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Methods and techniques in deep learning : (Record no. 35747)

000 -LEADER
fixed length control field 09002cam a2200457 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field jomaaum
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230921133256.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr |||||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220818s2023 nju ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119910657
International Standard Book Number 1119910676
International Standard Book Number 9781119910664
International Standard Book Number 1119910668
International Standard Book Number 9781119910695
International Standard Book Number 1119910692
International Standard Book Number 9781119910671
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1002/9781119910695
Source of number or code doi
041 ## - Language
Language code of text/sound track or separate title eng
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK6592.M55
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.38480285
Edition number 23
Item number S237
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Santra, Avik,
Relator term author
9 (RLIN) 45900
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Methods and techniques in deep learning :
Remainder of title advancements in mmwave radar solutions /
Statement of responsibility, etc Avik Santra, Souvik Hazra, Lorenzo Servadei, Thomas Stadelmayer, Michael Stephan, Anand Dubey, Infineon Technologies, Munich, Germany
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Hoboken, New Jersey :
Name of publisher, distributor, etc John Wiley & Sons, Inc.,
Date of publication, distribution, etc 2023.
300 ## - PHYSICAL DESCRIPTION
Extent xxiv, 312 p. :
Other physical details ill. ;
Dimensions 24 cm.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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
506 ## - RESTRICTIONS ON ACCESS NOTE
Terms governing access Available to OhioLINK libraries
520 ## - SUMMARY, ETC.
Summary, etc "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"--
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Millimeter wave radar
General subdivision Data processing
9 (RLIN) 45901
Topical term or geographic name as entry element Radar targets
General subdivision Identification
-- Data processing.
9 (RLIN) 45902
Topical term or geographic name as entry element Radar receiving apparatus
General subdivision Data processing.
9 (RLIN) 45903
Topical term or geographic name as entry element Deep learning (Machine learning)
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Ohio Library and Information Network.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading Santra, Avik.
Title Methods and techniques in deep learning
Place, publisher, and date of publication Hoboken, New Jersey : John Wiley & Sons, Inc., [2023]
International Standard Book Number 9781119910657
Record control number (DLC) 2022036520
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified OhioLINK
Public note Connect to resource
Uniform Resource Identifier https://rave.ohiolink.edu/ebooks/ebc2/9781119910695
Materials specified Wiley Online Library
Public note Connect to resource
Uniform Resource Identifier https://onlinelibrary.wiley.com/doi/book/10.1002/9781119910695
Materials specified Wiley Online Library
Public note Connect to resource (off-campus)
Uniform Resource Identifier http://proxy.ohiolink.edu:9099/login?url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781119910695
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Lost status Cost, normal purchase price Withdrawn status Source of acquisition Cost, replacement price Damaged status Barcode Current location Public note Full call number
2023-09-21AUM Main Library2023-09-21 2023-09-21 Book 71.95 بنك الكتب الطبية والأكاديمية66.51 AUM-026134AUM Main Libraryinv 202301028621.38480285 S237