Network Architecture (network + architecture)

Distribution by Scientific Domains
Distribution within Engineering

Kinds of Network Architecture

  • neural network architecture

  • Selected Abstracts

    Adaptive routing algorithms and buffer management in multihop metropolitan area networks

    Paolo Giacomazzi
    The present study is devoted to multihop Metropolitan Area Network architectures characterized by some buffer capacity at the nodes and routing algorithm slightly more complex than those already present in the literature. A new adaptive routing algorithm and a new buffer management scheme are presented, and their performance are compared to that of the best algorithms known in the literature. Both the new proposals exhibit significant improvements in throughput and delay with respect to the older ones, for various MAN topologies, sizes and buffer dimensions. These results, which in some cases are very close to the theoretical optimum, are achieved with an implementation complexity well within the limits of present day technology. [source]

    A neuroanatomically grounded Hebbian-learning model of attention,language interactions in the human brain

    Max Garagnani
    Abstract Meaningful familiar stimuli and senseless unknown materials lead to different patterns of brain activation. A late major neurophysiological response indexing ,sense' is the negative component of event-related potential peaking at around 400 ms (N400), an event-related potential that emerges in attention-demanding tasks and is larger for senseless materials (e.g. meaningless pseudowords) than for matched meaningful stimuli (words). However, the mismatch negativity (latency 100,250 ms), an early automatic brain response elicited under distraction, is larger to words than to pseudowords, thus exhibiting the opposite pattern to that seen for the N400. So far, no theoretical account has been able to reconcile and explain these findings by means of a single, mechanistic neural model. We implemented a neuroanatomically grounded neural network model of the left perisylvian language cortex and simulated: (i) brain processes of early language acquisition and (ii) cortical responses to familiar word and senseless pseudoword stimuli. We found that variation of the area-specific inhibition (the model correlate of attention) modulated the simulated brain response to words and pseudowords, producing either an N400- or a mismatch negativity-like response depending on the amount of inhibition (i.e. available attentional resources). Our model: (i) provides a unifying explanatory account, at cortical level, of experimental observations that, so far, had not been given a coherent interpretation within a single framework; (ii) demonstrates the viability of purely Hebbian, associative learning in a multilayered neural network architecture; and (iii) makes clear predictions on the effects of attention on latency and magnitude of event-related potentials to lexical items. Such predictions have been confirmed by recent experimental evidence. [source]

    The generation of rhythmic activity in dissociated cultures of rat spinal cord

    Jürg Streit
    Abstract Locomotion in vertebrates is controlled by central pattern generators in the spinal cord. The roles of specific network architecture and neuronal properties in rhythm generation by such spinal networks are not fully understood. We have used multisite recording from dissociated cultures of embryonic rat spinal cord grown on multielectrode arrays to investigate the patterns of spontaneous activity in randomised spinal networks. We were able to induce similar patterns of rhythmic activity in dissociated cultures as in slice cultures, although not with the same reliability and not always with the same protocols. The most reliable rhythmic activity was induced when a partial disinhibition of the network was combined with an increase in neuronal excitability, suggesting that both recurrent synaptic excitation and neuronal excitability contribute to rhythmogenesis. During rhythmic activity, bursts started at several sites and propagated in variable ways. However, the predominant propagation patterns were independent of the protocol used to induce rhythmic activity. When synaptic transmission was blocked by CNQX, APV, strychnine and bicuculline, asynchronous low-rate activity persisted at ,,50% of the electrodes and ,,70% of the sites of burst initiation. Following the bursts, the activity in the interval was transiently suppressed below the level of intrinsic activity. The degree of suppression was proportional to the amount of activity in the preceding burst. From these findings we conclude that rhythmic activity in spinal cultures is controlled by the interplay of intrinsic neuronal activity and recurrent excitation in neuronal networks without the need for a specific architecture. [source]

    To breathe or not to breathe?

    That is the question
    Our understanding of the role of the brain in respiratory rhythm generation and regulation began the early nineteenth century. Over the next 150 years the neuronal groups in the medulla oblongata and pons that were involved in eupnoea and in gasping were identified by techniques involving the lesioning of areas of the lower brainstem, several transections across the brainstem and focal electrical stimulation. An incomplete picture emerged that stressed the importance of the ventral medulla. Subsequent electrophysiological studies in in vivo, in situ and in vitro preparations have revealed the importance of restricted groups of neurones in this area, within the Bötzinger and pre-Bötzinger nuclei, that are the essential kernel for rhythm generation. The outputs to the spinal motoneurones responsible for the patterning of inspiratory and expiratory discharge are shaped by inputs from these neurones and others within the respiratory complex that determine the activity of respiratory bulbospinal neurones. It is clear that the developmental stage of the preparation is often critical for the pattern of respiratory activity that is generated and that these patterns have important physiological consequences. The models that are currently considered to explain rhythmogenesis are critically evaluated. The respiratory network is subject to regulation from peripheral and central chemoreceptors, amongst other afferent inputs, which act to ensure respiratory homeostasis. The roles of peripheral chemoreceptors as primarily O2 sensors are considered, and the evolution of ideas surrounding their roles is described. New insights into the transduction mechanisms of chemoreception in the carotid body and chemosensitive areas of the ventral medullary surface, specifically in monitoring CO2 levels, are reviewed. As new experimental tools, both genetic and cellular, are emerging, it can be expected that the detailed network architecture and synaptic interactions that pattern respiratory activity in relation to behavioural activity will be revealed over the next years. [source]

    Producing Supramolecular Functional Materials Based on Fiber Network Reconstruction

    Shaokun Tang
    Abstract Here, the creation of new supramolecular functional materials based on the reconstruction of three-dimensional interconnecting self-organized nanofiber networks by a surfactant is reported. The system under investigation is N -lauroyl- L -glutamic acid di- n -butylamide in propylene glycol. The architecture of networks is implemented in terms of surfactants, e.g. sorbitan monolaurate. The elastic performance of the soft functional material is either weakened or strengthened (up to 300% for the current system) by reconstructing the topology of a fiber network. A topology transition of gel fiber network from spherulite-like to comb-like to spherulite-like is performed with the introduction of this surfactant. The Span 20 molecules are selectively adsorbed on the side surfaces of the crystalline fibers and promote the nucleation of side branches, giving rise to the transformation of the network architecture from spherulite-like topology to comb-like topology. At high surfactant concentrations, the occurrence of micelles may provide an increasing number of nucleation centers for spherulitic growth, leading to the reformation of spherulite-like topology. An analysis on fiber network topology supports and verifies a perfect agreement between the topological behavior and the rheological behavior of the functional materials. The approach identified in this study opens up a completely new avenue in designing and producing self-supporting supramolecular functional materials with designated macroscopic properties. [source]

    Development of terabit-class super-networking technologies

    Junichi Murayama Member
    Abstract We propose terabit-class super-networking technologies, designed to improve the scalability, reliability and performance of optical Internet protocol networks. Our technologies comprise both intra- and interlayer traffic engineering technologies. The intralayer technologies include an optical path protection scheme, an electrical load-balancing scheme and a distributed content-caching scheme. These provide an effective and economical way of improving performance and reliability. The interlayer technologies include both traffic-driven and application-driven optical cut-through control schemes and a policy control scheme. These provide an effective and economical way of improving scalability and performance. The feasibility of our technologies has been verified by means of experiments using prototype systems. The results showed that the different techniques can be combined to form a single network architecture for dynamic optical path control. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

    Assessing the predictive performance of artifIcial neural network-based classifiers based on different data preprocessing methods, distributions and training mechanisms

    Adrian Costea
    We analyse the implications of three different factors (preprocessing method, data distribution and training mechanism) on the classification performance of artificial neural networks (ANNs). We use three preprocessing approaches: no preprocessing, division by the maximum absolute values and normalization. We study the implications of input data distributions by using five datasets with different distributions: the real data, uniform, normal, logistic and Laplace distributions. We test two training mechanisms: one belonging to the gradient-descent techniques, improved by a retraining procedure, and the other is a genetic algorithm (GA), which is based on the principles of natural evolution. The results show statistically significant influences of all individual and combined factors on both training and testing performances. A major difference with other related studies is the fact that for both training mechanisms we train the network using as starting solution the one obtained when constructing the network architecture. In other words we use a hybrid approach by refining a previously obtained solution. We found that when the starting solution has relatively low accuracy rates (80,90%) the GA clearly outperformed the retraining procedure, whereas the difference was smaller to non-existent when the starting solution had relatively high accuracy rates (95,98%). As reported in other studies, we found little to no evidence of crossover operator influence on the GA performance. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Dynamic power management in new architecture of wireless sensor networks

    Chuan Lin
    Abstract Dynamic power management (DPM) technology has been widely used in sensor networks. Though many specific technical challenges remain and deserve much further study, the primary factor currently limiting progress in sensor networks is not these challenges but is instead the lack of an overall sensor network architecture. In this paper, we first develop a new architecture of sensor networks. Then we modify the sleep state policy developed by Sinha and Chandrakasan in (IEEE Design Test Comput. 2001; 18(2):62,74) and deduce that a new threshold satisfies the sleep-state transition policy. Under this new architecture, nodes in deeper sleep states consume lower energy while asleep, but require longer delays and higher latency costs to awaken. Implementing DPM with considering the battery status and probability of event generation will reduce the energy consumption and prolong the whole lifetime of the sensor networks. We also propose a new energy-efficient DPM, which is a modified sleep state policy and combined with optimal geographical density control (OGDC) (Wireless Ad Hoc Sensor Networks 2005; 1(1,2):89,123) to keep a minimal number of sensor nodes in the active mode in wireless sensor networks. Implementing dynamic power management with considering the battery status, probability of event generation and OGDC will reduce the energy consumption and prolong the whole lifetime of the sensor networks. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    WHOMoVeS: An optimized broadband sensor network for military vehicle tracking

    Mohamed Hamdi
    Abstract With the advance of sensing technologies and their applications, advanced sensor networks are gaining increasing interest. For certain sensitive applications, heterogeneous sensors can be deployed in the monitored space to ensure scalability, high-speed communication, and long network lifetime. Hybrid sensor networks have capabilities to combine the use of both resource-rich and resource-impoverished sensor nodes. This paper proposes a heterogeneous broadband sensor network architecture for military vehicle tracking. Powerful sensor devices with good bandwidth and energy capabilities are used as a communication backbone while energy sensors are used to track moving targets. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Active network architecture and management

    Roy Ladner
    Access and retrieval of meteorological and oceanographic data from heterogeneous sources in a distributed system presents many issues. There are a number of features of the TEDServices system that illustrate active network management for such data. There is a self-aware or intelligent aspect with respect to the mechanisms for shutdown, data ordering, and propagation of data orders. Intelligent cache management and collaborative application sharing process are other features of the active network management. Additionally a very important capability is the implementation of resumable object streams, which allows either the client or server side of a request to lose network connection, regain it, and the request will continue where it left off. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1123,1138, 2007. [source]

    Broadband Internet network management software platform and systems in KT

    Jae-Hyoung Yoo
    The high penetration rate of Internet access services in Korea has created an operational environment that is different from those of other countries, and thus the level of customer needs takes on different aspects. To meet the operational environment, customer needs and rapid time to market requirements, Korea Telecom (KT) developed a scalable and flexible Internet network management system software (NMS S/W) platform, on which many NMSs have been implemented. This paper introduces KT's Internet network architecture, broadband services and operational environment. It also presents the software architecture of the NMS platform and application functions of major NMSs that are implemented on the platform. Furthermore, the future perspective of KT's network management technologies is described. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

    M. S. Alam
    Abstract The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source,drain extension, which simultaneously improves maximum frequency of oscillation ,max because of lower gate to drain capacitance, and intrinsic gain AV0,=,gm/gds, due to lower output conductance gds. The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current Id on drain,source Vds and gate,source Vgs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (Jds,10,µA/µm) improvement was observed in both third-order-intercept IIP3 (,10,dBm) and intrinsic gain AV0 (,20,dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of Id with respect to gate voltage and lower gds in FinFET compared to bulk MOSFET. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Efficient IP-multicast via Inmarsat BGAN, a 3GPP satellite network

    Paul Febvre
    Abstract This paper outlines a number of challenges associated with supporting IP-multicast services efficiently across the Inmarsat Broadband Global Area Network (BGAN) 3GPP-based satellite network operating over the Imarsat-4 satellite constellation. The paper presents a network architecture that extends the 3GPP reference architecture to allow IP-multicast to be delivered when the Core Network is in a 3GPP Release-4 (non-MBMS compliant) configuration. This paper further extends the service and system concepts defined in 3GPP MBMS to provide improved flexibility and accountability, and improved scalability and efficiency when operating with the Inmarsat-4 BGAN TDM/TDMA air interface. This paper describes a number of radio resource management techniques that were deployed in a test system and the validation testing that was undertaken to support multimedia distribution and VoIP-based netted communications applications. The tuning of application and system behaviour to achieve acceptable performance is described in outline. Copyright © 2007 John Wiley & Sons, Ltd. [source]

    Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues

    Manfred M Fischer
    In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure for parameter estimation, such as the Alopex procedure. A benchmark comparison against the standard origin constrained gravity model and the two,stage neural network approach, suggested by Openshaw (1998), illustrates the superiority of the proposed model in terms of the generalization performance measured by ARV and SRMSE. [source]


    Shie-Yui Liong
    ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one-lead day to seven-lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four-lead day to seven-lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool. [source]

    Structure and Properties of Poly(, -caprolactone) Networks with Modulated Water Uptake

    Jorge L. Escobar Ivirico
    Abstract Summary: A PCL macromonomer was obtained by the reaction of PCL diol with methacrylic anhydride. The effective incorporation of the polymerizable end groups was assessed by FT-IR and 1H NMR spectroscopy. PCL networks were then prepared by photopolymerization of the PCL macromonomer. Furthermore, the macromonomer was copolymerized with HEA, with the aim of tailoring the hydrophilicity of the system. A set of hydrophilic semicrystalline copolymer networks were obtained. The phase microstructure of the new system and the network architecture was investigated by DSC, IR, DMS, TG, dielectric spectroscopy and water sorption studies. The presence of the hydrophilic units in the system prevented PCL crystallization on cooling; yet there was no effect on the glass transition process. The copolymer networks showed microphase separation and the , relaxation of the HEA units moved to lower temperatures as the amount of PCL in the system increased. Ideal structure, compatible with the experimental results, for the hydrophilized poly(, -caprolactone) networks with modulated water uptake. [source]

    Symmetry and bifurcation in vestibular system

    Marty Golubitsky
    The vestibular system in almost all vertebrates, humans included, controls balance by employing a set of six semicircular canals, three in each inner ear, to detect angular accelerations of the head. Signals from the canals are transmitted to neck motoneurons and activate eight corresponding muscle groups. These signals may be either excitatory or inhibitory, depending on the direction of acceleration. McCollum and Boyle have observed that in the cat the network of neurons concerned possesses octahedral symmetry, a structure deduced from the known innervation patterns (connections) from canals to muscles. We re-derive the octahedral symmetry from mathematical features of the probable network architecture, and model the movement of the head in response to the activation patterns of the muscles concerned. We assume that connections among neck muscles can be modeled by a ,coupled cell network', a system of coupled ODEs whose variables correspond to the eight muscles, and that network also has octahedral symmetry. The network and its symmetries imply that these ODEs must be equivariant under a suitable action of the octahedral group. Using results of Ashwin and Podvigina, we show that with the appropriate group actions, there are six possible spatiotemporal patterns of time-periodic states that can arise by Hopf bifurcation from an equilibrium corresponding to natural head motions. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Arteriolar network architecture and vasomotor function with ageing in mouse gluteus maximus muscle

    Shawn E. Bearden
    Physical diminishes with ageing, but little is known of how the microvascular supply to skeletal muscle fibres is affected. To test the hypothesis that ageing alters blood flow control, we investigated network architecture and vasomotor responses of arterioles in the gluteus maximus muscle of young (2,3 months), adult (12,14 months) and old (18,20 months) C57BL6 male mice (n= 83) (Young, Adult and Old, respectively). Microvascular casts revealed that the total number, length and surface area of arteriolar segments (diameter, 10,50 ,m) were not significantly different across age-groups. However, for arterioles with diameter of 30 ,m, tortuosity and branch angles increased with age (P < 0.05). In anaesthetized mice, second-order (2A) distributing arterioles had similar resting (17 ± 1 ,m) and maximal (37 ± 1 ,m) diameters and similar responsiveness to cumulative (10,10,10,4m) superfusion of acetylcholine or phenylephrine. With superfusate oxygen level raised from 0 to 21%, 2A arteriolar constriction in Young (11 ± 1 ,m) was greater (P < 0.05) than Adult and Old (5 ± 1 ,m). Observed 1 mm upstream from microiontophoresis of ACh (1 ,A, 1 s), conducted vasodilatation was 10 ± 1 ,m in Young, 17 ± 1 ,m in Adult and 6 ± 1 ,m in Old (P < 0.05). With muscle contractions (2, 4 and 8 Hz; 30 s) arteriolar diameter increased similarly across age-groups (6 ± 1, 11 ± 1 and 18 ± 1 ,m, respectively). Muscle mass and active tension were similar across age-groups yet postcontraction vasodilatation recovered more rapidly in Old versus Adult and Young (P < 0.05). With arteriolar network architecture maintained during ageing, the impairment in conducted vasodilatation and attenuation of postcontraction vasodilatation may compromise exercise tolerance. [source]

    An overview of the heterogeneous telescope network system: Concept, scalability and operation

    R.R. White
    Abstract In the coming decade there will be an avalanche of data streams devoted to astronomical exploration opening new windows of scientific discovery. The shear volume of data and the diversity of event types (Kantor 2006; Kaiser 2004; Vestrand & Theiler & Wozniak 2004) will necessitate; the move to a common language for the communication of event data, and enabling telescope systems with the ability to not just simply respond, but to act independently in order to take full advantage of available resources in a timely manner. Developed over the past three years, the Virtual Observatory Event (VOEvent) provides the best format for carrying these diverse event messages (White et al. 2006a; Seaman & Warner 2006). However, in order for the telescopes to be able to act independently, a system of interoperable network nodes must be in place, that will allow the astronomical assets to not only issue event notifications, but to coordinate and request specific observations. The Heterogeneous Telescope Network (HTN) is a network architecture that can achieve the goals set forth and provide a scalable design to match both fully autonomous and manual telescope system needs (Allan et al. 2006a;White et al. 2006b; Hessman 2006b). In this paper we will show the design concept of this meta-network and nodes, their scalable architecture and complexity, and how this concept can meet the needs of institutions in the near future. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Network and service architecture for emerging services based on home sensor networks

    Harish Viswanathan
    Sensor networks in the home can enable a variety of applications such as home monitoring and control, home security, home energy management, and home health care. Current state-of-the-art solutions typically target a single sensor application and do not take advantage of the established infrastructure of the broadband service provider, such as a telco operator or cable provider. In this paper, we propose an alternative solution that provides a comprehensive and scalable service platform for multiple parallel home sensor applications, even from third party providers. We highlight the advantages that a broadband service provider holds for providing these emerging high margin services, and derive a suitable end-to-end network architecture. We describe the functions of each of the main components and some of their interfaces, and pay particular attention to one of the key technological challenges: the commissioning and management of the home sensor network. In particular, we describe a laboratory implementation that demonstrates the feasibility of automatic commissioning and remote management of the sensor network. © 2009 Alcatel-Lucent. [source]

    Evolving modular networks with genetic algorithms: application to nonlinear time series

    EXPERT SYSTEMS, Issue 4 2004
    A.S. Cofiño
    Abstract: A key problem of modular neural networks is finding the optimal aggregation of the different subtasks (or modules) of the problem at hand. Functional networks provide a partial solution to this problem, since the inter-module topology is obtained from domain knowledge (functional relationships and symmetries). However, the learning process may be too restrictive in some situations, since the resulting modules (functional units) are assumed to be linear combinations of selected families of functions. In this paper, we present a non-parametric learning approach for functional networks using feedforward neural networks for approximating the functional modules of the resulting architecture; we also introduce a genetic algorithm for finding the optimal intra-module topology (the appropriate balance of neurons for the different modules according to the complexity of their respective tasks). Some benchmark examples from nonlinear time-series prediction are used to illustrate the performance of the algorithm for finding optimal modular network architectures for specific problems. [source]

    Use of neural networks for the prediction of frictional drag and transmission of axial load in horizontal wellbores

    Tanvir Sadiq
    Abstract The use of mud motors and other tools to accomplish forward motion of the bit in extended reach and horizontal wells allows avoiding large amounts of torque caused by rotation of the whole drill string. The forward motion of the drill string, however, is resisted by excessive amount of friction. In the presence of large compressive axial loads, the drill pipe or coiled tubing tends to buckle into a helix in horizontal boreholes. This causes additional frictional drag resisting the transmission of axial load (resulting from surface slack-off force) to the bit. As the magnitude of the frictional drag increases, a buckled pipe may become ,locked-up' making it almost impossible to drill further. In case of packers, the frictional drag may inhibit the transmission of set-up load to the packer. A prior knowledge of the magnitude of frictional drag for a given axial load and radial clearance can help avoid lock-up conditions and costly failure of the tubular. In this study a neural network model, for the prediction of frictional drag and axial load transmission in horizontal wellbores, is presented. Several neural network architectures were designed and tested to obtain the most accurate prediction. After cross-validation of the Back Propagation Neural Network (BPNN) algorithm, a two-hidden layer model was chosen for simultaneous prediction of frictional drag and axial load transmission. A comparison of results obtained from BPNN and General Regression Neural Network (GRNN) algorithms is also presented. Copyright © 2002 John Wiley & Sons, Ltd. [source]

    Using virtual topologies to manage inter-domain QoS in next-generation networks

    Ricardo B. Freitas
    Recently, several computer fields have turned to virtualization as a way to simplify complex problems. In this context, the Virtual Topology Service (VTS) was created to manage the advertisement and acquisition of virtual topologies (abstractions of the network status of a domain) and their use in inter-domain resource reservation to provide end-to-end quality of service (QoS). As an effort to create new network architectures which could attend current requirements like mobility and context-aware applications and support autonomous, heterogeneous and mobile domains next-generation networks (NGNs) emerged, with Ambient Networks (AN) as one of its instances. With an ever increasing multitude of online applications, end-to-end QoS has become increasingly important, especially for media and real-time uses. In this context, in order to better manage inter-domain QoS in these new networks, better coping with mobile nodes and domains, this work presents a new design and implementation of the VTS, adapted to the AN environment. The new VTS stores resource reservation information to enable the reuse of these reservations when re-establishing QoS after a node/domain movement. This implementation was tested on a real NGN prototype and showed considerable time saving when compared to QoS re-establishment without reusing the reservations. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    UniFAFF: a unified framework for implementing autonomic fault management and failure detection for self-managing networks

    Ranganai Chaparadza
    Today's network management, as known within the Fault, Configuration, Accounting, Performance, Security (FCAPS) management framework, is moving towards the definition and implementation of ,self-managing' network functions, with the aim of eliminating or drastically reducing human intervention in some of the complex aspects or daunting tasks of network management. The fault management plane of the FCAPS framework deals with the following functions: fault detection, fault diagnosis, localization or isolation, and fault removal. Task automation is at the very heart of self-managing (autonomic) nodes and networks, meaning that all functions and processes related to fault management must be automated as much as possible within the functionalities of self-managing (autonomic) nodes and networks, in order for us to talk about autonomic fault management. At this point in time there are projects calling for implementing new network architectures that are flexible to support on-demand functional composition for context- or situation-aware networking. A number of such projects have started, under the umbrella of the so-called clean-slate network designs. Therefore, this calls for open frameworks for implementing self-managing (autonomic) functions across each of the traditional FCAPS management planes. This paper presents a unified framework for implementing autonomic fault management and failure detection for self-managing networks, a framework we are calling UniFAFF. Copyright © 2008 John Wiley & Sons, Ltd. [source]


    Moon Seong Rang
    ABSTRACT: The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall-runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km2 watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth-order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea. [source]

    Hydrogen-bonded assemblies of 5,10,15,20-tetrakis(4-hydroxyphenyl)porphyrin with dimethylformamide, dimethylacetamide and water

    Sophia Lipstman
    The title free base porphyrin compound forms hydrogen-bonded adducts with N,N -dimethylformamide, C44H30N4O4·4C3H7NO, (I), a mixture of N,N -dimethylformamide and water, C44H30N4O4·4C3H7NO·H2O, (II), and a mixture of N,N -dimethylacetamide and water, C44H30N4O4·6C3H7NO·2H2O, (III). Total solvation of the four hydroxy functions of the porphyrin molecules characterizes all three compounds, thus preventing its supramolecular association into extended network architectures. In (I), the asymmetric unit consist of two five-component adduct species, while in (III), the nine-component entities reside on centres of inversion. This report provides the first structural characterizations of the free base tetra(hydroxyphenyl)porphyrin. It also demonstrates that the presence of strong Lewis bases, such as dimethylformamide or dimethylacetamide, in the crystallization mixture prevents direct supramolecular networking of the porphyrin ligands via O,H...O,H hydrogen bonds, due to their competing O,H...N(base) interaction with the hydroxy functions. The crystal packing of compounds (I),(III) resembles that of other hydrogen-bonding-assisted tetraarylporphyrin clathrates. [source]

    Converged network common charging controller function

    Xiang Yang Li
    With the emergence of converged networks, circuit switched (CS), packet switched (PS), and IP Multimedia Subsystem (IMS) domains and application servers (AS) are now merging to combine all kinds of services, business modes, and network architectures. This can cause chaos among various charging mechanisms within the networks. This paper presents a common charging controller function (CCCF) between the charging trigger function (CTF) of individual network elements (NEs) and the charging system. The CCCF operates with the common NE-independent charging control layer to serve various network elements in CS, PS, and IMS domains and application servers. It also simplifies NE-dependent CTFs for accounting metrics collection (AMC), regardless of whether the charging mechanisms are online or offline. The CCCF maintains a single charging characteristic database that stores the charging mechanism of each subscriber's service usage and forwards subscriber accounting data to the charging systems. By including the CCCF in networks, the IMS and pre-IMS charging systems for existing and new customers can be integrated without replacing deployed products, resulting in significant cost savings for telecommunications vendors and customers. © 2008 Alcatel-Lucent. [source]

    Ethernet aggregation and core network models for effcient and reliable IPTV services

    Christian Hermsmeyer
    With the growing interest on wireline network architectures for residential triple-play and business Ethernet services there is a renewed demand for efficient and reliable packet-based transport capabilities between the content providers and the end users. Voice and data traffic carried over a variety of access technologies is collected via technology-specific access networks (e.g., digital subscriber line [xDSL], passive optical network [xPON], and wireless fidelity [WiFi]). Metro and core networks need to aggregate the various user flows from different access network nodes and provide scalable and cost-effective distribution of various flow types (e.g., Internet access, voice, video on demand, and broadcast TV services) to the relevant service access points. Varying quality of service and resiliency requirements for these services are being reflected in a new breed of converged Ethernet and optical network elements with capabilities to interwork the bearer-planes of these two networking technologies seamlessly. Network elements based on Ethernet/Optical converged technology are able to select the most fitting mechanisms from each networking technology to meet the transport requirements for each individual service demand better while providing significantly enhanced implementation and operational efficiencies. This paper discusses network architecture models and network elements addressing these goals. © 2007 Alcatel-Lucent. [source]

    VoIP network architectures and QoS strategy

    Bharat T. Doshi
    Voice over IP (VoIP) has received much attention in recent years with the promise of lower costs, as well as new revenue-generating services. Cost and services advantages of carrying voice over IP compared to over the current circuit network are made possible by a common high-capacity packet infrastructure for voice, data, and multimedia services. An important requirement of such a packet infrastructure is the ability to provide public-switched telephone network (PSTN) grade quality without excessive over-provisioning. In this paper, we describe an approach to offer AbsoluteQoSÔ to voice and other demanding applications over a general-purpose packet network. AbsoluteQoS is defined as the ability to provide an engineered bound on call-blocking and quantitative QoS guarantee that calls-in-progress will receive. The proposed strategy is based on key innovations in architectures and protocols, as well as business models of PSTN and packet networks. © 2003 Lucent Technologies Inc. [source]

    Direct Associations or Internal Transformations?

    Exploring the Mechanisms Underlying Sequential Learning Behavior
    Abstract We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predicts differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. [source]