基于方差的奇异值分解域鲁棒零水印算法

针对已提出的奇异值分解域鲁棒水印算法往往将水印嵌入在奇异值矩阵的奇异值上,利用奇异值分解产生的两个正交矩阵提出一种基于方差的鲁棒零水印算法.将图像分割成互不重叠的子块,对每个子块进行奇异值分解,通过判断每个子块U矩阵第一列元素平方的方差与V矩阵第一列元素平方的方差之间的大小关系产生零水印序列.算法实质上没有对原始图像做...     

一种三维矩阵的奇异值分解算法

提出了一种三维矩阵的奇异值分解算法,该法适合处理具有三维矩阵数据的模式识别和分类模型等领域实际问题,该算法与二维矩阵奇异值分解算法类似,通过求解约束条件极值问题获得,该算法与已有的三线性分解算法比较,相对简单,计算速度快,适合处理数据量大的实际问题,该算法也很容易推广到更高维阵列的光谱数据。     

基于奇异值分解的数字波前拟合算法

提出了基于奇异值分解、采用泽尼克多项式拟合干涉波前的算法,该算法直接从线性方程组入手,对矩阵进行奇异值分解分解,在求解逆矩阵的过程中,采用阈值法对奇异值的倒数进行非常规的置换(∞→0),可直接得到系数向量。理论分析和实验证明,相对于传统的格拉姆施密特正交法,该算法可首先通过求解条件数判断线性方程矩阵是否奇异,对于解决病态方程组或奇异矩阵的最小二乘问题,有很好的稳定性,避免了由最小二乘构造的法方程组出现病态而引入的计算误差,且易于编程。     

Products, Coproducts, and Singular Value Decomposition

Products and coproducts may be recognized as morphisms in a monoidal tensor category of vector spaces. To gain invariant data of these morphisms, we can use singular value decomposition which attaches singular values, i.e. generalized eigenvalues, to these maps. We show, for the case of Grassmannand Clifford products, that twist maps significantly alter these data reducing degeneracies. Since non group like coproducts give rise to non classical behavior of the algebra of functions, makeing them noncommutative, we hope to be able to learn more about such geometries. A remarkabe thechnicallity is that the coproduct for positive singular values of eigenvectors in A yields directly corresponding eigenvectors in A⊗ A.     

基于集合经验模态分解和奇异值分解的激光雷达信号去噪

为了提高差分光柱像运动激光雷达(DCIM雷达)探测信噪比,提出了一种基于集合经验模态分解(EEMD)和奇异值分解(SVD)的混合降噪法.由EEMD获得含噪信号多层模态分量,根据各模态分量之间互相关系数的差分量确定主要噪声并予以滤除,利用奇异值分解识别模态分量中的残余噪声并提取有用信号.利用混合降噪法EEMD-SVD和EEMD方法分别对模拟仿真信号和实测激光雷达信号进行降噪处理.结果表明,当模拟噪声标准差在0.05~0.2之间时,相比与未降噪直接反演的湍流廓线,EEMD-SVD方法降噪后反演的湍流廓线信噪比提高了2.718 7dB~6.921 5dB,相应的EEMD方法提高了1.446 1dB~3.366 1dB;两个不同时段DCIM雷达降噪前后反演廓线与探空廓线的对比发现,EEMD-SVD和EEMD两种方法降噪后反演廓线较之于未降噪的反演廓线,信噪比最大提高了2.526 5dB和2.155 6dB.EEMD-SVD的降噪效果优于EEMD,能够更有效地识别和滤除噪声,较大地提高了原始信号的信噪比,获得更准确的大气湍流廓线反演结果.     

Jacobi Operators with Singular Continuous Spectrum

We consider a semilinear elliptic system which includes the model system of the W-strings in the cosmology as a special case. We prove existence of multi-string solutions and obtain precise asymptotic decay estimates near infinity for the solutions. As a special case of this result we solve an open problem posed by Yang [14]. AMS Subject Classifications (2000): 35J45, 35J60, 37K40, 70S15     

解析动态光谱的奇异值分解最小二乘法的改进

本文改进了处理动态光谱的奇异值分解最小二乘法（SVDLS），通过对模拟的蛋白质二级结构的动态圆二色谱和小牛胸腺DNA在电化学过程中动态紫外光谱的分析，表明该方法不仅给出动态过程中所包含的组分数，而且可同时获得各组分的光谱和各组分分数的分布曲线，是多种动态光谱数据处理的有效的无模型方法。     

基于奇异值分解和中位数绝对偏差的拉曼成像数据去噪方法

拉曼成像是一种无损伤、无需标记的光谱成像技术,它可以提供样品的不同组分的分子指纹信息以及空间分布特征,相比其他成像技术有着更重要的应用。但是拉曼散射的截面积小,灵敏度低,加上在很多实验中为了观察某些组分的动态分布而缩短扫描时间,导致最终得到的成像数据被噪声干扰,因此往往需要对信号进行去噪处理。常规的算法一般都是基于一个给定的数学模型对光谱进行处理,容易造成过滤波,使得信号失真;另外,在处理拉曼成像数据时,常规算法往往是对数据进行逐条光谱去噪,从而忽略了多条光谱之间的相互关系,导致最终的拉曼图像仍然受许多噪点干扰。因此,提出了一种基于奇异值分解和中位数绝对偏差的拉曼成像的信号处理方法,用于拉曼成像数据的去噪处理。该方法首先对拉曼成像数据进行奇异值分解,获得一个奇异值矩阵与两个正交矩阵;然后通过中位数绝对偏差法对奇异值矩阵中的各奇异值进行离群值检测,选取前k个被连续标记的离群值作为要保留的奇异值,并将其余的奇异值赋值为零,得到新的奇异值矩阵;最后用新的奇异值矩阵与两个正交矩阵重新求解得到去噪后的拉曼成像数据。实验中,首先验证了中位数绝对偏差法确定前k个奇异值的正确性,其次分别从处理后的图像质量和信号波形两方面对比了该算法与常规算法的去噪效果。结果证明,中位数绝对偏差法可以快速地确定出合理的k值大小,而且,依据该k值处理后的成像数据不仅在成像质量上消除了大量的噪点,使得组分的空间分布特征清晰可见,也在信号波形上较完美地保留了微小谱峰,并恢复光谱信号。该算法不同于常规算法,能同时对整个拉曼成像数据进行处理,并保留光谱之间的统计特征,是一种更加有效的拉曼成像数据的去噪方法。     

Singular Value Decomposition Solution of the Schrödinger Equation in the Presence of Exchange Terms

A new numerical method of solving integro-differential equations appearing in the theory of atomic and nuclear scattering systems has been devised. It is termed Singular Value Decomposition Method (SVD). It consists in expanding the exchange kernel into a number of separable terms by means of the Singular Value Decomposition and then iterating over the remainder. In this paper, we extend our SVD method to the scattering of low energy electron-helium which has been the subject of interest, both theoretically and experimentally. We compare our results with the Moments Method which is widely used. The Moments Method consists of making an expansion of the solution into an especially favorable basis that takes care of the non-exchange part of the Hamiltonian.     

Singular Lagrangian, Hamiltonization and Jacobi last multiplier for certain biological systems

We study the construction of singular Lagrangians using Jacobi’s last multiplier (JLM). We also demonstrate the significance of the last multiplier in Hamiltonian theory by explicitly constructing the Hamiltonian of the Host-Parasite model and a Lotka-Volterra mutualistic system, both of which are well known first-order systems of differential equations arising in biology.     

基于迭代TSVD的NMR二维谱反演算法

针对现有基于TSVD的二维反演算法中截断位置判断不准确、容易产生虚假峰等问题,提出了一种改进的方法. 首先,通过逐步求精的方法对L曲线上的拐角位置进行定位,得到迭代的最大截断位置;然后,根据反演核奇异值的集中程度获取迭代的最小截断位置;最后,在给定的截断位置范围内从小到大进行TSVD,每次迭代都以上一次迭代的结果为依据. 对仿真数据和实验数据的反演结果表明,该算法都能够得到比较好的二维反演效果. 与现有基于TSVD的方法相比,该算法具有更高的鲁棒性,能够得到更清晰的二维谱,可满足实际应用需求.     

奇异值分解用于可调谐二极管激光吸收光谱技术去除系统噪声

可调谐二极管激光吸收光谱(TDLAS)技术用于气体浓度检测时,会受到谐波检测中基线漂移及噪声的影响,因此如何去除系统噪声一直是研究的热点。分析了连续截断信号和构造hankel矩阵两种不同方法下,奇异值分解(SVD)对TDLAS系统检测的理论意义。将二次谐波信号分别用该方法进行矩阵化排列和奇异值分解,选取适当阈值将部分奇异值置零并重构矩阵,得到了这两种方法对基线纠漂和去噪的不同效果。实验证明,奇异值分解方法不需加入额外系统部件、不需通零气扣除背景,就能够快速有效地去除TDLAS系统噪声,而构造hankel矩阵的方法适用于去除高频噪声,连续截断信号的方法适用于进行基线纠漂。将该方法应用于实际TDLAS系统氨气检测时的二次谐波,系统噪声去除率达80%。     

Adomian Decomposition Method Using Integrating Factor

This paper proposes a new Adomian decomposition method by using integrating factor. Nonlinear models are solved by this method to get more reliable and efficient numerical results. It can also solve ordinary differential equations where the traditional one fails. Besides, the complete error analysis for this method is presented.     

Using the Complex Jacobi Method to Simulate Kerr Non-linear Photonic Components

The iterative complex Jacobi technique has been extended to simulate the third order Kerr effect in wavelength scale dielectric structures. This method solves the Helmholtz equation in a discrete finite simulation space by an iterative process. An update equation refines the field values at each iteration step, until a desired accuracy is achieved. We have extended the iterative process with an extra calculation step which allows simulating materials with the non-linear third order Kerr effect. Our adjustment of the discrete field operators in the update equation also introduces PMLs as absorbing boundaries and the total field/scattered field formalism as field source for this method.     

Adomian Decomposition Method Using Integrating Factor

This paper proposes a new Adomian decomposition method by using integrating factor.Nonlinear models are solved by this method to get more reliable and efficient numerical results.It can also solve ordinary differential equations where the traditional one fails.Besides,the complete error analysis for this method is presented.     

Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

We present a hybrid singular spectrum analysis(SSA) and fuzzy entropy method to filter noisy nonlinear time series.With this approach,SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise,while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component.We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey-GIass attractors,as well as improving the multi-step prediction quality of these two series in noisy environments.     

基于模态分解的扩散火焰动态响应特性分析

针对扩散火焰燃烧不稳定性,以钝体扰流扩散火焰为对象,研究不同频率声波激励下火焰动态响应特性,通过傅里叶变换(FFT)与本征正交分解法(POD)分析火焰的图像的空间分布的频谱特性.结果 表明,火焰振荡的主要模态为火焰整体的横向振荡、尾流区的频闪振荡与纵向振荡,当外加声波激励频率与火焰自身不稳定频率接近时会增强火焰的频闪振...