La couleur est un critère important dans de nombreux secteurs d'activité pour identifier, comparer ou encore contrôler la qualité de produits. Cette tâche est souvent assumée par un opérateur humain qui effectue un contrôle visuel. Malheureusement la subjectivité de celui-ci rend ces contrôles peu fiables ou répétables. Pour contourner ces limitations, l'utilisation d'une caméra RGB permet d'acquérir et d'extraire des propriétés photométriques. Cette solution est facile à mettre en place et offre une rapidité de contrôle. Cependant, elle est sensible au phénomène de métamérisme. La mesure de réflectance spectrale est alors la solution la plus appropriée pour s'assurer de la conformité colorimétrique entre des échantillons et une référence. Ainsi dans l'imprimerie, des spectrophotomètres sont utilisés pour mesurer des patchs uniformes imprimés sur une bande latérale. Pour contrôler l'ensemble d'une surface imprimée, des caméras multi-spectrales sont utilisées pour estimer la réflectance de chaque pixel. Cependant, elles sont couteuses comparées aux caméras conventionnelles. Dans ces travaux de recherche, nous étudions l'utilisation d'une caméra RGB pour l'estimation de la réflectance dans le cadre de l'imprimerie. Nous proposons une description spectrale complète de la chaîne de reproduction pour réduire le nombre de mesures dans les phases d'apprentissage et pour compenser les limitations de l'acquisition. Notre première contribution concerne la prise en compte des limitations colorimétriques lors de la caractérisation spectrale d'une caméra. La deuxième contribution est l'exploitation du modèle spectrale de l'imprimante dans les méthodes d'estimation de réflectance. / Color is a major criterion for many sectors to identify, to compare or simply to control the quality of products. This task is generally assumed by a human operator who performs a visual inspection. Unfortunately, this method is unreliable and not repeatable due to the subjectivity of the operator. To avoid these limitations, a RGB camera can be used to capture and extract the photometric properties. This method is simple to deploy and permits a high speed control. However, it's very sensitive to the metamerism effects. Therefore, the reflectance measurement is the more reliable solution to ensure the conformity between samples and a reference. Thus in printing industry, spectrophotometers are used to measure uniform color patches printed on a lateral band. For a control of the entire printed surface, multispectral cameras are used to estimate the reflectance of each pixel. However, they are very expensive compared to conventional cameras. In this thesis, we study the use of an RGB camera for the spectral reflectance estimation in the context of printing. We propose a complete spectral description of the reproduction chain to reduce the number of measurements in the training stages and to compensate for the acquisition limitations. Our first main contribution concerns the consideration of the colorimetric limitations in the spectral characterization of a camera. The second main contribution is the exploitation of the spectral printer model in the reflectance estimation methods.

Doctor of Philosophy / Department of Mathematics / John S. Maginnis / In this work, we find the module structure of the cohomology of the group of four by four upper triangular matrices (with ones on the diagonal) with entries from the field on three elements modulo its center. Some of the relations amongst the generators for the cohomology ring are also given. This cohomology is found by considering a certain split extension. We show that the associated Lyndon-Hochschild-Serre spectral sequence collapses at the second page by illustrating a set of generators for the cohomology ring from generating elements of the second page. We also consider two other extensions using more traditional techniques. In the first we introduce some new results giving degree four and five differentials in spectral sequences associated to extensions of a general class of groups and apply these to both the extensions.

M.Sc.

Beginning in the 1970s, and aided by the advancement and an increased prevalence of computers, spectral music emerged as an important development in twentieth century music. Spectral composers, as exemplified by Gérard Grisey and Tristan Murail, took the harmonic spectra of sounds as the fundamental materials of composition. The resulting music placed an emphasis on texture and gradually evolving forms. The generation of composers immediately following the spectralists assimilated their techniques into distinct and varying styles. Finnish composer Kaija Saariaho uses spectral techniques to create an aesthetic that generates form and progression from a sound/noise axis. In her piece Du cristal…à la fumée, a number of pendulum and half-pendulum gestures build up texture and form. The accompanying original composition Axiom Unearthed employs similar pendulum gestures and uses spectral techniques to generate melody and harmony in an aesthetic divergent from traditional spectral pieces.

The ability to probe molecular electronic and vibrational structures gives rise to optical absorption spectroscopy, which is a credible tool used in molecular quantification and classification with high sensitivity, low limit of detection (LoD), and immunity to electromagnetic noises. Spectra are sensitive to slight analyte variations, so they are often used to identify a sample’s components. This thesis proposes several methods for quick classification and quantification of analysts based on their absorbance spectra. functional Principal Component Analysis (fPCA) is employed for feature extraction and dimension reduction. For 1,000-pixel spectra data, fPCA can capture the majority variance with as few output scores as the number of expected analytes. This reduces the amount of calculation required for the following machine learning algorithms. Further, the output scores are fed into XGBoost and logistic regression for classification, and fed into XGBoost and linear regression for quantification. Our models were tested on both synthesized datasets and experimentally acquired dataset. Our models demonstrated similar performance compared to deep learning but with much faster processing speeds. For the synthesized 30 dB dataset, our model XGBoost with fPCA could reach a micro-averaged f1 score of 0.9551 ± 0.0008, while FNN-OT [1] could obtain 0.940±0.001. fPCA helped the algorithms extract the feature of each analyte; furthermore, the output scores nearly had a linear relationship with their concentrations. It was much easier for the algorithm to find the mapping function between the inputs and the outputs with fPCA, which shortened the training and testing time. / Graduate

Hatchling marine turtles exhibit a positive phototaxis by crawling toward the lowest and brightest horizon when they emerge from nests on the beach at night, which should lead them to the ocean (“seafinding”). Previous research with cheloniid (loggerhead and green turtle) hatchlings demonstrated that the perceptual spectral sensitivities are well below the light available on the beach regardless of lunar phase. The goal of this research was to determine the perceptual spectral sensitivities of leatherback hatchlings, the most distantly related of all extant sea turtle species. This study revealed that, like cheloniids, leatherbacks are most sensitive to shorter wavelengths (< 500 nm). However, leatherbacks were 10 – 100x less sensitive than cheloniids at all tested wavelengths. This difference in sensitivity corresponds with increased crawl duration and circling behavior under new moon conditions when light levels are lowest and the difference in radiance between the landward and seaward direction is small. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Within our ecosystems, soil biota control an array of functions, such as nutrient cycling and decomposition, and have been pursued as a soil quality indicator. Though microbial communities are known to be a reflection of their environment, small scales dynamics within an agricultural system have been overlooked for many years leading to gaps when inferring on relative microbial values. To further asses our current microbial knowledge, two experiments analyzing microbial phospholipid fatty acid (PLFA) structures and enzyme activities sought out to determine temporal fluctuations, cycles, and driving force behind simulated daily microbial parameter outputs. Across both studies, temporal effects, cyclical structures, and common driving forces were recorded, but further validation and characterization is needed to solidify the temporal dynamics of the microbial community. Overall, this information serves as a valuable step towards determining the most viable tillage systems based on environmental conditions, and physical proof of small scale microbial fluctuations.

The three-dimensional (3D) structure of the genome plays a crucial role in gene expression regulation. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), sub-TADs, and chromatin loops which is relatively stable across cell-lines and even across species. These TADs dynamically reorganize during development of disease, and exhibit cell- and conditionspecific differences. Identifying such hierarchical structures and how they change between conditions is a critical step in understanding genome regulation and disease development. Despite their importance, there are relatively few tools for identification of TADs and even fewer for identification of hierarchies. Additionally, there are no publicly available tools for comparison of TADs across datasets. These tools are necessary to conduct large-scale genome-wide analysis and comparison of 3D structure. To address the challenge of TAD identification, we developed a novel sliding window-based spectral clustering framework that uses gaps between consecutive eigenvectors for TAD boundary identification. Our method, implemented in an R package, SpectralTAD, has automatic parameter selection, is robust to sequencing depth, resolution and sparsity of Hi-C data, and detects hierarchical, biologically relevant TADs. SpectralTAD outperforms four state-of-the-art TAD callers in simulated and experimental settings. We demonstrate that TAD boundaries shared among multiple levels of the TAD hierarchy were more enriched in classical boundary marks and more conserved across cell lines and tissues. SpectralTAD is available at http://bioconductor.org/packages/SpectralTAD/. To address the problem of TAD comparison, we developed TADCompare. TADCompare is based on a spectral clustering-derived measure called the eigenvector gap, which enables a loci-by-loci comparison of TAD boundary differences between datasets. Using this measure, we introduce methods for identifying differential and consensus TAD boundaries and tracking TAD boundary changes over time. We further propose a novel framework for the systematic classification of TAD boundary changes. Colocalization- and gene enrichment analysis of different types of TAD boundary changes revealed distinct biological functionality associated with them. TADCompare is available on https://github.com/dozmorovlab/TADCompare.

Various rendering techniques often use different approaches to the same aspects of the image synthesis process, mainly due to their complexity and constant development. Excluding global illumination algorithms, appearance descriptions are key distinguishing factors between the rendering systems. These descriptions might include BRDF models, support for spectral color representation, and even integration of advanced phenomena, such as fluores- cence. Unfortunately, as there are no standardized implementations of these features, their computations might not be completely accurate, which may result in their incorrect representation. This thesis describes an evaluation suite that methodically tests rendering algorithms based on their appearance reproduction capabilities. The core of the suite is a set of scenes that test five specific appearance phenomena - polarization, GGX reflectance, fluorescence, iridescence and the overall spectral accuracy. Each test case scenario contains as few scenes as possible while maximizing the number of covered aspects of the tested feature. For the user's convenience, we wrap the scenes inside an automatic workflow that runs the specified test case scenarios and displays the results. As a correctness metric, we provide manually verified reference images that are considered to...

Spectral density function (SDF) plays a critical role in spatio-temporal data analysis, where the data are analyzed in the frequency domain. Although many methods have been proposed for SDF estimation, real-world applications in many research fields, such as neuroscience and environmental science, call for better methodologies. In this thesis, we focus on the spectral density functions for time series and spatial data, develop new estimation algorithms, and use the estimators as features for clustering and classification purposes. The first topic is motivated by clustering electroencephalogram (EEG) data in the spectral domain. To identify synchronized brain regions that share similar oscillations and waveforms, we develop two robust clustering methods based on the functional data ranking of the estimated SDFs. The two proposed clustering methods use different dissimilarity measures and their performance is examined by simulation studies in which two types of contaminations are included to show the robustness. We apply the methods to two sets of resting-state EEG data collected from a male college student. Then, we propose an efficient collective estimation algorithm for a group of SDFs. We use two sets of basis functions to represent the SDFs for dimension reduction, and then, the scores (the coefficients of the basis) estimated by maximizing the penalized Whittle likelihood are used for clustering the SDFs in a much lower dimension. For spatial data, an additional penalty is applied to the likelihood to encourage the spatial homogeneity of the clusters. The proposed methods are applied to cluster the EEG data and the soil moisture data. Finally, we propose a parametric estimation method for the quantile spectrum. We approximate the quantile spectrum by the ordinary spectral density of an AR process at each quantile level. The AR coefficients are estimated by solving Yule- Walker equations using the Levinson algorithm. Numerical results from simulation studies show that the proposed method outperforms other conventional smoothing techniques. We build a convolutional neural network (CNN) to classify the estimated quantile spectra of the earthquake data in Oklahoma and achieve a 99.25% accuracy on testing sets, which is 1.25% higher than using ordinary periodograms.