This study used a water-soluble inclusion complex, fullerene/ß-cyclodextrins(C60/ß-CDs), as additive abrasive with conventional SiO2 slurry for the chemical mechanical polishing/planarization(CMP) of the sil...
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Because of the advancement of new technologies and the popularity of mobile devices, this study was designed to identify whether apps have a representative influence on companies' brand image. To fulfill this obje...
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The electrically evoked compound action potential (ECAP) has been used in various clinical studies and has become a key physiological signal for cochlear implants (CI). This study used four sensing electrodes to recor...
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ISBN:
(数字)9798350348958
ISBN:
(纸本)9798350348965
The electrically evoked compound action potential (ECAP) has been used in various clinical studies and has become a key physiological signal for cochlear implants (CI). This study used four sensing electrodes to record ECAP signals based on the alternating polarity approach. An electrical field imaging (EFI) result based on the finite element method was used to obtain the interface impedance, then ECAP simulation results were computed and compared with a patient's clinical ECAP measurements. Preliminary modeling results show that the interface impedance obtained by this EFI-based technique can improve the simulation accuracy of the ECAP model. The ECAP modeling result will be compared with clinical ECAP measurements to validate the model in the full paper.
Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynam...
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Diagnosis and evaluation of Parkinson’s disease (PD) by clinicians is normally dependent on several established clinical criteria. Measuring the severity level according to these criteria depends heavily on the docto...
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The development of a traffic monitoring system to analyze and improve traffic flow is fundamental to the efforts of efficient traffic management, enabling smarter and safer transportation infrastructure and contributi...
The development of a traffic monitoring system to analyze and improve traffic flow is fundamental to the efforts of efficient traffic management, enabling smarter and safer transportation infrastructure and contributing overall to applications of intelligent transportation systems (ITS). There are real-time traffic monitoring systems for tracking and analyzing; however, the accuracy of vehicle detection and classification in these systems can be influenced by adverse weather conditions, resulting in so gaps in the traffic data. In this paper, we propose a cloud-based system that supports vehicle detection using machine learning models, presents the detected vehicles in the real-time stream and statistical data of traffic flow, and supports machine learning model and camera management. Data collection using a combination of 2D and 3D cameras was experimented with various configurations and locations to ensure data integrity and clarity. The collected datasets were labeling for 2D and 3D machine learning model training, checking model accuracy and creating a frontend/backend to create a web application to interface data stats/stream. We successfully deployed a website that can stream a 2D live video of traffic flow, achieving 2D vehicle detection model accuracy of 89%, and data is collected in real-time and streamed onto AWS using a C++ plugin for Amazon Kinesis Video Streams. We also store statistical data in a database that is then visualized for traffic flow analysis.
In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are shaping the future of the global-scale quantum Internet. This paper investigates the collaboration among satellite, aerial, and terre...
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Injuries and sudden health crises at home are relatively frequent and urgently require medical expertise. This study presents an innovative application of vision-language models (VLMs) to enhance healthcare by improvi...
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Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild wo...
Video-based gait recognition has achieved impressive results in constrained scenarios. However, visual cameras neglect human 3D structure information, which limits the feasibility of gait recognition in the 3D wild world. Instead of extracting gait features from images, this work explores precise 3D gait features from point clouds and proposes a simple yet efficient 3D gait recognition framework, termed LidarGait. Our proposed approach projects sparse point clouds into depth maps to learn the representations with 3D geometry information, which outperforms existing point-wise and camera-based methods by a significant margin. Due to the lack of point cloud datasets, we build the first large-scale LiDAR-based gait recognition dataset, SUSTech1K, collected by a LiDAR sensor and an RGB camera. The dataset contains 25,239 sequences from 1,050 subjects and covers many variations, including visibility, views, occlusions, clothing, carrying, and scenes. Extensive experiments show that (1) 3D structure information serves as a significant feature for gait recognition. (2) LidarGait outperforms existing point-based and silhouette-based methods by a significant margin, while it also offers stable cross-view results. (3) The LiDAR sensor is superior to the RGB camera for gait recognition in the outdoor environment. The source code and dataset have been made available at https://***.
For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice ...
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