Internet of Multimedia Things IoMT Techniques and Applications 1st edition by Shailendra Shukla 0323900828 9780323900829

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ISBN-10 :  0323900828 

ISBN-13 :  9780323900829

Author : Shailendra Shukla 

Internet of Multimedia Things (IoMT): Techniques and Applications disseminates research efforts in the security and resilience of intelligent data-centric critical systems to support advanced research in this area. Sections cover the background of IoMT Architectures and Technologies, describe the problems that arise in IoMT Computing and protocols, and illustrate the application of IoMT on Industrial applications. The book will be beneficial for engineers, developers, solution designers, architects, system engineers and specialists from professional environments interested in the IoMT to seek appropriate solutions to their specific problems.

Internet of Multimedia Things (IoMT): Techniques and Applications 1st Table of contents:

Chapter 1: A review on Internet of Multimedia Things (IoMT) routing protocols and quality of service
Abstract
1.1. Introduction
1.2. Routing protocols in IoMT
1.3. Quality of Service (QoS) routing in IoMT
1.4. Conclusion and future directions
References
Chapter 2: Energy efficient data communication in Internet of Multimedia Things (IoMT)
Abstract
2.1. Introduction
2.2. Related work
2.3. System model
2.4. Proposed approach
2.5. Implementation and results
2.6. Conclusion
References
Chapter 3: Visual information processing and transmission in Wireless Multimedia Sensor Networks: a deep learning based practical approach
Abstract
3.1. Introduction
3.2. Literature review
3.3. Deep learning based practical approach for WMSN
3.4. Computer vision algorithms in WMSN
3.5. Conclusion and future considerations
References
Chapter 4: Cognitive radio-medium access control protocol for Internet of Multimedia Things (IoMT)
Abstract
4.1. Introduction
4.2. Difference in Internet of Things and Internet-of-Multimedia-Things
4.3. Internet-of-Multimedia-Things and cognitive radio
4.4. CR-MAC protocols for Internet-of-Multimedia-Things
4.5. Challenges in cognitive radio based IoMT network
4.6. Summary
References
Chapter 5: Multimedia nano communication for healthcare – noise analysis
Abstract
5.1. Introduction
5.2. Noise in nano communication and statistical tools
5.3. Fundamentals on various noises in MNC
5.4. Fundamentals on various noises in EMNC
5.5. Physical and/or stochastic models for noise in MNC
5.6. Physical and/or stochastic models for noise in EMNC
5.7. Simulation results of different noises under nano communication
5.8. Open research challenges on noises in nano communication
5.9. Summary
References
Chapter 6: The use of deep learning in image analysis for the study of oncology
Abstract
6.1. The difficulties in meeting demand
6.2. What is deep learning?
6.3. Deep learning techniques and processes
6.4. Data difficulties
6.5. The current uses of deep learning image analysis
6.6. The future of deep learning image analysis in the study of oncology
6.7. Conclusion
References
Chapter 7: Automatic analysis of the heart sound signal to build smart healthcare system
Abstract
7.1. Introduction
7.2. Literature survey
7.3. Methods and materials
7.4. Proposed methodology
7.5. Results and discussion
7.6. Conclusions
References
Chapter 8: Efficient single image haze removal using CLAHE and Dark Channel Prior for Internet of Multimedia Things
Abstract
8.1. Introduction
8.2. Background and related work
8.3. Analysis of adaptive contrast enhancement with DCP and its optimization
8.4. Results and discussion
8.5. Conclusion
References
Chapter 9: A supervised and unsupervised image quality assessment framework in real-time
Abstract
9.1. Introduction
9.2. Related work
9.3. Contributions
9.4. Definitions
9.5. Data quality
9.6. Framework
9.7. Experiments
9.8. Conclusion
References
Chapter 10: A computational approach to understand building floor plan images using machine learning techniques
Abstract
10.1. Introduction
10.2. Motivation of the problem
10.3. Problem statement
10.4. Literature survey
10.5. Descriptive narration generation from floor plan images
10.6. Application to smart homes and buildings
10.7. Conclusion

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