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Deep Learning Applications with Practical Measured Results in Electronics Industries
| Deep Learning Applications with Practical Measured Results in Electronics Industries |
| Autore | Kung Hsu-Yang |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (272 p.) |
| Soggetto non controllato |
faster region-based CNN
visual tracking intelligent tire manufacturing eye-tracking device neural networks A* information measure oral evaluation GSA-BP tire quality assessment humidity sensor rigid body kinematics intelligent surveillance residual networks imaging confocal microscope update mechanism multiple linear regression geometric errors correction data partition Imaging Confocal Microscope image inpainting lateral stage errors dot grid target K-means clustering unsupervised learning recommender system underground mines digital shearography optimization techniques saliency information gated recurrent unit multivariate time series forecasting multivariate temporal convolutional network foreign object data fusion update occasion generative adversarial network CNN compressed sensing background model image compression supervised learning geometric errors UAV nonlinear optimization reinforcement learning convolutional network neuro-fuzzy systems deep learning image restoration neural audio caption hyperspectral image classification neighborhood noise reduction GA MCM uncertainty evaluation binary classification content reconstruction kinematic modelling long short-term memory transfer learning network layer contribution instance segmentation smart grid unmanned aerial vehicle forecasting trajectory planning discrete wavelet transform machine learning computational intelligence tire bubble defects offshore wind multiple constraints human computer interaction Least Squares method |
| ISBN | 3-03928-864-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404080403321 |
Kung Hsu-Yang
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)
| Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) |
| Autore | Tang Bo |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (344 p.) |
| Soggetto non controllato |
FPGA
recurrence plot (RP) residual learning neural networks driver monitoring navigation depthwise separable convolution optimization dynamic path-planning algorithms object tracking sub-region cooperative systems convolutional neural networks DSRC VANET joystick road scene convolutional neural network (CNN) multi-sensor p-norm occlusion crash injury severity prediction deep leaning squeeze-and-excitation electric vehicles perception in challenging conditions T-S fuzzy neural network total vehicle mass of the front vehicle electrocardiogram (ECG) communications generative adversarial nets camera adaptive classifier updating Vehicle-to-X communications convolutional neural network predictive Geobroadcast infinity norm urban object detector machine learning automated-manual transition red light-running behaviors photoplethysmogram (PPG) panoramic image dataset parallel architectures visual tracking autopilot ADAS kinematic control GPU road lane detection obstacle detection and classification Gabor convolution kernel autonomous vehicle Intelligent Transport Systems driving decision-making model Gaussian kernel autonomous vehicles enhanced learning ethical and legal factors kernel based MIL algorithm image inpainting fusion terrestrial vehicle driverless drowsiness detection map generation object detection interface machine vision driving assistance blind spot detection deep learning relative speed autonomous driving assistance system discriminative correlation filter bank recurrent neural network emergency decisions LiDAR real-time object detection vehicle dynamics path planning actuation systems maneuver algorithm autonomous driving smart band the emergency situations two-wheeled support vector machine model global region biological vision automated driving |
| ISBN | 3-03921-376-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Machine Learning and Embedded Computing in Advanced Driver Assistance Systems |
| Record Nr. | UNINA-9910367757403321 |
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Tang Bo
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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