作者： Zhang, Yuxiang¹; Zhang, Mengmeng¹; Li, Wei¹; Wang, Shuai²; Tao, Ran¹ （¹School of Information and Electronics, Beijing Institute of Technology, Beijing Key Laboratory of Fractional Signals and Systems, Beijing; 100081, China;²Department of Chemistry, The University of Hong Kong, Hong Kong）

出处： arXiv 2022

作者： Wang, Jiaxin¹;Liu, Xia¹;Yang, Qingsheng¹;Tao, Ran²;Li, Ying²;Ma, Lianhua³; （¹Department of Engineering Mechanics, Beijing University of Technology, Beijing, 100124, China;²Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing, 100081, China;³School of Quality and Technical Supervision, Hebei University, Baoding, 071002, China）

出处： Composites Science and Technology 2022 Vol.219

摘要： Metamaterial is a new type of structure constructed by artificially designed geometries and possessing extraordinary physical properties. However, the ...

作者： Qi, Jixiang¹²;Chen, Zihao¹²;Jiang, Peng¹²;Hu, Wenxia²;Wang, Yonghuan¹²;Zhao, Zeang²;Cao, Xiaofei¹²;Zhang, Shushan¹²;Tao, Ran²;Li, Ying¹²;Fang, Daining²; （¹State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing, 100081, China;²Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures, Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing, 100081, China）

出处： Advanced Science 2022 Vol.9 No.1

摘要： Active mechanical metamaterials (AMMs) (or smart mechanical metamaterials) that combine the configurations of mechanical metamaterials and the active ...

作者： Yang, Yixiao¹; Tao, Ran¹; Wei, Kaixuan²; Fu, Ying²; （¹School of Information and Electronics, Beijing Institute of Technology, Beijing, China;²School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China）

出处： Neurocomputing 2022 Vol.510 P203-217

摘要： Compressive imaging aims to recover a latent image from under-sampled measurements, suffering from a serious ill-posed inverse problem. Recently, deep ...

作者： Huang, Gaowa¹;Zhang, Feng¹;Tao, Ran¹; （¹Beijing Institute of Technology, Beijing Key Laboratory of Fractional Signals and Systems, School of Information and Electronics, Beijing; 100081, China）

出处： IEEE Signal Processing Letters 2022 Vol.29 P1823-1827

摘要： The short-time fractional Fourier transform (STFRFT) has been shown to be a powerful tool for processing signals whose fractional frequencies vary wit ...

作者： Zhao, Xudong^{1, 2};Zhang, Mengmeng¹;Tao, Ran¹;Li, Wei¹;Liao, Wenzhi^{3, 4};Philips, Wilfried²; （¹Beijing Institute of Technology, School of Information and Electronics, Beijing; 100081, China;²Ghent University, Image Processing and Interpretation, IMEC Research Group, Ghent; 9000, Belgium;³Flanders Make, Lommel; 3920, Belgium;⁴Ghent University, Department of TELIN, Ghent; 9000, Belgium）

出处： IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 Vol.15 P5721-5733

摘要： Limitation of labeled samples has always been a challenge for hyperspectral image (HSI) classification. In real remote sensing applications, we encoun ...

作者： Gao, Chenzhong^{1, 2}; Li, Wei^{1, 2}; Tao, Ran^{1, 2}; Du, Qian³; （¹The School of Information and Electronics, Beijing Institute Technology, Beijing; 100081, China;²Beijing Key Laboratory of Fractional Signals and Systems, Beijing Institute of Technology, Beijing; 100081, China;³The Department of Electrical and Computer Engineering, Mississippi State University, MS; 39762, United States）

出处： arXiv 2022

摘要： Multi-source image registration is challenging due to intensity, rotation, and scale differences among the images. Considering the characteristics and ...

作者： Zhang, Yunzuo¹; Guo, Kaina¹; Tao, Ran² （¹Shijiazhuang Tiedao University, Shijaizhuang; 050043, China;²Beijing Institute of Technology, Beijing; 100081, China）

出处： IEEE Signal Processing Letters 2022 P2308-2312

作者： Wang, Ao^{1, 2}; Li, Wei^{1, 3}; Huang, Zhanchao^{1, 2}; Wu, Xin⁴; Jie, Feiran³; Tao, Ran^{1, 2} （¹Beijing Institute of Technology, School of Information and Electronics, Beijing; 100811, China;²Beijing Key Laboratory of Fractional Signals and Systems, Beijing; 100081, China;³Aviation Industry Corporation of China Ltd., Luoyang Institute of Electro-Optical Equipment, Luoyang; 471000, China;⁴Beijing University of Posts and Telecommunications, School of Computer Science (National Pilot Software Engineering School), Beijing; 100876, China）

出处： IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 P10027-10040

发明人： 王楠，李伟，陶然

申请人： 北京理工大学

申请号： 202210497703.4

申请日期： 2022.05.09

摘要： 本发明提供一种基于时空距离矩阵分析的遥感时间序列图像变化检测方法，包括以下步骤：S1：构建时空距离矩阵M；S2：基于时空距离矩阵的拓扑图结构；S3：基于图模型的变化断点检测。本发明充分考虑了遥感影像高维时间序列的变化规律，将拓扑图结构和高维时间序列的时空距离矩阵融合，创建了时间序列拓扑图结构，提出了 ...