Novel Application of Genetic Sequencing algorithms to optimisation of hardware resource sharing for DSP

Field programmable gate array (FPGA) technology is a powerful platform for implementing computationally complex, digital signal processing (DSP) systems. Applications that are multi-modal, however, are designed for worse case conditions. In this paper, genetic sequencing techniques are applied to give a more sophisticated decomposition of the algorithmic variations, thus allowing an unified hardware architecture which gives a 10-25% area saving and 15% power saving for a digital radar receiver.

Population genetics of C. Maenas : oceanography and larval dispersal

Decifrar a complexa interacção entre os ciclos de vida de espécies marinhas e a oceanografia revela-se fundamental para a compreensão do fluxo genético e da conectividade no meio marinho. Nas espécies marinhas com desenvolvimento indirecto o fluxo de genes entre populações depende da distância que separa as populações, bem como da interacção entre a duração do desenvolvimento larvar, do comportamento das larvas e dos padrões de circulação oceânica. A conectividade larvar influencia uma variedade de processos como a dinâmica de stocks e de populações, a distribuição e limites geográficos das espécies, a estrutura genética das populações e a dispersão de espécies invasivas e reveste-se consequentemente de uma importância fundamental na identificação das unidades populacionais evolucionariamente relevantes e para a gestão e conservação marinhas. Os marcadores genéticos e os Modelos Individuais Acoplados a Modelos Físico-Biológicos (“ICPBMs”) são actualmente ferramentas fundamentais para o estudo dos padrões de dispersão larvar e para avaliar o nível de conectividade populacional. A presente tese respeita à avaliação das escalas espaciais de conectividade de populações de uma espécie costeira, o caranguejo Carcinus maenas, e utiliza conjuntamente informação de marcadores genéticos, análise de séries temporais de fornecimento de larvas e um modelo numérico de circulação oceânica. O primeiro capítulo introduz a temática da conectividade em espécies marinhas e inclui algumas referências aos métodos moleculares, analíticos e de modelação seguidos ao longo da tese. Através da utilização de múltiplas ferramentas – avaliação da estrutura genética geográfica de C. maenas na sua distribuição nativa com recurso a marcadores de DNA (microssatélites) (Capítulo 2), avaliação da estrutura genética temporal das larvas que formam os eventos de fornecimento larvar à Ria de Aveiro, NW Portugal (Capítulo 3), descrição da variabilidade inter-anual do fornecimento larvar à Ria de Aveiro, NW Portugal (Capítulo 4) e validação de um modelo ICPBM que descreve os padrões observados de fornecimento (Capítulo 5) – esta tese espera poder contribuir para uma melhor compreensão dos mecanismos que regulam o fluxo de genes e a conectividade entre populações de organismos marinhos. No Capítulo 6 são apresentadas as principais conclusões da investigação. A análise genética com recurso a microssatélites indicou que as populações de C. maenas são geneticamente homogéneas ao longo de várias centenas de km, dentro da distribuição nativa da espécie. Paralelamente, não foram encontrados indícios da existência de reprodução por “sweepstakes” em C. maenas de populações da costa oeste da Península Ibérica, visto que não se obtiveram diferenças genéticas significativas entre os eventos larvares. Também não se encontrou qualquer estrutura familiar entre as larvas que formam cada episódio de fornecimento, e não houve nenhuma redução significativa da variabilidade genética das larvas quando comparada com a de caranguejos adultos. A análise de séries temporais de suprimento de larvas na Ria de Aveiro em cinco anos estudados indica que este é um fenómeno episódico e variável, sendo os maiores episódios de fornecimento coincidentes com as marés vivas e acentuados por fortes ventos de sul. O modelo ICPBM foi validado com sucesso e parece fornecer uma estimativa realística das escalas espaciais e temporais de dispersão larvar, de acordo com as observações da estrutura genética e da ausência de reprodução por “sweepstake” em C. maenas da costa oeste da Península Ibérica

Synthesis of FPGA-based accelerators implementing recursive algorithms

O desenvolvimento de sistemas computacionais é um processo complexo, com múltiplas etapas, que requer uma análise profunda do problema, levando em consideração as limitações e os requisitos aplicáveis. Tal tarefa envolve a exploração de técnicas alternativas e de algoritmos computacionais para optimizar o sistema e satisfazer os requisitos estabelecidos. Neste contexto, uma das mais importantes etapas é a análise e implementação de algoritmos computacionais. Enormes avanços tecnológicos no âmbito das FPGAs (Field-Programmable Gate Arrays) tornaram possível o desenvolvimento de sistemas de engenharia extremamente complexos. Contudo, o número de transístores disponíveis por chip está a crescer mais rapidamente do que a capacidade que temos para desenvolver sistemas que tirem proveito desse crescimento. Esta limitação já bem conhecida, antes de se revelar com FPGAs, já se verificava com ASICs (Application-Specific Integrated Circuits) e tem vindo a aumentar continuamente. O desenvolvimento de sistemas com base em FPGAs de alta capacidade envolve uma grande variedade de ferramentas, incluindo métodos para a implementação eficiente de algoritmos computacionais. Esta tese pretende proporcionar uma contribuição nesta área, tirando partido da reutilização, do aumento do nível de abstracção e de especificações algorítmicas mais automatizadas e claras. Mais especificamente, é apresentado um estudo que foi levado a cabo no sentido de obter critérios relativos à implementação em hardware de algoritmos recursivos versus iterativos. Depois de serem apresentadas algumas das estratégias para implementar recursividade em hardware mais significativas, descreve-se, em pormenor, um conjunto de algoritmos para resolver problemas de pesquisa combinatória (considerados enquanto exemplos de aplicação). Versões recursivas e iterativas destes algoritmos foram implementados e testados em FPGA. Com base nos resultados obtidos, é feita uma cuidada análise comparativa. Novas ferramentas e técnicas de investigação que foram desenvolvidas no âmbito desta tese são também discutidas e demonstradas.

Optimisation of viscoelastic treatments using genetic algorithms

Viscoelastic treatments are one of the most efficient treatments, as far as passive damping is concerned, particularly in the case of thin and light structures. In this type of treatment, part of the strain energy generated in the viscoelastic material is dissipated to the surroundings, in the form of heat. A layer of viscoelastic material is applied to a structure in an unconstrained or constrained configuration, the latter proving to be the most efficient arrangement. This is due to the fact that the relative movement of both the host and constraining layers cause the viscoelastic material to be subjected to a relatively high strain energy. There are studies, however, that claim that the partial application of the viscoelastic material is just as efficient, in terms of economic costs or any other form of treatment application costs. The application of patches of material in specific and selected areas of the structure, thus minimising the extension of damping material, results in an equally efficient treatment. Since the damping mechanism of a viscoelastic material is based on the dissipation of part of the strain energy, the efficiency of the partial treatment can be correlated to the modal strain energy of the structure. Even though the results obtained with this approach in various studies are considered very satisfactory, an optimisation procedure is deemed necessary. In order to obtain optimum solutions, however, time consuming numerical simulations are required. The optimisation process to use the minimum amount of viscoelastic material is based on an evolutionary geometry re-design and calculation of the modal damping, making this procedure computationally costly. To avert this disadvantage, this study uses adaptive layerwise finite elements and applies Genetic Algorithms in the optimisation process.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.

Preface to the special section on algorithms and architectures for real-time control

Rapid developments in microelectronics and computer science continue to fuel new opportunities for real-time control engineers. The ever-increasing system complexity and sophistication, environmental legislation, economic competition, safety and reliability constitute some of the driving forces for the research themes presented at the IFAC Workshop on Algorithms and Architectures for Real-Time Control (AARTC'2000). The Spanish Society for Automatic Control hosted AARTC'2000, which was held at Palma de Maiorca, Spain, from 15 to 17 May. This workshop was the sixth in the series.

Comparison of on-line learning algorithms for RBF models in greenhouse environmental control problems

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.

Automatic parallelization of matricial algorithms

The introduction of parallel processing architectures allowed the real time impelemtation of more sophisticated control algorithms with tighter specifications in terms of sampling time. However, to take advantage of the processing power of these architectures the control engeneer, due to the lack of appropriate tools, must spend a considerable amount of time in the parallelizaton of the control algorithm.

Algorithms and architectures for real-time control

Algorithm and Architectures for Real-Time Control Workshop had the objective to investigate the state of the art and to present new research and application results in software and hardware for real-timecontrol, as well as to bring together engeneers and computer scientists who are researchers, developers and practitioners, both from the academic and the industrial world.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.

Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed.

Ecological genetics in the North Atlantic: environmental gradients and adaptation at specific loci

The North Atlantic intertidal community provides a rich set of organismal and environmental material for the study of ecological genetics. Clearly defined environmental gradients exist at multiple spatial scales: there are broad latitudinal trends in temperature, meso-scale changes in salinity along estuaries, and smaller scale gradients in desiccation and temperature spanning the intertidal range. The geology and geography of the American and European coasts provide natural replication of these gradients, allowing for population genetic analyses of parallel adaptation to environmental stress and heterogeneity. Statistical methods have been developed that provide genomic neutrality tests of population differentiation and aid in the process of candidate gene identification. In this paper, we review studies of marine organisms that illustrate associations between an environmental gradient and specific genetic markers. Such highly differentiated markers become candidate genes for adaptation to the environmental factors in question, but the functional significance of genetic variants must be comprehensively evaluated. We present a set of predictions about locus-specific selection across latitudinal, estuarine, and intertidal gradients that are likely to exist in the North Atlantic. We further present new data and analyses that support and contradict these simple selection models. Some taxa show pronounced clinal variation at certain loci against a background of mild clinal variation at many loci. These cases illustrate the procedures necessary for distinguishing selection driven by internal genomic vs. external environmental factors. We suggest that the North Atlantic intertidal community provides a model system for identifying genes that matter in ecology due to the clarity of the environmental stresses and an extensive experimental literature on ecological function. While these organisms are typically poor genetic and genomic models, advances in comparative genomics have provided access to molecular tools that can now be applied to taxa with well-defined ecologies. As many of the organisms we discuss have tight physiological limits driven by climatic factors, this synthesis of molecular population genetics with marine ecology could provide a sensitive means of assessing evolutionary responses to climate change.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

Landscape genetics of a seagrass species in a tidal mudflat lagoon

Tese de doutoramento, Ciências do Mar, da Terra e do Ambiente, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015