Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 22 for “"deconvolution methods"”.
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Theoretical and Experimental Concepts to Increase the Performance of Structured Illumination Microscopy
… the set-up is realised, the important aspect of deconvolution in SIM is highlighted and further developed, and finally novel SIM modalities introduced that improve its time-resolution, gentleness, and volumetric imaging capabilities. Based on the generalised theory, the computational steps for …
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Biomedical applications of holographic microscopy
… by replacing existing image reconstruction methods. We demonstrate the effectiveness of the algorithm by reconstructing biological samples and quantifying their structural similarity relative to spatial deconvolution methods. The approaches explored in this work could enable a standalone …
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Cellular Kaleidoscope: Unveiling Tissue Microenvironment in Health and Disease
… and bulk RNA data. Despite the abundance of methods, results often vary and lack reliability. One of the main challenges is selecting suitable methods, referencing data, and preprocessing for the condition and tissue of interest. This thesis aims to bridge this gap by developing a …
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Multichannel blind deconvolution in underwater acoustic channels
… techniques for solving the multichannel blind deconvolution problem and implemented these techniques in acoustic waveguide multiple environment. We developed a systematic way to build an efficient and accurate channel models incorporating a priori information about the expected Channel Impulse …
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Unsupervised Signal Deconvolution for Multiscale Characterization of Tissue Heterogeneity
… by most global molecular and genomic profiling methods. While signal deconvolution has widespread applications in many real-world problems, there are significant limitations associated with existing methods, mainly unrealistic assumptions and heuristics, leading to inaccurate or incorrect …
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Mathematical Modeling and Deconvolution for Molecular Characterization of Tissue Heterogeneity
… from complex tissues. Existing computational methods performing data deconvolution from mixed subtype signals almost exclusively rely on supervising information, requiring subtype-specific markers, the number of subtypes, or subtype compositions in individual samples. We develop a fully …
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wavelet domain inversion and joint deconvolution/interpolation of geophysical data
… framework and an algorithm for combining linear deconvolution methods with geostatistical interpolation techniques. This allows for sparsely sampled data to aid in image deblurring problems, or, conversely, noisy and blurred data to aid in sample interpolation. In order to overcome difficulties …
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Energy allocation and transmission scheduling for wireless and space communications
… framework and an algorithm for combining linear deconvolution methods with geostatistical interpolation techniques. This allows for sparsely sampled data to aid in image deblurring problems, or, conversely, noisy and blurred data to aid in sample interpolation. In order to overcome difficulties …
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Characterisation of the Tumour Microenvironment in Ovarian Cancer
… gene signatures by intersecting state-of-the-art deconvolution methods that predict immune cell populations using bulk RNA data was developed. ConsensusTME improved accuracy and sensitivity of T cell and leukocyte deconvolutions in ovarian cancer samples. As previously observed in the case report, …
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Computational Advancements for Solving Large-scale Inverse Problems
… ill-posed and large-scale. Iterative projection methods have dramatically reduced the computational costs of solving large-scale inverse problems, and regularization methods have been critical in obtaining stable estimations by applying prior information of unknowns via Bayesian inference. …
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Multiscale Modeling and Simulation of Turbulent Geophysical Flows
… flow computations, several types of multiscale methods were systematically developed and tested for a variety of physical settings including barotropic and stratified wind-driven large scale ocean circulation models, decaying and forced two-dimensional turbulence simulations, as well as several …
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Mixed-ligand diruthenium complexes: theoretical modelling and interpretation of electronic absorption spectra
… natural transition orbitals (NTOs) and spectrum deconvolution methods have aided in characterizing the electronic transitions of the prominent UV band at 250-350 nm as a combination of δ(Ru₂) → π*(Np,C), π(Cl) → π*(Cp,Np) and π(Cl) → π*(Cₐ) transitions, where the subscript “a” and “p” represents …
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Investigating Tumour-Immune Cell Interactions in Paediatric Malignancies
… leukaemia. We next utilised well established deconvolution methods to investigate the TME in medulloblastoma and discovered five transcriptionally distinct clusters, including one that was composed of WNT, SHH, Group 3, and Group 4 tumours, which were associated with differential TME …
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Optimal filters for deconvolution of transient signals in the presence of noise
This dissertation presents different methods for the deconvolution of time domain signals. The techniques developed in this work are frequency domain filtering techniques. and are suitable for the type of deconvolution problems encountered in time domain reflectometry (TOR). They include a …
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Deconvolution and sparsity based image restoration
Deconvolution and sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear …
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Deconvolution and sparsity based image restoration
Deconvolution and sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear …
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DEVELOPMENT OF A BIOINFORMATICS FRAMEWORK TO IDENTIFY CELL SUBPOPULATIONS FROM BULK TRANSCRIPTIONAL DATA
… extremely significant. For this reason, several deconvolution tools have been developed to infer (deconvolve) the signals of each constituent cell type from bulk gene expression data. Historically, these tools have been mainly developed to define leukocyte proportions, and their performance has …
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Integrating Bulk and Single-Cell Transcriptomics to Investigate Intratumor Heterogeneity
… signals from all diverse cellular sources. Bulk deconvolution with single-cell/nucleus (sc/sn) RNA-seq data has emerged as a powerful approach to dissect both cellular composition and cell-type-specific expression patterns, yet the technological discrepancy across sequencing platforms limits …
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Catechyl-lignin tissues in Vanilla orchid and Candlenut: structure/property studies
… coat and candlenut shell determined using peak deconvolution methods were about half of Southern Yellow Pine crystallinity. DMA was used to measure Tg in vanilla seed coat and candlenut shell. Measurements were conducted in solvent-submersion mode using organic plasticizers to reduce the Tg to …
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Facial image restoration and retrieval through orthogonality
… image restoration method and three retrieval methods were proposed. Blur in facial images significantly impedes the efficiency of recognition approaches. However, most existing blind deconvolution methods cannot generate satisfactory results, due to their dependence on strong edges which are …
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