Nonlinear total least-squares variance component estimation for GM(1,1)model

The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.

Length estimation of extended targets based on bistatic high resolution range profile

The approach to estimate the length of extended targets by using the bistatic high resolution range profile（ H RRP） is analyzed in this paper. The relationship between the bistatic H RRP and the monostatic H RRP of extended targets are investigated. It is demonstrated by simulations that the target length measured by the bistatic H RRP is more meaningful and accurate than that by the monostatic HRRP,though the monostatic H RRP has been well developed and widely used in target recognizing and classification. The estimation results of a cone shaped target are present and compared at the end of the paper. To assure the reliability of the simulation,the bistatic H RRP is obtained through the scattering field data calculated by a fullwave numerical method,FE-BI-MLFMA.

IN-SITU DISTANCE RESOLVED OPTICALLY TRANSPARENT THIN-LAYER MICROSCOPIC FTIR SPECTROELECTROCEMISTRY.ESTIMATION OF THE ELECTROCHEMICAL CONCENTRATION-DISTANCE PROFILE

An in-situ optically transparent thin-layer microscopic FTIR spectroelectro- chemical cell was constructed.Using this cell,we characterize a concentration-distance profile in the electrochemical diffusion thin-layer by in-situ adjusting the focal point at different distances to the electrode surface.

JOINT ESTIMATION OF CARRIER FREQUENCY OFFSET AND POWER DELAY PROFILE FOR MIMO-OFDM SYSTEMS

This paper develops a Cyclic Prefix(CP)based joint Maximum-Likelihood(ML)estima-tion algorithm of Carrier Frequency Offset(CFO)and Power Delay Profile(PDP)for Multi-InputMulti-Output Orthogonal Frequency Division Multiplexing(MIMO-OFDM)systems.However,theexact solution of the joint ML estimation is very complex since it needs a search over amulti-dimensional domain.Thus a simplified method is proposed to estimate the CFO and the PDPiteratively via the alternating-projection method which could induce the multidimensional searchproblem to a sequence of simple one-dimensional searches.Simulations show that the proposed algo-rithm is more accurate and robust than the existing algorithms.

三维薛定谔方程组的线性Profile分解

为了研究线性薛定谔方程组解Strichartz估计的紧性缺失问题,针对H•^(1)(R^(3))×H•^(1)(R^(3))中的三维线性薛定谔方程组的有界解向量序列,使用解序列的Profile分解方法,构造为解向量子列的(1/√h_(n))U(t-t_(n))/h_(n)^(2),(x-x_(n))/h_(n)类型的Profile分解和.其中,U是线性薛定谔方程组的解向量,在Strichartz范数估计下具有一个很小的余项.首先确定伸缩变换参数序列族,利用傅里叶变换和迭代的思想确定Profile分解族.其次验证了Profile分解和的收敛性,说明了Strichartz范数下余项的收敛性.最后证明了当线性薛定谔方程组的解序列有界时,都可以分解为解向量子列和的形式.

MPEG-4 Simple Visual Profile编码特性剖析及实现

针对低比特率通信,对MPEG-4 Simple Visual Profile的编码特性进行剖析,并对影响编码速度最大运动估计与运动补偿进行讨论,提出实现MPEG-4 Simple Visual Profile编码模式,通过对待压缩帧数据的定义提出MB码流的构成,为MPEG-4编码器在移动通信设备上的实现建立一个研究的基础平台。

MPEG-4 Simple Visual Profile编码特性剖析及实现

针对低比特率通信,对MPEG-4 Simple Visual Profile的编码特性剖析,并对影响编码速度最大运动估计与运动补偿进行讨论,提出实现MPEG-4 Simple Visual Profile编码模式,通过对待压缩帧数据的定义提出 MB码流的构成,为MPEG-4编码器在移动通信设备上的实现建立一个研究的基础平台。

Online LS-SVM for function estimation and classification

An online algorithm for training LS-SVM (Least Square Support VectorMachines) was proposed for the application of function estimation and classification. Online LS-SVMmeans that LS-SVM can be trained in an incremental way, and can be pruned to get sparseapproximation in a decremental way. When a SV (Support Vector) is added or removed, the onlinealgorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Onlinealgorithm is especially useful to realistic function estimation problem such as systemidentification. The experiments with benchmark function estimation problem and classificationproblem show the validity of this online algorithm.

THE SUPERIORITY OF EMPIRICAL BAYES ESTIMATION OF PARAMETERS IN PARTITIONED NORMAL LINEAR MODEL

In this article,the empirical Bayes（EB）estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares（LS）estimator are investigated under mean square error matrix（MSEM）criterion.

NONLINEAR STABILITY OF PLANAR SHOCK PROFILES FOR THE GENERALIZED KdV-BURGERS EQUATION IN SEVERAL DIMENSIONS

This paper is concerned with the nonlinear stability of planar shock profiles to the Cauchy problem of the generalized KdV-Burgers equation in two dimensions. Our analysis is based on the energy method developed by Goodman [5] for the nonlinear stability of scalar viscous shock profiles to scalar viscous conservation laws and some new decay estimates on the planar shock profiles of the generalized KdV-Burgers equation.

NEW EFFICIENT ORDER-RECURSIVE LEAST-SQUARES ALGORITHMS

Order-recursive least-squares(ORLS)algorithms are applied to the prob-lems of estimation and identification of FIR or ARMA system parameters where a fixedset of input signal samples is available and the desired order of the underlying model isunknown.On the basis of several universal formulae for updating nonsymmetric projec-tion operators,this paper presents three kinds of LS algorithms,called nonsymmetric,symmetric and square root normalized fast ORLS algorithms,respectively.As to the au-thors’ knowledge,the first and the third have not been so far provided,and the second isone of those which have the lowest computational requirement.Several simplified versionsof the algorithms are also considered.

Novel channel estimation model for OFDM in time-varying channel

A new channel estimation method for orthogonal frequency division multiplexing （OFDM） system with large subcarriers and serious intercarrier interference （ICI） is proposed. The channel frequency-domain （ CFD ） matrix of each delay path is factorized to the product of a diagonal delay matrix and a circular ICI matrix in this model. To reduce the coefficient number, the circular ICI ma- trix is squeezed by using Hamming-window as the reshaping pulse in the transmitter. Meanwhile, the elements of the diagonal delay matrix are approximated with a discrete prolate spheroidal basis ex- pansion model （DPS-BEM）. A least-square （LS） estimator is used to estimate the reduced channel coefficients. The proposed method is theoretically derived and simulated. The simulation results in- dicate that the model has good performance and is appropriate for various channel environments. The method also has low complexity and good spectral efficiency.

LEAST-SQUARES MIXED FINITE ELEMENT METHOD FOR A CLASS OF STOKES EQUATION

A least-squares mixed finite element method was formulated for a class of Stokes equations in two dimensional domains. The steady state and the time-dependent Stokes' equations were considered. For the stationary equation, optimal H-t and L-2-error estimates are derived under the standard regularity assumption on the finite element partition ( the LBB-condition is not required). Far the evolutionary equation, optimal L-2 estimates are derived under the conventional Raviart-Thomas spaces.

Estimation of Open Channel Flow Parameters by Using Genetic Algorithm

The present study involves estimation of open channel flow parameters having different bed materials invoking data of Gradual Varied Flow (GVF). Use of GVF data facilitates estimation of flow parameters. The necessary data base was generated by conducting laboratory. In the present study, the efficacy of the Genetic Algorithm (GA) optimization technique is assessed in estimation of open channel flow parameters from the collected experimental data. Computer codes are developed to obtain optimal flow parameters Optimization Technique. Applicability, adequacy and robustness of the developed code are tested using sets of theoretical data generated by experimental work. A simulation model was developed to compute GVF depths at preselected discrete sections for given downstream head and discharge rate. This model is linked to an optimizer to estimate optimal value of decision variables. The proposed model is employed to a set of laboratory data for three bed materials. Application of proposed model reveals that optimal value of fitting parameter ranges from 1.42 to 1.48 as the material gets finer and optimal decision variable ranges from 0.015 to 0.024. The optimal estimates of Manning’s n of three different bed conditions of experimental channel appear to be higher than the corresponding reported/Strickler’s estimates.

Robust Frequency Estimation Under Additive Mixture Noise

In many applications such as multiuser radar communications and astrophysical imaging processing,the encountered noise is usually described by the finite sum ofα-stable(1≤α<2)variables.In this paper,a new parameter estimator is developed,in the presence of this new heavy-tailed noise.Since the closed-formPDF of theα-stable variable does not exist exceptα=1 andα=2,we take the sum of the Cauchy(α=1)and Gaussian(α=2)noise as an example,namely,additive Cauchy-Gaussian(ACG)noise.The probability density function(PDF)of the mixed random variable,can be calculated by the convolution of the Cauchy’s PDF and Gaussian’s PDF.Because of the complicated integral in the PDF expression of the ACG noise,traditional estimators,e.g.,maximum likelihood,are analytically not tractable.To obtain the optimal estimates,a new robust frequency estimator is devised by employing the Metropolis-Hastings(M-H)algorithm.Meanwhile,to guarantee the fast convergence of the M-H chain,a new proposal covariance criterion is also devised,where the batch of previous samples are utilized to iteratively update the proposal covariance in each sampling process.Computer simulations are carried out to indicate the superiority of the developed scheme,when compared with several conventional estimators and the Cramér-Rao lower bound.

Interval Estimation in a Two Parameter Weibull Distribution Based on Type-2 Censored Data

In this paper, we consider the construction of the approximate profile- likelihood confidence intervals for parameters of the 2-parameter Weibull distribution based on small type-2 censored samples. In previous research works, the traditional Wald method has been used to construct approximate confidence intervals for the 2-parameter Weibull distribution under type-2 censoring scheme. However, the Wald technique is based on normality assumption and thus may not produce accurate interval estimates for small samples. The profile-likelihood and Wald confidence intervals are constructed for the shape and scale parameters of the 2-parameter Weibull distribution based on simulated and real type-2 censored data, and are hence compared using confidence length and coverage probability.

Estimation of a Linear Model in Terms of Intra-Class Correlations of the Residual Error and the Regressors

Objectives: The objective is to analyze the interaction of the correlation structure and values of the regressor variables in the estimation of a linear model when there is a constant, possibly negative, intra-class correlation of residual errors and the group sizes are equal. Specifically: 1) How does the variance of the generalized least squares (GLS) estimator (GLSE) depend on the regressor values? 2) What is the bias in estimated variances when ordinary least squares (OLS) estimator is used? 3) In what cases are OLS and GLS equivalent. 4) How can the best linear unbiased estimator (BLUE) be constructed when the covariance matrix is singular? The purpose is to make general matrix results understandable. Results: The effects of the regressor values can be expressed in terms of the intra-class correlations of the regressors. If the intra-class correlation of residuals is large, then it is beneficial to have small intra-class correlations of the regressors, and vice versa. The algebraic presentation of GLS shows how the GLSE gives different weight to the between-group effects and the within-group effects, in what cases OLSE is equal to GLSE, and how BLUE can be constructed when the residual covariance matrix is singular. Different situations arise when the intra-class correlations of the regressors get their extreme values or intermediate values. The derivations lead to BLUE combining OLS and GLS weighting in an estimator, which can be obtained also using general matrix theory. It is indicated how the analysis can be generalized to non-equal group sizes. The analysis gives insight to models where between-group effects and within-group effects are used as separate regressors.

三维热方程的profile分解

研究了热方程的profile分解问题,主要思想是运用Sobolev不等式和Strichartz估计给出Sobolev嵌入的紧性缺失原因。并以一族正交profile分解和的形式证明在时空意义下余项是趋于零的。

1~H NMR-based serum metabolic profiling in compensatedand decompensated cirrhosis

AIM: To study the metabolic profiling of serum samples from compensated and decompensated cirrhosis patients. METHODS: A pilot metabolic profiling study was conducted using three groups: compensated cirrhosis patients (n = 30), decompensated cirrhosis patients (n = 30) and healthy controls (n = 30). A 1H nuclear magnetic resonance (NMR)-based metabonomics approach was used to obtain the serum metabolic profiles of the samples. The acquired data were processed by multivariate principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model was capable of distinguishing between decompensated and compensated cirrhosis patients, with an R2Y of 0.784 and a Q2Y of 0.598. Twelve metabolites, such as pyruvate, phenylala-nine and succinate, were identified as the most influential factors for the difference between the two groups. The validation of the diagnosis prediction showed that the accuracy of the OPLS-DA model was 85% (17/20). CONCLUSION: 1H NMR spectra combined with pattern recognition analysis techniques offer a new way to diagnose compensated and decompensated cirrhosis in the future.

A novel adaptive image zooming scheme via weighted least-squares estimation

A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimation that can achieve arbitrary integer-ratio zoom （WLS-AIZ） For a given zooming ratio n, every pixel in a low-resolution （LR） image is associated with an n x n block of high-resolution （HR） pixels in the HR image. In WLS-AIZ, the LR image is interpolated using the bilinear method in advance. Model parameters of every n×n block are worked out through weighted least-square estimation. Subsequently, each pixel in the n × n block is substituted by a combination of its eight neighboring HR pixels using estimated parameters. Finally, a refinement strategy is adopted to obtain the ultimate HR pixel values. The proposed algorithm has significant adaptability to local image structure. Extensive experiments comparing WLS-AIZ with other state of the art image zooming methods demonstrate the superiority of WLS-AIZ. In terms of peak signal to noise ratio （PSNR）, structural similarity index （SSIM） and feature similarity index （FSIM）, WLS-AIZ produces better results than all other image integer-ratio zoom algorithms.