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Big data analytics for cyber-physical systems : machine learning for the Internet of Things /
Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interacti...
| Cote: | Libro Electrónico |
|---|---|
| Autres auteurs: | , , |
| Format: | Électronique eBook |
| Langue: | Inglés |
| Publié: |
Amsterdam :
Elsevier,
2019.
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| Édition: | First edition. |
| Sujets: | |
| Accès en ligne: | Texto completo |
Table des matières:
- Intro; Title page; Table of Contents; Copyright; Contributors; Foreword; Acknowledgments; Introduction; Chapter 1: Data analytics and processing platforms in CPS; Abstract; 1 Open source versus proprietary software; 2 Data types; 3 Easy data visualization using code; 4 Statistical measurements in CPS data; 5 Statistical methods, models, and techniques: Brief introduction; 6 Analytics and statistics versus ML techniques; 7 Data charts; 8 Machine logs analysis and dashboarding; 9 Conclusion; Chapter 2: Fundamentals of data analysis and statistics; Abstract; 1 Introduction
- 2 Useful software tools3 Fundamentals of statistics; 4 Regression: Fitting functional models to the data; 5 Minimizing redundancy: Factor analysis and principle component analysis; 6 Explore unknown data: Cluster analysis; 7 Conclusion; Chapter 3: Density-based clustering techniques for object detection and peak segmentation in expanding data fields; Abstract; 1 Introduction; 2 Related work; 3 A brief introduction to density-based clustering; 4 Formal extensions of density-based clustering; 5 Clustering strategy for time-expandable data sets; 6 Evaluation and results; 7 Conclusion
- Chapter 4: Security for a regional network platform in IoTAbstract; 1 Introduction; 2 Regional network security; 3 Proactive distributed authentication framework for a regional network; 4 Discussion; 5 Function implementations; 6 Network setup and performance evaluations; 7 Conclusions; Chapter 5: Inference techniques for ultrasonic parking lot occupancy sensing based on smart city infrastructure; Abstract; 1 Introduction; 2 Related work; 3 Fundamentals and background; 4 System setup and architecture; 5 Data annotation and trainging methodoloy; 6 Proposed method; 7 Evaluation and results
- 8 Conclusion and future workChapter 6: Portable implementations for heterogeneous hardware platforms in autonomous driving systems; Abstract; 1 Advanced driver-assistance systems; 2 Programming challenges; 3 Parallel programming approaches; 4 Unification; 5 Summary; Chapter 7: AI-based sensor platforms for the IoT in smart cities; Abstract; 1 Introduction; 2 Function units of an IoT sensor; 3 More than one sensor element; 4 The communication interface; 5 Embedded O/S requirements; 6 Artificial intelligence embedded; 7 Classification and regression using machine learning algorithms
- 8 Learning process required9 AI-based IoT sensor system; 10 Decentralized intelligence; 11 Conclusions; Chapter 8: Predicting energy consumption using machine learning; Abstract; Acknowledgments; 1 Introduction; 2 Data profiling; 3 Learning from data; 4 Related work; 5 Further thoughts; Chapter 9: Reinforcement learning and deep neural network for autonomous driving; Abstract; 1 Introduction; 2 Signal model; 3 Machine learning; 4 Simulation; 5 Conclusion and future work