Different Number Of Nodes Research Articles

Outage threshold extraction for maximising the capacity of wireless ad hoc networks

In this article, the authors develop an ‘analytical closed-form expression’ for the outage capacity of a wireless ad hoc network. First, a series of practical modulation schemes including binary phase shift keying (BPSK) and quadrature amplitude modulation (QAM) are considered. Then, a proper formulation for the total network capacity is presented based on the cumulative distribution function (CDF) of the signal to interference power ratio (SIR). A closed-form expression for this CDF is also analytically derived. Subsequently, the outage capacity of the network is examined. By investigating the effect of the outage threshold, it is shown that an optimum threshold value exists that maximises the outage capacity. This optimum value is obtained in the networks with different number of nodes. The analytical results are verified by conducting simulation studies.

Returns to scale, productivity and efficiency in US banking (1989-2000): the neural distance function revisited

Productivity and efficiency analyses have been indispensable tools for evaluating firms’ performance in the banking sector. In this context, the use of Artificial Neural Networks (ANNs) has been recently proposed in order to obtain a globally flexible functional form which is capable of approximating any existing output distance function while enabling the a priori imposition of the theoretical properties dictated by production theory, globally. Previous work has proposed and estimated the so-called Neural Distance Function (NDF) which has numerous advantages when compared to widely adopted specifications. In this paper, we carefully refine some of the most critical characteristics of the NDF. First, we relax the simplistic assumption that each equation has the same number of nodes because it is not expected to approximate reality with any reasonable accuracy and different numbers of nodes are allowed for each equation of the system. Second, we use an activation function which is known to achieve faster convergence compared to the conventional NDF model. Third, we use a relevant approach for technical efficiency estimation based on the widely adopted literature. Fitting the model to a large panel data we illustrate our proposed approach and estimate the Returns to Scale, the Total Factor Productivity and the Technical Efficiency in US commercial banking (1989-2000). Our approach provides very satisfactory results compared to the conventional model, a fact which implies that the refined NDF model successfully expands and improves the conventional NDF approach.

Twisted Torus Topologies for Enhanced Interconnection Networks

Many current parallel computers are built around a torus interconnection network. Machines from Cray, HP, and IBM, among others, make use of this topology. In terms of topological advantages, square (2D) or cubic (3D) tori would be the topologies of choice. However, for different practical reasons, 2D and 3D tori with different number of nodes per dimension have been used. These mixed-radix topologies are not edge symmetric, which translates into poor performance due to an unbalanced use of network resources. In this work, we analyze twisted 2D and 3D mixed-radix tori that remove the network bottlenecks present in nontwisted ones. Such topologies recover edge symmetry, and consequently, balance the utilization of their links. The distance-related properties of twisted tori together with a full characterization of their bisection bandwidth are described in this paper. A simulation-based performance evaluation has been carried out to assess the network performance under synthetic and trace-driven workloads. The obtained results show noticeable and consistent performance gains (up to an increase of 74 percent in accepted load). In addition, we propose scalable and practicable packet routing mechanisms and wiring layouts for these interconnection systems. The complexity of the architectural proposals is similar to the one exhibited by routing and folding mechanisms in standard tori.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

Deployment and Validation of GMPLS-Controlled Multi-layer Integrated Routing over the ASON/GMPLS CARISMA Test-bed

The efficient accommodation of subwavelength client flows on optical channels is a current challenge for the optimization of resources in GMPLS-controlled optical networks. While the capacity of the optical channels usually exceeds 10 Gbps, connection requests show finer granularity. This paper concentrates on the design and implementation of the ASON/GMPLS CARISMA test-bed a GMPLS-controlled grooming-capable transport network. Through the paper, the operation of a GMPLS-controlled multi-layer network architecture is introduced, reviewing those implementation issues that come into light. Then, an experimental evaluation is conducted on two alternative network scenarios with different number of nodes and nodal degree. From the results, GMPLScontrolled grooming makes a good trade-off between network blocking probability and E/O port usage when compared to all-optical and opaque solutions, leading to enhanced network performance while reducing network capital expenditures (CAPEX) .

Analytical thermal models for small induction motors

PurposeThermal analysis is an important design aspect and becoming a more important component of the electric motor design process due to the push for reduced weights and costs and increased efficiency. The purpose of this paper is to demonstrate that the accuracy of analytical thermal models depends on the accuracy of the thermal resistance computation and on the number of nodes in the equivalent thermal circuit.Design/methodology/approachIn this paper, several thermal analytical models with different numbers of nodes are compared against with each other and with experimental data.FindingsIt is demonstrated that the more sophisticated and detailed model having a larger number of nodes can be used to calibrate the simpler but faster models with less nodes. The models are all for the same range of small totally enclosed non‐ventilated induction motors.Originality/valueThis paper shows that simplified and/or detailed analytical thermal models can be successfully used in predicting the temperature rise in small induction motors. The level of detail and accuracy of these models strongly depends on the number of nodes and how the thermal resistances are set up. The calibration process for various reduced nodal model has been successfully described. Once calibrated, the reduced node thermal models give both satisfactory accuracy and allow very fast and robust thermal calculations.

Generalized projective synchronization between two different general complex dynamical networks with delayed coupling

In this Letter, generalized projective synchronization (GPS) between two different complex dynamical networks with delayed coupling is investigated. Two complex networks are distinct if they have diverse node dynamics, or different number of nodes, or different topological structures. By using the adaptive control scheme, a sufficient synchronization criterion for this GPS is derived based on the LaSalle invariance principle. Three corollaries are also obtained. It is noticed that the synchronization speed sensitively depends on the adjustable positive constants μ i . Furthermore, the coupling configuration matrix is not necessary to be symmetric or irreducible, and the inner coupling matrix need not be symmetric. In addition, the node dynamic need not satisfy the very strong and conservative uniformly Lipschitz condition. Numerical simulations further demonstrate the feasibility and effectiveness of the theoretical results.

Ant Colony Optimization Technique Applied in Network Routing Problem

In this paper, the Ant Colony Optimization Technique has been applied in different network models with different number of nodes and structure to find the shortest path with optimum throughput. Three variations of the Ant Colony Optimization Technique, ACO1, ACO2 and ACO3 has been proposed and applied on different standard network models and the results has been analyzed and concluded. A Tabu list is also maintained for a network with large number of nodes and results were collected to find the optimum size of the Tabu list in one of the algorithms proposed here. Experiments have also been performed by varying the load of the network. Here the throughput and the reliability of the network has been specially taken as the performance factor of the network.

ADPF Algorithm for Target Tracking in WSN

Particle filtering (PF) has been widely used in solving nonlinear/non Gaussian filtering problems. Inferring to the target tracking in a wireless sensor network (WSN), distributed PF (DPF) was used due to the limitation of nodes’ computing capacity. In this paper, a novel filtering method—asynchronous DPF (ADPF) for target tracking in WSN is proposed. There are two keys in the proposed algorithm. Firstly, instead of transferring value and weight of particles, Gaussian mixture model (GMM) is used to approximate the posteriori distribution, and only GMM parameters need to be transferred which can reduce the bandwidth and power consumption. Secondly, in order to use sampling information effectively, when target moving to the next cluster head region, the GMM parameters are transfer to the next cluster head, and combine with the new local GMM parameters to compose the new GMM parameters incrementally. The ADPF can also deal with the situation of different number of nodes in different cluster when using the dynamic cluster structure. The proposed ADPF is compared to some other DPF for WSN target tracking, and the experimental results show that not only the precision is improved, but also the bandwidth and power is reduced.

動作パターンとシンボルを相互変換する原始シンボル空間における動作パターンの内挿・外挿

Simple and easy human-interfaces for designing motion patterns of humanoids or human-like computer graphics have been required. One of the promising approaches is associating motion patterns with some symbol representations, and then generating new motion patterns by synthesizing those symbol representations. The proto-symbol space method based on continuous hidden Markov model (HMM) was such an approach. This method not only abstracts a symbol representation called proto-symbol but also generates new motion patterns by interpolating two proto-symbols in the proto-symbol space constructed with Kullback-Leibler divergence of the proto-symbols. Extrapolation of the proto-symbols as well as synthesis of HMMs that have different number of nodes, however, could not be achieved. In this paper, we propose an algorithm to solve these issues. We further discuss identification of synthesized proto-symbols and estimation of synthetic coefficients. To acquire accurate estimations, Bhattacharyya distance is introduced in the proto-symbol space construction. Finally, we show the feasibility of our approach with simulation experiments by a humanoid robot. This proto-symbol space could be a first synthetic step toward understanding symbolization mechanism according to Deacon's symbol developmental model.

Prediction of surface roughness in the end milling machining using Artificial Neural Network

This paper presents the ANN model for predicting the surface roughness performance measure in the machining process by considering the Artificial Neural Network (ANN) as the essential technique for measuring surface roughness. A revision of several previous studies associated with the modelling issue is carried out to assess how capable ANN is as a technique to model the problem. Based on the studies conducted by previous researchers, the abilities and limitations of the ANN technique for predicting surface roughness are highlighted. Utilization of ANN-based modelling is also discussed to show the required basic elements for predicting surface roughness in the milling process. In order to investigate how capable the ANN technique is at estimating the prediction value for surface roughness, a real machining experiment is referred to in this study. In the experiment, 24 samples of data concerned with the milling operation are collected based on eight samples of data of a two-level DOE 2 k full factorial analysis, four samples of centre data, and 12 samples of axial data. All data samples are tested in real machining by using uncoated, TiAIN coated and SN TR coated cutting tools of titanium alloy (Ti-6A1-4V). The Matlab ANN toolbox is used for the modelling purpose with some justifications. Feedforward backpropagation is selected as the algorithm with traingdx, learngdx, MSE, logsig as the training, learning, performance and transfer functions, respectively. With three nodes in the input layer and one node in the output layer, eight networks are developed by using different numbers of nodes in the hidden layer which are 3–1–1, 3–3–1, 3–6–1, 3–7–1, 3–1–1–1, 3–3–3–1, 3–6–6–1 and 3–7–7–1 structures. It was found that the 3–1–1 network structure of the SN TR coated cutting tool gave the best ANN model in predicting the surface roughness value. This study concludes that the model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.

SASHA—A Quality-Oriented Handover Algorithm for Multimedia Content Delivery to Mobile Users

The convergence of the existing network access technologies to a common IP-based architecture and the increase in popularity of accessing video content over the Internet makes IPTV a promising solution for media and entertainment industries. Additionally, video content delivery to the increasingly popular mobile devices over heterogeneous wireless networks makes IPTV even more appealing. However the distribution of multimedia content over heterogeneous wireless networks to mobile devices involves significant technical challenges related to mobility management and quality of service provisioning. The existing solutions do not consider quality of service as a decision making parameter for mobility management in general and handover management in particular. This paper proposes the smooth adaptive soft handover algorithm (SASHA), a novel quality-aware approach to handover based on load balancing among different networks using a comprehensive, quality of multimedia streaming (QMS), function for decision making. SASHA represents the handover management solution at the core of the more comprehensive multimedia mobility management system (M3S), a quality oriented mobility management framework for multimedia applications which maximizes user perceived quality by efficiently exploiting all available communication resources. Simulation-based testing results are presented, outlining the performance of SASHA in different mobility scenarios. The evaluation is performed for different number of nodes performing handover simultaneous and for various situations in terms of networks' overlapping area. The results shown indicate how SASHA outperforms other three mobility management solutions in terms of quality, scalability and resilience to the dynamics of the networks' overlapping area.

The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation

There seems to be a renewed trade-off between processes of concentration and dispersal in airline networks. This development calls for a framework that allows for a systematic assessment of the overall evolution of the spatiality of airline networks. To this end, we develop an analytical framework based on a set of spatial interaction indices, which allows measuring the degree of hierarchical differentiation (i.e. dominance and connectivity) in a network. Moreover, a normalization of these indices through a systematic comparison with the corresponding values for a rank size distribution makes it possible to engage in meaningful longitudinal analyses and/or to compare the spatiality of networks with different numbers of nodes and/or links. Our framework is tested by applying it to data on air passenger flows within Europe, and its usefulness is discussed in the context of research on air traffic liberalization.

Supporting Service Differentiation and Improving Channel Utilization with an Enhanced IEEE 802.11 DCF

Fair allocation of bandwidth and maximization of channel utilization are two important issues when designing a contention-based wireless medium access control (MAC) protocol. However, fulfilling both design goals at the same time is very difficult. Considering the problem in the IEEE 802.11 wireless local area networks (WLANs), in this work we propose a method using a p-persistent enhanced DCF, called P-IEEE 802.11 DCF, to achieve weighted fairness and efficient channel utilization among multiple priority classes in a WLAN. Its key idea is that when the back-off timer of a node reaches zero, the transmission probability is properly controlled to reflect the relative weights among data traffic flows so as to maximize the aggregate throughput and to minimize the frame delay at the same time. In particular, we obtain the optimal transmission probability based on a theoretical analysis, and also provide an approximation to this probability. The derived optimal and approximation are all evaluated numerically and simulated with different scenarios. The results show that the proposed method can fulfill our design goals under different numbers of priority classes and different numbers of nodes.

Exploring Flow Structures Around a Simplified ICE2 Train Subjected to A 30° Side Wind Using LES

Large-eddy simulation (LES) is conducted to investigate the flow around a simplified ICE2 train under side wind conditions at 30° yaw angle. The Reynolds number based on the height of the train and the free stream velocity is 2×105. Two computations on two different meshes with different numbers of nodes are made to check the effect of the mesh resolution on the results. The fine and the coarse meshes give similar results meaning that the results are mesh independent. The results are also verified against available experimental data. Good agreement is obtained between the LES results and the experimental data. The LES results show that two flow regimes exist in the wake. The first flow regime consists of steady vortex lines in the upper part of the wake flow. It changes into unsteady shedding after a distance of about five train heights from their onset on the surface of the train. The second flow regime is the unsteady movement of the lower part of the wake vortices. They attach and detach from the surface of the train in a regular fashion. The time-averaged flow and the instantaneous flow around the ICE2 train at 30° yaw angle are explored.

An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

This paper presents an artificial neural network (ANN) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton fabrics dyed using commercial reactive dyes. Achieving the expected efficiency in the application of stripping process is a very important aspect for the success of the reproduction. In the study, the predictions of L* and ΔE colorimetric values of stripped cotton samples for different stripping applications by artificial neural network are reported. We set up different network structures with different number of nodes in the hidden layer, the number of inputs and MSE of results as stopping criteria in order to get the best fitting model. According to the result of the best neural network models predicting L* and ΔE, we achieved 97 % of R for both of them. We are able to predict the L* value of the stripped samples using some working parameters as inputs with only 1.2 % error. We think that our results are very promising and the predictions of L* and ΔE values of stripped samples before applying any process are possible using the ANN model set up in the study, especially for L*.

Multicasting in IEEE 802.17 resilient packet ring

By using destination stripping and shortest-path routing, the new standard IEEE 802.17 resilient packet ring (RPR) allows for spatial reuse, resulting in a significantly increased capacity compared to source-stripping legacy ring networks. We show that for multicast traffic the performance of RPR reduces to that of legacy ring networks that do not support spatial reuse. We propose a bandwidth-efficient and cost-sensitive multicast approach for RPR networks that exploits RPR's built-in topology discovery and supplementary time-to-live field to enable spatial reuse for multicast traffic. By means of analysis and simulation we investigate the proposed multicast approach in terms of transmission, multicast, and reception capacities as well as throughput efficiency for different numbers of nodes and multicast fanout under various traffic scenarios. Our findings show that for multicast traffic, the transmission capacity and multicast capacity of RPR are increased significantly, in particular for small to medium multicast group sizes, and a multicast throughput efficiency of 100% is achieved. Besides the performance gain, the proposed multicast approach is able to maintain RPR's simplicity.

Reading Strategies and Hypertext Comprehension

The literature on assessing the cognitive processes involved in hypertext comprehension during the past 15 years has yielded contradictory results. In this article we explore a possible factor affecting this situation, mainly the fact that previous works did not control for the potential effects on comprehension of reading strategies in hypertext. In Experiment 1, results showed that reading strategies selectively affect the textbase and the situation model level. The number of different nodes read mainly affected the textbase, whereas the reading order influenced the situation model. In Experiment 2, the analysis of reading strategies replicated the effect of knowledge and coherence found in the literature on linear text comprehension (McNamara & Kintsch, 1996), but it was not replicated in hypertext. Low-knowledge participants learned more by following a high coherent reading order, whereas high-knowledge participants learned more by reading the hypertext in a low-coherence order. We discuss the theoretical and methodological consequences of this approach for the study of hypertext comprehension.

First principles studies of magnesium oxide clusters by parallelized Tohoku University Mixed-Basis program TOMBO

We report the results of first principles calculations on neutral magnesium oxide clusters (MgO) n n ⩽ 13, using the parallelized all-electron Tohoku University Mixed-Basis Orbitals (TOMBO) program and local density approximation for the exchange-correlation energy. It uses both plane waves and atomic orbitals as the basis functions. Various isomers are studied for each size to find the lowest energy structures and to identify the magic clusters. Clusters with n = 4, 6, 9, and 12 are found to be magic in agreement with the available experimental data. The performance of the parallelized TOMBO is also studied by using different number of nodes on Hitachi SR8000 supercomputer.

Small Worlds Among Interlocking Directors: Network Structure and Distance in Bipartite Graphs

We describe a methodology to examine bipartite relational data structures as exemplified in networks of corporate interlocking. These structures can be represented as bipartite graphs of directors and companies, but direct comparison of empirical datasets is often problematic because graphs have different numbers of nodes and different densities. We compare empirical bipartite graphs to simulated random graph distributions conditional on constraints implicit in the observed datasets. We examine bipartite graphs directly, rather than simply converting them to two 1-mode graphs, allowing investigation of bipartite statistics important to connection redundancy and bipartite connectivity. We introduce a new bipartite clustering coefficient that measures tendencies for localized bipartite cycles. This coefficient can be interpreted as an indicator of inter-company and inter-director closenesss but high levels of bipartite clustering have a cost for long range connectivity. We also investigate degree distributions, path lengths, and counts of localized subgraphs. Using this new approach, we compare global structural properties of US and Australian interlocking company directors. By comparing observed statistics against those from the simulations, we assess how the observed graphs are structured, and make comparisons between them relative to the simulated graph distributions. We conclude that the two networks share many similarities and some differences. Notably, both structures tend to be influenced by the clustering of directors on boards, more than by the accumulation of board seats by individual directorss that shared multiple board memberships (multiple interlocks) are an important feature of both infrastructures, detracting from global connectivity (but more so in the Australian case)s and that company structural power may be relatively more diffuse in the US structure than in Australia.