Although utilization of polarimetry techniques for recognition of military and civilian targets is well established in the narrowband context, it is not yet fully established in a broadband sense as compared to planetary area of research. The concept of combining polarimetry together with certain areas of broadband technology and thus forming a robust signature and feature set has been the main theme of this thesis. This is important, as basing the feature set on multiple types of signatures can increase the accuracy of the recognition process. In this thesis, the concept of radar target recognition based upon a polarization signature in a broadband context is examined. A proper UWB radar signal can excite the target dominant resonances and, consequently, reveal information about the target principle dimensions; while diversity in the polarization domain revealed information about the target shape. The target dimensions are used to classify the target, and then information about its shape is used to identify it. Fused together and inferred from the target characteristic polarization states, it was verified that the polarization information at dominant resonant frequencies have both a physical interpretation and attributes (as seen in section ‎3.4.3) related to the target symmetry, linearity, and orientation. In addition, this type of information has the ability to detect the presence of major scattering mechanisms such as strong specular reflection as in the case of the cylinder flat ends. Throughout the thesis, simulated canonical targets with similar resonant frequencies were used, and thus identification of radar targets was based solely on polarization information. In this framework, the resonant frequencies were merely identified as peaks in the frequency response for simple or low damping targets such as thin metal wires, or alternatively identified as the imaginary parts of the complex poles for complex or high damping targets with significant diameter and dielectric properties. Therefore, the main contribution of this thesis originates from the ability to integrate the optimum polarization states in a broadband context for improved target recognition performance. In this context, the spectral dispersion originating from the broad nature of the radar signal, the lack of accuracy in extracting the target resonances, the robustness of the polarization feature set, the representation of these states in time domain, and the feature set modelling with spatial variation are among the important issues addressed with several approaches presented to overcome them. The general approach considered involved a subset of “representative” times in the time domain, or correspondingly, “representative frequencies” in the frequency domain with which to associate optimum polarization states with each member of the subset are used. The first approach in chapter ‎3 involved the polarization representation by a set of frequency bands associated with the target resonant frequencies. This type of polarization description involved the formulation of a wideband scattering matrix to accommodate the broad nature of the signal presentation with appropriate bandwidth selection for each resonance; good estimation of the optimum polarization states in this procedure was achievable even for low signal-to-noise ratios. The second approach in chapter ‎4 extended the work of chapter ‎3 and involved the modification of the optimum polarization states by their associated powers. In addition, this approach included an identification algorithm based on the nearest neighbour technique. To identify the target, the identification algorithm involved the states at a set of resonant frequencies to give a majority vote. Then, a comparison of the performance of the modified polarization states and the original states demonstrated good improvement when the modified set is used. Generally, the accuracy of the resonance set estimate is more reliable in the time domain than the frequency domain, especially for resonances well localized in time. Therefore, the third approach in chapter ‎5 deals with the optimum states in the time domain where the extension to a wide band context was possible by the virtue of the polarization information embodied in the energy of the resonances. This procedure used a model-based signature to model the target impulse response as a set of resonances. The relevant resonance parameters, in this case, the resonant frequency and its associated energy, were extracted using the Matrix Pencil of Function algorithm. Again, this approach of sparse representation is necessary to find descriptors from the target impulse response that are time-invariant, and at the same time, can relate robustly to the target physical characteristics. A simple target such as a long wire showed that indeed polarization information contained in the target resonance energies could reflect the target physical attributes. In addition, for noise-corrupted signals and without any pulse averaging, the accuracy in estimating the optimum states was sufficiently good for signal to noise ratios above 20dB. Below this level, extraction of some members of the resonance set are not possible. In addition, using more complex wire models of aircraft, these time-based optimum states could distinguish between similar dimensional targets with small structural differences, e.g. different wing dihedral angles. The results also showed that the dominant resonance set has members belonging to different structural sections of the target. Therefore, incorporation of a time-based polarization set can give the full target physical characteristics. In the final procedure, a statistical Kernel function estimated the feature set derived previously in chapter ‎3, with aspect angle. After sampling the feature set over a wide set of angular aspects, a criterion based on the Bayesian error bisected the target global aspect into smaller sectors to decrease the variance of the estimate and, subsequently, decrease the probability of error. In doing so, discriminative features that have acceptable minimum probability of error were achievable. The minimum probability of error criterion and the angular bisection of the target could separate the feature set of two targets with similar resonances.

The present thrust in the electronics industry towards integrating multiple functions on a single chip while operating at very high frequencies has highlighted the need for efficient Electronic Design Automation (EDA) tools to shorten the design cycle and capture market windows. However, the increasing complexity in modern circuit design has made simulation a computationally cumbersome task. The notion of model order reduction has emerged as an effective tool to address this difficulty. Typically, there are numerous approaches and several issues involved in the implementation of model-order reduction techniques. Among the important ones of those issues is the problem of determining a suitable order (or size) for the reduced system. An optimal order would be the minimal order that enables the reduced system to capture the behavior of the original (more complex and larger) system up to a user-defined frequency. The contribution presented in this thesis describes a new approach aimed at determining the order of the reduced system. The proposed approach is based on approximating the impulse response of the original system in the time-domain. The core methodology in obtaining that approximation is based on numerically inverting the Laplace-domain of the representation of the impulse response from the complex-domain (s-domain) into the time-domain. The main advantage of the proposed approach is that it allows the order selection algorithm to operate directly on the time-domain form of the impulse response. It is well-known that numerically generating the impulse response in the time-domain is very difficult and its not impossible, since it requires driving the original network with the Dirac-delta function, which is a mathematical abstraction rather than a concrete waveform that can be implemented on a digital computer. However, such a difficulty is avoided in the proposed approach since it uses the Laplace-domain image of the impulse response to obtain its time-domain representation. The numerical simulations presented in the thesis demonstrate that using the time-domain waveform of the impulse response, computed using the proposed approach and properly filtered with a Butterworth filter, guides the order selection algorithm to select a smaller order, i.e., the reduced system becomes more compact in size. The phrase "smaller or more compact" in this context refers to the comparison with existing techniques currently in use, which seek to generate some form of time-domain approximations for the impulse response through driving the original network with pulse-shaped function (e.g., Gaussian pulse).

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

abstract: Data imbalance and data noise often coexist in real world datasets. Data imbalance affects the learning classifier by degrading the recognition power of the classifier on the minority class, while data noise affects the learning classifier by providing inaccurate information and thus misleads the classifier. Because of these differences, data imbalance and data noise have been treated separately in the data mining field. Yet, such approach ignores the mutual effects and as a result may lead to new problems. A desirable solution is to tackle these two issues jointly. Noting the complementary nature of generative and discriminative models, this research proposes a unified model fusion based framework to handle the imbalanced classification with noisy dataset. The phase I study focuses on the imbalanced classification problem. A generative classifier, Gaussian Mixture Model (GMM) is studied which can learn the distribution of the imbalance data to improve the discrimination power on imbalanced classes. By fusing this knowledge into cost SVM (cSVM), a CSG method is proposed. Experimental results show the effectiveness of CSG in dealing with imbalanced classification problems. The phase II study expands the research scope to include the noisy dataset into the imbalanced classification problem. A model fusion based framework, K Nearest Gaussian (KNG) is proposed. KNG employs a generative modeling method, GMM, to model the training data as Gaussian mixtures and form adjustable confidence regions which are less sensitive to data imbalance and noise. Motivated by the K-nearest neighbor algorithm, the neighboring Gaussians are used to classify the testing instances. Experimental results show KNG method greatly outperforms traditional classification methods in dealing with imbalanced classification problems with noisy dataset. The phase III study addresses the issues of feature selection and parameter tuning of KNG algorithm. To further improve the performance of KNG algorithm, a Particle Swarm Optimization based method (PSO-KNG) is proposed. PSO-KNG formulates model parameters and data features into the same particle vector and thus can search the best feature and parameter combination jointly. The experimental results show that PSO can greatly improve the performance of KNG with better accuracy and much lower computational cost. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2014

Várias propostas têm surgido nos últimos anos para problemas que envolvem a predição de observações futuras em modelos mistos, contudo, para os casos em que o problema trata-se em atribuir valores para os efeitos aleatórios de novos grupos existem poucos trabalhos. Tamura, Giampaoli e Noma (2013) propuseram um método que consiste na computação das distâncias entre o novo grupo e os grupos com efeitos aleatórios conhecidos, baseadas nos valores das covariáveis, denominado Método de Predição do Vizinho Mais Próximo ou NNPM (Nearest Neighbors Prediction Method), na sigla em inglês, considerando o modelo logístico misto. O objetivo deste presente trabalho foi o de estender o método NNPM para o modelo Poisson misto, além da obtenção de intervalos de confiança para as predições, para tais fins, foram propostas novas medidas de desempenho da predição e o uso da metodologia Bootstrap para a criação dos intervalos. O método de predição foi aplicado em dois conjuntos de dados reais e também no âmbito de estudos de simulação, em ambos os casos, obtiveram-se bons desempenhos. Dessa forma, a metodologia NNPM apresentou-se como um método de predição muito satisfatório também no caso Poisson misto. / Many proposals have been created in the last years for problems in the prediction of future observations in mixed models, however, there are few studies for cases that is necessary to assign random effects values for new groups. Tamura, Giampaoli and Noma (2013) proposed a method that computes the distances between a new group and groups with known random effects based on the values of the covariates, named as Nearest Neighbors Prediction Method (NNPM), considering the mixed logistic model. The goal of this dissertation was to extend the NNPM for the mixed Poisson model, in addition to obtaining confidence intervals for predictions. To attain such purposes new prediction performance measures were proposed as well as the use of Bootstrap methodology for the creation of intervals. The prediction method was applied in two sets of real data and in the simulation studies framework. In both cases good performances were obtained. Thus, the NNPM proved to be a viable prediction method also in the mixed Poisson case.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

A presente dissertação propõe métodos para mineração de dados para diagnóstico de câncer de mama (CM) baseado na seleção de variáveis. Partindo-se de uma revisão sistemática, sugere-se um método para a seleção de variáveis para classificação das observações (pacientes) em duas classes de resultado, benigno ou maligno, baseado na análise citopatológica de amostras de célula da mama de pacientes. O método de seleção de variáveis para categorização das observações baseia-se em 4 passos operacionais: (i) dividir o banco de dados original em porções de treino e de teste, e aplicar a ACP (Análise de Componentes Principais) na porção de treino; (ii) gerar índices de importância das variáveis baseados nos pesos da ACP e na percentagem da variância explicada pelos componentes retidos; (iii) classificar a porção de treino utilizando as técnicas KVP (k-vizinhos mais próximos) ou AD (Análise Discriminante). Em seguida eliminar a variável com o menor índice de importância, classificar o banco de dados novamente e calcular a acurácia de classificação; continuar tal processo iterativo até restar uma variável; e (iv) selecionar o subgrupo de variáveis responsável pela máxima acurácia de classificação e classificar a porção de teste utilizando tais variáveis. Quando aplicado ao WBCD (Wisconsin Breast Cancer Database), o método proposto apresentou acurácia média de 97,77%, retendo uma média de 5,8 variáveis. Uma variação do método é proposta, utilizando quatro diferentes tipos de kernels polinomiais para remapear o banco de dados original; os passos (i) a (iv) acima descritos são então aplicados aos kernels propostos. Ao aplicar-se a variação do método ao WBCD, obteve-se acurácia média de 98,09%, retendo uma média de 17,24 variáveis de um total de 54 variáveis geradas pelo kernel polinomial recomendado. O método proposto pode auxiliar o médico na elaboração do diagnóstico, selecionando um menor número de variáveis (envolvidas na tomada de decisão) com a maior acurácia, obtendo assim o maior acerto possível. / This dissertation presents a data mining method for breast cancer (BC) diagnosis based on selected features. We first carried out a systematic literature review, and then suggested a method for feature selection and classification of observations, i.e., patients, into benign or malignant classes based on patients’ breast tissue measures. The proposed method relies on four operational steps: (i) split the original dataset into training and testing sets and apply PCA (Principal Component Analysis) on the training set; (ii) generate attribute importance indices based on PCA weights and percent of variance explained by the retained components; (iii) classify the training set using KNN (k-Nearest Neighbor) or DA (Discriminant Analysis) techniques, eliminate irrelevant features and compute the classification accuracy. Next, eliminate the feature with the lowest importance index, classify the dataset, and re-compute the accuracy. Continue such iterative process until one feature is left; and (iv) choose the subset of features yielding the maximum classification accuracy, and classify the testing set based on those features. When applied to the WBCD (Wisconsin Breast Cancer Database), the proposed method led to average 97.77% accurate classifications while retaining average 5.8 features. One variation of the proposed method is presented based on four different types of polynomial kernels aimed at remapping the original database; steps (i) to (iv) are then applied to such kernels. When applied to the WBCD, the proposed modification increased average accuracy to 98.09% while retaining average of 17.24 features from the 54 variables generated by the recommended kernel. The proposed method can assist the physician in making the diagnosis, selecting a smaller number of variables (involved in the decision-making) with greater accuracy, thereby obtaining the highest possible accuracy.

Orientador: Eduardo Alves do Valle Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T08:10:33Z (GMT). No. of bitstreams: 1 Silva_EliezerdeSouzada_M.pdf: 2350845 bytes, checksum: dd31928bd19312563101a08caea74d63 (MD5) Previous issue date: 2014 / Resumo: A crescente disponibilidade de conteúdo multimídia é um desafio para a pesquisa em Recuperação de Informação. Usuários querem não apenas ter acesso aos documentos multimídia, mas também obter semântica destes documentos, de modo que a capacidade de encontrar um conteúdo específico em grandes coleções de documentos textuais e não textuais é fundamental. Nessas grandes escalas, sistemas de informação multimídia de recuperação devem contar com a capacidade de executar a busca por semelhança de forma eficiente. No entanto, documentos multimídia são muitas vezes representados por descritores multimídia representados por vetores de alta dimensionalidade, ou por outras representações complexas em espaços métricos. Fornecer a possibilidade de uma busca por similaridade eficiente para esse tipo de dados é extremamente desafiador. Neste projeto, vamos explorar uma das famílias mais citado de soluções para a busca de similaridade, o Hashing Sensível à Localidade (LSH - Locality-sensitive Hashing em inglês), que se baseia na criação de funções de hash que atribuem, com maior probabilidade, a mesma chave para os dados que são semelhantes. O LSH está disponível apenas para um punhado funções de distância, mas, quando disponíveis, verificou-se ser extremamente eficiente para arquiteturas com custo de acesso uniforme aos dados. A maioria das funções LSH existentes são restritas a espaços vetoriais. Propomos dois métodos novos para o LSH, generalizando-o para espaços métricos quaisquer utilizando particionamento métrico (centróides aleatórios e k-medoids). Apresentamos uma comparação com os métodos LSH bem estabelecidos em espaços vetoriais e com os últimos concorrentes novos métodos para espaços métricos. Desenvolvemos uma modelagem teórica do comportamento probalístico dos algoritmos propostos e demonstramos algumas relações e limitantes para a probabilidade de colisão de hash. Dentre os algoritmos propostos para generelizar LSH para espaços métricos, esse desenvolvimento teórico é novo. Embora o problema seja muito desafiador, nossos resultados demonstram que ela pode ser atacado com sucesso. Esta dissertação apresentará os desenvolvimentos do método, a formulação teórica e a discussão experimental dos métodos propostos / Abstract: The increasing availability of multimedia content poses a challenge for information retrieval researchers. Users want not only have access to multimedia documents, but also make sense of them --- the ability of finding specific content in extremely large collections of textual and non-textual documents is paramount. At such large scales, Multimedia Information Retrieval systems must rely on the ability to perform search by similarity efficiently. However, Multimedia Documents are often represented by high-dimensional feature vectors, or by other complex representations in metric spaces. Providing efficient similarity search for that kind of data is extremely challenging. In this project, we explore one of the most cited family of solutions for similarity search, the Locality-Sensitive Hashing (LSH), which is based upon the creation of hashing functions which assign, with higher probability, the same key for data that are similar. LSH is available only for a handful distance functions, but, where available, it has been found to be extremely efficient for architectures with uniform access cost to the data. Most existing LSH functions are restricted to vector spaces. We propose two novel LSH methods (VoronoiLSH and VoronoiPlex LSH) for generic metric spaces based on metric hyperplane partitioning (random centroids and K-medoids). We present a comparison with well-established LSH methods in vector spaces and with recent competing new methods for metric spaces. We develop a theoretical probabilistic modeling of the behavior of the proposed algorithms and show some relations and bounds for the probability of hash collision. Among the algorithms proposed for generalizing LSH for metric spaces, this theoretical development is new. Although the problem is very challenging, our results demonstrate that it can be successfully tackled. This dissertation will present the developments of the method, theoretical and experimental discussion and reasoning of the methods performance / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica

Orientador: Adriana Vitorino Rossi / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Química / Made available in DSpace on 2018-08-20T02:46:28Z (GMT). No. of bitstreams: 1 Favaro_MarthaMariaAndreotti_D.pdf: 3734314 bytes, checksum: 08002efe51b2f18e9a942c3b818270b7 (MD5) Previous issue date: 2012 / Resumo: Antocianinas (ACYS) são corantes naturais responsáveis pela coloração de frutas, hortaliças, flores e grãos. Novas perspectivas de usos de antocianinas em diversos segmentos industriais estimulam estudos analíticos para sistematizar a identificação e a classificação de fontes e extratos desses corantes. Neste trabalho foram utilizadas fontes de ACYS como frutas típicas brasileiras: AMORA (Morus nigra), amora preta (Rubus sp.), jabuticaba (Myrciaria cauliflora), jambolão (Syzygium cumini), jussara (Euterpe edulis Mart.), morango (Fragaria x ananassa Duch) e uva (Vitis vinífera e Vitis vinífera L. Brasil); hortaliças: alface roxa (Lactuca sativa), berinjela (Solanum melongena), cebola roxa (Allium cepa), rabanete (Raphanus sativus), repolho roxo (Brassica oleraceae) e flores: beijo-turco (Impatiens walleriana), gerânio (Pelargonium hortorum e Pelargonium peltatum L.), hibisco (Hibiscus sinensis e Hibiscus syriacus) e hortênsia (Hydrangea macrophylla). A literatura descreve diversas técnicas para análise de ACYS em vegetais e seus extratos, com destaque para cromatografia líquida de alta eficiência (HPLC), espectrometria de massas (MS) e espectrofotometria (UV-Vis), sendo que todas elas foram aplicadas neste trabalho, incluindo-se espectrofotometria de reflectância e a técnica de eletromigração em capilares cromatografia eletrocinética micelar (MEKC). As ferramentas quimiométricas utilizadas no tratamento dos dados foram análise de componentes principais (PCA) e método do vizinho mais próximo (KNN). Os modelos quimiométricos de classificação obtidos apresentaram-se robustos com erros de previsão de menos de 30 % sendo possível identificar as fontes de ACYS, o solvente extrator, a idade dos extratos e dados sobre sua estabilidade e condições de armazenamento. Os resultados apontaram que dados obtidos de técnicas analíticas simples como espectrofotometria de absorção e sem necessidade de preparo de amostra como reflectância difusa na região do visível são comparáveis a resultados de técnicas mais sofisticadas e caras como HPLC e MEKC e até superam o potencial de algumas informações obtidas por MS / Abstract: Anthocyanins (ACYS) are natural dyes responsible for color in fruits, vegetables, flowers and grains. New perspectives for use of anthocyanins in various industries stimulate analytical studies to systematize the identification and classification of sources and extracts of these dyes. In this work, typical Brazilian fruits: mulberry (Morus nigra), blackberry (Rubus sp), jaboticaba (Myrciaria cauliflora), jambolan (Syzygium cumini), jussara fruit (Euterpe edulis Mart.), strawberry (Fragaria x ananassa Duch) and grapes (Vitis vinifera and Vitis vinifera L. 'Brazil'); vegetables: red lettuce (Lactuca sativa), eggplant (Solanum melongena), purple onion (Allium cepa), radish (Raphanus sativus), red cabbage (Brassica oleracea) and flowers, Buzy Lizzie (Impatiens walleriana), geranium (Pelargonium hortorum and Pelargonium peltatum L.), hibiscus (Hibiscus sinensis and Hibiscus syriacus) and hydrangea (Hydrangea macrophylla) were used as sources of ACYS. The literature describes several techniques for analyzing ACYS in vegetables and their extracts, with emphasis on high performance liquid chromatography (HPLC), mass spectrometry (MS) and spectrophotometry (UV-VIS). All of these techniques were applied in this work, including reflectance spectrophotometry and micellar electrokinetic chromatography (MEKC) which is one of the capillary electromigration techniques. The chemometric tools used in data handling were the principal component analysis (PCA) and the K-nearest neighbor method (KNN). The chemometric classification models obtained are robust with predict errors of less than 30 %. It is possible to identify the sources of ACYS, the extractor solvent, the age of the extracts, their stability and storage conditions. The results show that data obtained from simple analytical techniques such as absorption spectroscopy and diffuse reflectance in the visible region (sample preparation is not needed) are comparable to results of those obtained from sophisticated and expensive techniques such as HPLC and MEKC. These techniques also surpass the information obtained by MS / Doutorado / Quimica Analitica / Doutor em Ciências

La technique des K-plus proches voisins (K-Nearest Neighbors (KNN) en Anglais) est une méthode efficace pour trouver des données similaires au sein d'un grand ensemble de données. Au fil des années, un grand nombre d'applications ont utilisé les capacités du KNN pour découvrir des similitudes dans des jeux de données de divers domaines tels que les affaires, la médecine, la musique, ou l'informatique. Bien que des années de recherche aient apporté plusieurs approches de cet algorithme, sa mise en œuvre reste un défi, en particulier aujourd'hui alors que les quantités de données croissent à des vitesses inimaginables. Dans ce contexte, l'exécution du KNN sur de grands ensembles pose deux problèmes majeurs: d'énormes empreintes mémoire et de très longs temps d'exécution. En raison de ces coût élevés en termes de ressources de calcul et de temps, les travaux de l'état de l'art ne considèrent pas le fait que les données peuvent changer au fil du temps, et supposent toujours que les données restent statiques tout au long du calcul, ce qui n'est malheureusement pas du tout conforme à la réalité. Nos contributions dans cette thèse répondent à ces défis. Tout d'abord, nous proposons une approche out-of-core pour calculer les KNN sur de grands ensembles de données en utilisant un seul ordinateur. Nous préconisons cette approche comme un moyen moins coûteux pour faire passer à l'échelle le calcul des KNN par rapport au coût élevé d'un algorithme distribué, tant en termes de ressources de calcul que de temps de développement, de débogage et de déploiement. Deuxièmement, nous proposons une approche out-of-core multithreadée (i.e. utilisant plusieurs fils d'exécution) pour faire face aux défis du calcul des KNN sur des données qui changent rapidement et continuellement au cours du temps. Après une évaluation approfondie, nous constatons que nos principales contributions font face aux défis du calcul des KNN sur de grands ensembles de données, en tirant parti des ressources limitées d'une machine unique, en diminuant les temps d'exécution par rapport aux performances actuelles, et en permettant le passage à l'échelle du calcul, à la fois sur des données statiques et des données dynamiques. / The K-Nearest Neighbors (KNN) is an efficient method to find similar data among a large set of it. Over the years, a huge number of applications have used KNN's capabilities to discover similarities within the data generated in diverse areas such as business, medicine, music, and computer science. Despite years of research have brought several approaches of this algorithm, its implementation still remains a challenge, particularly today where the data is growing at unthinkable rates. In this context, running KNN on large datasets brings two major issues: huge memory footprints and very long runtimes. Because of these high costs in terms of computational resources and time, KNN state-of the-art works do not consider the fact that data can change over time, assuming always that the data remains static throughout the computation, which unfortunately does not conform to reality at all. In this thesis, we address these challenges in our contributions. Firstly, we propose an out-of-core approach to compute KNN on large datasets, using a commodity single PC. We advocate this approach as an inexpensive way to scale the KNN computation compared to the high cost of a distributed algorithm, both in terms of computational resources as well as coding, debugging and deployment effort. Secondly, we propose a multithreading out-of-core approach to face the challenges of computing KNN on data that changes rapidly and continuously over time. After a thorough evaluation, we observe that our main contributions address the challenges of computing the KNN on large datasets, leveraging the restricted resources of a single machine, decreasing runtimes compared to that of the baselines, and scaling the computation both on static and dynamic datasets.