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Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

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ISBN: 9783039213757 9783039213764 Year: Pages: 344 DOI: 10.3390/books978-3-03921-376-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR

Keywords

Vehicle-to-X communications --- Intelligent Transport Systems --- VANET --- DSRC --- Geobroadcast --- multi-sensor --- fusion --- deep learning --- LiDAR --- camera --- ADAS --- object tracking --- kernel based MIL algorithm --- Gaussian kernel --- adaptive classifier updating --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- joystick --- two-wheeled --- terrestrial vehicle --- path planning --- infinity norm --- p-norm --- kinematic control --- navigation --- actuation systems --- maneuver algorithm --- automated driving --- cooperative systems --- communications --- interface --- automated-manual transition --- driver monitoring --- visual tracking --- discriminative correlation filter bank --- occlusion --- sub-region --- global region --- autonomous vehicles --- driving decision-making model --- the emergency situations --- red light-running behaviors --- ethical and legal factors --- T-S fuzzy neural network --- road lane detection --- map generation --- driving assistance --- autonomous driving --- real-time object detection --- autonomous driving assistance system --- urban object detector --- convolutional neural networks --- machine vision --- biological vision --- deep learning --- convolutional neural network --- Gabor convolution kernel --- recurrent neural network --- enhanced learning --- autonomous vehicle --- crash injury severity prediction --- support vector machine model --- emergency decisions --- relative speed --- total vehicle mass of the front vehicle --- perception in challenging conditions --- obstacle detection and classification --- dynamic path-planning algorithms --- drowsiness detection --- smart band --- electrocardiogram (ECG) --- photoplethysmogram (PPG) --- recurrence plot (RP) --- convolutional neural network (CNN) --- squeeze-and-excitation --- residual learning --- depthwise separable convolution --- blind spot detection --- machine learning --- neural networks --- predictive --- vehicle dynamics --- electric vehicles --- FPGA --- GPU --- parallel architectures --- optimization --- panoramic image dataset --- road scene --- object detection --- deep learning --- convolutional neural network --- driverless --- autopilot --- deep leaning --- object detection --- generative adversarial nets --- image inpainting --- n/a

Deep Learning Applications with Practical Measured Results in Electronics Industries

Authors: --- --- ---
ISBN: 9783039288632 / 9783039288649 Year: Pages: 272 DOI: 10.3390/books978-3-03928-864-9 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2020-06-09 16:38:57
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Abstract

This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

Keywords

computational intelligence --- offshore wind --- forecasting --- machine learning --- neural networks --- neuro-fuzzy systems --- humidity sensor --- data fusion --- nonlinear optimization --- multiple linear regression --- GSA-BP --- geometric errors correction --- kinematic modelling --- lateral stage errors --- Imaging Confocal Microscope --- K-means clustering --- data partition --- Least Squares method --- deep learning --- multivariate time series forecasting --- multivariate temporal convolutional network --- CNN --- hyperspectral image classification --- information measure --- transfer learning --- neighborhood noise reduction --- visual tracking --- update occasion --- update mechanism --- background model --- network layer contribution --- saliency information --- geometric errors --- rigid body kinematics --- lateral stage errors --- imaging confocal microscope --- MCM uncertainty evaluation --- dot grid target --- smart grid --- foreign object --- binary classification --- convolutional network --- image inpainting --- content reconstruction --- instance segmentation --- underground mines --- intelligent surveillance --- residual networks --- compressed sensing --- image compression --- image restoration --- discrete wavelet transform --- intelligent tire manufacturing --- digital shearography --- faster region-based CNN --- tire bubble defects --- tire quality assessment --- unmanned aerial vehicle --- UAV --- trajectory planning --- GA --- A* --- multiple constraints --- recommender system --- human computer interaction --- eye-tracking device --- deep learning --- oral evaluation --- generative adversarial network --- neural audio caption --- gated recurrent unit --- long short-term memory --- deep learning --- machine learning --- supervised learning --- unsupervised learning --- reinforcement learning --- optimization techniques

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