Sensor Analysis for the Internet of Things (Record no. 85590)
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000 -LEADER | |
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fixed length control field | 03319nam a22005055i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-031-01526-7 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730164329.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 220601s2018 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783031015267 |
-- | 978-3-031-01526-7 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 621,382 |
100 1# - AUTHOR NAME | |
Author | Stanley, Michael. |
245 10 - TITLE STATEMENT | |
Title | Sensor Analysis for the Internet of Things |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2018. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XXIII, 113 p. |
490 1# - SERIES STATEMENT | |
Series statement | Synthesis Lectures on Algorithms and Software in Engineering, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | List of Figures -- List of Tables -- Preface -- Acknowledgments -- Nomenclature -- Introduction -- Sensors -- Sensor Fusion -- Machine Learning for Sensor Data -- IoT Sensor Applications -- Concluding Remarks and Summary -- Bibliography -- Authors' Biographies. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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. |
700 1# - AUTHOR 2 | |
Author 2 | Lee, Jongmin. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01526-7 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2018. |
336 ## - | |
-- | text |
-- | txt |
-- | rdacontent |
337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
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347 ## - | |
-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal processing. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Signal, Speech and Image Processing. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 1938-1735 |
912 ## - | |
-- | ZDB-2-SXSC |
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