network 37

1.6.2 Convolutional Neural Networks (CNN) 37

1.6.3 Recurrent Neural Network (RNN) 43

1.6.4 Autoencoder & Variational Autoencoder 47

1.6.5 Generative Adversial Network 51

1.6.6 Transformer 54

1.7 Training a Neural Network 56

1.7.1 Forward Pass & Backpropagation 57

1.7.2 Optimizers 62

1.7.3 Loss Functions 65

1.8 Questions to the Reader 66

Bibliography 68

2 Deep Metric Learning 75

2.1 Introduction 78

2.2 Pairwise methods 79

2.2.1 Contrastive Loss 79

2.2.2 Triplet Loss 80

2.2.3 Quadruplet Loss 81

2.2.4 N-Pair Loss 82

2.2.5 Big Picture 83

2.3 End-to-end Learning 84

2.3.1 Cosine Similarity 86

2.3.2 Euclidean Distance 95

2.3.3 Big Picture 100

2.4 Proxy methods 103

2.5 Advanced Methods 103

2.5.1 Statistical Distance 104

2.5.2 Structured Metric Learning 108

2.6 Application Gesture Sensing 110

2.6.1 Radar System Design 111

2.6.2 Data Set and Preparation 112

2.6.3 Architecture and Metric Learning Procedure 114

2.6.4 Results 123

2.7 Questions to the Reader 129

Bibliography 130

3 Deep Parametric Learning 135

3.1 Introduction 135

3.2 Radar Parametric Neural Network 140

3.2.1 2D Sinc Filters 142

3.2.2 2D Morlet Wavelets 143

3.2.3 Adaptive 2D Sinc Filters 145

3.2.4 Complex Frequency Extraction Layer 146

3.3 Multilevel Wavelet Decomposition Network 150

3.4 Application Activity Classification 153

3.4.1 Proposed Parametric Networks 155

3.4.2 State-of-art Networks 158

3.4.3 Results & Discussion 160

3.5 Conclusion 167

3.6 Question to Readers 168

Bibliography 168

4 Deep Reinforcement Learning 173

4.1 Useful Notation and Equations 173

4.1.1 Markov Decision Process 173

4.1.2 Solving the Markov Decision Process 174

4.1.3 Bellman Equations 175

4.2 Introduction 175

4.3 On-Policy Reinforcement Learning 179

4.4 Off-Policy Reinforcement Learning 180

4.5 Model-Based Reinforcement Learning 180

4.6 Model-Free Reinforcement Learning 181

4.7 Value-Based Reinforcement Learning 181

4.8 Policy-Based Reinforcement Learning 183

4.9 Online Reinforcement Learning 183

4.10 Offline Reinforcement Learning 184

4.11 Reinforcement Learning with

Discrete Actions 184

4.12 Reinforcement Learning with

Continuous Actions 185

4.13 Reinforcement Learning Algorithms

for Radar Applications 185

4.14 Application Tracker's Parameter Optimization 189

4.14.1 Motivation 190

4.14.2 Background 192

4.14.3 Approach 202

4.14.4 Experimental 208

4.14.5 Outcomes of the proposed Ap