5 resultados para Indoor environments

em Universidade Federal do Rio Grande do Norte(UFRN)


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In this thesis, a frequency selective surface (FSS) consists of a two-dimensional periodic structure mounted on a dielectric substrate, which is capable of selecting signals in one or more frequency bands of interest. In search of better performance, more compact dimensions, low cost manufacturing, among other characteristics, these periodic structures have been continually optimized over time. Due to its spectral characteristics, which are similar to band-stop or band-pass filters, the FSSs have been studied and used in several applications for more than four decades. The design of an FSS with a periodic structure composed by pre-fractal elements facilitates the tuning of these spatial filters and the adjustment of its electromagnetic parameters, enabling a compact design which generally has a stable frequency response and superior performance relative to its euclidean counterpart. The unique properties of geometric fractals have shown to be useful, mainly in the production of antennas and frequency selective surfaces, enabling innovative solutions and commercial applications in microwave range. In recent applications, the FSSs modify the indoor propagation environments (emerging concept called wireless building ). In this context, the use of pre-fractal elements has also shown promising results, allowing a more effective filtering of more than one frequency band with a single-layer structure. This thesis approaches the design of FSSs using pre-fractal elements based on Vicsek, Peano and teragons geometries, which act as band-stop spatial filters. The transmission properties of the periodic surfaces are analyzed to design compact and efficient devices with stable frequency responses, applicable to microwave frequency range and suitable for use in indoor communications. The results are discussed in terms of the electromagnetic effect resulting from the variation of parameters such as: fractal iteration number (or fractal level), scale factor, fractal dimension and periodicity of FSS, according the pre-fractal element applied on the surface. The analysis of the fractal dimension s influence on the resonant properties of a FSS is a new contribution in relation to researches about microwave devices that use fractal geometry. Due to its own characteristics and the geometric shape of the Peano pre-fractal elements, the reconfiguration possibility of these structures is also investigated and discussed. This thesis also approaches, the construction of efficient selective filters with new configurations of teragons pre-fractal patches, proposed to control the WLAN coverage in indoor environments by rejecting the signals in the bands of 2.4~2.5 GHz (IEEE 802.11 b) and 5.0~6.0 GHz (IEEE 802.11a). The FSSs are initially analyzed through simulations performed by commercial software s: Ansoft DesignerTM and HFSSTM. The fractal design methodology is validated by experimental characterization of the built prototypes, using alternatively, different measurement setups, with commercial horn antennas and microstrip monopoles fabricated for low cost measurements

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Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark

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The localization of mobile robots in indoor environments finds lots of problems such as accumulated errors and the constant changes that occur at these places. A technique called global vision intends to localize robots using images acquired by cameras placed in such a way that covers the place where the robots movement takes place. Localization is obtained by marks put on top of the robot. Algorithms applied to the images search for the mark on top of the robot and by finding the mark they are able to get the position and orientation of the robot. Such techniques used to face some difficulties related with the hardware capacity, fact that limited their execution in real time. However, the technological advances of the last years changed that situation and enabling the development and execution of such algorithms in plain capacity. The proposal specified here intends to develop a mobile robot localization system at indoor environments using a technique called global vision to track the robot and acquire the images, all in real time, intending to improve the robot localization process inside the environment. Being a localization method that takes just actual information in its calculations, the robot localization using images fit into the needs of this kind of place. Besides, it enables more accurate results and in real time, what is exactly the museum application needs.

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Location systems have become increasingly part of people's lives. For outdoor environments, GPS appears as standard technology, widely disseminated and used. However, people usually spend most of their daily time in indoor environments, such as: hospitals, universities, factories, buildings, etc. In these environments, GPS does not work properly causing an inaccurate positioning. Currently, to perform the location of people or objects in indoor environments no single technology could reproduce for indoors the same result achieved by GPS for outdoors environments. Due to this, it is necessary to consider use of information from multiple sources using diferent technologies. Thus, this work aims to build an Adaptable Platform for Indoor location. Based on this goal, the IndoLoR platform is proposed. This platform aims to allow information reception from diferent sources, data processing, data fusion, data storage and data retrieval for the indoor location context.

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Location systems have become increasingly part of people's lives. For outdoor environments, GPS appears as standard technology, widely disseminated and used. However, people usually spend most of their daily time in indoor environments, such as: hospitals, universities, factories, buildings, etc. In these environments, GPS does not work properly causing an inaccurate positioning. Currently, to perform the location of people or objects in indoor environments no single technology could reproduce for indoors the same result achieved by GPS for outdoors environments. Due to this, it is necessary to consider use of information from multiple sources using diferent technologies. Thus, this work aims to build an Adaptable Platform for Indoor location. Based on this goal, the IndoLoR platform is proposed. This platform aims to allow information reception from diferent sources, data processing, data fusion, data storage and data retrieval for the indoor location context.