Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
Intimate coupling of Ti2 photocatalysis and biodegradation (ICPB) offers potential for degrading biorecalcitrant and toxic organic compounds much better than possible with conventional wastewater treatments. This study reports on using a novel sponge-type, Ti2-coated biofilm carrier that shows significant adherence of Ti2 to its exterior and the ability to accumulate biomass in its interior (protected from UV light and free radicals). First, this carrier was tested for ICPB in a continuous-flow photocatalytic circulating-bed biofilm reactor (PCBBR) to mineralize biorecalcitrant organic: 2,4,5-trichlorophenol (TCP). Four mechanisms possibly acting of ICPB were tested separately: TCP adsorption, UV photolysis/photocatalysis, and biodegradation. The carrier exhibited strong TCP adsorption, while photolysis was negligible. Photocatalysis produced TCP-degradation products that could be mineralized and the strong adsorption of TCP to the carrier enhanced biodegradation by relieving toxicity. Validating the ICPB concept, biofilm was protected inside the carriers from UV light and free radicals. ICPB significantly lowered the diversity of the bacterial community, but five genera known to biodegrade chlorinated phenols were markedly enriched. Secondly, decolorization and mineralization of reactive dyes by ICPB were investigated on a refined Ti2-coated biofilm carrier in a PCBBR. Two typical reactive dyes: Reactive Black 5 (RB5) and Reactive Yellow 86 (RY86), showed similar first-order kinetics when being photocatalytically decolorized at low pH (~4-5), which was inhibited at neutral pH in the presence of phosphate or carbonate buffer, presumably due to electrostatic repulsion from negatively charged surface sites on Ti2, radical scavenging by phosphate or carbonate, or both. In the PCBBR, photocatalysis alone with Ti2-coated carriers could remove RB5 and COD by 97% and 47%, respectively. Addition of biofilm inside macroporous carriers maintained a similar RB5 removal efficiency, but COD removal increased to 65%, which is evidence of ICPB despite the low pH. A proposed ICPB pathway for RB5 suggests that a major intermediate, a naphthol derivative, was responsible for most of the residual COD. Finally, three low-temperature sintering methods, called O, D and DN, were compared based on photocatalytic efficiency and Ti2 adherence. The DN method had the best Ti2-coating properties and was a successful carrier for ICPB of RB5 in a PCBBR.
ContributorsLi, Guozheng (Author) / Rittmann, Bruce E. (Thesis advisor) / Halden, Rolf (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Arizona State University (Publisher)
Created2011
Description
The goal of the study was twofold: (i) to investigate the synthesis of hematite-impregnated granular activated carbon (Fe-GAC) by hydrolysis of Fe (III) and (ii) to assess the effectiveness of the fabricated media in removal of arsenic from water. Fe-GAC was synthesized by hydrolysis of Fe(III) salts under two Fe (III) initial dosages (0.5M and 2M) and two hydrolysis periods (24 hrs and 72 hrs). The iron content of the fabricated Fe-GAC media ranged from 0.9% to 4.4% Fe/g of the dry media. Pseudo-equilibrium batch test data at pH = 7.7±0.2 in 1mM NaHCO3 buffered ultrapure water and challenge groundwater representative of the Arizona Mexico border region were fitted to a Freundlich isotherm model. The findings suggested that the arsenic adsorption capacity of the metal (hydr)oxide modified GAC media is primarily controlled by the surface area of the media, while the metal content exhibited lesser effect. The adsorption capacity of the media in the model Mexican groundwater matrix was significantly lower for all adsorbent media. Continuous flow short bed adsorber tests (SBA) demonstrated that the adsorption capacity for arsenic in the challenge groundwater was reduced by a factor of 3 to 4 as a result of the mass transport effects. When compared on metal basis, the iron (hydr)oxide modified media performed comparably well as existing commercial media for treatment of arsenic. On dry mass basis, the fabricated media in this study removed less arsenic than their commercial counterparts because the metal content of the commercial media was significantly higher.
ContributorsJain, Arti (Author) / Hristovski, Kiril (Thesis advisor) / Olson, Larry (Committee member) / Madar, David (Committee member) / Edwards, David (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Filtration for microfluidic sample-collection devices is desirable for sample selection, concentration, preprocessing, and downstream manipulation, but microfabricating the required sub-micrometer filtration structure is an elaborate process. This thesis presents a simple method to fabricate polydimethylsiloxane (PDMS) devices with an integrated membrane filter that will sample, lyse, and extract the DNA from microorganisms in aqueous environments. An off-the-shelf membrane filter disc was embedded in a PDMS layer and sequentially bound with other PDMS channel layers. No leakage was observed during filtration. This device was validated by concentrating a large amount of cyanobacterium Synechocystis in simulated sample water with consistent performance across devices. After accumulating sufficient biomass on the filter, a sequential electrochemical lysing process was performed by applying 5VDC across the filter. This device was further evaluated by delivering several samples of differing concentrations of cyanobacterium Synechocystis then quantifying the DNA using real-time PCR. Lastly, an environmental sample was run through the device and the amount of photosynthetic microorganisms present in the water was determined. The major breakthroughs in this design are low energy demand, cheap materials, simple design, straightforward fabrication, and robust performance, together enabling wide-utility of similar chip-based devices for field-deployable operations in environmental micro-biotechnology.
ContributorsLecluse, Aurelie (Author) / Meldrum, Deirdre (Thesis advisor) / Chao, Joseph (Thesis advisor) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2011
Description
As the use of engineered nanomaterials (ENMs) in consumer products becomes more common, the amount of ENMs entering wastewater treatment plants (WWTPs) increases. Investigating the fate of ENMs in WWTPs is critical for risk assessment and pollution control. The objectives of this dissertation were to (1) quantify and characterize titanium (Ti) in full-scale wastewater treatment plants, (2) quantify sorption of different ENMs to wastewater biomass in laboratory-scale batch reactors, (3) evaluate the use of a standard, soluble-pollutant sorption test method for quantifying ENM interaction with wastewater biomass, and (4) develop a mechanistic model of a biological wastewater treatment reactor to serve as the basis for modeling nanomaterial fate in WWTPs. Using titanium (Ti) as a model material for the fate of ENMs in WWTPs, Ti concentrations were measured in 10 municipal WWTPs. Ti concentrations in pant influent ranged from 181 to 3000 µg/L, and more than 96% of Ti was removed, with effluent Ti concentrations being less than 25 µg/L. Ti removed from wastewater accumulated in solids at concentrations ranging from 1 to 6 µg Ti/mg solids. Using transmission electron microscopy, spherical titanium oxide nanoparticles with diameters ranging from 4 to 30 nm were found in WWTP effluents, evidence that some nanoscale particles will pass through WWTPs and enter aquatic systems. Batch experiments were conducted to quantify sorption of different ENM types to activated sludge. Percentages of sorption to 400 mg TSS/L biomass ranged from about 10 to 90%, depending on the ENM material and functionalization. Natural organic matter, surfactants, and proteins had a stabilizing effect on most of the ENMs tested. The United States Environmental Protection Agency's standard sorption testing method (OPPTS 835.1110) used for soluble compounds was found to be inapplicable to ENMs, as freeze-dried activated sludge transforms ENMs into stable particles in suspension. In conjunction with experiments, we created a mechanistic model of the microbiological processes in membrane bioreactors to predict MBR, extended and modified this model to predict the fate of soluble micropollutants, and then discussed how the micropollutant fate model could be used to predict the fate of nanomaterials in wastewater treatment plants.
ContributorsKiser, Mehlika Ayla (Author) / Westerhoff, Paul K (Thesis advisor) / Rittmann, Bruce E. (Committee member) / Hristovski, Kiril D (Committee member) / Arizona State University (Publisher)
Created2011
Description
As the demand for power increases in populated areas, so will the demand for water. Current power plant technology relies heavily on the Rankine cycle in coal, nuclear and solar thermal power systems which ultimately use condensers to cool the steam in the system. In dry climates, the amount of water to cool off the condenser can be extremely large. Current wet cooling technologies such as cooling towers lose water from evaporation. One alternative to prevent this would be to implement a radiative cooling system. More specifically, a system that utilizes the volumetric radiation emission from water to the night sky could be implemented. This thesis analyzes the validity of a radiative cooling system that uses direct radiant emission to cool water. A brief study on potential infrared transparent cover materials such as polyethylene (PE) and polyvinyl carbonate (PVC) was performed. Also, two different experiments to determine the cooling power from radiation were developed and run. The results showed a minimum cooling power of 33.7 W/m2 for a vacuum insulated glass system and 37.57 W/m2 for a tray system with a maximum of 98.61 Wm-2 at a point when conduction and convection heat fluxes were considered to be zero. The results also showed that PE proved to be the best cover material. The minimum numerical results compared well with other studies performed in the field using similar techniques and materials. The results show that a radiative cooling system for a power plant could be feasible given that the cover material selection is narrowed down, an ample amount of land is available and an economic analysis is performed proving it to be cost competitive with conventional systems.
ContributorsOvermann, William (Author) / Phelan, Patrick (Thesis advisor) / Trimble, Steve (Committee member) / Taylor, Robert (Committee member) / Arizona State University (Publisher)
Created2011
Description
A recent joint study by Arizona State University and the Arizona Department of Transportation (ADOT) was conducted to evaluate certain Warm Mix Asphalt (WMA) properties in the laboratory. WMA material was taken from an actual ADOT project that involved two WMA sections. The first section used a foamed-based WMA admixture, and the second section used a chemical-based WMA admixture. The rest of the project included control hot mix asphalt (HMA) mixture. The evaluation included testing of field-core specimens and laboratory compacted specimens. The laboratory specimens were compacted at two different temperatures; 270 °F (132 °C) and 310 °F (154 °C). The experimental plan included four laboratory tests: the dynamic modulus (E*), indirect tensile strength (IDT), moisture damage evaluation using AASHTO T-283 test, and the Hamburg Wheel-track Test. The dynamic modulus E* results of the field cores at 70 °F showed similar E* values for control HMA and foaming-based WMA mixtures; the E* values of the chemical-based WMA mixture were relatively higher. IDT test results of the field cores had comparable finding as the E* results. For the laboratory compacted specimens, both E* and IDT results indicated that decreasing the compaction temperatures from 310 °F to 270 °F did not have any negative effect on the material strength for both WMA mixtures; while the control HMA strength was affected to some extent. It was noticed that E* and IDT results of the chemical-based WMA field cores were high; however, the laboratory compacted specimens results didn't show the same tendency. The moisture sensitivity findings from TSR test disagreed with those of Hamburg test; while TSR results indicated relatively low values of about 60% for all three mixtures, Hamburg test results were quite excellent. In general, the results of this study indicated that both WMA mixes can be best evaluated through field compacted mixes/cores; the results of the laboratory compacted specimens were helpful to a certain extent. The dynamic moduli for the field-core specimens were higher than for those compacted in the laboratory. The moisture damage findings indicated that more investigations are needed to evaluate moisture damage susceptibility in field.
ContributorsAlossta, Abdulaziz (Author) / Kaloush, Kamil (Thesis advisor) / Witczak, Matthew W. (Committee member) / Mamlouk, Michael S. (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study investigated the difference in biofilm growth between pristine polypropylene microplastics and aged polypropylene microplastics. The microplastics were added to Tempe Town Lake water for 4 weeks. Each week the microplastic biofilms were quantified. Comparing the total biofilm counts, the results showed that the aged microplastic biofilms were larger than the pristine each week. By week 3 the aged microplastic counts had almost doubled in size increasing from 324 to 626 Colony Forming Units per gram in just one week. There was a significant difference in the diversity found from week 1 to week 4. About 40% of the diversity for the pristine microplastic biofilm was seen as light-yellow dots and about 60% of these dots were seen on the aged microplastic biofilms in both weeks. As the microplastics were submerged in the lake water, new phenotypes emerged varying from week 1 to week 4 and from pristine to aged microplastic biofilms. Generally, it was found that as the microplastics stay in the environment there is more biofilm on the particles. The aged microplastics have a larger amount of biofouling, and the pristine microplastic biofilms were found to have more diversity of phenotypes.
ContributorsMushro, Noelle Sararose (Author) / Perreault, Francois (Thesis director) / Jay, Oswald (Committee member) / Civil, Environmental and Sustainable Eng Program (Contributor) / School of Sustainable Engineering & Built Envirnmt (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In many classication problems data samples cannot be collected easily, example in drug trials, biological experiments and study on cancer patients. In many situations the data set size is small and there are many outliers. When classifying such data, example cancer vs normal patients the consequences of mis-classication are probably more important than any other data type, because the data point could be a cancer patient or the classication decision could help determine what gene might be over expressed and perhaps a cause of cancer. These mis-classications are typically higher in the presence of outlier data points. The aim of this thesis is to develop a maximum margin classier that is suited to address the lack of robustness of discriminant based classiers (like the Support Vector Machine (SVM)) to noise and outliers. The underlying notion is to adopt and develop a natural loss function that is more robust to outliers and more representative of the true loss function of the data. It is demonstrated experimentally that SVM's are indeed susceptible to outliers and that the new classier developed, here coined as Robust-SVM (RSVM), is superior to all studied classier on the synthetic datasets. It is superior to the SVM in both the synthetic and experimental data from biomedical studies and is competent to a classier derived on similar lines when real life data examples are considered.
ContributorsGupta, Sidharth (Author) / Kim, Seungchan (Thesis advisor) / Welfert, Bruno (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011