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Big data analytics for cyber-physica...

Dartmann, Guido,

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Big data analytics for cyber-physical systems = machine learning for the Internet of Things /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big data analytics for cyber-physical systems/ edited by Guido Dartmann, Houbing Song, Anke Schmeink.
Reminder of title:	machine learning for the Internet of Things /
other author:	Dartmann, Guido,
Published:	Amsterdam :Elsevier, : 2019.,
Description:	1 online resource (xxii, 373 p.) :ill.
[NT 15003449]:	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
[NT 15003449]:	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
[NT 15003449]:	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
[NT 15003449]:	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
[NT 15003449]:	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
Subject:	Big data. -
Online resource:	https://www.sciencedirect.com/science/book/9780128166376
ISBN:	9780128166468 (electronic bk.)

Big data analytics for cyber-physical systems = machine learning for the Internet of Things /
Big data analytics for cyber-physical systemsmachine learning for the Internet of Things /[electronic resource] :edited by Guido Dartmann, Houbing Song, Anke Schmeink. - First edition. - Amsterdam :Elsevier,2019. - 1 online resource (xxii, 373 p.) :ill.

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

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 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.

ISBN: 9780128166468 (electronic bk.)Subjects--Topical Terms:

2045508
Big data.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QA76.9.B45 / B54 2019eb

Dewey Class. No.: 005.7

Big data analytics for cyber-physical systems = machine learning for the Internet of Things /
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245 0 0 $a Big data analytics for cyber-physical systems $h [electronic resource] : $b machine learning for the Internet of Things / $c edited by Guido Dartmann, Houbing Song, Anke Schmeink.
250 $a First edition.
260 $a Amsterdam : $b Elsevier, $c 2019.
300 $a 1 online resource (xxii, 373 p.) : $b ill.
505 0 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
520 $a 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 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.
650 0 $a Big data. $3 2045508
650 0 $a Discourse analysis, Narrative. $3 555054
650 0 $a Truthfulness and falsehood. $3 584326
650 0 $a Online social networks. $3 624374
655 0 $a Electronic books. $2 lcsh $3 542853
700 1 $a Dartmann, Guido, $e editor. $3 3508606
700 1 $a Song, Houbing, $e editor. $3 3235302
700 1 $a Schmeink, Anke, $e editor. $3 3508607
856 4 0 $u https://www.sciencedirect.com/science/book/9780128166376