897 resultados para Sensor Data Visualization


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Acoustic techniques have been used for many years to find and locate leaks in buried water distribution systems. Hydrophones and accelerometers are typically used as sensors. Although geophones could be used as well, they are not generally used for leak detection. A simple acoustic model of the pipe and the sensors has been proposed previously by some of the authors of this paper, and their model was used to explain some of the features observed in measurements. However, simultaneous measurements of a leak using all three sensor-types in controlled conditions for plastic pipes has not been reported to-date and hence they have not yet been compared directly. This paper fills that gap in knowledge. A set of measurements was made on a bespoke buried plastic water distribution pipe test rig to validate the previously reported analytical model. There is qualitative agreement between the experimental results and the model predictions in terms of the differing filtering properties of the pipe-sensor systems. A quality measure for the data is also presented, which is the ratio of the bandwidth over which the analysis is carried out divided by the centre frequency of this bandwidth. Based on this metric, the accelerometer was found to be the best sensor to use for the test rig described in this paper. However, for a system in which the distance between the sensors is large or the attenuation factor of the system is high, then it would be advantageous to use hydrophones, even though they are invasive sensors.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The head is the most important and specialized region in the body because it contains a range of specialized organs and, because it has interconnections between specialized organs, there is a great overlap of images. Thus, computed tomography (CT) helps in diagnosing diseases in this region, such as oral conditions, as they provide millimetric slices or cuts and demonstrate the relationship between the various anatomical structures involved, in volume and depth. Within dentistry, CT helps in the identification of pathological processes such as infection, tumors, visualization of embedded teeth and bone bed. This study aimed to assess the density of the mandibular alveolar bone at a determined point to later predict how periodontal disease is involved in bone resorption. For this, we performed a blind retrospective study (n = 124) of the CT scan files of dog skulls at FMVZ-UNESP in order to determine the density of the jaw bone using a Hounsfield scale, in the region of the dental apex of the cranial root of the first molar tooth in dogs. The results obtained were evaluated using mean and standard deviation (27.28 +/- 9.53 HU) in order to predict the normal density of the mandibular alveolar bone in the studied region. Thus, this data analysis allows a more concise evaluation of bone resorption of mandibular alveolar bone and, therefore, provides an adequate surgical planning in cases of osteosynthesis given mainly by the presence of installed periodontal disease.

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Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.

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A new selective sensor based on molecularly imprinted polymers (MIPs) was developed for the determination of hexazinone (HXZ) in environmental samples. MIPs were synthesized using a non-covalent approach, and selection of the monomers employed in the polymerization reaction was carried out by molecular modeling. Three functional monomers with high (2-vinylpyridine (MP17)) and intermediate (methacrylic acid (MP12) and acrylamide (MP5)) energies of binding to the template (HXZ) were selected for preparation of the MIPs, in order to conduct comparative studies and validate the theoretical data. For sensor construction, carbon pastes were modified with each MIP or NIP (non-imprinted polymer), and HXZ determination was performed using differential pulse adsorptive cathodic stripping voltammetry (DPAdCSV). All parameters affecting the sensor response were optimized. In HCl at pH 2.5, the sensor prepared with MP17 (5% w/w in the paste) showed a dynamic linear range between 1.9 × 10−11 and 1.1 × 10−10 mol L−1, and a detection limit of 2.6 × 10−12 mol L−1, under the following conditions: accumulation time of 200 s at a potential of −0.5V, scan rate of 50 mVs−1, pulse amplitude of 60 mV, and pulse width of 50 ms. The sensor was selective in the presence of other similar compounds, and was successfully applied to the analysis of HXZ in river water samples.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Ciência da Computação - IBILCE

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The electrical measures of the soil have been used as variables that correlate with its characteristics. This study aimed at developing an electrical capacitance sensor of low cost, to evaluate its performance on the field and verify the correlation between the measurements of electrical capacitance with physical properties (sand, silt and clay) and chemical properties of soil (pH, MO, P resin, H + Al, K, Ca, Mg, SB, CTC and V%) and the moisture content. The data sampling was performed at the farm named "Capão da Onça" which belongs to the State University of Ponta Grossa. The samples collection was conducted in an area of approximately 13 hectares, totalizing 81 samples. In each sampling the electrical capacitance of the soil was measured. After the sensor withdrawal, soil samples were collected and sent to be analysed in the laboratory of the College of Agronomics Science of the Paulista State University. The measuring instrument used to collect data on electric capacitance of the soil a digital multimeter was used. The data were submitted to the analysis of correlation and regression. The developed system presented a low cost and it was capable to measuring variation of the electrical capacitance of the soil. The obtained measures satisfactorily correlated with the levels of clay and sand, and weakly with the moisture content. This had demonstrated the possibility to use a sensor to verify the soil texture in not homogeneous areas. The measures of the electrical capacitance of the soil obtained by the sensor had significantly correlated with the soil attributes: calcium, magnesium, pH, SB and CTC. These results had demonstrated the possibility to use a sensor for soil fertility control.

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The integration of CMOS cameras with embedded processors and wireless communication devices has enabled the development of distributed wireless vision systems. Wireless Vision Sensor Networks (WVSNs), which consist of wirelessly connected embedded systems with vision and sensing capabilities, provide wide variety of application areas that have not been possible to realize with the wall-powered vision systems with wired links or scalar-data based wireless sensor networks. In this paper, the design of a middleware for a wireless vision sensor node is presented for the realization of WVSNs. The implemented wireless vision sensor node is tested through a simple vision application to study and analyze its capabilities, and determine the challenges in distributed vision applications through a wireless network of low-power embedded devices. The results of this paper highlight the practical concerns for the development of efficient image processing and communication solutions for WVSNs and emphasize the need for cross-layer solutions that unify these two so-far-independent research areas.

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Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

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Empirical approaches and, more recently, physical approaches, have grounded the establishment of logical connections between radiometric variables derived from remote data and biophysical variables derived from vegetation cover. This study was aimed at evaluating correlations of dendrometric and density data from canopies of Eucalyptus spp., as collected in Capao Bonito forest unit, with radiometric data from imagery acquired by the TM/Landsat-5 sensor on two orbital passages over the study site (dates close to field data collection). Results indicate that stronger correlations were identified between crown dimensions and canopy height with near-infrared spectral band data (rho(s)4), irrespective of the satellite passage date. Estimates of spatial distribution of dendrometric data and canopy density (D) using spectral characterization were consistent with the spatial distribution of tree ages during the study period. Statistical tests were applied to evaluate performance disparities of empirical models depending on which date data were acquired. Results indicated a significant difference between models based on distinct data acquisition dates.