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EB

EB

ONLINE

London : Academic Press, 2022

1 online zdroj

ISBN 9780323900829 (e-kniha)

ISBN 9780323858458 (print)

ISBN 0323858457

Print version: ISBN 0323858457 ISBN 9780323858458

Print version: Internet of multimedia things (IoMT) ISBN 9780323858458

Obsahuje rejstřík

001932228

Contributors // CHAPTER 1 A review on Internet of Multimedia Things (loMT) routing protocols and quality of service - Dinesh Singh, Ashish Kumar Maurya, Rupesh Kumar Dewang, and Niharika Keshari // 1.1 Introduction // 1.2 Routing protocols in IoMT // 1.2.1 Fault tolerant routing protocol // 1.2.2 DDSV routing protocol // 1.2.3 Optimal routing for multihop social-based D2D communications // 1.2.4 Green-RPL routing protocol // 1.2.5 Context-aware and load balancing RPL (CLRPL) // 1.2.6 Energy-Harvesting-Aware (EHA) routing protocol // 1.2.7 Optimized 3-D deployment with lifetime constraint (03DwLC) protocol // 1.3 Quality of Service (QoS) routing in IoMT // 1.3.1 Traffic-aw are QoS routing protocol // 1.3.2 QoS-aware and Heterogeneously Clustered Routing (QHCR) protocol //1.3.3 QoS in wireless multimedia sensor network based IoMT // 1.3.4 SAMS framework for WMSN_based IoMT //1.4 Conclusion and future directions // References // CHAPTER 2 Energy efficient data communication in Internet of Multimedia Things (IoMT) - Shailendra Shukla, Asheesh Kumar Mani Tripathi, and Amit Kumar Singh // 2.1 Introduction // 2.2 Related work // 2.2.1 Routing protocol for IoMT // 2.2.2 Boundary detection and virtual coordinate algorithms // 2.3 System model // 2.3.1 Problem description // 2.4 Proposed approach // 2.4.1 Working of algorithm 35 // 2.4.2 Phase I: boundary detection algorithms 36 // 2.4.3 Phase II: assignment of virtual coordinates to boundary // 2.5 Implementation and results 40 // 2.5.1 Preliminaries 40 // 2.5.2 Simulation setup 40 // 2.6 Conclusion 43 // References 44 // CHAPTER 3 Visual information processing and transmission in / Wireless Multimedia Sensor Networks: a deep learning based practical approach 47 - Yasar Abbas Ur Rehman and Muhammad Tariq // 3.1 Introduction // 3.2 Literature review // 3.3 Deep learning based practical approach for WMSN //

3.3.1 Convolutional Neural Networks // 3.3.2 Activation units // 3.3.3 Max pooling // 3.3.4 Batch normalization // 3.3.5 Regularization // 3.3.6 Transfer learning // 3.3.7 Recurrent Neural Networks // 3.3.8 Auto-Encoders // 3.3.9 Generative Adversarial Networks // 3.3.10 Mobile neural networks // 3.4 Computer vision algorithms in WMSN // 3.4.1 Object detection // 3.4.2 Semantic segmentation // 3.4.3 Image restoration, superresolution, and seman colorization // 3.5 Conclusion and future considerations // References // CHAPTER 4 Cognitive radio-medium access control protocol for Internet of Multimedia Things (loMT) - Shweta Pandit, Prabhat Thakur, Alok Kumar, and Ghanshyam Singh // 4.1 Introduction // 4.2 Difference in Internet of Things and Internet-of-MultimediaThings // 4.2.1 Architecture // 4.2.2 Performance parameters 71 // 4.2.3 Scalar data of loT and big data of loMT 72 // 4.3 Internet-of-Multimedia-Things and cognitive radio 73 // 4.3.1 Feasibility of cognitive radio to multimedia traffic 73 // 4.3.2 Why cognitive radio is a potential candidate for // Internet-of-Multimedia-Things? 73 // 4.3.3 Cognitive radio in loMT network 75 // 4.4 CR-MAC protocols for Internet-of-Multimedia-Things 77 // 4.5 Challenges in cognitive radio based loMT network 88 // 4.5.1 Spectrum sensing 89 // 4.5.2 Spectrum sharing/management 90 // 4.5.3 Spectrum mobility 92 // 4.5.4 Miscellaneous challenges 93 // 4.6 Summary 95 // References 95 // CHAPTER 5 Multimedia nano communication for healthcare noise analysis 99 - Shyam Pratap Singh, Vivek K. Dwivedi, and Ghanshyam Singh // 5.1 Introduction 99 // 5.2 Noise in nano communication and statistical tools 100 // 5.3 Fundamentals on various noises in MNC 102 // 5.3.1 Additive inverse Gaussian (IG) noise 104 // 5.3.2 Normal inverse Gaussian noise 105 // 5.3.3 Stable distribution noise 105 // 5.3.4 Modified Nakagami distribution noise 105 //

5.3.5 Radiation absorption noise 106 // 5.3.6 Sampling and counting noise 106 // 5.3.7 Molecular displacement noise 107 // 5.3.8 Drug delivery noise 107 // 5.3.9 Reactive obstacle noise 108 // 5.3.10 External noise 109 // 5.3.11 System noise 109 // 5.4 Fundamentals on various noises in EMNC 110 // 5.4.1 Johnson-Nyquist noise/thermal noise 110 // 5.4.2 Black-body noise ill // 5.4.3 Doppler-shift-induced noise ill // 5.4.4 Molecular absorption noise 112 // 5.4.5 Body radiation noise 112 // 5.5 Physical and/or stochastic models for noise in MNC 113 // 5.5.1 Additive inverse Gaussian noise (AIGN) 113 // 5.5.2 Normal inverse Gaussian noise (NIGN) 113 // 5.5.3 Stable distribution noise 114 // viji Contents // ui Ui cn // Eo 01 // 5.5.4 Modified Nakagami distribution noise // 5.5.5 Radiation absorption noise // 5.5.6 Sampling and counting noise // 5.5.7 Molecular displacement noise // 5.5.8 Drug delivery noise // 5.5.9 Reactive obstacle noise // 5.5.10 External noise // 5.5.11 System noise // Physical and/or stochastic models for noise in EMNC // 5.6.1 Johnson-Nyquist noise/thermal noise // 5.6.2 Black-body noise // 5.6.3 Doppler-shift-induced noise // 5.6.4 Molecular absorption noise // 5.6.5 Body radiation noise // 5.7 Simulation results of different noises under nano communication // 5.8 Open research challenges on noises in nano communication // 5.8.1 Challenges on the noises in MNC // 5.8.2 Challenges on the noises in EMNC // Summary // References // CHAPTER 6 The use of deep learning in image analysis for the study of oncology 133 - Bailey Janeczko and Gautam Srivastava // 6.1 The difficulties in meeting demand 133 // 6.1.1 The medical imaging equipment issue 134 // 6.2 What is deep learning? 135 // 6.2.1 Neuron’s and activation functions 136 // 6.2.2 Feedforward neural networks 138 // 6.2.3 Recurrent neural networks 139 // 6.2.4 Long-short term memory model 140 //

6.2.5 Convolutional neural networks 140 // 6.3 Deep learning techniques and processes 142 // 6.3.1 B ackpropagation 142 // 6.3.2 Image segmentation 142 // 6.3.3 Object localization in the study of oncology 142 // 6.4 Data difficulties 143 // 6.4.1 Stepping away from supervised-learning 143 // 6.4.2 Dimension reduction 144 // 6.4.3 Synthetic data 145 // 6.5 The current uses of deep learning image analysis 147 // 6.6 The future of deep learning image analysis in the study of oncology 147 // 6.7 Conclusion // References // CHAPTER 7 Automatic analysis of the heart sound signal to build smart healthcare system - Puneet Kumar Jain and Om Prakash Mahela // 7.1 Introduction // 7.1.1 Motivation // 7.1.2 Contributions // 7.2 Literature survey // 7.2.1 Denoising algorithms // 7.2.2 Segmentation algorithms // 7.2.3 Feature extraction and classification algorithms // 7.3 Methods and materials // 7.3.1 Theoretical background about TQWT // 7.3.2 HSMM based heart sound signal segmentation // 7.3.3 Machine learning based classification algorithms // 7.4 Proposed methodology // 7.4.1 Data preprocessing // 7.4.2 TQWT based denoising algorithm // 7.4.3 Segmentation using Springer’s HSMM algorithm 169 // 7.4.4 Feature extraction // 7.4.5 Classification of heart sound signal // 7.5 Results and discussion // Performance evaluation metrics // Results using the SVM method // Results using KNN method // Results using ensemble method // Comparison of the proposed method with other methods // 7.6 Conclusions // References // CHAPTER 8 Efficient single image haze removal using CLAHE and Dark Channel Prior for Internet of Multimedia Things 189 - Prateek Ishwar Khade and Amitesh Singh Rajput // 8.1 Introduction // 8.1.1 Efficient multimedia processing for loMT // 8.2 Background and related work // 8.2.1 Dark Channel Prior (DCP) // 8.2.2 Contrast-Limited Adaptive Histogram Equalization (CLAHE) 192 //

8.3 Analysis of adaptive contrast enhancement with DCP and its optimization 193 // 8.3.1 Adaptive contrast enhancement with DCP 193 // 8.3.2 Optimization for computational advantage 193 // 8.4 Results and discussion 195 // 8.4.1 Quality of haze removal 196 // 8.4.2 Performance assessment 199 // 8.4.3 Discussion 199 // 8.5 Conclusion 200 // References 200 // CHAPTER 9 A supervised and unsupervised image quality // assessment framework in real-time 203 - Zahi Al Chami, Chady Abou Jaoude, and Richard Chbeir // 9.1 Introduction 203 // 9.1.1 Motivating scenario 204 // 9.2 Related work 205 // 9.2.1 Full-reference image quality assessment 206 // 9.2.2 No-reference image quality assessment 207 // 9.3 Contributions 208 // 9.4 Definitions 209 // 9.4.1 Data model 210 // 9.4.2 Data manipulation functions 210 // 9.5 Data quality 211 // 9.5.1 Neural network architecture for image quality assessment 211 // 9.5.2 Face alignment anomaly detection 215 // 9.5.3 Image score 217 // 9.6 Framework 217 // 9.6.1 Stream processing module 217 // 9.6.2 Back-end module 218 // 9.7 Experiments 221 // 9.7.1 Experimental setup 221 // 9.7.2 Experimental protocol 222 // 9.7.3 Results 224 // 9.8 Conclusion 229 // References 229 // CHAPTER 10 A computational approach to understand building // floor plan images using machine learning // techniques 233 - Shreya Goyal, Chiranjoy Chattopadhyay, and Gaurav Bhatnagar // 10.1 Introduction // 10.2 Motivation of the problem // 10.3 Problem statement // 10.3.1 Brief description of the work done // 10.4 Literature survey // 10.4.1 State of the art in graphic recognition // 10.4.2 State of the art in floor plan analysis // 10.4.3 Publicly available floor plan datasets // 10.4.4 Symbol spotting in document images // 10.4.5 Image description generation // 10.4.6 Evaluation of text generation // 10.5 Descriptive narration generation from floor plan images //

10.5.1 System overview // 10.5.2 Room annotation learning model // 10.5.3 Bag of decor (BoD) // 10.5.4 Semistructured description generation // 10.5.5 Experimental findings // 10.5.6 Results for description generation // 10.6. Application to smart homes and buildings // Conclusion // References // Index

(OCoLC)1331021604

GBC2J1664