A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-...A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.展开更多
针对能量受限多中继网络的物理层安全中断性能问题,提出了基于无线携能通信(Simultaneous Wireless Information and Power Transfer,SWIPT)的多中继网络物理层安全传输方案。该方案在中继节点处采用中继选择策略以及混合功率和时间分...针对能量受限多中继网络的物理层安全中断性能问题,提出了基于无线携能通信(Simultaneous Wireless Information and Power Transfer,SWIPT)的多中继网络物理层安全传输方案。该方案在中继节点处采用中继选择策略以及混合功率和时间分割协议来实现网络安全速率最大化。对于提出的网络安全中断性能问题,首先计算出任意链路的安全中断概率闭合表达式,然后利用瑞利衰落信道的独立性和高斯切比雪夫等式,推导出了网络安全中断概率闭合表达式。为了进一步分析理论结果,推导出了在高发射功率下的网络安全中断概率闭合表达式。仿真结果验证了理论分析的正确性。仿真结果表明,增加网络中继节点数量可以显著地降低网络安全中断概率。与功率分割协议和时间切换协议相比,低发射功率下采用混合功率分割和时间转换协议能有效地提高网络安全中断性能。展开更多
Generalized linear mixed models(GLMMs)have been widely used in contemporary ecology studies.However,determination of the relative importance of collinear predictors(i.e.fixed effects)to response variables is one of th...Generalized linear mixed models(GLMMs)have been widely used in contemporary ecology studies.However,determination of the relative importance of collinear predictors(i.e.fixed effects)to response variables is one of the challenges in GLMMs.Here,we developed a novel R package,glmm.hp,to decompose marginal R2^(2)explained by fixed effects in GLMMs.The algorithm of glmm.hp is based on the recently proposed approach‘average shared variance’i.e.used for multivariate analysis.We explained the principle and demonstrated the use of this package by simulated dataset.The output of glmm.hp shows individual marginal R2^(2)s that can be used to evaluate the relative importance of predictors,which sums up to the overall marginal R2^(2).Overall,we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes.展开更多
基金Supported by the National Natural Science Foundation of China(60505004,60773061)~~
文摘A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.
文摘针对能量受限多中继网络的物理层安全中断性能问题,提出了基于无线携能通信(Simultaneous Wireless Information and Power Transfer,SWIPT)的多中继网络物理层安全传输方案。该方案在中继节点处采用中继选择策略以及混合功率和时间分割协议来实现网络安全速率最大化。对于提出的网络安全中断性能问题,首先计算出任意链路的安全中断概率闭合表达式,然后利用瑞利衰落信道的独立性和高斯切比雪夫等式,推导出了网络安全中断概率闭合表达式。为了进一步分析理论结果,推导出了在高发射功率下的网络安全中断概率闭合表达式。仿真结果验证了理论分析的正确性。仿真结果表明,增加网络中继节点数量可以显著地降低网络安全中断概率。与功率分割协议和时间切换协议相比,低发射功率下采用混合功率分割和时间转换协议能有效地提高网络安全中断性能。
基金This work was supported by the National Natural Science Foundation of China(32271551)the Metasequoia funding of Nanjing Forestry University.Conflict of interest statement.The authors declare that they have no conflict of interest.
文摘Generalized linear mixed models(GLMMs)have been widely used in contemporary ecology studies.However,determination of the relative importance of collinear predictors(i.e.fixed effects)to response variables is one of the challenges in GLMMs.Here,we developed a novel R package,glmm.hp,to decompose marginal R2^(2)explained by fixed effects in GLMMs.The algorithm of glmm.hp is based on the recently proposed approach‘average shared variance’i.e.used for multivariate analysis.We explained the principle and demonstrated the use of this package by simulated dataset.The output of glmm.hp shows individual marginal R2^(2)s that can be used to evaluate the relative importance of predictors,which sums up to the overall marginal R2^(2).Overall,we believe the glmm.hp package will be helpful in the interpretation of GLMM outcomes.
