{"product_id":"intelligent-multimodal-data-processing-9781119571384","title":"Intelligent MultiModal Data Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA comprehensive review of the most recent applications of intelligent multi-modal data processing\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003e\u003ci\u003eIntelligent Multi-Modal Data Processing\u003c\/i\u003e contains a review of the most recent applications of data processing. The Editors and contributors ? noted experts on the topic ? offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices.   \u003c\/p\u003e\u003cp\u003eDesigned as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eList of contributors xv\u003c\/p\u003e \u003cp\u003eSeries Preface xix\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSoham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Areas of Application for Multimodal Signal 1\u003c\/p\u003e \u003cp\u003e1.1.1 Implementation of the Copyright Protection Scheme 1\u003c\/p\u003e \u003cp\u003e1.1.2 Saliency Map Inspired Digital Video Watermarking 1\u003c\/p\u003e \u003cp\u003e1.1.3 Saliency Map Generation Using an Intelligent Algorithm 2\u003c\/p\u003e \u003cp\u003e1.1.4 Brain Tumor Detection Using Multi-Objective Optimization 2\u003c\/p\u003e \u003cp\u003e1.1.5 Hyperspectral Image Classification Using CNN 2\u003c\/p\u003e \u003cp\u003e1.1.6 Object Detection for Self-Driving Cars 2\u003c\/p\u003e \u003cp\u003e1.1.7 Cognitive Radio 2\u003c\/p\u003e \u003cp\u003e1.2 Recent Challenges 2\u003c\/p\u003e \u003cp\u003eReferences 3\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Progressive Performance of Watermarking Using Spread Spectrum Modulation 5\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eArunothpol Debnath, Anirban Saha, Tirtha Sankar Das, Abhishek Basu, and Avik Chattopadhyay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 5\u003c\/p\u003e \u003cp\u003e2.2 Types of Watermarking Schemes 9\u003c\/p\u003e \u003cp\u003e2.3 Performance Evaluation Parameters of a Digital Watermarking Scheme 10\u003c\/p\u003e \u003cp\u003e2.4 Strategies for Designing the Watermarking Algorithm 11\u003c\/p\u003e \u003cp\u003e2.4.1 Balance of Performance Evaluation Parameters and Choice of Mathematical Tool 11\u003c\/p\u003e \u003cp\u003e2.4.2 Importance of the Key in the Algorithm 13\u003c\/p\u003e \u003cp\u003e2.4.3 Spread Spectrum Watermarking 13\u003c\/p\u003e \u003cp\u003e2.4.4 Choice of Sub-band 14\u003c\/p\u003e \u003cp\u003e2.5 Embedding and Detection of a Watermark Using the Spread Spectrum Technique 15\u003c\/p\u003e \u003cp\u003e2.5.1 General Model of Spread Spectrum Watermarking 15\u003c\/p\u003e \u003cp\u003e2.5.2 Watermark Embedding 17\u003c\/p\u003e \u003cp\u003e2.5.3 Watermark Extraction 18\u003c\/p\u003e \u003cp\u003e2.6 Results and Discussion 18\u003c\/p\u003e \u003cp\u003e2.6.1 Imperceptibility Results for Standard Test Images 20\u003c\/p\u003e \u003cp\u003e2.6.2 Robustness Results for Standard Test Images 20\u003c\/p\u003e \u003cp\u003e2.6.3 Imperceptibility Results for Randomly Chosen Test Images 22\u003c\/p\u003e \u003cp\u003e2.6.4 Robustness Results for Randomly Chosen Test Images 22\u003c\/p\u003e \u003cp\u003e2.6.5 Discussion of Security and the key 24\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 31\u003c\/p\u003e \u003cp\u003eReferences 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Secured Digital Watermarking Technique and FPGA Implementation 41\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRanit Karmakar, Zinia Haque, Tirtha Sankar Das, and Rajeev Kamal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.1.1 Steganography 41\u003c\/p\u003e \u003cp\u003e3.1.2 Cryptography 42\u003c\/p\u003e \u003cp\u003e3.1.3 Difference between Steganography and Cryptography 43\u003c\/p\u003e \u003cp\u003e3.1.4 Covert Channels 43\u003c\/p\u003e \u003cp\u003e3.1.5 Fingerprinting 43\u003c\/p\u003e \u003cp\u003e3.1.6 Digital Watermarking 43\u003c\/p\u003e \u003cp\u003e3.1.6.1 Categories of Digital Watermarking 44\u003c\/p\u003e \u003cp\u003e3.1.6.2 Watermarking Techniques 45\u003c\/p\u003e \u003cp\u003e3.1.6.3 Characteristics of Digital Watermarking 47\u003c\/p\u003e \u003cp\u003e3.1.6.4 Different Types of Watermarking Applications 48\u003c\/p\u003e \u003cp\u003e3.1.6.5 Types of Signal Processing Attacks 48\u003c\/p\u003e \u003cp\u003e3.1.6.6 Performance Evaluation Metrics 49\u003c\/p\u003e \u003cp\u003e3.2 Summary 50\u003c\/p\u003e \u003cp\u003e3.3 Literary Survey 50\u003c\/p\u003e \u003cp\u003e3.4 System Implementation 51\u003c\/p\u003e \u003cp\u003e3.4.1 Encoder 52\u003c\/p\u003e \u003cp\u003e3.4.2 Decoder 53\u003c\/p\u003e \u003cp\u003e3.4.3 Hardware Realization 53\u003c\/p\u003e \u003cp\u003e3.5 Results and Discussion 55\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 57\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Intelligent Image Watermarking for Copyright Protection 69\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSubhrajit Sinha Roy, Abhishek Basu, and Avik Chattopadhyay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 69\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 72\u003c\/p\u003e \u003cp\u003e4.3 Intelligent Techniques for Image Watermarking 75\u003c\/p\u003e \u003cp\u003e4.3.1 Saliency Map Generation 75\u003c\/p\u003e \u003cp\u003e4.3.2 Image Clustering 77\u003c\/p\u003e \u003cp\u003e4.4 Proposed Methodology 78\u003c\/p\u003e \u003cp\u003e4.4.1 Watermark Insertion 78\u003c\/p\u003e \u003cp\u003e4.4.2 Watermark Detection 81\u003c\/p\u003e \u003cp\u003e4.5 Results and Discussion 82\u003c\/p\u003e \u003cp\u003e4.5.1 System Response for Watermark Insertion and Extraction 83\u003c\/p\u003e \u003cp\u003e4.5.2 Quantitative Analysis of the Proposed Watermarking Scheme 85\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 90\u003c\/p\u003e \u003cp\u003eReferences 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Video Summarization Using a Dense Captioning (DenseCap) Model 97\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSourav Das, Anup Kumar Kolya, and Arindam Kundu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 97\u003c\/p\u003e \u003cp\u003e5.2 Literature Review 98\u003c\/p\u003e \u003cp\u003e5.3 Our Approach 101\u003c\/p\u003e \u003cp\u003e5.4 Implementation 102\u003c\/p\u003e \u003cp\u003e5.5 Implementation Details 108\u003c\/p\u003e \u003cp\u003e5.6 Result 110\u003c\/p\u003e \u003cp\u003e5.7 Limitations 127\u003c\/p\u003e \u003cp\u003e5.8 Conclusions and Future Work 127\u003c\/p\u003e \u003cp\u003eReferences 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 A Method of Fully Autonomous Driving in Self-Driving Cars Based on Machine Learning and Deep Learning 131\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHarinandan Tunga, Rounak Saha, and Samarjit Kar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 131\u003c\/p\u003e \u003cp\u003e6.2 Models of Self-Driving Cars 131\u003c\/p\u003e \u003cp\u003e6.2.1 Prior Models and Concepts 132\u003c\/p\u003e \u003cp\u003e6.2.2 Concept of the Self-Driving Car 133\u003c\/p\u003e \u003cp\u003e6.2.3 Structural Mechanism 134\u003c\/p\u003e \u003cp\u003e6.2.4 Algorithm for theWorking Procedure 134\u003c\/p\u003e \u003cp\u003e6.3 Machine Learning Algorithms 135\u003c\/p\u003e \u003cp\u003e6.3.1 Decision Matrix Algorithms 135\u003c\/p\u003e \u003cp\u003e6.3.2 Regression Algorithms 135\u003c\/p\u003e \u003cp\u003e6.3.3 Pattern Recognition Algorithms 135\u003c\/p\u003e \u003cp\u003e6.3.4 Clustering Algorithms 137\u003c\/p\u003e \u003cp\u003e6.3.5 Support Vector Machines 137\u003c\/p\u003e \u003cp\u003e6.3.6 Adaptive Boosting 138\u003c\/p\u003e \u003cp\u003e6.3.7 TextonBoost 139\u003c\/p\u003e \u003cp\u003e6.3.8 Scale-Invariant Feature Transform 140\u003c\/p\u003e \u003cp\u003e6.3.9 Simultaneous Localization and Mapping 140\u003c\/p\u003e \u003cp\u003e6.3.10 Algorithmic Implementation Model 141\u003c\/p\u003e \u003cp\u003e6.4 Implementing a Neural Network in a Self-Driving Car 142\u003c\/p\u003e \u003cp\u003e6.5 Training and Testing 142\u003c\/p\u003e \u003cp\u003e6.6 Working Procedure and Corresponding Result Analysis 143\u003c\/p\u003e \u003cp\u003e6.6.1 Detection of Lanes 143\u003c\/p\u003e \u003cp\u003e6.7 Preparation-Level Decision Making 146\u003c\/p\u003e \u003cp\u003e6.8 Using the Convolutional Neural Network 147\u003c\/p\u003e \u003cp\u003e6.9 Reinforcement Learning Stage 147\u003c\/p\u003e \u003cp\u003e6.10 Hardware Used in Self-Driving Cars 148\u003c\/p\u003e \u003cp\u003e6.10.1 \u003ci\u003eLIDAR \u003c\/i\u003e148\u003c\/p\u003e \u003cp\u003e6.10.2 \u003ci\u003eVision-Based Cameras \u003c\/i\u003e149\u003c\/p\u003e \u003cp\u003e6.10.3 Radar 150\u003c\/p\u003e \u003cp\u003e6.10.4 \u003ci\u003eUltrasonic Sensors \u003c\/i\u003e150\u003c\/p\u003e \u003cp\u003e6.10.5 \u003ci\u003eMulti-Domain Controller (MDC) \u003c\/i\u003e150\u003c\/p\u003e \u003cp\u003e6.10.6 \u003ci\u003eWheel-Speed Sensors \u003c\/i\u003e150\u003c\/p\u003e \u003cp\u003e6.10.7 Graphics Processing Unit (GPU) 151\u003c\/p\u003e \u003cp\u003e6.11 Problems and Solutions for SDC 151\u003c\/p\u003e \u003cp\u003e6.11.1 \u003ci\u003eSensor Disjoining \u003c\/i\u003e151\u003c\/p\u003e \u003cp\u003e6.11.2 \u003ci\u003ePerception Call Failure \u003c\/i\u003e152\u003c\/p\u003e \u003cp\u003e6.11.3 \u003ci\u003eComponent and Sensor Failure \u003c\/i\u003e152\u003c\/p\u003e \u003cp\u003e6.11.4 \u003ci\u003eSnow \u003c\/i\u003e152\u003c\/p\u003e \u003cp\u003e6.11.5 Solutions 152\u003c\/p\u003e \u003cp\u003e6.12 Future Developments in Self-Driving Cars 153\u003c\/p\u003e \u003cp\u003e6.12.1 \u003ci\u003eSafer Transportation \u003c\/i\u003e153\u003c\/p\u003e \u003cp\u003e6.12.2 \u003ci\u003eSafer Transportation Provided by the Car \u003c\/i\u003e153\u003c\/p\u003e \u003cp\u003e6.12.3 \u003ci\u003eEliminating Traffic Jams \u003c\/i\u003e153\u003c\/p\u003e \u003cp\u003e6.12.4 \u003ci\u003eFuel Efficiency and the Environment \u003c\/i\u003e154\u003c\/p\u003e \u003cp\u003e6.12.5 \u003ci\u003eEconomic Development \u003c\/i\u003e154\u003c\/p\u003e \u003cp\u003e6.13 Future Evolution of Autonomous Vehicles 154\u003c\/p\u003e \u003cp\u003e6.14 Conclusion 155\u003c\/p\u003e \u003cp\u003eReferences 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Problem of Interoperability of Fusion Sensory Data from the Internet of Things 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDoaa Mohey Eldin, Aboul Ella Hassanien, and Ehab E. Hassanein\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 157\u003c\/p\u003e \u003cp\u003e7.2 Internet of Things 158\u003c\/p\u003e \u003cp\u003e7.2.1 Advantages of the IoT 159\u003c\/p\u003e \u003cp\u003e7.2.2 Challenges Facing Automated Adoption of Smart Sensors in the IoT 159\u003c\/p\u003e \u003cp\u003e7.3 Data Fusion for IoT Devices 160\u003c\/p\u003e \u003cp\u003e7.3.1 The Data Fusion Architecture 160\u003c\/p\u003e \u003cp\u003e7.3.2 Data Fusion Models 161\u003c\/p\u003e \u003cp\u003e7.3.3 Data Fusion Challenges 161\u003c\/p\u003e \u003cp\u003e7.4 Multi-Modal Data Fusion for IoT Devices 161\u003c\/p\u003e \u003cp\u003e7.4.1 Data Mining in Sensor Fusion 162\u003c\/p\u003e \u003cp\u003e7.4.2 Sensor Fusion Algorithms 163\u003c\/p\u003e \u003cp\u003e7.4.2.1 Central Limit Theorem 163\u003c\/p\u003e \u003cp\u003e7.4.2.2 Kalman Filter 163\u003c\/p\u003e \u003cp\u003e7.4.2.3 Bayesian Networks 164\u003c\/p\u003e \u003cp\u003e7.4.2.4 Dempster-Shafer 164\u003c\/p\u003e \u003cp\u003e7.4.2.5 Deep Learning Algorithms 165\u003c\/p\u003e \u003cp\u003e7.4.2.6 A Comparative Study of Sensor Fusion Algorithms 168\u003c\/p\u003e \u003cp\u003e7.5 A Comparative Study of Sensor Fusion Algorithms 170\u003c\/p\u003e \u003cp\u003e7.6 The Proposed Multimodal Architecture for Data Fusion 175\u003c\/p\u003e \u003cp\u003e7.7 Conclusion and Research Trends 176\u003c\/p\u003e \u003cp\u003eReferences 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Implementation of Fast, Adaptive, Optimized Blind Channel Estimation for Multimodal MIMO-OFDM Systems Using MFPA 183\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShovon Nandi, Narendra Nath Pathak, and Arnab Nandi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 183\u003c\/p\u003e \u003cp\u003e8.2 Literature Survey 185\u003c\/p\u003e \u003cp\u003e8.3 STBC-MIMO-OFDM Systems for Fast Blind Channel Estimation 187\u003c\/p\u003e \u003cp\u003e8.3.1 Proposed Methodology 187\u003c\/p\u003e \u003cp\u003e8.3.2 OFDM-Based MIMO 188\u003c\/p\u003e \u003cp\u003e8.3.3 STBC-OFDM Coding 188\u003c\/p\u003e \u003cp\u003e8.3.4 Signal Detection 189\u003c\/p\u003e \u003cp\u003e8.3.5 Multicarrier Modulation (MCM) 189\u003c\/p\u003e \u003cp\u003e8.3.6 Cyclic Prefix (CP) 190\u003c\/p\u003e \u003cp\u003e8.3.7 Multiple Carrier-Code Division Multiple Access (MC-CDMA) 191\u003c\/p\u003e \u003cp\u003e8.3.8 Modified Flower Pollination Algorithm (MFPA) 192\u003c\/p\u003e \u003cp\u003e8.3.9 Steps in the Modified Flower Pollination Algorithm 192\u003c\/p\u003e \u003cp\u003e8.4 Characterization of Blind Channel Estimation 193\u003c\/p\u003e \u003cp\u003e8.5 Performance Metrics and Methods 195\u003c\/p\u003e \u003cp\u003e8.5.1 Normalized Mean Square Error (NMSE) 195\u003c\/p\u003e \u003cp\u003e8.5.2 Mean Square Error (MSE) 196\u003c\/p\u003e \u003cp\u003e8.6 Results and Discussion 196\u003c\/p\u003e \u003cp\u003e8.7 Relative Study of Performance Parameters 198\u003c\/p\u003e \u003cp\u003e8.8 Future Work 201\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Spectrum Sensing for Cognitive Radio Using a Filter Bank Approach 205\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSrijibendu Bagchi and Jawad Yaseen Siddiqui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 205\u003c\/p\u003e \u003cp\u003e9.1.1 Dynamic Exclusive Use Model 206\u003c\/p\u003e \u003cp\u003e9.1.2 Open Sharing Model 206\u003c\/p\u003e \u003cp\u003e9.1.3 Hierarchical Access Model 206\u003c\/p\u003e \u003cp\u003e9.2 Cognitive Radio 207\u003c\/p\u003e \u003cp\u003e9.3 Some Applications of Cognitive Radio 208\u003c\/p\u003e \u003cp\u003e9.4 Cognitive Spectrum Access Models 209\u003c\/p\u003e \u003cp\u003e9.5 Functions of Cognitive Radio 210\u003c\/p\u003e \u003cp\u003e9.6 Cognitive Cycle 211\u003c\/p\u003e \u003cp\u003e9.7 Spectrum Sensing and Related Issues 211\u003c\/p\u003e \u003cp\u003e9.8 Spectrum Sensing Techniques 213\u003c\/p\u003e \u003cp\u003e9.9 Spectrum Sensing in Wireless Standards 216\u003c\/p\u003e \u003cp\u003e9.10 Proposed Detection Technique 218\u003c\/p\u003e \u003cp\u003e9.11 Numerical Results 221\u003c\/p\u003e \u003cp\u003e9.12 Discussion 222\u003c\/p\u003e \u003cp\u003e9.13 Conclusion 223\u003c\/p\u003e \u003cp\u003eReferences 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Singularity Expansion Method in Radar Multimodal Signal Processing and Antenna Characterization 231\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNandan Bhattacharyya and Jawad Y. Siddiqui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 231\u003c\/p\u003e \u003cp\u003e10.2 Singularities in Radar Echo Signals 232\u003c\/p\u003e \u003cp\u003e10.3 Extraction of Natural Frequencies 233\u003c\/p\u003e \u003cp\u003e10.3.1 Cauchy Method 233\u003c\/p\u003e \u003cp\u003e10.3.2 Matrix Pencil Method 233\u003c\/p\u003e \u003cp\u003e10.4 SEM for Target Identification in Radar 234\u003c\/p\u003e \u003cp\u003e10.5 Case Studies 236\u003c\/p\u003e \u003cp\u003e10.5.1 Singularity Extraction from the Scattering Response of a Circular Loop 236\u003c\/p\u003e \u003cp\u003e10.5.2 Singularity Extraction from the Scattering Response of a Sphere 237\u003c\/p\u003e \u003cp\u003e10.5.3 Singularity Extraction from the Response of a Disc 238\u003c\/p\u003e \u003cp\u003e10.5.4 Result Comparison with Existing Work 239\u003c\/p\u003e \u003cp\u003e10.6 Singularity Expansion Method in Antennas 239\u003c\/p\u003e \u003cp\u003e10.6.1 Use of SEM in UWB Antenna Characterization 240\u003c\/p\u003e \u003cp\u003e10.6.2 SEM for Determining Printed Circuit Antenna Propagation Characteristics 241\u003c\/p\u003e \u003cp\u003e10.6.3 Method of Extracting the Physical Poles from Antenna Responses 241\u003c\/p\u003e \u003cp\u003e10.6.3.1 Optimal Time Window for Physical Pole Extraction 241\u003c\/p\u003e \u003cp\u003e10.6.3.2 Discarding Low-Energy Singularities 242\u003c\/p\u003e \u003cp\u003e10.6.3.3 Robustness to Signal-to-Noise Ratio (SNR) 243\u003c\/p\u003e \u003cp\u003e10.7 Other Applications 243\u003c\/p\u003e \u003cp\u003e10.8 Conclusion 243\u003c\/p\u003e \u003cp\u003eReferences 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Conclusion 249\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSoham Sarkar, Abhishek Basu, and Siddhartha Bhattacharyya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 250\u003c\/p\u003e \u003cp\u003eIndex 253\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407087051095,"sku":"9781119571384","price":98.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119571384.jpg?v=1730498132","url":"https:\/\/bookcurl.com\/products\/intelligent-multimodal-data-processing-9781119571384","provider":"Book Curl","version":"1.0","type":"link"}