The Role of Network Topologies in the Optical Core of IP-over-WDM Networks with Static Wavelength Routing

In this paper, we present a performance analysis of network topologies for the optical core of IP-over-WDM networks with static wavelength routing. The performance analysis is focused on regular degree four topologies, and, for comparison purposes, degree three topologies are also considered. It is shown that the increase of the nodal degree from three (degree three topology with smallest diameter) to four (degree four topology with smallest diameter) improves the network performance if a larger number of wavelengths per link is available. However, the influence of wavelength interchange on the nodal degree gain is small. The performance of regular degree four topologies with smallest diameter is also compared with the performance of mesh–torus topologies (which are also degree four topologies), and it is shown that the blocking probability of degree four topologies with smallest diameter is about two orders of magnitude lower than the blocking probability of mesh–torus topologies. It is also presented a performance comparison of WDM-based networks with nodal degrees ranging from two to five and it is shown that the increase of the nodal degree from two to three leads to high nodal degree gains, while de increase of the nodal degree from four to five leads to low nodal degree gains. These results show that degree three and degree four topologies are very attractive for use in the optical core of IP-over-WDM networks.     

弹性分组环技术

弹性分组环技术是一种新兴的城域网技术。作者主要讨论了弹性分组环技术的特点和优势,并且简要介绍了弹性分组环技术目前在国内城域网建设中的一些应用。弹性分组环技术集以太网的经济性、IP的智能化、光纤环网的丰富带宽和可靠性于一体,是目前宽带IP城域网运营商的最佳组网方案。     

Nanoweb Formation: 2D Self‐Assembly of Semiconductor Gallium Oxide Nanowires/Nanotubes

This paper reports a method to produce networks of crystalline gallium oxide comprised of one‐dimensional (1D) nanostructures. Because of the unique arrangement of wires, these crystalline networks are termed as ‘nanowebs’. Nanowebs are of great technological interest since they contain wire densities of the order of 10⁹ cm^–2. A possible mechanism for the fast self‐assembly of crystalline metal oxide nanowires involves multiple nucleation and coalescence via oxidation–reduction reactions at the molecular level. The preferential growth of nanowires parallel to the substrate enabled them to coalesce into regular polygonal networks. The individual segments of the polygonal network consist of both nanowires and nanotubules of β‐gallium oxide. Individual wire properties contribute to a nanoweb’s overall capacity and the implications for devices based on nanowebs are expected to be enormous.     

Positively Thermo‐Sensitive Monodisperse Core–Shell Microspheres

In this paper, we report on a novel family of monodisperse thermo‐sensitive core–shell hydrogel microspheres that is featured with high monodispersity and positively thermo‐responsive volume phase transition characteristics with tunable swelling kinetics, i.e., the particle swelling is induced by an increase rather than a decrease in temperature. The microspheres were fabricated in a three‐step process. In the first step, monodisperse poly(acrylamide‐co‐styrene) seeds were prepared by emulsifier‐free emulsion polymerization. In the second step, poly(acrylamide) or poly[acrylamide‐co‐(butyl methacrylate)] shells were fabricated on the microsphere seeds by free radical polymerization. In the third step, the core–shell microspheres with poly‐ (acrylamide)/poly(acrylic acid) based interpenetrating polymer network (IPN) shells were finished by a method of sequential IPN synthesis. The proposed monodisperse core–shell microspheres provide a new mode of the phase transition behavior for thermo‐sensitive “smart” or “intelligent” monodisperse micro‐actuators that is highly attractive for targeting drug delivery systems, chemical separations, sensors, and so on.     

Experiments of Fast Learning with High Order Boltzmann Machines

This work reports the results obtained with the application of High Order Boltzmann Machines without hidden units to construct classifiers for some problems that represent different learning paradigms. The Boltzmann Machine weight updating algorithm remains the same even when some of the units can take values in a discrete set or in a continuous interval. The absence of hidden units and the restriction to classification problems allows for the estimation of the connection statistics, without the computational cost involved in the application of simulated annealing. In this setting, the learning process can be sped up several orders of magnitude with no appreciable loss of quality of the results obtained.     

上海市电话局用户接入网发展状况

上海市电话局用户接入网建设经历了调研、网络规划、启动实施和进一步完善、规范四个怅阶段、现已基本形成了覆盖政府机关、大专院校、医院、商务大楼及高层建筑、新建住宅小区、区级以上图书馆等范围的用户光纤网络，为加快经济建设步伐、满足多层次用户需求、拓展电信新业务打下了坚实基础。     

神经网络离散变结构控制的算法及其应用

本文首次将自适应线性神经元（Ａｄａｌｉｎｅ）用于离散变结构控制。以滑模面Ｓ（ｋ）＝０作为Ａｄａｌｉｎｅ元的希望输出，作为Ａｄａｌｉｎｅ元的实际输出，通过ＬＭＳ算法在线调整权值可保证滑态可达条件的满足。应用所提出的控制策略对泵控马达速度伺服系统进行控制，仿真表明控制效果是令人满意的。     

数字程控交换机与专网中的多媒体通信

本文以一个多媒体通信应用VISIT为实例,介绍了多媒体通信在数字程控交换机及专网中的应用情况。     

A combined queueing network and stochastic Petri-net approach for evaluating the performability of fault-tolerant computer systems

In this paper the performability analysis of fault-tolerant computer systems using a hierarchical decomposition technique is presented. A special class of queueing network (QN) models, the so-called BCMP [4], and generalized stochastic Petri nets (GSPN) [1] which are often used to separately model performance and reliability respectively, have been combined in order to preserve the best modelling features of both.

A conceptual model is decomposed into GSPN and BCMP submodels, which are solved in isolation. Then, the remaining GSPN portion of the model is aggregated with flow-equivalents of BCMP models, in order to compute performability measures. The substitutes of BCMP models are presented by means of simple GSPN constructs, thereby preserving the 1st and 2nd moments of the throughput. A simple example of a data communication system where failed transmissions are corrected, is presented.     

Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.