Implementação de conversor de polarização linear-circular para antenas de microfita usando metasuperfícies

This work aims to propose a new model of metasurface with simplified basic cell, able to convert linearly polarized signals generated by planar antenna array in circularly polarized signals, for the ISM frequency band (2.45 GHz), with good bandwidth of return loss and axial ratio. To study the behavior of the proposed structure, the metasurface is coupled to three different structures. First, initial tests are made with the metasurface coupled to a microstrip antenna in its simple configuration. Then the metasurface is coupled to an array with two elements of patch type. And later it is coupled to an optimized array, that uses a stub in its main feed, to get a better impedance matching. The structures are analyzed numerically through Ansoft HFSS™, and to validate these results, the structures are characterized experimentally. The characteristics of transmissions simulated and measures are presented. A good agreement between simulated and measured results was obtained. The structure proposed here has the advantage of meeting the desired characteristics, with a simple geometry to be built using a low-cost substrate (FR-4).

Implementação de conversor de polarização linear-circular para antenas de microfita usando metasuperfícies

This work aims to propose a new model of metasurface with simplified basic cell, able to convert linearly polarized signals generated by planar antenna array in circularly polarized signals, for the ISM frequency band (2.45 GHz), with good bandwidth of return loss and axial ratio. To study the behavior of the proposed structure, the metasurface is coupled to three different structures. First, initial tests are made with the metasurface coupled to a microstrip antenna in its simple configuration. Then the metasurface is coupled to an array with two elements of patch type. And later it is coupled to an optimized array, that uses a stub in its main feed, to get a better impedance matching. The structures are analyzed numerically through Ansoft HFSS™, and to validate these results, the structures are characterized experimentally. The characteristics of transmissions simulated and measures are presented. A good agreement between simulated and measured results was obtained. The structure proposed here has the advantage of meeting the desired characteristics, with a simple geometry to be built using a low-cost substrate (FR-4).

Artificial neural network nonlinear equalizer for coherent optical OFDM

We propose a novel low-complexity artificial neural network (ANN)-based nonlinear equalizer (NLE) for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) and compare it with the recent inverse Volterra-series transfer function (IVSTF)-based NLE over up to 1000 km of uncompensated links. Demonstration of ANN-NLE at 80-Gb/s CO-OFDM using 16-quadrature amplitude modulation reveals a Q-factor improvement after 1000-km transmission of 3 and 1 dB with respect to the linear equalization and IVSTF-NLE, respectively.

Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas

Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.

The role of material characterisation in the crush modelling of thermoplastic composite structures

The predictive capability of high fidelity finite element modelling, to accurately capture damage and crush behaviour of composite structures, relies on the acquisition of accurate material properties, some of which have necessitated the development of novel approaches. This paper details the measurement of interlaminar and intralaminar fracture toughness, the non-linear shear behaviour of carbon fibre (AS4)/thermoplastic Polyetherketoneketone (PEKK) composite laminates and the utilisation of these properties for the accurate computational modelling of crush. Double-cantilever-beam (DCB), four-point end-notched flexure (4ENF) and Mixed-mode bending (MMB) test configurations were used to determine the initiation and propagation fracture toughness in mode I, mode II and mixed-mode loading, respectively. Compact Tension (CT) and Compact Compression (CC) test samples were employed to determine the intralaminar longitudinal tensile and compressive fracture toughness. V-notched rail shear tests were used to measure the highly non-linear shear behaviour, associated with thermoplastic composites, and fracture toughness. Corresponding numerical models of these tests were developed for verification and yielded good correlation with the experimental response. This also confirmed the accuracy of the measured values which were then employed as input material parameters for modelling the crush behaviour of a corrugated test specimen.

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Application of multivariable control using artificial neural networks in a debutanizer distillation column

LOPES, Jose Soares Batista et al. Application of multivariable control using artificial neural networks in a debutanizer distillation column.In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING - COBEM, 19, 5-9 nov. 2007, Brasilia. Anais... Brasilia, 2007

Topology Optimization of continuum structures with epsilon-relaxed stress constraints

Topology optimization of linear elastic continuum structures is a challenging problem when considering local stress constraints. The reasons are the singular behavior of the constraint with the density design variables, combined with the large number of constraints even for small finite element meshes. This work presents an alternative formulation for the s-relaxation technique, which provides an workaround for the singularity of the stress constraint. It also presents a new global stress constraint formulation. Derivation of the sensitivities for the constraint by the adjoint method is shown. Results for single and multiple load cases show the potential of the new formulation.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.

Hydric behavior of earth materials and the effects of their stabilization with cement or lime: study on repair mortars for historical rammed earth structures

Earthen building materials bear interesting environmental advantages and are the most appropriate to conserve historical earth constructions. To improve mechanical properties, these materials are often stabilized with cement or lime, but the impact of the stabilizers on the water transport properties, which are also critical, has been very rarely evaluated. We have tested four earth-based repair mortars applied on three distinct and representative rammed earth surfaces. Three mortars are based on earth collected from rammed earth buildings in south of Portugal and the fourth mortar is based on a commercial clayish earth. The main objective of the work was over the commercial earth mortar, applied stabilized and not stabilized on the three rammed earth surfaces to repair, to assess the influence of the stabilizers. The other three earth mortars (not stabilized) were applied on each type of rammed earth, representing the repair only made with local materials. The four unstabilized earth materials depicted nonlinear dependence on t1/2 during capillary suction. This behaviour was probably due to clay swelling. Stabilization with any of the four tested binders enabled the linear dependence of t1/2 expected from Washburn's equation, probably because the swelling did not take place in this case. However, the stabilizers also increased significantly the capillary suction and the capillary porosity of the materials. This means that, in addition to increasing the carbon footprint, stabilizers like cement and lime have functional disadvantages that discourage its use in repair mortars for raw earth construction.

Solid-liquid interactions in microscale structures and devices

Liquid-solid interactions become important as dimensions approach mciro/nano-scale. This dissertation focuses on liquid-solid interactions in two distinct applications: capillary driven self-assembly of thin foils into 3D structures, and droplet wetting of hydrophobic micropatterned surfaces. The phenomenon of self-assembly of complex structures is common in biological systems. Examples include self-assembly of proteins into macromolecular structures and self-assembly of lipid bilayer membranes. The principles governing this phenomenon have been applied to induce self-assembly of millimeter scale Si thin films into spherical and other 3D structures, which are then integrated into light-trapping photovoltaic (PV) devices. Motivated by this application, we present a generalized analytical study of the self-folding of thin plates into deterministic 3D shapes, through fluid-solid interactions, to be used as PV devices. This study consists of developing a model using beam theory, which incorporates the two competing components — a capillary force that promotes folding and the bending rigidity of the foil that resists folding into a 3D structure. Through an equivalence argument of thin foils of different geometry, an effective folding parameter, which uniquely characterizes the driving force for folding, has been identified. A criterion for spontaneous folding of an arbitrarily shaped 2D foil, based on the effective folding parameter, is thus established. Measurements from experiments using different materials and predictions from the model match well, validating the assumptions used in the analysis. As an alternative to the mechanics model approach, the minimization of the total free energy is employed to investigate the interactions between a fluid droplet and a flexible thin film. A 2D energy functional is proposed, comprising the surface energy of the fluid, bending energy of the thin film and gravitational energy of the fluid. Through simulations with Surface Evolver, the shapes of the droplet and the thin film at equilibrium are obtained. A critical thin film length necessary for complete enclosure of the fluid droplet, and hence successful self-assembly into a PV device, is determined and compared with the experimental results and mechanics model predictions. The results from the modeling and energy approaches and the experiments are all consistent. Superhydrophobic surfaces, which have unique properties including self-cleaning and water repelling are desired in many applications. One excellent example in nature is the lotus leaf. To fabricate these surfaces, well designed micro/nano- surface structures are often employed. In this research, we fabricate superhydrophobic micropatterned Polydimethylsiloxane (PDMS) surfaces composed of micropillars of various sizes and arrangements by means of soft lithography. Both anisotropic surfaces, consisting of parallel grooves and cylindrical pillars in rectangular lattices, and isotropic surfaces, consisting of cylindrical pillars in square and hexagonal lattices, are considered. A novel technique is proposed to image the contact line (CL) of the droplet on the hydrophobic surface. This technique provides a new approach to distinguish between partial and complete wetting. The contact area between droplet and microtextured surface is then measured for a droplet in the Cassie state, which is a state of partial wetting. The results show that although the droplet is in the Cassie state, the contact area does not necessarily follow Cassie model predictions. Moreover, the CL is not circular, and is affected by the micropatterns, in both isotropic and anisotropic cases. Thus, it is suggested that along with the contact angle — the typical parameter reported in literature quantifying wetting, the size and shape of the contact area should also be presented. This technique is employed to investigate the evolution of the CL on a hydrophobic micropatterned surface in the cases of: a single droplet impacting the micropatterned surface, two droplets coalescing on micropillars, and a receding droplet resting on the micropatterned surface. Another parameter which quantifies hydrophobicity is the contact angle hysteresis (CAH), which indicates the resistance of the surface to the sliding of a droplet with a given volume. The conventional methods of using advancing and receding angles or tilting stage to measure the resistance of the micropatterned surface are indirect, without mentioning the inaccuracy due to the discrete and stepwise motion of the CL on micropillars. A micronewton force sensor is utilized to directly measure the resisting force by dragging a droplet on a microtextured surface. Together with the proposed imaging technique, the evolution of the CL during sliding is also explored. It is found that, at the onset of sliding, the CL behaves as a linear elastic solid with a constant stiffness. Afterwards, the force first increases and then decreases and reaches a steady state, accompanied with periodic oscillations due to regular pinning and depinning of the CL. Both the maximum and steady state forces are primarily dependent on area fractions of the micropatterned surfaces in our experiment. The resisting force is found to be proportional to the number of pillars which pin the CL at the trailing edge, validating the assumption that the resistance mainly arises from the CL pinning at the trailing edge. In each pinning-and-depinning cycle during the steady state, the CL also shows linear elastic behavior but with a lower stiffness. The force variation and energy dissipation involved can also be determined. This novel method of measuring the resistance of the micropatterned surface elucidates the dependence on CL pinning and provides more insight into the mechanisms of CAH.

Artificial olfaction system for on-site odour measurement

Odour impacts and concerns are an impediment to the growth of the Australian chicken meat industry. To manage these, the industry has to be able to demonstrate the efficacy of its odour reduction strategies scientifically and defensibly; however, it currently lacks reliable, cost effective and objective tools to do so. This report describes the development of an artificial olfaction system (AOS) to measure meat chicken farm odour. This report describes the market research undertaken to determine the demand for such a tool, the development and evaluation of three AOS prototypes, data analysis and odour prediction modelling, and the development of two complementary odour measurement tools, namely, a volatile organic compound (VOC) pre-concentrator and a field olfactometer. This report is aimed at investors in poultry odour research and those charged with, or interested in, assessment of odour on chicken farms, including farm managers, integrators, their consultants, regulators and researchers. The findings will influence the focus of future environmental odour measurement research.

Linear non-local diffusion problems in metric measure spaces

The aim of this paper is to provide a comprehensive study of some linear non-local diffusion problems in metric measure spaces. These include, for example, open subsets in ℝN, graphs, manifolds, multi-structures and some fractal sets. For this, we study regularity, compactness, positivity and the spectrum of the stationary non-local operator. We then study the solutions of linear evolution non-local diffusion problems, with emphasis on similarities and differences with the standard heat equation in smooth domains. In particular, we prove weak and strong maximum principles and describe the asymptotic behaviour using spectral methods.