ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

Supplier's selection model based on an empirical study

The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, there were identified five broad selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. After the identification criteria, a survey was elaborated and companies were contacted in order to understand which factors have more weight in their decisions to choose the partners. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Value Analysis. The goal of the paper it's to supply a selection reference model that can represent an orientation/pattern for a decision making on the suppliers/partners selection process

Fifth harmonic and sag impact on PMSG wind turbines with a balancing new strategy for capacitor voltages

This paper deals with the computing simulation of the impact on permanent magnet synchronous generator wind turbines due to fifth harmonic content and grid voltage decrease. Power converter topologies considered in the simulations are the two-level and the three-level ones. The three-level converters are limited by unbalance voltages in the DC-link capacitors. In order to lessen this limitation, a new control strategy for the selection of the output voltage vectors is proposed. Controller strategies considered in the simulation are respectively based on proportional integral and fractional-order controllers. Finally, a comparison between the results of the simulations with the two controller strategies is presented in order to show the main advantage of the proposed strategy. (C) 2014 Elsevier Ltd. All rights reserved.

Shear strength of adhesively bonded polyolefins with minimal surface preparation

Interest in polyethylene and polypropylene bonding has increased in the last years. However, adhesive joints with adherends which are of low surface energy and which are chemically inert present several difficulties. Generally, their high degree of chemical resistance to solvents and dissimilar solubility parameters limit the usefulness of solvent bonding as a viable assembly technique. One successful approach to adhesive bonding of these materials involves proper selection of surface pre-treatment prior to bonding. With the correct pre-treatment it is possible to glue these materials with one or more of several adhesives required by the applications involved. A second approach is the use of adhesives without surface pre-treatment, such as hot melts, high tack pressure-sensitive adhesives, solvent-based specialty adhesives and, more recently, structural acrylic adhesives as such 3M DP-8005® and Loctite 3030®. In this paper, the shear strengths of two acrylic adhesives were evaluated using the lap shear test method ASTM D3163 and the block shear test method ASTM D4501. Two different industrial polyolefins (polyethylene and polypropylene) were used for adherends. However, the focus of this study was to measure the shear strength of polyethylene joints with acrylic adhesives. The effect of abrasion was also studied. Some test specimens were manually abraded using 180 and 320 grade abrasive paper. An additional goal of this work was to examine the effect of temperature and moisture on mechanical strength of adhesive joints.

Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Factors associated with exclusive breastfeeding in the first six months of life in Brazil: a systematic review

ABSTRACT OBJECTIVE To identify factors associated with exclusive breastfeeding in the first six months of life in Brazil. METHODS Systematic review of epidemiological studies conducted in Brazil with exclusive breastfeeding as outcome. Medline and LILACS databases were used. After the selection of articles, a hierarchical theoretical model was proposed according to the proximity of the variable to the outcome. RESULTS Of the 67 articles identified, we selected 20 cross-sectional studies and seven cohort studies, conducted between 1998 and 2010, comprising 77,866 children. We identified 36 factors associated with exclusive breastfeeding, being more often associated the distal factors: place of residence, maternal age and education, and the proximal factors: maternal labor, age of the child, use of a pacifier, and financing of primary health care. CONCLUSIONS The theoretical model developed may contribute to future research, and factors associated with exclusive breastfeeding may subsidize public policies on health and nutrition.

Body weight gain during adulthood and uterine myomas: Pró-Saúde Study

This study intended to investigate whether body weight gain during adulthood is associated with uterine myomas. 1,560 subjects were evaluated in a Pró-Saúde Study. Weight gain was evaluated in a continuous fashion and also in quintiles. Odds ratios and 95% confidence intervals were estimated through logistic regression models that were adjusted for education levels, color/race, body mass indices at age 20, age of menarche, parity, use of oral contraceptive methods, smoking, health insurance, and the Papanicolaou tests. No relevant differences were observed regarding the presence of uterine myomas among weight gain quintiles in that studied population.

Global localization with non-quantized local image features

In the field of appearance-based robot localization, the mainstream approach uses a quantized representation of local image features. An alternative strategy is the exploitation of raw feature descriptors, thus avoiding approximations due to quantization. In this work, the quantized and non-quantized representations are compared with respect to their discriminativity, in the context of the robot global localization problem. Having demonstrated the advantages of the non-quantized representation, the paper proposes mechanisms to reduce the computational burden this approach would carry, when applied in its simplest form. This reduction is achieved through a hierarchical strategy which gradually discards candidate locations and by exploring two simplifying assumptions about the training data. The potential of the non-quantized representation is exploited by resorting to the entropy-discriminativity relation. The idea behind this approach is that the non-quantized representation facilitates the assessment of the distinctiveness of features, through the entropy measure. Building on this finding, the robustness of the localization system is enhanced by modulating the importance of features according to the entropy measure. Experimental results support the effectiveness of this approach, as well as the validity of the proposed computation reduction methods.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Low-power 6-bit 1-GS/s two-channel pipeline ADC with open-loop amplification using amplifiers with local-feedback

IEEE International Symposium on Circuits and Systems, pp. 2258 – 2261, Seattle, EUA

Solving Conflicting Beliefs with a Distributed Belief Revision Approach

The ability to solve conflicting beliefs is crucial for multi- agent systems where the information is dynamic, incomplete and dis- tributed over a group of autonomous agents. The proposed distributed belief revision approach consists of a distributed truth maintenance sy- stem and a set of autonomous belief revision methodologies. The agents have partial views and, frequently, hold disparate beliefs which are au- tomatically detected by system’s reason maintenance mechanism. The nature of these conflicts is dynamic and requires adequate methodolo- gies for conflict resolution. The two types of conflicting beliefs addressed in this paper are Context Dependent and Context Independent Conflicts which result, in the first case, from the assignment, by different agents, of opposite belief statuses to the same belief, and, in the latter case, from holding contradictory distinct beliefs. The belief revision methodology for solving Context Independent Con- flicts is, basically, a selection process based on the assessment of the cre- dibility of the opposing belief statuses. The belief revision methodology for solving Context Dependent Conflicts is, essentially, a search process for a consensual alternative based on a “next best” relaxation strategy.

A MOSFET-only wideband LNA exploiting thermal noise canceling and gain optimization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

A saúde fora de portas: o projeto de literacia da informação da ESTeSL

Introdução – A pesquisa de informação realizada pelos estudantes de ensino superior em recursos eletrónicos não corresponde necessariamente ao domínio de competências de pesquisa, análise, avaliação, seleção e bom uso da informação recuperada. O conceito de literacia da informação ganha pertinência e destaque, na medida em que abarca competências que permitem reconhecer quando é necessária a informação e de atuar de forma eficiente e efetiva na sua obtenção e utilização. Objetivo – A meta da Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL) foi a formação em competências de literacia da informação, fora da ESTeSL, de estudantes, professores e investigadores. Métodos – A formação foi integrada em projetos nacionais e internacionais, dependendo dos públicos-alvo, das temáticas, dos conteúdos, da carga horária e da solicitação da instituição parceira. A Fundação Calouste Gulbenkian foi o promotor financeiro privilegiado. Resultados – Decorreram várias intervenções em território nacional e internacional. Em 2010, em Angola, no Instituto Médio de Saúde do Bengo, formação de 10 bibliotecários sobre a construção e a gestão de uma biblioteca de saúde e introdução à literacia da informação (35h). Em 2014, decorrente do ERASMUS Intensive Programme, o OPTIMAX (Radiation Dose and Image Quality Optimisation in Medical Imaging) para 40 professores e estudantes de radiologia (oriundos de Portugal, Reino Unido, Noruega, Países Baixos e Suíça) sobre metodologia e pesquisa de informação na MEDLINE e na Web of Science e sobre o Mendeley, enquanto gestor de referências (4h). Os trabalhos finais deste curso foram publicados em formato de ebook (http://usir.salford.ac.uk/34439/1/Final%20complete%20version.pdf), cuja revisão editorial foi da responsabilidade dos bibliotecários. Ao longo de 2014, na Escola Superior de Educação, Escola Superior de Dança, Instituto Politécnico de Setúbal e Faculdade de Medicina de Lisboa e, ao longo de 2015, na Universidade Aberta, Escola Superior de Comunicação Social, Instituto Egas Moniz, Faculdade de Letras de Lisboa e Centro de Linguística da Universidade de Lisboa foram desenhados conteúdos sobre o uso do ZOTERO e do Mendeley para a gestão de referências bibliográficas e sobre uma nova forma de fazer investigação. Cada uma destas sessões (2,5h) envolveu cerca de 25 estudantes finalistas, mestrandos e professores. Em 2015, em Moçambique, no Instituto Superior de Ciências da Saúde, decorreu a formação de 5 bibliotecários e 46 estudantes e professores (70h). Os conteúdos ministrados foram: 1) gestão e organização de uma biblioteca de saúde (para bibliotecários); 2) literacia da informação: pesquisa de informação na MEDLINE, SciELO e RCAAP, gestores de referências e como evitar o plágio (para bibliotecários e estudantes finalistas de radiologia). A carga horária destinada aos estudantes incluiu a tutoria das monografias de licenciatura, em colaboração com mais duas professoras do projeto. Para 2016 está agendada formação noutras instituições de ensino superior nacionais. Perspetiva-se, ainda, formação similar em Timor-Leste, cujos conteúdos, datas e carga horária estão por agendar. Conclusões – Destas iniciativas beneficia a instituição (pela visibilidade), os bibliotecários (pelo evidenciar de competências) e os estudantes, professores e investigadores (pelo ganho de novas competências e pela autonomia adquirida). O projeto de literacia da informação da ESTeSL tem contribuído de forma efetiva para a construção e para a produção de conhecimento no meio académico, nacional e internacional, sendo a biblioteca o parceiro privilegiado nesta cultura de colaboração.

Exploiting the bin-class histograms for feature selection on discrete data

In machine learning and pattern recognition tasks, the use of feature discretization techniques may have several advantages. The discretized features may hold enough information for the learning task at hand, while ignoring minor fluctuations that are irrelevant or harmful for that task. The discretized features have more compact representations that may yield both better accuracy and lower training time, as compared to the use of the original features. However, in many cases, mainly with medium and high-dimensional data, the large number of features usually implies that there is some redundancy among them. Thus, we may further apply feature selection (FS) techniques on the discrete data, keeping the most relevant features, while discarding the irrelevant and redundant ones. In this paper, we propose relevance and redundancy criteria for supervised feature selection techniques on discrete data. These criteria are applied to the bin-class histograms of the discrete features. The experimental results, on public benchmark data, show that the proposed criteria can achieve better accuracy than widely used relevance and redundancy criteria, such as mutual information and the Fisher ratio.