Reprint
Sensor Data Fusion Analysis for Broad Applications
Edited by
July 2024
334 pages
- ISBN978-3-7258-1589-0 (Hardback)
- ISBN978-3-7258-1590-6 (PDF)
https://doi.org/10.3390/books978-3-7258-1590-6 (registering)
Print copies available soon
This book is a reprint of the Special Issue Sensor Data Fusion Analysis for Broad Applications that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary
Nowadays, there are many fields of application where different sensors are used to collect sensitive data. A good analysis of this data allows for improving the performance of a system, as well as making it more efficient and secure. New technologies have made it increasingly possible to analyze larger amounts of data, which has allowing for the area of sensor data fusion analysis to undergo exponential growth. The objective of this reprint is to immerse the reader in the latest advances in this area, showing applications in very different fields that demonstrate its relevance.
Format
- Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
data fusion; iterative learning; fault detection; pitch system; wind turbines; computer vision; self-driving car; smart product delivery; Internet of Things; convolution neural network; Raspberry Pi 3; clustering; feature selection; management zone delineation; precision agriculture; acoustic emissions; capsule neural network; convolutional neural network; source localization; time of arrival; sensor placement; route detection; evolutionary computing; Acrylonitrile-Butadiene-Styrene; low-level data fusion; multiblock-partial least squares (MB-PLS); multivariate statistical process control; polymer production; quality prediction; real-time monitoring; response-oriented sequential alternation (ROSA); smart manufacturing; data fusion; feature extraction; industrial prognosis; sparkling wine; biogenic amines; elemental composition; wine quality; winemaking practices; principal component analysis; data fusion approach; ESN; recurrent neural networks; sensor data fusion; edge computing; industry 4.0; fault detection; deep learning; localization; inertial navigation system; extended Kalman filter; mobile robot; autonomous ground vehicle; TinyML; machine learning; edge analytics; energy harvesting; health care; security; safety; fire safety; prescribed performance tracking control; terminal sliding mode control; disturbance observer; magnetic levitation systems; machine learning; information extraction; object spatial; smart cities; gis detection; mobile robot; self-localization; odometry; sensor fusion; long short-term memory; object detection; sensor fusion; early-fusion; computer vision; RGB camera; thermal camera; 3D LiDAR; non-contact monitoring; neonates; sensor fusion; neural network; face detection; n/a