Effects of a radial electric field on the thinning of a slightly viscoelastic liquid jet

In this work, we study numerically the thinning of a perfectly conducting, slightly viscoelastic liquid jet under a radial electric field by using a one-dimensional model. In the presence of the electric field, the viscoelastic jet develops into a stable beads-on-a-string structure with a uniform thickness filament as in the non-electrified case, but the \( 1/3De \) (De: the Deborah number) exponential law of filament thinning and polymer stress growth holds no more. The electric field decelerates the thinning of the jet and induces periodic oscillations of the flow properties. The balance between the surface tension, viscoelastic stress and electrostatic force is absent in the filament, and as a result, the extensional flow there becomes unsteady.     

基于嵌入式操作系统的无线电力监测系统设计

为了对各个用电设备或用电用户的电力使用情况进行精细化监测,从而对电力评估、管理起到基础性作用,设计了一种基于Contiki嵌入式操作系统的无线电力监测系统,以6LoWPAN作为无线通信协议;该系统由终端节点、路由节点、接入节点、GPRS模块及计算机数据中心构成;介绍了基于TV3154、TA5212及信号调理滤波网络、ADE7653电力测量、MSP430F1611主控及CC2420射频的终端节点、接入节点硬件设计方法;介绍了基于Contiki,结合6LoWPAN无线协议及I²C、UART等串口通信协议的终端节点、接入节点软件设计方法;实验结果表明,系统对电压、电流、功率的测量误差均保持在2% 以内,能够实现组网,两个节点之间的无线通信距离可达80 m。     

基于小波变换与脉冲耦合神经网络的人脸识别

脉冲耦合神经网络（Pulse Coupled Neural Network,PCNN）是基于生物视觉特性而提出的新一代人工神经网络,它在数字图像处理及人工智能等领域具有广泛应用前景.本文通过研究PCNN理论模型及其工作特性的基础上提出了一种提取人脸特征的方法.首先利用小波变换提取人脸图像低频特征,降低人脸图像的维度,然后利用简化的PCNN提取小波低频系数重构后的人脸图像的相应时间序列,并以此作为人脸识别的特征序列.最后利用时间序列和欧式距离完成人脸的识别过程.本文通过ORL人脸库进行实验证明了该方法的有效性.     

非平稳信号度量方法综述

非平稳性度量是非平稳信号处理中的重要课题。尽管已有的度量方法在原理上有较大差异,缺少统一的理论框架和度量标准,但这些非平稳信号度量方法都在其相应领域发挥了重要作用。本文对随机信号非平稳度量方法进行了综述,系统地总结、归纳了不同领域现有的随机信号非平稳性度量方法,包括基于统计量和动力学参数的时域方法、时频域参数方法、信号建模参数方法以及特定类别信号的非平稳性度量方法,给出了各种度量方法的应用,并展望了非平稳性度量的未来发展方向。     

“技能-能力-思维”三层次大学计算机基础教学

当前因受“狭议工具论”错误认识的影响致使非计算机专业大学计算机基础教育受到冲击,计算机基础课程的学时也被缩减.针对这一问题,提出了以计算思维培养为核心,“技能-能力-计算思维”三层次培养目标的教学改革.对课程内容进行了面向知识能力、计算思维教学的内容重构与组织,给出了具体的教学内容模块,同时探讨了计算思维培养教学方法.     

Direct generation of extended STL file from unorganized point data

An extended STL file format is presented in this paper. Differing with existing solutions, it proposes a new format to produce and store triangles. The format uses a cluster unit composed of several triangles. The main advantages of the format are that it contains both geometry and topological information and has improved storage capability. Direct generation of the extended STL from the scanned data has a great advantage in that it can reduce the time and error in modeling process. In order to obtain the format from unorganized point cloud, a new triangulation algorithm was introduced. The algorithm is based on reconstructing the relative Delaunay triangulation of the sample points on the surface. Other advantages of the extended STL format were also presented in this paper.     

Quantifying blood flow and wall shear stresses in the outflow tract of chick embryonic hearts

Wall shear stresses (WSS) exerted by blood flow on cardiac tissues modulate growth and development of the heart. To study the role of hemodynamic conditions on cardiac morphogenesis, here, we present a methodology that combines imaging and finite element modeling to quantify the in vivo blood flow dynamics and WSS in the cardiac outflow tract (OFT) of early chicken embryos (day 3 out of 21-day incubation period). We found a distinct blood flow field and heterogeneous distribution of WSS in the chicken embryonic heart OFT, which have physiological implications for OFT morphogenesis.     

A topology-preserving optimization algorithm for polycube mapping

We present an effective optimization framework to compute polycube mapping. Composed of a set of small cubes, a polycube well approximates the geometry of the free-form model yet possesses great regularity; therefore, it can serve as a nice parametric domain for free-form shape modeling and analysis. Generally, the more cubes are used to construct the polycube, the better the shape can be approximated and parameterized with less distortion. However, corner points of a polycube domain are singularities of this parametric representation, so a polycube domain having too many corners is undesirable. We develop an iterative algorithm to seek for the optimal polycube domain and mapping, with the constraint on using a restricted number of cubes (therefore restricted number of corner points). We also use our polycube mapping framework to compute an optimal common polycube domain for multiple objects simultaneously for lowly distorted consistent parameterization.     

Bilayer segmentation of webcam videos using tree-based classifiers

This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a novel motion representation, referred to as "motons," inspired by research in object recognition. Second, we propose estimating the segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo systems.     

Color face recognition based on quaternion matrix representation

There are several methods to recognize and reconstruct a human face image. The principal component analysis (PCA) is a successful approach because of its effective extraction of the global feature and excellent reconstruction of face image. However, the crucial shortcomings of PCA are its low recognition rate and overfitting of feature extraction which leads to the dependence of training data on training samples. In this paper, a modified two-dimension principal component analysis (2DPCA) and bidirectional principal component analysis (BDPCA) methods based on quaternion matrix are proposed to recognize and reconstruct a color face image. In these methods, the spatial distribution information of color images is used to represent a color face, and the 2DPCA or BDPCA feature of color face image is extracted by reducing the dimensionality in both column and row directions. A method obtaining orthogonal eigenvector set of quaternion matrix is proposed. Numerous experiments show that the present approach based on quaternion matrix can effectively smooth the overfitting issue and substantially enhance the recognition rate.