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298 results on '"Ma, Mengyu"'

22. HiVision: Rapid Visualization of Large-Scale Spatial Vector Data

Author

Ma, Mengyu, Wu, Ye, Ouyang, Xue, Chen, Luo, Li, Jun, and Jing, Ning

Subjects
Computer Science - Graphics, Computer Science - Databases
Abstract

Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration., Comment: 19 pages 16 figures

Published
2020
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33. A Dynamic Transmission Design via Deep Multi-task Learning for Supporting Multiple Applications in Vehicular Networks

Author

He, Zhixing, Ma, Mengyu, Wang, Chao, Liu, Fuqiang, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin (Sherman), Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Gao, Honghao, editor, Wun, Jun, editor, Yin, Jianwei, editor, Shen, Feifei, editor, Shen, Yulong, editor, and Yu, Jun, editor

Published
2022
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39. Two-Stage Path Planning for Long-Distance Off-Road Path Planning Based on Terrain Data.

Author

Zheng, Xudong, Ma, Mengyu, Zhong, Zhinong, Yang, Anran, Chen, Luo, and Jing, Ning

Subjects
*POINT processes
Abstract

In the face of increasing demands for tasks such as mountain rescue, geological exploration, and military operations in complex wilderness environments, planning an efficient walking route is crucial. To address the inefficiency of traditional two-dimensional path planning, this paper proposes a two-stage path planning algorithm. First, an improved Probabilistic Roadmap (PRM) algorithm is used to quickly and roughly determine the initial path. Then, the morphological dilation is applied to process the grid points of the initial path, retaining the surrounding area of the initial path for a precise positioning of the search range. Finally, the idea of the A∗ algorithm is applied to achieve precise path planning in the refined search range. During the process of constructing the topology map, we utilized parallelization acceleration strategies to expedite the graph construction. In order to verify the effectiveness of the algorithm, we used terrain data to construct a wilderness environment model, and tests were conducted on off-road path planning tasks with different terrains and distances. The experimental results show a substantial enhancement in the computational efficiency of the proposed algorithm relative to the conventional A∗ algorithm by 30 to 60 times. [ABSTRACT FROM AUTHOR]

Published
2024
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45. IBA-Buffer: Interactive Buffer Analysis Method for Big Geospatial Data

Author

Wu, Ye, Ma, Mengyu, Chen, Luo, Zhong, Zhinong, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Tan, Ying, editor, and Shi, Yuhui, editor

Published
2019
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