Simultaneous linearization of a class of pairs of involutions with normally hyperbolic composition

In this paper we obtain a result on simultaneous linearization for a class of pairs of involutions whose composition is normally hyperbolic. This extends the corresponding result when the composition of the involutions is a hyperbolic germ of diffeomorphism. Inside the class of pairs with normally hyperbolic composition, we obtain a characterization theorem for the composition to be hyperbolic. In addition, related to the class of interest, we present the classification of pairs of linear involutions via linear conjugacy. © 2012 Elsevier Masson SAS.

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Política de avaliação e trabalho docente no ensino médio

A pesquisa versa sobre avaliação e trabalho docente no ensino médio, tendo como objeto de investigação e análise as políticas de avaliação que vêm sendo instituídas a partir dos anos 1990, com recorte específico no denominado novo Enem e suas repercussões sobre o trabalho docente. O estudo possui como objetivo geral analisar e compreender as reformas instituídas na educação brasileira a partir da década de 1990, com foco na avaliação externa, especificamente na implementação do Enem, enquanto um processo de avaliação implementado no bojo de uma nova regulação educacional, e suas possíveis repercussões sobre o trabalho docente nesse nível de ensino, última etapa da educação básica, tendo como lócus a Região Metropolitana do Cariri – CE. Quanto à metodologia adotada, optou-se pela abordagem de pesquisa qualitativa, enfocando o complexo universo das políticas de avaliação externa e do trabalho docente no Ensino médio, lançando mão, para a coleta de dados, da pesquisa exploratória, revisão bibliográfica, análise documental e entrevistas não-diretivas. O tratamento dos dados foi realizado com base na análise de conteúdo, a partir de exaustiva análise das informações levantadas que, cotejadas com o referencial teórico, permitiu a emersão de algumas categorias de análise, como: avaliações externas, trabalho docente, regulação da educação e accountability. Como síntese dos resultados aferidos, destacamos que: - A Reforma do Aparelho do Estado Brasileiro, implementada a partir da década de 1990, instituiu o “Estado avaliador”, pautado, dentre outros, pela desresponsabilização do Estado para com as políticas sociais, pelo foco nos resultados, na excelência, na performatividade e na obtenção da eficiência e eficácia educacional, instituindo mecanismos de controle, no formato de avaliações, para a promoção da regulação da educação, de modo a assegurar os valores dominantes no contexto educacional escolar, controlando seus resultados; - são fortes as repercussões das políticas educacionais inscritas sob a lógica mercadológica sobre o ensino médio, dado que o mesmo vem sofrendo alterações significativas nas últimas décadas, em decorrência do “Estado avaliador” e da crescente centralidade das avaliações externas; - as avaliações externas de larga escala, com destaque para o Enem, privilegiam o accountability, por meio dos fenômenos da desresponsabilização do Estado, da crescente responsabilização da escola e dos profissionais da educação, da meritocracia e da privatização da educação, promovendo a intensificação do trabalho docente; - o atual modelo de avaliação de larga escala impõe ênfase aos produtos ou resultados em detrimento do processo, focando-se no trato individual de instituições ou estudantes, por meio de dados predominantemente quantitativos, resultando em classificação e rankeamento, estimulando a competição entre as instituições educacionais e entre os sujeitos; - esse processo tem repercutido sobre o trabalho docente, intensificando-o, à medida que os professores, à revelia de suas condições objetivas de trabalho, que são extremamente precárias na maioria das escolas públicas, tendem a ser responsabilizados, individualmente, pelo êxito ou fracasso de seus alunos; - por fim, constatamos que, não obstante novas atribuições e responsabilidades estarem sendo imputadas ao professor, inclusive com a imposição unilateral de metas a serem atingidas, não há, em contrapartida às exigências postas pelo Enem, uma efetiva política de Estado voltada para a valorização dos profissionais docentes no Ceará, seja pela via da carreira, da remuneração e/ou da formação continuada.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Optimum-Path Forest Applied for Breast Masses Classification

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems.

Toward Satellite-Based Land Cover Classification Through Optimum-Path Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Exploring different approaches for music genre classification

In this letter, we present different approaches for music genre classification. The proposed techniques, which are composed of a feature extraction stage followed by a classification procedure, explore both the variations of parameters used as input and the classifier architecture. Tests were carried out with three styles of music, namely blues, classical, and lounge, which are considered informally by some musicians as being “big dividers” among music genres, showing the efficacy of the proposed algorithms and establishing a relationship between the relevance of each set of parameters for each music style and each classifier. In contrast to other works, entropies and fractal dimensions are the features adopted for the classifications.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of croplands using indicator kriging and fuzzy classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Semiautomatic object-space road extraction combining a stereoscopic image pair and a TIN-Based DTM

In this letter, a semiautomatic method for road extraction in object space is proposed that combines a stereoscopic pair of low-resolution aerial images with a digital terrain model (DTM) structured as a triangulated irregular network (TIN). First, we formulate an objective function in the object space to allow the modeling of roads in 3-D. In this model, the TIN-based DTM allows the search for the optimal polyline to be restricted along a narrow band that is overlaid upon it. Finally, the optimal polyline for each road is obtained by optimizing the objective function using the dynamic programming optimization algorithm. A few seed points need to be supplied by an operator. To evaluate the performance of the proposed method, a set of experiments was designed using two stereoscopic pairs of low-resolution aerial images and a TIN-based DTM with an average resolution of 1 m. The experimental results showed that the proposed method worked properly, even when faced with anomalies along roads, such as obstructions caused by shadows and trees.

Patch-based local histograms and contour estimation for static foreground classification

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)