多移动信标辅助的分布式节点定位方法

为提高移动信标辅助定位算法的定位精度,避免重复扫描待定位节点,提出了一种使用多个移动信标的定位方法。这些信标在遍历网络时保持一定相对位置关系,使用TDoA技术测距并为未知节点提供距离信息辅助其定位。提出了基于最优覆盖策略的2种移动信标路径规划方法。仿真结果表明,所提出的算法具有较高的定位精度,且所使用的移动路径性能较高。     

Depth map enhancement based on color and depth consistency

Current low-cost depth sensing techniques, such as Microsoft Kinect, still can achieve only limited precision. The resultant depth maps are often found to be noisy, misaligned with the color images, and even contain many large holes. These limitations make it difficult to be adopted by many graphics applications. In this paper, we propose a computational approach to address the problem. By fusing raw depth values with image color, edges and smooth priors in a Markov random field optimization framework, both misalignment and large holes can be eliminated effectively, our method thus can produce high-quality depth maps that are consistent with the color image. To achieve this, a confidence map is estimated for adaptive weighting of different cues, an image inpainting technique is introduced to handle large holes, and contrasts in the color image are also considered for an accurate alignment. Experimental results demonstrate the effectiveness of our method.     

Four-mobile-beacon assisted localization in three-dimensional wireless sensor networks

Localization is one of key technologies of wireless sensor networks, and the mobile beacon assisted localization method is promising. For the three-dimensional sensor networks, this paper proposes a four-mobile-beacon assisted weighted centroid localization method. The four beacons form a regular tetrahedron while traversing the region and broadcast packets including their positions simultaneously. The ordinary sensor nodes estimate their locations using weighted centroid method. It also presents Layered-Scan trajectory of mobile beacons which consists of several parallel layers of Scan. This method can localize all the sensor nodes with appropriate parameters. Two sets of simulations are performed using Matlab to compare this method with other methods and evaluate tradeoffs between localization performance and different parameters. Simulation results show that the proposed method outperforms weighted centroid and multilateration methods using single mobile beacon.     

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.     

Rootkit隐藏技术与检测方法研究

近些年来,恶意代码攻击的目的由破坏炫耀向经济利益转变,因而更加注重自身的隐藏.Rootkit具有隐蔽性强、特权级高等特点,成为主机安全的严重威胁.硬件虚拟化技术出现后,Rootkit扩展到了操作系统外部,对检测技术提出了新的挑战.本文总结了近几年网络安全领域中Rootkit隐藏技术和检测技术的研究进展,分析了各自面临的问题,并基于对Rootkit隐藏技术和检测技术的分析,探讨Rootkit检测技术的发展趋势.     

求解多段图问题的并行动态规划算法

多段图问题是一类特殊的单源最短路径问题。在串行动态规划算法的两种实现方法的基础上,根据图中顶点的编号,提出两种在集群环境下进行任务分割的并行化求解方法,并使用MPI进行实现。实验结果表明,所提出的算法具有较高的加速比和较低的通信复杂度、时间复杂度。算法不限于某种结构的集群,通用性强。