Abstract Introduction Sign language is a collection of gestures, postures, movements, and facial expressions used by deaf people. The Brazilian sign language is Libras. The use of Libras has been increased among the deaf communities, but is still not disseminated outside this community. Sign language recognition is a field of research, which intends to help the deaf community communication with non-hearing-impaired people. In this context, this paper describes a new method for recognizing hand configurations of Libras - using depth maps obtained with a Kinect® sensor. Methods The proposed method comprises three phases: hand segmentation, feature extraction, and classification. The segmentation phase is independent from the background and depends only on pixel value. The feature extraction process is independent from rotation and translation. The features are extracted employing two techniques: (2D)2LDA and (2D)2PCA. The classification employs two classifiers: a novelty classifier and a KNN classifier. A robust database is constructed for classifier evaluation, with 12,200 images of Libras and 200 gestures of each hand configuration. Results The best accuracy obtained was 96.31%. Conclusion The best gesture recognition accuracy obtained is much higher than the studies previously published. It must be emphasized that this recognition rate is obtained for different conditions of hand rotation and proximity of the depth camera, and with a depth camera resolution of only 640×480 pixels. This performance must be also credited to the feature extraction technique, and to the size standardization and normalization processes used previously to feature extraction step.

Abstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features.

Introduction According to the Global TB control report of 2013, “Tuberculosis (TB) remains a major global health problem. In 2012, an estimated 8.6 million people developed TB and 1.3 million died from the disease. Two main sputum smear microscopy techniques are used for TB diagnosis: Fluorescence microscopy and conventional microscopy. Fluorescence microscopy is a more expensive diagnostic method because of the high costs of the microscopy unit and its maintenance. Therefore, conventional microscopy is more appropriate for use in developing countries. Methods This paper presents a new method for detecting tuberculosis bacillus in conventional sputum smear microscopy. The method consists of two main steps, bacillus segmentation and post-processing. In the first step, the scalar selection technique was used to select input variables for the segmentation classifiers from four color spaces. Thirty features were used, including the subtractions of the color components of different color spaces. In the post-processing step, three filters were used to separate bacilli from artifact: a size filter, a geometric filter and a Rule-based filter that uses the components of the RGB color space. Results In bacillus identification, an overall sensitivity of 96.80% and an error rate of 3.38% were obtained. An image database with 120-sputum-smear microscopy slices of 12 patients with objects marked as bacillus, agglomerated bacillus and artifact was generated and is now available online. Conclusions The best results were obtained with a support vector machine in bacillus segmentation associated with the application of the three post-processing filters.

INTRODUCTION: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition: the novelty classifier. METHODS: The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. RESULTS: In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%. CONCLUSION: For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.

Este trabalho apresenta um estudo de caso de um processo de melhoria aplicado a uma indústria de remanufatura de cartuchos de toner. Inicialmente, é feita uma abordagem do processo de remanufatura procurando salientar as principais características que o diferenciam de um processo de manufatura convencional e a importância do mesmo diante das preocupações ambientais atuais. Através de três indicadores de desempenho propostos no trabalho fez-se um diagnóstico do processo de remanufatura quanto ao aproveitamento de peças recolhidas do campo, atendimento de pedidos dos clientes e quantidade de defeitos. No diagnóstico inicial realizado, os valores obtidos para esses critérios situaram-se abaixo das metas estabelecidas na empresa. Através da metodologia PDCA, identificaram-se oportunidades de melhorias em pontos críticos do processo de fabricação. A implantação dessas melhorias resultou em ganhos nos indicadores de desempenho da ordem de 12%. Algumas ferramentas clássicas foram utilizadas para auxiliar na implementação da metodologia PDCA destacando-se brainstorming, diagrama de Ishikawa e Controle Estatístico de Processo.

This paper presents an improvement process applied in one remanufacturing toner cartridge industry. It, initially, presents a mapping of the stages of this process by attempting to highlight the main characteristics, which distinguish it from a conventional manufacturing process and emphasizes its importance as an of eco-efficient process aiming at achieving sustainable development. Through three performance indicators proposed by the work, a diagnosis of the existing process was carried out as to the reuse of parts gasthered in the field, fulfilment of customers'orders and quantity of production faults. At the initial diagnosis carried out, the values obtained for theses criteria were located below the established targets proposed by the company. Through the PDCA tool, improvement opportunities in critical points in the manufacturing process were identified. The setting up of these improvements resulted in gains in the performance indicators of nearly 12%. Some classical tools were applied in PDCA implementation, as brainstorming Ishikawa diagram and statistic process control.