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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) / John Ball, Bo Tang
| Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) / John Ball, Bo Tang |
| Autore | Ball John |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (344 p.) |
| Soggetto topico | History of engineering and technology |
| 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 |
9783039213764
3039213768 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367757403321 |
Ball John
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Ubiquitous Technologies for Emotion Recognition
| Ubiquitous Technologies for Emotion Recognition |
| Autore | Banos Oresti |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (174 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
advanced driver-assistance systems (ADAS)
affective computing artificial intelligence brain computer interface (BCI) computer vision consumer preferences convolutional recurrent neural network deep convolutional neural network deep learning deep neural network (DNN) dimensionality reduction driver health risk driver stress state EEG signal electroencephalogram (EEG) emotion analysis emotion recognition facial expression analysis facial recognition Gaussian kernel human computer interaction human-robot interaction image processing image-mining intelligent speech signal processing IR imaging Laplacian prior line segment feature analysis logistic regression machine learning micro facial expressions mobile tool neuromarketing optical flow pattern recognition real-time processing self-management interview application social robots supervised learning support vector machine (SVR) texture descriptors thermal IR imaging video processing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557366303321 |
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Banos Oresti
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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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