Nightclubs and restaurant bars have become major sources of noise pollution particularly in areas close to residential dwellings. The purpose of this study was to investigate the impact of noise emanating from nightclubs and restaurant bars on the community of Melville, Johannesburg. This study followed both qualitative and quantitative research methods. A total of 100 respondents were randomly sampled within the study area. Qualitative data was collected using a structured questionnaire. A calibrated sound level meter was used to measure environmental noise levels at 10 different measuring points. The research finding revealed that about:  87% of noise levels measured with the sound level meter did not comply with officially acceptable levels of 40dB at night.  69% of respondents indicated that the main source of noise is pollution is nightclubs.  78% of respondents described noise as annoying, disturbing and unwanted.  57% of respondents indicated that members of their household have suffered from sleeping disorders due to noise activities at night disrupting their sleep patterns and resulting in irritability and fatigue. The noise measurements were taken on weekends and public holidays during the day from 10h00 to 14h30 and at night from 22h00 to 02h30. The research findings revealed that the residents of Melville experienced high level of noise at night with nightclub as major source of noise and as a result the majority of the sampled population complained about irritability, fatigue and sleeping disorders due to exposure to noise. The outcome of this research indicated the need of health education on the adverse effects of noise pollution and the need of sound insulation at places of entertainment. Implementation of a noise management policy is needed in order to effectively control and manage the noise pollution in its area of jurisdiction and regular noise level monitoring by constantly taking noise measurements by law enforcements officers. / Department of Environmental Sciences / M. Sc. (Environmental Management)

L’étude de la structure microscopique des fibres du coeur offre une nouvelle approche pour expliquer les maladies du coeur et pour trouver des moyens de thérapie efficaces. L’imagerie de tenseur de diffusion par résonance magnétique (DTMR) ou l’imagerie de tenseur de diffusion (DTI) fournit actuellement un outil unique pour étudier les structures tridimensionnelles (3D) de fibres cardiaques in vivo. Cependant, DTI est connu pour souffrir des temps d'acquisition longs, ce qui limite considérablement son application pratique et clinique. Les méthodes traditionnelles pour l’acquisition et la reconstruction de l’image ne peuvent pas résoudre ce problème. La motivation principale de cette thèse est alors d’étudier des techniques d'imagerie rapide en reconstruisant des images de haute qualité à partir des données fortement sous-échantillonnées. La méthode adoptée est basée sur la nouvelle théorie de l’acquisition comprimée (CS). Plus précisément, nous étudions l’utilisation de la théorie de CS pour l’imagerie par résonance magnétique (IRM) et DTI cardiaque. Tout d'abord, nous formulons la reconstruction de l’image par résonance magnétique (MR) comme un problème d'optimisation avec les contraintes de trames ajustées guidées par les données (TF) et de variation totale généralisée (TGV) dans le cadre de CS, dans lequel, le TF guidé par les données est utilisé pour apprendre de manière adaptative un ensemble de filtres à partir des données fortement sous-échantillonné afin d’obtenir une meilleure approximation parcimonieuse des images, et le TGV est dédié à régulariser de façon adaptative les régions d'image et à réduire ainsi les effets d'escalier. Ensuite, nous proposons une nouvelle méthode CS qui emploie conjointement la parcimonie et la déficience de rang pour reconstruire des images de DTMR cardiaques à partir des données de l'espace k fortement sous-échantillonnées. Puis, toujours dans le cadre de la théorie CS, nous introduisons la contrainte de rang faible et la régularisation de variation totale (TV) dans la formulation de la reconstruction par CS. Deux régularisations TV sont considérées: TV locale (i.e. TV classique) et TV non locale (NLTV). Enfin, nous proposons deux schémas de sous-échantillonnage radial aléatoire (angle d’or et angle aléatoire) et une méthode d’optimisation avec la contrainte de faible rang et la régularisation TV pour traiter des données espace k fortement sous-échantillonnées en DTI cardiaque. Enfin, nous comparons nos méthodes avec des stratégies existantes de sous-échantillonnage radial telles que l’angle uniforme, l’angle uniforme perturbé aléatoirement, l’angle d’or et l’angle aléatoire. / The investigation of the micro fiber structures of the heart provides a new approach to explaining heart disease and investigating effective therapy means. Diffusion tensor magnetic resonance (DTMR) imaging or diffusion tensor imaging (DTI) currently provides a unique tool to image the three-dimensional (3D) fiber structures of the heart in vivo. However, DTI is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Classical acquisition and reconstruction methods do not allow coping with the problem. The main motivation of this thesis is then to investigae fast imaging techniques by reconstructing high-quality images from highly undersampled data. The methodology adopted is based on the recent theory of compressed sensing (CS). More precisely, we address the use of CS for magnetic resonance imaging (MRI) and cardiac DTI. First, we formulate the magnetic resonance (MR) image reconstruction as a problem of optimization with data-driven tight frame (TF) and total generalized variation (TGV) constraints in the framework of CS, in which the data-driven TF is used to adaptively learn a set of filters from the highly under-sampled data itself to provide a better sparse approximation of images and the TGV is devoted to regularizing adaptively image regions and thus supprressing staircase effects. Second, we propose a new CS method that employs joint sparsity and rank deficiency prior to reconstruct cardiac DTMR images from highly undersampled k-space data. Then, always in the framework of CS theory, we introduce low rank constraint and total variation (TV) regularizations in the CS reconstruction formulation, to reconstruct cardiac DTI images from highly undersampled k-space data. Two TV regularizations are considered: local TV (i.e. classical TV) and nonlocal TV (NLTV). Finally, we propose two randomly perturbed radial undersampling schemes (golden-angle and random angle) and the optimization with low rank constraint and TV regularizations to deal with highly undersampled k-space acquisitons in cardiac DTI, and compare the proposed CS-based DTI with existing radial undersampling strategies such as uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle.

This thesis deals with the method of distributed ray tracing focusing on optimalization of this method. The method uses simulation of some attributes of light by distributing rays of lights and it produces high quality and partly realistic images. The price for realitic effects is the high computational complexity of the method. The thesis analysis the theory connected with these aspects. A large part describes optimalizations of this method, i.e. searching for the nearest triangle intersection using kd-trees, quasi random sampling with faster convergence, the use of SSE instruction set and fast ray - triangle intersection. These optimalizations brought a noticable speed - up. The thesis includes description of implementation of these techniques. The implementation itself emphasises the practical usability including generating some advanced animations and universal description of objects.

KUBÍNOVÁ, Zuzana. The effect of elevated CO2 concentration and irradiation on quantitative parameters of mesophyll cells of Norway spruce. Prague, 2010. 74 p. Master's degree thesis. Faculty of Science, Charles University in Prague. Abstract The aim of the present thesis was to choose and adjust a suitable methodology for counting particles in 3D space, which would be suitable for unbiased estimation of chloroplast number in needle mesophyll cells. The disector method was used for the first time to determine the number of chloroplasts. This method enables unbiased estimation of chloroplast number in needle volume from optical sections captured from fresh free-hand sections by confocal microscope. The sections did not need any pre-processing. Another aim was to compare selected photosynthetic and anatomical characteristics of sun and shade Norway spruce needles, which were grown under different CO2 concentration. The trees were grown for eight years in ambient (during the experiment increasing from 357 up to 370 µmol CO2 ∙ mol-1 ) CO2 concentration or elevated (700 µmol ∙ mol-1 ) CO2 concentration in special glass domes on an experimental research site of the Institute of Systems Biology and Ecology, Academy of Sciences of the Czech Republic at Bílý Kříž in Moravskoslezské Beskydy mountains. The sun needles...

6 Abstract Temperate forests serve as long term carbon storage and are affected by increasing carbon dioxide (CO2) concentration in the atmosphere. Norway spruce (Picea abies (L.) Karst.) is the most abundant conifer in the forests of the Czech Republic, therefore I studied the response of its photosynthetic aparatus to elevated CO2 concentration. The aim of my thesis was to analyze the impact of CO2 concentration on chloroplast ultrastructure in both shaded and exposed needles, focusing on the volume density of starch in the median cross-sections of mesophyll cell chloroplasts. The next aim of the study was to test whether the chloroplasts of the first subepidermal layer of mesophyll are representative for the whole needle with respect to starch volume density. The study was performed on eleven years-old Norway spruce trees that had been exposed to ambient or elevated concentration of CO2 for six years; the experiment had been carried out at the Bílý Kříž experimental station in the Beskids Mountains in cultivation chambers with automatically adjustable windows. First year needles of trees grown under abient (382-395ppm) or elevated (700 ppm) CO2 concentration were collected in October 2011. The needles were used to prepare ultrathin sections and the images of median chloroplast cross-sections were...

En este trabajo se aborda el problema de controlar un proceso cuya salida se muestrea de forma irregular. Para ello se propone utilizar un predictor que estima las salidas del proceso en instantes regulares de tiempo más un controlador convencional que calcula la acción de control a partir de las estimaciones del predictor (técnica conocida como control inferencial). La predicción consiste en estimar las variables de salida que se desean controlar a partir de las mediciones realizadas con diversos sensores utilizando para ello un modelo matemático del proceso. El filtro de Kalman permite hacer la predicción de forma óptima si las perturbaciones tienen una distribución gaussiana de media cero, pero con el inconveniente de requerir un elevado coste computacional cuando se utilizan diferentes sensores con retardos temporales variantes. En este trabajo se propone una estrategia de predicción alternativa de bajo coste computacional cuyo diseño se basa en el conocimiento de la disponibilidad de mediciones y de los retardos (del proceso, del sistema de medición o del sistema de transmisión de datos) y de la naturaleza de las perturbaciones. Los predictores propuestos minimizan el error de predicción frente al muestreo aleatorio con retardos variantes, perturbaciones, ruido de medida, error de modelado, retardos en la acción de control e incertidumbre en los tiempos de medición. Las diferentes estrategias de diseño que se proponen se clasifican según el tipo de información que se dispone de las perturbaciones y del coste computacional requerido. Se han planteado los diseños para sistemas monovariables, multivariables, lineales y no lineales. Asimismo, también se ha elaborado una forma más eficiente de incluir mediciones escasas con retardo en el filtro de Kalman, con el objetivo de reducir el coste computacional de la predicción. En este trabajo se demuestra que los sistemas de control inferencial que utilizan los predictores propuestos cumplen con el principio de sep / Peñarrocha Alós, I. (2006). Sensores virtuales para procesos con medidas escasas y retardos temporales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/3882