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Minghao Liu, Jianxiang Wang, Qingxi Luo, Lingbo Sun and Enming Wang
Exploring spatial anisotropy features and capturing spatial interactions during urban change simulation is of great significance to enhance the effectiveness of dynamic urban modeling and improve simulation accuracy. Addressing the inadequacies of curren...
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Julien Mercier, Nicolas Chabloz, Gregory Dozot, Olivier Ertz, Erwan Bocher and Daniel Rappo
Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design im...
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Tongyang Xu, Yuan Liu, Zhaotai Ma, Yiqiang Huang and Peng Liu
As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogen...
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Liangkun Yu, Xiang Sun, Rana Albelaihi and Chen Yi
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the co...
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Tai Huang, Kuangang Fan, Wen Sun, Weichao Li and Haoqi Guo
This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) alg...
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