基于小波域LS方法的图象超分辨率重构算法

为了能够有效地重构出高分辨率图象，提出了一种基于小波域最小二乘法(LS)的图象超分辨率重构算法．该算法是利用多尺度边缘的自相似性，由低分辨率图象通过预测来得到高分辨率图象小波变换的3个高频通道，以实现图象超分辨率重构．由于该算法保持了图象边缘附近的几何正则性，因而能够重构出较高质量的图象．同时，由于小波系数的预测只在边缘处进行，因此该算法具有较小的计算复杂度．实验表明，该算法较好地实现了图象超分辨率重构．     

Outliers, inliers and the generalized least trinuned squares estimator in system identification

The least trimmed squares estimator (LTS) is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. We show that the LTS estimate can be obtained by a simple algorithm with the complexity O( N In N) for large N, where N is the number of measurements. We also show that though the LTS is robust in terms of the outliers, it is sensitive to the inliers. The concept of the inliers is introduced. Moreover, the Generalized Least Trimmed Squares estimator (GLTS) together with its solution are presented that reduces the effect of both the outliers and the inliers.     

A random least-trimmed-squares identification algorithm

The Least-trimmed-squares (LTS) estimator is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. In this paper, we propose a random LTS algorithm which has a low computational complexity that can be calculated a priori as a function of the required error bound and the confidence interval. Moreover, if the number of data points goes to infinite, the algorithm becomes a deterministic one that converges to the true LTS in some probability sense.     

基于小波函数的数据拟合方法

本把小波函数引入离散数据拟合领域，将小波函数与数据拟合的常用方法－最小二乘法相结合，给出了一种新型的数据拟合工具，中详细讨论了该方法的处理论和实施步骤，由于小波函数具有良好的局部性质，该方法在提高似合精度方面具有传统方法不可替代的优点。     

基于角色的访问控制

信息安全技术领域一个非常重要的方面是访问控制。文章描述了近年提出的一种新的访问控制技术RBAC。详细介绍RBAC的基本要素及NIST的RBAC四层模型,讨论比较了RBAC模型与TCSEC中的MAC和DAC的区别,最后分析了NISTRBAC四层模型的一些不完善之处。     

形位误差的数据处理

本文介绍了适用MATLAB语言处理的有关形位误差评定的数学模型 ,以及相应数据处理方法 ,并给出典型实例。     

A least squares approach to principal component analysis for interval valued data

Principal Component Analysis (PCA) is a well-known technique, the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA), recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to analyze how MR-PCA works, the results of a simulation study and two applications on chemical data are proposed.     

基于电涡流传感器的电机铁心叠厚控制

本文提出了一种基于电涡流传感器的双探头测量电机硅钢片轧板厚度以控制铁心叠厚的方法.介绍了电涡流传感器的工作原理及双探头测厚法基本原理.以最小二乘法高次多项式拟合传感器的输出特性,达到较高的精度,保证了叠厚控制的准确性.设计了实验系统,实验结果验证了该方法的可行性.     

捕捉与提取影像片断中的人物动作

该文详细分析了运用自适应最小二乘法捕捉和提取影像中人物动作行为的实验过程。并通过实验总结出线性预测,迭代调整,恢复丢失特征点等方法,并对自适应最小二乘法进行了改进。实验结果已经应用于新加坡南洋理工大学对影像中的个体动作进行动画复原的项目中,对于其它图像处理应用领域也有广阔的应用前景。     

Analysis of the Kalman filter based estimation algorithm: an orthogonal decomposition approach

In this paper we shall provide new analysis on some fundamental properties of the Kalman filter based parameter estimation algorithms using an orthogonal decomposition approach based on the excited subspace. A theoretical analytical framework is established based on the decomposition of the covariance matrix, which appears to be very useful and effective in the analysis of a parameter estimation algorithm with the existence of an unexcited subspace. The sufficient and necessary condition for the boundedness of the covariance matrix in the Kalman filter is established. The idea of directional tracking is proposed to develop a new class of algorithms to overcome the windup problem. Based on the orthogonal decomposition approach two kinds of directional tracking algorithms are proposed. These algorithms utilize a time-varying covariance matrix and can keep stable even in the case of unsufficient and/or unbounded excitation.