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Sensor analysis for the Internet of ...

Stanley, Michael,

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Sensor analysis for the Internet of things /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sensor analysis for the Internet of things // Michael Stanley, Jongmin Lee.
Author:	Stanley, Michael,
other author:	Lee, Jongmin,
Description:	1 online resource (139 p.)
[NT 15003449]:	Sensor analysis for the Internet of things -- Abstract; Keywords -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- Nomenclature -- 1 Introduction -- 2 Sensors -- 3 Sensor Fusion -- 4 Machine Learning for Sensor Data -- 5 IoT Sensor Applications -- 6 Concluding Remarks and Summary -- Bibliography -- Authors' Biographies.
Subject:	Multisensor data fusion. -
Online resource:	https://portal.igpublish.com/iglibrary/search/MCPB0006379.html
ISBN:	9781681732879

Sensor analysis for the Internet of things /
Stanley, Michael,

Sensor analysis for the Internet of things /Michael Stanley, Jongmin Lee. - 1 online resource (139 p.) - Synthesis lectures on algorithms and software in engineering ;17. - Synthesis lectures on algorithms and software in engineering ;17..

Includes bibliographical references and index.

Sensor analysis for the Internet of things -- Abstract; Keywords -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- Nomenclature -- 1 Introduction -- 2 Sensors -- 3 Sensor Fusion -- 4 Machine Learning for Sensor Data -- 5 IoT Sensor Applications -- 6 Concluding Remarks and Summary -- Bibliography -- Authors' Biographies.

While it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals. Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types. We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.

Mode of access: World Wide Web.

ISBN: 9781681732879Subjects--Topical Terms:

581966
Multisensor data fusion.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: TK7872.D48

Dewey Class. No.: 681.2

Sensor analysis for the Internet of things /
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100 1 $a Stanley, Michael, $e author. $3 3440540
245 1 0 $a Sensor analysis for the Internet of things / $c Michael Stanley, Jongmin Lee.
264 1 $a [San Rafael, California] : $b Morgan & Claypool Publishers, $c 2018.
300 $a 1 online resource (139 p.)
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490 1 $a Synthesis lectures on algorithms and software in engineering ; $v 17
504 $a Includes bibliographical references and index.
505 0 $a Sensor analysis for the Internet of things -- Abstract; Keywords -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- Nomenclature -- 1 Introduction -- 2 Sensors -- 3 Sensor Fusion -- 4 Machine Learning for Sensor Data -- 5 IoT Sensor Applications -- 6 Concluding Remarks and Summary -- Bibliography -- Authors' Biographies.
520 $a While it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals. Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types. We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.
538 $a Mode of access: World Wide Web.
650 0 $a Multisensor data fusion. $3 581966
650 0 $a Internet of things. $3 2057703
650 0 $a Sensor networks. $3 581965
650 0 $a Machine learning. $3 533906
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Lee, Jongmin, $e author. $3 3440541
830 0 $a Synthesis lectures on algorithms and software in engineering ; $v 17. $3 3440542
856 4 0 $u https://portal.igpublish.com/iglibrary/search/MCPB0006379.html