ORCID Profile
0000-0001-7408-6052
Current Organisation
University of Queensland
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Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4388946
Publisher: Elsevier BV
Date: 12-2019
Publisher: The Electromagnetics Academy
Date: 2018
Publisher: AIP Publishing
Date: 04-2020
DOI: 10.1063/5.0004211
Abstract: This study presents a fast-computed analytical model to compute the magnetic field generated by a cone with arbitrarily uniform magnetization. Based on the charge model, algebraic and geometrical analyses, the analytical expressions of the axial, azimuthal, and radial components of the magnetic field produced by the cone at any given point in space are derived. The model was in excellent agreement with the Finite Element Model (FEM), as is proved in this research. It is demonstrated that it took an average of less than 1 ms to execute each expression at a single point on the modern personal computer, whereas the FEM simulation consumed 779 s. Moreover, using the developed model, the magnetic field distribution of a cone used in magnetic resonance imaging with varying magnetization is analyzed. As a result, the distributions of the axial and radial components are a cosine-like wave with opposite directions, except for the case where the slope angle of the magnetization is equal to π/2 or, in other words, the magnetization is axial. On the other hand, the distribution of the azimuthal component is a sine-like wave, except for the noted case where the magnetization is axial.
Publisher: World Scientific Pub Co Pte Ltd
Date: 27-02-2023
DOI: 10.1142/S0217984923400110
Abstract: In this paper, an efficient approach for traffic sign detection, classification, and localization is introduced. An integration of the tiling technique with the RetinaFace model and the MobileNetV1-SSD was proposed for traffic detection and classification. The combination of the extraction of the region of interest with a depth estimation model, namely the AANet+, was introduced for the traffic sign localization task. All models were developed based on the transfer learning technique and existing datasets, including the Zalo and ApolloScape datasets. The accuracy and the computational efficiency of the approach are evaluated. Experimental results show that the novel traffic sign detection and classification method outperform the existing ones with an average precision of 77.2%. Moreover, the computing performance achieved is 5 FPS on Jetson Nano and 50 FPS on Jetson Xavier. For the traffic sign localization, the relative error can be reduced to 3.78%, and the computing time is 1.965 s air on Jetson Nano and 0.128 s air on Jetson Xavier, while the existing method has lower accuracy and is more time-consuming.
Publisher: The Electromagnetics Academy
Date: 2020
Publisher: The Electromagnetics Academy
Date: 2019
Publisher: Elsevier BV
Date: 10-2022
No related grants have been discovered for Van Tai Nguyen.