Tandem mass spectrometry (MS/MS) is a powerful tool for identifying peptide sequences. In a typical experiment, incorrect peptide identifications may result due to noise contained in the MS/MS spectra and to the low quality of the spectra. Filtering methods are widely used to remove the noise and improve the quality of the spectra before the subsequent spectra identification process. However, existing filtering methods often use features and empirically assigned weights. These weights may not reflect the reality that the contribution (reflected by weight) of each feature may vary from dataset to dataset. Therefore, filtering methods that can adapt to different datasets have the potential to improve peptide identification results. This thesis proposes two adaptive filtering methods; denoising and quality assessment, both of which improve efficiency and effectiveness of peptide identification. First, the denoising approach employs an adaptive method for picking signal peaks that is more suitable for the datasets of interest. By applying the approach to two tandem mass spectra datasets, about 66% of peaks (likely noise peaks) can be removed. The number of peptides identified later by peptide identification on those datasets increased by 14% and 23%, respectively, compared to previous work (Ding et al., 2009a). Second, the quality assessment method estimates the probabilities of spectra being high quality based on quality assessments of the individual features. The probabilities are estimated by solving a constraint optimization problem. Experimental results on two datasets illustrate that searching only the high-quality tandem spectra determined using this method saves about 56% and 62% of database searching time and loses 9% of high-quality spectra. Finally, the thesis suggests future research directions including feature selection and clustering of peptides.

Le traitement d’images médicales a pour but d’aider les médecins dans leur diagnostic et d’améliorer l’interprétation des résultats. Les scanners tomo-densitométriques (scanners X) sont des outils d’imagerie médicale utilisés pour reconstruire des images 3D du corps humain.De nos jours, il est très important de sécuriser les images médicales lors de leur transmission, leur stockage, leur visualisation ou de leur partage entre spécialistes. Par exemple, dans la criminalistique des images, la capacité d’identifier le système d’acquisition d’une image à partir de cette dernière seulement, est un enjeu actuel.Dans cette thèse, nous présentons une première analyse du problème d’identification des scanners X. Pour proposer une solution à ce type de problèmes, nous nous sommes basés sur les méthodes d’identification d’appareils photo. Elles reposent sur l’extraction de l’empreinte des capteurs. L’objectif est alors de détecter sa présence dans les images testées. Pour extraire le bruit, nous utilisons un filtre de Wiener basé sur une transformation en ondelettes. Ensuite, nous nous appuyons sur les propriétés relatives aux images médicales pour proposer des solutions avancées pour l’identification des scanners X. Ces solutions sont basées sur une nouvelle conception de leur empreinte, cette dernière étant définie en trois dimensions et sur les trois couches : os, tissu et air.Pour évaluer notre travail, nous avons généré des résultats sur un ensemble de données réelles acquises avec différents scanners X. Finalement, nos méthodes sont robustes et donnent une précision d’authentification élevée. Nous sommes en mesure d’identifier quelle machine a servi pour l’acquisition d’une image 3D et l’axe selon lequel elle a été effectuée. / Medical image processing aims to help the doctors to improve the diagnosis process. Computed Tomography (CT) Scanner is an imaging medical device used to create cross-sectional 3D images of any part of the human body. Today, it is very important to secure medical images during their transmission, storage, visualization and sharing between several doctors. For example, in image forensics, a current problem consists of being able to identify an acquisition system from only digital images. In this thesis, we present one of the first analysis of CT-Scanner identification problem. We based on the camera identification methods to propose a solution for such kind of problem. It is based on extracting a sensor noise fingerprint of the CT-Scanner device. The objective then is to detect its presence in any new tested image. To extract the noise, we used a wavelet-based Wiener denoising filter. Then, we depend on the properties of medical images to propose advanced solutions for CT-Scanner identification. These solutions are based on new conceptions in the medical device fingerprint that are the three dimension fingerprint and the three layers one. To validate our work, we applied our experiments on multiple real data images of multiple CT-Scanner devices. Finally, our methods that are robust, give high identification accuracy. We were able to identify the acquisition CT-Scanner device and the acquisition axis.

A global view of power system's frequency opens up a new window to the "world" of large system's dynamics. With the aid of global positioning system (GPS), measurements from different locations can be time-synchronized; therefore, a system-wide observation and analysis would be possible. As part of the U.S. nation-wide power frequency monitoring network project (FNET), the first part of the study focuses on utilizing system simulation as a tool to assess the frequency measurement accuracy needed to observe frequency oscillations from events such as remote generation drops in three U.S. power systems. Electromechanical wave propagation phenomena during system disturbances, such as generation trip, load rejection and line opening, have been observed and discussed. Further uniform system models are developed to investigate the detailed behaviors of wave propagation. Visualization tool is developed to help to view frequency behavior simulations. Frequency replay from simulation data provides some insights of how these frequency electromechanical waves propagate when major events occur. The speeds of electromechanical wave propagation in different areas of the U.S. systems, as well as the uniform models were estimated and their characteristics were discussed. Theoretical derivation between the generator's mechanical powers and bus frequencies is provided and the delayed frequency response is illustrated. Field-measured frequency data from FNET are also examined. Outlier removal and wavelet-based denoising signal processing techniques are applied to filter out spikes and noises from measured frequency data. System's frequency statistics of three major U.S. power grids are investigated. Comparison between the data from phasor measurement unit (PMU) at a high voltage substation and from FNET taken from 110 V outlets at distribution level illustrates the close tracking between the two. Several generator trip events in the Eastern Interconnection System and the Western Electricity Coordinating Council system are recorded and the frequency patterns are analyzed. Our trigger program can detect noticeable frequency drop or rise and sample results are shown in a 13 month period. In addition to transient states' observation, the quasi-steady-state, such as oscillations, can also be observed by FNET. Several potential applications of FNET in the areas of monitoring & analysis, system control, model validation, and others are discussed. Some applications of FNET are still beyond our imagination. / Ph. D.

A Microscopia Eletrônica de Baixa Energia (LEEM) é uma recente e poderosa modalidade para o estudo de superfície passível de uma grande quantidade de degradações, como ruídos e borramento. Ainda incipiente na literatura, este trabalho visou a análise e identificação das fontes de degradações presentes em vídeos, além da utilização de um conjunto de técnicas de remoção de ruído e borramento para a restauração de dados LEEM. Além disso, foram desenvolvidas duas novas técnicas de filtragem de vídeo como intuito de preservar detalhes pequenos e texturas presentes. Na etapa de análise foi constatado que as imagens LEEM possuem uma grande quantidade e variedade de ruídos, sendo o Gaussiano o mais preponderante. Foi também estimada a Função de Espalhamento de Ponto (PSF) do microscópio utilizado, visando o emprego de técnicas de redução de borramento. Este trabalho também analisou a combinação de técnicas de redução de borramento com as técnicas de filtragem do ruído Gaussiano existente. Foi constatado que as técnicas não locais, como Non-Local Means (NLM) eBlock-Matching 3-D (BM3D), proveem uma maior capacidade de filtragem das imagens LEEM, preservando descontinuidades. Ainda nesta análise, identificou-se que algumas técnicas de redução de borramento não são efetivas em imagens LEEM, exceto a técnica Richardson-Lucy (RL) que suprimiu grande parte do borramento sem adicionar mais degradação. A indesejável remoção de pequenas estruturas e texturas pelas técnicas de filtragem existentes motivou o desenvolvimento de duas novas técnicas de filtragem de ruído Gaussiano (NLM3D-LBP-MSB eNLM3D-LBP-Adaptive) que mostraram resultados superiores para filtragem de imagens com grande quantidade de textura. Porém, em imagens com muitas regiões homogêneas o BM3D foi superior. Avaliações quantitativas foram realizadas sobre imagens artificiais. Em imagens LEEM reais, realizou-se um experimento qualitativo em que observadores avaliaram visualmente o resultado de restaurações por diversas técnicas existentes e as propostas neste trabalho. O experimento comprovou que os métodos de filtragem não locais foram superiores, principalmente quando combinados com o método RL. Os métodos propostos produziram bons resultados, entretanto, inferiores aos exibidos pelas técnicas NLM eBM3D. Este trabalho demonstrou que as técnicas de filtragem não locais são as mais adequadas para dados LEEM. Além disso, a técnica RL mostrou-se eficaz na redução de borramento. / Low Energy Electronic Microscopy (LEEM) is a recent and powerful surface science image modality prone to considerable amounts of degradations, such as noise and blurring. Still not fully addressed in the literature, this worked aimed at analysing and identifying the sources of degradation in LEEM videos, as well as the adequacy of existing noise reduction and deblurring techniques for LEEM data. This work also presented two new noise reduction techniques aimed at preserving texture and small details. Our analysis has revealed that LEEM images exhibit a large amount and variety of noises, with Gaussian noise being the most frequent. To handle the deblurring issue, the Point Spread Function (PSF) for the microscopeused in the experiments has also been estimated. This work has also studied the combination of deblurring and denoising techniques for Gaussian noise. Results have shown that non-local techniques such as Non-Local Means (NLM) and Block-Matching 3-D (BM3D) are more adequate for filtering LEEM images, while preserving discontinuities. We have also shown that some deblurring techniques are not suitable for LEEM images, except the RichardsonLucy (RL) approach which coped with most of the blur without the addition of extra degradation. The undesirable removal of small structures and texture by the existing denoising techniques encouraged the development of two novel Gaussian denoising techniques (NLM3D-LBP-MSB and NLM3D-LBP-Adaptive) which exhibited good results for images with a large amount of texture. However, BM3D was superior for images with large homogeneous regions. Quantitative experiments have been carried out for synthetic images. For real LEEM images, a qualitative analysis has been conducted in which observers visually assessed restoration results for existing techniques and also the two proposed ones. This experiment has shown that non-local denoising methodswere superior, especially when combined with theRL method. The proposed methods produced good results, but were out performed by NLM and BM3D. This work has shown that non-local denoising techniques are more adequate for LEEM data. Also, theRL technique is very efficient for deblurring purposes.

A Microscopia Eletrônica de Baixa Energia (LEEM) é uma recente e poderosa modalidade para o estudo de superfície passível de uma grande quantidade de degradações, como ruídos e borramento. Ainda incipiente na literatura, este trabalho visou a análise e identificação das fontes de degradações presentes em vídeos, além da utilização de um conjunto de técnicas de remoção de ruído e borramento para a restauração de dados LEEM. Além disso, foram desenvolvidas duas novas técnicas de filtragem de vídeo como intuito de preservar detalhes pequenos e texturas presentes. Na etapa de análise foi constatado que as imagens LEEM possuem uma grande quantidade e variedade de ruídos, sendo o Gaussiano o mais preponderante. Foi também estimada a Função de Espalhamento de Ponto (PSF) do microscópio utilizado, visando o emprego de técnicas de redução de borramento. Este trabalho também analisou a combinação de técnicas de redução de borramento com as técnicas de filtragem do ruído Gaussiano existente. Foi constatado que as técnicas não locais, como Non-Local Means (NLM) eBlock-Matching 3-D (BM3D), proveem uma maior capacidade de filtragem das imagens LEEM, preservando descontinuidades. Ainda nesta análise, identificou-se que algumas técnicas de redução de borramento não são efetivas em imagens LEEM, exceto a técnica Richardson-Lucy (RL) que suprimiu grande parte do borramento sem adicionar mais degradação. A indesejável remoção de pequenas estruturas e texturas pelas técnicas de filtragem existentes motivou o desenvolvimento de duas novas técnicas de filtragem de ruído Gaussiano (NLM3D-LBP-MSB eNLM3D-LBP-Adaptive) que mostraram resultados superiores para filtragem de imagens com grande quantidade de textura. Porém, em imagens com muitas regiões homogêneas o BM3D foi superior. Avaliações quantitativas foram realizadas sobre imagens artificiais. Em imagens LEEM reais, realizou-se um experimento qualitativo em que observadores avaliaram visualmente o resultado de restaurações por diversas técnicas existentes e as propostas neste trabalho. O experimento comprovou que os métodos de filtragem não locais foram superiores, principalmente quando combinados com o método RL. Os métodos propostos produziram bons resultados, entretanto, inferiores aos exibidos pelas técnicas NLM eBM3D. Este trabalho demonstrou que as técnicas de filtragem não locais são as mais adequadas para dados LEEM. Além disso, a técnica RL mostrou-se eficaz na redução de borramento. / Low Energy Electronic Microscopy (LEEM) is a recent and powerful surface science image modality prone to considerable amounts of degradations, such as noise and blurring. Still not fully addressed in the literature, this worked aimed at analysing and identifying the sources of degradation in LEEM videos, as well as the adequacy of existing noise reduction and deblurring techniques for LEEM data. This work also presented two new noise reduction techniques aimed at preserving texture and small details. Our analysis has revealed that LEEM images exhibit a large amount and variety of noises, with Gaussian noise being the most frequent. To handle the deblurring issue, the Point Spread Function (PSF) for the microscopeused in the experiments has also been estimated. This work has also studied the combination of deblurring and denoising techniques for Gaussian noise. Results have shown that non-local techniques such as Non-Local Means (NLM) and Block-Matching 3-D (BM3D) are more adequate for filtering LEEM images, while preserving discontinuities. We have also shown that some deblurring techniques are not suitable for LEEM images, except the RichardsonLucy (RL) approach which coped with most of the blur without the addition of extra degradation. The undesirable removal of small structures and texture by the existing denoising techniques encouraged the development of two novel Gaussian denoising techniques (NLM3D-LBP-MSB and NLM3D-LBP-Adaptive) which exhibited good results for images with a large amount of texture. However, BM3D was superior for images with large homogeneous regions. Quantitative experiments have been carried out for synthetic images. For real LEEM images, a qualitative analysis has been conducted in which observers visually assessed restoration results for existing techniques and also the two proposed ones. This experiment has shown that non-local denoising methodswere superior, especially when combined with theRL method. The proposed methods produced good results, but were out performed by NLM and BM3D. This work has shown that non-local denoising techniques are more adequate for LEEM data. Also, theRL technique is very efficient for deblurring purposes.