APS-FeW: Improving TCP throughput over multihop adhoc networks

TCP is a reliable transport protocol tuned to perform well over traditional wired networks. Although it performs well for wired networks, TCP’s implicit assumption that any packet loss is due to congestion is not valid any longer in mobile ad hoc networks. It is observed that TCP induces the over-action of routing protocol and reduces the performance of the connection. Fraction window increment (FeW) scheme for TCP improves the connection performance by limiting TCP’s aggressiveness. But to some extent, this limitation is too strict in that it eliminates the possibility to deliver more bytes under the same congestion window. To solve this problem, we propose an adaptive packet size (APS) scheme to work on top of FeW for TCP. The proposed scheme utilizes the advantages of both legacy TCP and FeW to achieve high performance over multihop 802.11 networks. Extensive simulation results demonstrate that APS over FeW outperforms FeW alone by 10–25% according to different scenarios, e.g., chain-topology, grid-topology, and random-topology with mobility.     

Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework

Particle filtering is frequently used for visual tracking problems since it provides a general framework for estimating and propagating probability density functions for nonlinear and non-Gaussian dynamic systems. However, this algorithm is based on a Monte Carlo approach and the cost of sampling and measurement is a problematic issue, especially for high-dimensional problems. We describe an alternative to the classical particle filter in which the underlying density function has an analytic representation for better approximation and effective propagation. The techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities with Gaussian mixtures, where all relevant parameters are automatically determined. The proposed analytic approach is shown to perform more efficiently in sampling in high-dimensional space. We apply the algorithm to real-time tracking problems and demonstrate its performance on real video sequences as well as synthetic examples.     

Constructive approximation to real function by wavelet neural networks

We present a type of single-hidden layer feed-forward wavelet neural networks. First, we give a new and quantitative proof of the fact that a single-hidden layer wavelet neural network with n + 1 hidden neurons can interpolate n + 1 distinct samples with zero error. Then, without training, we constructed a wavelet neural network X _a (x, A), which can approximately interpolate, with arbitrary precision, any set of distinct data in one or several dimensions. The given wavelet neural network can uniformly approximate any continuous function of one variable.     

Femtosecond laser-induced silicon surface morphology in water confinement

This article investigates the use of femtosecond laser induced surface morphology on silicon wafer surface in water confinement. Unlike irradiation of silicon surfaces in the air, there are no laser induced periodic structures, but irregular roughness is formed when the silicon wafer is ablated under water. The unique discovery of a smoothly processed silicon surface in water confinement under certain laser parameter combinations may help improve laser direct micromachining surface quality in industrial applications.     

Imbalanced text classification: A term weighting approach

The natural distribution of textual data used in text classification is often imbalanced. Categories with fewer examples are under-represented and their classifiers often perform far below satisfactory. We tackle this problem using a simple probability based term weighting scheme to better distinguish documents in minor categories. This new scheme directly utilizes two critical information ratios, i.e. relevance indicators. Such relevance indicators are nicely supported by probability estimates which embody the category membership. Our experimental study using both Support Vector Machines and Naïve Bayes classifiers and extensive comparison with other classic weighting schemes over two benchmarking data sets, including Reuters-21578, shows significant improvement for minor categories, while the performance for major categories are not jeopardized. Our approach has suggested a simple and effective solution to boost the performance of text classification over skewed data sets.     

一种高效的SPKI/SDSI2.0策略分析算法

信任管理方法提供了一种新的思路,弥补了传统授权机制应用于分布式系统的不足.SPKI/SDSI2.0是目前较普及的信任管理系统,系统中的每个主体都可以发放证书.在一个特定的系统状态中,系统管理员需要知道关于系统的一些"特性",如某一主体是否有权访问被保护资源、一个本地名有哪些成员等.当证书数量庞大时,这些问题需要借助一定的工具才能回答.但以前的算法均集中于对授权问题的讨论,没有考虑与名字相关的系统策略分析,且分析效率偏低.提出了一种基于逻辑的SPKI/SDSI2.0策略分析算法EPAAS,从本质上拓宽了策略分析的领域,利用它不仅可以分析SPKI/SDSI2.0的授权问题及名字问题,还可以将这两类问题结合起来对系统策略进行综合查询;此外,EPAAS将策略分析的时间复杂度由原先算法的O(n<'3>/l)降至O(n),提高了分析效率.EPAAS用标准的Datalog程序表示SPKI/SDSI2.0的系统状态,以Datalog程序的最小Herbrand模型作为它的语义,证明了该语义的可靠性.     

工作流资源分配中约束规则的建模研究

传统工作流系统在对任务节点进行资源分配时,只进行了组织、角色、人员的分配,而较少对不同任务节点上执行人间的潜在关系进行建模.根据企业的实际应用需求,将这种潜在的约束关系提取出来进行建模,并以规则的形式进行定义.最后采用强有力的推理工具--规则引擎--来完成对定义的规则冲突检测以及规则的实现.     

面向攻击图构建的网络连通性分析

针对目前网络攻击图构建系统的需求,设计网络连通性分析算法。通过对网络拓扑及防火墙规则进行离线分析,可以判断网络中由若干台过滤设备分隔的任意2台主机间的连通性。引入关键实体集的概念,结合经典的Apriori算法提出一种快速有效的获取关键实体集的方法。分析对比表明,关键实体集可以在连通性分析过程中为网络中各节点的重要性评估提供有力依据。     

动量抖动符号误差恒模盲多用户检测算法

针对直扩码分多址通信系统中存在的多址干扰抑制问题,采用加入动量项加快算法收敛的方法,提出一种基于动量抖动符号误差的恒模盲多用户检测算法,在理论上证明该算法的收敛性。通过计算机仿真对该算法与其他算法进行比较,结果证明该算法有较好的收敛性、跟踪性和鲁棒性。     

离散萤火虫优化算法求解概率旅行商问题

对随机组合优化问题中的概率旅行商问题(PTSP)的理论和方法进行了研究分析,采用现代进化算法中有代表性发展优势的萤火虫优化算法(FA),提出一种离散萤火虫优化算法(DFA)以求解.其中引入了新的学习机制使其相比原始的萤火虫优化算法,更容易搜索到全局最优解,有更好的收敛性能.实验中用TSPLIB中的经典实例进行测试来验证其可行性.考察了萤火虫数量和进化迭代次数对求解结果性能的影响,并将DFA与GA、PSO和ACO等其他著名的进化计算算法进行性能比较.实验结果证实了DFA无论对固定访问概率,还是访问概率为区间内随机数等不同情况,都具有良好的有效性和高效性,因此对求解随机组合优化系列问题的有效解决具有一定参考和借鉴价值.