数字混沌扩频序列的盲估计

混沌序列具有非线性、宽带类噪声、大的码族、任意长的周期且容易产生的特性,因此在扩频通信系统中很有实用价值。本文将文献[1]中对伪随机扩频序列盲估计的方法推广应用到混沌扩频通信中,并对其不足提出了改进方法。在只知道扩频码周期而无其它任何先验知识的条件下,利用特征值分析的方法可以对数字混沌直扩序列进行盲估计,仿真实验证明了该方法的有效性。     

Numerical experiments of preconditioned Krylov subspace methods solving the dense non-symmetric systems arising from BEM

Discretization of boundary integral equations leads, in general, to fully populated non-symmetric linear systems of equations. An inherent drawback of boundary element method (BEM) is that, the non-symmetric dense linear systems must be solved. For large-scale problems, the direct methods require expensive computational cost and therefore the iterative methods are perhaps more preferable. This paper studies the comparative performances of preconditioned Krylov subspace solvers as bi-conjugate gradient (Bi-CG), generalized minimal residual (GMRES), conjugate gradient squared (CGS), quasi-minimal residual (QMR) and bi-conjugate gradient stabilized (Bi-CGStab) for the solution of dense non-symmetric systems. Several general preconditioners are also considered and assessed. The results of numerical experiments suggest that the preconditioned Krylov subspace methods are effective approaches solving the large-scale dense non-symmetric linear systems arising from BEM.     

基于下垂控制模式的独立微电网小干扰稳定分析

从电力系统的运行原理出发,开展独立微电网系统运行稳定性研究对于促进分布式发电技术的发展具有重要意义。针对独立微电网系统运行稳定性问题,以李雅普若夫判据和特征值分析法为基础,对基于下垂控制模式的独立微电网系统的小扰动问题展开分析。首先,构建基于下垂控制模式的独立微电网系统数学建模,并基于线性化理论进行小扰动线性化分析,得到系统小扰动线性化状态矩阵;其次,基于李雅普若夫判据理论,对系统小扰动线性化状态矩阵的特征值进行分析,并逐一改变系统各个参数取值范围得到状态矩阵特征值变化的根轨迹;最后,基于状态矩阵特征值变化根轨迹确定系统各参数初步优化结果,并对比各参数优化前后的系统状态矩阵特征值分布情况,理论分析及实验验证表明初步优化后的系统运行可靠性增强。该研究成果可为微电网控制参数的优化选取提供思路和方法。     

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.     

一种模式匹配快速算法

在定义模式串的特征值之后,给出了判断两等长串匹配的必要条件以及两相邻子串的特征值之间的递推关系.在此基础上,提供一种模式匹配快速算法,其时间复杂度可达O(n).该算法彻底避免了回溯现象,执行效率要比RK算法高.     

Instability in leapfrog and forward–backward schemes: Part II: Numerical simulations of dam break

Following Sun’s approach [17], Shuman smoothing instead of conventional diffusion terms is used in a simple two-time step semi-implicit finite volume scheme to simulate dam break. When the Courant number is less than one, the absolute value of amplification factor of the 1D linearized shallow-water equations is 1 in this new scheme. Compared with the characteristic-based semi-Lagrangian schemes and the Riemann solver, this scheme produces excellent results of free water depth and speed of the shock. Numerical simulations show that the water inside the dam initially moves away radially until water almost depletes near the center; then the water moves back to the center and forms a vertical water column there. This paper proves that Shuman smoothing can be used not only in the linearized shallow-water equations discussed in Sun [17] but also in the nonlinear wave equations to control instability around shocks.     

MR-SMP: A symmetric tridiagonal eigensolver for multi-core architectures

The computation of eigenvalues and eigenvectors of symmetric tridiagonal matrices arises frequently in applications; often as one of the steps in the solution of Hermitian and symmetric eigenproblems. While several accurate and efficient methods for the tridiagonal eigenproblem exist, their corresponding implementations usually target uni-processors or large distributed memory systems. Our new eigensolver MR³-SMP is instead specifically designed for multi-core and many-core general purpose processors, which today have effectively replaced uni-processors. We show that in most cases MR³-SMP is faster and achieves better speedups than state-of-the-art eigensolvers for uni-processors and distributed-memory systems.     

Prüfer method for periodic and semi-periodic sturm-liouville eigenvalue problems ∗

In reference [19], the authors developed a shooting algorithm for Sturm-Liouville eigenvalue problems associated with periodic and semi-periodic boundary conditions. The technique is based on the application of the Floquet theory, and it has proven to be efficient for computing eigenvalues. However, the performance of this technique depends upon the choice of the starting eigenvalues. In the present paper, we continue our study and employ the Prüfer method. An attractive property of this method is that eigenvalues can usually be accurately computed even when no information on the eigenvalue distribution is provided. Sufficient conditions for convergence, error bounds and a procedure to improve the stability are discussed. Some numerical examples are given to illustrate the effectiveness of the proposed method.     

对称矩阵的改进Cholesky分解在特征值问题中的应用

利用对称短阵的改进Cholesky分解，估计在给定区间(a，b)内对称短阵A特征值的个数,进而发展成一种求对称短阵特征值的近似算法。