Employing 2D Projections for Fast Visual Exploration of Large Fiber Tracking Data

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Slow-fast systems on algebraic varieties bordering piecewise-smooth dynamical systems

This article extends results contained in Buzzi et al. (2006) [4], Llibre et al. (2007, 2008) [12,13] concerning the dynamics of non-smooth systems. In those papers a piecewise C-k discontinuous vector field Z on R-n is considered when the discontinuities are concentrated on a codimension one submanifold. In this paper our aim is to study the dynamics of a discontinuous system when its discontinuity set belongs to a general class of algebraic sets. In order to do this we first consider F :U -> R a polynomial function defined on the open subset U subset of R-n. The set F-1 (0) divides U into subdomains U-1, U-2,...,U-k, with border F-1(0). These subdomains provide a Whitney stratification on U. We consider Z(i) :U-i -> R-n smooth vector fields and we get Z = (Z(1),...., Z(k)) a discontinuous vector field with discontinuities in F-1(0). Our approach combines several techniques such as epsilon-regularization process, blowing-up method and singular perturbation theory. Recall that an approximation of a discontinuous vector field Z by a one parameter family of continuous vector fields is called an epsilon-regularization of Z (see Sotomayor and Teixeira, 1996 [18]; Llibre and Teixeira, 1997 [15]). Systems as discussed in this paper turn out to be relevant for problems in control theory (Minorsky, 1969 [16]), in systems with hysteresis (Seidman, 2006 [17]) and in mechanical systems with impacts (di Bernardo et al., 2008 [5]). (C) 2011 Elsevier Masson SAS. All rights reserved.

Sliding Vector Fields via Slow-Fast Systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Fast Large Scale Iris Database Classification with Optimum-Path Forest Technique: A Case Study

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

A New and Fast Technique to Generate Offspring after Germ Cells Transplantation in Adult Fish: The Nile Tilapia (Oreochromis niloticus) Model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Electrospray ionization mass spectrometry screening of piperidine alkaloids from Senna spectabilis (Fabaceae) extracts: Fast identification of new constituents and co-metabolites

The fragmentation pattern of a homologous series of piperidine alkaloids isolated from S. spectabilis was investigated using electrospray ionization tandem mass spectrometry (ESI-MS/MS). The ESI-MS and ESI-MS/MS analyses of EtOH extracts and fractions from flowers and fruits of S. spectabilis allowed to elucidate the structures of four new compounds. The identification of these co-metabolites, based on the fragmentation patterns of previously isolated compounds, and further confirmed by accurate mass spectrometry defines this technique as a powerful tool to determine the metabolomic profile of species which has pharmacological importance. ©2005 Sociedade Brasileira de Química.

Fast preparative separation of flavones from capitula of Eriocaulon ligulatum (Vell.) L.B.Smith (Eriocaulaceae) by High-speed countercurrent chromatography (HSCCC)

High-speed countercurrent chromatography (HSCCC) is a leading method for the fast separation of natural products from plants. It was used for the preparative isolation of two flavone monoglucosides present in the capitula of Eriocaulon ligulatum (Veil.) L.B.Smith (Eriocaulaceae). This species, known locally as botão-dourado, is exported to Europe, Japan and North America as an ornamental species, constituting an important source of income for the local population of Minas Gerais State, Brazil. The solvent system, optimized in tests prior to the HSCCC run, consisted of the two phases of the mixture ethyl acetate: n-propanol: water (140:8:80, v/v/v), which led to the successful separation of 6-methoxyluteolin-7-O-β-D-allopyranoside and 6-methoxyapigenin-7-O-β-D-allopyranoside in only 3 hours. The two flavonoids were identified by NMR (1-D and 2-D) and ESI-MS, comparing their spectra with published data.

Microwave-induced fast decalcification of rat bone for electron microscopic analysis: An ultrastructural and cytochemical study

Bone decalcification is a time-consuming process. It takes weeks and preservation of the tissue structure depends on the quality and velocity of the demineralization process. In the present study, a decalcification methodology was adapted using microwaving to accelerate the decalcification of rat bone for electron microscopic analysis. The ultrastructure of the bone decalcified by microwave energy was observed. Wistar rats were perfused with paraformaldehyde and maxillary segments were removed and fixed in glutaraldehyde. Half of specimens were decalcified by conventional treatment with immersion in Warshawsky solution at 4oC during 45 days, and the other half of specimens were placed into the beaker with 20 mL of the Warshawsky solution in ice bath and thereafter submitted to irradiation in a domestic microwave oven (700 maximum power) during 20 s/350 W/±37°C. In the first day, the specimens were irradiated 9 times and stored at 40°C overnight. In the second day, the specimens were irradiated 20 times changing the solution and the ice after each bath. After decalcification, some specimens were postfixed in osmium tetroxide and others in osmium tetroxide and potassium pyroantimonate. The specimens were observed under transmission electron microscopy. The results showed an increase in the decalcification rate in the specimens activated by microwaving and a reduction of total experiment time from 45 days in the conventional method to 48 hours in the microwave-aided method.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Morphological changes in the fast vs slow fiber profiles of the urethras of diabetic pregnant rats

Background: This study was undertaken to test the hypothesis that diabetes and pregnancy detrimentally affect the normal function of urethral striated muscles in rats, providing a model for additional studies related to urinary incontinence. The aim of this study was to evaluate morphological alterations in the urethral striated muscles of diabetic pregnant rats. Materials and Methods: Twenty female Wistar rats were distributed into four experimental groups of five rats as follows: virgin, pregnant, diabetic virgin, and diabetic pregnant. Diabetes was induced using streptozotocin administration (40 mg/kg i.v.). The rats were lethally anesthetized, and the urethra and vagina were extracted as a unit. Cryostat sections (6 μm thick) were cut and stained with hematoxylin-eosin, and immunohistochemical procedures were performed and subjected to morphological and semi quantitative analysis. Results: The urethral striated muscle from the diabetic pregnant rats presented with the following variations: thinning and atrophy, disorganization and disruption associated with the colocalization of fast and slow fibers and a steady decrease in the proportion of fast vs slow fibers. Conclusion: Diabetes and pregnancy impair the urethral striated muscle and alter its fiber type distribution. © Copyright G. Marini et al., 2011.