A passive tree-based backbone construction scheme for MANETs

Backbone-based broadcast schemes are effective in alleviating the broadcast storm problem in mobile ad hoc networks (MANETs). However, periodically hello packets used to maintain a backbone usually lead to extra control overhead. In this paper, passive tree-based backbone construction scheme (PTBCS) is proposed as a backbone construction scheme for MANETs. Different from other schemes, each node in PTBCS determines its role by intercepting packet transmissions in the air during a special waiting period. Hence, its most remarkable advantage is that there are no periodical packet transmissions specially for backbone construction. The property that the nodes selected by PTBCS make up a connected dominating set (CDS) of the network is proven with several ideal assumptions. Simulation results show that PTBCS is effective when compared with some other typical backbone construction schemes.     

Generalized predictive control of linear systems with actuator arrearage faults

Arrearage phenomenon commonly exists in actuators. With arrearages, the actuator cannot achieve the target suggested by the controller, so the control performance degrades. Deadzones and stuck faults can be considered to be two special classes of arrearages, which are studied widely; however, more general arrearage phenomenon cannot be described and treated by these forms. In this paper, a general formulation is introduced for this phenomenon, which is referred as actuator arrearage faults. Based on this formulation, an improved generalized predictive control scheme is proposed for a class of single-input single-output linear systems. With arrearage faults, the system becomes a stochastic process. Hence, a sufficient condition, which guarantees the output tracking error to be uniformly ultimately bounded in the mean-square sense, is presented in this paper. Simulations are given to illustrate the feasibility and effectiveness of the proposed scheme.     

混合式P2P系统中基于缓存的多项查询方法研究

首先从混合式P2P网络拓扑结构出发,结合DHT思想,提出了基于DHT的层次化P2P网络模型.其次根据在文档集巨大的情况下,用户提交的查询不可能"面面俱到",实际用来回答查询的文档仅仅是文档集中很小的一部分这一思想,在层次化P2P模型的超级节点中建立了分布式缓存,运用分布式索引与缓存技术,提出一种新的方法来解决多项查询问题.即由多项查询中的某个关键字key,根据hash函数定位到负责该key的超级节点,查询该节点上的分布式索引得到缓存具体存储位置,最终将结果返回给用户,如若缓存中没有所要查询的内容,则广播该查询,同时根据系统中的历史广播查询信息来计算某个待选缓存项的利益值,利益最大的待选项加入缓存.一般针对多项查询的泛洪算法往往会造成巨大的网络信息量,提出的方法牺牲了超级节点上一小部分的存储力,缓解了多项查询造成的网络拥挤现象.同时,基于DHT的层次化P2P模型也具有很好的稳定性,不会因为大量节点的动态加入或者退出而无法进行多项查询.     

Reachability determination in acyclic Petri nets by cell enumeration approach

Reachability is one of the most important behavioral properties of Petri nets. We propose in this paper a novel approach for solving the fundamental equation in the reachability analysis of acyclic Petri nets, which has been known to be NP-complete. More specifically, by adopting a revised version of the cell enumeration method for an arrangement of hyperplanes in discrete geometry, we develop an efficient solution scheme to identify firing count vector solution(s) to the fundamental equation on a bounded integer set, with a complexity bound of O((nu)^n−m), where n is the number of transitions, m is the number of places and u is the upper bound of the number of firings for all individual transitions.     

基于DDC楼宇智能控制系统的设计

将楼宇中的电力、照明、空调、给排水、保安、通信和广播等多方面的设备通过现场总线联接进行控制与管理是楼宇智能化的一个主要的发展方向,本设计利用对上位机和下位机的软硬件系统的设计来实现楼宇智能化控制,达到预期的效果。

Abstract：

Building the power, lighting, air conditioning, plumbing, security, communications and broadcasting, and many other devices via field bus connection for intelligent building control and management is a key to the development of the main, this design is th     

共享数据工程(SHADE)体系结构及实现描述

文章在深入研究DIICOE的基础上,描述了DIICOE中共享数据工程(SHADE)的体系结构及实现方式。     

计算机犯罪侦查途径的探讨

近年来计算机犯罪案件呈迅速增长趋势,为有效控制和侦破此类案件,急需寻找一套行之有效的侦查办案途径.归纳各种犯罪手段,分别针对单机犯罪、专用网络犯罪和互联网犯罪,提出三种解决方案.     

A new criterion of delay-dependent asymptotic stability for Hopfield neural networks with time delay.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.     

Equilibria and their bifurcations in a recurrent neural network involving iterates of a transcendental function.

Some practical models contain so complicated mathematical expressions that it is hard to determine the number and distribution of all equilibria, not mentioning the qualitative properties and bifurcations of those equilibria. The three-node recurrent neural network system with two free weight parameters, originally introduced by Ruiz, Owens, and Townley in 1997, is such a system, for which the equation of equilibria involves transcendental function tanh(x) and its iterates. Not computing coordinates of its equilibria, in this paper, we display an effective technique to determine the number and distribution of its equilibria. Without full information about equilibria, our method enables to further study qualitative properties of those equilibria and discuss their saddle node, pitchfork, and Hopf bifurcations by approximating center manifolds.     

A new crossover operator in genetic programming for object classification.

The crossover operator has been considered "the centre of the storm" in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper introduces an approach with a new crossover operator in GP for object recognition, particularly object classification. In this approach, a local hill-climbing search is used in constructing good building blocks, a weight called looseness is introduced to identify the good building blocks in individual programs, and the looseness values are used as heuristics in choosing appropriate crossover points to preserve good building blocks. This approach is examined and compared with the standard crossover operator and the headless chicken crossover (HCC) method on a sequence of object classification problems. The results suggest that this approach outperforms the HCC, the standard crossover, and the standard crossover operator with hill climbing on all of these problems in terms of the classification accuracy. Although this approach spends a bit longer time than the standard crossover operator, it significantly improves the system efficiency over the HCC method.