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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 |
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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Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities
| Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities |
| Autore | Alsina-Pagès Rosa Ma |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (240 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
motor
mechanical fault detection RMS sound drill safety pattern bearing fan shaft road traffic noise noise events intermittency ratio urban sites classification noise monitoring real-time noise mapping wireless sensor networks noise mapping noise mitigation DYNAMAP project outdoors noise sound level meter digital signal processing multirate filters dynamic noise maps anomalous noise events individual impact aggregate impact WASN sensor nodes urban and suburban environments noise control sensor concept road traffic noise model dynamic model acoustics smart cities deep learning long short-term memory temporal forecast p-u sensor p-p sensor noise Adrienne stabilization damping acoustic impedance road surfaces low-cost sensors networks noise sources regression analysis contribution analysis vehicle interior noise acoustic sensor design acoustic event detection map generation public information END CNOSSOS-EU |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557779403321 |
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Alsina-Pagès Rosa Ma
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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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