905 resultados para automated registration


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In this article, we show how the use of state-of-the-art methods in computer science based on machine perception and learning allows the unobtrusive capture and automated analysis of interpersonal behavior in real time (social sensing). Given the high ecological validity of the behavioral sensing, the ease of behavioral-cue extraction for large groups over long observation periods in the field, the possibility of investigating completely new research questions, and the ability to provide people with immediate feedback on behavior, social sensing will fundamentally impact psychology.

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This paper deals with the structural properties of a-Si:H/a-Si1-xCx: H multilayers deposited by glow-discharge decomposition of SiH4 and SiH4 and CH4 mixtures. The main feature of the rf plasma reactor is an automated substrate holder. The plasma stabilization time and its influence on the multilayer obtained is discussed. A series of a-Si:H/a-Si1-xCx: H multilayers has been deposited and characterized by secondary ion mass spectrometry (SIMS), X-ray diffraction (XRD) and transmission electron microscopy (TEM). No asymmetry between the two types of interface has been observed. The results show that the multilayers present a very good periodicity and low roughness. The difficulty of determining the abruptness of the multilayer at the nanometer scale is discussed.

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In this work, we propose a method for prospective motion correction in MRI using a novel image navigator module, which is triggered by a free induction decay (FID) navigator. Only when motion occurs, the image navigator is run and new positional information is obtained through image registration. The image navigator was specifically designed to match the impact on the magnetization and the acoustic noise of the host sequence. This detection-correction scheme was implemented for an MP-RAGE sequence and 5 healthy volunteers were scanned at 3T while performing various head movements. The correction performance was demonstrated through automated brain segmentation and an image quality index whose results are sensitive to motion artifacts.

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Living bacteria or yeast cells are frequently used as bioreporters for the detection of specific chemical analytes or conditions of sample toxicity. In particular, bacteria or yeast equipped with synthetic gene circuitry that allows the production of a reliable non-cognate signal (e.g., fluorescent protein or bioluminescence) in response to a defined target make robust and flexible analytical platforms. We report here how bacterial cells expressing a fluorescence reporter ("bactosensors"), which are mostly used for batch sample analysis, can be deployed for automated semi-continuous target analysis in a single concise biochip. Escherichia coli-based bactosensor cells were continuously grown in a 13 or 50 nanoliter-volume reactor on a two-layered polydimethylsiloxane-on-glass microfluidic chip. Physiologically active cells were directed from the nl-reactor to a dedicated sample exposure area, where they were concentrated and reacted in 40 minutes with the target chemical by localized emission of the fluorescent reporter signal. We demonstrate the functioning of the bactosensor-chip by the automated detection of 50 μgarsenite-As l(-1) in water on consecutive days and after a one-week constant operation. Best induction of the bactosensors of 6-9-fold to 50 μg l(-1) was found at an apparent dilution rate of 0.12 h(-1) in the 50 nl microreactor. The bactosensor chip principle could be widely applicable to construct automated monitoring devices for a variety of targets in different environments.

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Recent standardization efforts in e-learning technology have resulted in a number of specifications, however, the automation process that is considered essential in a learning management system (LMS) is a lessexplored one. As learning technology becomes more widespread and more heterogeneous, there is a growing need to specify processes that cross the boundaries of a single LMS or learning resource repository. This article proposes to obtain a specification orientated to automation that takes on board the heterogeneity of systems and formats and provides a language for specifying complex and generic interactions. Having this goal in mind, a technique based on three steps is suggested. The semantic conformance profiles, the business process management (BPM) diagram, and its translation into the business process execution language (BPEL) seem to be suitable for achieving it.

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Automation or semi-automation of learning scenariospecifications is one of the least exploredsubjects in the e-learning research area. There isa need for a catalogue of learning scenarios and atechnique to facilitate automated retrieval of stored specifications. This requires constructing anontology with this goal and is justified inthis paper. This ontology must mainlysupport a specification technique for learning scenarios. This ontology should also be useful in the creation and validation of new scenarios as well as in the personalization of learning scenarios or their monitoring. Thus, after justifying the need for this ontology, a first approach of a possible knowledge domain is presented. An example of a concrete learning scenario illustrates some relevant concepts supported by this ontology in order to define the scenario in such a way that it could be easy to automate.

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The spectrophotometric determination of Cd(II) using a flow injection system provided with a solid-phase reactor for cadmium preconcentration and on-line reagent preparation, is described. It is based on the formation of a dithizone-Cd complex in basic medium. The calibration curve is linear between 6 and 300 µg L-1 Cd(II), with a detection limit of 5.4 µg L-1, an RSD of 3.7% (10 replicates in duplicate) and a sample frequency of 11.4 h-1. The proposed method was satisfactorily applied to the determination of Cd(II) in surface, well and drinking waters.

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Vehicle operations in underwater environments are often compromised by poor visibility conditions. For instance, the perception range of optical devices is heavily constrained in turbid waters, thus complicating navigation and mapping tasks in environments such as harbors, bays, or rivers. A new generation of high-definition forward-looking sonars providing acoustic imagery at high frame rates has recently emerged as a promising alternative for working under these challenging conditions. However, the characteristics of the sonar data introduce difficulties in image registration, a key step in mosaicing and motion estimation applications. In this work, we propose the use of a Fourier-based registration technique capable of handling the low resolution, noise, and artifacts associated with sonar image formation. When compared to a state-of-the art region-based technique, our approach shows superior performance in the alignment of both consecutive and nonconsecutive views as well as higher robustness in featureless environments. The method is used to compute pose constraints between sonar frames that, integrated inside a global alignment framework, enable the rendering of consistent acoustic mosaics with high detail and increased resolution. An extensive experimental section is reported showing results in relevant field applications, such as ship hull inspection and harbor mapping

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A liquid chromatography-tandem mass spectrometry method with atmospheric pressure chemical ionization (LC-APCI/MS/MS) was validated for the determination of etoricoxib in human plasma using antipyrin as internal standard, followed by on-line solid-phase extraction. The method was performed on a Luna C18 column and the mobile phase consisted of acetonitrile:water (95:5, v/v)/ammonium acetate (pH 4.0; 10 mM), run at a flow rate of 0.6 mL/min. The method was linear in the range of 1-5000 ng/mL (r²>0.99). The lower limit of quantitation was 1 ng/mL. The recoveries were within 93.72-96.18%. Moreover, method validation demonstrated acceptable results for the precision, accuracy and stability studies.