Certain fractional calculus and integral transform results of incomplete ℵ-functions with applications

In this paper, we study certain interesting and useful properties of incomplete $\aleph$-functions. The incomplete $\aleph$-function is an extension of the $\aleph$-function. We find several useful classical integral transforms of these functions. Further, we examine the fractional calculus with the incomplete $\aleph$-functions and point out several special cases. Finally, we give the applications of incomplete $\aleph$-functions in detecting glucose supply in human blood.     

飞秒激光光斑轮廓的傅里叶描述子重建及特征提取

在利用飞秒激光对单晶硅材料进行微尺度烧蚀时,衍生的等离子体发光光斑中蕴含着大量的加工信息。如何有效提取光斑图像的几何特征信息,对研究飞秒激光烧蚀加工工艺过程具有重要意义。轮廓特征是描述激光光斑最重要的特征之一,本文着重分析了等离子体光斑轮廓特征与光斑运动规律间的关系及光斑运动情况分类方法。首先,考虑到光斑微弱的特性,对光斑图像进行增强处理。其次,采用链码方法提取光斑轮廓特征,并利用傅里叶描述子低频特性,选取少量的描述子重建光斑图像轮廓特征,减少了复杂的计算量,有效提高了光斑图像分类的速度与质量。最后,运用Hu不变矩特征证明傅里叶描述子用于提取激光光斑对轮廓特征的有效性。     

A Barban-Davenport-Halberstam theorem for integers with fixed number of prime divisors

The purpose of this paper is to study the distribution of integers with a given number prime divisors over arithmetic progressions, via using the large-sieve inequality, Huxley-Hooley contour and the zero-density estimate, and present a Barban-Davenport-Halberstam type theorem for it.     

High speed VLSI architecture for improved region based active contour segmentation technique

Active contour segmentation is an important stage in image analysis applications. In this article, an improved region based active contour segmentation is proposed. The proposed active contour model speeds up the contour convergence by up to 40% while maintaining the advantages of a local region based active contour model by reducing the number of iterations. Moreover, we propose a low-complexity pipelined VLSI architecture for improved region based active contour model targeting FPGA and 90 nm ASIC platforms. The proposed pipelined design offers an increased speed of operation. Its complexity is independent of the size of image.     

Optimization of injection‐molding process for mechanical properties of polypropylene components via a generalized regression neural network

This study analyzed contour distortions, wear and tensile properties of polypropylene (PP) components applied in the interior coffer of automobiles. A hybrid method integrating a trained generalized regression neural network (GRNN) and a sequential quadratic programming (SQP) method is proposed to determine an optimal parameter setting of the injection‐molding process. The specimens were prepared under different injection‐molding conditions by changing melting temperatures, injection speeds, and injection pressures. Average contour distortions at six critical locations, wear and tensile properties were selected as the quality targets. Sixteen experimental runs, based on a Taguchi orthogonal array table, were utilized to train the GRNN and then the SQP method was applied to search for an optimal setting. The trained GRNN was capable of predicting average contour distortions, wear and tensile properties at various injection‐molding conditions. In addition, the analysis of variance (ANOVA) was implemented to identify significant factors for the molding process and the proposed algorithm was compared with traditional schemes like the Taguchi method and the design of experiments (DOE) approach. Copyright © 2007 John Wiley & Sons, Ltd.     

The Problem of Linear Conjugation on a Closed Riemann Surface

In this paper, we present a general solution of the scalar Riemann problem on a closed Riemann surface in the case of a compound contour in the class of piecewise meromorphic functions multiple of a given divisor. All the results are known and belong to the author [15–17], except for the existence theorems and properties of basic functionals and also properties of a θ-function. The solution of the problem in a ‘special case’ has been announced by the author but not published [15]. Similar problems and some applications are considered in [1, 2, 12]. The main results of the paper were obtained by the author during his collaboration with Professor G. S. Litvinchuk, and this paper is devoted to his cherished memory. Received: April 13, 2007. Accepted: June 13, 2008.     

基于机器视觉的工件定位和识别

根据机器视觉系统具有非接触性、实时性、稳定性及高精度的特点。文中采用机器视觉技术对工件的定位和识别进行了研究。通过求取工件轮廓中心矩和轮廓轴线二阶导数的最小值,求得工件的中心坐标和旋转角度;通过提取模板工件的Hu不变矩与工件轮廓的Hu不变矩,利用轮廓的相似度量函数识别工件类型。实验结果表明,通过提取工件轮廓矩能在旋转的情况下精确地定位和识别工件。     

基于局部尺度特征描述和改进DTW技术的局部轮廓匹配算法

基于轮廓的图像匹配是计算机视觉领域中的重要问题,但是目前尚未有较成熟的算法能够很好地解决局部轮廓匹配问题及非相似变换和非刚体变换引起的轮廓形变问题.根据局部轮廓结构在产生形变时具有相对稳定性的规律及融合轮廓局部信息和全局信息的轮廓描述思想,本文提出了一种具有尺度、旋转、平移不变性,形变鲁棒性和初始点无关性的局部尺度轮廓描述算法.在此基础上,针对线性匹配方法效果不佳以及传统DTW技术约束路径的线性度不满足轮廓采样特性要求的问题,提出一种基于改进DTW技术的轮廓匹配算法,即结合轮廓采样特性设置九宫格的路径约束条件,以旋转角度为参数,计算全局最佳匹配路径.实验结果表明,对于存在尺度、平移、旋转及形变关系的两轮廓,该方法能较好地实现轮廓间的局部匹配,并且其匹配准确率平均约为92%,较HD算法提高了30%,较传统DTW算法提高了26%.     

一种具有深度值的莫尔轮廓图

提出一种具有深度值的莫尔轮廓图。将一称为高标的正圆锥与被测物一起置于粗光栅之后 ,平行光照明 ,远场拍照。在所拍图片上高标的高度值 (已知 )除以高标上的等高线数即可求得相邻等高线的深度值 ,进而直接在莫尔轮廓图上读出任一点到基准点的深度差值     

Observation of flux qubit states with the help of a superconducting differential double contour interferometer

The critical current of a new structure, the superconducting differential double contour interferometer (DDCI), investigated recently, depends on the parity of the sum of quantum numbers of the two superconducting loops connected in two points by two Josephson junctions. The theory confirms that the DDCI structure can be used for the ideal readout of quantum states of the flux qubit. Large jumps in the critical current and voltage enables to observe continuously the change in time the state of the flux qubit. Such observations can have fundamental importance for the investigation of macroscopic quantum systems with strongly discrete spectrum such as the flux qubit. The DDCI structure can also be used for precise measurement of a very weak magnetic field.