智能网技术—未来发展趋势

本文从智能网（ＩＮ）的概念、主要组成、产品演进、发展史以及人们对智能网所持的各种观点等方面全面介绍了智能网；在分析亚洲电信市场现状的同时，指出向亚洲进行ＩＮ技术转让是寻求合作伙伴的最佳途径。     

A Distributed Virtual Backbone Development Scheme for Ad-Hoc Wireless Networks

The virtual backbone is an approach for solving routing problems in ad-hoc wireless networks. The virtual backbone approach features low latency, moderate routing overhead and is a hybrid scheme that uses the table-driven and on-demand routing protocols. This work presents a distributed virtual backbone development scheme for ad-hoc wireless networks. Using clustering, distributed labeling and heuristic Steiner tree techniques, our scheme outperforms other schemes in terms of the size and stability of the virtual backbone and the virtual backbone change rate. Experimental results demonstrate that our scheme has lower overhead than traditional table-driven and on-demand routing schemes. This revised version was published online in July 2006 with corrections to the Cover Date.     

Channel Adaptive Shortest Path Routing for Ad Hoc Networks

1　IntroductionAdhocnetworksareformedwithoutrequiringthepreexistinginfrastructureorcentralizedadminis tration ,incontrasttocellularnetworks.Asidefromtheoriginalmilitaryapplication ,ithasapplicationinpublicsafetyandcommercialareas,butadaptiveprotocolsarerequiredinorderforthemtodoso .Twoimportantcharacteristicsofacommunicationlinkinadhocnetworksareitsunreliabilityanditsvariability .Thelinksinsuchanetworkareunreli ablebecauseoffading ,interference,noise,andper hapsthefailureofthetransmittingorrec…     

Universal Platform for Scalable Semiconductor-Superconductor Nanowire Networks

Semiconductor-superconductor hybrids are commonly used in research on topological quantum computation. Traditionally, top-down approaches involving dry or wet etching are used to define the device geometry. These often aggressive processes risk causing damage to material surfaces, giving rise to scattering sites particularly problematic for quantum applications. Here, a method that maintains the flexibility and scalability of selective area grown nanowire networks while omitting the necessity of etching to create hybrid segments is proposed. Instead, it takes advantage of directional growth methods and uses bottom-up grown indium phosphide (InP) structures as shadowing objects to obtain selective metal deposition. The ability to lithographically define the position and area of these objects and to grow a predefined height ensures precise control of the shadowed region. The approach by growing indium antimonide nanowire networks with well-defined aluminium and lead (Pb) islands is demonstrated. Cross-section cuts of the nanowires reveal a sharp, oxide-free interface between semiconductor and superconductor. By growing InP structures on both sides of in-plane nanowires, a combination of platinum and Pb can independently be shadow deposited, enabling a scalable and reproducible in situ device fabrication. The semiconductor-superconductor nanostructures resulting from this approach are at the forefront of material development for Majorana based experiments.     

光时分复用技术的发展与展望

光时分复用技术不但是提高光纤通信容量的一个重要手段,而且还是全光网络的一种重要技术方案,本文对光时分复用系统及网络技术进行了综述,并对其发展前景进行了展望。     

一种精确计算容差网络的新算法

论述了应用Markov区间迭代法对含有容差的网络进行节点电压分析。文中首先介绍Markov区间选代法的基本方法，然后把这种方法应用于容差网络的节点电压计算，并把计算结果与Hansen迭代法进行比较。结果表明，这种方法具有较高的计算精度，它不仅能够用于电路的分析而且也能够用于容差网络的故障诊断。     

A Neural Network-Based Blind Multiuser Receiver for DS-CDMA Communication Systems

The Universal Mobile Telecommunications System (UMTS) which is based on Wideband-Code Division Multiple Access (W-CDMA) techniques is one of the most important broadband wireless communication systems. Adaptive Blind Multiuser Detection was widely considered for mobile receivers. The main drawback of this approach is that it achieves the optimum solution after a certain number of bit times. This paper deals with a new neural network approach in order to reduce the convergence time in different application environments. In particular, a modified Kennedy-Chua neural network, based on the Hopfield model is proposed. The neural network stability was investigated by means of a suitable analytical approach, while the performance of the proposed receiver scheme was derived by means of computer simulations. The numerical results shown in this paper highlight a fast convergence behavior of the proposed scheme, in particular under multipath-fading conditions. This revised version was published online in July 2006 with corrections to the Cover Date.     

前景广阔的虚拟专用网业务

虚拟专用网是利用公众网资源为客户构成专用网 的一种业务。本文首先回顾了ＶＰＮ的发展简史，指出经济的全球化带动了ＶＰＮ的迅速发展，市场需求和技术发展是两大主要推动力。ＶＰＮ业务可使运营公司，电信客户和最终用户均能受益，其发展趋势是提供数据业务。最后简述了ＶＰＮ在我国的发展情况。     

普遍故障下神经网络隐层冗余容错的分析与应用

隐层神经元冗余是提高神经网络容错性的一个有效的方法,在神经网络分类器的容错设计中,这一方法得到了良好的效果,对单故障可以做到完全容错.但是这一应用仅仅只能应用于输出层为硬限幅函数的前向网络,并且只证明了对网络中单故障有效.在实际应用中,网络中的各个节点和权值的故障往往是普遍存在的,因此本文提出了一种隐层冗余结构,对普遍故障存在下隐层神经元冗余容错方法做以评估,得出的结论是应用这种隐层神经元冗余结构可以减小网络的全局故障率;并提出了针对一般前向神经网络的实用的隐层神经元容错方法,这种方法可以有效地提高网络在普遍故障下的容错能力.     

Optical multicast over wavelength-routed WDM networks: A survey

With the rapid development of optical networking technology, it is now a realizable technique to support point-to-multipoint connections directly on the optical layer, giving rise to optical multicast. The topic of optical multicast has attracted much enthusiasm in recent years for the reason that it will not only make full use of the abundant bandwidth provided by optical fibers, but also take full advantage of multicast over the traditional point-to-point connection approach. In this paper, we present a comprehensive review of optical multicast over wavelength-routed WDM networks, covering the development of both data plane and control plane designs. In particular, we provide an up-to-date state-of-the-art review on the multicast routing and wavelength assignment problem. To the best of our knowledge, this is the most thorough and comprehensive review conducted so far on this topic in the literature.     

Network properties of mobile tactical scenarios

The mobile tactical network is a practical implementation of the mobile ad hoc network. Formed across tactical radios operating in the military very high frequency and low ultrahigh frequency bands, the mobile tactical network has distinctive characteristics when compared with generic mobile ad hoc networks, in particular with respect to its network topological behaviors and connectivity attributes. These characteristics must be understood and considered when selecting suitable network protocols. To this end, in this paper, a network science‐based systematic modeling approach is applied to analyze typical deployment scenarios and identify fundamental tactical network properties. The novel framework employs realistic scenario models as well as radio physical layer performance parameters and channel models to effectively capture the dynamic network behavior that needs to be considered for protocol design. The results provide critical insights and guidance to the development of tactical network solutions. © Her Majesty the Queen in Right of Canada 2013     

Prediction-based energy map for wireless sensor networks

A fundamental issue in the design of a wireless sensor network is to devise mechanisms to make efficient use of its energy, and thus, extend its lifetime. The information about the amount of available energy in each part of the network is called the energy map and can be useful to increase the lifetime of the network. In this paper, we address the problem of constructing the energy map of a wireless sensor network using prediction-based approach. Simulation results compare the performance of a prediction-based approach with a naive one in which no prediction is used. Results show that the prediction-based approach outperforms the naive in a variety of parameters. We also investigate the possibility of sampling the energy information in some nodes in the network in order to diminish the number of energy information packets. Results show that the use of sampling techniques produce more constant error curves.     

Underwater acoustic sensor networks: research challenges

Underwater sensor nodes will find applications in oceanographic data collection, pollution monitoring, offshore exploration, disaster prevention, assisted navigation and tactical surveillance applications. Moreover, unmanned or autonomous underwater vehicles (UUVs, AUVs), equipped with sensors, will enable the exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the characteristics of the underwater channel are detailed. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed and a cross-layer approach to the integration of all communication functionalities is suggested. Furthermore, open research issues are discussed and possible solution approaches are outlined.     

Mobile Station Positioning Using GSM Cellular Phone and Artificial Neural Networks

In this paper, we describe a novelapproach to mobile station positioning using a GSMmobile phone. The approach is based on the use of aninherent feature of the GSM cellular system (themobile phone continuously measures radio signalstrengths from a number of the nearest base stations(antennas)) and on the use of this information to estimatethe phone's location. The current values of the signalstrengths are processed by a trained artificial neuralnetwork executed at the computer attached to themobile phone to estimate the position of the mobilestation in real time. The neural network configurationis obtained by using a genetic algorithm that searchesthe space of specific neural network types anddetermines which one provides the best locationestimation results. Two general methods are explored:the first is based on using a neural network forclassification and the second uses functionapproximation. The experimental results are reportedand discussed.     

A gradient‐based binary feedback scheme for congestion control in computer networks

In this paper, we propose a binary feedback scheme for congestion control in computer networks using a gradient‐based mechanism which employs neural network modelling of the system dynamics. The optimal direction for rate adjustment at the source is based on a single bit feedback signal which depends upon the sign of the sensitivity of the system performance index with respect to queue input rate. The paper presents the scheme and the analysis that went into the choice of the various decision mechanisms. Simulation results are presented to show the performance of the gradient based technique as compared to the conventional queue‐based approach. Copyright © 2000 John Wiley & Sons, Ltd.     

Multi-scale spatial–temporal attention graph convolutional networks for driver fatigue detection

With the development of deep learning, fatigue detection technology for drivers has achieved remarkable achievements. Although the image-based approach achieves good accuracy, it inevitably leads to greater model complexity, which is unsuitable for mobile terminal devices. Luckily, human skeletal data significantly reduces the impact of noise and input data volume while retaining valid information, and it can better deal with real-world driving scenarios with the benefit of robustness in complex driving situations. This paper proposes a lightweight multi-scale spatio-temporal attention graph convolutional network (MS-STAGCN) to efficiently utilize skeleton data to identify driver states by aggregating locally and globally valid face information, which achieves good performance even for lightweight design. The experimental results show that the method achieves 92.4% accuracy on the NTHU-DDD dataset, which can be applied to fatigue detection tasks of the driver in real-world driving scenarios in the future.     

采用偏射路由算法的全光分组网络的性能分析

本文提出采用统一的马尔可夫链方法分析存储转发路由和偏射路由算法的网络，并在具体计算偏射概率时，采用了递推的算法。着重分析了偏射路由算法在无存储器、有单个存储器及有两个存储器的情况下，ＭａｎｈａｔｔａｎＳｔｒｅｅｔＮｅｔｗｏｒｋ和ＳｈｕｆｌｅＮｅｔ网络的性能，包括网络吞吐量、数据包的平均跳转次数和数据包跳转次数的概率分布，并对ＭａｎｈａｔａｎＳｔｒｅｅｔＮｅｔｗｏｒｋ和Ｓｈｕｆ－ｆｌｅＮｅｔ两种网络进行了简单的比较。     

A Computer-Aided Design Technique for Lossless Matching Networks with Mixed, Lumped and Distributed Elements

A computer-aided method for the design of lossless broadband matching networks with lumped elements and commensurate transmission lines is presented. The method is based on combining the simplifield real frequency technique with the algebraic network decomposition by Fettweis. To show the application of the Computer-Aided Design (CAD) approach, an UHF antenna matching problem is solved.     

A novel modeling approach for vertical handover based on dynamic k-partite graph in heterogeneous networks

The future network world will be embedded with different generations of wireless technologies, such as 3G, 4G and 5G. At the same time, the development of new devices equipped with multiple interfaces is growing rapidly in recent years. As a consequence, the vertical handover protocol is developed in order to provide ubiquitous connectivity in the heterogeneous wireless environment. Indeed, by using this protocol, the users have opportunities to be connected to the Internet through a variety of wireless technologies at any time and anywhere. The main challenge of this protocol is how to select the best access network in terms of Quality of Service (QoS) for users. For that, many algorithms have been proposed and developed to deal with the issue in recent studies. However, all existing algorithms permit only the selection of one access network from the available networks during the vertical handover process. To cope with this problem, in this paper we propose a new approach based on k-partite graph. Firstly, we introduce k-partite graph theory to model the vertical handover problem. Secondly, the selection of the best path is performed by a robust and lightweight mechanism based on cost function and Dijkstra’s algorithm. The experimental results show that the proposed approach can achieve better performance of QoS than the existing algorithms for FTP traffic and video streaming.     

一种基于模型的可分解贝叶斯在线强化学习

针对贝叶斯强化学习中参数个数巨大,收敛速度慢,无法实现在线学习的问题,提出一种基于模型的可分解贝叶斯强化学习方法.首先,将学习参数进行可分解表示,降低学习参数的个数;然后,根据先验知识和观察数据采用贝叶斯方法来学习,最优化探索和利用二者之间的平衡关系;最后,采用基于点的贝叶斯强化学习方法实现学习过程的快速收敛,从而达到在线学习的目的.仿真结果表明该算法能够满足实时系统性能的要求.