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Recent progress in fundamental biological sciences and medicine has considerably increased the quantity ofdata that can be studied and processed. The main limitation now is not retrieving data, but rather extractinguseful biological insights from the large datasets accumulated. More recent advances have provided detailedhigh-density data regarding metabolism (metabolomics) and protein expression (proteomics). Clearly, no single analytic methods, can provide a comprehensive understanding. Rather, the ability to link available datatogether in a coherent manner is required to obtain a complete view. The improving application of MachineLearning (ML) techniques provides the means to make continuous progress in processing complex data sets.A brief discussion is offered on the advantages of ML, the state-of-the-art in Deep Learning (DL) for proteinpredictions and the importance of ML in biological data processing. Noise stemming from incorrect classification or arbitrary/ambiguous labelling of data may arise when ML techniques are applied to large datasets. Furthermore, the stochasticity of biological systems needs to be considered for correctly evaluating theoutputs. Here we show the potential of a workflow to respond biological questions taking into consideration aperturbation of the biological data.  For controlling the applicability of models and maximizing the predictivity, in silico filtering schemescan usefully be applied as an “Ockham’s razor” before using any ML technique. After reviewing differentDL approaches for protein prediction purposes, this work shows that a computational approach in filteringsteps is a valuable tool for proteins classification when biological features are not fully annotated or reviewed.The in silico approach has identified putative proline transporters in fungi and plants as well as carotenoidbiosynthetic gene products in the plant family Brassicaceae. The proposed method is suitable for extractingfeatures of classification and then maximizing the use of a DL approach.

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Although high-level synthesis has been researched for many years, synthesizing minimum hardware implementations under a throughput constraint for computationally intensive algorithms remains a challenge. In this thesis, three important techniques are studied carefully and applied in an integrated way to meet this challenging synthesis requirement. The first is pipeline scheduling, which generates a pipelined schedule that meets the throughput requirement. The second is module selection, which decides the most appropriate circuit module for each operation. The third is resource sharing, which reuses a circuit module by sharing it between multiple operations. This work shows that combining module selection and resource sharing while performing pipeline scheduling can significantly reduce the hardware area, by either using slower, more area-efficient circuit modules or by time-multiplexing faster, larger circuit modules, while meeting the throughput constraint. The results of this work show that the combined approach can generate on average 43% smaller hardware than possible when a single technique (resource sharing or module selection) is applied. There are four major contributions of this work. First, given a fixed throughput constraint, it explores all feasible frequency and data introduction interval design points that meet this throughput constraint. This enlarged pipelining design space exploration results in superior hardware architectures than previous pipeline synthesis work because of the larger sapce. Second, the module selection algorithm in this work considers different module architectures, as well as different pipelining options for each architecture. This not only addresses the unique architecture of most FPGA circuit modules, it also performs retiming at the high-level synthesis level. Third, this work proposes a novel approach that integrates the three inter-related synthesis techniques of pipeline scheduling, module selection and resource sharing. To the author's best knowledge, this is the first attempt to do this. The integrated approach is able to identify more efficient hardware implementations than when only one or two of the three techniques are applied. Fourth, this work proposes and implements several algorithms that explore the combined pipeline scheduling, module selection and resource sharing design space, and identifies the most efficient hardware architecture under the synthesis constraint. These algorithms explore the combined design space in different ways which represents the trade off between algorithm execution time and the size of the explored design space.

This thesis introduces methods for adding user-guided depth-aware effects to images captured with a consumer-grade stereo camera with minimal user interaction. In particular, we present methods for highlighted depth-of-field, haze, depth-of-field, and image relighting. Unlike many prior methods for adding such effects, we do not assume prior scene models or require extensive user guidance to create such models, nor do we assume multiple input images. We also do not require specialized camera rigs or other equipment such as light-field camera arrays, active lighting, etc. Instead, we use only an easily portable and affordable consumer-grade stereo camera. The depth is calculated from a stereo image pair using an extended version of PatchMatch Stereo designed to compute not only image disparities but also normals for visible surfaces. We also introduce a pipeline for rendering multiple effects in the order they would occur physically. Each can be added, removed, or adjusted in the pipeline without having to reapply subsequent effects. Individually or in combination, these effects can be used to enhance the sense of depth or structure in images and provide increased artistic control. Our interface also allows editing the stereo pair together in a fashion that preserves stereo consistency, or the effects can be applied to a single image only, thus leveraging the advantages of stereo acquisition even to produce a single photograph.

<p>Information about the conditions of specific fields and assets is critical for farm managers to make operational decisions. Location, rainfall, windspeed, soil moisture, and temperature are examples of metrics that influence the ability to perform certain tasks. Monitoring these events in real time and being able to store historical data can be done using Internet of Things (IoT) devices such as sensors. The abilities of this technology have previously been communicated, yet few farmers have adopted these connected devices into their work. A lack of reliable internet connection, the high annual cost of current on-market systems, and a lack of technical awareness have all contributed to this disconnect. One technology that can better meet the demand of farmers is LoRaWAN because of its long range, low power, and low cost. To assist farmers in implementing this technology on their farms the goal was to build a LoRaWAN network with several sensors to measure metrics such as weather data, distribute these systems locally, and provide context to the operation of IoT networks. By leveraging readily available commercial hardware and opens source software two examples of standalone networks were created with sensor data stored locally and without a dependence on internet connectivity. The first use case was a kit consisting of a gateway and small PC mounted to a tripod with 6 individual sensors and cost close to $2200 in total. An additional design was prepared for a micro-computer-based version using a Raspberry Pi, which made improvements to the original design. These adjustments included a lower cost and complication of hardware, software with more open-source community support, and cataloged steps to increase approachability. Given outside factors, the PC architecture was chosen for mass distribution. Over one year, several identical units were produced and given to farms, extension educators, and vocational agricultural programs. From this series of deployments, all units survived the growing season without damage from the elements, general considerations about the chosen type of sensors and their potential drawbacks were made, the practical observed average range for packet acceptance was 3 miles, and battery life among sensors remained usable after one year. The Pi-based architecture was implemented in an individual use case with instructions to assist participation from any experience level. Ultimately, this work has introduced individuals to the possibilities of creating and managing their own network and what can be learned from a reasonably simple, self-managed data pipeline.</p>

[pt] A avaliação não destrutiva ultrassônica é de extrema importância na indústria de óleo e gás, principalmente para ativos e estruturas sujeitos a condições que aceleram os mecanismos de falha. Apesar de amplamente difundidos, os métodos ultrassônicos não destrutivos dependem de uma força de trabalho especializada, sendo, portanto, suscetíveis a erros e demorados. Nesse contexto, métodos de reconhecimento de padrões, como o aprendizado de máquina, se encaixam convenientemente para solucionar os desafios da tarefa. Assim, este trabalho tem como objetivo a aplicação de técnicas de inteligência artificial para abordar a interpretação de dados adquiridos por meio de avaliação não destrutiva ultrassônica no contexto da indústria de óleo e gás. Para tanto, esta dissertação envolve três estudos de caso. Primeiramente, sinais de ondas guiadas ultrassônicas são usados para classificar os defeitos presentes em juntas soldadas de compósito termoplástico. Os resultados mostraram que, ao usar atributos extraídos com modelos autoregressivos, a acurácia do modelo de aprendizado de máquina melhora em pelo menos 72,5 por cento. Em segundo lugar, dados ultrassônicos em formato de imagens são usados para construir um sistema de diagnóstico de solda automático. A estrutura proposta resultou em um modelo computacionalmente eficiente, capaz de realizar classificações com acurácia superior à 99 por cento. Por fim, dados obtidos por simulação numérica foram usados para criar um modelo de aprendizado profundo visando estimar a severidade de defeitos semelhantes à corrosão em dutos. Resultados de R2 superiores a 0,99 foram alcançados. / [en] Ultrasonic non-destructive evaluation is of extreme importance in the oil and gas industry, especially for assets and structures subjected to conditions that accelerate failure mechanisms. Despite being widely spread, ultrasonic non-destructive methods depend on a specialized workforce, thus being errorprone and time-consuming. In this context, pattern recognition methods, like machine learning, fit conveniently to solve the challenges of the task. Hence, this work aims at applying artificial intelligence techniques to address the interpretation of data acquired through ultrasonic non-destructive evaluation in the context of the oil and gas industry. For that purpose, this dissertation involves three case studies. Firstly, ultrasonic guided wave signals are used to classify defects present in welded thermoplastic composite joints. Results have shown that, when using features extracted with autoregressive models, the accuracy of the machine learning model improves by at least 72.5 percent. Secondly, ultrasonic image data is used to construct an automatic weld diagnostic system. The proposed framework resulted in a lightweight model capable of performing classification with over 99 percent accuracy. Finally, simulation data was used to create a deep learning model for estimating the severity of corrosion-like defects in pipelines. R2 results superior to 0.99 were achieved.

[pt] Sabe-se que os anéis e tubulações elásticas de paredes finas estão sujeitos a instabilidades quando sob tensões compressivas. Um exemplo particularmente interessante é a flambagem de um anel elástico sob uma pressão hidrostática. A carga de flambagem é fortemente influenciada pela natureza seguidora da força devida à pressão hidrostática e, se esse efeito for desprezado, a previsão da carga de flambagem crítica pode ser de até 50 por cento para anéis esbeltos. Este trabalho estuda, usando uma formulação variacional não linear, as características de flambagem e vibração de anéis e tubulações apoiados em uma fundação elástica de Pasternak, sendo a fundação de Winkler considerada como um caso particular. Primeiro, a equação de movimento do anel pré-carregado é derivada e a solução analítica dos problemas de autovalor é obtida. A análise paramétrica mostra a influência dos parâmetros geométricos e físicos do anel e da fundação na carga crítica, frequências naturais e relação não linear carga-frequência, considerando o efeito da força seguidora da pressão hidrostática. Adicionalmente, o efeito da fundação nas deformações pré-críticas é estudado. Finalmente, o efeito de uma imperfeição geométrica inicial é avaliado usando o método de Galerkin. Os resultados mostram que os parâmetros da fundação de Pasternak têm um efeito considerável na carga e modo crítico, na frequência fundamental do anel. / [en] It is known that thin-walled elastic rings and pipes are subject to instability when under a state of compressive stresses. A particularly interesting example is the buckling of an elastic ring under hydrostatic pressure. The buckling load is strongly influenced by the following nature of the force due to the hydrostatic pressure and, if this effect is neglected, the forecast of the critical buckling load can be up to 50 per cent for slender rings. This work studies, using a non-linear variational formulation, the buckling and vibration characteristics of rings and pipes supported by a Pasternak elastic foundation, the Winkler foundation being considered as a particular case. First, the equation of motion of the preloaded ring is derived and the analytical solution of the eigenvalue problems is obtained. Parametric analysis shows the influence of the geometric and physical parameters of the ring and the foundation on the critical load, natural frequencies and nonlinear load-frequency relationship, considering the force following effect of the hydrostatic pressure. Additionally, the effect of the foundation on pre-critical deformations is studied. Finally, the effect of an initial geometric imperfection is assessed using the Galerkin method. The results show that the parameters of the Pasternak foundation have a considerable effect on the critical load and mode as well as on the natural frequencies of the ring.

Starting with tensor calculus and the variational form of the Hamiltonian functional, a generalized theory is formulated for doubly curved thin shells. The formulation avoids geometric approximations commonly adopted in other formulations. The theory is then specialized for cylindrical and toroidal shells as special cases, both of interest in the modeling of straight and elbow segments of pipeline systems. Since the treatment avoids geometric approximations, the cylindrical shell theory is believed to be more accurate than others reported in the literature. By adopting a set of consistent geometric approximations, the present theory is shown to revert to the well known Flugge shell theory. Another set of consistent geometric approximations is shown to lead to the Donnell-Mushtari-Vlasov (DMV) theory. A general closed form solution of the theory is developed for cylinders under general harmonic loads. The solution is then used to formulate a family of exact shape functions which are subsequently used to formulate a super-convergent finite element. The formulation efficiently and accurately captures ovalization, warping, radial expansion, and other shell behavioural modes under general static or harmonic forces either in-phase or out-of-phase. Comparisons with shell solutions available in Abaqus demonstrate the validity of the formulation and the accuracy of its predictions. The generalized thin shell theory is then specialized for toroidal shells. Consistent sets of approximations lead to three simplified theories for toroidal shells. The first set of approximations has lead to a theory comparable to that of Sanders while the second set of approximation has lead to a theory nearly identical to the DMV theory for toroidal shells. A closed form solution is then obtained for the governing equation. Exact shape functions are then developed and subsequently used to formulate a finite element. Comparisons with Abaqus solutions show the validity of the formulation for short elbow segments under a variety of loading conditions. Because of their efficiency, the finite elements developed are particularly suited for the analysis of long pipeline systems.

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