Otimização de Algoritmos Criptográficos para Redes de Sensores e Atuadores Sem-fio para Poços do Tipo Plunger Lift

Wireless sensors and actuators Networks specified by IEEE 802.15.4, are becoming increasingly being applied to instrumentation, as in instrumentation of oil wells with completion Plunger Lift type. Due to specific characteristics of the environment being installed, it s find the risk of compromising network security, and presenting several attack scenarios and the potential damage from them. It`s found the need for a more detailed security study of these networks, which calls for use of encryption algorithms, like AES-128 bits and RC6. So then it was implement the algorithms RC6 and AES-128, in an 8 bits microcontroller, and study its performance characteristics, critical for embedded applications. From these results it was developed a Hybrid Algorithm Cryptographic, ACH, which showed intermediate characteristics between the AES and RC6, more appropriate for use in applications with limitations of power consumption and memory. Also was present a comparative study of quality of security among the three algorithms, proving ACH cryptographic capability.

Algoritmos de alinhamento múltiplo e técnicas de otimização para esses algoritmos utilizando Ant Colony

Pós-graduação em Ciência da Computação - IBILCE

Otimização multimodal através de novas técnicas baseadas em clusterização nebulosa

Neste trabalho, é proposta uma nova família de métodos a ser aplicada à otimização de problemas multimodais. Nestas técnicas, primeiramente são geradas soluções iniciais com o intuito de explorar o espaço de busca. Em seguida, com a finalidade de encontrar mais de um ótimo, estas soluções são agrupadas em subespaços utilizando um algoritmo de clusterização nebulosa. Finalmente, são feitas buscas locais através de métodos determinísticos de otimização dentro de cada subespaço gerado na fase anterior com a finalidade de encontrar-se o ótimo local. A família de métodos é formada por seis variantes, combinando três esquemas de inicialização das soluções na primeira fase e dois algoritmos de busca local na terceira. A fim de que esta nova família de métodos possa ser avaliada, seus constituintes são comparados com outras metodologias utilizando problemas da literatura e os resultados alcançados são promissores.

Aperfeiçoamento do algoritmo algébrico sequencial para a identificação de variações abruptas de impedância acústica via otimização

Neste trabalho são utilizados a técnica baseada na propagação de ondas acústicas e o método de otimização estocástica Luus-Jaakola (LJ) para solucionar o problema inverso relacionado à identificação de danos em barras. São apresentados o algoritmo algébrico sequencial (AAS) e o algoritmo algébrico sequencial aperfeiçoado (AASA) que modelam o problema direto de propagação de ondas acústicas em uma barra. O AASA consiste nas modificações introduzidas no AAS. O uso do AASA resolve com vantagens o problema de identificação de danos com variações abruptas de impedância. Neste trabalho são obtidos, usando-se o AAS-LJ e o AASA-LJ, os resultados de identificação de cinco cenários de danos. Três deles com perfil suave de impedância acústica generalizada e os outros dois abruptos. Além disso, com o objetivo de simular sinais reais de um experimento, foram introduzidos variados níveis de ruído. Os resultados alcançados mostram que o uso do AASA-LJ na resolução de problemas de identificação de danos em barras é bastante promissor, superando o AAS-LJ para perfis abruptos de impedância.

Otimização de imagens de mamografia computadorizada

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Redução de ordem de modelos matemáticos lineares usando algoritmos genéticos

Nesta dissertação apresenta-se o problema de redução de ordem de modelos dinâmicos lineares, sob o ponto de vista de otimização via Algoritmos Genéticos. Uma função custo, obtida a partir da norma dos coeficientes do numerador da função de transferência do erro entre o modelo original e o reduzido, e minimizada por meio de um algoritmo genético, com consequente calculo dos parâmetros do modelo reduzido. O procedimento e aplicado em alguns exemplos que demonstram a validade da abordagem.

Otimização de injeção sobre tecidos pré-impregnados

A otimização é aplicada constantemente como método de aperfeiçoamento, desde os problemas quotidianos, aos problemas mais complexos de matemática e engenharia. A Natureza há milhões de anos que aplica eficazmente otimização em todos os seus ínfimos sistemas de forma a manter o equilíbrio. Estes processos naturais de aperfeiçoamento serviram de inspiração a diversos autores para o desenvolvimento de algoritmos de otimização. Quatro algoritmos bio-inspirados, na sua forma de otimização mono-objetivo e um algoritmo multi-objetivo são utilizados para efetuar otimização do problema do ciclo de injeção. A indústria dos moldes como conhecida pioneira no desenvolvimento tecnológico, necessita de constante aperfeiçoamento e otimização dos seus processos. A redução do tempo de ciclo é um dos principais focos de otimização pois determina fortemente o custo da peça a produzir. A procura de novos conceitos de injeção, em que se pretende produzir peças com gradientes funcionais, levaram à criação de uma nova ferramenta molde para o processamento de tecidos pré impregnados com polímero (prepreg). A introdução de novas etapas no ciclo de injeção, criaram a necessidade de uma nova formulação, de forma a modelar o processamento dos prepregs. A otimização efetuada pelos algoritmos bio-inspirados à nova formulação pretende minimizar o tempo de ciclo e obter parâmetros de processamento ideais, tais como as dimensões ideais dos canais de alimentação temperaturas e pressões de processamento. A otimização mono-objetivo tem um único objetivo, a minimização do tempo de ciclo. A otimização multi-objetivo tem como objetivos a minimização do tempo de ciclo, da queda de pressão e volume de abastecimento. Para que o princípio da sobre moldação fosse aplicado a esta nova técnica de processamento, são apresentados os métodos existentes, para que se possa criar um paralelismo e prever a adesão do polímero ao tecido. Da mesma forma é apresentado o estudo efetuado sobre a compatibilidade de materiais e as suas condições de processamento. Como resultado final, surge a ferramenta que permite a sobre-moldação, ou seja, o molde projetado, respeitando as dimensões e parâmetros obtidos pelo melhor resultado dos algoritmos mono-objetivo.

Estudo avaliativo de um algoritmo genético auto-organizável e multiobjetivo utilizando aprendizado de máquina para aplicações de telecomunicações

This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

Uma abordagem multiobjetivo para o problema de corte de estoque unidimensional

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Planejamento de sistemas de distribuição de energia elétrica de média tensão através de um algoritmo de busca dispersa

Pós-graduação em Engenharia Elétrica - FEIS

Bio-Optical Properties of the Inner Continental Shelf off Santos Estuarine System, Southeastern Brazil, and their Implications for Ocean Color Algorithm Performance

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

Algoritmos de otimização para decisões de localização de facilidades e distribuição em sistemas multiníveis de transporte rodoviário de carga

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Análise e otimização de superfícies seletivas de Frequência utilizando redes neurais artificiais e algoritmos de otimização natural

The bidimensional periodic structures called frequency selective surfaces have been well investigated because of their filtering properties. Similar to the filters that work at the traditional radiofrequency band, such structures can behave as band-stop or pass-band filters, depending on the elements of the array (patch or aperture, respectively) and can be used for a variety of applications, such as: radomes, dichroic reflectors, waveguide filters, artificial magnetic conductors, microwave absorbers etc. To provide high-performance filtering properties at microwave bands, electromagnetic engineers have investigated various types of periodic structures: reconfigurable frequency selective screens, multilayered selective filters, as well as periodic arrays printed on anisotropic dielectric substrates and composed by fractal elements. In general, there is no closed form solution directly from a given desired frequency response to a corresponding device; thus, the analysis of its scattering characteristics requires the application of rigorous full-wave techniques. Besides that, due to the computational complexity of using a full-wave simulator to evaluate the frequency selective surface scattering variables, many electromagnetic engineers still use trial-and-error process until to achieve a given design criterion. As this procedure is very laborious and human dependent, optimization techniques are required to design practical periodic structures with desired filter specifications. Some authors have been employed neural networks and natural optimization algorithms, such as the genetic algorithms and the particle swarm optimization for the frequency selective surface design and optimization. This work has as objective the accomplishment of a rigorous study about the electromagnetic behavior of the periodic structures, enabling the design of efficient devices applied to microwave band. For this, artificial neural networks are used together with natural optimization techniques, allowing the accurate and efficient investigation of various types of frequency selective surfaces, in a simple and fast manner, becoming a powerful tool for the design and optimization of such structures

Otimização de superfícies seletivas de frequência com elementos pré-fractais utilizando rede neural MLP e algoritmos de busca populacional

This thesis describes design methodologies for frequency selective surfaces (FSSs) composed of periodic arrays of pre-fractals metallic patches on single-layer dielectrics (FR4, RT/duroid). Shapes presented by Sierpinski island and T fractal geometries are exploited to the simple design of efficient band-stop spatial filters with applications in the range of microwaves. Initial results are discussed in terms of the electromagnetic effect resulting from the variation of parameters such as, fractal iteration number (or fractal level), fractal iteration factor, and periodicity of FSS, depending on the used pre-fractal element (Sierpinski island or T fractal). The transmission properties of these proposed periodic arrays are investigated through simulations performed by Ansoft DesignerTM and Ansoft HFSSTM commercial softwares that run full-wave methods. To validate the employed methodology, FSS prototypes are selected for fabrication and measurement. The obtained results point to interesting features for FSS spatial filters: compactness, with high values of frequency compression factor; as well as stable frequency responses at oblique incidence of plane waves. This thesis also approaches, as it main focus, the application of an alternative electromagnetic (EM) optimization technique for analysis and synthesis of FSSs with fractal motifs. In application examples of this technique, Vicsek and Sierpinski pre-fractal elements are used in the optimal design of FSS structures. Based on computational intelligence tools, the proposed technique overcomes the high computational cost associated to the full-wave parametric analyzes. To this end, fast and accurate multilayer perceptron (MLP) neural network models are developed using different parameters as design input variables. These neural network models aim to calculate the cost function in the iterations of population-based search algorithms. Continuous genetic algorithm (GA), particle swarm optimization (PSO), and bees algorithm (BA) are used for FSSs optimization with specific resonant frequency and bandwidth. The performance of these algorithms is compared in terms of computational cost and numerical convergence. Consistent results can be verified by the excellent agreement obtained between simulations and measurements related to FSS prototypes built with a given fractal iteration

Otimização de modelos de predição da perda de propagação aplicáveis em 3,5GHZ utilizando algoritmos genéticos

The telecommunications industry has experienced recent changes, due to increasing quest for access to digital services for data, video and multimedia, especially using the mobile phone networks. Recently in Brazil, mobile operators are upgrading their networks to third generations systems (3G) providing to users broadband services such as video conferencing, Internet, digital TV and more. These new networks that provides mobility and high data rates has allowed the development of new market concepts. Currently the market is focused on the expansion of WiMAX technology, which is gaining increasingly the market for mobile voice and data. In Brazil, the commercial interest for this technology appears to the first award of licenses in the 3.5 GHz band. In February 2003 ANATEL held the 003/2002/SPV-ANATEL bidding, where it offered blocks of frequencies in the range of 3.5 GHz. The enterprises who purchased blocks of frequency were: Embratel, Brazil Telecom (Vant), Grupo Sinos, Neovia and WKVE, each one with operations spread in some regions of Brazil. For this and other wireless communications systems are implemented effectively, many efforts have been invested in attempts to developing simulation methods for coverage prediction that is close to reality as much as possible so that they may become believers and indispensable tools to design wireless communications systems. In this work wasm developed a genetic algorithm (GA's) that is able to optimize the models for predicting propagation loss at applicable frequency range of 3.5 GHz, thus enabling an estimate of the signal closer to reality to avoid significant errors in planning and implementation a system of wireless communication