886 resultados para Detecção heteródina sintética


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Investiga as interferências da violência doméstica na fase diagnóstica, que antecede a Psicoterapia Breve, sua importância no quadro clínico apresentado pelo paciente, se a violência foi verbalizada pelo paciente e a forma como ela foi captada pelo terapeuta e levada em consideração na fase diagnóstica da Psicoterapia Breve Infantil. Foram investigados sinais e sintomas de 29 pacientes de Psicoterapia Breve Infantil. A avaliação das entrevistas iniciais que compõem a fase diagnóstica da Psicoterapia Breve, foi realizada por dois juizes independentes, com índice de concordância igual a 0,87. Foram detectados sinais e sintomas de violência doméstica em 24 dos 29 casos analisados, o que corresponde a 83% do total de pacientes. A violência doméstica foi considerada significativa para uma situação-problema a ser trabalhada em análise em 54% dos casos, o que mostra que é uma situação de limiar, em que 50% das percepções são verdadeiras e a outra metade são falsas. Dos 13 casos em que a violência doméstica foi significativa pelos juízes, apenas 54% estavam incluídas na compreensão psicodinâmica da queixa, pelo terapeuta. A inclusão da violência doméstica no foco da Psicoterapia Breve foi observada somente em 2 casos, o que corresponde a 07% da amostra geral, muito perto da incidência de mecanismos psicóticos que prejudicam a socialização (08%). Os resultados obtidos foram discutidos segundo a literatura psicanalítica, que considera a violência como a parte agressiva do self que ainda não está integrada ao self, permanecendo dissociada do conluio da família e às vezes do próprio psicoterapeuta. Tais resistências em lidar com a violência não permitem que a agressividade seja usada de forma construtiva pelo self, promovendo a socialização da criança pelo fortalecimento dos sentimentos de amizade e favorecendo o desenvolvimento de vínculos emocionais saudáveis na dupla criança e psioterapeuta.(AU)

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The detection and diagnosis of faults, ie., find out how , where and why failures occur is an important area of study since man came to be replaced by machines. However, no technique studied to date can solve definitively the problem. Differences in dynamic systems, whether linear, nonlinear, variant or invariant in time, with physical or analytical redundancy, hamper research in order to obtain a unique solution . In this paper, a technique for fault detection and diagnosis (FDD) will be presented in dynamic systems using state observers in conjunction with other tools in order to create a hybrid FDD. A modified state observer is used to create a residue that allows also the detection and diagnosis of faults. A bank of faults signatures will be created using statistical tools and finally an approach using mean squared error ( MSE ) will assist in the study of the behavior of fault diagnosis even in the presence of noise . This methodology is then applied to an educational plant with coupled tanks and other with industrial instrumentation to validate the system.

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Recent studies have shown evidence of log-periodic behavior in non-hierarchical systems. An interesting fact is the emergence of such properties on rupture and breakdown of complex materials and financial failures. These may be examples of systems with self-organized criticality (SOC). In this work we study the detection of discrete scale invariance or log-periodicity. Theoretically showing the effectiveness of methods based on the Fourier Transform of the log-periodicity detection not only with prior knowledge of the critical point before this point as well. Specifically, we studied the Brazilian financial market with the objective of detecting discrete scale invariance in Bovespa (Bolsa de Valores de S˜ao Paulo) index. Some historical series were selected periods in 1999, 2001 and 2008. We report evidence for the detection of possible log-periodicity before breakage, shown its applicability to the study of systems with discrete scale invariance likely in the case of financial crashes, it shows an additional evidence of the possibility of forecasting breakage

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This thesis is part of research on new materials for catalysis and gas sensors more active, sensitive, selective. The aim of this thesis was to develop and characterize cobalt ferrite in different morphologies, in order to study their influence on the electrical response and the catalytic activity, and to hierarchize these grains for greater diffusivity of gas in the material. The powders were produced via hydrothermal and solvothermal, and were characterized by thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, transmission electron microscopy (electron diffraction, highresolution simulations), and energy dispersive spectroscopy. The catalytic and electrical properties were tested in the presence of CO and NO2 gases, the latter in different concentrations (1-100 ppm) and at different temperatures (room temperature to 350 ° C). Nanooctahedra with an average size of 20 nm were obtained by hydrothermal route. It has been determined that the shape of the grains is mainly linked to the nature of the precipitating agent and the presence of OH ions in the reaction medium. By solvothermal method CoFe2O4 spherical powders were prepared with grain size of 8 and 20 nm. CoFe2O4 powders exhibit a strong response to small amounts of NO2 (10 ppm to 200 ° C). The nanooctahedra have greater sensitivity than the spherical grains of the same size, and have smaller response time and shorter recovery times. These results were confirmed by modeling the kinetics of response and recovery of the sensor. Initial tests of catalytic activity in the oxidation of CO between temperatures of 100 °C and 350 °C show that the size effect is predominant in relation the effect of the form with respect to the conversion of the reaction. The morphology of the grains influence the rate of reaction. A higher reaction rate is obtained in the presence of nanooctahedra. In order to improve the detection and catalytic properties of the material, we have developed a methodology for hierarchizing grains which involves the use of carbonbased templates.

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The conventional control schemes applied to Shunt Active Power Filters (SAPF) are Harmonic extractor-based strategies (HEBSs) because their effectiveness depends on how quickly and accurately the harmonic components of the nonlinear loads are identified. The SAPF can be also implemented without the use of the load harmonic extractors. In this case, the harmonic compensating term is obtained from the system active power balance. These systems can be considered as balanced-energy-based schemes (BEBSs) and their performance depends on how fast the system reaches the equilibrium state. In this case, the phase currents of the power grid are indirectly regulated by double sequence controllers with two degrees of freedom, where the internal model principle is employed to avoid reference frame transformation. Additionally the DSC controller presents robustness when the SAPF is operating under unbalanced conditions. Furthermore, SAPF implemented without harmonic detection schemes compensate simultaneously harmonic distortion and reactive power of the load. Their compensation capabilities, however, are limited by the SAPF power converter rating. Such a restriction can be minimized if the level of the reactive power correction is managed. In this work an estimation scheme for determining the filter currents is introduced to manage the compensation of reactive power. Experimental results are shown for demonstrating the performance of the proposed SAPF system.

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Valve stiction, or static friction, in control loops is a common problem in modern industrial processes. Recently, many studies have been developed to understand, reproduce and detect such problem, but quantification still remains a challenge. Since the valve position (mv) is normally unknown in an industrial process, the main challenge is to diagnose stiction knowing only the output signals of the process (pv) and the control signal (op). This paper presents an Artificial Neural Network approach in order to detect and quantify the amount of static friction using only the pv and op information. Different methods for preprocessing the training set of the neural network are presented. Those methods are based on the calculation of centroid and Fourier Transform. The proposal is validated using a simulated process and the results show a satisfactory measurement of stiction.

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The time series analysis has played an increasingly important role in weather and climate studies. The success of these studies depends crucially on the knowledge of the quality of climate data such as, for instance, air temperature and rainfall data. For this reason, one of the main challenges for the researchers in this field is to obtain homogeneous series. A time series of climate data is considered homogeneous when the values of the observed data can change only due to climatic factors, i.e., without any interference from external non-climatic factors. Such non-climatic factors may produce undesirable effects in the time series, as unrealistic homogeneity breaks, trends and jumps. In the present work it was investigated climatic time series for the city of Natal, RN, namely air temperature and rainfall time series, for the period spanning from 1961 to 2012. The main purpose was to carry out an analysis in order to check the occurrence of homogeneity breaks or trends in the series under investigation. To this purpose, it was applied some basic statistical procedures, such as normality and independence tests. The occurrence of trends was investigated by linear regression analysis, as well as by the Spearman and Mann-Kendall tests. The homogeneity was investigated by the SNHT, as well as by the Easterling-Peterson and Mann-Whitney-Pettit tests. Analyzes with respect to normality showed divergence in their results. The von Neumann ratio test showed that in the case of the air temperature series the data are not independent and identically distributed (iid), whereas for the rainfall series the data are iid. According to the applied testings, both series display trends. The mean air temperature series displays an increasing trend, whereas the rainfall series shows an decreasing trend. Finally, the homogeneity tests revealed that all series under investigations present inhomogeneities, although they breaks depend on the applied test. In summary, the results showed that the chosen techniques may be applied in order to verify how well the studied time series are characterized. Therefore, these results should be used as a guide for further investigations about the statistical climatology of Natal or even of any other place.

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The expansion of cultivated areas with genetically modified crops (GM) is a worldwide phenomenon, stimulating regulatory authorities to implement strict procedures to monitor and verify the presence of GM varieties in agricultural crops. With the constant growing of plant cultivating areas all over the world, consumption of aflatoxin-contaminated food also increased. Aflatoxins correspond to a class of highly toxic contaminants found in agricultural products that can have harmful effects on human and animal health. Therefore, the safety and quality evaluation of agricultural products are important issues for consumers. Lateral flow tests (strip tests) is a promising method for the detection both proteins expressed in GM crops and aflatoxins-contaminated food samples. The advantages of this technique include its simplicity, rapidity and cost-effective when compared to the conventional methods. In this study, two novel and sensitive strip tests assay were developed for the identification of: (i) Cry1Ac and Cry8Ka5 proteins expressed in GM cotton crops and; (ii) aflatoxins from agricultural products. The first strip test was developed using a sandwhich format, while the second one was developed using a competitive format. Gold colloidal nanoparticles were used as detector reagent when coated with monoclonal antibodies. An anti-species specific antibody was sprayed at the nitrocellulose membrane to be used as a control line. To validate the first strip test, GM (Bollgard I® e Planta 50- EMBRAPA) and non-GM cotton leaf (Cooker 312) were used. The results showed that the strip containing antibodies for the identification of Cry1Ac and Cry8Ka5 proteins was capable of correctly distinguishing between GM samples (positive result) and non-GM samples (negative result), in a high sensitivity manner. To validate the second strip test, artificially contaminated soybean with Aspergillus flavus (aflatoxin-producing fungus) was employed. Food samples, such as milk and soybean, were also evaluated for the presence of aflatoxins. The strip test was capable to distinguish between samples with and without aflatoxins samples, at a sensitivity concentration of 0,5 μg/Kg. Therefore, these results suggest that the strip tests developed in this study can be a potential tool as a rapid and cost-effective method for detection of insect resistant GM crops expressing Cry1Ac and Cry8Ka5 and aflatoxins from food samples.

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The expansion of cultivated areas with genetically modified crops (GM) is a worldwide phenomenon, stimulating regulatory authorities to implement strict procedures to monitor and verify the presence of GM varieties in agricultural crops. With the constant growing of plant cultivating areas all over the world, consumption of aflatoxin-contaminated food also increased. Aflatoxins correspond to a class of highly toxic contaminants found in agricultural products that can have harmful effects on human and animal health. Therefore, the safety and quality evaluation of agricultural products are important issues for consumers. Lateral flow tests (strip tests) is a promising method for the detection both proteins expressed in GM crops and aflatoxins-contaminated food samples. The advantages of this technique include its simplicity, rapidity and cost-effective when compared to the conventional methods. In this study, two novel and sensitive strip tests assay were developed for the identification of: (i) Cry1Ac and Cry8Ka5 proteins expressed in GM cotton crops and; (ii) aflatoxins from agricultural products. The first strip test was developed using a sandwhich format, while the second one was developed using a competitive format. Gold colloidal nanoparticles were used as detector reagent when coated with monoclonal antibodies. An anti-species specific antibody was sprayed at the nitrocellulose membrane to be used as a control line. To validate the first strip test, GM (Bollgard I® e Planta 50- EMBRAPA) and non-GM cotton leaf (Cooker 312) were used. The results showed that the strip containing antibodies for the identification of Cry1Ac and Cry8Ka5 proteins was capable of correctly distinguishing between GM samples (positive result) and non-GM samples (negative result), in a high sensitivity manner. To validate the second strip test, artificially contaminated soybean with Aspergillus flavus (aflatoxin-producing fungus) was employed. Food samples, such as milk and soybean, were also evaluated for the presence of aflatoxins. The strip test was capable to distinguish between samples with and without aflatoxins samples, at a sensitivity concentration of 0,5 μg/Kg. Therefore, these results suggest that the strip tests developed in this study can be a potential tool as a rapid and cost-effective method for detection of insect resistant GM crops expressing Cry1Ac and Cry8Ka5 and aflatoxins from food samples.

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The increasing demand in electricity and decrease forecast, increasingly, of fossil fuel reserves, as well as increasing environmental concern in the use of these have generated a concern about the quality of electricity generation, making it well welcome new investments in generation through alternative, clean and renewable sources. Distributed generation is one of the main solutions for the independent and selfsufficient generating systems, such as the sugarcane industry. This sector has grown considerably, contributing expressively in the production of electricity to the distribution networks. Faced with this situation, one of the main objectives of this study is to propose the implementation of an algorithm to detect islanding disturbances in the electrical system, characterized by situations of under- or overvoltage. The algorithm should also commonly quantize the time that the system was operating in these conditions, to check the possible consequences that will be caused in the electric power system. In order to achieve this it used the technique of wavelet multiresolution analysis (AMR) for detecting the generated disorders. The data obtained can be processed so as to be used for a possible predictive maintenance in the protection equipment of electrical network, since they are prone to damage on prolonged operation under abnormal conditions of frequency and voltage.

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In this study, our goal was develop and describe a molecular model of the enzyme-inhibiting interaction which can be used for an optimized projection of a Microscope Force Atomic nanobiosensor to detect pesticides molecules, used in agriculture, to evaluate its accordance with limit levels stipulated in valid legislation for its use. The studied herbicide (imazaquin) is a typical member of imidazolinone family and is an inhibitor of the enzymatic activity of Acetohydroxiacid Synthase (AHAS) enzyme that is responsible for the first step of pathway for the synthesis of side-chains in amino acids. The analysis of this enzyme property in the presence of its cofactors was made to obtain structural information and charge distribution of the molecular surface to evaluate its capacity of became immobilized on the Microscopy Atomic Force tip. The computational simulation of the system, using Molecular Dynamics, was possible with the force-field parameters for the cofactor and the herbicides obtained by the online tool SwissParam and it was implemented in force-field CHARMM27, used by software GROMACS; then appropriated simulations were made to validate the new parameters. The molecular orientation of the AHAS was defined based on electrostatic map and the availability of the herbicide in the active site. Steered Molecular Dynamics (SMD) Simulations, followed by quantum mechanics calculations for more representative frames, according to the sequential QM/MM methodology, in a specific direction of extraction of the herbicide from the active site. Therefore, external harmonic forces were applied with similar force constants of AFM cantilever for to simulate herbicide detection experiments by the proposed nanobiosensor. Force value of 1391 pN and binding energy of -14048.52 kJ mol-1 were calculated.

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In several areas of health professionals (pediatricians, nutritionists, orthopedists, endocrinologists, dentists, etc.) are used in the assessment of bone age to diagnose growth disorders in children. Through interviews with specialists in diagnostic imaging and research done in the literature, we identified the TW method - Tanner and Whitehouse as the most efficient. Even achieving better results than other methods, it is still not the most used, due to the complexity of their use. This work presents the possibility of automation of this method and therefore that its use more widespread. Also in this work, they are met two important steps in the evaluation of bone age, identification and classification of regions of interest. Even in the radiography in which the positioning of the hands were not suitable for TW method, the identification algorithm of the fingers showed good results. As the use AAM - Active Appearance Models showed good results in the identification of regions of interest even in radiographs with high contrast and brightness variation. It has been shown through appearance, good results in the classification of the epiphysis in their stages of development, being chosen the average epiphysis finger III (middle) to show the performance. The final results show an average percentage of 90% hit and misclassified, it was found that the error went away just one stage of the correct stage.

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Avanços recentes nas técnicas de análise automática de documentos, possibilitaram o reconhecimento de aspectos subjetivos. Dentre algumas tarefas da análise de sentimentos, destaca-se a classificação da polaridade do texto, ou seja, o quão negativa ou positiva são as opiniões expressadas nele. Contudo, por ser uma área ainda em desenvolvimento, métodos criados para estas análises, na maioria, são para língua inglesa, o que dificulta sua utilização em textos escritos em português. Assim, esse trabalho tem como objetivo o estudo e a implementação de uma ferramenta que, conterá um algoritmo de classificação de sentimentos, sendo ele capaz de avaliar a polaridade de textos extraídos de mídias sociais, baseando-se em técnicas da mineração de textos.