Machine Learning in Sensors and Imaging

doi:10.3390/books978-3-0365-3754-2

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Machine Learning in Sensors and Imaging

Edited by

April 2022

302 pages

ISBN978-3-0365-3753-5 (Hardback)
ISBN978-3-0365-3754-2 (PDF)

https://doi.org/10.3390/books978-3-0365-3754-2

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This book is a reprint of the Special Issue Machine Learning in Sensors and Imaging that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications.

This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

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Keywords

star image; image denoising; reinforcement learning; maximum likelihood estimation; mixed Poisson–Gaussian likelihood; machine learning-based classification; non-uniform foundation; stochastic analysis; vehicle–pavement–foundation interaction; forest growing stem volume; coniferous plantations; variable selection; texture feature; random forest; red-edge band; on-shelf availability; semi-supervised learning; deep learning; image classification; machine learning; explainable artificial intelligence; wildfire; risk assessment; Naïve bayes; transmission-line corridors; image encryption; compressive sensing; plaintext related; chaotic system; convolutional neural network; color prior model; object detection; piston error detection; segmented telescope; BP artificial neural network; modulation transfer function; computer vision; intelligent vehicles; extrinsic camera calibration; structure from motion; convex optimization; temperature estimation; machine learning; BLDC; electric machine protection; touchscreen; capacitive; display; SNR; stylus; machine learning; laser cutting; quality monitoring; artificial neural network; burr formation; cut interruption; fiber laser; semi-supervised; fuzzy; deep learning; noisy; real-world; plankton; marine; activity recognition; wearable sensors; imbalanced activities; sampling methods; path planning; Q-learning; neural network; YOLO algorithm; computer vision; robot arm; target reaching; obstacle avoidance