this paper describes the research carried out in the 3 years of the PhD in Information and Communication technology for Health (ICth) on the topic of Cognitive computing Systems for Neuroncology at the Department of E...
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Compared withthe traditional Yee grid FDTD, the hexagonal grid is more complicated to implement the absorbing boundary. Here, we present an absorbing boundary approach for the finite-difference time-domain (FDTD) met...
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In this paper, we propose a unique LiShu Guo-shaped patch antenna design. the antenna features an L-shaped feeding probe and utilizes characteristic mode analysis to optimize the feeding position, resulting in the cre...
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this paper presents the development and implementation of an innovative electric wheelchair which is controlled using Tongue Drive System (TDS) utilizing magnetic Hall Effect sensors. Traditional wheelchair control me...
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In this paper, we have designed a system for contactless temperature measurement and identification device by using Arduino and Maix Dock M1W Development Board. the system mainly consists of a temperature measurement ...
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In Dempster-Shafer evidence theory, basic probability assignment (BPA) plays a important role in representing uncertain and unknown information. How to generate highquality BPA is essential, which can promote further ...
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Fatigue driving is a longstanding hazard in the field of road traffic safety. the factors such as longtime driving, lack of sleep, and work pressure result in driver fatigue which impair driver ability to detect and r...
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ISBN:
(纸本)9798400708305
Fatigue driving is a longstanding hazard in the field of road traffic safety. the factors such as longtime driving, lack of sleep, and work pressure result in driver fatigue which impair driver ability to detect and respond to sudden situations, and increase the risk of serious traffic accidents. this paper proposes a novel method for driver fatigue detection based on YOLOv5 algorithm. Firstly, a fatigue detection dataset is collected and created for this purpose. the YOLOv5 algorithm is then utilized to detect driver multiple facial fatigue features such as yawning, closed eyes, and head nodding, and the driver's fatigue status is determined based on the statistical frequency of the three fatigue characteristics. Furthermore, the YOLOv5 trained model is deployed on the Tensor Processing Unit (TPU) computing device. the experimental results demonstrated that the YOLOv5 algorithm model achieved a mAP@0.5 of 98.51% when evaluated on the validation sets. the driver fatigue detection system deployed on the TPU computing device can perform at 25 fps with an average accuracy of 93.8%. this system can monitor the driver's status in real time in practical applications, timely remind drivers to pay attention to safety, and help reduce the risk of traffic accidents caused by fatigue driving.
Over the last decade, Bitcoin and Ethereum have become cryptocurrencies that have attracted the attention of the financial world withtheir potential for business transactions and the use of new blockchain technology....
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this paper introduces a planar fixed-frequency beam-scanning antenna using a controllable slotted substrate integrated waveguide (SIW). the controllable SIW incorporates subwavelength slots and varactor diodes to enab...
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the purpose of this work is to build a salary prediction system using machine learning techniques. the experiments are done using the data from 1994 census database which has 32,561 records of employee data. the techn...
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