Design and Analysis of Optical Interconnection Networks for a Dataflow Parallel Computer

This work shows the design, simulation, and analysis of two optical interconnection networks for a Dataflow parallel computer architecture. To verify the optical interconnection network performance on the Dataflow architecture, we have analyzed the load balancing among the processors during the parallel programs executions. The load balancing is a very important parameter because it is directly associated to the dataflow parallelism degree. This article proves that optical interconnection networks designed with simple optical devices can provide efficiently the dataflow requirements of a high performance communication system.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Optimal placement of piezoelectric sensor/actuators for smart structures vibration control

Smart material technology has become an area of increasing interest for the development of lighter and stronger structures that are able to incorporate actuator and sensor capabilities for collocated control. In the design of actively controlled structures, the determination of the actuator locations and the controller gains is a very important issue. For that purpose, smart material modeling, modal analysis methods, and control and optimization techniques are the most important ingredients to be taken into account. The optimization problem to be solved in this context presents two interdependent aspects. The first is related to the discrete optimal actuator location selection problem, which is solved in this paper using genetic algorithms. The second is represented by a continuous variable optimization problem, through which the control gains are determined using classical techniques. A cantilever Euler-Bernoulli beam is used to illustrate the presented methodology.

Invasive Africanized honeybees change the structure of native pollination networks in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Voltage responsivity of pyroelectric sensor

Composites polymer-ceramic using castor oil-based polyurethane (PU) as non-ferroelectric matrix and Lead Zirconate Titanate (PZT) as ceramic powder have been prepared at thin films form by spin coating. The samples are poled by appropriated electric field to show piezo and pyroelectric activity. The pyroelectric coefficient p(T) at 343 K is obtained to be equal 5.8 X 10(-5) C m(-2) K-1 for a composite with 32 vol.% of ceramic. The figure of merit of this composite is six times higher than of PZT ceramic. The voltage responsivity of the pyroelectric is reduced when the thickness of the sample increases. It was used modulated white light as radiation source to excite the sensor film. The electric signal of the sensor decreases with the light modulation frequency by 1/f. (C) 1999 Elsevier B.V. S.A. All rights reserved.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Influence of distance, geometry and number of control stations on quality of local geodetic networks for the purpose of georeferencing of rural properties

The implementation of local geodetic networks for georeferencing of rural properties has become a requirement after publication of the Georeferencing Technical Standard by INCRA. According to this standard, the maximum distance of baselines to GNSS L1 receivers is of 20 km. Besides the length of the baseline, the geometry and the number of geodetic control stations are other factors to be considered in the implementation of geodetic networks. Thus, this research aimed to examine the influence of baseline lengths higher than the regulated limit of 20 km, the geometry and the number of control stations on quality of local geodetic networks for georeferencing, and also to demonstrate the importance of using specific tests to evaluate the solution of ambiguities and on the quality of the adjustment. The results indicated that the increasing number of control stations has improved the quality of the network, the geometry has not influenced on the quality and the baseline length has influenced on the quality; however, lengths higher than 20 km has not interrupted the implementation, with GPS L1 receiver, of the local geodetic network for the purpose of georeferencing. Also, the use of different statistical tests, both for the evaluation of the resolution of ambiguities and for the adjustment, have enabled greater clearness in analyzing the results, which allow that unsuitable observations may be eliminated.

Beta Diversity of Plant-Pollinator Networks and the Spatial Turnover of Pairwise Interactions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of biomimetic sensor for fast and sensitive detection of Norfloxacin

A biomimetic sensor is proposed as a promising new analytical method for determination of norfloxacin (NF) in pharmaceuticals. The sensor was prepared by modifying a glassy carbon electrode surface with a Nafion® membrane doped with poly(copper phthalocyanine) complex [poly-CuPc]. Amperometric measurements carried out with the sensor under an applied potential of -0.05 V vs Ag|AgCl in 0.1 mol L-1 acetic acid containing 1.5 × 10-3 mol L-1 hydrogen peroxide showed a linear response range from 2.0 × 10-4 to 1.2 × 10-3 mol L-1. Selectivity and interference studies were also performed. A sensor response mechanism is proposed, based on the experimental evidence. Recovery studies were carried out using environmental samples, in order to evaluate the sensor’s potential for use with these sample classes. Finally, sensor performance was evaluated using analyses of commercial formulations.

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Spectrum Distribution in Cognitive Radio: Error Correcting Codes Perspective

Cognitive radio is a growing zone in wireless communication which offers an opening in complete utilization of incompetently used frequency spectrum: deprived of crafting interference for the primary (authorized) user, the secondary user is indorsed to use the frequency band. Though, scheming a model with the least interference produced by the secondary user for primary user is a perplexing job. In this study we proposed a transmission model based on error correcting codes dealing with a countable number of pairs of primary and secondary users. However, we obtain an effective utilization of spectrum by the transmission of the pairs of primary and secondary users' data through the linear codes with different given lengths. Due to the techniques of error correcting codes we developed a number of schemes regarding an appropriate bandwidth distribution in cognitive radio.

Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Development of an electrochemical sensor for determination of dissolved oxygen by nickel-salen polymeric film modified electrode

An amperometric oxygen sensor based on a polymeric nickel-salen (salen = N,N'-ethylene bis(salicylideneiminato)) film coated platinum electrode was developed. The sensor was constructed by electropolymerization of nickel-salen complex at platinum electrode in acetonitrile/tetrabutylammonium perchlorate by cyclic voltammetry. The voltammetric behavior of the sensor was investigated in 0.5 mol L-1 KCl solution in the absence and presence of molecular oxygen. Thus, with the addition of oxygen to the solution, the increase of cathodic peak current (at -0.25 V vs. saturated calomel electrode (SCE)) of the modified electrode was observed. This result shows that the nickel-salen film on electrode surface promotes the reduction of oxygen. The reaction can be brought about electrochemically, where the nickel(II) complex is first reduced to a nickel(I) complex at the electrode surface. The nickel(I) complex then undergoes a catalytic oxidation by the molecular oxygen in solution back to the nickel(II) complex, which can then be electrochemically re-reduced to produce an enhancement of the cathodic current. The Tafel plot analyses have been used to elucidate the kinetics and mechanism of the oxygen reduction. A plot of the cathodic current vs. the dissolved oxygen concentration for chronoamperometry (fixed potential = -0.25 V vs. SCE) at the sensor was linear in the 3.95-9.20 mg L-1 concentration range and the concentration limit was 0.17 mg L-1 O-2. The proposed electrode is useful for the quality control and routine analysis of dissolved oxygen in commercial samples and environmental water. The results obtained for the levels of dissolved oxygen are in agreement with the results obtained with a commercial O-2 sensor. (C) 2012 Elsevier B.V. All rights reserved.