This paper mainly researches on the signless laplacian spectral radius of bipartite graphs Dr(m1,m2;n1,n2). We consider how the signless laplacian spectral radius of Dr(m1,m2;n1,n2)?changes under some special cases. A...This paper mainly researches on the signless laplacian spectral radius of bipartite graphs Dr(m1,m2;n1,n2). We consider how the signless laplacian spectral radius of Dr(m1,m2;n1,n2)?changes under some special cases. As application, we give two upper bounds on the signless laplacian spectral radius of Dr(m1,m2;n1,n2), and determine the graphs that obtain the upper bounds.展开更多
In this article the computation of the Structured Singular Values (SSV) for the delay eigenvalue problems and polynomial eigenvalue problems is presented and investigated. The comparison of bounds of SSV with the well...In this article the computation of the Structured Singular Values (SSV) for the delay eigenvalue problems and polynomial eigenvalue problems is presented and investigated. The comparison of bounds of SSV with the well-known MATLAB routine mussv is investigated.展开更多
In this paper, we characterize the trees with the largest Laplacian and adjacency spectral radii among all trees with fixed number of vertices and fixed maximal degree, respectively.
将随机矩阵的非渐近谱理论应用到协作频谱感知中,对接收信号样本协方差矩阵的最大特征值和最小特征值进行分析,该文提出一种精确的最大最小特征值差(Exact Maximum Minimum Eigenvalue Difference,EMMED)的协作感知算法。对于任意给定...将随机矩阵的非渐近谱理论应用到协作频谱感知中,对接收信号样本协方差矩阵的最大特征值和最小特征值进行分析,该文提出一种精确的最大最小特征值差(Exact Maximum Minimum Eigenvalue Difference,EMMED)的协作感知算法。对于任意给定的协作用户个数K和采样点数N,首先推导了最大最小特征值之差的精确概率密度函数(Probability Density Function,PDF)和累积分布函数(Cumulative Distribution Function,CDF),然后利用该分布函数设计了所提算法的判决阈值。理论分析表明,EMMED算法的判决阈值较已有的渐进最大最小特征值差(Asymptotic Maximum Minimum Eigenvalue Difference,AMMED)检测更为精确,算法无需主用户信号特征并且能够对抗噪声不确定度影响。仿真结果表明,存在噪声不确定度的感知环境下,EMMED算法较已有的精确最大特征值(Exact Maximum Eigenvalue,EME)和EMMER等频谱感知算法具有更好的检测性能。展开更多
文摘This paper mainly researches on the signless laplacian spectral radius of bipartite graphs Dr(m1,m2;n1,n2). We consider how the signless laplacian spectral radius of Dr(m1,m2;n1,n2)?changes under some special cases. As application, we give two upper bounds on the signless laplacian spectral radius of Dr(m1,m2;n1,n2), and determine the graphs that obtain the upper bounds.
文摘In this article the computation of the Structured Singular Values (SSV) for the delay eigenvalue problems and polynomial eigenvalue problems is presented and investigated. The comparison of bounds of SSV with the well-known MATLAB routine mussv is investigated.
基金Foundation item： the National Natural Science Foundation of China （No. 10601001） the Natural Science Foundation of Anhui Province （Nos. 050460102+3 种基金 070412065） Natural Science Foundation of Department of Education of Anhui Province （No. 2005kj005zd） Project of Anhui University on leading Researchers Construction Foundation of Innovation Team on Basic Mathematics of Anhui University.
文摘In this paper, we characterize the trees with the largest Laplacian and adjacency spectral radii among all trees with fixed number of vertices and fixed maximal degree, respectively.
文摘将随机矩阵的非渐近谱理论应用到协作频谱感知中,对接收信号样本协方差矩阵的最大特征值和最小特征值进行分析,该文提出一种精确的最大最小特征值差(Exact Maximum Minimum Eigenvalue Difference,EMMED)的协作感知算法。对于任意给定的协作用户个数K和采样点数N,首先推导了最大最小特征值之差的精确概率密度函数(Probability Density Function,PDF)和累积分布函数(Cumulative Distribution Function,CDF),然后利用该分布函数设计了所提算法的判决阈值。理论分析表明,EMMED算法的判决阈值较已有的渐进最大最小特征值差(Asymptotic Maximum Minimum Eigenvalue Difference,AMMED)检测更为精确,算法无需主用户信号特征并且能够对抗噪声不确定度影响。仿真结果表明,存在噪声不确定度的感知环境下,EMMED算法较已有的精确最大特征值(Exact Maximum Eigenvalue,EME)和EMMER等频谱感知算法具有更好的检测性能。
