TY  - GEN
AU  - Nam,Hyoungsik
AU  - Nam,Hyoungsik
TI  - Machine Learning in Sensors and Imaging
SN  - books978-3-0365-3754-2
PY  - 2022///
CY  - Basel
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Technology: general issues
KW  - bicssc
KW  - History of engineering & technology
KW  - star image
KW  - image denoising
KW  - reinforcement learning
KW  - maximum likelihood estimation
KW  - mixed Poisson-Gaussian likelihood
KW  - machine learning-based classification
KW  - non-uniform foundation
KW  - stochastic analysis
KW  - vehicle-pavement-foundation interaction
KW  - forest growing stem volume
KW  - coniferous plantations
KW  - variable selection
KW  - texture feature
KW  - random forest
KW  - red-edge band
KW  - on-shelf availability
KW  - semi-supervised learning
KW  - deep learning
KW  - image classification
KW  - machine learning
KW  - explainable artificial intelligence
KW  - wildfire
KW  - risk assessment
KW  - Naïve bayes
KW  - transmission-line corridors
KW  - image encryption
KW  - compressive sensing
KW  - plaintext related
KW  - chaotic system
KW  - convolutional neural network
KW  - color prior model
KW  - object detection
KW  - piston error detection
KW  - segmented telescope
KW  - BP artificial neural network
KW  - modulation transfer function
KW  - computer vision
KW  - intelligent vehicles
KW  - extrinsic camera calibration
KW  - structure from motion
KW  - convex optimization
KW  - temperature estimation
KW  - BLDC
KW  - electric machine protection
KW  - touchscreen
KW  - capacitive
KW  - display
KW  - SNR
KW  - stylus
KW  - laser cutting
KW  - quality monitoring
KW  - artificial neural network
KW  - burr formation
KW  - cut interruption
KW  - fiber laser
KW  - semi-supervised
KW  - fuzzy
KW  - noisy
KW  - real-world
KW  - plankton
KW  - marine
KW  - activity recognition
KW  - wearable sensors
KW  - imbalanced activities
KW  - sampling methods
KW  - path planning
KW  - Q-learning
KW  - neural network
KW  - YOLO algorithm
KW  - robot arm
KW  - target reaching
KW  - obstacle avoidance
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/5335
UR  - https://directory.doabooks.org/handle/20.500.12854/80994
ER  -