Machine Learning in Sensors and Imaging
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, mach...
Gorde:
| Formatua: | Online |
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| Hizkuntza: | ingelesa |
| Argitaratua: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Gaiak: | |
| Sarrera elektronikoa: | ONIX_20220506_9783036537535_60 |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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| _version_ | 1869516368823451648 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-80994 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-809942024-04-09T23:15:57Z Machine Learning in Sensors and Imaging Nam, Hyoungsik 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 BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2022-05-06T11:20:29Z 2022-05-06T11:20:29Z 2022 book ONIX_20220506_9783036537535_60 9783036537535 9783036537542 https://directory.doabooks.org/handle/20.500.12854/80994 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5335 https://mdpi.com/books/pdfview/book/5335 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3754-2 10.3390/books978-3-0365-3754-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036537535 9783036537542 302 Basel open access |
| spellingShingle | 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 BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Machine Learning in Sensors and Imaging |
| title | Machine Learning in Sensors and Imaging |
| title_full | Machine Learning in Sensors and Imaging |
| title_fullStr | Machine Learning in Sensors and Imaging |
| title_full_unstemmed | Machine Learning in Sensors and Imaging |
| title_short | Machine Learning in Sensors and Imaging |
| title_sort | machine learning in sensors and imaging |
| topic | 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 BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | 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 BLDC electric machine protection touchscreen capacitive display SNR stylus laser cutting quality monitoring artificial neural network burr formation cut interruption fiber laser semi-supervised fuzzy noisy real-world plankton marine activity recognition wearable sensors imbalanced activities sampling methods path planning Q-learning neural network YOLO algorithm robot arm target reaching obstacle avoidance thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20220506_9783036537535_60 |