Learning in an Ambient Intelligent World: Enabling Technologies and Practices

The rapid evolution of information and communication technology opens a wide spectrum of opportunities to change our surroundings into an Ambient Intelligent (AmI) world. AmI is a vision of future information society, where people are surrounded by a digital environment that is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user friendliness, user empowerment, and more effective service support, with an aim to bring information and communication technology to everyone, every home, every business, and every school, thus improving the quality of human life. AmI unprecedentedly enhances learning experiences by endowing the users with the opportunities of learning in context, a breakthrough from the traditional education settings. In this survey paper, we examine some major characteristics of an AmI learning environment. To deliver a feasible and effective solution to ambient learning, we overview a few latest developed enabling technologies in context awareness and interactive learning. Associated practices are meanwhile reported. We also describe our experience in designing and implementing a smart class prototype, which allows teachers to simultaneously instruct both local and remote students in a context-aware and natural way.     

Addressing agent loss in vehicle formations and sensor networks

In this paper, we address the problem of agent loss in vehicle formations and sensor networks via two separate approaches: (1) perform a ‘self‐repair’ operation in the event of agent loss to recover desirable information architecture properties or (2) introduce robustness into the information architecture a priori such that agent loss does not destroy desirable properties. We model the information architecture as a graph G(V, E), where V is a set of vertices representing the agents and E is a set of edges representing information flow amongst the agents. We focus on two properties of the graph called rigidity and global rigidity, which are required for formation shape maintenance and sensor network self‐localization, respectively. For the self‐repair approach, we show that while previous results permit local repair involving only neighbours of the lost agent, the repair cannot always be implemented using only local information. We present new results that can be applied to make the local repair using only local information. We describe implementation and illustrate with algorithms and examples. For the robustness approach, we investigate the structure of graphs with the property that rigidity or global rigidity is preserved after removing any single vertex (we call the property as 2‐vertex‐rigidity or 2‐vertex‐global‐rigidity, respectively). Information architectures with such properties would allow formation shape maintenance or self‐localization to be performed even in the event of agent failure. We review a characterization of a class of 2‐vertex‐rigidity and develop a separate class, making significant strides towards a complete characterization. We also present a characterization of a class of 2‐vertex‐global‐rigidity. Copyright © 2008 John Wiley & Sons, Ltd.     

Study of folklore and mythological traditions using intellectual data mining

This work is devoted to the development and substantiation of intellectual data mining as applied to studying folklore and mythological traditions. The approach is based on use of the functions of distance between traditions. The examples of application of the methods developed to investigate the interrelation between folklore traditions of the American continent are considered.     

Interoperability of the national system of electronic digital signatures

The reasons of the absence of interoperability of the National system of electronic digital signatures (EDS) are analyzed based on systems analysis. An attempt is made to substantiate the minimax (minimum costs and maximum results) way of solving interoperability problems in EDS infrastructure in both Ukraine and foreign states.     

Investigation of stability of nonlinear continuous-discrete models of economic dynamics using vector Lyapunov function. I

Modification and extension of procedures of strict analysis of stability and estimates of domains of attraction based on the reduction method with sublinear vector Lyapunov function is given for sufficiently wide class of nonlinear stabilization systems of continuous objects using piecewise constant control formed using discrete state measurements. The new type of realization of heterogeneous comparison systems in the form of interconnected differential and discrete impulse subsystems with varying right-hand side allowing to avoid preliminary discretization of the original system and thus increasing accuracy of investigations is proposed. In the case of nonlinearities (including those with respect to control and measurements), limited by semi-homogeneous functions. Constructive conditions of exponential stability with necessary quantitative estimates are formulated. Application to investigation of stability of economic growth in the Phillips-Bergstrom model with discrete monetary regulation is presented. The paper consists of two parts. In this part, the studied models are described, the studied stability property is defined, procedures for construction of vector Lyapunov function and comparison system are given, and some of their specific features are established.     

Learning Situation Models in a Smart Home

This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.     

FaceSeg: Automatic Face Segmentation for Real-Time Video

Segmenting human faces automatically is very important for face recognition and verification, security system, and computer vision. In this paper, we present an accurate segmentation system for cutting human faces out from video sequences in real-time. First, a learning based face detector is developed to rapidly find human faces. To speed up the detection process, a face rejection cascade is constructed to remove most of negative samples while retaining all the face samples. Then, we develop a coarse-to-fine segmentation approach to extract the faces based on a min-cut optimization. Finally, a new matting algorithm is proposed to estimate the alpha-matte based on an adaptive trimap generation method. Experimental results demonstrate the effectiveness and robustness of our proposed method that can compete with the well-known interactive methods in real-time.     

基于WSNs的脉搏血氧饱和度中央监护系统设计

设计了基于无线传感器网络(WSNs)的脉搏血氧饱和度中央监护系统;系统利用WSNs的各节点位置随机分布,自组织网络等特性,结合脉搏血氧饱和度监测仪,应用无线的方式完成了病人脉搏血氧饱和度监测的任务;其中一个传感器节点与血氧仪相连进行数据采集;其它的节点组成多跳路由通讯网络,把血氧仪采集到的数据传到中央监控站(host computer)里;最后通过一个友好的图形用户界面来接收和显示所有的监测患者的实时监测数据,并经分析、处理后实现自动报警,自动记录;试验结果表明具有很高的测量精确度,且在设备的实用性、方便性、可管理性等方面有了极大提高。     

飞机分布式配电系统总线监控器的设计

对基于MIL-STD-1553B多路传输数据总线先进飞机的分布式配电系统的结构和功能进行了分析,重点论述了总线监控器的功能及应用软件的设计及实现;配电系统的消息数量多而且传输频率高,因此如何实现实时监控消息是软件设计的关键;采用Windows消息驱动机制和多线程概念,并结合实时内存数据库技术,很好地解决了实时问题;总线通信试验表明,系统运行可靠,达到了功能要求,具有很好的监控效果,可用于分布式配电系统中。     

空时互反馈人脸检测与跟踪系统的算法与实现

针对视频序列人脸检测与跟踪算法复杂、实时性要求较强这一相互矛盾的特点,设计了基于数字媒体处理器DM642的空时互反馈人脸检测与跟踪系统;在空域,利用主成分分析和最优阈值检测人脸;在时域,根据前后帧概率密度函数的相对距离确定跟踪目标,利用卡尔曼滤波器对检测出人脸的位置进行预测,形成了空时互反馈闭环算法;实践表明,该系统运行稳定可靠,能满足实时视频监控系统的要求。