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)

Table of 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.