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Nous avons travaillé sur le développement d’un instrument de mesure de la luminescence induite par un faisceau d’ions lourds (nucléons  12) et d’énergie de l’ordre du MeV/nucléons. Basé sur une méthode de comptage de photons uniques obtenus par coïncidences, le dispositif permet d’obtenir sur 16 voies à la fois un spectre en énergie dans le domaine proche UV-visible-proche IR (185-920 nm) et la réponse temporelle sur la gamme ns-µs, avec un échantillonnage de 100 ps. Des mesures en température peuvent être réalisées depuis la température ambiante jusqu’à 30K.Ce travail met particulièrement l’accent sur les méthodes d’extraction des données : Une fois montrée la nécessité de déconvoluer les signaux, on s’intéresse dans un premier temps à évaluer différents profils instrumentaux modélisés et reconstruit à partir de mesures. A cet effet, un travail de caractérisation temporelle de chaque constituant du dispositif est mené. Puis ces profils instrumentaux sont utilisés dans deux méthodes de déconvolution par moindres carrés d’abord puis par maximum d’entropie ensuite.Deux matériaux types sont testés : Le Titanate de Strontium pour l’étude de la dynamique de l’excitation électronique, et un scintillateur plastique commercial, le BC400, pour l’étude du vieillissement et de la baisse des performances en fonction de la fluence. Dans les deux cas on a pu mettre en évidence la présence d’une composante ultra rapide de constante de temps subnanoseconde. / We developed an instrument for measuring the luminescence induced by a heavy ion beam (nucleons  12) and energy in the range of MeV / nucleon. Based on a single photon counting method obtained by coincidences, the device can provide in the same run a 16-channel energy spectrum in the UV-visible- IR region (185-920 nm) and a time-resolved response in the range of ns up to µs for each channel. Temperature measurements can be performed from room temperature down to 30K.This work places particular emphasis on data extraction methods: Once the need to deconvolve the signals demonstrated the evaluation of different instrument profiles (simulated and reconstructed from measurements) leads to a systematic temporal characterization of each component of the device. Then, these instrumental profiles are used in two deconvolution methods: least squares first followed by maximum entropy method.Two typical materials are tested: the Strontium Titanate for the study of the dynamics of the electronic excitation, and a commercial scintillator, the BC400, for the study of the aging and the decrease of performances with fluence. In both cases, we have been able to highlight the presence of an ultrafast component of subnanosecond time constant.

Maintenance of aging buried infrastructure and reinforced concrete are critical issues in the United States. Inexpensive non-destructive techniques for mapping and imaging infrastructure and defects are an integral component of maintenance. Ground penetrating radar (GPR) is a widely-used non-destructive tool for locating buried infrastructure and for imaging rebar and other features of interest to civil engineers. Conventional acquisition and interpretation of GPR profiles is based on the arrival times of strong reflected/diffracted returns, and qualitative interpretation of return amplitudes. Features are thereby generally well located, but their material properties are only qualitatively assessed. For example, in the typical imaging of buried pipes, the average radar wave velocity through the overlying soil is estimated, but the properties of the pipe itself are not quantitatively resolved. For pipes on the order of the radar wavelength (<5-35 cm), pipe dimensions and infilling material remain ambiguous. Full waveform inversion (FWI) methods exploit the entire radar return rather than the time and peak amplitude. FWI can generate better quantitative estimates of subsurface properties. In recent decades FWI methods, developed for seismic oil exploration, have been adapted and advanced for GPR with encouraging results. To date, however, FWI methods for GPR data have not been specifically tuned and applied on surface collected common offset GPR data, which are the most common type of GPR data for engineering applications. I present an effective FWI method specifically tailored for common-offset GPR data. This method is composed of three main components, the forward modeling, wavelet estimation and inversion tools. For the forward modeling and iterative data inversion I use two open-source software packages, gprMax and PEST. The source wavelet, which is the most challenging component that guarantees the success of the method, is estimated with a novel Sparse Blind Deconvolution (SBD) algorithm that I have developed. The present dissertation indicates that with FWI, GPR can yield better quantitative estimates, for example, of both the diameters of small pipes and rebar and their electromagnetic properties (permittivity, conductivity). Also better estimates of electrical properties of the surrounding media (i.e. soil or concrete) are achieved with FWI.

A latent factor model for count data is popularly applied when deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the estimators can enjoy much better accuracy by utilizing the extra information. However, such an advantage quickly disappears in the presence of excessive zeros. To correctly account for such a phenomenon, we propose a zero-inflated non-negative matrix factorization that models excessive zeros in both mixed and pure samples and derive an effective multiplicative parameter updating rule. In simulation studies, our method yields smaller bias comparing to other deconvolution methods. We applied our approach to gene expression from brain tissue as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF. In zero-inflated non-negative matrix factorization (iNMF) for the deconvolution of mixed signals of biological data, pure-samples play a significant role by solving the identifiability issue as well as improving the accuracy of estimates. One of the main issues of using single-cell data is that the identities(labels) of the cells are not given. Thus, it is crucial to sort these cells into their correct types computationally. We propose a nonlinear latent variable model that can be used for sorting pure-samples as well as grouping mixed-samples via deep neural networks. The computational difficulty will be handled by adopting a method known as variational autoencoding. While doing so, we keep the NMF structure in a decoder neural network, which makes the output of the network interpretable.

This thesis deals with the topic of 3D image processing for digital holographic microscopy - numerical refocusing. This method allows to perform mathematically accurate defocus correction on image of a sample captured away from the sample plane and it was applicable only for images that were made made using coherent illumination source. It has been generalized to a form in which it is also applicable to devices that use incoherent (non-monochromatic or extended) illumination sources. Another presented achievement concerns hologram processing. The advanced hologram processing method enables obtaining more data mainly concerning precision of quantities from one hologram — normally, one would have to capture multiple holograms to get those. Both methods have been verified experimentally.

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This project aims to obtain and interpret the measurements of the mean longitudinal magnetic field of the chemically peculiar star V1148 Ori. To achieve this aim 12 spectropolarimetric observations obtained by the CFHT using the spectropolarimeter ESPaDOnS were used. The method used to extract the magnetic field signatures from the spectra is called least-squares deconvolution. This method yields line-averaged profiles with a high signal-to-noise ratio. These mean line profiles are necessary to compute the mean longitudinal field. Results of the mean longitudinal field measurements were plotted as a function of the rotational phase, and to this, a sinusoidal function describing a dipolar field was fitted. The dipolar field parameters were computed for two different stellar radii. Inconsistent values for the stellar radii were obtained from the literature, and therefore we calculated two values for each of the parameters. For the surface polar field strength, we found BR1 = 17.38±0.30 kG and BR2 = 12.81±0.22 kG. The calculations involving one of the stellar radii gave results more consistent with previous findings. However, the discrepancy in parameter values could not be accounted for by the small uncertainties. So no definite conclusions can be drawn about the dipolar field parameters. Our fit aligns well with our longitudinal field measurements, no clear indication of any significant deviation from our model assumption, which suggests that the mean longitudinal field is consistent with a large-scale dipolar-like structure.

Cancer is a dangerous disease caused by mutations in the host's genome that makes the cells proliferateuncontrollably and disrupts bodily functions. The immune system tries to prevent this, but tumours have methods ofdisrupting the immune system's ability to combat the cancer. These immunosuppression events can for examplehappen when the immune system interacts with the tumour to recognise it or try and destroy it. The tumours can bychanging their displayed proteins on the cell surface avoid detection or by excreting proteins, they can neutralisedangerous immune cells. This happens within the tumour microenvironment (TME), the immediate surrounding of atumour where there is a plethora of different cells both aiding and suppressing the tumour. Some of these cells arenot cancer cells but can still aid the tumour due to how the tumour has influenced them. For example, throughangiogenesis, where new blood vessels are formed which feeds the tumour. The interactions in the TME can be used as a target for immunotherapy, a field of treatments which improves theimmune system's own ability at defending against cancer. Immunotherapy can for example help the immune systemby guiding immune cells towards the tumour. It is therefore essential to understand the complex system ofinteractions within the TME to be able to create new methods of immunotherapy and thus treat cancers moreefficiently. Concurrently new methods of mapping what happens in a tissue have been developed in recent years,namely spatial transcriptomics (ST). It allows for the retrieval of transcriptomic information of cells throughsequencing while still retaining spatial information. However, the ST methods which capture the wholetranscriptome of the cells and reveal the cell-to-cell interactions are not of single-cell resolution yet. They capturemultiple cells in each spot, creating a mix of cells in the sequencing. This mix of cells can be detangled, and theproportions of each cell type revealed through the process of deconvolution. Deconvolution works by mapping thesingle cell expression profile of different cell types onto the ST data and figuring out what proportions of expressioneach cell type produces the expression of the mix. This reveals the cellular composition of the microenvironment.But since the interactions in the TME depend on the cells current expression we need to deconvolute according tophenotype and not just cell type. In this project we were able to create a tool which automatically finds phenotypes in the single-cell data and usesthose phenotypes to deconvolute ST data. Phenotypes are found using dimensionality reduction methods todifferentiate cells according to their contribution to the variability in the data. The resulting deconvoluted data wasthen used as the foundation for describing the growth of a cancer as a system of phenotype proportions in the tumourmicroenvironment. From this system a mathematical model was created which predicts the growth and couldprovide insight into how the phenotypes interact. The tool created worked as intended and the model explains thegrowth of a tumour in the TME with not just cancer cells phenotypes but other cell phenotypes as well. However, nonew interaction could be discovered by the final model and no phenotype found could provide us with new insightsto the structure of the TME. But our analysis was able to identify structures we expect to see in a tumour, eventhough they might not be so obvious, so an improved version of our tools might be able to find even more detailsand perhaps new, more subtle interactions.