基于Gabor变换和灰度梯度共生矩阵的人耳识别研究

人耳识别作为一种新兴的生物特征识别技术,具有其自身独特的优势。提出一种基于Gabor变换和灰度梯度共生矩阵的人耳身份识别方法。首先,利用Gabor变换和灰度-梯度共生矩阵融合提取人耳图像的纹理特征,然后采用K-NN分类器对特征进行分类。该方法用USTB人耳图像库做测试。实验结果表明介绍的提取人耳图像的纹理融合特征的方法优于只采用Gabor变换提取特征或是只提取灰度梯度共生矩阵的二次统计特征的性能。在明氏距离测度及K=1时,交叉验证识别率达到81.77%。     

Fully modified estimation of cointegrating vectors via var prewhitening: A simulation study

Summary In this paper we investigate, by simulation methods, the finite samples properties of the Fully Modified Least Squares (FMLS) estimator of cointegrating vectors when the long run covariance matrix is estimated via VAR prewhitening. We compare this estimator to the FMLS estimator based on an automatic or a fixed bandwidth kernel estimator of the long run covariance matrix. By and large, FMLS estimator based on VAR prewhitening perform better than FMLS based on fixed bandwidth or automatic bandwidth, with the latter behaving almost in the same way in finite samples. More importantly, the empirical distribution of a Wald test statistic built from VAR prewhitened FMLS is closer to the asymptoticχ ² distribution than those obtained from alternative kernel estimators. Thus, our findings strongly favor the use of VAR prewhitening in the FM correction of the OLS estimator. We would like to thank P. C. B. Phillips for his suggestions, two anonymous referees for detailed comments, and the participants of the IGIER (Milan), the University of Padova and the CIDE (Bologna) seminars for comments. This paper has been presented at the Econometric Society European Meeting 1984 held at Maastricht, Netherlands. We acknowledge financial support from MURST-Funds 40%. The usual disclaimers apply.     

汉英语码转换的句法特点和结构限制

通过分析实际语料验证波普拉克的“自由语素限制和对等限制”以及斯科顿的“主体语言构造模式”理论,可探讨汉英语码转换的句法特点和结构限制。研究得出,“主体语言构造模式”理论提出了更为合适的分析框架,更具有普遍性和说服力。     

A bidimensional class of optimality criteria involving φp and characteristic criteria

A new biparametric class of criteria for Optimal Experimental Design generalizing the families of φ_p and Characteristic Criteria is presented. Some properties of the Characteristic Criteria are provided: in particular, differentiability, monotonicity and convexity. A statistical interpretation is also offered. Optimal designs with respect to Characteristic Criteria are obtained for polynomial and compartmental models and an extension of the Michaelis-Menten model. A thorough discussion for the trigonometric model is given. The computed optimal designs are shown to be quite efficient for A-, D- and D_s -optimality. Thus, some of them appear as a natural compromise between different optimality criteria. A simulation in multiple linear regression confirms the quality of Characteristic designs for discovering significant differences between parameters.     

基于DSM的复杂研发项目价值流优化

针对传统设计结构矩阵(DSM)优化研发项目的不足,本文采用基于价值要素的DSM价值流优化方法,提出了活动的"费用时间比"参数和返工因子DSM矩阵,以综合反映活动的费用、时间及返工风险等价值要素对复杂研发项目活动排序优化的影响。进一步,将活动的"费用时间比"参数和返工因子作为优化参数,构建了相应的DSM价值流优化目标函数。对某案例优化计算表明,与传统方法相比,采用本文提出的价值流优化方法能明显降低由于返工而引起的项目费用增加值。     

A Note on Unicyclic Representations of Phase Type Distributions

ABSTRACT

In this note, we study the unicyclic representation introduced in O'Cinneide ^[21] O'Cinneide , C. A. Phase-type distributions: open problems and a few properties . Stochastic Models 1999 , 15 ( 4 ), 731 – 757 .[Taylor & Francis Online] , [Google Scholar]. First, we present a counterexample to the conjecture that every PH-representation has an equivalent unicyclic representation of the same order. Then we show that the conjecture holds if the order of the PH-representation is 3. We also introduce an algorithm for computing a unicyclic generator of order 3, which PH-majorizes the original PH-generator, for any PH-generator of order 3. For the general case, we develop a nonlinear program for computing unicyclic representations for PH-distributions.     

Bayesian Identification of Multivariate Autoregressive Processes

Identification is one of the most important stages of a time series analysis. This paper develops a direct Bayesian technique to identify the order of multivariate autoregressive processes. By employing the conditional likelihood function and a matrix normal-Wishart prior density, or Jeffrey' vague prior, the proposed identification technique is based on deriving the exact posterior probability mass function of the model order in a convenient form. Then one may easily evaluate the posterior probabilities of the model order and choose the order that maximizes the posterior mass function to be the suitable order of the time series data being analyzed. Assuming the bivariate autoregressive processes, a numerical study, with different prior mass functions, is carried out to assess the efficiency of the proposed technique. The analysis of the numerical results supports the adequacy of the proposed technique in identifying the orders of multivariate autoregressive processes.     

Optimal Configuration of a Square Array Group Testing Algorithm

We consider the optimal configuration of a square array group testing algorithm (denoted A2) to minimize the expected number of tests per specimen. For prevalence greater than 0.2498, individual testing is shown to be more efficient than A2. For prevalence less than 0.2498, closed form lower and upper bounds on the optimal group sizes for A2 are given. Arrays of dimension 2 × 2, 3 × 3, and 4 × 4 are shown to never be optimal. The results are illustrated by considering the design of a specimen pooling algorithm for detection of recent HIV infections in Malawi.     

Fundamental Equations of BLUE and BLUP in the Multivariate Linear Model with Applications

Ordinary least squares estimator (OLSE), best linear unbiased estimator (BLUE), and best linear unbiased predictor (BLUP) in the general linear model with new observations are generalized to the general multivariate linear model. The fundamental equations of BLUE and BLUP in the multivariate linear model are derived by two methods, including the vectorization method and projection method. By using the matrix rank method, some new results of linear BLUE-sufficiency, linear BLUP-sufficiency, and the equality of OLSE, BLUE, and BLUP are given in the multivariate linear model.