移动场景下的智能信道预测方法

针对无线通信信道估计老化问题,本文提出了一种基于卷积神经网络的信道预测方法,该方法通过联合轨迹预测和信道重构实现。首先,采用卷积神经网络学习从规划路线和移动终端所在位置到移动方向映射,进而预测出轨迹上多个目标位置;其次,采用卷积神经网络学习从目标位置附近K个位置项的信道,到目标位置信道间映射,用于实现预测轨迹的信道估计。本文利用Wireless InSite为移动方向预测和信道重构模型的训练及测试生成充足的样本,包括规划路线和通过射线跟踪方法获取的信道等。仿真结果表明,本文所提出的方法能有效地估计目标位置的信道特性,与K值较小的K-近邻插值方法和基于全连接神经网络的信道预测方法相比,其信道估计总相对误差更低且鲁棒性较好。     

Recognition of ClO－ and Cellular Imaging with Xanthene-based Fluorescent Probes北大核心CSCD

Selective recognition of hypochlorite ion（ClO－）in intracellular biological environment is important for research and application in cellular physiology and pathology. A novel fluorescent molecule, 6-bis （dimethylamino）-9-（1-（3-methyl-benzothiazole）-trimethyl）-xanthene（R-1）,is designed and synthesized with a combination of dimethylamino-substituted xanthene and benzothiazole segments through continuous carbon-carbon double bonds. In the presence of ClO－ ,the fluorescence emission of R-1 at 600 nm is significantly enhanced（the quantum yield is as high as 20. 3%）,and other reactive oxygen species do not affect the recognition of ClO－. Fluorescence imaging experiments demonstrate that probe R-1 can detect ClO－ in human lung adenocarinoma （A549）cells and emit red fluorescence,indicating the potential application of R-1 in ClO－ biological imaging. © 2022, Science Press (China). All rights reserved.