自适应传输范围VANETs车间通信性能分析

动态拓扑结构以及变化的车联网VANETs(vehicular ad hoc networks)给车间通信连接提出挑战。为此,提出基于自适应传输范围建立车间通信方案。首先通过车辆速度、车间距以及车到达率分布知识,推导车辆自适应的传输范围以及传输功率,并得出连接中断的概率;同时,推导同向车间通信连接和反向车间通信连接的分布。最后,通过仿真验证理论推导其正确性。结果表明:车辆自适应传输范围能够有力地提高车间通信连接概率。     

无人机辅助智能交通系统中面向视频多播的资源优化

在智能交通系统（Intelligent Transportation System, ITS）中,实时交通视频可以辅助网联车做出更理智的决策。然而,由于车载传感器位置和数量的限制,车辆无法全面掌握交通环境,不利于行车安全;此外,当多个车辆用户请求相同的视频内容时,传统的单播传输模式存在效率低下的问题。为了解决这些问题,基于非正交多址接入（Non-Orthogonal Multiple Access, NOMA）和可伸缩视频编码（Scalable Video Coding, SVC）技术,提出了一种无人机（Unmanned Aerial Vehicle, UAV）辅助ITS的交通视频多播方案。通过联合优化车辆分组和功率分配策略,最大化车辆接收的长期视频质量。将该优化问题建模为一个马尔可夫决策过程（Markov Decision Process, MDP）,并采用Soft Actor-Critic(SAC)算法来求解。大量仿真结果表明,该算法具有很强的探索能力,而且收益性能优于传统Actor-Critic(AC)算法。     

装甲车辆状态信息远程监控系统设计与实现

从装甲车辆实车实验需求出发,分析了装甲车辆的传感器设备,以车辆状态采集器、GPS(全球定位系统)接收机为信息采集端,利用数据电台轮询方式时分多址传输的机制,实现了对装甲车辆实车实验时车辆状态、车辆位置等信息的显示和远程监控,并能在车辆状态超出设定阈值后向实验车辆发送报警信息.     

VPN技术在数字电影传输中的应用

随着电影数字化技术的发展,计算机网络安全技术中的虚拟专用网(VPN,vitual private network)技术可利用公共通信网络(如因特网)实现影片数据安全的保密的通信,只需要将进行影片传输的两个端点均连接在公共通信网上,当需要进行影片数据传输时,通过端点上的VPN设备在公共网上建立一条虚拟的专用通信通道,就可以将所有影片数据均经过加密后再在网上传输,同时可确保机密数据的安全传输.     

罗德与施瓦茨携手华为成功调试完成V2X模块及RSU生产测试方案

<正>近日,罗德与施瓦茨公司(以下简称RS公司)和华为公司共同宣布调试完成LTE-V2X模块DA2300以及路边单元RSU5210生产测试方案。该模块支持最新的3GPP Release14 PC5通信模式,工作在5.8/5.9GHz band46D和band47。华为RSU是全球首款支持Uu口以及PC5通信模式的路边单元。不久之前,华为公司使用RS·CMW500宽带无线通信测试仪以及RS·SMBV100A矢量信号发生器完成LTE-V2X (也可以简称V2X)     

GPS交通调度管理系统的设计与仿真

GPS技术为各种交通工具提供了实时导航定位能力,GPS车辆交通调度管理系统利用全球卫星定位系统、全球移动通信系统、地理信息系统,结合计算机网络通信技术对车辆进行指挥调度和监控.驾驶员和监控中心能够随时通过GSM短消息业务中心传送和查询指定车辆的GPS定位信息.仿真验证了车载导航定位子系统及基站采用的伪距解算软件.     

车载物联网子系统浅析

随着信息技术与汽车电子技术的飞速发展,车载物联网应运而生,该系统通过将车辆进行计算机网络互联,从而大幅提高未来交通系统的安全和使用效率。车载物联网可以利用在行驶中的车辆间建立无线通信,也可以利用过路车辆和路边基站之间建立无线通信。因此,车载物联网将成为未来智能交通系统的重要组成部分,其系统组成复杂应用广泛。     

基于通信网络的集中式空调远程监控系统研究

针对集中式空调系统远程监控需求,阐述一套基于通信网络的集中式空调远程监控系统的实现方案。该方案主要包括集中式空调系统远程监控软件、手机客户端软件、无线数据传输单元(DTU)以及集中式空调系统可编程控制器(PLC) 4个核心子项。PLC将采集到的数据通过DTU和通信网络全透明传输到云服务系统,基于微信服务号研发的手机客户端通过云服务系统实现对机组的控制。实际运行结果表明,该方案在保证云服务系统接入能力不低于10万个节点的基础上,借助虚拟专用拨号网(VPDN)专用通道实现智能终端对集中式空调机组的远程监控等多项核心功能,同时降低运营成本,提高自动化水平。     

一种基于多维矩阵的无线传感器网络密钥预分配算法研究

密钥分配是无线传感器网络通信安全的基础.在低成本、低功耗、资源受限的传感器节点上现在可行的密钥分配方案是基于对称密码体制的密钥预分配模型,随机预分配模型的优点是分配方法简便,缺点是节点问不能保证安全连通,连通概率与节点密钥存储量、抗俘获能力成反比,确定预分配模型的优点是任意节点间可建立直接安全通信,缺点是密钥协商的通信和计算开销较大.在多项式与LU矩阵分解的确定密钥预分配方案的基础上、挺出了一种基于多维矩阵的改进方案.分析研究表明对比传统方案,在保证安全连通的基础上,降低了存储量和通信计算的开销,提高了节点抗俘获能力与动态扩展能力.     

ZigBee技术在航空电源车自动检测上的应用

介绍了ZigBee技术的概念、特点,并给出了基于ZigBee技术的航空电源车自动检测与控制系统.通过对ZigBee技术在航空电源车自动检测上的应用进行分析,提出了该系统的硬件与软件设计,实现了数据的采集和无线传输.ZigBee技术在航空电源车自动检测上的应用,即利用ZigBee无线传感器对航空电源车数据进行采集,然后以ZigBee无线传输模式将这些数据传送到保障指挥中心,计算机对这些数据进行分析整理后,为保障指挥人员准确判断航空电源车状态提供依据,因此能够及时发现航空电源车的故障,减少了装备维修所需的时间,大大提高了装备保障的效率.     

A tutorial survey of topics in wireless networking: Part I

In this two part paper, we provide a survey of recent and emerging topics in wireless networking. We view the area of wireless networking as dealing with problems of resource allocation so that the various connections that utilise the network achieve their desired performance objectives. In the first part of the paper, we first survey the area by providing a taxonomy of wireless networks as they have been deployed. Then, we provide a quick tutorial on the main issues in the wireless ‘physical’ layer, which is concerned with transporting bits over the radio frequency spectrum. Then, we proceed to discuss some resource allocation formulations in CDMA(code division multiple access) cellular networks and OFDMA(orthogonal frequency division multiple access) networks. In the second part of the paper, we first analyse random access wireless networks and pay special attention to 802·11 (Wi-Fi) networks.We then survey some topics in ad hoc multihop wireless networks, where we discuss arbitrary networks, as well as some theory of dense random networks. Finally, we provide an overview of the technical issues in the emerging area of wireless sensor networks.     

Networks and Rapid Technological Change: Novel Evidence from the Canadian Biotech Industry

Former studies on inter-organizational relationships have fallen short of properly identifying networks and elucidating factors that cause firms to entertain relationships in one type of network rather than another. This study adopts a methodology that is both empirical and comparative and thus constitutes a methodological departure from these previous studies. It also explicitly accounts for two structural characteristics of networks, namely, interactions and learning, to identify four types of networks, accumulative advantage networks, follow-the-trend networks, homophilies and multiconnectivity networks. It then investigates factors that cause firms to participate in any of these networks rather than another. Results show that even though networking is a common practice among biotech firms, most of them would rather keep this activity to a minimum. In addition, participation in these networks is found to vary according to the firm's size, stage of development and its sector of activity.     

On reaction network implementations of neural networks

This paper is concerned with the utilization of deterministically modelled chemical reaction networks for the implementation of (feed-forward) neural networks. We develop a general mathematical framework and prove that the ordinary differential equations (ODEs) associated with certain reaction network implementations of neural networks have desirable properties including (i) existence of unique positive fixed points that are smooth in the parameters of the model (necessary for gradient descent) and (ii) fast convergence to the fixed point regardless of initial condition (necessary for efficient implementation). We do so by first making a connection between neural networks and fixed points for systems of ODEs, and then by constructing reaction networks with the correct associated set of ODEs. We demonstrate the theory by constructing a reaction network that implements a neural network with a smoothed ReLU activation function, though we also demonstrate how to generalize the construction to allow for other activation functions (each with the desirable properties listed previously). As there are multiple types of ‘networks’ used in this paper, we also give a careful introduction to both reaction networks and neural networks, in order to disambiguate the overlapping vocabulary in the two settings and to clearly highlight the role of each network’s properties.     

A tutorial survey of topics in wireless networking: Part II

This is the second part of the survey of recent and emerging topics in wireless networking. We provide an overview of the area of wireless networking as that of dealing with problems of resource allocation so that the various connections that utilise the network achieve their desired performance objectives. In Part I we provided a taxonomy of wireless networks as they have been deployed. We then provided a quick survey of the main issues in the wireless ‘physical’ layer. We then discussed some resource allocation formulations in CDMA (code division multiple access) cellular networks and OFDMA (orthogonal frequency division multiple access) networks. In this part we begin with a discussion of random access wireless networks. We first provide an overview of the evolution of random access networks from Aloha to the currently popular 802·11 (Wi-Fi) networks. We then analyse the performance of the 802· 11 random access protocol. We briefly discuss the problem of optimal association of nodes to Wi-Fi access points. Next, we consider topics in ad hoc multihop wireless networks. We first discuss topology and cross layer control. For the latter, we describe the important maximum weight link scheduling algorithm. The connectivity and capacity of randomly deployed networks are then analysed. Finally, we provide an overview of the technical issues in the emerging area of wireless sensor networks.     

Topological properties of robust biological and computational networks

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields.     

Heterogeneous Hyperedge Convolutional Network

Graph convolutional networks (GCNs) have been developed as a general and powerful tool to handle various tasks related to graph data. However, current methods mainly consider homogeneous networks and ignore the rich semantics and multiple types of objects that are common in heterogeneous information networks (HINs). In this paper, we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph convolutional network architecture that operates on HINs. Specifically, we extract the rich semantics by different metastructures and adopt hyperedge to model the interactions among metastructure-based neighbors. Due to the powerful information extraction capabilities of metastructure and hyperedge, HHCN has the flexibility to model the complex relationships in HINs by setting different combinations of metastructures and hyperedges. Moreover, a metastructure attention layer is also designed to allow each node to select the metastructures based on their importance and provide potential interpretability for graph analysis. As a result, HHCN can encode node features, metastructure-based semantics and hyperedge information simultaneously by aggregating features from metastructure-based neighbors in a hierarchical manner. We evaluate HHCN by applying it to the semi-supervised node classification task. Experimental results show that HHCN outperforms state-of-the-art graph embedding models and recently proposed graph convolutional network models.     

用于光学信息处理的WTA电子网络的实现及其特性分析

给出了一种光信号处理用ＷＴＡ电子网络的实现方法，测试了网络的灵敏度，并分析了可能对其产生影响的因素，制作了３２单元的ＷＴＡ网络器件。网络能在几百微秒的时间内找出输入最大者，分辨精度为１ｍＶ。     

The diminishing role of hubs in dynamical processes on complex networks

It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here, we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics when compared with intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein–protein interaction network and a neural network, suggesting that it might indeed be a widespread property in nature.     

Spectrum of genetic diversity and networks of clonal organisms.

Clonal reproduction characterizes a wide range of species including clonal plants in terrestrial and aquatic ecosystems, and clonal microbes such as bacteria and parasitic protozoa, with a key role in human health and ecosystem processes. Clonal organisms present a particular challenge in population genetics because, in addition to the possible existence of replicates of the same genotype in a given sample, some of the hypotheses and concepts underlying classical population genetics models are irreconcilable with clonality. The genetic structure and diversity of clonal populations were examined using a combination of new tools to analyse microsatellite data in the marine angiosperm Posidonia oceanica. These tools were based on examination of the frequency distribution of the genetic distance among ramets, termed the spectrum of genetic diversity (GDS), and of networks built on the basis of pairwise genetic distances among genets. Clonal growth and outcrossing are apparently dominant processes, whereas selfing and somatic mutations appear to be marginal, and the contribution of immigration seems to play a small role in adding genetic diversity to populations. The properties and topology of networks based on genetic distances showed a 'small-world' topology, characterized by a high degree of connectivity among nodes, and a substantial amount of substructure, revealing organization in subfamilies of closely related individuals. The combination of GDS and network tools proposed here helped in dissecting the influence of various evolutionary processes in shaping the intra-population genetic structure of the clonal organism investigated; these therefore represent promising analytical tools in population genetics.     

Influence Diffusion Model in Multiplex Networks

The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest influence. Up to now, most of the research has tended to focus on monolayer network rather than on multiplex networks. But in the real world, most individuals usually exist in multiplex networks. Multiplex networks are substantially different as compared with those of a monolayer network. In this paper, we integrate the multi-relationship of agents in multiplex networks by considering the existing and relevant correlations in each layer of relationships and study the problem of unbalanced distribution between various relationships. Meanwhile, we measure the distribution across the network by the similarity of the links in the different relationship layers and establish a unified propagation model. After that, place on the established multiplex network propagation model, we propose a basic greedy algorithm on it. To reduce complexity, we combine some of the characteristics of triggering model into our algorithm. Then we propose a novel MNStaticGreedy algorithm which is based on the efficiency and scalability of the StaticGreedy algorithm. Our experiments show that the novel model and algorithm are effective, efficient and adaptable.