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Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Helena Lenasi | |
dc.date.accessioned | 2022-05-03T09:53:36Z | - |
dc.date.available | 2022-05-03T09:53:36Z | - |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9781789842531 9781789842548 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/147693 | - |
dc.language.iso | eng | |
dc.publisher | IntechOpen | |
dc.relation.isbasedon | 10.5772/intechopen.68656 | |
dc.relation.uri | https://www.intechopen.com/books/endothelial-dysfunction-old-concepts-and-new-challenges | |
dc.rights.uri | CC BY-NC-ND (姓名標示-非商業性-禁止改作) | |
dc.source | DOAB | |
dc.subject.classification | Diseases of the circulatory (Cardiovascular) system | |
dc.subject.other | Vehicle-to-X communications | |
dc.subject.other | Intelligent Transport Systems | |
dc.subject.other | VANET | |
dc.subject.other | DSRC | |
dc.subject.other | Geobroadcast | |
dc.subject.other | multi-sensor | |
dc.subject.other | fusion | |
dc.subject.other | deep learning | |
dc.subject.other | LiDAR | |
dc.subject.other | camera | |
dc.subject.other | ADAS | |
dc.subject.other | object tracking | |
dc.subject.other | kernel based MIL algorithm | |
dc.subject.other | Gaussian kernel | |
dc.subject.other | adaptive classifier updating | |
dc.subject.other | perception in challenging conditions | |
dc.subject.other | obstacle detection and classification | |
dc.subject.other | dynamic path-planning algorithms | |
dc.subject.other | joystick | |
dc.subject.other | two-wheeled | |
dc.subject.other | terrestrial vehicle | |
dc.subject.other | path planning | |
dc.subject.other | infinity norm | |
dc.subject.other | p-norm | |
dc.subject.other | kinematic control | |
dc.subject.other | navigation | |
dc.subject.other | actuation systems | |
dc.subject.other | maneuver algorithm | |
dc.subject.other | automated driving | |
dc.subject.other | cooperative systems | |
dc.subject.other | communications | |
dc.subject.other | interface | |
dc.subject.other | automated-manual transition | |
dc.subject.other | driver monitoring | |
dc.subject.other | visual tracking | |
dc.subject.other | discriminative correlation filter bank | |
dc.subject.other | occlusion | |
dc.subject.other | sub-region | |
dc.subject.other | global region | |
dc.subject.other | autonomous vehicles | |
dc.subject.other | driving decision-making model | |
dc.subject.other | the emergency situations | |
dc.subject.other | red light-running behaviors | |
dc.subject.other | ethical and legal factors | |
dc.subject.other | T-S fuzzy neural network | |
dc.subject.other | road lane detection | |
dc.subject.other | map generation | |
dc.subject.other | driving assistance | |
dc.subject.other | autonomous driving | |
dc.subject.other | real-time object detection | |
dc.subject.other | autonomous driving assistance system | |
dc.subject.other | urban object detector | |
dc.subject.other | convolutional neural networks | |
dc.subject.other | machine vision | |
dc.subject.other | biological vision | |
dc.subject.other | deep learning | |
dc.subject.other | convolutional neural network | |
dc.subject.other | Gabor convolution kernel | |
dc.subject.other | recurrent neural network | |
dc.subject.other | enhanced learning | |
dc.subject.other | autonomous vehicle | |
dc.subject.other | crash injury severity prediction | |
dc.subject.other | support vector machine model | |
dc.subject.other | emergency decisions | |
dc.subject.other | relative speed | |
dc.subject.other | total vehicle mass of the front vehicle | |
dc.subject.other | perception in challenging conditions | |
dc.subject.other | obstacle detection and classification | |
dc.subject.other | dynamic path-planning algorithms | |
dc.subject.other | drowsiness detection | |
dc.subject.other | smart band | |
dc.subject.other | electrocardiogram (ECG) | |
dc.subject.other | photoplethysmogram (PPG) | |
dc.subject.other | recurrence plot (RP) | |
dc.subject.other | convolutional neural network (CNN) | |
dc.subject.other | squeeze-and-excitation | |
dc.subject.other | residual learning | |
dc.subject.other | depthwise separable convolution | |
dc.subject.other | blind spot detection | |
dc.subject.other | machine learning | |
dc.subject.other | neural networks | |
dc.subject.other | predictive | |
dc.subject.other | vehicle dynamics | |
dc.subject.other | electric vehicles | |
dc.subject.other | FPGA | |
dc.subject.other | GPU | |
dc.subject.other | parallel architectures | |
dc.subject.other | optimization | |
dc.subject.other | panoramic image dataset | |
dc.subject.other | road scene | |
dc.subject.other | object detection | |
dc.subject.other | deep learning | |
dc.subject.other | convolutional neural network | |
dc.subject.other | driverless | |
dc.subject.other | autopilot | |
dc.subject.other | deep leaning | |
dc.subject.other | object detection | |
dc.subject.other | generative adversarial nets | |
dc.subject.other | image inpainting | |
dc.subject.other | n/a | |
dc.title | Endothelial Dysfunction - Old Concepts and New Challenges | |
dc.type | 電子教科書 | |
dc.classification | 醫學類 | |
Theme: | 教科書-醫學類 |
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