Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Monitoring of process control systems is extremely important for industries to ensure the quality of the product and the safety of the process. Predictive controllers, also known by MPC (Model Predictive Control), usually has a well performance initially. However, after a period, many factors contribute to the deterioration of its performance. This highlights the importance of monitoring the MPC control systems. In this work, tools based on multivariate statistical methods are discussed and applied to the problem of monitoring and Performance Assessment of predictive controllers. The methods presented here are: PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Both are techniques that use data collected directly from the process. The first is widely used in Performance Assessment of predictive controllers. The second is a more recent technique that has arisen, mainly in order to be used in fault detection systems. The analyzes are made when applied in simulated processes characteristic of the petrochemical industry operating under MPC control. / O monitoramento de sistemas de controle de processos é extremamente importante no que diz respeito às indústrias, para garantir a qualidade do que é produzido e a segurança do processo. Os controladores preditivos, também conhecidos pela sigla em inglês MPC (Model Predictive Control), costumam ter um bom desempenho inicialmente. Entretanto, após um certo período, muitos fatores contribuem para a deterioração de seu desempenho. Isto evidencia a importância do monitoramento dos sistemas de controle MPC. Neste trabalho aborda-se ferramentas, baseada em métodos estatísticos multivariados, aplicados ao problema de monitoramento e avaliação de desempenho de controladores preditivos. Os métodos aqui apresentados são: o PCA (Análise por componentes principais) e o ICA (Análise por componentes independentes). Ambas são técnicas que utilizam dados coletados diretamente do processo. O primeiro é largamente utilizado na avaliação de desempenho de controladores preditivos. Já o segundo, é uma técnica mais recente que surgiu, principalmente, com o intuito de ser utilizado em sistemas de detecção de falhas. As análises são feitas quando aplicadas em processos simulados característicos da indústria petroquímica operando sob controle MPC.

Identify some digestive features in people through Electrogastrogram (EGG) is important because this is a cheap, non-invasive and less bother way than traditional endoscopy procedure. This work evaluates the learning behavior of Artificial Neural Networks (ANN) and Hidden Markov Model (HMM) on components extracted by Independent Component Analysis (ICA) algorithms. In this research, an experiment was made with statistical analysis that shows the relationship between neutral, negative or positive images and digestive reactions. Training some classifiers with an EGG signal database, where the emotional states of individuals are known during processing, would it be possible to carry out the other way? Meaning, just from the EGG signal, estimate the emotional state of individuals. The initial challenge is to treat the EGG signal, which is mixed with the signals from other organs such as heart and lung. For this, the FastICA and Tensorial Methods algorithms were used, in order to produce a set of independent components, where one can identify the stomach component. Then, the EGG signal classification is performed through ANN and HMM models. The results have shown that extracting only the stomach signal component before the experiment can reduce the learning error rate in classifiers. / Identificar características digestivas de pessoas através da Eletrogastrografia (EGG) é importante pois esta costuma ser uma opção barata, não-invasiva e incomoda menos que o tradicional procedimento de Endoscopia. Este trabalho avalia o comportamento do aprendizado das Redes Neurais Artificiais (RNA) e do Modelo Oculto de Markov (HMM) diante de componentes extraídas por algoritmos de Análise de Componentes Independentes (ICA). Nesta pesquisa é realizado um experimento com análise estatística cujo objetivo apresenta a relação entre a visualização de imagens neutras, negativas ou positivas e as reações digestivas. Treinando alguns classificadores com uma base de dados de sinais EGG, onde se conhece os estados emocionais dos indivíduos durante a sua obtenção, seria possível realizar o caminho inverso? Em outras palavras, apenas a partir dos sinais EGG, pode-se estimar o estado emocional de indivíduos? O desafio inicial é tratar o sinal EGG que encontra-se misturado aos sinais de outros órgãos como coração e pulmão. Para isto foi utilizado o algoritmo FastICA e os métodos tensoriais, com o intuito de produzir um conjunto de componentes independentes onde se possa identificar a componente do estômago. Em seguida, a classifição do sinal EGG é realizada por meio dos modelos de RNA e HMM. Os resultados mostraram que classificar apenas as componentes com mais presença da frequência do sinal do estômago pode reduzir a taxa de erro do aprendizado dos classificadores no experimento realizado.

Este trabalho estuda o modelo de análise em componentes independentes (ICA) para misturas instantâneas, aplicado na separação de sinais de áudio. Três algoritmos de separação de misturas instantâneas são avaliados: FastICA, PP (Projection Pursuit) e PearsonICA; possuindo dois princípios básicos em comum: as fontes devem ser independentes estatisticamente e não-Gaussianas. Para analisar a capacidade de separação dos algoritmos foram realizados dois grupos de experimentos. No primeiro grupo foram geradas misturas instantâneas, sinteticamente, a partir de sinais de áudio pré-definidos. Além disso, foram geradas misturas instantâneas a partir de sinais com características específicas, também geradas sinteticamente, para avaliar o comportamento dos algoritmos em situações específicas. Para o segundo grupo foram geradas misturas convolutivas no laboratório de acústica do LPS. Foi proposto o algoritmo PP, baseado no método de Busca de Projeções comumente usado em sistemas de exploração e classificação, para separação de múltiplas fontes como alternativa ao modelo ICA. Embora o método PP proposto possa ser utilizado para separação de fontes, ele não pode ser considerado um método ICA e não é garantida a extração das fontes. Finalmente, os experimentos validam os algoritmos estudados. / This work studies Independent Component Analysis (ICA) for instantaneous mixtures, applied to audio signal (source) separation. Three instantaneous mixture separation algorithms are considered: FastICA, PP (Projection Pursuit) and PearsonICA, presenting two common basic principles: sources must be statistically independent and non-Gaussian. In order to analyze each algorithm separation capability, two groups of experiments were carried out. In the first group, instantaneous mixtures were generated synthetically from predefined audio signals. Moreover, instantaneous mixtures were generated from specific signal generated with special features, synthetically, enabling the behavior analysis of the algorithms. In the second group, convolutive mixtures were probed in the acoustics laboratory of LPS at EPUSP. The PP algorithm is proposed, based on the Projection Pursuit technique usually applied in exploratory and clustering environments, for separation of multiple sources as an alternative to conventional ICA. Although the PP algorithm proposed could be applied to separate sources, it couldnt be considered an ICA method, and source extraction is not guaranteed. Finally, experiments validate the studied algorithms.

Made available in DSpace on 2016-08-17T14:52:55Z (GMT). No. of bitstreams: 1 Natalia Costa Leite Abreu.pdf: 841490 bytes, checksum: 00ff55b62f0819b502a66a2304564bf4 (MD5) Previous issue date: 2003-12-11 / The constant search for the improvement and strengthening of the relationship between humans and machines turning it more natural is common place. Consequently, the recognition of speech will turn, easier and practical the handling of equipments supplied with the capacity to understand the human speech. In this sense and with the use of the available knowledge information in the literature as how the human brain processes informations, some suggested methods try to simulate this ability in the computer, especially devoted to the extraction of a speech signal of mixed sounds, attempting, for example to increase the recognition and comprehension rate. The extraction of speech can be obtained by measures of a single-channel or multiple the channels. In order to extract the speech in a single channel, it is proposed here to use the speech characteristics introducing the concept of efficient codification, that tries to imitate the way the auditory cortex gets information using the method of Independent Component Analysis (ICA), getting the basis functions of the input signals and retrieving the estimated signal even when we add interferences to it. Our simulations also prove the efficiency of our method against reverberation effects and the recovery of speech signal by the handling of basis function of other speech signals. This technique can be used efficiently both to extract a single speech, as well as highlighting new ways of approaching the speech/speaker recognition problem. / A constante busca para aperfeiçoar e estreitar o relacionamento entre homens e máquinas, tornando-o mais natural, não é nenhuma novidade. Conseqüentemente, o reconhecimento da voz possibilitará uma manipulação mais fácil e prática de equipamentos dotados com a capacidade de compreender a fala humana. Neste sentido e utilizando-se dos conhecimentos disponíveis na literatura de como o cérebro humano processa informações, alguns métodos propostos procuram simular computacionalmente essa habilidade, voltados principalmente à extração de um sinal de voz de uma mistura de sons, na tentativa de, por exemplo, aumentar a taxa de reconhecimento e inteligibilidade. A extração da voz pode ser obtida usando medidas de um único ou múltiplos canais. Para extrair uma voz em um único canal, propomos usar as características da voz pelo conceito de codificação eficiente, que procura imitar o modo como o córtex auditivo trata as informações, utilizando-se da técnica de Análise de Componentes Independentes (ICA), obtendo as funções bases dos sinais de entrada e recuperando o sinal estimado, mesmo quando são adicionadas interferências. Através de simulações comprovamos também a eficiência da técnica usada, primeiro, na recuperação de um sinal de voz com a utilização das funções bases de outro sinal e, segundo, frente a efeitos de reverberação. Esta técnica pode ser usada para extrair uma única fala eficazmente, como também prenuncia um modo novo de chegar ao problema de reconhecimento da fala/orador.

Made available in DSpace on 2016-08-17T14:53:21Z (GMT). No. of bitstreams: 1 dissertacao Simone Cristina.pdf: 915238 bytes, checksum: 6eb097a7ebfb66da176cd431d9883ba3 (MD5) Previous issue date: 2012-07-24 / The ovarian cancer is difficult to diagnose in the early stages of development. In this work we bring a study of a new method that gave us great accuracy rates based on a bioinformatics tool called surface enhanced for laser desorption and ionization (SELDI-TOF) used to generate proteomic patterns which is one of the technologies advanced in the diagnosis. Our goal is to contribute to effectiveness of this tool, which already helps diagnosis earlier, our methodology uses independent component analysis (ICA) for feature extraction and neural networks to classify between malignancy and no malignancy in a database of the research center cancer in the U.S.A. Our work rates obtained acurracy 97%, 98% specificity and 96% sensitivity. / O câncer de ovário possui difícil diagnóstico nas primeiras fases de desenvolvimento. Neste trabalho trazemos um estudo de um novo método que nos deu ótimas taxas de precisão baseado em uma ferramenta da bio-informática chamada superfície mehorada a laser para ionização e dessorção (SELDI-TOF) usada para geração de padrões proteômicos que é uma das tecnologias mais avançada no auxílio ao diagnóstico. Nosso objetivo é contribuir para eficácia desta esta ferramenta, que já auxilia o dignóstico precoce, nossa metodologia usa análise de componentes independentes (ICA) para extração de caractéristicas e redes neurais para classificar entre malignidade e não malignidade em uma base de dados do centro de pesquisa do câncer nos EUA. Nosso trabalho obteve taxas de 97% de acurária, 98% de especifidade e 96 % de sensibilidade.

Le problème de la restauration est rencontré dans domaines très variés notamment en traitement de signal et de l'image. Il correspond à la récupération des données originales à partir de données observées. Dans le cas de données multidimensionnelles, la résolution de ce problème peut se faire par différentes approches selon la nature des données, l'opérateur de transformation et la présence ou non de bruit. Dans ce travail, nous avons traité ce problème, d'une part, dans le cas des données discrètes en présence de bruit. Dans ce cas, le problème de restauration est analogue à celui de la segmentation. Nous avons alors exploité les modélisations dites chaînes de Markov couples et triplets qui généralisent les chaînes de Markov cachées. L'intérêt de ces modèles réside en la possibilité de généraliser la méthode de calcul de la probabilité à posteriori, ce qui permet une segmentation bayésienne. Nous avons considéré ces méthodes pour des observations bi-dimensionnelles et nous avons appliqué les algorithmes pour une séparation sur des documents issus de manuscrits scannés dans lesquels les textes des deux faces d'une feuille se mélangeaient. D'autre part, nous avons attaqué le problème de la restauration dans un contexte de séparation aveugle de sources. Une méthode classique en séparation aveugle de sources, connue sous l'appellation "Analyse en Composantes Indépendantes" (ACI), nécessite l'hypothèse d'indépendance statistique des sources. Dans des situations réelles, cette hypothèse n'est pas toujours vérifiée. Par conséquent, nous avons étudié une extension du modèle ACI dans le cas où les sources peuvent être statistiquement dépendantes. Pour ce faire, nous avons introduit un processus latent qui gouverne la dépendance et/ou l'indépendance des sources. Le modèle que nous proposons combine un modèle de mélange linéaire instantané tel que celui donné par ACI et un modèle probabiliste sur les sources avec variables cachées. Dans ce cadre, nous montrons comment la technique d'Estimation Conditionnelle Itérative permet d'affaiblir l'hypothèse usuelle d'indépendance en une hypothèse d'indépendance conditionnelle / The restoration problem is usually encountered in various domains and in particular in signal and image processing. It consists in retrieving original data from a set of observed ones. For multidimensional data, the problem can be solved using different approaches depending on the data structure, the transformation system and the noise. In this work, we have first tackled the problem in the case of discrete data and noisy model. In this context, the problem is similar to a segmentation problem. We have exploited Pairwise and Triplet Markov chain models, which generalize Hidden Markov chain models. The interest of these models consist in the possibility to generalize the computation procedure of the posterior probability, allowing one to perform bayesian segmentation. We have considered these methods for two-dimensional signals and we have applied the algorithms to retrieve of old hand-written document which have been scanned and are subject to show through effect. In the second part of this work, we have considered the restoration problem as a blind source separation problem. The well-known "Independent Component Analysis" (ICA) method requires the assumption that the sources be statistically independent. In practice, this condition is not always verified. Consequently, we have studied an extension of the ICA model in the case where the sources are not necessarily independent. We have introduced a latent process which controls the dependence and/or independence of the sources. The model that we propose combines a linear instantaneous mixing model similar to the one of ICA model and a probabilistic model on the sources with hidden variables. In this context, we show how the usual independence assumption can be weakened using the technique of Iterative Conditional Estimation to a conditional independence assumption

Functional brain connectivity is a marker of the brain state. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. A proposed approach blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity where each pattern contains three signatures (spatial, temporal, and spectral). The decomposition takes into account the trilinear structure of EEG data, as compared to the standard approaches of electrode averaging, electrode subset selection or using standard frequency bands. The simultaneously acquired BOLD fMRI data were decomposed by Independent Component Analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and functional connectivity network states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of functional connectivity network states and the fluctuations of EEG spectral patterns. Three patterns related to the dynamics of functional connectivity network states were found. Previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. This work suggests that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

The separation of independent sources from mixed observed data is a fundamental problem in many practical situations. A typical example is speech recordings made in an acoustic environment in the presence of background noise or other speakers. Problems of signal separation are explored by a group of methods called Blind Source Separation. Blind Source Separation (BSS) consists on estimating a set of N unknown sources from P observations resulting from the mixture of these sources and unknown background. Some existing solutions for instantaneous mixtures are reviewed and in Matlab implemented , i.e Independent Componnent Analysis (ICA) and Time-Frequency Analysis (TF). The acoustic signals recorded in real environment are not instantaneous, but convolutive mixtures. In this case, an ICA algorithm for separation of convolutive mixtures in frequency domain is introduced and in Matlab implemented. This diploma thesis examines the useability and comparisn of proposed separation algorithms.

This thesis deals with the study of contactless and non-invasive methods for estimating heart and respiratory rate. Non-contact measurement involves sensing persons by using camera and the values of the physiological parameters are then assessed from the sets of image sequences by using suitable approaches. The theoretical part is devoted to description of the various methods and their implementation. The practical part describes the design and realization of the experiment for contactless detection of heart and respiratory rate. The experiment was carried out on 10 volunteers with a known heart and respiratory rate, which was covered by using of a sophisticated system BIOPAC. Processing and analysis of the measured data was conducted in software environment Matlab. Finally, results from contactless detection were compared with the reference from measurement system BIOPAC. Experiment results are statistically evaluated and discussed.

This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.