多种类病毒相互作用下网络信道破坏程度估计

在多种类病毒的相互作用下,网络信道破坏程度呈现较强的非线性特点,不能准确约束病毒在相互作用下的破坏性.传统的网络破坏程度估计的过程中,仅仅以单一病毒破坏程度叠加的方式去计算参数,计算具有较强的主观性,没有准确描述不同病毒的破坏作用,对评估结果误差影响较大.为解决上述问题,提出一种基于粒子滤波算法的多种类病毒相互作用下网络信道破坏程度估计方法,利用欧式距离方法对全部病毒进行分类,获取不同类型病毒的网络信道破坏程度值.通过粒子滤波算法对单一个体约束的优势,将每一个不同类型的病毒看作一个粒子,并进行训练.根据训练结果,利用粒子滤波算法对当前时刻网络信道破坏程度进行估计,完成整体评估.实验结果表明,利用改进算法进行多种类病毒相互作用下网络信道破坏程度估计,能够有效提高估计的准确率.     

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.     

喀麦隆曼维莱水电站枢纽布置设计

介绍了曼维莱水电站枢纽布置设计及优化过程,在合同方案基础上,根据最新的补充勘测资料及多方案研究对比,对坝线选择、引水发电系统方案选择和厂区枢纽布置及优化设计工作做了详细的介绍,可供同类工程参考。     

用于腺苷检测的基于级联扩增核酸适体电化学传感界面的构建研究

该文报道了一种基于酶级联扩增的电化学核酸适体检测腺苷的新方法。当腺苷存在时,滚环扩增的引物通过E.coli DNA连接酶的作用与通过巯基组装在金电极上的固定探针连接。在pHi29 DNA聚合酶的作用下,滚环扩增反应进行并产生一条与环形探针完全互补的长的单链,然后将大量金纳米粒子标记的核酸探针与滚环扩增的产物杂交。该传感界面电化学行为的结果表明,通过酶级联扩增的方法提高了检测腺苷的阻抗响应灵敏度,且选择性和重现性良好。用于腺苷的检测时,其线性范围为2.0μmol/L~100μmol/L,最低检测浓度为2.0μmol/L,表明该传感界面的设计可作为一种通用方法而有望用于其它目标物的检测。     

IT时代的第三种文化产业机遇

人类社会一直存在着并行不悖的科技与人文的两种文化与文化产业。如今,出现了第三种文化与第三种文化产业。第三种文化产业的思想基础是图灵的可计算原理;其物质基础是微处理器智力内核。第三种文化产业将打破两种文化产业界线,用人工智能与智能化工具等现代科技手段,实现人文文化领域中的艺术创作,这是人类文化领域中已经开始的没有硝烟的产业战争。     

探讨计算机网络专业学生的实训教学课程设置

计算机网络专业的学生的实训内容通常是与今后的工作相关联的,这种关联的程度越高,说明实训的效果越好,也可以从一个侧面说明教学越成功。所以,实训项目不应该是凭空设想的,也不应该是几年如一日的照搬照抄的,应该是符合市场需求的,与时俱进的。     

Sampling average approximation method for a class of stochastic Nash equilibrium problems

We consider a class of stochastic Nash equilibrium problems (SNEP). Under some mild conditions, we reformulate the SNEP as a stochastic mixed complementarity problem (SMCP). We apply the well-known sample average approximation (SAA) method to solve the SMCP. We further introduce a semismooth Newton method to solve the SAA problems. The comprehensive convergence analysis is given as well. In addition, we demonstrate the proposed approach on a stochastic Nash equilibrium model in the wholesale gas–oil markets.     

Mediating and inducing quantum correlation between two separated qubits by one-dimensional plasmonic waveguide

We investigate the dynamics of quantum correlation between two separated qubits trapped in one-dimensional plasmonic waveguide. It is found that for a class of initial states, the quantum discord shows a sudden change phenomenon during the dynamical evolution. Furthermore, we demonstrate that the quantum discord can be enhanced if a proper product of the plasmon wave number and two qubits distance is chosen. Finally, we find that the non-zero quantum discord between two qubits can be created for the states without initial quantum discord during the time evolution of the system.     

Image mosaicking for polyhedral scene and in particular singly visible surfaces

Image mosaic construction is about stitching together a number of images about the same scene to construct a single image with a larger field of view. The majority of the previous work was rooted at the use of a single image-to-image mapping termed planar homography for representing the imaged scene. However, the mapping is applicable only to cases where the imaged scene is either a single planar surface, or very distant from the cameras, or imaged under a pure rotation of the camera, and that greatly limits the range of applications of the mosaicking methods. This paper presents a novel mosaicking solution for scenes that are polyhedral (thus consisting of multiple surfaces) and that are pictured possibly in closed range of the camera. The solution has two major advantages. First, it requires only a few correspondences over the entire scene, not correspondences over every surface patch in it to work. Second, it conquers a seemingly impossible task—warping image data of surfaces that are visible in only one of the input images, which we refer to as the singly visible surfaces, to another viewpoint to constitute the mosaic there. We also provide a detail analysis of what determines whether a singly visible surface could be mosaicked or not. Experimental results on real image data are presented to illustrate the performance of the method.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.