First we construct a covariance matrix from sample images , then compute the eigenvalues and corresponding eigenvectors of the covariance matrix , construct a feature matrix with the eigenvectors . then every images in database can be projected into the feature matrix and gain a projection vector , so does the input image . then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors 我们首先根据采集的样本图像构造一个协方差矩阵，然后求取该矩阵的特征值，以这些矩阵特征值对应的特征向量构造出一个特征空间，然后将输入图像向该特征空间映射，将获取的映射系数与样本库中图像的映射系数进行距离计算，根据计算出的距离判定输入图像与样本图像间的匹配程度。
In the presence of impulsive noise , the two received signals are combinated , so the estimated impulse response of the channel is the eigenvector of its covariation matrix corresponding to the smallest eigenvalue , which can be realized adaptively using generalization of the normalized least mean - norm ( generalized nlmp ) algorithm 该算法在脉冲噪声环境下，组合两个接收信号，使其共变矩阵最小特征值对应的特征向量为信道的估计，并基于广义归一化最小平均范数（广义nlmp ）方法自适应得到该特征向量，从而获得时延估计。
First we construct a covariance matrix from sample images , then compute the eigenvalues and corresponding eigenvectors of the covariance matrix , construct a feature matrix with the eigenvectors . then every image in database can be projected into the feature matrix and gain a projection vector , so does the input image . then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors 然后，根据采集的样本图像构造一个协方差矩阵，求取该矩阵的特征值，以这些矩阵特征值对应的特征向量构造一个特征空间，将输入图像向该特征空间映射，计算获取的映射系数与样本库中各类图像的映射系数的欧基里德距离，根据计算出的距离判定输入图像与样本图像间的匹配程度。
The 1 - dimension was decomposed by harr wavelet in depth of 2 - layer , then we could construct a characteristic information from the low frequency part of the wavelet coefficients and compare it with the corresponding information of the standard characters , this led to a rapid and efficient character recognition . the total experimental character recognition rate was above 92 % 将投影得到数据进行2层小波分解，从其中各层平滑分量提取一个特征信息，将其与模板字符对应的特征信息进行分析比较，实现了字符的快速高效识别，识别率达到92以上。
Firstly , it introduced the import principium resolving thread and steps of analytic hierarchy process . secondly , it erected model of population modernization , which based on the steps of analytic hierarchy process . lastly , it got the maximal eigenvalue of dissymmetric matrix and corresponding eigenvector with sas , and normalized the eigenvector to get weight value 本章分为两部分，第一部介绍分析法的提出、原理以及解决问题的思路和步骤；第二部建立模型确定权重，根据层次分析法的原理和步骤，建立人口现代化指标体系的模型，运用sas求解所构造的非对称判断矩阵的最大特征值，从而得出所对应的特征向量，变形后得到权重。
