Denim production is one of the leading sub-sectors of textile industry which basically generates highly colored indigo dyeing effluents. In the present study, ozonation was applied to the indigo-dyeing effluent(COD=820 mg/L / color=5500 Pt-Co), and to the whole effluent from a denim-producing plant before(COD=2750 mg/L / color=3950 Pt-Co) and after(COD=800 mg/L,color=3700 Pt-Co) biological treatment for degradation/detoxification purposes. Ozonation was also tried in the wastewater of the plant(COD=3100 mg/L / color=4500 Pt-Co) that would be produced after some foreseen cleaner production measures / caustic recovery and reusing of dyeing process wastewater. When applied to indigo-dyeing wastewater / ozonation provided 95% color and 61% COD removals at 1320 mg/h ozone dose within 60 minutes The optimum pH was evaluated as 4 when indigo-dyeing wastewater exposed to ozonation at different pHs(1.6-12.3). On the other side, ozonation applied as pre-treatment to the plant effluent provided 86% color and 46% COD removals with 3240 mg/h ozone dose in 70 minutes. Less satisfactory results were obtained when ozonation was tried in the wastewater after measures, with 86% color and 31% COD removals at 3960 mg/h ozone dose in 80 minutes. When applied to the biologically-treated effluent / at 420 mg/h ozone dose and within 40 minutes, ozonation removed 47% of influent COD and 96% of influent color indicating better performance of ozonation in postoxidation. In order to assess possible improvements on ozonation with the addition of H2O2, different concentrations were tried, but no significant improvement was obtained. The improvement in BOD5/COD index was also determined for the ozonetreated plant effluent to measure the effects of ozonation on biodegradability and found out that BOD5/COD ratio has improved to 0.39 from 0.22.

SeaWiFS was collecting ocean color data since 1997. This means chlorophyll-a data for more than ten years. Since, SeaWiFS Chl-a data is validated for Black Sea this data set can be used for analysis. Nevertheless, the data is not gap free due to cloud effect. One of the main objectives of this work is to obtain a gap free, complete Chl-a data set for the Black Sea. For this purpose DINEOF method will be used.

This research has successfully developed a multiple fluorescence detection method for high throughput capillary electrophoresis detection using a digitally-modulated light source and a spectrum detection system. A commercial available LCD (liquid crystal device) projector is adoped to replace the spacially-filttered light source (Hg lamp) in a conventional fluorescence microscopy. The LCD projector can be digitally controlled by a computer to create the three primary colors of RGB (red, green, and blue) for fluorescence excitation in the analytes. The emitted light from the fluorescent samples is then collected using a UV-VIS-NIR spectrometer through a ultimode fiber. Delicate optical components, such as filter wheel or acousto-optic filtering system, for filtering different excitation light sources can be excluded with this simple and novel approach. In addition, the desired wavelength for the excitation light can be selected quickly and smoothly without vibration problems come with the mechanical optical components. Three fluorescent dyes (Atto 647N, Rhodamine B, Fluorescein) with different excitation and emission wavelength has been used to demonstrate the proposed digitally-modulated light source system for high throughput CE system. The optimal operation conditions for obtaining best detection signal-to-noise ratio for different fluorescence dyes are firstly determined. In addition, the current study proposes a mixed-color light (visually in purple) composed of two specific primary lights (red and blue) to simultaneously excite a mixed sample composed of two fluorescent dyes (Atto 647N and FITC). Separation and detection of the mixed fluoresce samples using a single excitation illumination using the proposed digital-modulated CE system is successfully demonstrated. Finally, a single-strand DNA biosample is used to confirmed the proposed system is feasible of adopting in the bio-analytical applications. The technique proposed in this study has shown its potential to be a high throughput CE detection system.

The self-organizing map (SOM) is an unsupervised learning algorithm which has been successfully applied to various applications. One of advantages of SOM is it maintains an incremental property to handle data on the fly. In the last several decades, there have been variants of SOM used in many application domains. In this dissertation, two new SOM algorithms are developed for image quantization and compression. The first algorithm is a sample-size adaptive SOM algorithm that can be used for color quantization of images to adapt to the variations of network parameters and training sample size. The sweep size of neighborhood function is modulated by the size of the training data. In addition, the minimax distortion principle which is modulated by training sample size is used to search the winning neuron. Based on the sample-size adaptive self-organizing map, we use the sampling ratio of training data, rather than the conventional weight change between adjacent sweeps, as a stop criterion. As a result, it can significantly speed up the learning process. Experimental results show that the proposed sample-size adaptive SOM achieves much better PSNR quality, and smaller PSNR variation under various combinations of network parameters and image size. The second algorithm is a novel classified SOM method for edge preserving quantization of images using an adaptive subcodebook and weighted learning rate. The subcodebook sizes of two classes are automatically adjusted in training iterations based on modified partial distortions that can be estimated incrementally. The proposed weighted learning rate updates the neuron efficiently no matter of how large the weighting factor is. Experimental results show that the proposed classified SOM method achieves better quality of reconstructed edge blocks and more spread out codebook and incurs a significantly less computational cost as compared to the competing methods.

Problems in automated video surveillance analysis caused by illumination changes are explored, and solutions are presented. Controlled experiments are first conducted to measure the responses of color targets to changes in lighting intensity and spectrum. Surfaces of dissimilar color are found to respond significantly differently. Illumination compensation model error is reduced by 70% to 80% by individually optimizing model parameters for each distinct color region, and applying a model tuned for one region to a chromatically different region increases error by a factor of 15. A background model--called BigBackground--is presented to extract large, stable, chromatically self-similar background features by identifying the dominant colors in a scene. The stability and chromatic diversity of these features make them useful reference points for quantifying illumination changes. The model is observed to cover as much as 90% of a scene, and pixels belonging to the model are 20% more stable on average than non-member pixels. Several illumination compensation techniques are developed to exploit BigBackground, and are compared with several compensation techniques from the literature. Techniques are compared in terms of foreground / background classification, and are applied to an object tracking pipeline with kinematic and appearance-based correspondence mechanisms. Compared with other techniques, BigBackground-based techniques improve foreground classification by 25% to 43%, improve tracking accuracy by an average of 20%, and better preserve object appearance for appearance-based trackers. All algorithms are implemented in C or C++ to support the consideration of runtime performance. In terms of execution speed, the BigBackground-based illumination compensation technique is measured to run on par with the simplest compensation technique used for comparison, and consistently achieves twice the frame rate of the two next-fastest techniques.

<p>The aim of the study is to investigate how double-consciousness operates through contrastive color imagery in Nella Larsen’s novel Quicksand. A focal point of the analysis is to show how Larsen thematizes the ability to benefit from bright colors and how color choice determines the quality and level of freedom in life.</p><p>Together with W. E. B. Du Bois’s theory of double-consciousness, a few other literary works by writers of the Harlem Renaissance have been considered in order to further support my arguments. I link these other writers’ perspectives to Quicksand and to the novel’s theme of color as a path to freedom.</p><p>In Quicksand, a broader path of colors, more bright than dull, leads to freedom, as is made evident through the novel’s connection of bright colors with Harlem’s freedom of expression. Furthermore, a narrow path of colors is contrastively figured as the course towards tragedy, which is clearly seen in the novel through the example of the protagonist Helga’s “sinking” due to an absence of color.</p>

Includes vita. / Title from PDF of title page. / Document formatted into pages; contains 95 pages. / Thesis (Ph.D.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: A Covariate Structure Model was used to examine a possible relationship between colorism as a component of body image and global psychological functioning in the hypothesized model. Two hundred-fifty-five African-American women were recruited from undergraduate psychology and social work classes to participate in the current study from state and community colleges in the southeastern United States, and from social groups and church organizations. The results indicated that the hypothesized model was not a good fit to the data; therefore, the model was re-evaluated. The results of the analysis of the revised model indicated that the revised model was not plausible. However, the model does suggest that there is a relationship between the body image when the definition includes issues of colorism and psychological functioning as measured by levels of depression and self-esteem. Therefore, the model provides direction to be taken in future research. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 77-80).

Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. Includes bibliographical references (leaves 371-389).

A Covariate Structure Model was used to examine a possible relationship between colorism as a component of body image and global psychological functioning in the hypothesized model. Two hundred-fifty-five African-American women were recruited from undergraduate psychology and social work classes to participate in the current study from state and community colleges in the southeastern United States, and from social groups and church organizations. The results indicated that the hypothesized model was not a good fit to the data; therefore, the model was re-evaluated. The results of the analysis of the revised model indicated that the revised model was not plausible. However, the model does suggest that there is a relationship between the body image when the definition includes issues of colorism and psychological functioning as measured by levels of depression and self-esteem. Therefore, the model provides direction to be taken in future research.