Análise de séries temporais de multicaminho em estações de monitoramento contínuo

Data from reference stations are widely used in GNSS (Global Navigation Satellite System) positioning, and can be used in relative positioning or network-based positioning concept. Positioning accuracy will be directly influenced by errors in signals collected in these stations. In this paper, it is aimed at evaluating these data quality using temporal series of multipath index MP1 and MP2. A statistical study of temporal series with 7 years of daily observations related to 7 stations from RBMC (Rede Brasileira de Monitoramento Contínuo) was accomplished. In order to investigate trends and seasonality a linear regression model, correlograms, and Fourier periodograms were used. We also used a harmonic adjust to identify peaks on temporal series. At last, the possible causes of seasonality found in some stations were discussed. It was also possible to identify peaks in MP values of March and October months (mainly in stations located near geomagnetic equator).

Desenvolvimento de uma plataforma de software para a modelagem digital de terrenos baseada em TIN

Topographical surfaces can be represented with a good degree of accuracy by means of maps. However these are not always the best tools for the understanding of more complex reliefs. In this sense, the greatest contribution of this work is to specify and to implement the architecture of an opensource software system capable of representing TIN (Triangular Irregular Network) based digital terrain models. The system implementation follows the object oriented programming and generic paradigms enabling the integration of various opensource tools such as GDAL, OGR, OpenGL, OpenSceneGraph and Qt. Furthermore, the representation core of the system has the ability to work with multiple topological data structures from which can be extracted, in constant time, all the connectivity relations between the entities vertices, edges and faces existing in a planar triangulation what helps enormously the implementation for real time applications. This is an important capability, for example, in the use of laser survey data (Lidar, ALS, TLS), allowing for the generation of triangular mesh models in the order of millions of points.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

Interferência do método de previsão populacional no desempenho hidráulico de uma rede coletora de esgoto sanitário

The non existence of sewage services creates an unhealthy environmental, causing impact extremely prejudicial to the human health. In Brazil, this item represents the biggest deficit in the basic sanitation these days, and only about 50% of the population is attended. The sewage collection network is a needful system for the basic sanitation. Between the parameters of the project of a system of collection and sewage transportation, the definition of the population that will be attended is one of the most important variables, and that should be done based on criterion of population growth and in accordant to directrix presents in the comprehensive city plan. In this paper, were evaluated methods of growth based on the geometric growth rate, on the population density and on the number of residents per dwelling, and also mathematics models of growth prediction, emphasizing in the geometric growth and on the logistic curve method. That way, the goal of this paper were value the hydraulic performance in one of the watersheds delimited by the sewage network magnification work existent in the city of Cacoal–RO. Using simulation with different scenarios, the results pointed that the hydraulic performance of the system were strongly affected by the population determination method used. For the same drained area the diameters range from 150 to 250 mm as well as the trench’s deep, where some stretches exceeded the maximum values contained in the standard. Besides, the variation of the sewage contribution rate due the population variation estimated in different scenarios, led to alterations in others important project parameters, such as slope, shallow pool, tensile stress and spreading velocity

SPECT (Single photon emission tomography): Gama câmara, reconstrução tomográfica e características funcional

The SPECT (Single Photon Emission Computed Tomography) systems are part of a medical image acquisition technology which has been outstanding, because the resultant images are functional images complementary to those that give anatomic information, such as X-Ray CT, presenting a high diagnostic value. These equipments acquire, in a non-invasive way, images from the interior of the human body through tomographic mapping of radioactive material administered to the patient. The SPECT systems are based on the Gamma Camera detection system, and one of them being set on a rotational gantry is enough to obtain the necessary data for a tomographic image. The images obtained from the SPECT system consist in a group of flat images that describe the radioactive distribution on the patient. The trans-axial cuts are obtained from the tomographic reconstruction techniques. There are analytic and iterative methods to obtain the tomographic reconstruction. The analytic methods are based on the Fourier Cut Theorem (FCT), while the iterative methods search for numeric solutions to solve the equations from the projections. Within the analytic methods, the filtered backprojection (FBP) method maybe is the simplest of all the tomographic reconstruction techniques. This paper's goal is to present the operation of the SPECT system, the Gamma Camera detection system, some tomographic reconstruction techniques and the requisites for the implementation of this system in a Nuclear Medicine service

Segurança em redes sem fio: estudo sobre o desenvolvimento de conjuntos de dados para comparação de IDS

Pós-graduação em Engenharia Elétrica - FEIS

Correlação e visualização de alertas de segurança em redes de computadores

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Nanotechnology-based drug delivery systems for melanoma antitumoral therapy: a review

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effects of Protein A in Detection of Canine Distemper Virus through immunosensor construction

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

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Root niche separation can explain avoidance of seasonal drought stress and vulnerability of overstory trees to extended drought in a mature Amazonian forest

Large areas of Amazonian evergreen forest experience seasonal droughts extending for three or more months, yet show maximum rates of photosynthesis and evapotranspiration during dry intervals. This apparent resilience is belied by disproportionate mortality of the large trees in manipulations that reduce wet season rainfall, occurring after 2-3 years of treatment. The goal of this study is to characterize the mechanisms that produce these contrasting ecosystem responses. A mechanistic model is developed based on the ecohydrological framework of TIN (Triangulated Irregular Network)-based Real Time Integrated Basin Simulator + Vegetation Generator for Interactive Evolution (tRIBS+VEGGIE). The model is used to test the roles of deep roots and soil capillary flux to provide water to the forest during the dry season. Also examined is the importance of "root niche separation," in which roots of overstory trees extend to depth, where during the dry season they use water stored from wet season precipitation, while roots of understory trees are concentrated in shallow layers that access dry season precipitation directly. Observational data from the Tapajo's National Forest, Brazil, were used as meteorological forcing and provided comprehensive observational constraints on the model. Results strongly suggest that deep roots with root niche separation adaptations explain both the observed resilience during seasonal drought and the vulnerability of canopy-dominant trees to extended deficits of wet season rainfall. These mechanisms appear to provide an adaptive strategy that enhances productivity of the largest trees in the face of their disproportionate heat loads and water demand in the dry season. A sensitivity analysis exploring how wet season rainfall affects the stability of the rainforest system is presented. Citation: Ivanov, V. Y., L. R. Hutyra, S. C. Wofsy, J. W. Munger, S. R. Saleska, R. C. de Oliveira Jr., and P. B. de Camargo (2012), Root niche separation can explain avoidance of seasonal drought stress and vulnerability of overstory trees to extended drought in a mature Amazonian forest, Water Resour. Res., 48, W12507, doi:10.1029/2012WR011972.

Compact and autonomous multiwavelength microanalyzer for in-line and in situ colorimetric determinations

Nowadays, the attainment of microsystems that integrate most of the stages involved in an analytical process has raised an enormous interest in several research fields. This approach provides experimental set-ups of increased robustness and reliability, which simplify their application to in-line and continuous biomedical and environmental monitoring. In this work, a novel, compact and autonomous microanalyzer aimed at multiwavelength colorimetric determinations is presented. It integrates the microfluidics (a three-dimensional mixer and a 25 mm length "Z-shape" optical flow-cell), a highly versatile multiwavelength optical detection system and the associated electronics for signal processing and drive, all in the same device. The flexibility provided by its design allows the microanalyzer to be operated either in single fixed mode to provide a dedicated photometer or in multiple wavelength mode to obtain discrete pseudospectra. To increase its reliability, automate its operation and allow it to work under unattended conditions, a multicommutation sub-system was developed and integrated with the experimental set-up. The device was initially evaluated in the absence of chemical reactions using four acidochromic dyes and later applied to determine some key environmental parameters such as phenol index, chromium(VI) and nitrite ions. Results were comparable with those obtained with commercial instrumentation and allowed to demonstrate the versatility of the proposed microanalyzer as an autonomous and portable device able to be applied to other analytical methodologies based on colorimetric determinations.

Cooperative communication and distributed detection in wireless sensor networks

Recent progress in microelectronic and wireless communications have enabled the development of low cost, low power, multifunctional sensors, which has allowed the birth of new type of networks named wireless sensor networks (WSNs). The main features of such networks are: the nodes can be positioned randomly over a given field with a high density; each node operates both like sensor (for collection of environmental data) as well as transceiver (for transmission of information to the data retrieval); the nodes have limited energy resources. The use of wireless communications and the small size of nodes, make this type of networks suitable for a large number of applications. For example, sensor nodes can be used to monitor a high risk region, as near a volcano; in a hospital they could be used to monitor physical conditions of patients. For each of these possible application scenarios, it is necessary to guarantee a trade-off between energy consumptions and communication reliability. The thesis investigates the use of WSNs in two possible scenarios and for each of them suggests a solution that permits to solve relating problems considering the trade-off introduced. The first scenario considers a network with a high number of nodes deployed in a given geographical area without detailed planning that have to transmit data toward a coordinator node, named sink, that we assume to be located onboard an unmanned aerial vehicle (UAV). This is a practical example of reachback communication, characterized by the high density of nodes that have to transmit data reliably and efficiently towards a far receiver. It is considered that each node transmits a common shared message directly to the receiver onboard the UAV whenever it receives a broadcast message (triggered for example by the vehicle). We assume that the communication channels between the local nodes and the receiver are subject to fading and noise. The receiver onboard the UAV must be able to fuse the weak and noisy signals in a coherent way to receive the data reliably. It is proposed a cooperative diversity concept as an effective solution to the reachback problem. In particular, it is considered a spread spectrum (SS) transmission scheme in conjunction with a fusion center that can exploit cooperative diversity, without requiring stringent synchronization between nodes. The idea consists of simultaneous transmission of the common message among the nodes and a Rake reception at the fusion center. The proposed solution is mainly motivated by two goals: the necessity to have simple nodes (to this aim we move the computational complexity to the receiver onboard the UAV), and the importance to guarantee high levels of energy efficiency of the network, thus increasing the network lifetime. The proposed scheme is analyzed in order to better understand the effectiveness of the approach presented. The performance metrics considered are both the theoretical limit on the maximum amount of data that can be collected by the receiver, as well as the error probability with a given modulation scheme. Since we deal with a WSN, both of these performance are evaluated taking into consideration the energy efficiency of the network. The second scenario considers the use of a chain network for the detection of fires by using nodes that have a double function of sensors and routers. The first one is relative to the monitoring of a temperature parameter that allows to take a local binary decision of target (fire) absent/present. The second one considers that each node receives a decision made by the previous node of the chain, compares this with that deriving by the observation of the phenomenon, and transmits the final result to the next node. The chain ends at the sink node that transmits the received decision to the user. In this network the goals are to limit throughput in each sensor-to-sensor link and minimize probability of error at the last stage of the chain. This is a typical scenario of distributed detection. To obtain good performance it is necessary to define some fusion rules for each node to summarize local observations and decisions of the previous nodes, to get a final decision that it is transmitted to the next node. WSNs have been studied also under a practical point of view, describing both the main characteristics of IEEE802:15:4 standard and two commercial WSN platforms. By using a commercial WSN platform it is realized an agricultural application that has been tested in a six months on-field experimentation.