Construction of a pulse-coupled dipole network capable of fear-like and relief-like responses

The challenge for neuroscience as an interdisciplinary programme is the integration of ideas among the disciplines to achieve a common goal. This paper deals with the problem of deriving a pulse-coupled neural network that is capable of demonstrating behavioural responses (fear-like and relief-like). Current pulse-coupled neural networks are designed mostly for engineering applications, particularly image processing. The discovered neural network was constructed using the method of minimal anatomies approach. The behavioural response of a level-coded activity-based model was used as a reference. Although the spiking-based model and the activity-based model are of different scales, the use of model-reference principle means that the characteristics that is referenced is its functional properties. It is demonstrated that this strategy of dissection and systematic construction is effective in the functional design of pulse-coupled neural network system with nonlinear signalling. The differential equations for the elastic weights in the reference model are replicated in the pulse-coupled network geometrically. The network reflects a possible solution to the problem of punishment and avoidance. The network developed in this work is a new network topology for pulse-coupled neural networks. Therefore, the model-reference principle is a powerful tool in connecting neuroscience disciplines. The continuity of concepts and phenomena is further maintained by systematic construction using methods like the method of minimal anatomies.     

Leader-following consensus for a class of second-order nonlinear multi-agent systems

This paper deals with the leader-following consensus problem for a class of multi-agent systems with nonlinear dynamics and directed communication topology. The control input of the leader agent is assumed to be unknown to all follower agents. A distributed adaptive nonlinear control law is constructed using the relative state information between neighboring agents, which achieves leader-following consensus for any directed communication graph that contains a spanning tree with the root node being the leader agent. Compared with previous results, the nonlinear functions are not required to satisfy the globally Lipschitz or Lipschitz-like condition and the adaptive consensus protocol is in a distributed fashion. A numerical example is given to verify our proposed protocol.     

Information system business affecting impact evaluation method

To effectively analyze the impact of continuous business interruption on the information system,an information system business affecting impact evaluation method (IBAIE) is proposed.First,we identify the main businesses of the system and quantify their vulnerability.Then,we take the businesses as the nodes of network topology and weight nodes based on the business significance.The orderly association between businesses is taken as the edges which are weighted based on the association between asset and business,and the directed weighted business network topology is obtained.Finally,the method of directed weighted network structure entropy is used to evaluate the change of system business network structure during the period from business interruption to recovery.Experiments show that this method has obvious advantages in evaluating accuracy compared to other methods,and can be applied in ensuring information system security.     

Multi-formation control of nonlinear leader-following multi-agent systems

This paper deals with the multi-formation control problem for nonlinear leader-following multi-agent systems. Both the fixed topology case and the switching topology case are considered. The neighbor-based multi-formation control protocols are proposed under the assumption that for one subgroup, the total information received from other subgroups is zero. Then, based on the Lyapunov stability theory combined with the algebraic graph theory, sufficient conditions are established to ensure that the leader-following multi-agent systems with nonlinear dynamics can reach and maintain the desired multi-formation control. Finally, simulation examples are provided to illustrate the effectiveness of the theoretical results.     

Optimal topology design of continuum structures with stress concentration alleviation via level set method

Although the phenomenon of stress concentration is of paramount importance to engineers when they are designing load‐carrying structures, stiffness is often used as the solely concerned objective or constraint functional in the studies of optimal topology design of continuum structures. Sometimes this will lead to optimal designs with severe stress concentrations that may be highly responsible for the fracture, creep, and fatigue of structures. The aim of the present work is to develop some effective numerical techniques for designing stiff structures with less stress concentrations. This is achieved by introducing some specific stress measures, which are sensitive to the existence of high local stresses, in the problem formulation and resolving the corresponding optimization problem numerically in a level set framework. Our study indicates that with use of the proposed numerical schemes, some intrinsic difficulties in stress‐related topology optimization of continuum structures can be overcome in a natural way. Copyright © 2012 John Wiley & Sons, Ltd.     

基于蜂窝网络拓扑的无线传感网定位算法

针对无线传感网中DV-Hop定位算法过于依赖信标节点数量以及定位精度低的问题,提出基于蜂窝网络拓扑的定位算法(LABCNT)。通过对网络中节点的有向筛选,选取满足特定条件的节点,构造一个蜂窝的网络拓扑结构,得到网络上节点的相对位置;根据拓扑结构中的第二个信标节点确定网络上节点的绝对位置;将已定位的节点升级为协作节点,采用最小二乘算法对域内其它未知节点进行定位。仿真结果表明,与DH-RLS、IDVH-LA、IDVH-HCHEC算法相比,LABCNT算法的定位精度较高,所需信标节点数量最少。     

Free-scaling your data center

The increasing popularity of both small and large private clouds and expanding public clouds poses new requirements to data center (DC) architectures. First, DC architectures should be incrementally scalable allowing the creation of DCs of arbitrary size with consistent performance characteristics. Second, initial DC deployments should be incrementally expandable supporting small-scale upgrades without decreasing operation efficiency. A DC architecture possessing both properties satisfies the requirement of free-scaling.Recent work in DC design focuses on traditional performance and scalability characteristics, therefore resulting in symmetric topologies whose upgradability is coarse-grained at best. In our earlier work we proposed Scafida, an asymmetric, scale-free network inspired DC topology which scales incrementally and has favorable structural characteristics. In this paper, we build on Scafida and propose a full-fledged DC architecture achieving free-scaling called FScafida. Our main contribution is threefold. First, we propose an organic expansion algorithm for FScafida; this combined with Scafida’s flexible original design results in a freely scalable architecture. Second, we introduce the Effective Source Routing mechanism that provides near-shortest paths, multi-path and multicast capability, and low signaling overhead by exploiting the benefits of the FScafida topology. Third, we show based on extensive simulations and a prototype implementation that FScafida is capable of handling the traffic patterns characteristic of both enterprise and cloud data centers, tolerates network equipment failures to a high degree, and allows for high bisection bandwidth.     

Balancing CapEx reduction and network stability with stable routing–virtual topology capacity adjustment (SR–VTCA)

This paper investigates the merits of the SR–VTCA (stable routing–virtual topology capacity adjustment) approach as a mechanism to find a beneficial trade-off between network stability and reduction in capital expenditures (CapEx). These are two main objectives for the entities that own the optical infrastructure, such as network operators (NOs), and those also acting as Internet service providers (ISPs). The SR–VTCA scheme is a novel approach to adapt transparent optical networks to time-varying traffic by adjusting the number of lightpaths between node pairs, while keeping the IP routing unchanged. Lightpath bundling (LB) and anycast (AS) switching are combined in SR–VTCA operation to advertise lightpath additions/removals to the IP layer as mere adjustments (increments or decrements) in the capacity, allowing to keep the IP routing stable, and thus, simplifying control plane operations. On the contrary, a fully-reconfigurable (FR) network design, where IP routing can be also modified, would increase the burden in the control plane, but at a higher CapEx reduction, since the optical infrastructure is used more efficiently. In this work, we investigate the CapEx overprovision introduced by SR–VTCA with respect to a FR scheme. In order to do this, SR–VTCA planning problem is first modeled as a MILP formulation. A heuristic procedure based on traffic domination is then proposed to solve large instances of the problem. Exhaustive experiments are conducted comparing the SR–VTCA solutions obtained by the aforementioned MILP and heuristic proposal with solutions found by other optimization methods presented in the literature to solve the FR planning problem. Finally, the results show that SR–VTCA can achieve similar results to the FR case in terms of CapEx reduction, while a huge number of IP reroutings are saved by maintaining IP stability. Thus, SR–VTCA provides an advantageous balance between CapEx overprovisioning and the control plane overhead associated with IP rerouting.     

基于多粒度拓扑图的无线传感器网络逐级精化溯源方法

针对溯源数据分段传输方法要求所有分段准确到达基站（BS）后才能解码，鲁棒性较弱的问题，提出一种无线传感器网络（WSN）溯源逐级精化方法。首先，在BS端利用商空间划分理论将较大的WSN拓扑图划分为由少量抽象节点组成的较粗粒度的拓扑图；然后，利用字典编码溯源的方式分段传输溯源；最后，在BS端根据依次到达的分段进行逐级精化解码，实现了在BS端由粗到细逐级精化解码溯源的过程，且BS可以根据前期解码出的较粗粒度下的溯源信息判断是否放弃此数据还是须采用更细粒度的数据进行深入评估。理论分析、仿真与实验数据均表明，与传统分段方法相比，所提方法平均压缩比提高约51.8%，平均能量消耗降低约50.5%。     

Hierarchical Correlation Clustering in Multiple 2D Scalar Fields

Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.