Rotation,scaling and translation invariant texture recognition by Bessel-Fourier moments

The ideal of Bessel-Fourier moments (BFMs) for image analysis and only rotation invariant image cognition has been proposed recently. In this paper, we extend the previous work and propose a new method for rotation, scaling and translation (RST) invariant texture recognition using Bessel-Fourier moments. Compared with the others moments based methods, the radial polynomials of Bessel-Fourier moments have more zeros and these zeros are more evenly distributed. It makes Bessel-Fourier moments more suitable for invariant texture recognition as a generalization of orthogonal complex moments. In the experiment part, we got three testing sets of 16, 24 and 54 texture images by way of translating, rotating and scaling them separately. The correct classification percentages (CCPs) are compared with that of orthogonal Fourier-Mellin moments and Zernike moments based methods in both noise-free and noisy condition. Experimental results validate the conclusion of theoretical derivation: BFM performs better in recognition capability and noise robustness in terms of RST texture recognition under both noise-free and noisy condition when compared with orthogonal Fourier-Mellin moments and Zernike moments based methods.     

Predicting temporal centrality in Opportunistic Mobile Social Networks based on social behavior of people

Predicting the centrality of nodes is a significant problem for different applications in Opportunistic Mobile Social Networks (OMSNs). However, when calculating such metrics, current studies focused on analyzing static networks that do not change over time or using aggregated contact information over a period of time. Furthermore, the centrality measured in the past is not verified whether it is useful as a predictor for the future. In this paper, in order to capture the dynamic behavior of people, we focus on predicting nodes’ future centrality (importance) from the temporal perspective using real mobility traces in OMSNs. Three important centrality metrics, namely betweenness, closeness, and degree centrality, are considered. Through real trace-driven simulations, we find that nodes’ future centrality is highly predictable due to natural social behavior of people. Then, based on the observations in the simulation, we design several reasonable prediction methods to predict nodes’ future temporal centrality. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed methods. The results show that the Recent Weighted Average Method performs best in the MIT Reality trace, and the recent Uniform Average Method performs best in the Infocom 06 trace. Furthermore, we also evaluate the impact of parameters m and w on the performance of the proposed methods and find proper values of different parameters for each proposed method at the same time.     

Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node’s perspective i.e., migrating the service of faulty node to it’s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don’t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level.     

北京市水资源状态与供需压力研究

针对北京市水资源短缺与用水需求不断增长的矛盾,将北京市水资源状态分成四个紧缺等级——无紧缺、轻度紧缺、中度紧缺和重度紧缺,形成了水资源状态指数,综合反映了北京市水资源动态变化;水资源供需压力分成四个压力等级——无压力、轻度压力、中度压力和重度压力,形成了供需压力指数,综合反映了北京市水资源供需状态。利用这两个指数对近年来北京市水资源状态和供需压力情况进行了综合评价。     

虚拟天文台技术在HXMT卫星任务中应用的设想

随着国际上天文项目的发展与推进,天文数据呈爆炸式增长。如何从浩瀚如海的大数据集中快速检索所需信息,已成为各国科研人员共同面对的问题。虚拟天文台（Virtual Observatory,VO）为满足对大规模天文数据进行处理分析、检索访问和共享发布等需求,提供了一系列 VO 标准协议和集成化服务。开发符合 VO 标准协议的天文数据发布与检索系统、集成数据访问发布和可视化分析服务,符合当前的发展趋势。本文在回顾 VO发展历程的基础上,对 VO 体系结构、VO 标准协议等若干关键技术进行概括阐述。同时,基于 VO的技术框架,提出硬 X 射线调制望远镜（Hard X-ray ModulationTelescope,HXMT）卫星数据发布与检索系统的初步设想,设计符合卫星任务需求的体系结构,并选取相关的 VO 标准协议加以实现。     

区域自动站发布显示系统的研究

阐述了自动站和电子显示屏的工作原理。通过研发自动站现场发布显示系统实现了自动站与电子显示屏硬件的无缝连接,通过显示系统能每分钟在电子显示屏上更新气象要素内容;为推进气象预警工作的实施提供了一种方案和思考。     

为互联网和数据中心业务定制的服务器——云计算资源池的最佳选择

随着云计算技术的不断发展,基于云计算定制的服务器在互联网和数据中心的应用日益广泛,各种解决方案层出不穷。本文通过分析服务器和云计算的技术特点,比较各种硬件解决方案的异同,确定最适合云计算资源池应用的硬件支撑方案。     

一种面向机器人的超市形态建模方法

结合超市经营客观实际,围绕环境、商品、货架形态建模过程遇到的体系结构、关键症结解决对策等展开深入讨论,给出了问题处置策略、指导原则与技术路线。