Análise morfológica neural e da densidade de espinhos dendríticos da amígdala medial de ratas durante o ciclo estral

A amígdala medial (AMe) é um núcleo superficial do complexo amigdalói-de, ocupando seu aspecto rostromedial. A AMe modula uma série de comportamen-tos, além de modular a memória e o aprendizado associado a estímulos olfativos e visuais. Em ratos, é uma estrutura sexualmente dimórfica e está dividida em quatro subnúcleos: ântero-dorsal (AMeAD), ântero-ventral (AMeAV), póstero-dorsal (AMePD) e póstero-ventral (AMePV). A AMe apresenta células com características morfológicas variadas e receptores para hormônios gonadais amplamente e heterogeneamente distribuídos entre todos os seus subnúcleos. O presente trabalho teve como obje-tivos caracterizar a morfologia dos neurônios dos subnúcleos AMeAD, AMeAV, AMePD e AMePV de ratas na fase de diestro e verificar a densidade de espinhos dendríticos de neurônios dos subnúcleos AMeAD, AMePD e AMePV de ratas nas fa-ses de diestro, pró-estro, estro e metaestro. Para tal, foram utilizadas ratas Wistar (N=24) que, após a identificação da fase do ciclo estral, foram anestesiadas e per-fundidas, tiveram seus encéfalos retirados e seccionados (cortes coronais de espes-sura de 100 e 200 µm), submetidos à técnica de Golgi. A seguir, os neurônios foram selecionados e desenhados com auxílio de câmara clara acoplada a um fotomicroscópio. Na avaliação da morfologia dos neurônios, observou-se que eles são do tipo multipolar, células estreladas e bitufted, sendo encontrados em todos os subnúcleos da AMe de ratas na fase de diestro além de células com corpos celulares arredondados, fusiformes, piriformes, ovais e com características piramidais. Para a quanti-ficação da densidade de espinhos dendríticos, foram desenhados os primeiros 40 µm de 8 ramos dendríticos de 6 fêmeas por fase do ciclo estral e por subregião da AMe. Os resultados da contagem dos espinhos dendríticos foram submetidos a ANOVA de uma via e ao teste de Newman-Keuls. Verificou-se que, em diestro, a densidade de espinhos nas regiões AMeAD, AMePD e AMePV, foi maior quando comparada às demais fases do ciclo estral. Além disso, em diestro, a AMePD apresentou a maior densidade quando comparada com as regiões AMeAD e AMePV. O estudo mostrou que os neurônios da AMe de ratas estudadas na fase de diestro apresentaram morfologia variada e a densidade de espinhos dendríticos va-riou na AMeAD, AMePD e AMePV durante o ciclo estral de ratas, especialmente na AMePD. Os resultados obtidos sugerem a plasticidade induzida por esteróides se-xuais na morfologia e na fisiologia da AMe de ratas.

Rumo a um desenho técnico de um sistema de apoio à decisão para uma reconfiguração do soft systems methodology: o caso do planejamento sistêmico

A multidisciplinaridade da tomada de decisão sofre com as peculiaridades de qualquer campo multidisciplinar. A falta de comunicação, muitas vezes, gera problemas e as respostas que podem ser encontradas dentro de outras áreas. Os Métodos de Estruturação de Problemas são respostas para os questionamentos atuais nas escolas de administração e negócios, principalmente o uso multimetodológico destes com outros métodos. Tendo o Soft Systems Metholodogy – SSM – como base, e a incorporação do Strategic Options Development and Analysis – SODA – ao processo do SSM, Georgiou (2012) apresenta o Planejamento Sistêmico em sua configuração mais recente. Visando buscar uma ferramenta computacional que atenda os pressupostos do SSM, e que incorpore as especificações da configuração do Planejamento Sistêmico, definem-se uma notação para o método e uma formalização das para as comunicações existentes entre os elementos, subsistemas, sistema e ambiente e, com isso, torna-se possível controlar o uso do método de forma iterativa. Para demonstrar tal uso, apresenta-se uma análise de um caso real e demonstra as dificuldades encontradas na utilização da Notação e Comunicação definida. Posteriormente, apresenta-se um desenho técnico de uma ferramenta computacional modular e que pode ser usada de forma integrada com outras ferramentas de outros métodos. Como resultado, têm-se o avanço na definição de padrões no uso das ferramentas do SSM, na apresentação dos aspectos sistêmicos do Planejamento Sistêmico, na apresentação de um uso iterativo do método e na apresentação de um desenho técnico para uma ferramenta computacional.

Identificação não linear usando uma rede fuzzy wavelet neural network modificada

In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed

Implementação e análise de desempenho dos protocolos de criptografia neural e Diffie-Hellman em sistemas RFID utilizando uma plataforma embarcada

RFID (Radio Frequency Identification) identifies object by using the radio frequency which is a non-contact automatic identification technique. This technology has shown its powerful practical value and potential in the field of manufacturing, retailing, logistics and hospital automation. Unfortunately, the key problem that impacts the application of RFID system is the security of the information. Recently, researchers have demonstrated solutions to security threats in RFID technology. Among these solutions are several key management protocols. This master dissertations presents a performance evaluation of Neural Cryptography and Diffie-Hellman protocols in RFID systems. For this, we measure the processing time inherent in these protocols. The tests was developed on FPGA (Field-Programmable Gate Array) platform with Nios IIr embedded processor. The research methodology is based on the aggregation of knowledge to development of new RFID systems through a comparative analysis between these two protocols. The main contributions of this work are: performance evaluation of protocols (Diffie-Hellman encryption and Neural) on embedded platform and a survey on RFID security threats. According to the results the Diffie-Hellman key agreement protocol is more suitable for RFID systems

Contribuição para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)

This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

Equalização neural aplicada a sistemas com modulação bidimensional em fibra óptica

Nowadays, optic fiber is one of the most used communication methods, mainly due to the fact that the data transmission rates of those systems exceed all of the other means of digital communication. Despite the great advantage, there are problems that prevent full utilization of the optical channel: by increasing the transmission speed and the distances involved, the data is subjected to non-linear inter symbolic interference caused by the dispersion phenomena in the fiber. Adaptive equalizers can be used to solve this problem, they compensate non-ideal responses of the channel in order to restore the signal that was transmitted. This work proposes an equalizer based on artificial neural networks and evaluates its performance in optical communication systems. The proposal is validated through a simulated optic channel and the comparison with other adaptive equalization techniques

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

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

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

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.

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Application of intelligent systems for specification of automotive equipments using LEDs

The advantages offered by the electronic component light emitting diode ( LED) have caused a quick and wide application of this device in replacement of incandescent lights. However, in its combined application, the relationship between the design variables and the desired effect or result is very complex and it becomes difficult to model by conventional techniques. This work consists of the development of a technique, through artificial neural networks, to make possible to obtain the luminous intensity values of brake lights using LEDs from design data. (C) 2005 Elsevier B.V. All rights reserved.

A neural network approach for robust nonlinear parameter estimation in presence of unknown-but-bounded errors

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. This paper presents a novel approach to solve robust parameter estimation problem for nonlinear model with unknown-but-bounded errors and uncertainties. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Using neural networks for estimation of aquifer dynamical behavior

The systems of water distribution from groundwater wells can be monitored using the changes observed on its dynamical behavior. In this paper, artificial neural networks are used to estimate the depth of the dynamical water level of groundwater wells in relation to water flow, operation time and rest time. Simulation results are presented to demonstrate the validity of the proposed approach. These results have shown that artificial neural networks can be effectively used for the identification and estimation of parameters related to systems of water distribution.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Design and analysis of an efficient neural network model for solving nonlinear optimization problems

This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.