Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Frequent occurrence of ocular artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In the present paper, a novel and robust technique is proposed to eliminate ocular artifacts from EEG signals in real time. Independent Component Analysis (ICA) is used to decompose EEG signals. The features of topography and power spectral density of those components are extracted. Moreover, we introduce manifold learning algorithm, a recently popular dimensionality reduction technique, to reduce the dimensionality of initial features, and then those new features are fed to a classifier to identify ocular artifacts components. A k-nearest neighbor classifier is adopted to classify components because classification results show that manifold learning with the nearest neighbor algorithm works best. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove ocular artifacts effectively from EEG signals with little distortion of the underlying brain signals and be satisfied the real-time application.     

Determinants of software quality: A survey of information systems project managers

Software quality is important for the success of any information systems (IS). In this research, we find the determinants of software quality. We used five attributes for software quality: system reliability, maintainability, ease of use, usefulness, and relevance. By surveying 112 IS project managers, we collected data about their perceptions on the software quality attributes and their determinants. We arrived at six factors through exploratory factor analysis. We determined the individual factors that impacted the software quality attributes; for example, reliability is associated with responsiveness of IS department; ease of use is influenced by the capabilities of users and attitude of management; and usefulness is impacted by capabilities of IS department and responsiveness of IS department. We show that organizational factors are more important than technical factors in impacting software quality in IS projects. We provide implications of our research to practice and to future research.     

复杂网络生存性评估方法研究

该文针对复杂网络的特点,首先给出了复杂网络生存性的一个新测度——容忍度。在此基础之上,给出了生存性测度的新定义,针对复杂网络无标度性的特点,给出了复杂网络生存性评估的新方法,并以互联网抽样数据为例进行了网络生存性分析。最后对复杂网络生存性研究的思路进行了探讨,指出从网络拓扑结构出发,研究拓扑结构的各种属性对网络生存性的影响,将是复杂网络生存性研究的一个有效而新颖的思路。     

浅谈《JAVA程序设计》中线程的教学

线程是《JAVA程序设计》这门课程学习的一个难点。针对线程部分在JAVA教学中的地位和特点,结合学生的特点采取恰当的教学手段,使学生能深入理解线程的理论并能运用理论作简单的线程编程。     

稀土萃取过程组分含量的RBF软测量建模

稀土萃取分离过程的自动控制一直是稀土工业急待解决的关键难题,要实现稀土萃取分离过程的自动控制,首先必须解决串级萃取槽中各稀土组分含量的在线检测,而传统的检测又有很大的问题,软测量技术在稀土萃取过程控制中的应用研究开始活跃起来。该文是基于RBF网络的稀土萃取过程组分含量进行软测量建模的研究。     

高校多媒体网络教室的病毒防治

本文针对高校多媒体网络教室面临的计算机病毒、网络攻击等安全问题,首先分析了病毒的传播途径,然后探讨了计算机病毒防治的方法,从技术上和管理等方面有的放矢地采取安全防治对策,为师生创造安全的实验教学环境。     

辩论支持系统的设计与实现

本文阐述了辩论支持系统开发的背景和意义,介绍了基于J2EE的系统需求,分析了软件的结构和主要功能模块及所用的关键技术框架,并在J2EE平台上开发实现了系统。     

Stabilization of linear systems with input delay and saturation—A parametric Lyapunov equation approach

This paper studies the problem of stabilizing a linear system with delayed and saturating feedback. It is known that the eigenstructure assignment‐based low‐gain feedback law (globally) stabilizes a linear system in the presence of arbitrarily large delay in its input, and semi‐globally stabilizes it when the input is also subject to saturation, as long as all its open‐loop poles are located in the closed left‐half plane. Based on a recently developed parametric Lyapunov equation‐based low‐gain feedback design method, this paper presents alternative, but simpler and more elegant, feedback laws that solve these problems. The advantages of this new approach include its simplicity, the capability of giving explicit conditions to guarantee the stability of the closed‐loop system, and the ease in scheduling the low‐gain parameter on line to achieve global stabilization in the presence of actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.     

FacetAtlas: multifaceted visualization for rich text corpora

Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.     

High-dimensional kNN joins with incremental updates

The k Nearest Neighbor (kNN) join operation associates each data object in one data set with its k nearest neighbors from the same or a different data set. The kNN join on high-dimensional data (high-dimensional kNN join) is a very expensive operation. Existing high-dimensional kNN join algorithms were designed for static data sets and therefore cannot handle updates efficiently. In this article, we propose a novel kNN join method, named kNNJoin ⁺, which supports efficient incremental computation of kNN join results with updates on high-dimensional data. As a by-product, our method also provides answers for the reverse kNN queries with very little overhead. We have performed an extensive experimental study. The results show the effectiveness of kNNJoin⁺ for processing high-dimensional kNN joins in dynamic workloads.