The globally Bi-3-connected property of the honeycomb rectangular torus

The honeycomb rectangular torus is an attractive alternative to existing networks such as mesh-connected networks in parallel and distributed applications because of its low network cost and well-structured connectivity. Assume that m and n are positive even integers with n ? 4. It is known that every honeycomb rectangular torus HReT(m,n) is a 3-regular bipartite graph. We prove that in any HReT(m,n), there exist three internally-disjoint spanning paths joining x and y whenever x and y belong to different partite sets. Moreover, for any pair of vertices x and y in the same partite set, there exists a vertex z in the partite set not containing x and y, such that there exist three internally-disjoint spanning paths of G-{z} joining x and y. Furthermore, for any three vertices x, y, and z of the same partite set there exist three internally-disjoint spanning paths of G-{z} joining x and y if and only if n ? 6 or m = 2.     

无线传感器网络中多重覆盖算法的研究

在无线传感网器络中的硬件资源普遍受限的情况下,形成优秀的覆盖控制策略己成为无线传感器网络研究领域的热点课题。本文提出了一种能量有效的多重物理覆盖算法,在保障覆盖与连通性的前提下,以能量、覆盖度为衡量指标,采用调度机制实现节点轮换活跃与休眠,有效地提高网络生存时间。仿真实验结果表明,与目前典型算法相比,提出的算法在网络生存时间、能量消耗与消亡节点数上具有显著的优势。     

异构数据库数据迁移的研究与实现

数据是业务运转的基础．是企业的宝贵资产。企业在系统升级过程中往往会遇到数据迁移问题,由于新系统与旧系统经常存在数据结构异构的情况．使数据迁移工作难以实现。开发一款异构数据库数据迁移工具,该工具通过框架程序在ODBC（Open Database Connectivity）开发平台以及XML技术的支持下实现数据结构异构的数据库数据迁移。     

基于自组网络的武器火力饱和分配研究

针对武器火力分配中网络自组连通情况及能否饱和分配问题,研究了网络自组及饱和攻击数计算方法和匈牙利分配算法,并对其进行了改进.首先根据有效通信距离和火力单元之间距离求解任意两火力单元的连通概率,得出连通概率矩阵,将矩阵中数值规范化,并利用准平方法计算得出网络连接矩阵;然后,根据敌我双方信息,构建火力分配模型;最后,求出各个目标饱和攻击数,运用改进匈牙利算法构建火力分配模型,并根据目标饱和情况进行二次分配.仿真结果表明,在自组网的基础上,运用改进后的匈牙利算法可以有效的解决饱和分配问题.     

Skitter与Ark探测架构下AS级Internet拓扑分析

选取CAIDA授权的AS级2003年1月—2007年12月的Skitter数据及2008年1月—12月的Ark数据进行层层深入的对比分析,以说明Internet拓扑探测架构的改变对拓扑探测结果的影响。首先统计了Skitter及Ark架构下AS级Internet拓扑的多种宏观特征,分析了Skitter与Ark在各特征值上的异同,进而分析了网络的幂率特征与网络连通性以及拓扑核数的演化,指出幂率性质以及高聚类性质在Internet拓扑中是真实存在的,不随探测方式的改变而消失。     

The Strong Property of Morphological Connected Alternated Filters

This paper studies connectivity aspects that arise in image operators that process connected components. The focus is on morphological image analysis (i.e., on increasing image operators) and, in particular, on a robustness property satisfied by certain morphological filters that is denominated the strong property. The behavior of alternated compositions of openings and closings is investigated under certain assumptions, particularly connectedness and a connected component preserving condition. It is shown that these conditions cannot in general guarantee the strong property of certain connected alternated filters because of issues related to the locality of the filters. As treated in the paper, there have been a series of misunderstandings in the literature concerning this topic, and it is important to clarify them. The root cause of those problems is discussed, and a solution is indicated. The class of connected openings and closings used to build connected alternated filters should therefore be defined to avoid such situations, since the strong property of alternated filters should be a distinctive characteristic of this class.

Design of neural network-based estimator for tool wear modeling in hard turning

Hard turning with cubic boron nitride (CBN) tools has been proven to be more effective and efficient than traditional grinding operations in machining hardened steels. However, rapid tool wear is still one of the major hurdles affecting the wide implementation of hard turning in industry. Better prediction of the CBN tool wear progression helps to optimize cutting conditions and/or tool geometry to reduce tool wear, which further helps to make hard turning a viable technology. The objective of this study is to design a novel but simple neural network-based generalized optimal estimator for CBN tool wear prediction in hard turning. The proposed estimator is based on a fully forward connected neural network with cutting conditions and machining time as the inputs and tool flank wear as the output. Extended Kalman filter algorithm is utilized as the network training algorithm to speed up the learning convergence. Network neuron connection is optimized using a destructive optimization algorithm. Besides performance comparisons with the CBN tool wear measurements in hard turning, the proposed tool wear estimator is also evaluated against a multilayer perceptron neural network modeling approach and/or an analytical modeling approach, and it has been proven to be faster, more accurate, and more robust. Although this neural network-based estimator is designed for CBN tool wear modeling in this study, it is expected to be applicable to other tool wear modeling applications.     

The Topology of Transportation Networks: A Comparison Between Different Economies

This paper presents an analysis of the topology of transportation networks within different systems of cities. Urban entities and their components are complex systems by their nature; there is no central force that affects their spatial structure. Thus, we study transportation networks within different countries as complex networks. Based on the above, we consider cities as nodes, while direct air and railways routes represent the links. We present characteristics of these networks including their degree and clustering coefficient. Transportation networks can be used as an indicator of economic activity between cities. Cities with strong economic relationship are characterized by high volume of connectivity. Our findings suggest that the topology of the analyzed transportation networks can be used to classify the countries they belong to based on their economic development.

Digital Steiner sets and Matheron semi-groups

There are various ways to define digital convexity in Zⁿ$Z^n$. The proposed approach focuses on structuring elements (and not the sets under study), whose digital versions should allow to construct hierarchies of operators satisfying Matheron semi-groups law _γλγμ=_γmax(λ,_μ)${\gamma }_{\lambda }{\gamma }_{\mu }={\gamma }_{\max (\lambda \text{,}\mu )}$, where λ$\lambda$ is a size factor. In Rⁿ$R^n$ the convenient class is the Steiner one. Its elements are Minkowski sums of segments. We prove that it admits a digital equivalent when the segments of Zⁿ$Z^n$ are Bezout. The conditions under which the Steiner sets are convex in Zⁿ$Z^n$, and are connected, are established. The approach is then extended to structuring elements that vary according to the law of perspective, and also to anamorphoses, so that the digital Steiner class and its properties can extend to digital spaces as a sphere or a torus.     

Connectivity preserving localized coverage algorithm for area monitoring using wireless sensor networks

Efficient network coverage and connectivity are the requisites for most Wireless Sensor Network (WSN) deployments, particularly those concerned with area monitoring. Due to the resource constraints of the sensor nodes, redundancy of coverage area must be reduced for effective utilization of the available resources. If two nodes have the same coverage area in their active state, and if both the nodes are activated simultaneously, it leads to redundancy in network and wastage of precious sensor resources. In this paper, we address the problem of network coverage and connectivity and propose an efficient solution to maintain coverage, while preserving the connectivity of the network. The proposed solution aims to cover the area of interest (AOI), while minimizing the count of the active sensor nodes. The overlap region of two sensor nodes varies with the distance between the nodes. If the distance between two sensor nodes is maximized, the overall coverage area of these nodes will also be maximized. Also, to preserve the connectivity of the network, each sensor node must be in the communication range of at least one other node. Results of simulation of the proposed solution indicate up to 95% coverage of the area, while consuming very less energy of 9.44 J per unit time in the network, simulated in an area of 2500 m².