An algorithm on fairness verification of mobile sink routing in wireless sensor network

Congestion and starvation will occur among some nodes due to the emerging serious unfairness, which is derived from the limited communication capabilities of all nodes and sink or in the case of a mobile sink moving to a new place. The problem to be solved is to balance the network and keep the fairness for all nodes. For this purpose, this paper focuses on verifying the fairness of mobile sink routing based on both state and action, which is realized mainly by composing Labeled Kripke Transition Systems (LKTS). First, an approach is presented by LKTS to model node behaviors. Second, a notion of Fair Computational Tree Logic (CTL) is introduced to describe the fairness formulae in branching time transitions, and four kinds of fairness assumptions are defined for fairness verification. Moreover, in order to avoid the problem of state-space explosion, Bounded model Checking to explore states and transitions on-the-fly until a witness is found, while Strong Connected Components algorithm is used to pick up fair paths under fairness constraints of Fair CTL. The experimental results show the superiority of our method by the savings in memory and time consumptions during the mobile sink routing process.     

Grey self-organizing map based intrusion detection

Grey self-organizing map(GSOM) model is proposed and applied in the detection of intrusion.Through the improvement of the weight adjustment using the GRC(grey relational coefficient),the training results of SOM get better.In the detection of deny of service(DOS) attacks,this model can consider the relativity of the data set of DOS attacks.Finally,the experiments on the DOS data set confirm their validities and feasibilities over this GSOM model.     

Local consistent hierarchical Hough Match for image re-ranking

Geometric image re-ranking is a widely adopted phrase to refine the large-scale image retrieval systems built based upon popular paradigms such as Bag-of-Words (BoW) model. Its main idea can be treated as a sort of geometric verification targeting at reordering the initial returning list by previous similarity ranking metrics, e.g. Cosine distance over the BoW vectors between query image and reference ones. In the literature, to guarantee the re-ranking accuracy, most existing schemes requires the initial retrieval to be conducted by using a large vocabulary (codebook), corresponding to a high-dimensional BoW vector. However, in many emerging applications such as mobile visual search and massive-scale retrieval, the retrieval has to be conducted by using a compact BoW vector to accomplish the memory or time requirement. In these scenarios, the traditional re-ranking paradigms are questionable and new algorithms are urgently demanded. In this paper, we propose an accurate yet efficient image re-ranking algorithm specific for small vocabulary in aforementioned scenarios. Our idea is inspired by Hough Voting in the transformation space, where votes come from local feature matches. Most notably, this geometry re-ranking can easily been aggregated to the cutting-edge image based retrieval systems yielding superior performance with a small vocabulary and being able to store in mobile end facilitating mobile visual search systems. We further prove that its time complexity is linear in terms of the re-ranking instance, which is a significant advantage over the existing scheme. In terms of mean Average Precision, we show that its performance is comparable or in some cases better than the state-of-the-art re-ranking schemes.     

Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

It is novel to apply three-dimensional(3D) light field imaging technology to recognize two-dimensional(2D) fake pedestrians. In this paper, we propose a parallel support vector machine(SVM) method based on 3D light field imaging(light field camera) and machine learning techniques. A light field(LF) camera with robust sensors, which is able to record rich 3D information, is used as hardware equipment. Histogram of oriented gradient(HOG) feature extraction algorithm and SVM classification method a...     

基于矩阵半张量积的信息物理融合系统状态不透明性分析与控制

状态不透明性作为一种重要的机密属性,能够表征入侵者窃取系统隐私信息的能力。针对带有不可观测事件的信息物理融合系统(CPSs),该文提出一种基于矩阵半张量积(STP)的代数状态空间方法,并且分析与验证CPSs的状态不透明性。首先利用矩阵STP理论对CPSs的状态演化进行建模,得到系统的动态代数表达式,然后利用STP运算的特性,给出验证系统当前状态不透明性的充分必要代数条件。最后,通过数值仿真算例验证了方法的有效性。该文提出的基于矩阵STP方法为CPSs相关隐私分析与安全控制研究提供了一个新的思路和框架。     

Automatic diagnosis of mobile communication networks under imprecise parameters

In the last years, self-organization of cellular networks is becoming a crucial aspect of network management due to the increasing complexity of the networks. Automatic fault identification, i.e. diagnosis, is the most difficult task in self-healing. In this paper, a model based on discrete bayesian networks (BNs) is proposed for diagnosis of radio access networks of cellular systems. Normally, inaccuracies are unavoidable in the parameters of the model (interval limits for discretized symptoms and probabilities in the BN). In order to enhance the performance of BNs, a methodology to model the “continuity” in the human reasoning is presented, named smooth bayesian networks (SBNs). SBNs are intended to decrease the sensitivity of diagnosis accuracy to imprecision in the definition of the model parameters. An empirical research campaign has been carried out in a live GSM/GPRS network in order to assess the performance of the proposed techniques. Results have shown that SBNs outperform traditional BNs when there is inaccuracy in the model parameters.     

基于小波变换的三维网格数字水印技术研究

大部分三维数字水印算法更多地关注水印的鲁棒性而忽视了三维模型的视觉效果。为了解决这一问题,提出了一种基于小波变换的三维网格数字水印新算法,该算法以典型的三角网格模型作为水印载体。提取三维模型中的显著区域,并将三维模型进行旋转归一化处理,以此保证对三维模型平移、缩放或旋转攻击都有很强的鲁棒性;将三维模型转化到球面坐标下并进行小波变换,在低频和高频系数下嵌入水印;经过小波逆变换得到嵌入水印后的三维模型,实验结果表明提出的算法既对多类攻击方式具有鲁棒性,又保持了三维模型的视觉效果。     

复杂网络上同时考虑感染延迟和非均匀传播的SIR模型

为了能更有效地分析和理解传染性疾病的传播,提出了一个新颖的SIR模型,在这个传播模型里同时考虑了影响疾病传播行为的2个因素:感染延迟和非均匀传播.基于平均场理论和大量的数值仿真,给出了疾病传播临界值的解析公式,并发现感染延迟和非均匀传播对临界值影响截然不同:感染延迟能够在很大程度上减小传播阈值,促进疾病在人群中的传播;而非均匀传播能够增大传播临界值,阻碍疾病的大规模传播.当前的研究结果有助于深入理解真实复杂系统中的疾病传播行为,充分考虑感染延迟、传播机制和实际人群的拓扑结构等因素在疾病传播中的作用,从而为制定有效的传染病预防和控制措施提供理论依据.     

集装箱码头场桥协同调度研究

针对集装箱港口场桥调度过程中场桥移动路径具有冲突性的特点,提出了将集中决策和多agent建模相结合的优化方法,充分发挥集中决策的高效性和多agent建模的灵活性。通过数值实验和以往的调度方法进行了比较,结果显示得到的调度结果具有良好的可行性。     

基于仿真的机场行李处理模式评估

仿真建模在机场运行中的运用日趋增多,但此类仿真系统大多以旅客和飞机为仿真的实体,针对行李流程的专项研究则相对较少。以行李为研究对象,在现行行李作业流程基础上提出了改进的作业流程,并应用仿真软件Arena建立了相关的仿真模型,研究方法和成果对行李系统的规划设计具有重要参考价值。