La transcription automatique de musique polyphonique consiste à estimer automatiquernent les notes présentes dans un enregistrement via trois de leurs attributs : temps d'attaque, durée et hauteur. Pour traiter ce problème, il existe une classe de méthodes dont le principe est de modéliser un signal comme une somme d'éléments de base, porteurs d'informations symboliques. Parmi ces techniques d'analyse, on trouve l'analyse probabiliste en composantes latentes (PLCA). L'objet de cette thèse est de proposer des variantes et des améliorations de la PLCA afin qu'elle puisse mieux s'adapter aux signaux musicaux et ainsi mieux traiter le problème de la transcription. Pour cela, un premier angle d'approche est de proposer de nouveaux modèles de signaux, en lieu et place du modèle inhérent à la PLCA, suffisamment expressifs pour pouvoir s'adapter aux notes de musique possédant simultanément des variations temporelles de fréquence fondamentale et d'enveloppe spectrale. Un deuxième aspect du travail effectué est de proposer des outils permettant d'aider l'algorithme d'estimation des paramètres à converger vers des solutions significatives via l'incorporation de connaissances a priori sur les signaux à analyser, ainsi que d'un nouveau modèle dynamique. Tous les algorithmes ainsi imaginés sont appliqués à la tâche de transcription automatique. Nous voyons également qu'ils peuvent être directement utilisés pour la séparation de sources, qui consiste à séparer plusieurs sources d'un mélange, et nous proposons deux applications dans ce sens. / Automatic music transcription consists in automatically estimating the notes in a recording, through three attributes: onset time, duration and pitch. To address this problem, there is a class of methods which is based on the modeling of a signal as a sum of basic elements, carrying symbolic information. Among these analysis techniques, one can find the probabilistic latent component analysis (PLCA). The purpose of this thesis is to propose variants and improvements of the PLCA, so that it can better adapt to musical signals and th us better address the problem of transcription. To this aim, a first approach is to put forward new models of signals, instead of the inherent model 0 PLCA, expressive enough so they can adapt to musical notes having variations of both pitch and spectral envelope over time. A second aspect of this work is to provide tools to help the parameters estimation algorithm to converge towards meaningful solutions through the incorporation of prior knowledge about the signals to be analyzed, as weil as a new dynamic model. Ali the devised algorithms are applie to the task of automatic transcription. They can also be directly used for source separation, which consists in separating several sources from a mixture, and Iwo applications are put forward in this direction

Nous proposons de traiter l'extraction de la mélodie principale, ainsi que la séparation de l'instrument jouant cette mélodie. La première tâche appartient au domaine de la recherche d'information musicale (MIR) : nous cherchons à indexer les morceaux de musique à l'aide de leur mélodie. La seconde application est la séparation aveugle de sources sonores (BASS) : extraire une piste audio pour chaque source présente dans un mélange sonore. La séparation de la mélodie principale et de l'accompagnement et l'extraction de cette mélodie sont traitées au sein d'un même cadre statistique. Le modèle pour l'instrument principal est un modèle de production source/filtre. Il suppose deux états cachés correspondant à l'état du filtre et de la source. Le modèle spectral choisi permet de prendre compte les fréquences fondamentales de l'instrument désiré et de séparer ce dernier de l'accompagnement. Deux modèles de signaux sont proposés, un modèle de mélange de gaussiennes amplifiées (GSMM) et un modèle de mélange instantané (IMM). L'accompagnement est modélisé par un modèle spectral plus général. Cinq systèmes sont proposés, trois systèmes fournissent la mélodie sous forme de séquence de fréquences fondamentales, un système fournit les notes de la mélodie et le dernier système sépare l'instrument principal de l'accompagnement. Les résultats en estimation de la mélodie et en séparation sont du niveau de l'état de l'art, comme l'ont montré nos participations aux évaluations internationales (MIREX'08, MIREX'09 et SiSEC'08). Nous avons ainsi réussi à intégrer de la connaissance musicale améliorant les résultats de travaux antérieurs sur la séparation de sources sonores.

Atmospheric aerosol is an important component of our atmosphere influencing human health, regional and global atmospheric chemistry and climate. The organic component of submicron aerosol contributes around 50% of its mass and is a complex mixture of tens of thousands of compounds. Real-time aerosol mass spectrometry was the major measurement tool used in this work. The Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS) can quantitatively measure the chemical composition and size distribution of non-refractory submicron aerosol (NR-PM1). The mass spectra provided by the instrument every few minutes contain information about aerosol sources and processes. This thesis uses the HR-ToF-AMS measurements in two areas of Greece to quantify the contributions of organic aerosol sources to the corresponding organic aerosol levels. Local and regional air pollution sources were monitored and characterized in two sites during intensive campaigns. The first campaign took place during the fall of 2011 (September 24 to October 23) in Finokalia, Crete, a remote-background coastal site without any major human activity. The aim of the study was to quantify the extent of oxidation of the organic aerosol (OA) during autumn, a season neither too hot nor cold, with reduced solar radiation in comparison to summer. The second one took place during the winter of 2012 (February 26 to March 5), in the third major city of Greece, Patras. The measurements were conducted in the campus of the Technological Educational Institute of Patras (TEI), in order to quantify the severity of the wintertime air pollution problem in the area and its sources. The contributions of traffic and residential wood burning were the foci of that study. The Finokalia site is isolated and far away from anthropogenic sources of pollution, making it ideal for the study of organic aerosol coming from different directions, usually exposed to high levels of atmospheric oxidants. The fine PM measured during the Finokalia Atmospheric Measurement Experiment (FAME-11) by the AMS and a Multi Angle Absorption Photometer (MAAP) was mostly ammonium sulfate and bisulfate (60%), organic compounds (34%), and BC (5%). The aerosol sampled originated mainly from Turkey during the first days of the study, but also from Athens and Northern Greece during the last days of the campaign. By performing Positive Matrix Factorization (PMF) analysis on the AMS organic spectra for the whole dataset the organic aerosol (OA) composition could be explained by two components: a low volatility factor (LV-OOA) and a semi-volatile one (SV-OOA). Hydrocarbon-like organic aerosol (HOA) was not present, consistent with the lack of strong local sources. The second field campaign took place in the suburbs of the city of Patras, 4 km away from the city center during the winter of 2012. During this 10-day campaign, organics were responsible for 70% during the day and 80% during the evening of the total PM1. The OA mean concentration during that period was approximately 20 μg m-3 and reaching hourly maximum values as high as 85 μg m-3. Sulfate ions and black carbon followed with 10% and 7% of the PM1. PMF analysis of the organic mass spectra of PM1 explained the OA observations with four sources: cooking (COA), traffic (HOA), biomass burning (BBOA), and oxygenated aerosol (OOA), related to secondary formation and long range transport. On average, BBOA represented 58% of the total OM, followed by OOA with 18%, COA and HOA, with the last two contributing of the same percentage (12%). / --

Atmospheric aerosols have adverse effects on human health, air quality, and visibility and frequently result in severe pollution events, particularly in urban areas. However, the sources of aerosols and the processes governing their behaviour in the atmosphere, including those which lead to high concentrations, are not well understood thus limit our ability to accurately assess and forecast air quality. Presented here are the first long-term chemical composition measurements from an urban environment using an Aerodyne compact Time-of-Flight Aerosol Mass Spectrometer (cToF-AMS). Organic aerosols (OA) were observed to account for a significant fraction (44%) of the total non-refractory submicron mass during 2012 at the urban background site in North Kensington, London, followed by nitrate (28%), sulphate (14%), ammonium (13%), and chloride (1%). The sources and components of OA were determined using Positive Matrix Factorisation (PMF) and attributed as hydrocarbon-like OA (HOA), cooking OA (COA), solid fuel OA (SFOA), type 1 oxygenated OA (OOA1), and type 2 oxygenated OA (OOA2), where HOA, COA, and SFOA were observed to be of equal importance across the year. The concentration of secondary OA increased during the summer yet the extent of oxidation, as defined by the oxygen content, showed no variability during the year. The main factors governing the diurnal, monthly, and seasonal trends observed in all organic and inorganic species were meteorological conditions, specific nature of the sources, and availability of precursors. Regional and transboundary pollution influenced total aerosol concentrations and high concentration events were observed to be governed by different factors depending on season. High-Resolution ToF-AMS measurements were used to further probe OA behaviour, where two SFOA factors were derived from PMF analysis in winter, which likely represent differences in burn conditions. In the summer an OA factor was identified, likely of primary origin, which was observed to be strongly associated with organic nitrates and anthropogenic emissions. This work uses instruments and techniques that have not previously been used in this way in an urban environment, where the results further the understanding of the chemical components of urban aerosols. Aerosol sources are likely to change in the future with increases in solid fuel burning as vehicular emissions decrease, with significant implications on air quality and health. Thus it is important to understand aerosol sources and behaviour in order to develop effective pollution abatement strategies.

Cette thèse traite de l'estimation de la concentration dans le sang artériel de molécules marquées par un radioélément émettant des positons. Cette concentration est appelée « fonction d'entrée artérielle B+ ». Elle doit être déterminée dans de nombreuses analyses en pharmacocinétique. Actuellement, elle est mesurée à l'aide d'une série de prélèvements artériels, méthode précise mais nécessitant un protocole contraignant. Des complications liées au caractère invasif de la méthode peuvent survenir (hématomes, infections nosocomiales).L'objectif de cette thèse est de s'affranchir de ses prélèvements artériels par l'estimation non-invasive de la fonction d'entrée B+ à l'aide d'un détecteur externe et d'un collimateur. Cela permet la reconstruction des vaisseaux sanguins afin de discriminer le signal artériel du signal contenu dans les autres tissus avoisinants. Les collimateurs utilisés en imagerie médicale ne sont pas adaptés à l'estimation de la fonction d'entrée artérielle B+ car leur sensibilité est très faible. Pour cette thèse, ils sont remplacés par des collimateurs codés, issus de la recherche en astronomie. De nouvelles méthodes pour utiliser des collimateurs à ouverture codée avec des algorithmes statistiques de reconstruction sont présentées.Des techniques de lancer de rayons et une méthode d'accélération de la convergence des reconstructions sont proposées. Une méthode de décomposition spatio-temporelle est également mise au point pour estimer efficacement la fonction d'entrée artérielle à partir d'une série d'acquisitions temporelles.Cette thèse montre qu'il est possible d'améliorer le compromis entre sensibilité et résolution spatiale en tomographie d'émission à l'aide de masques codés et d'algorithmes statistiques de reconstruction ; elle fournit également les outils nécessaires à la réalisation de tellesreconstructions. / This work deals with the estimation of the concentration of molecules in arterial blood which are labelled with positron-emitting radioelements. This concentration is called “ B+ arterial input function”. This concentration has to be estimated for a large number of pharmacokinetic analyses. Nowadays it is measured through series of arterial sampling, which is an accurate method but requiring a stringent protocol. Complications might occur during arterial blood sampling because this method is invasive (hematomes, nosocomial infections).The objective of this work is to overcome this risk through a non-invasive estimation of B+ input function with an external detector and a collimator. This allows the reconstruction of blood vessels and thus the discrimination of arterial signal from signals in other tissues.Collimators in medical imaging are not adapted to estimate B+ input function because their sensitivity is very low. During this work, they are replaced by coded-aperture collimators, originally developed for astronomy.New methods where coded apertures are used with statistical reconstruction algorithms are presented. Techniques for analytical ray-tracing and for the acceleration of reconstructions are proposed. A new method which decomposes reconstructions on temporal sets and on spatial sets is also developped to efficiently estimate arterial input function from series of temporal acquisitions.This work demonstrates that the trade-off between sensitivity and spatial resolution in PET can be improved thanks to coded aperture collimators and statistical reconstruction algorithm; it also provides new tools to implement such improvements.

Help documents are important in an organization to use the technology applications licensed from a vendor. Customers and internal employees frequently use and interact with the help documents section to use the applications and know about the new features and developments in them. Help documents consist of various knowledge base materials, question and answer documents and help content. In day- to-day life, customers go through these documents to set up, install or use the product. Recommending similar documents to the customers can increase customer engagement in the product and can also help them proceed without any hurdles. The main aim of this study is to build a recommendation system by exploring different machine-learning techniques to recommend the most relevant and similar help document to the user. To achieve this, in this study a hybrid-based recommendation system for help documents is proposed where the documents are recommended based on similarity of the content using content-based filtering and similarity between the users using collaborative filtering. Finally, the recommendations from content-based filtering and collaborative filtering are combined and ranked to form a comprehensive list of recommendations. The proposed approach is evaluated by the internal employees of the company and by external users. Our experimental results demonstrate that the proposed approach is feasible and provides an effective way to recommend help documents.