26 resultados para bluetooth,ble,beacon,localizzazione,indoor

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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Systems for indoor positioning using radio technologies are largely studied due to their convenience and the market opportunities they offer. The positioning algorithms typically derive geographic coordinates from observed radio signals and hence good understanding of the indoor radio channel is required. In this paper we investigate several factors that affect signal propagation indoors for both Bluetooth and WiFi. Our goal is to investigate which factors can be disregarded and which should be considered in the development of a positioning algorithm. Our results show that technical factors such as device characteristics have smaller impact on the signal than multipath propagation. Moreover, we show that propagation conditions differ in each direction. We also noticed that WiFi and Bluetooth, despite operating in the same radio band, do not at all times exhibit the same behaviour.

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We present a geospatial model to predict the radiofrequency electromagnetic field from fixed site transmitters for use in epidemiological exposure assessment. The proposed model extends an existing model toward the prediction of indoor exposure, that is, at the homes of potential study participants. The model is based on accurate operation parameters of all stationary transmitters of mobile communication base stations, and radio broadcast and television transmitters for an extended urban and suburban region in the Basel area (Switzerland). The model was evaluated by calculating Spearman rank correlations and weighted Cohen's kappa (kappa) statistics between the model predictions and measurements obtained at street level, in the homes of volunteers, and in front of the windows of these homes. The correlation coefficients of the numerical predictions with street level measurements were 0.64, with indoor measurements 0.66, and with window measurements 0.67. The kappa coefficients were 0.48 (95%-confidence interval: 0.35-0.61) for street level measurements, 0.44 (95%-CI: 0.32-0.57) for indoor measurements, and 0.53 (95%-CI: 0.42-0.65) for window measurements. Although the modeling of shielding effects by walls and roofs requires considerable simplifications of a complex environment, we found a comparable accuracy of the model for indoor and outdoor points.

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Some free-living amoebae, including some species of the genus Acanthamoeba, can cause infections in humans and animals. These organisms are known to cause granulomatous amebic encephalitis (GAE) in predominantly immune-deficient persons. In the present study, we isolated a potentially human pathogenic Acanthamoeba isolate originating from a public heated indoor swimming pool in Switzerland. The amoebae, thermophilically preselected by culture at 37 degrees C, subsequently displayed a high thermotolerance, being able to grow at 42 degrees C, and a marked cytotoxicity, based on a co-culture system using the murine cell line L929. Intranasal infection of Rag2-immunodeficient mice resulted in the death of all animals within 24 days. Histopathology of brains and lungs revealed marked tissue necrosis and hemorrhagic lesions going along with massive proliferation of amoebae. PCR and sequence analysis, based on 18S rDNA, identified the agent as Acanthamoeba lenticulata. In summary, the present study reports on an Acanthamoeba isolate from a heated swimming pool suggestive of being potentially pathogenic to immunocompromised persons.

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This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.