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Machine Learning in Sensors and Imaging
| Machine Learning in Sensors and Imaging |
| Autore | Nam Hyoungsik |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (302 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
activity recognition
artificial neural network BLDC BP artificial neural network burr formation capacitive chaotic system color prior model compressive sensing computer vision coniferous plantations convex optimization convolutional neural network cut interruption deep learning display electric machine protection explainable artificial intelligence extrinsic camera calibration fiber laser forest growing stem volume fuzzy image classification image denoising image encryption imbalanced activities intelligent vehicles laser cutting machine learning machine learning-based classification marine maximum likelihood estimation mixed Poisson-Gaussian likelihood modulation transfer function Naïve bayes neural network noisy non-uniform foundation object detection obstacle avoidance on-shelf availability path planning piston error detection plaintext related plankton Q-learning quality monitoring random forest real-world red-edge band reinforcement learning risk assessment robot arm sampling methods segmented telescope semi-supervised semi-supervised learning SNR star image stochastic analysis structure from motion stylus target reaching temperature estimation texture feature touchscreen transmission-line corridors variable selection vehicle-pavement-foundation interaction wearable sensors wildfire YOLO algorithm |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566484703321 |
Nam Hyoungsik
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sensors for Vital Signs Monitoring
| Sensors for Vital Signs Monitoring |
| Autore | Yang Jong-Ryul |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (141 p.) |
| Soggetto topico |
Energy industries & utilities
Technology: general issues |
| Soggetto non controllato |
3D+t modeling
4D CT absolute distance measurement cage deformation cardiopulmonary resuscitation (CPR) carotid blood flow (CBF) cerebral circulation compensatory reserve cone-beam computed tomography continuous-wave radar coronary artery cross-correlation CW radar decomposition level denoising doppler cardiogram electroencephalogram (EEG) envelope detection frequency discrimination frequency-shift keying radar heartbeat accuracy improvement heartbeat detection hemodynamic data low-contrast object medical monitoring modulation transfer function mother wavelet function noise power spectrum non-rigid registration optimal filter passenger detection physiology radar feature vector radar machine learning radar signal processing signal decomposition signal-to-noise-ratio ultra-high resolution vital signs vital-signs monitoring wavelet transform wearable sensors |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557554603321 |
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Yang Jong-Ryul
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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