7 resultados para Least mean squares methods

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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It is well known that constant-modulus-based algorithms present a large mean-square error for high-order quadrature amplitude modulation (QAM) signals, which may damage the switching to decision-directed-based algorithms. In this paper, we introduce a regional multimodulus algorithm for blind equalization of QAM signals that performs similar to the supervised normalized least-mean-squares (NLMS) algorithm, independently of the QAM order. We find a theoretical relation between the coefficient vector of the proposed algorithm and the Wiener solution and also provide theoretical models for the steady-state excess mean-square error in a nonstationary environment. The proposed algorithm in conjunction with strategies to speed up its convergence and to avoid divergence can bypass the switching mechanism between the blind mode and the decision-directed mode. (c) 2012 Elsevier B.V. All rights reserved.

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Proposed is a symbol-based decision-directed algorithm for blind equalisation of quadrature amplitude modulation (QAM) signals using a decision feedback scheme. Independently of QAM order, it presents: (i) an error equal to zero when the equaliser output coincides with the transmitted signal; (ii) simultaneous recovery of the modulus and phase of the signal; (iii) a misadjustment close to that of the normalised least-mean squares algorithm; (iv) fast convergence; and (v) the avoidance of degenerative solutions. Additionally, its stability is ensured when the step-size is properly chosen.

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Purpose: To determine the proportion of blindness and investigate the relationships between risk factors based on clinical characteristics and development of blindness in patients with primary open-angle glaucoma (POAG) treated for at least 15 years. Methods: A retrospective observational chart review was performed with 403 patients referred to a tertiary level hospital, each with a diagnosis of primary open-angle glaucoma, treated for at least 15 years. Blindness attributable to glaucoma was defined based on visual acuity and/or visual field tests. Variables considered to be possible risk factors for blindness were evaluated using odds ratio (OR), confidence interval (95% CI), and univariate and multivariate analyses. Results: Thirty-one patients became blind [13/53 (24.5%) - unilaterally and 18/53 (34%) - bilaterally] during the follow-up period of treatment (19.5 +/- 4.6 years, range 15-31 years). Multivariate statistics with regression analysis revealed that persistency on initial therapy <= 6 months was significantly associated with blindness, both unilateral (OR: 8.4; 95% CI: 1.3-56.4) and bilateral (OR: 7.2; 95% CI: 1.3-39.6). Other potential factors such as race, age, gender or number of medications were not associated with blindness. Conclusion: Blindness from primary open-angle glaucoma was not uncommon in this population of treated patients after the long follow-up period proposed. Persistence rates with the first therapy, as measured by a medical decision to change, were low. Persistence <= 6 months was statistically associated with the development of unilateral and bilateral blindness from glaucoma.

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A new method for analysis of scattering data from lamellar bilayer systems is presented. The method employs a form-free description of the cross-section structure of the bilayer and the fit is performed directly to the scattering data, introducing also a structure factor when required. The cross-section structure (electron density profile in the case of X-ray scattering) is described by a set of Gaussian functions and the technique is termed Gaussian deconvolution. The coefficients of the Gaussians are optimized using a constrained least-squares routine that induces smoothness of the electron density profile. The optimization is coupled with the point-of-inflection method for determining the optimal weight of the smoothness. With the new approach, it is possible to optimize simultaneously the form factor, structure factor and several other parameters in the model. The applicability of this method is demonstrated by using it in a study of a multilamellar system composed of lecithin bilayers, where the form factor and structure factor are obtained simultaneously, and the obtained results provided new insight into this very well known system.

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The objective of this study was to investigate, in a population of crossbred cattle, the obtainment of the non-additive genetic effects for the characteristics weight at 205 and 390 days and scrotal circumference, and to evaluate the consideration of these effects in the prediction of breeding values of sires using different estimation methodologies. In method 1, the data were pre-adjusted for the non-additive effects obtained by least squares means method in a model that considered the direct additive, maternal and non-additive fixed genetic effects, the direct and total maternal heterozygosities, and epistasis. In method 2, the non-additive effects were considered covariates in genetic model. Genetic values for adjusted and non-adjusted data were predicted considering additive direct and maternal effects, and for weight at 205 days, also the permanent environmental effect, as random effects in the model. The breeding values of the categories of sires considered for the weight characteristic at 205 days were organized in files, in order to verify alterations in the magnitude of the predictions and ranking of animals in the two methods of correction data for the non-additives effects. The non-additive effects were not similar in magnitude and direction in the two estimation methods used, nor for the characteristics evaluated. Pearson and Spearman correlations between breeding values were higher than 0.94, and the use of different methods does not imply changes in the selection of animals.

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Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

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RATIONALE: The interaction between lungs and chest wall influences lung volume, that determines lung history during respiration cycle. In this study, the influence of chest wall mechanics on respiratory system is assessed by the evaluation of inspiration pressure-volume curve (PV curve) under three different situations: closed-chest, open-chest and isolated lung. The PV curve parameters in each situation allow us to further understand the role played by different chest wall elements in the respiratory function. Methods: Twenty-four male Wistar rats (236 ± 29 g) were used. The animals were weighted and then anesthetized with xylazine 2% (0,5mL/kg) and ketamine 10% (0,9mL/kg), exsanguinated and later tracheostomized with a metallic cannula (14 gauge). The cannula was connected to an automatic small animal insufflator. This setup was connected to a pressure transducer (32 samples/s). The 24 animals were randomly separated in three groups: (i) closed chest, (ii) open chest and (iii) isolated lung. The rats were insufflated with 20mL quasi-statically (constant speed of 0,1mL/s). Insufflated volume and measured pressure data were kept and PV curves were obtained for all animals. The PV curves were fitted (non-linear least squares) against the sigmoid equation (1) to obtain the sigmoid equation parameters (a,b,c,d). Elastance measurements were obtained from linear regression of pressure/volume measurements in a 0,8s interval before and after the calculated point. Results: The parameters a, b and c showed no significant change, but the parameter d showed a significant variation among the three groups. The initial elastance also varied between open and closed chest, indicating the need of a higher pressure for the lung expansion, as can be seen in Table 1. Table 1: Mean and Standard Deviation of parameters obtained for each protocol. Protocol: Closed Chest – a (mL) -0.35±0.33; b (mL) 13.93±0.89; c (cm H2O) 21.28±2.37; d (cm H2O) 6.17±0.84; r²** (%) 99.4±0.14; Initial Elastance* (cm H2)/mL) 12.72±6.66; Weight (g) 232.33±5.72. Open Chest - a (mL) 0.01±0.28; b (mL) 14.79±0.54; c (cm H2O) 19.47±1.41; d (cm H2O) 3.50±0.28; r²** (%) 98.8±0.34; Initial Elastance* (cm H2)/mL) 28.68±2.36; Weight (g) 217.33±7.97. Isolated Lung - a (mL) -0.09±0.46; b (mL) 14.22±0.75; c (cm H2O) 21.76±1.43; d (cm H2O) 4.24±0.50; r²** (%) 98.9±0.19; Initial Elastance* (cm H2)/mL) 7.13±8.85; Weight (g) 224.33±16.66. * Elastance measures in the 0-0,1 mL range. ** Goodness of sigmoid fit versus measured data Conclusion: A supporting effect of the chest wall was observed at the initial moments of inspiration, observed as a higher initial elastance in open chest situations than in closed chest situations (p=0,00001). The similar initial elastance for the isolated lung and closed chest may be explained by the specific method used for the isolated lung experiment. As the isolated lung is supported by the trachea vertically, the weight of the tissue may have a similar effect of the residual negative pressure in the thorax, responsible for maintaining the residual volume.