The recent technological advances in environmental monitoring coupled with the increasingly stringent effluent requirements being placed on waste treatment systems makes it vital to have a more complete understanding of how specific compounds in waste streams can impact wastewater treatment processes. Since activated sludge processes are recognized as one of the most often applied technologies in wastewater treatment, this study assesses the impacts of select toxic synthetic organic compounds (SOCs) on the activated sludge communities in two types of wastewater treatment reactors: a completely-mixed activated sludge reactor (CMAS) and a sequencing batch reactor (SBR). Commonly applied activated sludge monitoring parameters, such as solids analysis and substrate removal, are collected and correlated to the results of microscopic image analysis (IA) and direct gradient gel electrophoresis (DGGE) to monitor the response of the activated sludge communities to variations in operational conditions, including the incorporation of SOCs in the influent feed and varying the solids retention time. The results of this research indicate that the response of the activated community is highly dependent on the reactor configuration. The CMAS settling performance was more strongly correlated to the shape parameters, and the SBR settling performance was more strongly correlated to the size parameters, which is qualitatively supported by particle settling theory when considering that SBR flocs were found to be larger than the CMAS flocs. The SBR began to exhibit larger floc sizes and had a higher sludge volume index with the incorporation of SOCs, while the CMAS flocs became more spherical after SOCs were incorporated and exhibited more discrete settling. The molecular analysis results revealed that the community structure within the activated sludge system was transient in response to environmental variations. Banding patterns indicated that samples were more similar to other samples taken from the same reactor under the same operational conditions. Thus, as operational conditions were varied, sample banding patterns would also change, indicating transitions in the genetic composition, and ultimately the dominant species present, in response to environmental changes.

The accurate identification of parasites allows for the quick diagnosis and treatment of infections. Current state-of-the-art identification techniques require a trained technician to examine prepared specimens by microscope or other molecular methods. In an effort to automate the process and better facilitate the field identification of parasites, approaches are developed to utilize LabVIEW and MATLAB, which are commercially available image processing software packages, for parasite egg identification. The goal of this project is to investigate different image processing techniques and descriptors for the detection and characterization of the following parasite eggs: Ascaris lumbricoides, Taenia sp., and Paragonimus westermani. One manual approach and four automated approaches are used to locate the parasite eggs and gather parasite characterization data. The manual approach uses manual measurements of the parasite eggs within the digital images. The four automated approaches are LabVIEW Vision Assistant scripts, MATLAB separation code, MATLAB cross-section grayscale analysis, and MATLAB edge signature analysis. Forty-four separate measurements were analyzed through the four different approaches. Two types of statistical tests, single factor global Analysis of Variance (ANOVA) test and Multiple Comparison tests, are used to demonstrate that parasite eggs can be differentiated. Thirty-six of the measurements proved to be statistically significant in the differentiation of at least two of the parasite egg types. Of the thirty-six measurements, seven proved to be statistically significant in the differentiation of all three parasite egg types. These results have shown that it is feasible to develop an automated parasite egg detection and identification algorithm through image processing. The automated image processing techniques have proven successful at differentiating parasite eggs from background material. This initial research will be the foundation for future software structure, image processing techniques, and measurements that should be used for automated parasite egg detection. / Master of Science

Cette thèse s'inscrit dans le contexte de diagnostic assisté par ordinateur pour la localisation subcellulaire des protéines dans les images microscopiques. L'objectif est la conception et le développement d'un système de classification automatique permettant d'identifier le compartiment cellulaire dans lequel une protéine d'intérêt exerce son activité biologique. Afin de surmonter les difficultés rencontrées pour discerner les compartiments cellulaires présents dans les images microscopiques, les systèmes décrits dans la littérature proposent d'extraire plusieurs descripteurs associés à une combinaison de classifieurs. Dans cette thèse, nous proposons un schéma de classification différent répondant mieux aux besoins de généricité et de flexibilité pour traiter différentes bases d'images.Dans le but de fournir une caractérisation riche des images microscopiques, nous proposons un nouveau système de représentation permettant d'englober de multiples descripteurs visuels identifiés dans les différentes approches d'extraction de caractéristiques : locale, fréquentielle, globale et par région. Nous formulons ensuite le problème de fusion et de sélection des caractéristiques sous forme d'un problème de sélection de noyaux. Basé sur l'apprentissage de noyaux multiples (MKL), les tâches de sélection et de fusion de caractéristiques sont considérées simultanément. Les expériences effectuées montrent que la plateforme de classification proposée est à la fois plus simple, plus générique et souvent plus performante que les autres approches de la littérature. Dans le but d'approfondir notre étude sur l'apprentissage de noyaux multiples, nous définissons un nouveau formalisme d'apprentissage MKL réalisé en deux étapes. Cette contribution consiste à proposer trois termes régularisant liés à la résolution du problème d'apprentissage des poids associés à une combinaison linéaire de noyaux, problème reformulé en un problème de classification à vaste marge dans l'espace des couples. Le premier terme régularisant proposé assure une sélection parcimonieuse des noyaux. Les deux autres termes ont été conçus afin de tenir compte de la similarité entre les noyaux via une métrique basée sur la corrélation. Les différentes expérimentations réalisées montrent que le formalisme proposé permet d'obtenir des résultats de même ordre que les méthodes de référence, mais offrant l'avantage d'utiliser moins de fonctions noyaux. / This thesis arises in the context of computer aided analysis for subcellular protein localization in microscopic images. The aim is the establishment of an automatic classification system allowing to identify the cellular compartment in which a protein of interest exerts its biological activity. In order to overcome the difficulties in attempting to discern the cellular compartments in microscopic images, the existing state-of-art systems use several descriptors to train an ensemble of classifiers. In this thesis, we propose a different classification scheme wich better cope with the requirement of genericity and flexibility to treat various image datasets. Aiming to provide an efficient image characterization of microscopic images, a new feature system combining local, frequency-domain, global, and region-based features is proposed. Then, we formulate the problem of heterogeneous feature fusion as a kernel selection problem. Using multiple kernel learning, the problems of optimal feature sets selection and classifier training are simultaneously resolved. The proposed combination scheme leads to a simple and a generic framework capable of providing a high performance for microscopy image classification. Extensive experiments were carried out using widely-used and best known datasets. When compared with the state-of-the-art systems, our framework is more generic and outperforms other classification systems. To further expand our study on multiple kernel learning, we introduce a new formalism for learning with multiple kernels performed in two steps. This contribution consists in proposing three regularized terms with in the minimization of kernels weights problem, formulated as a classification problem using Separators with Vast Margin on the space of pairs of data. The first term ensures that kernels selection leads to a sparse representation. While the second and the third terms introduce the concept of kernels similarity by using a correlation measure. Experiments on various biomedical image datasets show a promising performance of our method compared to states of art methods.

This study aimed to explore the integration of varying angles in small-scale measurements and assess the light transmission performance of uncoated curtains. A total of four curtains were evaluated: blackout curtain samples 1 and 2 had a weft satin structure, while daytime curtain samples 3 and 4 consisted of a twill structure. The primary challenge was the inadequacy of a standardized test method for measuring the blackout effect, which led to inefficiencies and resource consumption for Company X. To address this, the study emphasized the need to investigate and establish a reliable and efficient evaluation method for uncoated curtains. By developing a standardized test method, it is possible to reduce waste material, labor costs, and resource consumption, enabling companies to operate more efficiently, sustainably, and responsibly. In the pursuit of understanding the blackout effect and evaluating the structural parameters of uncoated curtains, this study explored various test methods and parameters. More specifically, parameters such as air permeability, porosity, and thickness together with microscopic evaluation, were investigated to shed light on their influence on light transmission. The voids in the fabric were found to be a significant factor, along with porosity and air permeability, which demonstrated a correlation with lower values for the blackout curtains. The construction of the test method in a small-scale required a controlled space in the form of a lightbox. Light proofing of the box was required and successfully achieved in Prototype 1 using plywood, sealing strips, and fixtures, resulting in a lower lux value compared with the previous internal method. Objective measurements using the Konica CL-500A lux meter provided a repeatable and reliable test method with a lower tolerance value of 0,05 lx. The developed test method, theoretical Prototype 2, incorporates different sun angles by adjusting the curtain’s position, in the angles of 0°, 15°, 30°, 45° and 60°, and the rotation of the sample within 0° to 360° in intervals of 45°. The important parameters of weft density and the subsequent increase in weight were key factors in the results of light transmission, air permeability, and porosity. Furthermore, properties of the uncoated curtains such of continuous filaments and darker color showed a higher blackout effect. After the identification of complications with the lamp used in the measurement, there has been a focus on discussing the significance of a light diffuser. Additionally, alternative approaches to measure light transmission have been presented, such as digital image analysis and air permeability.

The master’s thesis deals with the methods and system for measurement of concentration of intracellular calcium in LabVIEW. The thesis included theoretical and practical parts. The thesis covers the theory of fluorescence principles, fluorescence methods and indicators for measurement of concentration of intracellular calcium. The procedure of measurement by means of fluorescence microscope, camera and indicators is also mentioned. Literary solution is presented. Block diagram of system for measurement of concentration of intracellular calcium at a graphical programming environment LabVIEW is presented in practical part of master’s thesis.

Master‘s thesis deals with the methods and system for measurement of concentration of intracellular calcium in LabVIEW. The thesis covers the theory of fluorescence principles, fluorescence methods and indicators for measurement of concentration of intracellular calcium. The procedure of measurement by means of fluorescence microscope, camera and indicators is also mentioned. The theory dealing with confocal microscopy is presented and two techniques are compared. Literary solution is presented. The practical part describes the program with a graphical user interface used for acquisition and analysis of data measuring dynamics of intracellular calcium concentration. The experimental part describes the preparation and measurement of cells and evaluation of experiments.