18 resultados para the Fuzzy Colour Segmentation Algorithm
em Repositório Científico do Instituto Politécnico de Lisboa - Portugal
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The calculation of the dose is one of the key steps in radiotherapy planning1-5. This calculation should be as accurate as possible, and over the years it became feasible through the implementation of new algorithms to calculate the dose on the treatment planning systems applied in radiotherapy. When a breast tumour is irradiated, it is fundamental a precise dose distribution to ensure the planning target volume (PTV) coverage and prevent skin complications. Some investigations, using breast cases, showed that the pencil beam convolution algorithm (PBC) overestimates the dose in the PTV and in the proximal region of the ipsilateral lung. However, underestimates the dose in the distal region of the ipsilateral lung, when compared with analytical anisotropic algorithm (AAA). With this study we aim to compare the performance in breast tumors of the PBC and AAA algorithms.
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The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman's method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.
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Nos tempos actuais os equipamentos para Aquecimento Ventilação e Ar Condicionado (AVAC) ocupam um lugar de grande importância na concepção, desenvolvimento e manutenção de qualquer edifício por mais pequeno que este seja. Assim, surge a necessidade premente de racionalizar os consumos energéticos optimizando-os. A alta fiabilidade desejada nestes sistemas obriga-nos cada vez mais a descobrir formas de tornar a sua manutenção mais eficiente, pelo que é necessário prevenir de uma forma proactiva todas as falhas que possam prejudicar o bom desempenho destas instalações. Como tal, torna-se necessário detectar estas falhas/anomalias, sendo imprescíndivel que nos antecipemos a estes eventos prevendo o seu acontecimento num horizonte temporal pré-definido, permitindo actuar o mais cedo possível. É neste domínio que a presente dissertação tenta encontrar soluções para que a manutenção destes equipamentos aconteça de uma forma proactiva e o mais eficazmente possível. A ideia estruturante é a de tentar intervir ainda numa fase incipiente do problema, alterando o comportamento dos equipamentos monitorizados, de uma forma automática, com recursos a agentes inteligentes de diagnóstico de falhas. No caso em estudo tenta-se adaptar de forma automática o funcionamento de uma Unidade de Tratamento de Ar (UTA) aos desvios/anomalias detectadas, promovendo a paragem integral do sistema apenas como último recurso. A arquitectura aplicada baseia-se na utilização de técnicas de inteligência artificial, nomeadamente dos sistemas multiagente. O algoritmo utilizado e testado foi construído em Labview®, utilizando um kit de ferramentas de controlo inteligente para Labview®. O sistema proposto é validado através de um simulador com o qual se conseguem reproduzir as condições reais de funcionamento de uma UTA.
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A large area colour imager optically addressed is presented. The colour imager consists of a thin wide band gap p-i-n a-SiC:H filtering element deposited on the top of a thick large area a-SiC:H(-p)/a-Si:H(-i)/a-SiC:H(-n) image sensor, which reveals itself an intrinsic colour filter. In order to tune the external applied voltage for full colour discrimination the photocurrent generated by a modulated red light is measured under different optical and electrical bias. Results reveal that the integrated device behaves itself as an imager and a filter giving information not only on the position where the optical image is absorbed but also on it wavelength and intensity. The amplitude and sign of the image signals are electrically tuneable. In a wide range of incident fluxes and under reverse bias, the red and blue image signals are opposite in sign and the green signal is suppressed allowing blue and red colour recognition. The green information is obtained under forward bias, where the blue signal goes down to zero and the red and green remain constant. Combining the information obtained at this two applied voltages a RGB colour image picture can be acquired without the need of the usual colour filters or pixel architecture. A numerical simulation supports the colour filter analysis.
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Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica
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Dissertação para obtenção do grau de Mestre em Engenharia Informática e de Computadores
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A fuzzy linguistic controller has been developed and implemented with the aim to cope with interactions between control loops due to coupling effects. To access the performance of the proposed approach several experiments have also been conducted using the classical PID controllers in the control loops. A mixing process has been used as test bed of all controllers experimented and the corresponding dynamic model has been derived. The successful results achieved with the fuzzy linguistic controllers suggests that they can be an alternative to classical controllers when in the presence of process plants where automatic control as to cope with coupling effects between control loops. © 2014 IEEE.
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In this work, we present results from teleseismic P-wave receiver functions (PRFs) obtained in Portugal, Western Iberia. A dense seismic station deployment conducted between 2010 and 2012, in the scope of the WILAS project and covering the entire country, allowed the most spatially extensive probing on the bulk crustal seismic properties of Portugal up to date. The application of the H-kappa stacking algorithm to the PRFs enabled us to estimate the crustal thickness (H) and the average crustal ratio of the P- and S-waves velocities V (p)/V (s) (kappa) for the region. Observations of Moho conversions indicate that this interface is relatively smooth with the crustal thickness ranging between 24 and 34 km, with an average of 30 km. The highest V (p)/V (s) values are found on the Mesozoic-Cenozoic crust beneath the western and southern coastal domain of Portugal, whereas the lowest values correspond to Palaeozoic crust underlying the remaining part of the subject area. An average V (p)/V (s) is found to be 1.72, ranging 1.63-1.86 across the study area, indicating a predominantly felsic composition. Overall, we systematically observe a decrease of V (p)/V (s) with increasing crustal thickness. Taken as a whole, our results indicate a clear distinction between the geological zones of the Variscan Iberian Massif in Portugal, the overall shape of the anomalies conditioned by the shape of the Ibero-Armorican Arc, and associated Late Paleozoic suture zones, and the Meso-Cenozoic basin associated with Atlantic rifting stages. Thickened crust (30-34 km) across the studied region may be inherited from continental collision during the Paleozoic Variscan orogeny. An anomalous crustal thinning to around 28 km is observed beneath the central part of the Central Iberian Zone and the eastern part of South Portuguese Zone.
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This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.
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Mestrado em Radioterapia.
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Projeto de Intervenção apresentado à Escola Superior de Educação de Lisboa para a obtenção de grau de Mestre em Didática da Língua Portuguesa no 1º e 2º CEB
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Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica - Ramo de Energia
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Trabalho de Projeto para obtenção do grau de Mestre em Engenharia de Eletrónica e Telecomunicações
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Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica Perfil Energia, Refrigeração e Climatização