LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.     

A feature extraction method for use with bimodal biometrics

Bimodal biometrics has been found to outperform single biometrics and are usually implemented using the matching score level or decision level fusion, though this fusion will enable less information of bimodal biometric traits to be exploited for personal authentication than fusion at the feature level. This paper proposes matrix-based complex PCA (MCPCA), a feature level fusion method for bimodal biometrics that uses a complex matrix to denote two biometric traits from one subject. The method respectively takes the two images from two biometric traits of a subject as the real part and imaginary part of a complex matrix. MCPCA applies a novel and mathematically tractable algorithm for extracting features directly from complex matrices. We also show that MCPCA has a sound theoretical foundation and the previous matrix-based PCA technique, two-dimensional PCA (2DPCA), is only one special form of the proposed method. On the other hand, the features extracted by the developed method may have a large number of data items (each real number in the obtained features is called one data item). In order to obtain features with a small number of data items, we have devised a two-step feature extraction scheme. Our experiments show that the proposed two-step feature extraction scheme can achieve a higher classification accuracy than the 2DPCA and PCA techniques.     

移动通讯及动态Web在原油计量中的应用

针对传统人工抄表计量方式存在的不足,利用先进的移动通讯技术和动态Web技术研制开发了新一代油田计量站原油分站计量系统,实现了计量数据的自动采集、无线远程传输和网络发布共享。     

软件技术专业实践教学体系的构建

简要介绍了软件技术专业实践教学体系构建的背景,结合具体课程给出实践教学体系构建过程,实践证明构建合理的实践教学体系是实现软件技术专业工学结合人才培养模式创新的必备条件和关键。     

基于Flex和Java的在线考试系统

随着计算机应用的迅猛发展,网络应用不断扩大,远程教育和虚拟大学的出现等等。传统的考试方式已经不能适应现代考试的需要。该文利用当前流行的Flex技术结合Java技术来构造RIA界面,实现在线考试系统,加强与学生的交互能力,减轻教师的工作负担及提高工作效率,与此同时也提高了考试的质量,从而使考试更趋于公证、客观。     

基于SOA技术的高校URP建设的研究

通过基于SOA技术的高校管理工作一体化信息系统软件的设计,综合性地量化学校各项重要信息,使信息一体化程度更为集中,帮助管理工作者、校务管理人员做出动态的管理决策调整,对于URP流程改造提供具体的实践模型参考。同时研究出一种能够应用于各类普通高校管理工作的无线与网络相结合的软件系统设计模型,提供一种可量化的工作数据系统样本,以比较低廉的代价解决校园信息化过程中的信息孤岛问题,进一步提升校园信息一体化程度。     

A new vertical‐alignment LC mode with high‐transmittance and fast‐response‐time features

Abstract— A novel pixel design for vertical‐alignment LCDs with superior transmittance has been developed. The new liquid‐crystal mode, refered to as the hole‐induced vertical‐alignment mode (Hi‐VA), uses a via hole of an organic layer on a TFT substrate to achieve multi‐domain alignment. Compared to the conventional design, the Hi‐VA mode has a transmittance of up to 135% with a contrast ratio of 2000:1. Moreover, the new structure is free from ITO patterning or protrusion on the color‐filter side, which makes the fabrication process simple and low cost.     

Combination framework of rendezvous algorithm for multi‐agent systems with limited sensing ranges

In this paper, we present a novel rendezvous algorithm framework ‐ the combination framework, which is named after the term convex combination in computational geometry. The convergence of the proposed algorithms is proved based on the novel tools from the recent results in graph theory and consensus study. It has been observed that the circumcenter algorithm can be considered as a special case of the combination framework. Moreover, the relevant analysis provides us a novel viewpoint on designing and the rendezvous rate of rendezvous algorithms for networked multi‐agent systems with limited sensing ranges. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Energy-aware weighted graph based dynamic topology control algorithm

In this paper a new energy-aware weighted dynamic topology control (WDTC) algorithm is proposed to extend the lifetime of wireless network and balance the nodes’ energy consumption. The idea is that each node builds its local minimum spanning tree (MST) based on the energy-aware weighted graph and the network topology is adjusted accordingly. It was proved theoretically that the topology under WDTC algorithm could preserve the network connectivity and a sufficient condition for the degree of no more than 6 was also given. Simulation shows that WDTC algorithm can effectively prolong the network lifetime and has good topological features.     

uPOSC4.5:一种针对不确定数据的PU学习决策树算法

目前绝大多数在PU学习场景下的学习算法都只能处理确定数据,然而在现实世界的许多应用中,如在传感器网络、市场分析和医学诊断等领域,数据的不确定性是普遍存在的.基于针对精确数据的PU学习算法POSC4. 5,提出了PU概率信息增益,并在此基础上提出了一种能在PU学习场景下学习不确定数据的决策树算法uPOSC4. 5(Uncertain POSC4. 5).在UCI数据集上的实验表明,uPOSC4. 5具有较好的分类准确率和健壮性,且其分类性能优于aUPNB.