小波分式滤波器

1．引言用小波处理实际问题时，对称性具有重要的意义．如果小波不具有对称性，则在信号重构时可能导致失真．我们知道，用多项式滤波器构造的正交小波不具有对称性，这是一个重要的缺欠．本文讨论了分式滤波器，它作为多项式滤波器的最自然的推广和进展，且包含了B样条小波滤波器，可随意构造出对称性小波函数，对实际应用提供了有意义的构造性方法．在小波计算中，为了回避hllrl＊r逆变换，人们通常喜欢用Mdl时算法山，即对尺度函数方程为造迭代格式为了得到迭代收敛（n。、v）条件，通常把滤波器其中以及时，迭代格式（2）逐点收敛于尺…     

正交小波变换的向量空间算法

本文揭示了一个事实，小波不仅可构成Ｌ２空间中的正交基，小波分解与重构滤波还可产生Ｎ维空间中的正交基．在本文提出修改的小波变换算法之下，Ｎ点信号的小波变换等价于Ｎ维空间中的正交变换．用该算法进行信号或图象压缩，无需对信号或图象进行周期延拓，可严格地在Ｎ维空间中进行．     

小波空间的非均匀采样及其重构

信号的采样问题，就是探讨采样集满足什么条件时，能够重建信号，如何重建信号．对于f(x)∈L^2(R)，这里证明了，当采样集满足一定的条件时，适当选择小波基，可以重建信号，并且考虑了用迭代重构算法来重建信号，得到了具体的逼近精度．     

紧支撑二元正交小波滤波器的构造

高维小波是处理多维信号的有力工具，张量积小波有其自身的缺点．本文给出矩形域上二元正交小波滤波器的一种参数化构造算法，二元小波滤波器的这种构造方法使我们能更方便地研究非张量积的二元正交小波．最后给出算例．     

一种利用多通道小波变换去噪的算法

通过对多通道小波和小波变换以及奇异信号的小波变换特性的讨论和分析,提出了一种利用M道小波变换去噪的算法,并利用此算法对加噪信号进行了模拟实验,实验结果表明,该算法简便,易于编程实现且去噪效果理想。     

正交小波包的构造

本文给出尺度因子ａ＝４时正交小波包的构造，推广了［２，４］引入的正交小波包，并给出相应的分解与再构造算法．本文引入的正交小波包具有保持信号ｆ∈Ｌ^２的线性相位，也讨论了尺度因子ａ＝ｋ（ｋ∈Ｚ，ｋ≥２）正交小波的构造     

小波分析在测试信号分析中的应用

本文给出了小波的基本原理和构造方法以及小波分析的快速算法，将小波分析方法（ＷＡＭ）引入到非平稳冲击信号的分析中，作为小波分析应用实例，对汽车齿轮箱的断齿测信号进行了分析，给出了分析结果     

区间[-1，1]上的α尺度双正交多小波的构造

本文研究了一元α尺度紧支撑、双正交多小波的构造．在区间[-1，1]，给出了利用α尺度双正交尺度向量构造α尺度双正交多小波的推导过程得到了一种有效的小波构造算法，并给出了数值算例．     

Slepian半小波基函数及其在无线通讯信号概率密度估计中的应用

文[20]引进了Slepian半小波基函数并讨论了这组基在概率度估计核方法中的应用[21]，Slepian半小波基函数具有极好的性质．包括多重尺度结构和局部非负性．更值得指出的是．与Gauss核不同，Slepian函数是与无线信号类似的具有平滑谱的有限带宽函数．在所有相同带宽的函数中．Slepian函数在特定的时同区域上具有最大能量．在逼近具有平滑谱的无线信号中．这些特性使得Slepian半小波核与Gauss核以及其他小波基相比具有潜在的优越性．美中不足的是．和其他核密度估计一样．Slepian核密度估计的算法设计具有一定的挑战性．幸运的是．我们注意到Slepian核可以被表示成卷积形式．这一观察具有重要的计算意义．本文主要讨论Slephn核密度估计的应用及其计算．我们首先设计了基于离散卷积的算法并讨论了这一算法的有效性．在文章的结尾，以Slepian核密度估计作为具有平滑谱的远程信号的衰减包络的模型为例．我们考查了Slepian核及其算法的性质．为了尝试数学理论与应用的紧密联系，本文的数值试验不仅采用了模拟数据而且包括了从无线通讯用户的硬件直接采集的实际数据．     

小波分析方法及其应用

１．小波和小波分析冉启文．小波分析方法及其应用．数理统计与管理．１９９９，１８（１），５２～５５“小波分析（ＷａｖｅｌｅｔＡｎａｌｙｓｉｓ）”是一种新的分析方法，它是继Ｆｏｕｒｉｅｒ分析之后纯粹数学和应用数学完美结合的又一光辉典范。小波分析的产生、发...     

Multifractal characterization of cerebrovascular dynamics in newborn rats

In this paper we study the cerebrovascular dynamics in newborn rats using the wavelet-based multifractal formalism in order to reveal effective markers of early pathological changes in the macro- and microcirculation at the hidden stage of the development of intracranial hemorrhage (ICH). We demonstrate that the singularity spectrum estimated with the wavelet-transform modulus maxima (WTMM) technique allows clear characterization of a reduced complexity of blood flow dynamics and changes of the correlation properties at the transformation of normal physiological processes into pathological dynamics that are essentially different at the level of large and small blood vessels.     

Wavelet-multigrid analysis of squeeze film characteristics of poroelastic bearings

The combined study of effects of poroelasticity and couple stresses on the performance of lubrication aspects of porelastic bearings in general and that of synovial joints in particular are analyzed. The modified form of Reynolds equation which incorporates the elastic nature of cartilage and Stokes couple-stress fluid as lubricant is derived and solved using a recently developed wavelet multigrid method. This method has greatest advantage of minimizing the errors using wavelet transforms in obtaining accurate solution as grid size tends to zero. It is found that, 6–7 cycles are required to obtain a reasonably accurate solution in the multigrid scheme, whereas, only one cycle is required to obtain the solution in the wavelet-multigrid method. Also, matrix of discrete wavelet-transform acts as a natural preconditioner producing rapid convergence. It is observed that, the poroelastic bearings with couple-stress fluid as lubricant provide enhancement in pressure and ensure the increased load carrying capacity compared with viscous fluids. This may be one of the reasons in the efficient lubrication and proper functioning of synovial joints.     

Multiobjective linear programming with context-dependent preferences

Multiobjective linear programming algorithms are typically based on value maximization. However, there is a growing body of experimental evidence showing that decision maker behavior is inconsistent with value maximization. Tversky and Simonson provide an alternative model for problems with a discrete set of choices. Their model, called the componential context model, has been shown to capture observed decision maker behavior. In this paper, an interactive multiobjective linear programming algorithm is developed which follows the rationale of Tversky and Simonson. The algorithm is illustrated with an example solved using standard linear programming software. Finally, an interactive decision support system based on this algorithm is developed to field test the usefulness of the algorithm. Results show that this algorithm compares favorably with an established algorithm in the field.     

An Arnoldi-Extrapolation algorithm for computing PageRank

The Arnoldi-type algorithm proposed by Golub and Greif [G. Golub, C. Greif, An Arnoldi-type algorithm for computing PageRank, BIT 46 (2006) 759-771] is a restarted Krylov subspace method for computing PageRank. However, this algorithm may not be efficient when the damping factor is high and the dimension of the search subspace is small. In this paper, we first develop an extrapolation method based on Ritz values. We then consider how to periodically knit this extrapolation method together with the Arnoldi-type algorithm. The resulting algorithm is the Arnoldi-Extrapolation algorithm. The convergence of the new algorithm is analyzed. Numerical experiments demonstrate the numerical behavior of this algorithm.     

An algorithm for the capacitated vehicle routing problem with route balancing

In this paper, we present a multi-objective evolutionary algorithm for the capacitated vehicle routing problem with route balancing. The algorithm is based on a formerly developed multi-objective algorithm using an explicit collective memory method, namely the extended virtual loser (EVL). We adapted and improved the algorithm and the EVL method for this problem. We achieved good results with this simple technique. In case of this problem the quality of the results of the algorithm is similar to that of other evolutionary algorithms.     

An Adaptive Interacting Wang–Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area that we feel deserves much further attention. Toward this aim, this article proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang–Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains—a feature that both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance of this new parallel adaptive Wang–Landau algorithm is studied in several applications. Through a Bayesian variable selection example, we demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm’s adaptive proposal to induce mode-jumping is illustrated through a Bayesian mixture modeling application. Last, through a two-dimensional Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models. Supplemental materials are available online.     

DQAINF: an algorithm for automatic integration of infinite oscillating tails

We describe an automatic quadrature routine which is specifically designed for real functions having a certain type of infinite oscillating tails. The algorithm is designed to integrate a vector function over an infinite interval. A FORTRAN implementation of the algorithm is included.The algorithm combines an adaptive subdivision strategy with extrapolation and requires that the decay of all the functions in the vector is the same. The algorithm is based on the assumption that the oscillating behavior is due to a periodic function with the property that it changes sign when evaluated at points of distance half a period. We assume that this period is known. The algorithm offers a choice of three different quasi-linear extrapolation procedures, namely the Euler transformation and two modifications of this transformation.This work was supported by The Norwegian Research Council for Science and the Humanities.     

解一类Hessian矩阵亏秩的修正BFGs算法及其局部Q-超线性收敛性

本文对凸函数在极值点的Hessian矩阵是秩亏一的情况下,给出了一类求解无约束优化问题的修正BFGS算法.算法的思想是对凸函数加上一个修正项,得到一个等价的模型,然后简化此模型得到一个修正的BFGS算法.文中证明了该算法是一个具有超线性收敛的算法,并且把修正的BFGS算法同Tensor方法进行了数值比较,证明了该算法对求解秩亏一的无约束优化问题更有效.     

Computing integral solutions of complementarity problems

In this paper an algorithm is proposed to find an integral solution of (nonlinear) complementarity problems. The algorithm starts with a nonnegative integral point and generates a unique sequence of adjacent integral simplices of varying dimension. Conditions are stated under which the algorithm terminates with a simplex, one of whose vertices is an integral solution of the complementarity problem under consideration.     

最小风险证券组合的结构分析和迭代算法

本文分析了最小风险组合证券投资的结构特征，并提出了一种组合证券风险最小化的迭代算法，证明了其收敛性．该算法操作简单，且易于处理不允许卖空情况下的证券组合问题．