Cluster Coordination and Photoluminescence Properties of alpha-Ag2WO4 Microcrystals

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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.

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.

A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

Frugivory and Seed Dispersal by Northern Pigtailed Macaques (Macaca leonina), in Thailand

Tropical rain forest conservation requires a good understanding of plant-animal interactions. Seed dispersal provides a means for plant seeds to escape competition and density-dependent seed predators and pathogens and to colonize new habitats. This makes the role and effectiveness of frugivorous species in the seed dispersal process an important topic. Northern pigtailed macaques (Macaca leonina) may be effective seed dispersers because they have a diverse diet and process seeds in several ways (swallowing, spitting out, or dropping them). To investigate the seed dispersal effectiveness of a habituated group of pigtailed macaques in Khao Yai National Park, Thailand, we examined seed dispersal quantity (number of fruit species eaten, proportion in the diet, number of feces containing seeds, and number of seeds processed) and quality (processing methods used, seed viability and germination success, habitat type and distance from parent tree for the deposited seeds, and dispersal patterns) via focal and scan sampling, seed collection, and germination tests. We found thousands of seeds per feces, including seeds up to 58 mm in length and from 88 fruit species. Importantly, the macaques dispersed seeds from primary to secondary forests, via swallowing, spitting, and dropping. Of 21 species, the effect of swallowing and spitting was positive for two species (i. e., processed seeds had a higher % germination and % viability than control seeds), neutral for 13 species (no difference in % germination or viability), and negative (processed seeds had lower % germination and viability) for five species. For the final species, the effect was neutral for spat-out seeds but negative for swallowed seeds. We conclude that macaques are effective seed dispersers in both quantitative and qualitative terms and that they are of potential importance for tropical rain forest regeneration. © 2013 Springer Science+Business Media New York.

Multi-scale lacunarity as an alternative to quantify and diagnose the behavior of prostate cancer

Prostate cancer is a serious public health problem accounting for up to 30% of clinical tumors in men. The diagnosis of this disease is made with clinical, laboratorial and radiological exams, which may indicate the need for transrectal biopsy. Prostate biopsies are discerningly evaluated by pathologists in an attempt to determine the most appropriate conduct. This paper presents a set of techniques for identifying and quantifying regions of interest in prostatic images. Analyses were performed using multi-scale lacunarity and distinct classification methods: decision tree, support vector machine and polynomial classifier. The performance evaluation measures were based on area under the receiver operating characteristic curve (AUC). The most appropriate region for distinguishing the different tissues (normal, hyperplastic and neoplasic) was defined: the corresponding lacunarity values and a rule's model were obtained considering combinations commonly explored by specialists in clinical practice. The best discriminative values (AUC) were 0.906, 0.891 and 0.859 between neoplasic versus normal, neoplasic versus hyperplastic and hyperplastic versus normal groups, respectively. The proposed protocol offers the advantage of making the findings comprehensible to pathologists. (C) 2014 Elsevier Ltd. All rights reserved.