Algoritmos genéticos e processamento paralelo aplicados à definição e treinamento de redes neurais perceptron de múltiplas camadas

ln this work, it was deveIoped a parallel cooperative genetic algorithm with different evolution behaviors to train and to define architectures for MuItiIayer Perceptron neural networks. MuItiIayer Perceptron neural networks are very powerful tools and had their use extended vastIy due to their abiIity of providing great resuIts to a broad range of appIications. The combination of genetic algorithms and parallel processing can be very powerful when applied to the Iearning process of the neural network, as well as to the definition of its architecture since this procedure can be very slow, usually requiring a lot of computational time. AIso, research work combining and appIying evolutionary computation into the design of neural networks is very useful since most of the Iearning algorithms deveIoped to train neural networks only adjust their synaptic weights, not considering the design of the networks architecture. Furthermore, the use of cooperation in the genetic algorithm allows the interaction of different populations, avoiding local minima and helping in the search of a promising solution, acceIerating the evolutionary process. Finally, individuaIs and evolution behavior can be exclusive on each copy of the genetic algorithm running in each task enhancing the diversity of populations

Implementação e avaliação de máquinas de comitê em um ambiente com múltiplos processadores embarcados em um único chip

The number of applications based on embedded systems grows significantly every year, even with the fact that embedded systems have restrictions, and simple processing units, the performance of these has improved every day. However the complexity of applications also increase, a better performance will always be necessary. So even such advances, there are cases, which an embedded system with a single unit of processing is not sufficient to achieve the information processing in real time. To improve the performance of these systems, an implementation with parallel processing can be used in more complex applications that require high performance. The idea is to move beyond applications that already use embedded systems, exploring the use of a set of units processing working together to implement an intelligent algorithm. The number of existing works in the areas of parallel processing, systems intelligent and embedded systems is wide. However works that link these three areas to solve any problem are reduced. In this context, this work aimed to use tools available for FPGA architectures, to develop a platform with multiple processors to use in pattern classification with artificial neural networks

Uma implementação paralela híbrida para o problema do caixeiro viajante usando algoritmos genéticos, GRASP e aprendizagem por reforço

The metaheuristics techiniques are known to solve optimization problems classified as NP-complete and are successful in obtaining good quality solutions. They use non-deterministic approaches to generate solutions that are close to the optimal, without the guarantee of finding the global optimum. Motivated by the difficulties in the resolution of these problems, this work proposes the development of parallel hybrid methods using the reinforcement learning, the metaheuristics GRASP and Genetic Algorithms. With the use of these techniques, we aim to contribute to improved efficiency in obtaining efficient solutions. In this case, instead of using the Q-learning algorithm by reinforcement learning, just as a technique for generating the initial solutions of metaheuristics, we use it in a cooperative and competitive approach with the Genetic Algorithm and GRASP, in an parallel implementation. In this context, was possible to verify that the implementations in this study showed satisfactory results, in both strategies, that is, in cooperation and competition between them and the cooperation and competition between groups. In some instances were found the global optimum, in others theses implementations reach close to it. In this sense was an analyze of the performance for this proposed approach was done and it shows a good performance on the requeriments that prove the efficiency and speedup (gain in speed with the parallel processing) of the implementations performed

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

Estudos de algumas ferramentas de coleta e visualização de dados e desempenho de aplicações paralelas no ambiente MPI

The last years have presented an increase in the acceptance and adoption of the parallel processing, as much for scientific computation of high performance as for applications of general intention. This acceptance has been favored mainly for the development of environments with massive parallel processing (MPP - Massively Parallel Processing) and of the distributed computation. A common point between distributed systems and MPPs architectures is the notion of message exchange, that allows the communication between processes. An environment of message exchange consists basically of a communication library that, acting as an extension of the programming languages that allow to the elaboration of applications parallel, such as C, C++ and Fortran. In the development of applications parallel, a basic aspect is on to the analysis of performance of the same ones. Several can be the metric ones used in this analysis: time of execution, efficiency in the use of the processing elements, scalability of the application with respect to the increase in the number of processors or to the increase of the instance of the treat problem. The establishment of models or mechanisms that allow this analysis can be a task sufficiently complicated considering parameters and involved degrees of freedom in the implementation of the parallel application. An joined alternative has been the use of collection tools and visualization of performance data, that allow the user to identify to points of strangulation and sources of inefficiency in an application. For an efficient visualization one becomes necessary to identify and to collect given relative to the execution of the application, stage this called instrumentation. In this work it is presented, initially, a study of the main techniques used in the collection of the performance data, and after that a detailed analysis of the main available tools is made that can be used in architectures parallel of the type to cluster Beowulf with Linux on X86 platform being used libraries of communication based in applications MPI - Message Passing Interface, such as LAM and MPICH. This analysis is validated on applications parallel bars that deal with the problems of the training of neural nets of the type perceptrons using retro-propagation. The gotten conclusions show to the potentiality and easinesses of the analyzed tools.

Análise de escalabilidade de uma implementação paralela do simulated annealing acoplado

This paper analyzes the performance of a parallel implementation of Coupled Simulated Annealing (CSA) for the unconstrained optimization of continuous variables problems. Parallel processing is an efficient form of information processing with emphasis on exploration of simultaneous events in the execution of software. It arises primarily due to high computational performance demands, and the difficulty in increasing the speed of a single processing core. Despite multicore processors being easily found nowadays, several algorithms are not yet suitable for running on parallel architectures. The algorithm is characterized by a group of Simulated Annealing (SA) optimizers working together on refining the solution. Each SA optimizer runs on a single thread executed by different processors. In the analysis of parallel performance and scalability, these metrics were investigated: the execution time; the speedup of the algorithm with respect to increasing the number of processors; and the efficient use of processing elements with respect to the increasing size of the treated problem. Furthermore, the quality of the final solution was verified. For the study, this paper proposes a parallel version of CSA and its equivalent serial version. Both algorithms were analysed on 14 benchmark functions. For each of these functions, the CSA is evaluated using 2-24 optimizers. The results obtained are shown and discussed observing the analysis of the metrics. The conclusions of the paper characterize the CSA as a good parallel algorithm, both in the quality of the solutions and the parallel scalability and parallel efficiency

Projeto de Sistemas Integrados de Propósito Geral Baseados em Redes em Chip Expandindo as Funcionalidades dos Roteadores para Execução de Operações: A plataforma IPNoSys

It bet on the next generation of computers as architecture with multiple processors and/or multicore processors. In this sense there are challenges related to features interconnection, operating frequency, the area on chip, power dissipation, performance and programmability. The mechanism of interconnection and communication it was considered ideal for this type of architecture are the networks-on-chip, due its scalability, reusability and intrinsic parallelism. The networks-on-chip communication is accomplished by transmitting packets that carry data and instructions that represent requests and responses between the processing elements interconnected by the network. The transmission of packets is accomplished as in a pipeline between the routers in the network, from source to destination of the communication, even allowing simultaneous communications between pairs of different sources and destinations. From this fact, it is proposed to transform the entire infrastructure communication of network-on-chip, using the routing mechanisms, arbitration and storage, in a parallel processing system for high performance. In this proposal, the packages are formed by instructions and data that represent the applications, which are executed on routers as well as they are transmitted, using the pipeline and parallel communication transmissions. In contrast, traditional processors are not used, but only single cores that control the access to memory. An implementation of this idea is called IPNoSys (Integrated Processing NoC System), which has an own programming model and a routing algorithm that guarantees the execution of all instructions in the packets, preventing situations of deadlock, livelock and starvation. This architecture provides mechanisms for input and output, interruption and operating system support. As proof of concept was developed a programming environment and a simulator for this architecture in SystemC, which allows configuration of various parameters and to obtain several results to evaluate it

Projeto e implementação de uma plataforma MP-SoC usando SystemC

This work presents the concept, design and implementation of a MP-SoC platform, named STORM (MP-SoC DirecTory-Based PlatfORM). Currently the platform is composed of the following modules: SPARC V8 processor, GPOP processor, Cache module, Memory module, Directory module and two different modles of Network-on-Chip, NoCX4 and Obese Tree. All modules were implemented using SystemC, simulated and validated, individually or in group. The modules description is presented in details. For programming the platform in C it was implemented a SPARC assembler, fully compatible with gcc s generated assembly code. For the parallel programming it was implemented a library for mutex managing, using the due assembler s support. A total of 10 simulations of increasing complexity are presented for the validation of the presented concepts. The simulations include real parallel applications, such as matrix multiplication, Mergesort, KMP, Motion Estimation and DCT 2D

Avaliação da execução de aplicações orientadas à dados na arquitetura de redes em chip IPNoSys

The increasing complexity of integrated circuits has boosted the development of communications architectures like Networks-on-Chip (NoCs), as an architecture; alternative for interconnection of Systems-on-Chip (SoC). Networks-on-Chip complain for component reuse, parallelism and scalability, enhancing reusability in projects of dedicated applications. In the literature, lots of proposals have been made, suggesting different configurations for networks-on-chip architectures. Among all networks-on-chip considered, the architecture of IPNoSys is a non conventional one, since it allows the execution of operations, while the communication process is performed. This study aims to evaluate the execution of data-flow based applications on IPNoSys, focusing on their adaptation against the design constraints. Data-flow based applications are characterized by the flowing of continuous stream of data, on which operations are executed. We expect that these type of applications can be improved when running on IPNoSys, because they have a programming model similar to the execution model of this network. By observing the behavior of these applications when running on IPNoSys, were performed changes in the execution model of the network IPNoSys, allowing the implementation of an instruction level parallelism. For these purposes, analysis of the implementations of dataflow applications were performed and compared

Escalonando uma aplicação paralela com demanda variável em ambiente de nuvem

This study proposes a solution responsible for scheduling data processing with variable demand in cloud environments. The system built check specific variables to the business context of a company incubated at Digital Metropole Institute of UFRN. Such a system generates an identification strategy machinery designs available in a cloud environment, focusing on processing performance, using data load balancing strategies and activities of parallelism in the software execution flow. The goal is to meet the seasonal demand within a standard time limit set by the company, controlling operating costs by using cloud services in the IaaS layer.

The influence of visual inter-hemispheric connections on spiking, assembly and LFP activities, and their phase relationship during figure-ground stimulation

Desde os descobrimentos pioneiros de Hubel e Wiesel acumulou-se uma vasta literatura descrevendo as respostas neuronais do córtex visual primário (V1) a diferentes estímulos visuais. Estes estímulos consistem principalmente em barras em movimento, pontos ou grades, que são úteis para explorar as respostas dentro do campo receptivo clássico (CRF do inglês classical receptive field) a características básicas dos estímulos visuais como a orientação, direção de movimento, contraste, entre outras. Entretanto, nas últimas duas décadas, tornou-se cada vez mais evidente que a atividade de neurônios em V1 pode ser modulada por estímulos fora do CRF. Desta forma, áreas visuais primárias poderiam estar envolvidas em funções visuais mais complexas como, por exemplo, a separação de um objeto ou figura do seu fundo (segregação figura-fundo) e assume-se que as conexões intrínsecas de longo alcance em V1, assim como as conexões de áreas visuais superiores, estão ativamente envolvidas neste processo. Sua possível função foi inferida a partir da análise das variações das respostas induzidas por um estímulo localizado fora do CRF de neurônios individuais. Mesmo sendo muito provável que estas conexões tenham também um impacto tanto na atividade conjunta de neurônios envolvidos no processamento da figura quanto no potencial de campo, estas questões permanecem pouco estudadas. Visando examinar a modulação do contexto visual nessas atividades, coletamos potenciais de ação e potenciais de campo em paralelo de até 48 eletrodos implantados na área visual primária de gatos anestesiados. Estimulamos com grades compostas e cenas naturais, focando-nos na atividade de neurônios cujo CRF estava situado na figura. Da mesma forma, visando examinar a influência das conexões laterais, o sinal proveniente da área visual isotópica e contralateral foi removido através da desativação reversível por resfriamento. Fizemos isso devido a: i) as conexões laterais intrínsecas não podem ser facilmente manipuladas sem afetar diretamente os sinais que estão sendo medidos, ii) as conexões inter-hemisféricas compartilham as principais características anatômicas com a rede lateral intrínseca e podem ser vistas como uma continuação funcional das mesmas entre os dois hemisférios e iii) o resfriamento desativa as conexões de forma causal e reversível, silenciando temporariamente seu sinal, permitindo conclusões diretas a respeito da sua contribuição. Nossos resultados demonstram que o mecanismo de segmentação figurafundo se reflete nas taxas de disparo de neurônios individuais, assim como na potência do potencial de campo e na relação entre sua fase e os padrões de disparo produzidos pela população. Além disso, as conexões laterais inter-hemisféricas modulam estas variáveis dependendo da estimulação feita fora do CRF. Observamos também uma influência deste circuito lateral na coerência entre potenciais de campo entre eletrodos distantes. Em conclusão, nossos resultados dão suporte à ideia de um mecanismo complexo de segmentação figura-fundo atuando desde as áreas visuais primárias em diferentes escalas de frequência. Esse mecanismo parece envolver grupos de neurônios ativos sincronicamente e dependentes da fase do potencial de campo. Nossos resultados também são compatíveis com a hipótese que conexões laterais de longo alcance também fazem parte deste mecanismo

Navegação cooperativa de um robô humanóide e um robô com rodas usando informação visual

This work presents a cooperative navigation systemof a humanoid robot and a wheeled robot using visual information, aiming to navigate the non-instrumented humanoid robot using information obtained from the instrumented wheeled robot. Despite the humanoid not having sensors to its navigation, it can be remotely controlled by infra-red signals. Thus, the wheeled robot can control the humanoid positioning itself behind him and, through visual information, find it and navigate it. The location of the wheeled robot is obtained merging information from odometers and from landmarks detection, using the Extended Kalman Filter. The marks are visually detected, and their features are extracted by image processing. Parameters obtained by image processing are directly used in the Extended Kalman Filter. Thus, while the wheeled robot locates and navigates the humanoid, it also simultaneously calculates its own location and maps the environment (SLAM). The navigation is done through heuristic algorithms based on errors between the actual and desired pose for each robot. The main contribution of this work was the implementation of a cooperative navigation system for two robots based on visual information, which can be extended to other robotic applications, as the ability to control robots without interfering on its hardware, or attaching communication devices

Seleção de features guiada por atenção visual em imagens com fóvea

Visual attention is a very important task in autonomous robotics, but, because of its complexity, the processing time required is significant. We propose an architecture for feature selection using foveated images that is guided by visual attention tasks and that reduces the processing time required to perform these tasks. Our system can be applied in bottom-up or top-down visual attention. The foveated model determines which scales are to be used on the feature extraction algorithm. The system is able to discard features that are not extremely necessary for the tasks, thus, reducing the processing time. If the fovea is correctly placed, then it is possible to reduce the processing time without compromising the quality of the tasks outputs. The distance of the fovea from the object is also analyzed. If the visual system loses the tracking in top-down attention, basic strategies of fovea placement can be applied. Experiments have shown that it is possible to reduce up to 60% the processing time with this approach. To validate the method, we tested it with the feature algorithm known as Speeded Up Robust Features (SURF), one of the most efficient approaches for feature extraction. With the proposed architecture, we can accomplish real time requirements of robotics vision, mainly to be applied in autonomous robotics

Odometria visual baseada em técnicas de structure from motion

Visual Odometry is the process that estimates camera position and orientation based solely on images and in features (projections of visual landmarks present in the scene) extraced from them. With the increasing advance of Computer Vision algorithms and computer processing power, the subarea known as Structure from Motion (SFM) started to supply mathematical tools composing localization systems for robotics and Augmented Reality applications, in contrast with its initial purpose of being used in inherently offline solutions aiming 3D reconstruction and image based modelling. In that way, this work proposes a pipeline to obtain relative position featuring a previously calibrated camera as positional sensor and based entirely on models and algorithms from SFM. Techniques usually applied in camera localization systems such as Kalman filters and particle filters are not used, making unnecessary additional information like probabilistic models for camera state transition. Experiments assessing both 3D reconstruction quality and camera position estimated by the system were performed, in which image sequences captured in reallistic scenarios were processed and compared to localization data gathered from a mobile robotic platform

Estudo das interações polifenol-proteína e das reações de escurecimento não-enzimático para o processamento de cajuína

The cashew, a fruit from Brazilian Northeast is used to produce juice due to its flavor and vitamin C richness. However, its acceptance is limited due to its astringency. Cajuína is a derivate product appreciated by its characteristic flavor, freshness and lack of astringency, due to tannin removal. Cajuína is a light yellow beverage made from clarified cashew juice and sterilized after bottling. It differs from the integral and concentrated juice by the clarification and thermal treatment steps. Many problems such as haze and excessive browning could appear if these steps are not controlled. The objective of this work was divided into two stages with the aim to supply process information in order to obtain a good quality product with uniform characteristics (sensory and nutritional). Polyphenol-protein interaction was studied at the clarification step, which is an empirical process, to provide values on the amount of clarifying solution (gelatin) that must be added to achieve a complete juice clarification. Clarification essays were performed with juice dilutions of 1:2 and 1:10 and the effect of metabissulfite and tannic acid addition was evaluated. It was not possible to establish a clarification point. Metabissulfite did not influenced the clarification process however tannic acid addition displaced the clarification point, showing the difficulty visual monitoring of the process. Thermal treatment of clarified juice was studied at 88, 100, 111 e 121 °C. To evaluate the non-enzymatic browning, vitamin C, 5-hidroximetilfurfural (5-HMF) and sugar variation were correlated with color parameters (reflectance spectra, color difference and CIELAB). Kinetic models were obtained for reflectance spectra, ascorbic acid and 5-HMF. It was observed that 5-HMF introduction followed a first order kinetic rate at the beginning of the thermal treatment and a zero order kinetic at later process stages. An inverse correlation was observed between absorbance at 420 nm and ascorbic acid degradation, which indicates that ascorbic acid might be the principal factor on cajuína non-enzymatic browning. Constant sugar concentration showed that this parameter did not contribute directly to the nonenzymatic browning. Optimization techniques showed showed that to obtain a high vitamin C and a low 5-HMF content, the process must be done at 120 ºC. With the water-bath thermal treatment, the 90 °C temperature promoted a lower ascorbic acid degradation at the expense of a higher 5-HMF level