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The Resource Sensor analysis for the Internet of things, Michael Stanley and Jongmin Lee

Sensor analysis for the Internet of things, Michael Stanley and Jongmin Lee

Label
Sensor analysis for the Internet of things
Title
Sensor analysis for the Internet of things
Statement of responsibility
Michael Stanley and Jongmin Lee
Creator
Contributor
Author
Subject
Language
eng
Summary
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
Member of
Cataloging source
CaBNVSL
http://library.link/vocab/creatorName
Stanley, Michael
Dewey number
681.2
Illustrations
illustrations
Index
no index present
LC call number
TK7872.D48
LC item number
S727 2018
Literary form
non fiction
Nature of contents
  • dictionaries
  • abstracts summaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Lee, Jongmin
http://library.link/vocab/subjectName
  • Multisensor data fusion
  • Internet of things
  • Sensor networks
  • Machine learning
Target audience
  • adult
  • specialized
Label
Sensor analysis for the Internet of things, Michael Stanley and Jongmin Lee
Instantiates
Publication
Note
Part of: Synthesis digital library of engineering and computer science
Bibliography note
Includes bibliographical references (pages 97-111)
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
  • 1. Introduction --
  • 2. Sensors -- 2.1 Accelerometer -- 2.1.1 Accelerometer placement -- 2.2 Magnetometer -- 2.2.1 Hard and soft iron magnetic compensation -- 2.2.2 Magnetometer placement -- 2.3 Gyro sensor -- 2.4 Pressure sensor/altimeters --
  • 3. Sensor fusion -- 3.1 Terminology -- 3.1.1 Degrees of freedom (DOF) -- 3.1.2 Axis/axes -- 3.1.3 Sensor module configurations -- 3.2 Basic quaternion math -- 3.2.1 Introduction and basic properties -- 3.2.2 Equality -- 3.2.3 Addition -- 3.2.4 Multiplication -- 3.2.5 Complex conjugate -- 3.2.6 Norm -- 3.2.7 Inverse -- 3.3 Orientation representations -- 3.3.1 Euler angles and rotation matrices -- 3.3.2 Quaternions -- 3.3.3 Conversions between representations -- 3.3.4 Orientation representation comparison -- 3.4 Virtual gyroscope -- 3.5 Kalman filtering for orientation estimation -- 3.5.1 Introduction to Kalman filters -- 3.5.2 Kalman filters for inertial sensor fusion -- 3.6 Tools -- 3.6.1 Numerical analysis -- 3.6.2 Tools to create fielded implementations --
  • 4. Machine learning for sensor data -- 4.1 Introduction -- 4.2 Sensor data acquisition -- 4.2.1 Structured vs. un-structured data -- 4.2.2 Data quality -- 4.2.3 Inherent variability -- 4.3 Feature extraction -- 4.3.1 Time-domain features -- 4.3.2 Frequency-domain features -- 4.3.3 Time-frequency features -- 4.3.4 Dimension reduction -- 4.3.5 Feature selection -- 4.4 Supervised learning -- 4.4.1 Linear discriminant analysis -- 4.4.2 Support vector machines -- 4.4.3 Kernel functions -- 4.5 Unsupervised learning -- 4.6 Remarks--learning from sensor data -- 4.7 Performance evaluation -- 4.8 Deep learning -- 4.9 Integration point of machine learning algorithms -- 4.10 Tools for machine learning --
  • 5. IoT sensor applications -- 5.1 Cloud platforms -- 5.2 Automotive industry -- 5.3 Unmanned aerial vehicles (UAV ) -- 5.4 Manufacturing and processing industry -- 5.5 Healthcare and wearables -- 5.6 Smart city and energy --
  • 6. Concluding remarks and summary -- Bibliography -- Authors' biographies
Control code
201802ASE017
Dimensions
unknown
Extent
1 PDF (xxiii, 113 pages)
File format
multiple file formats
Form of item
online
Isbn
9781681732886
Media category
electronic
Media MARC source
isbdmedia
Other control number
10.2200/S00827ED1V01Y201802ASE017
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System control number
  • (CaBNVSL)swl00408188
  • (OCoLC)1022184124
Label
Sensor analysis for the Internet of things, Michael Stanley and Jongmin Lee
Publication
Note
Part of: Synthesis digital library of engineering and computer science
Bibliography note
Includes bibliographical references (pages 97-111)
Carrier category
online resource
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type MARC source
rdacontent
Contents
  • 1. Introduction --
  • 2. Sensors -- 2.1 Accelerometer -- 2.1.1 Accelerometer placement -- 2.2 Magnetometer -- 2.2.1 Hard and soft iron magnetic compensation -- 2.2.2 Magnetometer placement -- 2.3 Gyro sensor -- 2.4 Pressure sensor/altimeters --
  • 3. Sensor fusion -- 3.1 Terminology -- 3.1.1 Degrees of freedom (DOF) -- 3.1.2 Axis/axes -- 3.1.3 Sensor module configurations -- 3.2 Basic quaternion math -- 3.2.1 Introduction and basic properties -- 3.2.2 Equality -- 3.2.3 Addition -- 3.2.4 Multiplication -- 3.2.5 Complex conjugate -- 3.2.6 Norm -- 3.2.7 Inverse -- 3.3 Orientation representations -- 3.3.1 Euler angles and rotation matrices -- 3.3.2 Quaternions -- 3.3.3 Conversions between representations -- 3.3.4 Orientation representation comparison -- 3.4 Virtual gyroscope -- 3.5 Kalman filtering for orientation estimation -- 3.5.1 Introduction to Kalman filters -- 3.5.2 Kalman filters for inertial sensor fusion -- 3.6 Tools -- 3.6.1 Numerical analysis -- 3.6.2 Tools to create fielded implementations --
  • 4. Machine learning for sensor data -- 4.1 Introduction -- 4.2 Sensor data acquisition -- 4.2.1 Structured vs. un-structured data -- 4.2.2 Data quality -- 4.2.3 Inherent variability -- 4.3 Feature extraction -- 4.3.1 Time-domain features -- 4.3.2 Frequency-domain features -- 4.3.3 Time-frequency features -- 4.3.4 Dimension reduction -- 4.3.5 Feature selection -- 4.4 Supervised learning -- 4.4.1 Linear discriminant analysis -- 4.4.2 Support vector machines -- 4.4.3 Kernel functions -- 4.5 Unsupervised learning -- 4.6 Remarks--learning from sensor data -- 4.7 Performance evaluation -- 4.8 Deep learning -- 4.9 Integration point of machine learning algorithms -- 4.10 Tools for machine learning --
  • 5. IoT sensor applications -- 5.1 Cloud platforms -- 5.2 Automotive industry -- 5.3 Unmanned aerial vehicles (UAV ) -- 5.4 Manufacturing and processing industry -- 5.5 Healthcare and wearables -- 5.6 Smart city and energy --
  • 6. Concluding remarks and summary -- Bibliography -- Authors' biographies
Control code
201802ASE017
Dimensions
unknown
Extent
1 PDF (xxiii, 113 pages)
File format
multiple file formats
Form of item
online
Isbn
9781681732886
Media category
electronic
Media MARC source
isbdmedia
Other control number
10.2200/S00827ED1V01Y201802ASE017
Other physical details
illustrations.
Reformatting quality
access
Specific material designation
remote
System control number
  • (CaBNVSL)swl00408188
  • (OCoLC)1022184124

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