| 000 | 04664naaaa2201177uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/80994 | ||
| 005 | 20220714171628.0 | ||
| 020 | _abooks978-3-0365-3754-2 | ||
| 020 | _a9783036537535 | ||
| 020 | _a9783036537542 | ||
| 024 | 7 |
_a10.3390/books978-3-0365-3754-2 _cdoi |
|
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 072 | 7 |
_aTB _2bicssc |
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| 072 | 7 |
_aTBX _2bicssc |
|
| 100 | 1 |
_aNam, Hyoungsik _4edt _91585861 |
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| 700 | 1 |
_aNam, Hyoungsik _4oth _91585861 |
|
| 245 | 1 | 0 | _aMachine Learning in Sensors and Imaging |
| 260 |
_aBasel _bMDPI - Multidisciplinary Digital Publishing Institute _c2022 |
||
| 300 | _a1 electronic resource (302 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aMachine 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. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc _4https://creativecommons.org/licenses/by/4.0/ |
||
| 546 | _aEnglish | ||
| 650 | 7 |
_aTechnology: general issues _2bicssc _9928609 |
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| 650 | 7 |
_aHistory of engineering & technology _2bicssc _91129967 |
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| 653 | _astar image | ||
| 653 | _aimage denoising | ||
| 653 | _areinforcement learning | ||
| 653 | _amaximum likelihood estimation | ||
| 653 | _amixed Poisson-Gaussian likelihood | ||
| 653 | _amachine learning-based classification | ||
| 653 | _anon-uniform foundation | ||
| 653 | _astochastic analysis | ||
| 653 | _avehicle-pavement-foundation interaction | ||
| 653 | _aforest growing stem volume | ||
| 653 | _aconiferous plantations | ||
| 653 | _avariable selection | ||
| 653 | _atexture feature | ||
| 653 | _arandom forest | ||
| 653 | _ared-edge band | ||
| 653 | _aon-shelf availability | ||
| 653 | _asemi-supervised learning | ||
| 653 | _adeep learning | ||
| 653 | _aimage classification | ||
| 653 | _amachine learning | ||
| 653 | _aexplainable artificial intelligence | ||
| 653 | _awildfire | ||
| 653 | _arisk assessment | ||
| 653 | _aNaïve bayes | ||
| 653 | _atransmission-line corridors | ||
| 653 | _aimage encryption | ||
| 653 | _acompressive sensing | ||
| 653 | _aplaintext related | ||
| 653 | _achaotic system | ||
| 653 | _aconvolutional neural network | ||
| 653 | _acolor prior model | ||
| 653 | _aobject detection | ||
| 653 | _apiston error detection | ||
| 653 | _asegmented telescope | ||
| 653 | _aBP artificial neural network | ||
| 653 | _amodulation transfer function | ||
| 653 | _acomputer vision | ||
| 653 | _aintelligent vehicles | ||
| 653 | _aextrinsic camera calibration | ||
| 653 | _astructure from motion | ||
| 653 | _aconvex optimization | ||
| 653 | _atemperature estimation | ||
| 653 | _aBLDC | ||
| 653 | _aelectric machine protection | ||
| 653 | _atouchscreen | ||
| 653 | _acapacitive | ||
| 653 | _adisplay | ||
| 653 | _aSNR | ||
| 653 | _astylus | ||
| 653 | _alaser cutting | ||
| 653 | _aquality monitoring | ||
| 653 | _aartificial neural network | ||
| 653 | _aburr formation | ||
| 653 | _acut interruption | ||
| 653 | _afiber laser | ||
| 653 | _asemi-supervised | ||
| 653 | _afuzzy | ||
| 653 | _anoisy | ||
| 653 | _areal-world | ||
| 653 | _aplankton | ||
| 653 | _amarine | ||
| 653 | _aactivity recognition | ||
| 653 | _awearable sensors | ||
| 653 | _aimbalanced activities | ||
| 653 | _asampling methods | ||
| 653 | _apath planning | ||
| 653 | _aQ-learning | ||
| 653 | _aneural network | ||
| 653 | _aYOLO algorithm | ||
| 653 | _arobot arm | ||
| 653 | _atarget reaching | ||
| 653 | _aobstacle avoidance | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/5335 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/80994 _70 _zDOAB: description of the publication |
| 999 |
_c2990635 _d2990635 |
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