SZNAJD MODEL AND PROPORTIONAL ELECTIONS: THE ROLE OF THE TOPOLOGY OF THE NETWORK

The Sznajd model (SM) has been employed with success in the last years to describe opinion propagation in a community. In particular, it has been claimed that its transient is able to reproduce some scale properties observed in data of proportional elections, in different countries, if the community structure (the network) is scale-free. In this work, we investigate the properties of the transient of a particular version of the SM, introduced by Bernardes and co-authors in 2002. We studied the behavior of the model in networks of different topologies through the time evolution of an order parameter known as interface density, and concluded that regular lattices with high dimensionality also leads to a power-law distribution of the number of candidates with v votes. Also, we show that the particular absorbing state achieved in the stationary state (or else, the winner candidate), is related to a particular feature of the model, that may not be realistic in all situations.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

Storing quantum states in bosonic dissipative networks

By considering a network of dissipative quantum harmonic oscillators, we deduce and analyse the optimum topologies which are able to store quantum superposition states, protecting them from decoherence, for the longest period of time. The storage is made dynamically, in that the states to be protected evolve through the network before being retrieved back in the oscillator where they were prepared. The decoherence time during the dynamic storage process is computed and we demonstrate that it is proportional to the number of oscillators in the network for a particular regime of parameters.

Estimating complex cortical networks via surface recordings-A critical note

We discuss potential caveats when estimating topologies of 3D brain networks from surface recordings. It is virtually impossible to record activity from all single neurons in the brain and one has to rely on techniques that measure average activity at sparsely located (non-invasive) recording sites Effects of this spatial sampling in relation to structural network measures like centrality and assortativity were analyzed using multivariate classifiers A simplified model of 3D brain connectivity incorporating both short- and long-range connections served for testing. To mimic M/EEG recordings we sampled this model via non-overlapping regions and weighted nodes and connections according to their proximity to the recording sites We used various complex network models for reference and tried to classify sampled versions of the ""brain-like"" network as one of these archetypes It was found that sampled networks may substantially deviate in topology from the respective original networks for small sample sizes For experimental studies this may imply that surface recordings can yield network structures that might not agree with its generating 3D network. (C) 2010 Elsevier Inc All rights reserved

Abelian torsion groups with a countably compact group topology

Comfort and Remus [W.W. Comfort, D. Remus, Abelian torsion groups with a pseudo-compact group topology, Forum Math. 6 (3) (1994) 323-337] characterized algebraically the Abelian torsion groups that admit a pseudocompact group topology using the Ulm-Kaplansky invariants. We show, under a condition weaker than the Generalized Continuum Hypothesis, that an Abelian torsion group (of any cardinality) admits a pseudocompact group topology if and only if it admits a countably compact group topology. Dikranjan and Tkachenko [D. Dikranjan. M. Tkachenko, Algebraic structure of small countably compact Abelian groups, Forum Math. 15 (6) (2003) 811-837], and Dikranjan and Shakhmatov [D. Dikranjan. D. Shakhmatov, Forcing hereditarily separable compact-like group topologies on Abelian groups, Topology Appl. 151 (1-3) (2005) 2-54] showed this equivalence for groups of cardinality not greater than 2(c). We also show, from the existence of a selective ultrafilter, that there are countably compact groups without non-trivial convergent sequences of cardinality kappa(omega), for any infinite cardinal kappa. In particular, it is consistent that for every cardinal kappa there are countably compact groups without non-trivial convergent sequences whose weight lambda has countable cofinality and lambda > kappa. (C) 2009 Elsevier B.V. All rights reserved.

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.

A model of competition and interbank network formation

This work aims to understand the interaction between competition and network formation in the banking market. Combining Matutes and Padilla (1994) and Matutes and Vives (2000), we build a model of imperfect bank competition for deposits in which an interbank relationship network is a key strategic decision: it affects banks’ profit and risk position. The competition level exerts influence in the banking network structure since it affects the network outcomes. As result, we have that different competition levels imply different network topologies. Specifically, greater competition imply denser networks. Finally, when we allow for the possibility of collusion, the denser network can come out in the least competitive environment.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Análise de desempenho de sistemas de comunicação sem-fio para monitoramento de unidade de produção de poços petrolíferos terrestres

The greater part of monitoring onshore Oil and Gas environment currently are based on wireless solutions. However, these solutions have a technological configuration that are out-of-date, mainly because analog radios and inefficient communication topologies are used. On the other hand, solutions based in digital radios can provide more efficient solutions related to energy consumption, security and fault tolerance. Thus, this paper evaluated if the Wireless Sensor Network, communication technology based on digital radios, are adequate to monitoring Oil and Gas onshore wells. Percent of packets transmitted with successful, energy consumption, communication delay and routing techniques applied to a mesh topology will be used as metrics to validate the proposal in the different routing techniques through network simulation tool NS-2

Uma metodologia para modelagem e avaliação da dependabilidade de redes industriais sem fio

Ensuring the dependability requirements is essential for the industrial applications since faults may cause failures whose consequences result in economic losses, environmental damage or hurting people. Therefore, faced from the relevance of topic, this thesis proposes a methodology for the dependability evaluation of industrial wireless networks (WirelessHART, ISA100.11a, WIA-PA) on early design phase. However, the proposal can be easily adapted to maintenance and expansion stages of network. The proposal uses graph theory and fault tree formalism to create automatically an analytical model from a given wireless industrial network topology, where the dependability can be evaluated. The evaluation metrics supported are the reliability, availability, MTTF (mean time to failure), importance measures of devices, redundancy aspects and common cause failures. It must be emphasized that the proposal is independent of any tool to evaluate quantitatively the target metrics. However, due to validation issues it was used a tool widely accepted on academy for this purpose (SHARPE). In addition, an algorithm to generate the minimal cut sets, originally applied on graph theory, was adapted to fault tree formalism to guarantee the scalability of methodology in wireless industrial network environments (< 100 devices). Finally, the proposed methodology was validate from typical scenarios found in industrial environments, as star, line, cluster and mesh topologies. It was also evaluated scenarios with common cause failures and best practices to guide the design of an industrial wireless network. For guarantee scalability requirements, it was analyzed the performance of methodology in different scenarios where the results shown the applicability of proposal for networks typically found in industrial environments

Uma nova proposta de antenas Quasi-Yagi banda larga para comunicação sem fio

ln this work, planar quasi- Y agi antennas are investigated based on the concept of the classic Y agi_Uda antennas. These antennas represent improvements on the topologies of the antennas existing printed because they present characteristics of broad bandwidth, excellent radiation diagrams and simple construction. New configurations are adapted for the driver of the antennas, introducing patches elements into the driver. These new configurations are named Patches Elements Anteonas (PEA). This adaptation is obtained from simulations that are executed usiog the software C8T Microwave 8tudio 5. After doing the optimizations, procedures for construction and measurement ofthe prototypes are executed in order to improve the performance of the antennas in such way that they could be used in wireless communication applications, such as Bluetooth, WLAN' s and Wi-Fi. Next, the quasi- Y agi antennas are studied in order to implement them in arrangements. The arrangements construction is based 00 the best driver configuration of the antenna developed in this work. First, a linear arrangement composed by two elements of quasi¬Yagi antennas is constructed in such way that the radiation characteristics and the mutual coupling effects could be analyzed. After that, a 90° angle arrangement composed by two elements is studied to observe the effect of circular polarization. Experiments are executed in order to evaluate the arrangements performance. The experimental results show that the analysis made in this work is efficient and accurate. The numerical values obtained for the analyzed parameters of each structure developed are compared with the experimental values. 80, it is possible to observe a good concordance between them. Finally, some future works proposals are presented

Desenvolvimento de uma arquitetura em hardware prototipada em FPGA para aplicações genéricas utilizando redes neurais artificiais embarcadas

This work proposes hardware architecture, VHDL described, developed to embedded Artificial Neural Network (ANN), Multilayer Perceptron (MLP). The present work idealizes that, in this architecture, ANN applications could easily embed several different topologies of MLP network industrial field. The MLP topology in which the architecture can be configured is defined by a simple and specifically data input (instructions) that determines the layers and Perceptron quantity of the network. In order to set several MLP topologies, many components (datapath) and a controller were developed to execute these instructions. Thus, an user defines a group of previously known instructions which determine ANN characteristics. The system will guarantee the MLP execution through the neural processors (Perceptrons), the components of datapath and the controller that were developed. In other way, the biases and the weights must be static, the ANN that will be embedded must had been trained previously, in off-line way. The knowledge of system internal characteristics and the VHDL language by the user are not needed. The reconfigurable FPGA device was used to implement, simulate and test all the system, allowing application in several real daily problems

Projeto em FPGA de um controlador unificado para correção de fator de potência em retificadores boost bidirecionais monofásicos

The use of Field Programmable Gate Array (FPGA) for development of digital control strategies for power electronics applications has aroused a growing interest of many researchers. This interest is due to the great advantages offered by FPGA, which include: lower design effort, high performance and highly flexible prototyping. This work proposes the development and implementation of an unified one-cycle controller for boost CFP rectifier based on FPGA. This controller can be applied to a total of twelve converters, six inverters and six rectifiers defined by four single phase VSI topologies and three voltage modulation types. The topologies considered in this work are: full-bridge, interleaved full-bridge, half-bridge and interleaved half-bridge. While modulations are classified in bipolar voltage modulation (BVM), unipolar voltage modulation (UVM) and clamped voltage modulation (CVM). The proposed project is developed and prototyped using tools Matlab/Simulink® together with the DSP Builder library provided by Altera®. The proposed controller was validated with simulation and experimental results

Estratégia de conversor para interligação de sistemas de geração eólica à rede elétrica

Currently, there are several power converter topologies applied to wind power generation. The converters allow the use of wind turbines operating at variable speed, enabling better use of wind forces. The high performance of the converters is being increasingly demanded, mainly because of the increase in the power generation capacity by wind turbines, which gave rise to various converter topologies, such as parallel or multilevel converters. The use of converters allow effective control of the power injected into the grid, either partially, for the case using partial converter, or total control for the case of using full converter. The back-to-back converter is one of the most used topologies in the market today, due to its simple structure, with few components, contributing to robust and reliable performance. In this work, is presented the implementation of a wind cogeneration system using a permanent magnet synchronous generator (PMSG) associated with a back-to-back power converter is proposed, in order to inject active power in an electric power system. The control strategy of the active power delivered to the grid by cogeneration is based on the philosophy of indirect control

Método de Otimização Topológica em Estruturas Tridimensionais

The topology optimization problem characterize and determine the optimum distribution of material into the domain. In other words, after the definition of the boundary conditions in a pre-established domain, the problem is how to distribute the material to solve the minimization problem. The objective of this work is to propose a competitive formulation for optimum structural topologies determination in 3D problems and able to provide high-resolution layouts. The procedure combines the Galerkin Finite Elements Method with the optimization method, looking for the best material distribution along the fixed domain of project. The layout topology optimization method is based on the material approach, proposed by Bendsoe & Kikuchi (1988), and considers a homogenized constitutive equation that depends only on the relative density of the material. The finite element used for the approach is a four nodes tetrahedron with a selective integration scheme, which interpolate not only the components of the displacement field but also the relative density field. The proposed procedure consists in the solution of a sequence of layout optimization problems applied to compliance minimization problems and mass minimization problems under local stress constraint. The microstructure used in this procedure was the SIMP (Solid Isotropic Material with Penalty). The approach reduces considerably the computational cost, showing to be efficient and robust. The results provided a well defined structural layout, with a sharpness distribution of the material and a boundary condition definition. The layout quality was proporcional to the medium size of the element and a considerable reduction of the project variables was observed due to the tetrahedrycal element