Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

An orthogonal basis for the hyperbolic hybrid polynomial space

Motivated by the wide usage of the Tchebyshev basis and Legendre basis in the algebra polynomial space, we construct an orthogonal basis with the properties of the H-Bézier basis in the hyperbolic hybrid polynomial space, which is similar to the Legendre basis and holds remarkable properties. Moreover, we derive the transformation matrices that map the H-Bézier basis and the orthogonal basis forms into each other. An example for approximating the degree reduction of the H- Bézier curves is sketched to illustrate the utility of the orthogonal basis.     

Boundary knot method for 2D and 3D Helmholtz and convection–diffusion problems under complicated geometry

The boundary knot method (BKM) of very recent origin is an inherently meshless, integration‐free, boundary‐type, radial basis function collocation technique for the numerical discretization of general partial differential equation systems. Unlike the method of fundamental solutions, the use of non‐singular general solution in the BKM avoids the unnecessary requirement of constructing a controversial artificial boundary outside the physical domain. The purpose of this paper is to extend the BKM to solve 2D Helmholtz and convection–diffusion problems under rather complicated irregular geometry. The method is also first applied to 3D problems. Numerical experiments validate that the BKM can produce highly accurate solutions using a relatively small number of knots. For inhomogeneous cases, some inner knots are found necessary to guarantee accuracy and stability. The stability and convergence of the BKM are numerically illustrated and the completeness issue is also discussed. Copyright © 2003 John Wiley & Sons, Ltd.     

网络分解法的误差分析

网络分解法是求解电大尺寸复杂结构电磁问题的重要方法之一,关于网络分解法误差分析的研究有着重要的理论意义和实用价值.本文给出了网络分解法的误差估计,并对于脉冲基函数转换成三角基函数的情况给出了三角基函数个数的最优公式,数值结果表明了分析的正确性.     

关于Imamura猜想1的一个结果

本文证明了GF(q)的特征为2且n为奇数时GF(q~n)在GF(q)上有自对偶正规基。     

AN EXTENDED MODELING OF TIDE-WAVE MOTION

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An implementation for a fast public-key cryptosystem

In this paper we examine the development of a high-speed implementation of a system to perform exponentiation in fields of the form GF(2 ⁿ ). For sufficiently large n, this device has applications in public-key cryptography. The selection of representation and observations on the structure of multiplication have led to the development of an architecture which is of low complexity and high speed. A VLSI implementation has being fabricated with measured throughput for exponentiation for cryptographic purposes of approximately 300 kilobits per second.     

Anthony D. Wagner: Award for Distinguished Scientific Early Career Contributions to Psychology.

Presents the citation for Anthony D. Wagner, who received the Award for Distinguished Scientific Early Career Contributions to Psychology (cognition and human learning) "for outstanding and innovative research on the neural basis of memory and executive control." A brief profile and a selected bibliography accompany the citation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Arithmetic operations inGF(2 m )

This article is concerned with various arithmetic operations inGF(2 ^m ). In particular we discuss techniques for computing multiplicative inverses and doing exponentiation. The method used for exponentiation is highly suited to parallel computation. All methods achieve much of their efficiency from exploiting a normal basis representation in the field.     

连续体结构的参数化水平集统一优化

针对传统分步式结构优化设计的不足,提出一种同时进行结构拓扑、形状和尺寸统一优化的设计方法.首先采用水平集函数描述统一的结构优化模型和几何尺寸边界,通过引入紧支径向插值基函数将结构拓扑优化变量、形状优化变量和尺寸优化变量变换为基函数的扩展系数;然后取该扩展系数为设计变量,借助一种参数的变化表达3种优化要素对结构性能的影响,将复杂的多变量优化问题变换为相对简单的参数优化问题,有利于与相对成熟的优化算法相结合提高求解效率;进一步用R函数将其融合为一个整体,构造出统一优化模型,并用最优化准则法进行求解.最后通过数值案例证明了该方法的有效性和精确性.