6 resultados para Sensory fusion

em Instituto Politécnico do Porto, Portugal


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Mestrado em Engenharia Electrotécnica e de Computadores

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Knowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.

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Nowadays the incredible grow of mobile devices market led to the need for location-aware applications. However, sometimes person location is difficult to obtain, since most of these devices only have a GPS (Global Positioning System) chip to retrieve location. In order to suppress this limitation and to provide location everywhere (even where a structured environment doesn’t exist) a wearable inertial navigation system is proposed, which is a convenient way to track people in situations where other localization systems fail. The system combines pedestrian dead reckoning with GPS, using widely available, low-cost and low-power hardware components. The system innovation is the information fusion and the use of probabilistic methods to learn persons gait behavior to correct, in real-time, the drift errors given by the sensors.

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Nowadays there is an increase of location-aware mobile applications. However, these applications only retrieve location with a mobile device's GPS chip. This means that in indoor or in more dense environments these applications don't work properly. To provide location information everywhere a pedestrian Inertial Navigation System (INS) is typically used, but these systems can have a large estimation error since, in order to turn the system wearable, they use low-cost and low-power sensors. In this work a pedestrian INS is proposed, where force sensors were included to combine with the accelerometer data in order to have a better detection of the stance phase of the human gait cycle, which leads to improvements in location estimation. Besides sensor fusion an information fusion architecture is proposed, based on the information from GPS and several inertial units placed on the pedestrian body, that will be used to learn the pedestrian gait behavior to correct, in real-time, the inertial sensors errors, thus improving location estimation.

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Using low cost portable devices that enable a single analytical step for screening environmental contaminants is today a demanding issue. This concept is here tried out by recycling screen-printed electrodes that were to be disposed of and by choosing as sensory element a low cost material offering specific response for an environmental contaminant. Microcystins (MCs) were used as target analyte, for being dangerous toxins produced by cyanobacteria released into water bodies. The sensory element was a plastic antibody designed by surface imprinting with carefully selected monomers to ensure a specific response. These were designed on the wall of carbon nanotubes, taking advantage of their exceptional electrical properties. The stereochemical ability of the sensory material to detect MCs was checked by preparing blank materials where the imprinting stage was made without the template molecule. The novel sensory material for MCs was introduced in a polymeric matrix and evaluated against potentiometric measurements. Nernstian response was observed from 7.24 × 10−10 to 1.28 × 10−9 M in buffer solution (10 mM HEPES, 150 mM NaCl, pH 6.6), with average slopes of −62 mVdecade−1 and detection capabilities below 1 nM. The blank materials were unable to provide a linear response against log(concentration), showing only a slight potential change towards more positive potentials with increasing concentrations (while that ofthe plastic antibodies moved to more negative values), with a maximum rate of +33 mVdecade−1. The sensors presented good selectivity towards sulphate, iron and ammonium ions, and also chloroform and tetrachloroethylene (TCE) and fast response (<20 s). This concept was successfully tested on the analysis of spiked environmental water samples. The sensors were further applied onto recycled chips, comprehending one site for the reference electrode and two sites for different selective membranes, in a biparametric approach for “in situ” analysis.

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A novel artificial antibody for troponin T (TnT) was synthesized by molecular imprint (MI) on the surface of multiwalled carbon nanotubes (MWCNT). This was done by attaching TnT to the MWCNT surface, and filling the vacant spaces by polymerizing under mild conditions acrylamide (monomer) in N,N′-methylenebisacrylamide (cross-linker) and ammonium persulphate (initiator). After removing the template, the obtained biomaterial was able to rebind TnT and discriminate it among other interfering species. Stereochemical recognition of TnT was confirmed by the non-rebinding ability displayed by non-imprinted (NI) materials, obtained by imprinting without a template. SEM and FTIR analysis confirmed the surface modification of the MWCNT. The ability of this biomaterial to rebind TnT was confirmed by including it as electroactive compound in a PVC/plasticizer mixture coating a wire of silver, gold or titanium. Anionic slopes of 50 mV decade−1 were obtained for the gold wire coated with MI-based membranes dipped in HEPES buffer of pH 7. The limit of detection was 0.16 μg mL−1. Neither the NI-MWCNT nor the MWCNT showed the ability to recognize the template. Good selectivity was observed against creatinine, sucrose, fructose, myoglobin, sodium glutamate, thiamine and urea. The sensor was tested successfully on serum samples. It is expected that this work opens new horizons on the design of new artificial antibodies for complex protein structures.