910 resultados para Ordered probit regression


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Precise and reproducible surface nanopatterning is the key for a successful ordered growth of GaN nanocolumns. In this work, we point out the main technological issues related to the patterning process, mainly surface roughness and cleaning, and mask adhesion to the substrate. We found that each of these factors, process-related, has a dramatic impact on the subsequent selective growth of the columns inside the patterned holes. We compare the performance of e-beam lithography, colloidal lithography, and focused ion beam in the fabrication of hole-patterned masks for ordered columnar growth. These results are applicable to the ordered growth of nanocolumns of different materials.

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Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

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We present a methodology for reducing a straight line fitting regression problem to a Least Squares minimization one. This is accomplished through the definition of a measure on the data space that takes into account directional dependences of errors, and the use of polar descriptors for straight lines. This strategy improves the robustness by avoiding singularities and non-describable lines. The methodology is powerful enough to deal with non-normal bivariate heteroscedastic data error models, but can also supersede classical regression methods by making some particular assumptions. An implementation of the methodology for the normal bivariate case is developed and evaluated.

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III-nitride nanorods have attracted much scientific interest during the last decade because of their unique optical and electrical properties [1,2]. The high crystal quality and the absence of extended defects make them ideal candidates for the fabrication of high efficiency opto-electronic devices such as nano-photodetectors, light-emitting diodes, and solar cells [1-3]. Nitride nanorods are commonly grown in the self-assembled mode by plasma-assisted molecular beam epitaxy (MBE) [4]. However, self-assembled nanorods are characterized by inhomogeneous heights and diameters, which render the device processing very difficult and negatively affect the electronic transport properties of the final device. For this reason, the selective area growth (SAG) mode has been proposed, where the nanorods preferentially grow with high order on pre-defined sites on a pre-patterned substrate

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We fabricate and characterize novel LEDs based on InGaN/GaN nanocolumns grown on patterned substrates, leading to the periodically ordered growth of emitters directly producing white light

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The basics of the self-assembled growth of GaN nanorods on Si(111) are reviewed. Morphology differences and optical properties are compared to those of GaN layers grown directly on Si(111). The effects of the growth temperature on the In incorporation in self-assembled InGaN nanorods grown on Si(111) is described. In addition, the inclusion of InGaN quantum disk structures into selfassembled GaN nanorods show clear confinement effects as a function of the quantum disk thickness. In order to overcome the properties dispersion and the intrinsic inhomogeneous nature of the self-assembled growth, the selective area growth of GaN nanorods on both, c-plane and a-plane GaN on sapphire templates, is addressed, with special emphasis on optical quality and morphology differences. The analysis of the optical emission from a single InGaN quantum disk is shown for both polar and non-polar nanorod orientations

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E-beam lithography was used to pattern a titanium mask on a GaN substrate with ordered arrays of nanoholes. This patterned mask served as a template for the subsequent ordered growth of GaN/InGaN nanorods by plasma-assisted molecular beam epitaxy. The mask patterning process was optimized for several holes configurations. The smallest holes were 30 nm in diameter with a pitch (center-to-center distance) of 100 nm only. High quality masks of several geometries were obtained that could be used to grow ordered GaN/InGaN nanorods with full selectivity (growth localized inside the nanoholes only) over areas of hundreds of microns. Although some parasitic InGaN growth occurred between the nanorods during the In incorporation, transmission electron microscopy and photoluminescence measurements demonstrated that these ordered nanorods exhibit high crystal quality and reproducible optical properties.

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This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

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Aplicación de simulación de Monte Carlo y técnicas de Análisis de la Varianza (ANOVA) a la comparación de modelos estocásticos dinámicos para accidentes de tráfico.

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Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

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In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. In addition to recording TOD, the cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also identified for use as the independent variables in the regression analysis. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajectory parame- ters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowledge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace.

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We present a model of Bayesian network for continuous variables, where densities and conditional densities are estimated with B-spline MoPs. We use a novel approach to directly obtain conditional densities estimation using B-spline properties. In particular we implement naive Bayes and wrapper variables selection. Finally we apply our techniques to the problem of predicting neurons morphological variables from electrophysiological ones.

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Quaternary-ordered double perovskite A2MM’O6 (M=Mo,W) semiconductors are a group of materials with a variety of photocatalytic and optoelectronic applications. An analysis focused on the optoelectronic properties is carried out using first-principles density-functional theory with several U orbital-dependent one-electron potentials applied to different orbital subspaces. The structural non-equivalence of the atoms resulting from the symmetry has been taken in account. In order to analyze optical absorption in these materials deeply, the absorption coefficients have been split into inter- and intra-non-equivalent species contributions. The results indicate that the effect of the A and M’ atoms on the optical properties are minimal whereas the largest contribution comes from the non-equivalent O atoms to M transitions.

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The growth of ordered arrays of InGaN/GaN nanocolumnar light emitting diodes by molecular beam epitaxy, emitting in the blue (441 nm), green (502 nm), and yellow (568 nm) spectral range is reported. The device active region, consisting of a nanocolumnar InGaN section of nominally constant composition and 250 to 500 nm length, is free of extended defects, which is in strong contrast to InGaN layers (planar) of similar composition and thickness. The devices are driven under pulsed operation up to 1300 A/cm2 without traces of efficiency droop. Electroluminescence spectra show a very small blue shift with increasing current, (almost negligible in the yellow device) and line widths slightly broader than those of state-of-the-art InGaN quantum wells.

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This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.