Dispersion Constraint Based Non-negative Sparse Coding Model

Based on advantages of basic non-negative sparse coding (NNSC) model, and considered the prior class constraint of image features, a novel NNSC model is discussed here. In this NNSC model, the sparseness criteria is selected as a two-parameter density estimation model and the dispersion ratio of within-class and between-class is used as the class constraint. Utilizing this NNSC model, image features can be extracted successfully. Further, the feature recognition task by using different classifiers can be implemented well. Simulation results prove that our NNSC model proposed is indeed effective in extracting image features and recognition task in application.     

Which algorithm should I choose: An evolutionary algorithm portfolio approach

Many good evolutionary algorithms have been proposed in the past. However, frequently, the question arises that given a problem, one is at a loss of which algorithm to choose. In this paper, we propose a novel algorithm portfolio approach to address the above problem for single objective optimization. A portfolio of evolutionary algorithms is first formed. Covariance Matrix Adaptation Evolution Strategy (CMA-ES), History driven Evolutionary Algorithm (HdEA), Particle Swarm Optimization (PSO2011) and Self adaptive Differential Evolution (SaDE) are chosen as component algorithms. Each algorithm runs independently with no information exchange. At any point in time, the algorithm with the best predicted performance is run for one generation, after which the performance is predicted again. The best algorithm runs for the next generation, and the process goes on. In this way, algorithms switch automatically as a function of the computational budget. This novel algorithm is named Multiple Evolutionary Algorithm (MultiEA). The predictor we introduced has the nice property of being parameter-less, and algorithms switch automatically as a function of budget. The following contributions are made: (1) experimental results on 24 benchmark functions show that MultiEA outperforms (i) Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO); (ii) Population-based Algorithm Portfolio (PAP); (iii) a multiple algorithm approach which chooses an algorithm randomly (RandEA); and (iv) a multiple algorithm approach which divides the computational budget evenly and execute all algorithms in parallel (ExhEA). This shows that it outperforms existing portfolio approaches and the predictor is functioning well. (2) Moreover, a neck to neck comparison of MultiEA with CMA-ES, HdEA, PSO2011, and SaDE is also made. Experimental results show that the performance of MultiEA is very competitive. In particular, MultiEA, being a portfolio algorithm, is sometimes even better than all its individual algorithms, and has more robust performance. (3) Furthermore, a positive synergic effect is discovered, namely, MultiEA can sometimes perform better than the sum of its individual EAs. This gives interesting insights into why an algorithm portfolio is a good approach. (4) It is found that MultiEA scales as well as the best algorithm in the portfolio. This suggests that MultiEA scales up nicely, which is a desirable algorithmic feature. (5) Finally, the performance of MultiEA is investigated on a real world problem. It is found that MultiEA can select the most suitable algorithm for the problem and is much better than choosing algorithms randomly.     

一种图像的连续性边缘提取方法

图像边缘提取是图像处理领域的重要环节,边缘提取的连续性显得尤为重要,其结果可以作为语义分割、测量的基础,为此提出一种图像的连续性边缘提取方法。首先采用卡尼(Canny)算法完成图像的边缘检测,得到粗分割结果。由于该类算法采用算子进行操作,不可避免地产生非连续性区域,因此利用两类模板对粗分割结果进行形态学膨胀操作,连续性边缘采用小半径膨胀,边缘末端采用大半径膨胀,以此构成一幅膨胀图连接这些非连续性区域。最后,将该膨胀图作为平均曲率运动(MCM)方程嵌入函数u的初始值,通过引入g函数在原图中进行迭代求解,完成图像边缘的连续性提取。实验结果验证了算法的实用性与有效性。     

基于跳数的防御无线传感器网络中虫洞攻击方案

虫洞攻击是一种针对无线传感器网络路由协议的特殊攻击,一般由至少两个合谋节点协同发起。合谋节点通过建立起一条高带宽高质量的私有信道来对数据进行吸引和传输,通过扰乱路由分组的传输达到破坏网络正常运行的目的。本文基于无线传感器网络反应式路由协议AODV协议,从网络管理者的角度出发,不引入额外的硬件辅助,也不需要节点之间时钟同步,提出IAODV(Improved AODV)协议。相比较于AODV协议,IAODV协议增加了源节点路由跳数判断机制和随机选择路由的虫洞攻击防御方案。用NS2仿真平台实现虫洞攻击模块的仿真和改进协议的仿真,结果证明了改进协议的有效性。     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

无线自组织网络中的自适应竞争窗口退避算法

基于无线自组织网络IEEE 802.11协议原理,改进Ad Hoc网络标准DCF算法,提出一种自动调整最小竞争窗口的退避算法——ABEB算法,建立算法的二维Markov链模型,导出其稳态分布概率,分析其性能并进行了仿真。研究结果表明,与标准DCF算法相比,该算法的吞吐量平均提高了18%,时延平均降低了11%。     

基于小波变换的图像配准

图像配准是信息融合处理中的重要环节。本文分析了图像配准的数学模型,并对小波变换进行了研究。基于小波理论,提出了一种高精度的图像配准方法。该方法利用小波变换将图像分成若干层次,按照互信息最大的原则对小波分解各层的近似分量求取其配准参数,最后通过迭代实现图像配准。实验结果表明,该方法配准精度高、可靠性好,较之传统的方法有明显的优越性。     

基于关联规则的分布式通信网告警相关性研究

描述了基于数据挖掘的通信网告警相关性分析.在分布式数据库中直接运用序列算法效率很低,因为这需要大量的额外通信.为此提出了一种有效的分布式关联规则挖掘算法--EDMA,它通过局部剪枝与全局剪枝来最小化候选项集数目和通信量.在局部站点上运用先进的压缩关联矩阵CMatrix统计局部项集支持数.此外还利用项目剪枝与交易剪枝共同来减少扫描时间.最后仿真验证了EDMA比其他经典分布式算法有更高的运算效率、更低的通信开销以及更好的可扩展性.     

纯2维Coifman小波的提升格式及其应用

为了降低纯2维Coifman小波分解和重构的运算量,提出了一种纯2维Coifman小波的提升格式。由于纯2维Coifman小波在完全重建性、消失矩、滤波器钻石型频带以及二进分母系数等方面具有突出的优点,使其在图像压缩编码中具有相当的竞争性。该提升格式既适用于图像的有损压缩,只要稍改变归一化系数,又适用于图像的无损压缩,同时还保持了1维小波提升格式在运算量和边界处理等方面所具有的优势,纯2维Coifman小波提升格式中的预测算子和更新算子所具有的特性为直接构造纯2维小波滤波器组提供了理论指导。仿真试验显示,纯2维Coifman小波滤波器组比用相同1维原型滤波器组构造的纯2维小波滤波器组压缩效果好。