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Sensor analysis for the Internet of things /

Bibliographic Details
Main Authors: Stanley, Michael (Engineer) (Author), Lee, Jongmin (Electrical engineer) (Author)
Corporate Author: Morgan & Claypool Publishers
Format: eBook
Language:English
Published: [San Rafael, California] : Morgan & Claypool, 2018.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on algorithms and software in engineering ; # 17.
Subjects:
Internet of things.
Machine learning.
Multisensor data fusion.
Sensor networks.
Internet of Things (IoT)
accelerometer
algorithms
deep learning
gyroscope
machine learning
magnetometer
pressure
sensor fusion
sensors
Electronic books.
Online Access:Connect to the full text of this electronic book (PDF)
Description
Abstract: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.
Item Description:Part of: Synthesis digital library of engineering and computer science.
Physical Description:1 online resource (xxiii, 113 pages) : illustrations.
Also available in print.
Format:Mode of access: World Wide Web.
Bibliography:Includes bibliographical references (pages 97-111).
ISBN:9781681732886
ISSN:1938-1735 ;
DOI:10.2200/S00827ED1V01Y201802ASE017
Access:Abstract freely available; full-text restricted to subscribers or individual document purchasers.