Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational res...Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe.展开更多
A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a tr...A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.展开更多
高精度车内定位技术是提供车内智能服务、进行车内用户行为习惯分析等应用的基础,有重要实用价值。低功耗蓝牙(BLE, bluetooth low energy)的RSSI(received signal strength indicator)值可用于定位系统的分析计算。针对无线信号传输易...高精度车内定位技术是提供车内智能服务、进行车内用户行为习惯分析等应用的基础,有重要实用价值。低功耗蓝牙(BLE, bluetooth low energy)的RSSI(received signal strength indicator)值可用于定位系统的分析计算。针对无线信号传输易受环境影响的问题,对车内定位提出了一种基于蓝牙多信道多RSSI值(multi-channel multi-RSSI values)的车内定位方法 VehLoc。接收端在传统的采集蓝牙RSSI信号的基础上,同时记录信号的信道来源,通过使用3个蓝牙信标在其不同信道的RSSI值对使用者终端在车内的位置进行粗细粒度与分布相结合的区域分析和位置判断。实验结果表明,VehLoc定位方法对车内5个主要位置的分类正确率均可达90%。展开更多
基金National Natural Science Foundations of China(41671452)China Postdoctoral Science Foundation Funded Project(2017M612510)。
文摘Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe.
基金supported by the National Key R&D Program of China under Grant 2021YFB1407001the National Natural Science Foundation of China (NSFC) under Grants 62001269 and 61960206006+2 种基金the State Key Laboratory of Rail Traffic Control and Safety (under Grants RCS2022K009)Beijing Jiaotong University, the Future Plan Program for Young Scholars of Shandong Universitythe EU H2020 RISE TESTBED2 project under Grant 872172
文摘A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.
文摘高精度车内定位技术是提供车内智能服务、进行车内用户行为习惯分析等应用的基础,有重要实用价值。低功耗蓝牙(BLE, bluetooth low energy)的RSSI(received signal strength indicator)值可用于定位系统的分析计算。针对无线信号传输易受环境影响的问题,对车内定位提出了一种基于蓝牙多信道多RSSI值(multi-channel multi-RSSI values)的车内定位方法 VehLoc。接收端在传统的采集蓝牙RSSI信号的基础上,同时记录信号的信道来源,通过使用3个蓝牙信标在其不同信道的RSSI值对使用者终端在车内的位置进行粗细粒度与分布相结合的区域分析和位置判断。实验结果表明,VehLoc定位方法对车内5个主要位置的分类正确率均可达90%。
