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040 _aN$T
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020 _a9780128166468
_q(electronic bk.)
020 _a0128166460
_q(electronic bk.)
020 _z9780128166376
035 _a(OCoLC)1108871637
050 4 _aQA76.9.B45
_bB54 2019eb
082 0 4 _a005.7
_223
245 0 0 _aBig data analytics for cyber-physical systems :
_bmachine learning for the Internet of Things /
_cedited by Guido Dartmann, Houbing Song, Anke Schmeink.
250 _aFirst edition.
264 1 _aAmsterdam :
_bElsevier,
_c2019.
300 _a1 online resource (xxii, 373 pages) :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
588 0 _aOnline resource; title from PDF title page (EBSCO, viewed July 18, 2019)
520 _aBig 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 interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science.
505 0 _aIntro; 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
505 8 _a2 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
505 8 _aChapter 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
505 8 _a8 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
505 8 _a8 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
650 0 _aBig data.
650 0 _aDiscourse analysis, Narrative.
_913647
650 0 _aTruthfulness and falsehood.
_934362
650 0 _aOnline social networks.
700 1 _aDartmann, Guido,
_eeditor.
_934363
700 1 _aSong, Houbing,
_eeditor.
_1https://id.oclc.org/worldcat/entity/E39PBJfRyKb4hF6rfgpWfbKyBP
_934364
700 1 _aSchmeink, Anke,
_eeditor.
_934365
758 _ihas work:
_aBig data analytics for cyber-physical systems (Text)
_1https://id.oclc.org/worldcat/entity/E39PCFCQRgMWyXWCHDbwVhJjBX
_4https://id.oclc.org/worldcat/ontology/hasWork
856 4 0 _3ScienceDirect
_uhttps://www.sciencedirect.com/science/book/9780128166376
999 _c216532
_d216532