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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

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
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Description
This thesis pursues a method to deregulate the electric distribution system and provide support to distributed renewable generation. A locational marginal price is used to determine prices across a distribution network in real-time. The real-time pricing may provide benefits such as a reduced electricity bill, decreased peak demand, and lower

This thesis pursues a method to deregulate the electric distribution system and provide support to distributed renewable generation. A locational marginal price is used to determine prices across a distribution network in real-time. The real-time pricing may provide benefits such as a reduced electricity bill, decreased peak demand, and lower emissions. This distribution locational marginal price (D-LMP) determines the cost of electricity at each node in the electrical network. The D-LMP is comprised of the cost of energy, cost of losses, and a renewable energy premium. The renewable premium is an adjustable function to compensate `green' distributed generation. A D-LMP is derived and formulated from the PJM model, as well as several alternative formulations. The logistics and infrastructure an implementation is briefly discussed. This study also takes advantage of the D-LMP real-time pricing to implement distributed storage technology. A storage schedule optimization is developed using linear programming. Day-ahead LMPs and historical load data are used to determine a predictive optimization. A test bed is created to represent a practical electric distribution system. Historical load, solar, and LMP data are used in the test bed to create a realistic environment. A power flow and tabulation of the D-LMPs was conducted for twelve test cases. The test cases included various penetrations of solar photovoltaics (PV), system networking, and the inclusion of storage technology. Tables of the D-LMPs and network voltages are presented in this work. The final costs are summed and the basic economics are examined. The use of a D-LMP can lower costs across a system when advanced technologies are used. Storage improves system costs, decreases losses, improves system load factor, and bolsters voltage. Solar energy provides many of these same attributes at lower penetrations, but high penetrations have a detrimental effect on the system. System networking also increases these positive effects. The D-LMP has a positive impact on residential customer cost, while greatly increasing the costs for the industrial sector. The D-LMP appears to have many positive impacts on the distribution system but proper cost allocation needs further development.
ContributorsKiefer, Brian Daniel (Author) / Heydt, Gerald T (Thesis advisor) / Shunk, Dan (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2011
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Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications

With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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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

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
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Description

Animals encounter information from different resources simultaneously, integrating input from multiple sensory systems before responding behaviorally. When different cues interact with one another, they may enhance, diminish, or have no impact on their responses. In this project, we test how the presence of chemical cues affect the perception of visual

Animals encounter information from different resources simultaneously, integrating input from multiple sensory systems before responding behaviorally. When different cues interact with one another, they may enhance, diminish, or have no impact on their responses. In this project, we test how the presence of chemical cues affect the perception of visual cues. Zebrafish (Danio rerio) often use both chemical cues and visual cues to communicate with shoal mates, to assess predation risk, and to locate food. For example, zebrafish rely on both olfactory cues and visual cues for kin recognition, and they frequently use both chemical and visual cues to search for and to capture prey. In zebrafish, the terminal nerve (TN) constitutes the olfacto-visual centrifugal pathway and connects the olfactory bulb with the retina, thus allowing olfactory perception also to activate visual receptors. Past studies have found that the presence of an olfactory cue can modulate visual sensitivity in zebrafish through the terminal nerve pathway. Alternatively, given that zebrafish are highly social, the presence of social chemical cues may distract individuals from responding to other visual cues, such as food and predator visual cues. Foraging and predator chemical cues, including chemical food cues and alarm cues, may also distract individuals from responding to non-essential visual cues. Here, we test whether the response to a visual cue either increases or decreases when presented in concert with alanine, an amino acid that represents the olfactory cues of zebrafish prey. We found that the presence of chemical cues did not affect whether zebrafish responded to visual cues, but that the fish took longer to respond to visual cues when chemical cues were also present. These findings suggest that different aspects of behavior could be affected by the interaction between sensory modalities. We also found that this impact of delayed response was significant only when the visual cue<br/>was weak compared to the strength of the chemical cue, suggesting that the salience of interacting cues may also have an influence on determining the outcomes of the interactions. Overall, the interactive effects of chemicals on an animal’s response to visual cues may also have wide-ranging impacts on behavior including foraging, mating, and evading predators, and the interaction of cues may affect different aspects of the same behavior.

ContributorsPuffer, Georgie Delilah (Author) / Martins, Emilia (Thesis director) / Suriyampola, Piyumika (Committee member) / Gerkin, Richard (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further

Humans use emotions to communicate social cues to our peers on a daily basis. Are we able to identify context from facial expressions and match them to specific scenarios? This experiment found that people can effectively distinguish negative and positive emotions from each other from a short description. However, further research is needed to find out whether humans can learn to perceive emotions only from contextual explanations.

ContributorsCulbert, Bailie (Author) / Hartwell, Leland (Thesis director) / McAvoy, Mary (Committee member) / School of Life Sciences (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Locusts are generalist herbivores meaning that they are able to consume a variety of plants. Because of their broad diet, and ability to respond rapidly to a favorable environment with giant swarms of voracious insects, they are dangerous pests. Their potential impacts on humans increase dramatically when individuals switch from

Locusts are generalist herbivores meaning that they are able to consume a variety of plants. Because of their broad diet, and ability to respond rapidly to a favorable environment with giant swarms of voracious insects, they are dangerous pests. Their potential impacts on humans increase dramatically when individuals switch from their solitarious phase to their gregarious phase where they congregate and begin marching and eventually swarming together. These swarms, often billions strong, can consume the vegetation of enormous swaths of land and can travel hundreds of kilometers in a single day producing a complex threat to food security. To better understand the biology of these important pests we explored the gut microbiome of the South American locust (Schistocerca cancellata). We hypothesized generally that the gut microbiome in this species would be critically important as has been shown in many other species. We extracted and homogenized entire guts from male S. cancellata, and then extracted gut microbiome genomic DNA. Genomic DNA was then confirmed on a gel. The initial extractions were of poor quality for sequencing, but subsequent extractions performed by collaborators during troubleshooting at Southern Illinois University Edwardsville proved more useful and were used for PCR. This resulted in the detections of the following bacterial genera in the gut of S. cancellata: Enterobacter, Enterococcus, Serratia, Pseudomonas, Actinobacter, and Weisella. With this data, we are able to speculate about the physiological roles that they hold within the locust gut generating hypotheses for further testing. Understanding the microbial composition of this species’ gut may help us better understand the locust in general in an effort to more sustainably manage them.

ContributorsGrief, Dustin (Author) / Overson, Rick (Thesis director) / Cease, Arianne (Committee member) / Peterson, Brittany (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Over 40% of adults in the United States are considered obese. Obesity is known to cause abnormal metabolic effects and lead to other negative health consequences. Interestingly, differences in metabolism and contractile performance between obese and healthy weight individuals are associated with differences in skeletal muscle fiber type composition between

Over 40% of adults in the United States are considered obese. Obesity is known to cause abnormal metabolic effects and lead to other negative health consequences. Interestingly, differences in metabolism and contractile performance between obese and healthy weight individuals are associated with differences in skeletal muscle fiber type composition between these groups. Each fiber type is characterized by unique metabolic and contractile properties, which are largely determined by the myosin heavy chain isoform (MHC) or isoform combination that the fiber expresses. In previous studies, SDS-PAGE single fiber analysis has been utilized as a method to determine MHC isoform distribution and single fiber type distribution in skeletal muscle. Herein, a methodological approach to analyze MHC isoform and fiber type distribution in skeletal muscle was fine-tuned for use in human and rodent studies. In the future, this revised methodology will be implemented to evaluate the effects of obesity and exercise on the phenotypic fiber type composition of skeletal muscle.

ContributorsOhr, Jalonna Rose (Author) / Katsanos, Christos (Thesis director) / Tucker, Derek (Committee member) / Serrano, Nathan (Committee member) / School of Life Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

In the United States, clinical testing is monitored by the federal and state governments, held to standards to ensure the safety and efficacy of these tests, as well as maintaining privacy for patients receiving a test. In order for the ABCTL to lawfully operate in the state of Arizona, it

In the United States, clinical testing is monitored by the federal and state governments, held to standards to ensure the safety and efficacy of these tests, as well as maintaining privacy for patients receiving a test. In order for the ABCTL to lawfully operate in the state of Arizona, it had to meet various legal criteria. These major legal considerations, in no particular order, are: Clinical Laboratory Improvement Amendments compliance; FDA Emergency Use Authorization (EUA); Health Insurance Portability and Accountability Act compliance; state licensure; patient, state, and federal result reporting; and liability. <br/>In this paper, the EUA pathway will be examined and contextualized in relation to the ABCTL. This will include an examination of the FDA regulations and policies that affect the laboratory during its operations, as well as a look at the different authorization pathways for diagnostic tests present during the COVID-19 pandemic.

ContributorsJenkins, Landon James (Co-author) / Espinoza, Hale Anna (Co-author) / Filipek, Marina (Co-author) / Ross, Nathaniel (Co-author) / Salvatierra, Madeline (Co-author) / Compton, Carolyn (Thesis director) / Rigoni, Adam (Committee member) / Stanford, Michael (Committee member) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Within the pediatric hospitalization experience, fear and anxiety are two emotions commonly felt by children of all ages. Hospitalized children can greatly benefit from interventions designed to help them cope with these emotions throughout their medical experiences. This study draws on each of our clinical experiences as volunteers at Phoenix

Within the pediatric hospitalization experience, fear and anxiety are two emotions commonly felt by children of all ages. Hospitalized children can greatly benefit from interventions designed to help them cope with these emotions throughout their medical experiences. This study draws on each of our clinical experiences as volunteers at Phoenix Children’s Hospital, and uses a qualitative analysis of three semi-structured interviews with currently employed Child Life Specialists to understand and analyze the use of medical play, a form of play intervention with a medical theme or medical equipment. We explore the goals and benefits of medical play for hospitalized pediatric patients, the process of using medical play as an intervention, including the activity design process, the assessments and adjustments made throughout the child’s hospitalization, and the considerations and limitations to implementing medical play activities. Ultimately, we found that the element of fun that defines play can be channeled into medical play activities implemented by skilled Child Life Specialists, who are experts in their field, in clinical settings to promote several different and beneficial goals, including pediatric patient coping.

ContributorsAguiar, Lara (Co-author) / Garciapeña, Danae (Co-author) / Loebenberg, Abby (Thesis director) / Swanson, Jodi (Committee member) / School of Life Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05