特征函数

In the fourth chapter, firstly it introduces the Boolean functions algebraic expressions of the 2-value clock-controlled stop-and-go generator and Gunther generator. It reveals the balanced property of the two kinds of Boolean functions, and studies the Walsh cycle spectrum and the autocorrelation function. It also obtains the coincidence rate of their output sequences with affine sum of some bits of input sequences, and analyzes their ability of resisting the best affine approximation cryptanalysis and differential cryptanalysis. Secondly, we properly present a new definition of the Best Affine Approximation, namely BAA on the Boolean vector functions, followed by the spectral characteristic of such defined BAA attacks through using the decomposition formula of the union distribution for random variables. A lower bound of such BAA attacks is proposed. Finally, we also study the spectral characteristic of the second kind of nonlinearity of Boolean vector functions, followed by a higher bound of such nonlinearity. Furthermore, the limited relationship between the second kind of nonlinearity of Boolean vector functions and the linear structure of the linear combination of every component is analyzed.

在第四章中，首先给出了2值密钥流"停走生成器"和"衮特生成器"中实际存在的布尔函数的代数表示，揭示了这两类布尔函数的平衡性，随后研究了它们的Walsh循环谱和自相关函数等，得到了它们的输出序列与输入序列中的某些bit的仿射项的符合率，分析了它们抵抗最佳仿射逼近攻击和差分攻击的能力；其次，我们合理地给出了布尔向量函数最佳仿射逼近的新定义，利用布尔随机变量联合分布的分解式考察了相应的谱特征，并给出了布尔向量函数与其最佳仿射逼近的符合率的一个下界；最后，我们还考察了布尔向量函数第二类非线性度的谱特征，给出了布尔向量函数第二类非线性度的一个上界，并揭示了布尔向量函数第二类非线性度与其各个分量的线性和的线性结构之间存在的制约关系。

It solves the problem that the unitary contour presentation can not correctly extract face contour in a face image which suffers from scale, rotation etc. The definition of the internal and external energy function is provided. At the same time, the global matching algorithm and local matching algorithm is given. The experiment shows that this presentation and the accompanying matching algorithm can be used to extract the face contour very well. So the image segmentation can be implemented by using it.②By analyzing the recognition principle of PCA method, we can conclude that the face images coming from different surrounding consist of different face image space. This is the essential reason that makes the generality of PCA method worse. Also, we give a measurement means to measure the distance from different face image space, so we can analyze face image space more conveniently.③We also construct various scale models and rotation pose models to detect the scale and rotating angle of face image to be recognized. The experiment results show that the detecting precision is very high. So it is good for face image feature extraction and face image representation.④Similarly, we construct local feature models of face image and utilize them to detect the local feature of face image. At the same time, we put forward a novel face image local feature detection algorithm, locating step by step. The experiment results show that this method can accurately detect the location of local face feature in a image.⑤A novel face image presentation model, dual attribute graph , is put forward. Firstly, it utilizes attribute graph to present the face image, then exact the local principal component coefficient and Gabor transform coefficient of thc pixels which corresponds to the nodes of the graph as the attribute of the nodes. This representation fully makes use of the statistical characteristic of the local face feature and utilizes Gabor transform to present the topographical structure of face image. So DAG has more general property.⑥Based on the DAG presentation, we give a DAG matching function and matching algorithm. During the design of the function and algorithm, the noise factor, e. g., lighting, scale and rotation pose are considered and tried to be eliminated. So the algorithm can give more general property.⑦A general face image recognition system is implemented. The experiment show the system can get better recognition performance under the noise surrounding of lighting, scale and rotation pose.

本文在上述研究的基础上，取得了如下主要研究成果：①构造了一个通用的人脸轮廓模型表示，解决了由于人脸图象尺度、旋转等因素而使得仅用单一轮廓表示无法正确提取人脸轮廓的问题，并给出了模型内、外能函数的定义，同时给出了模型的全局与局部匹配算法，实验表明，使用这种表示形式以及匹配算法，能够较好地提取人脸图象的轮廓，可实际用于人脸图象的分割；②深入分析了PCA方法的识别机制，得出不同成象条件下的人脸图象构成不同的人脸图象空间的结论，同时指出这也是造成PCA方法通用性较差的本质原因，并给出了不同人脸空间距离的一种度量方法，使用该度量方法能够直观地对人脸图象空间进行分析；③构造了各种尺度模板、旋转姿势模板以用于探测待识人脸图象的尺度、旋转角度，实验结果表明，探测精确度很高，从而有利于人脸图象特征提取，以及图象的有效表示；④构造了人脸图象的各局部特征模板，用于人脸图象局部特征的探测；同时提出了一种新的人脸图象局部特征探测法---逐步求精定位法，实验结果表明，使用这种方法能够精确地得到人脸图象各局部特征的位置；⑤提出了一种新的人脸图象表示法---双属性图表示法；利用属性图来表示人脸图象，并提取图节点对应图象位置的局部主成分特征系数以及Gabor变换系数作为图节点的属性，这种表示方法充分利用了人脸图象的局部特征的统计特性，并且使用Gabor变换来反映人脸图象的拓扑结构，从而使得双属性图表示法具有较强的通用性；⑥在双属性图表示的基础上，给出双属性图匹配函数及匹配算法，在函数及算法设计过程中，考虑并解决了光照、尺度、旋转姿势变化等因素对人脸图象识别的影响，使得匹配算法具有较强的通用性；⑦设计并实现了一个通用的人脸图象识别系统，实验结果表明，该系统在图象光照、尺度、旋转姿势情况下，得到了较好的识别效果。

Quantifying similarity/difference between two objects plays an important role in many contexts The quality of the similarity/difference scores can be improved by considering the semantic information related to the features of objects A flexible semantic distance function called X Dist is proposed, which can utilize the semantic information to measure the difference between two objects based on a solution to the transportation problem from linear optimization With a ground distance function for single features being a metric, X Dist is also a metric This property is very useful for making searching efficient, but is not investigated in the previous research Moreover, the experimental results show X Dist can be as good as the previously studied similarity measures in nearest neighbor searching, discriminative power and computing speed

量化对象间相似性/差别的方法具有广泛的用途，利用相关的语义信息能够得到更好的量化结果提出了一个量化对象间语义差别的距离函数XDist，它基于线性优化中的运输问题模型和相关的语义信息量化两个对象之间的差别在量化特征的差别函数是度量的情况下，XDist是一个度量，在提高搜索的效率方面具有优势，弥补了以往研究的不足，而且实验初步表明，此函数在最近邻查询效果、差别分辨力和计算速度方面能与已有函数相媲美1 引言量化对象间相似性/差别的方法具有广泛的用途，例如数据挖掘中的聚类；信息检索中的相似文档查询；协同过滤中的相似用户查询；CBR中的相似案例查询集合是对象特征表示的常用结构，集合的元素是与对象特征相关的项，它们可以

In this dissertation, we mainly use character matrix to research cryptology characteristic of Boolean function and m-ary logic function.Firstly, by using properties of character matrix's row and column we get an equivalence condition of bent function. Then we have an entirely construct method of 4 variable bent function.

本文利用特征矩阵研究了密码学中逻辑函数的相关问题，主要做的工作有：首先，根据Bent函数的自相关特征，利用特征矩阵给出了Bent函数的一个新的等价判别条件，并由此得到了4元Bent函数的一个完全构造方法。

To eliminate the limit, a hybrid objective function weighted by antitangent function is constructed, which behaves like an objective function of norm for small data points and like an objective function of norm for large data points, restraining the influence of false data points on inversion procedure.

为了解决这一问题，利用反正切函数作为加权因子构造了反正切混合目标函数，并利用模型试验进行了验证。简要阐述了最优化目标函数的构造方法；给出了利用反正切函数构造混合目标函数的基本原理，并对目标函数的微分特征和概率特征进行了分析；利用模型论述了反正切混合目标函数中控制参数μ和σ的意义，μ控制反正切函数变化的速度，σ控制数据误差项被处理或加权的方式。

As to the aspects of constructing methods for hedonic price index, based on the comparison of the current methods, this paper classifies them into four varieties, pooling data index, cross section index, hedonic-price-function-based modeling index and hedonic-price-function-based quality adjustment index.

并按该定义，根据特征价格指数使用特征价格函数的材料不同和使用的数据结构不同，给出了特征价格指数的分类方法和不同分类的定义。2、根据特征价格指数使用特征价格函数的材料不同和使用的数据结构不同，划分出四种主要的特征价格指数编制方法，即截面数据特征价格指数法、合并数据特征价格指数法、特征价格模拟法和特征价格质量调整法。

There is another type of orthogonality of eigenfunctions of the Sturm-Liouville problem as follows:∫badx+hymyn+Hymyn=0.

斯图姆—刘维尔型特征值问题中特征函数存在另外一类正交关系，就是：ba∫dx+hymyn+Hymyn=0，称此正交关系为第二类正交关系。

The results in this paper are the extendability of the results obtained by P.

最后，在上述两个结果的基础上给出了上述Sturm-Liouville算子的第n个正特征值所对应的特征函数在内的零点个数的计算公式，这是对P。

In this paper,we use the method of the estimated characteristic function to estimate a lower bound of the first eigenvalue on compact Riemann manifold.

本文利用特征函数梯度估计的方法对紧致Riemann流形上的第一特征值的下界进行了估计。

The paper analyzed the effects of the parameters Gand KI on the eigen-functionh of differential equations.

同时着重分析了重力参数G和匹配值KI对特征函数h的影响。

In the negative and interrogative forms, of course, this is identical to the non-emphatic forms.

。但是，在否定句或疑问句里，这种带有"do"的方法表达的效果却没有什么强调的意思。

Go down on one's knees;kneel down

屈膝跪下。。。下跪祈祷