All-sky search algorithms for monochromatic signals in bar gravitational wave detector data

In this paper we design and develop several filtering strategies for the analysis of data generated by a resonant bar gravitational wave (GW) antenna, with the goal of assessing the presence (or absence) therein of long-duration monochromatic GW signals, as well as the eventual amplitude and frequency of the signals, within the sensitivity band of the detector. Such signals are most likely generated in the fast rotation of slightly asymmetric spinning stars. We develop practical procedures, together with a study of their statistical properties, which will provide us with useful information on the performance of each technique. The selection of candidate events will then be established according to threshold-crossing probabilities, based on the Neyman-Pearson criterion. In particular, it will be shown that our approach, based on phase estimation, presents a better signal-to-noise ratio than does pure spectral analysis, the most common approach.

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data.

Next-generation sequencing (NGS) technologies have become the standard for data generation in studies of population genomics, as the 1000 Genomes Project (1000G). However, these techniques are known to be problematic when applied to highly polymorphic genomic regions, such as the human leukocyte antigen (HLA) genes. Because accurate genotype calls and allele frequency estimations are crucial to population genomics analyses, it is important to assess the reliability of NGS data. Here, we evaluate the reliability of genotype calls and allele frequency estimates of the single-nucleotide polymorphisms (SNPs) reported by 1000G (phase I) at five HLA genes (HLA-A, -B, -C, -DRB1, and -DQB1). We take advantage of the availability of HLA Sanger sequencing of 930 of the 1092 1000G samples and use this as a gold standard to benchmark the 1000G data. We document that 18.6% of SNP genotype calls in HLA genes are incorrect and that allele frequencies are estimated with an error greater than ±0.1 at approximately 25% of the SNPs in HLA genes. We found a bias toward overestimation of reference allele frequency for the 1000G data, indicating mapping bias is an important cause of error in frequency estimation in this dataset. We provide a list of sites that have poor allele frequency estimates and discuss the outcomes of including those sites in different kinds of analyses. Because the HLA region is the most polymorphic in the human genome, our results provide insights into the challenges of using of NGS data at other genomic regions of high diversity.

Evaluation of Software Development Practices through Error Analysis

This thesis studies evaluation of software development practices through an error analysis. The work presents software development process, software testing, software errors, error classification and software process improvement methods. The practical part of the work presents results from the error analysis of one software process. It also gives improvement ideas for the project. It was noticed that the classification of the error data was inadequate in the project. Because of this it was impossible to use the error data effectively. With the error analysis we were able to show that there were deficiencies in design and analyzing phases, implementation phase and in testing phase. The work gives ideas for improving error classification and for software development practices.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

The Detector Control Systems for the CMS Resistive Plate Chamber at LHC

The RPC Detector Control System (RCS) is the main subject of this PhD work. The project, involving the Lappeenranta University of Technology, the Warsaw University and INFN of Naples, is aimed to integrate the different subsystems for the RPC detector and its trigger chain in order to develop a common framework to control and monitoring the different parts. In this project, I have been strongly involved during the last three years on the hardware and software development, construction and commissioning as main responsible and coordinator. The CMS Resistive Plate Chambers (RPC) system consists of 912 double-gap chambers at its start-up in middle of 2008. A continuous control and monitoring of the detector, the trigger and all the ancillary sub-systems (high voltages, low voltages, environmental, gas, and cooling), is required to achieve the operational stability and reliability of a so large and complex detector and trigger system. Role of the RPC Detector Control System is to monitor the detector conditions and performance, control and monitor all subsystems related to RPC and their electronics and store all the information in a dedicated database, called Condition DB. Therefore the RPC DCS system has to assure the safe and correct operation of the sub-detectors during all CMS life time (more than 10 year), detect abnormal and harmful situations and take protective and automatic actions to minimize consequential damages. The analysis of the requirements and project challenges, the architecture design and its development as well as the calibration and commissioning phases represent themain tasks of the work developed for this PhD thesis. Different technologies, middleware and solutions has been studied and adopted in the design and development of the different components and a big challenging consisted in the integration of these different parts each other and in the general CMS control system and data acquisition framework. Therefore, the RCS installation and commissioning phase as well as its performance and the first results, obtained during the last three years CMS cosmic runs, will be

Determinação de metil-etil-cetona em amostras de urina com amostragem por micro extração em fase sólida (MEFS) em headspace associada à cromatografia gasosa com detector de ionização de chama (CG-DIC)

Methyl ethyl ketone (MEK) is a solvent commonly used in chemical, paint and shoe industry. The aim of this study was to develop and validate a method for urinary quantification of MEK, employing headspace solid phase micro extraction sampling (SPME) coupled to gas chromatography with flame ionization detection (GC-FID). The calibration curve (y=4.6851x-0.0011) presented good linearity with r²=0.9993. Accuracy (94-109%), intra-assay precision (4.07-5.91%) and inter-assay precision (3.03-5.62%) were acceptable. The quantification limit was 0.19 mg/L. This low cost method can be used routinely in the biological monitoring of occupational exposure to MEK, according to the requirements of the Brazilian legislation.

Determinação de etanol e voláteis relacionados em sangue e fluido oral por microextração em fase sólida em headspace associada à cromatografia gasosa com detector de ionização em chama

The aim of this study was to validate a method for the determination of acethaldehyde, methanol, ethanol, acetone and isopropanol employing solid-phase microextraction associated to gas chromatography with flame ionization detection. The operational conditions of SPME were optimized by response surface analysis. The calibration curves for all compounds were linear with r² > 0.9973. Accuracy (89.1-109.0%), intra-assay precision (1.8-8.5%) and inter-assay precision (2.2-8.2%) were acceptable. The quantification limit was 50 µg/mL. The method was applied to the meaurement of ethanol in blood and oral fluid of a group of volunteers. Oral fluid ethanol concentrations were not directly correlated with blood concentrations.

Desenvolvimento de métodos analíticos para determinação de agrotóxicos em sedimentos por cromatografia gasosa monodimensional e bidimensional abrangente com micro detector de captura de elétrons

The development of analytical methods for determination of eight pesticides of different chemical classes (trichlorfon, propanil, fipronil, propiconazole, trifloxystrobin, permethrin, difenoconazole and azoxystrobin) in sediments with gas chromatography-micro-electron capture detector (GC/µECD) and comprehensive two-dimensional gas chromatography with micro-electron capture detector (GCxGC/µECD) is described. These methods were applied to real sediment samples, and the best results were obtained using a 5% diphenyl-methylpolysiloxane column for 1D-GC. For GCxGC the same column was employed in the first dimension and a 50%-phenyl-methylpolysiloxane stationary phase was placed in the second dimension. Due to the superior peak capacity and selectivity of GCxGC, interfering matrix peaks were separated from analytes, showing a better performance of GCxGC.

Determinação rápida de oseltamivir em cápsulas por cromatografia líquida de ultraeficiência com detector por arranjo de diodos

A simple and rapid ultra-performance liquid chromatographic method for determination of oseltamivir in capsules was developed and validated. The mobile phase consisted of 5 mmol/L triethylammonium buffer (pH 3.0) and acetonitrile (70:30, v/v). Separation was performed in a Hypersil Gold® column, with octylsilil as stationary phase (100 x 2.1 mm, p.d. 1.9 µm). Chromatography run time was 1.2 min. The method presented adequate specificity, linearity, precision, ruggedness and accuracy and was adequate for determination of oseltamivir in capsules.

Determinação rápida de fármacos básicos em plasma por cromatografia a gás com detector de nitrogênio e fósforo

A simple and fast method for determination of 40 basic drugs in human plasma employing gas-chromatography with nitrogen-phosphorus detection was developed and validated. Drugs were extracted from 800 µL of plasma with 250 µL of butyl acetate at basic pH. Aliquots of the organic extract were directly injected on a column with methylsilicone stationary phase. Total chromatographic run time was 25 min. All compounds were detected in concentrations ranging from therapeutic to toxic levels, with intermediate precision CV% below 11.2 and accuracy in the range of 92-114%.

Determinação de bussulfano em plasma através de cromatografia líquida de alta eficiência com detector de arranjo de diodos e derivatização com dietilditiocarbamato de sódio

A high performance liquid chromatographic-diode array detection method for the determination of busulfan in plasma was developed and validated. Sample preparation consisted of protein precipitation followed by derivatization with sodium diethyldithiocarbamate and liquid-liquid extraction with methyl-tert-butyl ether. Chromatograms were monitored at 277 nm. Separation was carried out on a Lichrospher RP 18 column (5 µm, 250 x 4 mm). The mobile phase consisted of water and acetonitrile (20:80, v/v). The method presented adequate specificity, linearity, precision and accuracy and allowed reliable determination of busulfan in clinical plasma samples, being applied to three patients submitted to bone marrow transplantation.

Design and Analysis of Forward Error Control Coding and Signaling for Guaranteeing QoS in Wireless Broadcast Systems

Broadcasting systems are networks where the transmission is received by several terminals. Generally broadcast receivers are passive devices in the network, meaning that they do not interact with the transmitter. Providing a certain Quality of Service (QoS) for the receivers in heterogeneous reception environment with no feedback is not an easy task. Forward error control coding can be used for protection against transmission errors to enhance the QoS for broadcast services. For good performance in terrestrial wireless networks, diversity should be utilized. The diversity is utilized by application of interleaving together with the forward error correction codes. In this dissertation the design and analysis of forward error control and control signalling for providing QoS in wireless broadcasting systems are studied. Control signaling is used in broadcasting networks to give the receiver necessary information on how to connect to the network itself and how to receive the services that are being transmitted. Usually control signalling is considered to be transmitted through a dedicated path in the systems. Therefore, the relationship of the signaling and service data paths should be considered early in the design phase. Modeling and simulations are used in the case studies of this dissertation to study this relationship. This dissertation begins with a survey on the broadcasting environment and mechanisms for providing QoS therein. Then case studies present analysis and design of such mechanisms in real systems. The mechanisms for providing QoS considering signaling and service data paths and their relationship at the DVB-H link layer are analyzed as the first case study. In particular the performance of different service data decoding mechanisms and optimal signaling transmission parameter selection are presented. The second case study investigates the design of signaling and service data paths for the more modern DVB-T2 physical layer. Furthermore, by comparing the performances of the signaling and service data paths by simulations, configuration guidelines for the DVB-T2 physical layer signaling are given. The presented guidelines can prove useful when configuring DVB-T2 transmission networks. Finally, recommendations for the design of data and signalling paths are given based on findings from the case studies. The requirements for the signaling design should be derived from the requirements for the main services. Generally, these requirements for signaling should be more demanding as the signaling is the enabler for service reception.

Quantum Tomography with Phase Space Measurements

This thesis addresses the use of covariant phase space observables in quantum tomography. Necessary and sufficient conditions for the informational completeness of covariant phase space observables are proved, and some state reconstruction formulae are derived. Different measurement schemes for measuring phase space observables are considered. Special emphasis is given to the quantum optical eight-port homodyne detection scheme and, in particular, on the effect of non-unit detector efficiencies on the measured observable. It is shown that the informational completeness of the observable does not depend on the efficiencies. As a related problem, the possibility of reconstructing the position and momentum distributions from the marginal statistics of a phase space observable is considered. It is shown that informational completeness for the phase space observable is neither necessary nor sufficient for this procedure. Two methods for determining the distributions from the marginal statistics are presented. Finally, two alternative methods for determining the state are considered. Some of their shortcomings when compared to the phase space method are discussed.

The Shadow Moiré method using the phase shifting technique and digital image processing: computational implementation and application to the 3D-reconstruction of a buckled plate

The Shadow Moiré fringe patterns are level lines of equal depth generated by interference between a master grid and its shadow projected on the surface. In simplistic approach, the minimum error is about the order of the master grid pitch, that is, always larger than 0,1 mm, resulting in an experimental technique of low precision. The use of a phase shift increases the accuracy of the Shadow Moiré technique. The current work uses the phase shifting method to determine the surfaces three-dimensional shape using isothamic fringe patterns and digital image processing. The current study presents the method and applies it to images obtained by simulation for error evaluation, as well as to a buckled plate, obtaining excellent results. The method hands itself particularly useful to decrease the errors in the interpretation of the Moiré fringes that can adversely affect the calculations of displacements in pieces containing many concave and convex regions in relatively small areas.

On-site calibration of a phase fraction meter by an inverse technique

The formal calibration procedure of a phase fraction meter is based on registering the outputs resulting from imposed phase fractions at known flow regimes. This can be straightforwardly done in laboratory conditions, but is rarely the case in industrial conditions, and particularly for on-site applications. Thus, there is a clear need for less restrictive calibration methods regarding to the prior knowledge of the complete set of inlet conditions. A new procedure is proposed in this work for the on-site construction of the calibration curve from total flown mass values of the homogeneous dispersed phase. The solution is obtained by minimizing a convenient error functional, assembled with data from redundant tests to handle the intrinsic ill-conditioned nature of the problem. Numerical simulations performed for increasing error levels demonstrate that acceptable calibration curves can be reconstructed, even from total mass measured within a precision of up to 2%. Consequently, the method can readily be applied, especially in on-site calibration problems in which classical procedures fail due to the impossibility of having a strict control of all the input/output parameters.