椭圆曲线在WSN的密钥管理中的应用

随着无线传感器网络的广泛应用,其安全性问题也受到越来越多的关注。其中密钥管理是无线传感器网络安全的一个重要的组成部分,密钥管理的研究受到广泛的关注,而且提出来很多密钥的建立和管理的方案。由于传感器节点能量的限制．所提出的方案必须做到节能。因此,本文提出了一个基于椭圆曲线的加密方案。     

一种构造任意类三次三角曲线的方法

在自由曲线曲面造型中,一般多以多项式为基函数构造参数曲线曲面,而在三角函数空间中也能构造参数曲线曲面.给出了一种构造任意类三次三角参数曲线的方法,该法以三次多项式曲线的基本性质为基础,从而构造出的曲线与对应的三次多项式曲线具有几乎完全相似的性质,而且所构造的曲线能精确表示圆弧、椭圆弧、抛物线弧等二次曲线,为曲线曲面造型提供了一种新方法.     

改进的Koblitz曲线上数字签名算法

标量乘法的效率决定着椭圆曲线密码体制的性能,而JSF算法是当前最流行的计算椭圆曲线双标量乘的算法;Koblitz曲线上的快速标量乘算法是标量乘法研究的重要课题。Lee[12]算法采用Frobenius映射扩展正整数k并将其扩展后的系数改写成二进制形式有效地提高标量乘算法效率。将JSF应用到扩展后的系数中,以较小存储空间为代价来提高算法效率,并将算法运用到改进的ECDSA算法中,减少乘法运算次数,加速签名及验证过程,节约数字签名时间。     

Radical parametrizations of algebraic curves by adjoint curves

We present algorithms for parametrizing by radicals an irreducible curve, not necessarily plane, when the genus is less than or equal to 4 and the curve is defined over an algebraically closed field of characteristic zero. In addition, we also present an algorithm for parametrizing by radicals any irreducible plane curve of degree d having at least a point of multiplicity d−r, with 1≤r≤4 and, as a consequence, every irreducible plane curve of degree d≤5 and every irreducible singular plane curve of degree 6.     

Analyzing CAD competence with univariate and multivariate learning curve models

Understanding how learning occurs, and what improves or impedes the learning process is of importance to academicians and practitioners; however, empirical research on validating learning curves is sparse. This paper contributes to this line of research by collecting and analyzing CAD (computer-aided design) procedural and cognitive performance data for novice trainees during 16-weeks of training. The declarative performance is measured by time, and the procedural performance by the number of features used to construct a design part. These data were analyzed using declarative or procedural performance separately as predictors (univariate), or a combination of declarative or procedural predictors (multivariate). Furthermore, a method to separate the declarative and procedural components from learning curve data is suggested.     

Tangential cover for thick digital curves

The recognition of digital shapes is a deeply studied problem. The arithmetical framework, initiated by Reveillès [Géométrie discrète, calcul en nombres entiers et algorithmique, Thèse d’Etat, 1991], provides a powerful theoretical basis, as well as many algorithms to deal with digital objects. The tangential cover, first presented in Feschet and Tougne [Optimal time computation of the tangent of a discrete curve: application to the curvature, in: G. Bertrand, M. Couprie, L. Perroton (Eds.), 8th Discrete Geometry for Computer Imagery, Lecture Notes in Computer Science, vol. 1568, Springer, Berlin, 1999, pp. 31-40] and Feschet [Canonical representations of discrete curves, Pattern Anal. Appl. 8(1-2) (2005) 84-94] is a useful tool for representing geometric digital primitives. It computes the set of all maximal segments of a digital curve and permits either to obtain minimal length polygonalization or asymptotic convergence of tangents estimations. Nevertheless, the arithmetical approach does not tolerate the introduction of irregularities, which are however inherent to the acquisition of digital shapes. The present paper is an extension of Faure and Feschet [Tangential cover for thick digital curves, in: D. Coeurjolly, I. Sivignon, L. Tougne, F. Dupont (Eds.), DGCI 2008, Lecture Notes in Computer Science, vol. 4992, Springer, Berlin, 2008, pp. 358-369], in which we propose a new definition for a class of the so-called “thick digital curves” that applies well to a large class of digital object boundaries. We then propose an extension of the tangential cover to thick digital curves and provide an algorithm with an O(nlogn) time complexity, where n denotes the number of points of specific subparts of the thick digital curve. In order to keep up with this low complexity, some critical points must be taken into account. We describe all required implementation details in this paper.     

Fast Object Segmentation by Growing Minimal Paths from a Single Point on 2D or 3D Images

In this paper, we present a new method for segmenting closed contours and surfaces. Our work builds on a variant of the minimal path approach. First, an initial point on the desired contour is chosen by the user. Next, new keypoints are detected automatically using a front propagation approach. We assume that the desired object has a closed boundary. This a-priori knowledge on the topology is used to devise a relevant criterion for stopping the keypoint detection and front propagation. The final domain visited by the front will yield a band surrounding the object of interest. Linking pairs of neighboring keypoints with minimal paths allows us to extract a closed contour from a 2D image. This approach can also be used for finding an open curve giving extra information as stopping criteria. Detection of a variety of objects on real images is demonstrated. Using a similar idea, we can extract networks of minimal paths from a 3D image called Geodesic Meshing. The proposed method is applied to 3D data with promising results.

高分辨率超声Lamb波频散曲线测量与板厚估计

超声Lamb波在工业无损检测与 评价中具有广阔的应用前景。但受制于超声导波多模式频散与混叠的影响,如何实现高分辨率超声导波的模式分离与频散曲线提取,进而定量分析材料的健康状况一直是研究的热点与难点。本文旨在将现代信号处理中广泛采用的谱估计技术应用于超声Lamb波阵列信号分析,并通过提取频散曲线定量测量板厚度。以经典参数谱估计法中的Yule-Walker法与Burg法为例,实验测量和分析了3 mm,4 mm和 5 mm厚铝板中的宽带多模式Lamb波信号,准确地提取了铝板中的宽带Lamb波频散曲线,并比较了两种经典谱估计方法与二维傅氏变换法的性能,最终实现了铝板厚度估计。