复杂网络电输运性能与通信序列熵之间的关联

网络的电输运性能优化,不仅有助于理解网络的结构与功能关系,而且对于提升电气工程技术也有着非常重要的意义.从信息的角度看待网络,寻求影响网络电输运性能的信息结构测度是解决这一问题的有效途径.最近的研究表明,复杂网络的通信序列熵可以有效地量化网络的整体结构信息.本文将探讨其表征网络电输运性能的能力,其中主要研究了小世界网络、无标度网络、关联无标度网络、社团网络以及IEEE57等节点网络的通信序列熵和电输运性能之间的关联特性.研究结果表明,对于以上这些网络,它们的电输运性能是关于通信序列熵的单调递增函数,与通信序列熵成正关联特性.该规律的发现为设计高传输效率的电力网络提供了一个有效的策略,即可以通过提高网络的通信序列熵来优化其电输运性能.     

一种基于分层的量子分组传输方案及性能分析

大规模量子通信网络中,采用量子分组传输技术能有效提升发送节点的吞吐量,提高网络中链路的利用率,增强通信的抗干扰性能.然而量子分组的快速传输与路由器性能息息相关.路由器性能瓶颈将严重影响网络的可扩展性和链路的传输效率.本文提出一种量子通信网络分层结构,并根据量子密集编码和量子隐形传态理论,给出一种基于分层的量子分组信息传输方案,实现端到端的量子信息传输.该方案先将量子分组按照目的地址进行聚类,再按聚类后的地址进行传输.仿真结果表明,基于分层的量子分组信息传输方案能够有效减少量子分组信息在量子通信网络中的传输时间,并且所减少的时间与量子路由器性能与发送的量子分组数量有关.因此,本文提出的量子分组信息传输方案适用于大规模量子通信网络的构建.     

一种有效提高无标度网络负载容量的管理策略

现有研究表明明显的社团结构会显著降低网络的传输性能. 本文基于网络邻接矩阵的特征谱定义了链路对网络社团特性的贡献度, 提出一种通过逻辑关闭或删除对网络社团特性贡献度大的链路以提高网络传输性能的拓扑管理策略, 即社团弱化控制策略(CWCS 策略). 在具有社团结构的无标度网络上分别进行了基于全局最短路径路由和局部路由的仿真实验, 并与关闭连接度大的节点之间链路的HDF 策略进行了比较. 仿真实验结果显示, 在全局最短路径路由策略下, CWCS策略能更有效地提高网络负载容量, 并且网络的平均传输时间增加的幅度变小. 在局部路由策略下, 当调控参数0<α<2, 对网络负载容量的提升优于HDF策略. 关键词： 复杂网络 社团特性 负载容量 拓扑管理     

滑动去趋势波动分析与近似熵在动力学结构突变检测中的性能比较

近似熵（ApEn）被认为是一种有效的动力学结构突变检测方法. 将一种新的动力学结构检测方法——滑动去趋势波动分析（MDFA）与ApEn的检测结果进行了比较,检验了新方法的性能. 结果表明,新方法的检测结果几乎不依赖于子序列的长度,而ApEn虽然能在一定程度上识别系统的动力学结构突变,但其检测结果依赖于子序列长度,且不能准确地检测出突变点的位置. 因此,相对于ApEn方法而言,MDFA方法更适合于动力学结构突变检测,其优越性是显而易见的. 关键词： 滑动去趋势波动分析 近似熵 动力学结构突变     

单向耦合神经元网络中螺旋波的自发形成机制

构造一个具有单向耦合的二维神经元网络,引入信息传输熵来描述定向信息传输,采用Hindmarsh-Rose神经元模型研究网络中螺旋波等有序波自发产生的机制.数值模拟表明：适当选取耦合的强度和单向耦合的距离,网络可自发出现螺旋波、行波、靶波和平面波.各种有序波的产生与网络中出现信息间歇定向传输有关,网络出现单或多螺旋波时发生熵共振现象.噪声、抑制性耦合和排斥性耦合诱发螺旋波时网络中也存在信息间歇定向传输.首次发现自维持长平面波,其存在是由于网络存在持续的强信息定向传输.     

基于Bell态粒子和单光子混合的量子安全直接通信方案

为了提高量子安全直接通信的效率,本文提出了一种基于Bell态粒子和单光子混合的量子安全直接通信方案.该方案中Alice将所有Bell态粒子划分为两个序列S_A和S_B,先将S_B发给Bob进行第一次窃听检测,检测结果表示量子信道安全后再将信息序列编码在序列S_A和单光子序列S_S混合的量子态序列上;然后将已编码序列经过顺序重排和添加单光子检测粒子后发给合法接收方Bob.该方案避免了复杂的U变换,简化了方案的实现过程.同时顺序重排和检测粒子的结合保证了方案的安全性.另外3 bits经典信息加载在一个态上的编码规则大大提高了编码容量,从而使信息传输效率也得到提高.     

非一致通信时滞动力学网络上的接连滞后同步

在动力学网络中,节点与节点之间的通信通常存在时滞,并且不同节点之间的通信时滞往往是不同的(即非一致通信时滞),研究非一致通信时滞动力学网络上的接连滞后同步,更具现实意义.为此,本文首先构建含有非一致通信时滞的动力学网络模型.其次分别设计线性反馈控制和自适应反馈控制,利用Lyapunov函数方法,重点分析了该网络的接连滞后同步的稳定性,得到了同步稳定的充分条件.最后,选取蔡氏电路作为局部动力学,又分别选取了链式网络和星型网络这两种拓扑结构来验证理论结果的正确性和有效性.     

基于多路可视图的健康与心梗患者心电图信号复杂网络识别

可视图(visibility graph, VG)算法已被证明是将时间序列转换为复杂网络的简单且高效的方法,其构成的复杂网络在拓扑结构中继承了原始时间序列的动力学特性.目前,单维时间序列的可视图分析已趋于成熟,但应用于复杂系统时,单变量往往无法描述系统的全局特征.本文提出一种新的多元时间序列分析方法,将心梗和健康人的12导联心电图(electrocardiograph, ECG)信号转换为多路可视图,以每个导联为一个节点,两个导联构成可视图的层间互信息为连边权重,将其映射到复杂网络.由于不同人群的全连通网络表现为完全相同的拓扑结构,无法唯一表征不同个体的动力学特征,根据层间互信息大小重构网络,提取权重度和加权聚类系数,实现对不同人群12导联ECG信号的识别.为判断序列长度对识别效果的影响,引入多尺度权重度分布熵.由于健康受试者拥有更高的平均权重度和平均加权聚类系数,其映射网络表现为更加规则的结构、更高的复杂性和连接性,可以与心梗患者进行区分,两个参数的识别准确率均达到93.3%.     

基于混合光模式阵列的自由空间编码通信

光学涡旋的产生、传输与应用是当前光学领域的研究热点之一.光学涡旋具有轨道角动量,作为一种全新的自由度,丰富了目前光通信的方式.利用面向目标的共轭对称延拓傅里叶计算全息技术,基于空间光调制器,用单束激光直接产生混合光模式阵列进行编码通信.采用由单光涡和复合光涡构成的4种易于识别的模式组成2×2混合光模式阵列,进行灰度图像的编码传输.在接收端提取混合光模式阵列图的信息并进行解码,实现零误码的灰度图像再现.以传输一幅Lena图像为例,使用2×2混合光模式阵列进行编码通信,相对于传统单光涡编码通信,其信息容量可增加4倍.该方法光路简单易行,可扩展性强,进一步拓展使用4×4混合光模式阵列进行编码通信,信息容量提升16倍.提出的混合光模式阵列编码通信方法对于提高信息传输容量具有重要价值.     

基于蛛网结构的量子卫星广域网构建策略及性能仿真

量子卫星通信是通信领域的研究热点和前沿,具有理想的信息安全性和覆盖面广的优势,对于构建全球范围的量子卫星广域网具有重要意义,而远距离传输信息时网络的可靠性、安全性和路由中继等问题仍需改进.为了构建性能良好的量子卫星广域网,本文提出利用蜘蛛网作为一种独特的自然通信拓扑结构,将自然界蛛网演进为人工蛛网拓扑,量子信息的传输采用N阶量子隐形传态路由方案,其传输时延基本不变,在此基础上构建蛛网网络拓扑量子广域网传输模型,并对构建的网络模型的误码率、吞吐率、安全密钥生成率进行仿真分析.用抗毁度作为衡量网络拓扑结构可靠性的指标,以9节点环型网和9节点蛛网为例进行定量和定性分析,得出蛛网拓扑具有更高的可靠性.当噪声的平均功率谱密度给定且不存在中继时,量子态的传输距离越大误码率越大,这时要考虑引入中继;当传输距离和噪声功率谱密度一定的情况下,误码率随着中继节点个数的增多而减小,因此在蛛网拓扑下要选择合适的路由过程.随着量子卫星分发纠缠光子对成功概率的增大,吞吐率逐渐增加;随着网络中传输时延的增大,吞吐率逐渐减小,但在该路由方案下传输时延基本不变,且蛛网结构的传输时延很小,因此本文中提出的基于N阶量子隐形传态的蛛网网络拓扑量子广域网的吞吐率不会有明显的降低.当量子信息的传输距离不断增大时,网络密钥生成率逐渐减小;随着网络中继节点个数的增多,密钥生成率逐渐增加.由此可见,利用蛛网拓扑以及N阶量子隐形传态路由方案构建量子卫星广域网具有很好的优势.     

Communication on the structure of biological networks

Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of information in a network. Here we investigate the underlying undirected topology of different biological networks which support faster spreading of information and are better in communication. We analyse the good expansion property by using the spectral gap and communicability between nodes. Different epidemic models are also used to study the transmission of information in terms of spreading of disease through individuals (nodes) in those networks. Moreover, we explore the structural conformation and properties which may be responsible for better communication. Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of nodes, in neuronal networks, form a higher core in their structure. Our study shows that the underlying undirected topology in neuronal networks, in a significant way, is qualitatively different from the same in other biological networks and that they may have evolved in such a way that they inherit a (undirected) structure which is excellent and robust in communication.     

Communication boundaries in networks

We investigate and quantify the interplay between topology and the ability to send specific signals in complex networks. We find that in a majority of investigated real-world networks the ability to communicate is favored by the network topology at small distances, but disfavored at larger distances. We further suggest how the ability to locate specific nodes can be improved if information associated with the overall traffic in the network is available.     

The physics of communicability in complex networks

A fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network.     

Traffic dynamics on layered complex networks

To minimize traffic congestion, understanding how traffic dynamics depend on network structure is necessary. Many real-world complex systems can be described as multilayer structures. In this paper, we introduce the idea of layers to establish a traffic model of two-layer complex networks. By comparing different two-layer complex networks based on random and scale-free networks, we find that the physical layer is much more important to the network capacity of two-layer complex networks than the logical layer. Two-layer complex networks with a homogeneous physical topology are found to be more tolerant to congestion. Moreover, simulation results show that the heterogeneity of logical and physical topologies makes the packet-delivery process of two-layer networks more efficient in the free-flow state, without the occurrence of traffic congestion.     

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.     

异质化带宽分配下的复杂网络数据流负载问题研究

研究了带有连接边传输容量(带宽)约束的复杂网络上如何提升网络数据流负载问题. 在网络连接边带宽资源总量固定的条件下, 提出了一种异质化带宽分配方案. 引入 "受控边" 概念, 通过加入适当比例的 "受控边", 重新分配带宽资源, 并结合具有拥塞感知能力路由策略的数据流量模型, 利用带宽分配调节数据流量走向, 提高了带宽利用效率, 最终使得网络整体的负载能力较带宽匀质化分配时有显著提升. 分别在Barabási-Albert无标度网络和Watts-Strogtz (WS)小世界网络平台上仿真, 发现按照本文的带宽分配方案, WS小世界网络中节点连接边带宽与网络负载有较强的相关性, 节点连接边带宽分配最均衡的时候, 网络负载能力达到最大. 关键词： 异质化带宽分配 负载 介数 受控边     

Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

Many real communication networks, such as oceanic monitoring network and land environment observation network,can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue(HMQ) routing strategy and global forward-predicting hybrid minimum degree and queue(HMDQ) routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity.     

Optimal structure of complex networks for minimizing traffic congestion

To design complex networks to minimize traffic congestion, it is necessary to understand how traffic flow depends on network structure. We study data packet flow on complex networks, where the packet delivery capacity of each node is not fixed. The optimal configuration of capacities to minimize traffic congestion is derived and the critical packet generating rate is determined, below which the network is at a free flow state but above which congestion occurs. Our analysis reveals a direct relation between network topology and traffic flow. Optimal network structure, free of traffic congestion, should have two features: uniform distribution of load over all nodes and small network diameter. This finding is confirmed by numerical simulations. Our analysis also makes it possible to theoretically compare the congestion conditions for different types of complex networks. In particular, we find that network with low critical generating rate is more susceptible to congestion. The comparison has been made on the following complex-network topologies: random, scale-free, and regular.     

Optimized network structure and routing metric in wireless multihop ad hoc communication

Inspired by the Statistical Physics of complex networks, wireless multihop ad hoc communication networks are considered in abstracted form. Since such engineered networks are able to modify their structure via topology control, we search for optimized network structures, which maximize the end-to-end throughput performance. A modified version of betweenness centrality is introduced and shown to be very relevant for the respective modeling. The calculated optimized network structures lead to a significant increase of the end-to-end throughput. The discussion of the resulting structural properties reveals that it will be almost impossible to construct these optimized topologies in a technologically efficient distributive manner. However, the modified betweenness centrality also allows to propose a new routing metric for the end-to-end communication traffic. This approach leads to an even larger increase of throughput capacity and is easily implementable in a technologically relevant manner.     

Quantifying information loss on chaotic attractors through recurrence networks

We propose an entropy measure for the analysis of chaotic attractors through recurrence networks which are un-weighted and un-directed complex networks constructed from time series of dynamical systems using specific criteria. We show that the proposed measure converges to a constant value with increase in the number of data points on the attractor (or the number of nodes on the network) and the embedding dimension used for the construction of the network, and clearly distinguishes between the recurrence network from chaotic time series and white noise. Since the measure is characteristic to the network topology, it can be used to quantify the information loss associated with the structural change of a chaotic attractor in terms of the difference in the link density of the corresponding recurrence networks. We also indicate some practical applications of the proposed measure in the recurrence analysis of chaotic attractors as well as the relevance of the proposed measure in the context of the general theory of complex networks.