國立高雄大學圖資館 |

Methods and techniques in deep learning :advancements in mmwave radar solutions /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Methods and techniques in deep learning :Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey, Infineon Technologies, Munich, Germany.
其他題名:	advancements in mmwave radar solutions /
作者:	Santra, Avik,
面頁冊數:	xxiv, 312 Pages :illustrations ;24 cm.
標題:	Millimeter wave radarData processing.
電子資源:	https://ieeexplore.ieee.org/book/9962819
電子資源:	http://dx.doi.org/10.1002/9781119910695
ISBN:	9781119910671

Methods and techniques in deep learning :advancements in mmwave radar solutions /
Santra, Avik,

Methods and techniques in deep learning :advancements in mmwave radar solutions /Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey, Infineon Technologies, Munich, Germany. - xxiv, 312 Pages :illustrations ;24 cm.

Includes bibliographical references and index.

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 Approach 219 -- 4.15 Conclusion 220 -- 4.16 Questions to the Reader 220 -- Bibliography 221 -- 5 Cross-Modal Learning 229 -- 5.1 Introduction 229 -- 5.2 Self-Supervised Multi-Modal Learning 233 -- 5.2.1 Generating Audio Statistics 233 -- 5.2.2 Predicting sounds from images 234 -- 5.2.3 Audio Features Clustering 234 -- 5.2.4 Binary Coding Model 235 -- 5.2.5 Training 235 -- 5.2.6 Results 235 -- 5.3 Joint Embeddings Learning 237 -- 5.3.1 Feature Representations 237 -- 5.3.2 Joint-Embedding Learning 238 -- 5.3.3 Matching & Ranking 239 -- 5.3.4 Training Details & Result 239 -- 5.3.5 Discussion 241 -- 5.4 Multi-Modal Input 241 -- 5.4.1 Multi-modal Compact Bilinear Pooling 242 -- 5.4.2 VQA Architecture 243 -- 5.4.3 Training Details & Result 245 -- 5.4.4 Discussion 245 -- 5.5 Cross-Modal Learning 245 -- 5.5.1 Data Acquisition 246 -- 5.5.2 Cross-Modal Learning for Key-Point Detection 246 -- 5.5.3 Training Details & Result 247 -- 5.5.4 Discussion 249 -- 5.6 Application People Counting 250 -- 5.6.1 FMCW Radar System Design 251 -- 5.6.2 Data Acquisition 252 -- 5.6.3 Solution 1 253 -- 5.6.4 Solution 2 262 -- 5.7 Conclusion 265 -- 5.8 Questions to the Reader 265 -- Bibliography 267 -- 6 Signal Processing with Deep Learning 273 -- 6.1 Introduction 273 -- 6.2 Algorithm Unrolling 274 -- 6.2.1 Learning Fast Approximations of Sparse Coding 275 -- 6.2.2 Learned ISTA in radar processing 279 -- 6.3 Physics-inspired Deep Learning 282 -- 6.4 Processing-specific Network Architectures 284 -- 6.5 Deep Learning-aided Signal Processing 288 -- 6.6 Questions to the Reader 297 -- Bibliography 297 -- 7 Domain Adaptation 303 -- 7.1 Introduction 303 -- 7.2 Transfer Learning and Domain Adaptaton 304 -- 7.3 Categories of Domain Adaptation 307 -- 7.3.1 Common Data Shifts 307 -- 7.3.2 Methods of Domain Adaptation 308 -- 7.4 Domain Adaptation in Radar Processing 315 -- 7.4.1 Domain Adaptation with a different Sensor Type 316 -- 7.4.2 Domain Adaptation with different Radar Settings 318 -- 7.5 Summary 331 -- 7.6 Questions to the Reader 331 -- Bibliography 332 -- 8 Bayesian Deep Learning 339 -- 8.1 Learning Theory 341 -- 8.2 Bayesian Learning 343 -- 8.3 Bayesian Approximations 352 -- 8.4 Application VRU Classification 372 -- 8.4.1 VAE as Bayesian 373 -- xiii -- 8.4.2 Bayesian Metric Learning 377 -- 8.4.3 Kalman as Bayesian 383 -- 8.4.4 Results 387 -- 8.5 Summary 391 -- 8.6 Questions to the Reader 393 -- Bibliography 393 -- 9 Geometric Deep Learning 397 -- 9.1 Representation Learning in Graph Neural Network 399 -- 9.1.1 Fundamentals 399 -- 9.1.2 Learning Theory 401 -- 9.1.3 Embedding Learning 406 -- 9.2 Graph Representation Learning 407 -- 9.2.1 Convolution GNN 408 -- 9.2.2 Recurrent Graph Neural Networks (RGNN) 409 -- 9.2.3 Graph Autoencoders (GAE) 409 -- 9.2.4 Spatial-Temporal Graph Neural Networks (STGNN) 410 -- 9.2.5 Attention GNN 410 -- 9.2.6 Message-passing GNN 411 -- 9.3 Applications 413 -- 9.3.1 Application 1 Long-Range Gesture Recognition 413 -- 9.3.2 Application 2 Bayesian Anchor-Free Target Detection 426 -- 9.4 Conclusion 444 -- 9.5 Questions to the Reader 445 -- Bibliography 446.

"The advent of deep learning has transformed many fields and resulted in state-of-art solutions in computer vision, natural language processing and speech processing, etc. However, the application of deep learning algorithms to radars is still by and large at its nascent stage. A radar system consists of two parts: first, the radar hardware, including the RF transceiver, waveform generator, receiver unit, antenna and system packaging. State-of-art SiGe and CMOS are candidate technologies for mm-wave short-range radars and offer flexibility for integration and smaller form-factor. Second part is the sensing aspect, which relies on signal processing or deep learning algorithms that parses the radar return echo into meaningful target information facilitating a desired application"--

ISBN: 9781119910671

Standard No.: 10.1002/9781119910695doi

Source: 9962819IEEE

LCCN: 2022036521Subjects--Topical Terms:

943773
Millimeter wave radar
--Data processing.

LC Class. No.: TK6592.M55

Dewey Class. No.: 621.38480285

Methods and techniques in deep learning :advancements in mmwave radar solutions /
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245 1 0 $a Methods and techniques in deep learning : $b advancements in mmwave radar solutions / $c Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey, Infineon Technologies, Munich, Germany.
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504 $a Includes bibliographical references and index.
505 0 $a 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 Approach 219 -- 4.15 Conclusion 220 -- 4.16 Questions to the Reader 220 -- Bibliography 221 -- 5 Cross-Modal Learning 229 -- 5.1 Introduction 229 -- 5.2 Self-Supervised Multi-Modal Learning 233 -- 5.2.1 Generating Audio Statistics 233 -- 5.2.2 Predicting sounds from images 234 -- 5.2.3 Audio Features Clustering 234 -- 5.2.4 Binary Coding Model 235 -- 5.2.5 Training 235 -- 5.2.6 Results 235 -- 5.3 Joint Embeddings Learning 237 -- 5.3.1 Feature Representations 237 -- 5.3.2 Joint-Embedding Learning 238 -- 5.3.3 Matching & Ranking 239 -- 5.3.4 Training Details & Result 239 -- 5.3.5 Discussion 241 -- 5.4 Multi-Modal Input 241 -- 5.4.1 Multi-modal Compact Bilinear Pooling 242 -- 5.4.2 VQA Architecture 243 -- 5.4.3 Training Details & Result 245 -- 5.4.4 Discussion 245 -- 5.5 Cross-Modal Learning 245 -- 5.5.1 Data Acquisition 246 -- 5.5.2 Cross-Modal Learning for Key-Point Detection 246 -- 5.5.3 Training Details & Result 247 -- 5.5.4 Discussion 249 -- 5.6 Application People Counting 250 -- 5.6.1 FMCW Radar System Design 251 -- 5.6.2 Data Acquisition 252 -- 5.6.3 Solution 1 253 -- 5.6.4 Solution 2 262 -- 5.7 Conclusion 265 -- 5.8 Questions to the Reader 265 -- Bibliography 267 -- 6 Signal Processing with Deep Learning 273 -- 6.1 Introduction 273 -- 6.2 Algorithm Unrolling 274 -- 6.2.1 Learning Fast Approximations of Sparse Coding 275 -- 6.2.2 Learned ISTA in radar processing 279 -- 6.3 Physics-inspired Deep Learning 282 -- 6.4 Processing-specific Network Architectures 284 -- 6.5 Deep Learning-aided Signal Processing 288 -- 6.6 Questions to the Reader 297 -- Bibliography 297 -- 7 Domain Adaptation 303 -- 7.1 Introduction 303 -- 7.2 Transfer Learning and Domain Adaptaton 304 -- 7.3 Categories of Domain Adaptation 307 -- 7.3.1 Common Data Shifts 307 -- 7.3.2 Methods of Domain Adaptation 308 -- 7.4 Domain Adaptation in Radar Processing 315 -- 7.4.1 Domain Adaptation with a different Sensor Type 316 -- 7.4.2 Domain Adaptation with different Radar Settings 318 -- 7.5 Summary 331 -- 7.6 Questions to the Reader 331 -- Bibliography 332 -- 8 Bayesian Deep Learning 339 -- 8.1 Learning Theory 341 -- 8.2 Bayesian Learning 343 -- 8.3 Bayesian Approximations 352 -- 8.4 Application VRU Classification 372 -- 8.4.1 VAE as Bayesian 373 -- xiii -- 8.4.2 Bayesian Metric Learning 377 -- 8.4.3 Kalman as Bayesian 383 -- 8.4.4 Results 387 -- 8.5 Summary 391 -- 8.6 Questions to the Reader 393 -- Bibliography 393 -- 9 Geometric Deep Learning 397 -- 9.1 Representation Learning in Graph Neural Network 399 -- 9.1.1 Fundamentals 399 -- 9.1.2 Learning Theory 401 -- 9.1.3 Embedding Learning 406 -- 9.2 Graph Representation Learning 407 -- 9.2.1 Convolution GNN 408 -- 9.2.2 Recurrent Graph Neural Networks (RGNN) 409 -- 9.2.3 Graph Autoencoders (GAE) 409 -- 9.2.4 Spatial-Temporal Graph Neural Networks (STGNN) 410 -- 9.2.5 Attention GNN 410 -- 9.2.6 Message-passing GNN 411 -- 9.3 Applications 413 -- 9.3.1 Application 1 Long-Range Gesture Recognition 413 -- 9.3.2 Application 2 Bayesian Anchor-Free Target Detection 426 -- 9.4 Conclusion 444 -- 9.5 Questions to the Reader 445 -- Bibliography 446.
520 $a "The advent of deep learning has transformed many fields and resulted in state-of-art solutions in computer vision, natural language processing and speech processing, etc. However, the application of deep learning algorithms to radars is still by and large at its nascent stage. A radar system consists of two parts: first, the radar hardware, including the RF transceiver, waveform generator, receiver unit, antenna and system packaging. State-of-art SiGe and CMOS are candidate technologies for mm-wave short-range radars and offer flexibility for integration and smaller form-factor. Second part is the sensing aspect, which relies on signal processing or deep learning algorithms that parses the radar return echo into meaningful target information facilitating a desired application"-- $c Provided by publisher.
588 $a Description based on print version record and CIP data provided by publisher; resource not viewed.
650 0 $a Millimeter wave radar $x Data processing. $3 943773
650 0 $a Radar targets $x Identification $x Data processing. $3 943774
650 0 $a Radar receiving apparatus $x Data processing. $3 943775
650 0 $a Deep learning (Machine learning) $3 913129
776 0 8 $i Print version: $a Santra, Avik. $t Methods and techniques in deep learning $d Hoboken, New Jersey : John Wiley & Sons, Inc., [2023] $z 9781119910657 $w (DLC) 2022036520
856 4 0 $3 IEEE Xplore $u https://ieeexplore.ieee.org/book/9962819
856 4 0 $3 John Wiley $u http://dx.doi.org/10.1002/9781119910695
994 $a C0 $b TWNUK