929 resultados para SUBBARRIER FUSION
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Background - The eukaryotic cytosolic chaperonin CCT is a hetero-oligomeric complex formed by two rings connected back-to-back, each composed of eight distinct subunits (CCTalpha to CCTzeta). CCT complex mediates the folding, of a wide range of newly synthesised proteins including tubulin (alpha, beta and gamma) and actin, as quantitatively major substrates. Methodology/Principal findings - We disrupted the genes encoding CCTalpha and CCTdelta subunits in the ciliate Tetrahymena. Cells lacking the zygotic expression of either CCTalpha or CCTdelta showed a loss of cell body microtubules, failed to assemble new cilia and died within 2 cell cycles. We also show that loss of CCT subunit activity leads to axoneme shortening and splaying of tips of axonemal microtubules. An epitope-tagged CCTalpha rescued the gene knockout phenotype and localized primarily to the tips of cilia. A mutation in CCTalpha, G346E, at a residue also present in the related protein implicated in the Bardet Biedel Syndrome, BBS6, also caused defects in cilia and impaired CCTalpha localization in cilia. Conclusions/Significance - Our results demonstrate that the CCT subunits are essential and required for ciliary assembly and maintenance of axoneme structure, especially at the tips of cilia.
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Mestrado em Engenharia Electrotécnica e de Computadores
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A presente dissertação apresenta uma solução para o problema de modelização tridimensional de galerias subterrâneas. O trabalho desenvolvido emprega técnicas provenientes da área da robótica móvel para obtenção um sistema autónomo móvel de modelização, capaz de operar em ambientes não estruturados sem acesso a sistemas de posicionamento global, designadamente GPS. Um sistema de modelização móvel e autónomo pode ser bastante vantajoso, pois constitui um método rápido e simples de monitorização das estruturas e criação de representações virtuais das galerias com um elevado nível de detalhe. O sistema de modelização desloca-se no interior dos túneis para recolher informações sensoriais sobre a geometria da estrutura. A tarefa de organização destes dados com vista _a construção de um modelo coerente, exige um conhecimento exacto do percurso praticado pelo sistema, logo o problema de localização da plataforma sensorial tem que ser resolvido. A formulação de um sistema de localização autónoma tem que superar obstáculos que se manifestam vincadamente nos ambientes underground, tais como a monotonia estrutural e a já referida ausência de sistemas de posicionamento global. Neste contexto, foi abordado o conceito de SLAM (Simultaneous Loacalization and Mapping) para determinação da localização da plataforma sensorial em seis graus de liberdade. Seguindo a abordagem tradicional, o núcleo do algoritmo de SLAM consiste no filtro de Kalman estendido (EKF { Extended Kalman Filter ). O sistema proposto incorpora métodos avançados do estado da arte, designadamente a parametrização em profundidade inversa (Inverse Depth Parametrization) e o método de rejeição de outliers 1-Point RANSAC. A contribuição mais importante do método por nós proposto para o avanço do estado da arte foi a fusão da informação visual com a informação inercial. O algoritmo de localização foi testado com base em dados reais, adquiridos no interior de um túnel rodoviário. Os resultados obtidos permitem concluir que, ao fundir medidas inerciais com informações visuais, conseguimos evitar o fenómeno de degeneração do factor de escala, comum nas aplicações de localização através de sistemas puramente monoculares. Provámos simultaneamente que a correcção de um sistema de localização inercial através da consideração de informações visuais é eficaz, pois permite suprimir os desvios de trajectória que caracterizam os sistemas de dead reckoning. O algoritmo de modelização, com base na localização estimada, organiza no espaço tridimensional os dados geométricos adquiridos, resultando deste processo um modelo em nuvem de pontos, que posteriormente _e convertido numa malha triangular, atingindo-se assim uma representação mais realista do cenário original.
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O LSA/ISEP(Laboratório de sistemas Autónomos do Instituto Superior de Engenharia do Porto) tem vindo nos últimos anos a desenvolver sistemas robóticos inovadores para operação em ambiente marinho sendo o veículo de superfície autónomo ROAZ II um exemplo de renome internacional. Neste contexto, e tendo em vista a satisfação dos requisitos parciais conducentes à obtenção do grau de Mestre em Eng. Electrotécnica e de Computadores - Ramo de Sistemas Autónomos do ISEP, o presente trabalho visou a integração de um robô submarino operado remotamente (ROV) com o robô de superfície ROAZ II. Esta solução inovadora de operação coordenada e integrada de um ASV/ROV permite dotar o ASV de mobilidade e visão subaquática. Após a caracterização e análise de requisitos de diversos cenários operacionais foi apresentada uma arquitectura de controlo coordenado dos dois veículos baseada em manobras de controlo descritas por autómatos híbridos. Os dois veículos foram modelados e as manobras coordenadas projectadas foram validadas com um simulador em ambiente Matlab/Simulink. Foi desenvolvido um sistema de localização relativa do ROV através da fusão sensorial de um sistema INS com um sistema acústico USBL utilizando um filtro EKF. O veículo ROV (VideoRay) do LSA foi instrumentado com os sensores necessários e efectuada a integração de hardware e software com o ASV ROAZ II permitindo a operação remota. Foi realizada uma missão demonstrativa de inspecção de pilares subaquáticos em cenário real com a operação conjunta dos dois robôs.
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Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial
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In the last decade, local image features have been widely used in robot visual localization. To assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image to those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, we compare several candidate combiners with respect to their performance in the visual localization task. A deeper insight into the potential of the sum and product combiners is provided by testing two extensions of these algebraic rules: threshold and weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance. The voting method, whilst competitive to the algebraic rules in their standard form, is shown to be outperformed by both their modified versions.
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Pure tungsten and tantalum plates and tungsten-tantalum composites produced via mechanical alloying and spark plasma sintering were bombarded with He+ and D+ energetic ion beams and deuterium plasmas. The aim of this experiment is to study the effects caused by individual helium and deuterium exposures and to evidence that the modifications induced in the composites at different irradiation energies could be followed by irradiating the pristine constituent elements under the same experimental conditions, which is relevant considering the development of tailored composites for fusion applications. Higher D retentions, especially in tungsten, and superficial blistering are observed in both components after helium exposure. The blistering is magnified in the tantalum phase of composites due to its higher ductility and to water vapour production under deuterium irradiation. At lower irradiation energies the induced effects are minor. After plasma exposure, the presence of tantalum does not increase the D content in the composites. (C) 2013 Elsevier B.V. All rights reserved.
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The use of cytostatics drugs in anticancer therapy is increasing. Health care workers can be occupationally exposed to these drugs classified as carcinogenic, mutagenic or teratogenic. Workers may be exposed to this drug, being in the hospital settings the main focus dwelled upon the pharmacy, and nursing personnel. Although the potential therapeutic benefits of hazardous drugs outweigh the risks of side effects for ill patients, exposed health care workers can have the same side effects with no therapeutic benefit. The exposure to these substances is epidemiologically linked to cancer and nuclear changes detected by the cytokinesis-block micronucleus test (CBMN). This method is extensively used in molecular epidemiology, since it determines several biomarkers of genotoxicity, such as micronuclei (MN), which are biomarkers of chromosomes breakage or loss, nucleoplasmic bridges (NPB), common biomarkers of chromosome rearrangement, poor repair and/or telomeres fusion, and nuclear buds (NBUD), biomarkers of elimination of amplified DNA.
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Exposure in a hospital setting is normally due to the use of several antineoplastic drugs simultaneously. Nevertheless, the effects of such mixtures at the cell level and on human health in general are unpredictable and unique due to differences in practice of hospital oncology departments, in the number of patients, protection devices available, and the experience and safety procedures of medical staff. Health care workers who prepare or administer hazardous drugs or who work in areas where these drugs are used may be exposed to these agents in the air, on work surfaces, contaminated clothing, medical equipment, patient excreta, and other surfaces. These workers include specially pharmacists, pharmacy technicians, and nursing personnel. Exposures may occur through inhalation resulting from aerosolization of powder or liquid during reconstitution and spillage taking place while preparing or administering to patients, through Cytokinesis-block micronucleus test (CBMN) is extensively used in biomonitoring, since it determines several biomarkers of genotoxicity, such as micronuclei (MN), which are biomarkers of chromosomes breakage or loss, nucleoplasmic bridges (NPB), common biomarkers of chromosome rearrangement, poor repair and/or telomeres fusion, and nuclear buds (NBUD), biomarkers of elimination of amplified DNA.
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To avoid additional hardware deployment, indoor localization systems have to be designed in such a way that they rely on existing infrastructure only. Besides the processing of measurements between nodes, localization procedure can include the information of all available environment information. In order to enhance the performance of Wi-Fi based localization systems, the innovative solution presented in this paper considers also the negative information. An indoor tracking method inspired by Kalman filtering is also proposed.
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Relatório Final de Estágio apresentado à Escola Superior de Dança com vista à obtenção do Grau de Mestre em Ensino de Dança.
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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.
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In research on Silent Speech Interfaces (SSI), different sources of information (modalities) have been combined, aiming at obtaining better performance than the individual modalities. However, when combining these modalities, the dimensionality of the feature space rapidly increases, yielding the well-known "curse of dimensionality". As a consequence, in order to extract useful information from this data, one has to resort to feature selection (FS) techniques to lower the dimensionality of the learning space. In this paper, we assess the impact of FS techniques for silent speech data, in a dataset with 4 non-invasive and promising modalities, namely: video, depth, ultrasonic Doppler sensing, and surface electromyography. We consider two supervised (mutual information and Fisher's ratio) and two unsupervised (meanmedian and arithmetic mean geometric mean) FS filters. The evaluation was made by assessing the classification accuracy (word recognition error) of three well-known classifiers (knearest neighbors, support vector machines, and dynamic time warping). The key results of this study show that both unsupervised and supervised FS techniques improve on the classification accuracy on both individual and combined modalities. For instance, on the video component, we attain relative performance gains of 36.2% in error rates. FS is also useful as pre-processing for feature fusion. Copyright © 2014 ISCA.
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Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores
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In practice the robotic manipulators present some degree of unwanted vibrations. The advent of lightweight arm manipulators, mainly in the aerospace industry, where weight is an important issue, leads to the problem of intense vibrations. On the other hand, robots interacting with the environment often generate impacts that propagate through the mechanical structure and produce also vibrations. In order to analyze these phenomena a robot signal acquisition system was developed. The manipulator motion produces vibrations, either from the structural modes or from endeffector impacts. The instrumentation system acquires signals from several sensors that capture the joint positions, mass accelerations, forces and moments, and electrical currents in the motors. Afterwards, an analysis package, running off-line, reads the data recorded by the acquisition system and extracts the signal characteristics. Due to the multiplicity of sensors, the data obtained can be redundant because the same type of information may be seen by two or more sensors. Because of the price of the sensors, this aspect can be considered in order to reduce the cost of the system. On the other hand, the placement of the sensors is an important issue in order to obtain the suitable signals of the vibration phenomenon. Moreover, the study of these issues can help in the design optimization of the acquisition system. In this line of thought a sensor classification scheme is presented. Several authors have addressed the subject of the sensor classification scheme. White (White, 1987) presents a flexible and comprehensive categorizing scheme that is useful for describing and comparing sensors. The author organizes the sensors according to several aspects: measurands, technological aspects, detection means, conversion phenomena, sensor materials and fields of application. Michahelles and Schiele (Michahelles & Schiele, 2003) systematize the use of sensor technology. They identified several dimensions of sensing that represent the sensing goals for physical interaction. A conceptual framework is introduced that allows categorizing existing sensors and evaluates their utility in various applications. This framework not only guides application designers for choosing meaningful sensor subsets, but also can inspire new systems and leads to the evaluation of existing applications. Today’s technology offers a wide variety of sensors. In order to use all the data from the diversity of sensors a framework of integration is needed. Sensor fusion, fuzzy logic, and neural networks are often mentioned when dealing with problem of combing information from several sensors to get a more general picture of a given situation. The study of data fusion has been receiving considerable attention (Esteban et al., 2005; Luo & Kay, 1990). A survey of the state of the art in sensor fusion for robotics can be found in (Hackett & Shah, 1990). Henderson and Shilcrat (Henderson & Shilcrat, 1984) introduced the concept of logic sensor that defines an abstract specification of the sensors to integrate in a multisensor system. The recent developments of micro electro mechanical sensors (MEMS) with unwired communication capabilities allow a sensor network with interesting capacity. This technology was applied in several applications (Arampatzis & Manesis, 2005), including robotics. Cheekiralla and Engels (Cheekiralla & Engels, 2005) propose a classification of the unwired sensor networks according to its functionalities and properties. This paper presents a development of a sensor classification scheme based on the frequency spectrum of the signals and on a statistical metrics. Bearing these ideas in mind, this paper is organized as follows. Section 2 describes briefly the robotic system enhanced with the instrumentation setup. Section 3 presents the experimental results. Finally, section 4 draws the main conclusions and points out future work.