轨道交通中基于通信的列车控制(communication based train control, CBTC)系统采用多输入多输出(multiple-input multiple-output, MIMO)信道的LTE-M(long term evolution-metro)技术以确保可靠的车地通信,因此需要研究地铁隧道环境下M...轨道交通中基于通信的列车控制(communication based train control, CBTC)系统采用多输入多输出(multiple-input multiple-output, MIMO)信道的LTE-M(long term evolution-metro)技术以确保可靠的车地通信,因此需要研究地铁隧道环境下MIMO信道的统计特性.针对隧道内2×2 MIMO信道在列车移动特别是列车经过基站的过程中呈现的非稳态特性,建立了隧道环境下基于几何的单反射(geometrically based single bounce,GBSB)时变MIMO信道模型.推导了复信道增益以及时变统计特性,包括信道时变自相关函数(autocorrelation function, ACF)、信道时变功率谱密度(power spectral density, PSD)、信道时变互相关函数(cross correlation function, CCF)等.分析了列车运行速度、天线间距等参数对信道相关性的影响.结果表明,信道时变自相关性随列车运行速度增加而降低.当天线间隔增大,信道互相关性呈现波动性下降.仿真结果与实测结果相一致,验证了所提出模型的有效性.展开更多
A neural statistical approach to the reconstruction of novel viewpoint image us ing general regression neural networks(GRNN) is presented. Different color value will be obtained by watching the same surface point of a...A neural statistical approach to the reconstruction of novel viewpoint image us ing general regression neural networks(GRNN) is presented. Different color value will be obtained by watching the same surface point of an object from different viewpoints due to specular reflection, and the difference is related to the pos ition of viewpoint. The relationship between the position of viewpoint and the c olor of image is non linear, neural network is introduced to make curve fitting , where the inputs of neural network are only a few calibrated images with obvio us specular reflection. By training the neural network, network model is obtaine d. By inputing an arbitrary virtual viewpoint to the model, the image of the vir tual viewpoint can be computed. By using the method presented here, novel viewpo int image with photo realistic property can be obtained, especially images with obvious specular reflection can accurately be generated. The method is an image based rendering method, geometric model of the scene and position of lighting are not needed.展开更多
Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance ch...Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters.Because of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies.Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to retrieve.This study focuses on the microscopic structure of row crops.Regarding the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row crops.Meanwhile,the formula of clumping index is deduced theoretically.Then taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row crops.It is of great importance to introduce clumping index into BRDF model.In order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2008 are used as the validation dataset for the simulated values.The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops.In addition,the model is simple and easy to retrieve.In general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy BRDF.It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.展开更多
文摘A neural statistical approach to the reconstruction of novel viewpoint image us ing general regression neural networks(GRNN) is presented. Different color value will be obtained by watching the same surface point of an object from different viewpoints due to specular reflection, and the difference is related to the pos ition of viewpoint. The relationship between the position of viewpoint and the c olor of image is non linear, neural network is introduced to make curve fitting , where the inputs of neural network are only a few calibrated images with obvio us specular reflection. By training the neural network, network model is obtaine d. By inputing an arbitrary virtual viewpoint to the model, the image of the vir tual viewpoint can be computed. By using the method presented here, novel viewpo int image with photo realistic property can be obtained, especially images with obvious specular reflection can accurately be generated. The method is an image based rendering method, geometric model of the scene and position of lighting are not needed.
基金supported by National Natural Science Foundation of China (Grant Nos. 91025006, 40730525, 40871186 and 40801125)Special Funds for National High Technology Research and Development Program of China (Grant Nos. 2009AA12Z143 and 2009A122103)+1 种基金Major State Basic Research Project (973) (Grant No. 2007CB714402)"Simultaneous Remote Sensing and Ground-based Experiment in Heihe River Basin and Comprehensive Platform Construction" in the Chinese Academy of Sciences’ Action-Plan for West Development (the second phase) (Grant No. KZCX2-XB2-09)
文摘Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters.Because of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies.Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to retrieve.This study focuses on the microscopic structure of row crops.Regarding the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row crops.Meanwhile,the formula of clumping index is deduced theoretically.Then taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row crops.It is of great importance to introduce clumping index into BRDF model.In order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2008 are used as the validation dataset for the simulated values.The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops.In addition,the model is simple and easy to retrieve.In general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy BRDF.It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.
