Leg muscles recruitment pattern in soccer players and active individuals during isometric contractions

This study aimed to compare the torque, torque ratio (Hamstrings:Quadriceps - H:Q), electromyographic (EMG) activity and EMG ratio (knee flexors:knee extensors EMG) in soccer players (SG, N=10) and active subjects (AG, N=10). Subjects performed three maximal voluntary isometric knee extensions and flexions at 45° and 90° to determine the peak torque and EMG activity. Torque and EMG activity of the knee flexor (biceps femoris [BF] and semitendinosus [ST]) were divided by the torque and EMG activity of the knee extensor (vastuls lateralis [VL] and rectus femoris [RF]) to calculate torque ratios (H:Q) and EMG ratios (BF:VL, BF:RF, ST:VL, ST:RF). The flexion torque was significantly higher for SG (p<0.05) in 45° and 90°. EMG activity for SG was significantly higher in agonist contractions for VL, RF and ST, and significantly lower in antagonist contractions for RF and ST when compared to AG Torque and EMG ratios were similar between groups and there were good correlations between torque ratio and BF:VL ratio (r=0.71, p=0.02) and BF:RF ratio (r=0.81, p=0.004) at 45. The EMG results could overestimate the joint balance calculated using torque ratios. Differences in recruitment pattern between soccer players and non-athletes can be related to the training routines and the EMG ratios presents applicable in trained populations.

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.

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.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

Optimum-Path Forest pruning parameter estimation through Harmony Search

Pattern recognition in large amount of data has been paramount in the last decade, since that is not straightforward to design interactive and real time classification systems. Very recently, the Optimum-Path Forest classifier was proposed to overcome such limitations, together with its training set pruning algorithm, which requires a parameter that has been empirically set up to date. In this paper, we propose a Harmony Search-based algorithm that can find near optimal values for that. The experimental results have showed that our algorithm is able to find proper values for the OPF pruning algorithm parameter. © 2011 IEEE.

Differences in dietary pattern between obese and eutrophic children

Background: Excessive consumption of energy is a decisive factor of obesity, but a simple quantitative assessment of consumption between obese and eutrophic individuals not always explains the problem, raising questions about the importance of the qualitative aspects of food. Therefore, the purpose of this study was to evaluate the differences in nutrient composition and meal patterns between eutrophic and obese schoolchildren. Methods. The diet of 83 children (42 obese and 41 eutrophic), aged between 7 and 11 years of age, was assessed by two non-consecutive dietary recalls. After the software analysis of macro and micronutrients composition, the different types and amount of legumes, fruits and vegetables were analyzed to verify the dietary patterns. Results: No differences were verified in energy consumption between the groups (eutrophic = 1934.2 672.7 kcal, obese = 1835.8 621.2 kcal). In general, children showed consumption within the recommended ranges of carbohydrates, lipids and proteins. The average consumption of fiber was higher in the eutrophic group (20.7 g) when compared to the obese group (14.8 g). The dietary fiber was strongly correlated with the number of servings of beans (r = 0.77), when compared to fruits (r = 0.44) and leafy vegetables (r = 0.13). It was also observed that the higher the consumption of fiber and beans, the lower the proportion of dietary fat (r = -0.22) in the diet. Generally, there was a low consumption of fiber (20.7 g = eutrophic group/14.8 g = obese group), beans (1.1 portions in the eutrophic and obese groups), fruits (0.7 portions eutrophic group and 0.6 obese group) and vegetables (1.3 eutrophic group and 1.1 obese group). Conclusions: It is concluded that the obesity was more related to a dietary pattern of low intake of dietary fiber than excessive energy consumption and macronutrients imbalance. © 2011 de Oliveira et al; licensee BioMed Central Ltd.

Fault location in underground systems through optimum-path forest

In this paper we propose an accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the Time Domains Reflectometry method for signal acquisition, which was further analyzed by OPF and several other well known pattern recognition techniques. The results indicated that OPF and Support Vector Machines outperformed Artificial Neural Networks classifier. However, OPF has been much more efficient than all classifiers for training, and the second one faster for classification. © 2011 IEEE.

Evaluation of transverse changes in the dental arches according to growth pattern: a longitudinal study

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Urban-rural pattern of population distribution in Latin America and changes in this pattern during the last few decades

Includes bibliography

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In ELectric Vehicles

A multi-agent system with a percolation approach to simulate the driving pattern of Plug-In Electric Vehicle (PEV), especially suited to simulate the PEVs behavior on any distribution systems, is presented. This tool intends to complement information about the driving patterns database on systems where that kind of information is not available. So, this paper aims to provide a framework that is able to work with any kind of technology and load generated of PEVs. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with corresponding load level, and their relationships with the neighboring zones are represented as network probabilities. A percolation approach is used to characterize the autonomy of the battery of the PVEs to move through the city. The methodology is tested with data from a mid-size city real distribution system. The result shows the sub-area where the battery of PEVs will need to be recharge and gives the planners of distribution systems the necessary input for a medium to long term network planning in a smart grid environment. © 2012 IEEE.

Spurious shifts in the pattern of diurnal variation of sea level pressure of reanalysis datasets

The main purpose of this work is to report the presence of spurious discontinuities in the pattern of diurnal variation of sea level pressure of the three reanalysis datasets from: the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Science (R1), the NCEP and Department of Energy (R2), and the European Centre for Medium Range Weather Forecasting (ERA-40). Such discontinuities can be connected to the major changes in the global observing system that have occurred throughout reanalyses years. In the R1, the richest period in discontinuities is 1956-1958, coinciding with the start of modern radiosonde observation network. Rapid increase in the density of surface-based observations from 1967 also had an important impact on both R1 and ERA-40, with larger impact on R1. The reanalyses show discontinuities in the 1970s related to the assimilation of radiances measured by the Vertical Temperature Profile Radiometer and TIROS-N Operational Vertical Sounders onboard satellites. In the ERA-40, which additionally assimilated Special Sensor Microwave/Imager data, there are discontinuities in 1987-1989. The R1 also presents further discontinuities, in 1988-1993 likely connected to replacement/introduction of NOAA-series satellites with different biases, and to the volcanic eruption of Mount Pinatubo in June 1991, which is known to have severely affected measurements of infrared radiances for several years. The discontinuities in 1996-1998 might be partially connected to change in the type of radiosonde, from VIZ-B to VIZ-B2. The R2, which covers only satellite era (1979-on), shows discontinuities mainly in 1992, 1996-1997, and 2001. The discontinuities in 1992 and 2001 might have been caused by change in the satellite measurements and those in 1996-1997 by some changes in land-based observations network. © 2012 Springer-Verlag.

Development pattern and environment in Brazil

Includes bibliography