Matching Items (110)
Description
The purpose of the overall project is to create a simulated environment similar to Google map and traffic but simplified for education purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environment used in the project is ASU VIPLE (Visual IoT/Robotics Programming Language Environment). It is a visual programming environment for Computer Science education. VIPLE supports a number of devices and platforms, including a traffic simulator developed using Unity game engine. This thesis focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithm in VIPLE. The traffic data is generated from the recorded real traffic data published at Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation based on dynamic changing traffic data.
ContributorsZhang, Zhemin (Author) / Chen, Yinong (Thesis advisor) / Wang, Yalin (Thesis advisor) / De Luca, Gennaro (Committee member) / Arizona State University (Publisher)
Created2022
Description
Little is known about how cognitive and brain aging patterns differ in older adults with autism spectrum disorder (ASD). However, recent evidence suggests that individuals with ASD may be at greater risk of pathological aging conditions than their neurotypical (NT) counterparts. A growing body of research indicates that older adults with ASD may experience accelerated cognitive decline and neurodegeneration as they age, although studies are limited by their cross-sectional design in a population with strong age-cohort effects. Studying aging in ASD and identifying biomarkers to predict atypical aging is important because the population of older individuals with ASD is growing. Understanding the unique challenges faced as autistic adults age is necessary to develop treatments to improve quality of life and preserve independence. In this study, a longitudinal design was used to characterize cognitive and brain aging trajectories in ASD as a function of autistic trait severity. Principal components analysis (PCA) was used to derive a cognitive metric that best explains performance variability on tasks measuring memory ability and executive function. The slope of the integrated persistent feature (SIP) was used to quantify functional connectivity; the SIP is a novel, threshold-free graph theory metric which summarizes the speed of information diffusion in the brain. Longitudinal mixed models were using to predict cognitive and brain aging trajectories (measured via the SIP) as a function of autistic trait severity, sex, and their interaction. The sensitivity of the SIP was also compared with traditional graph theory metrics. It was hypothesized that older adults with ASD would experience accelerated cognitive and brain aging and furthermore, age-related changes in brain network topology would predict age-related changes in cognitive performance.
For both cognitive and brain aging, autistic traits and sex interacted to predict trajectories, such that older men with high autistic traits were most at risk for poorer outcomes. In men with autism, variability in SIP scores across time points trended toward predicting cognitive aging trajectories. Findings also suggested that autistic traits are more sensitive to differences in brain aging than diagnostic group and that the SIP is more sensitive to brain aging trajectories than other graph theory metrics. However, further research is required to determine how physiological biomarkers such as the SIP are associated with cognitive outcomes.
ContributorsSullivan, Georgia (Author) / Braden, Blair (Thesis advisor) / Kodibagkar, Vikram (Thesis advisor) / Schaefer, Sydney (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Tools designed to help match people with behaviors they identify as likely to lead to a successful behavioral outcome remain under-researched. This study assessed the effect of a participant-driven behavior-matching intervention on 1) the adoption of a new behavior related to fruit and vegetable (F&V) consumption, 2) study attrition, and 3) changes in F&V consumption. In this two-arm randomized controlled trial, 64 adults who did not meet standard F&V recommendations were allocated to an intervention (n=33) or control group (n=31). Participants in the intervention group ranked 20 F&V-related behaviors according to their perceived likelihood of engagement in the behavior and their perception of the behavior’s efficacy in increasing F&V consumption. Participants in the intervention group were subsequently shown the list of 20 behaviors in order of their provided rankings, with the highest-ranked behaviors at the top, and were asked to choose a behavior they would like to perform daily for 4 weeks. The control group chose from a random-order list of the same 20 behaviors to adopt daily for 4 weeks. During the study period, text messages were sent to all participants 90 minutes before their reported bedtime to collect Yes/No data reflecting successful behavior engagement each day. The binary repeated-measures data collected from the text messages was analyzed using mixed-effects logistic regression, differences in attrition were assessed using log-rank analysis, and change scores in F&V consumption were compared between the two groups using the Man-Whitney U test. P<0.05 indicated significance. The rate of successful behavior adoption did not differ significantly between the two groups (b=0.09, 95%CI= -0.81, 0.98, p=0.85). The log rank test results indicated that there was no significant difference in attrition between the two groups (χ2=2.68, df=1, p=0.10). F&V consumption increased significantly over the 4 weeks in the total sample (Z=-5.86, p<0.001), but no differences in F&V change scores were identified between the control and intervention groups (Z=-0.21, p=0.84). The behavior-matching tool assessed in this study did not significantly improve behavior adoption, study attrition, or F&V intake over 4 weeks.
ContributorsCosgrove, Kelly Sarah (Author) / Wharton, Christopher (Thesis advisor) / Adams, Marc (Committee member) / DesRoches, Tyler (Committee member) / Grebitus, Carola (Committee member) / Johnston, Carol (Committee member) / Arizona State University (Publisher)
Created2023
Description
As robots become increasingly integrated into the environments, they need to learn how to interact with the objects around them. Many of these objects are articulated with multiple degrees of freedom (DoF). Multi-DoF objects have complex joints that require specific manipulation orders, but existing methods only consider objects with a single joint. To capture the joint structure and manipulation sequence of any object, I introduce the "Object Kinematic State Machines" (OKSMs), a novel representation that models the kinematic constraints and manipulation sequences of multi-DoF objects. I also present Pokenet, a deep neural network architecture that estimates the OKSMs from the sequence of point cloud data of human demonstrations. I conduct experiments on both simulated and real-world datasets to validate my approach. First, I evaluate the modeling of multi-DoF objects on a simulated dataset, comparing against the current state-of-the-art method. I then assess Pokenet's real-world usability on a dataset collected in my lab, comprising 5,500 data points across 4 objects. Results showcase that my method can successfully estimate joint parameters of novel multi-DoF objects with over 25% more accuracy on average than prior methods.
ContributorsGUPTA, ANMOL (Author) / Gopalan, Nakul (Thesis advisor) / Zhang, Yu (Committee member) / Wang, Yalin (Committee member) / Arizona State University (Publisher)
Created2024
Description
In today's data-driven world, privacy is a significant concern. It is crucial to preserve the privacy of sensitive information while visualizing data. This thesis aims to develop new techniques and software tools that support Vega-Lite visualizations while maintaining privacy. Vega-Lite is a visualization grammar based on Wilkinson's grammar of graphics. The project extends Vega-Lite to incorporate privacy algorithms such as k-anonymity, l-diversity, t-closeness, and differential privacy. This is done by using a unique multi-input loop module logic that generates combinations of attributes as a new anonymization method. Differential privacy is implemented by adding controlled noise (Laplace or Exponential) to the sensitive columns in the dataset. The user defines custom rules in the JSON schema, mentioning the privacy methods and the sensitive column. The schema is validated using Another JSON Validation library, and these rules help identify the anonymization techniques to be performed on the dataset before sending it back to the Vega-Lite visualization server. Multiple datasets satisfying the privacy requirements are generated, and their utility scores are provided so that the user can trade-off between privacy and utility on the datasets based on their requirements. The interface developed is user-friendly and intuitive and guides users in using it. It provides appropriate feedback on the privacy-preserving visualizations generated through various utility metrics. This application is helpful for technical or domain experts across multiple domains where privacy is a big concern, such as medical institutions, traffic and urban planning, financial institutions, educational records, and employer-employee relations. This project is novel as it provides a one-stop solution for privacy-preserving visualization. It works on open-source software, Vega-Lite, which several organizations and users use for business and educational purposes.
ContributorsSekar, Manimozhi (Author) / Bryan, Chris (Thesis advisor) / Wang, Yalin (Committee member) / Cao, Zhichao (Committee member) / Arizona State University (Publisher)
Created2024
Description
Image denoising, a fundamental task in computer vision, poses significant challenges due to its inherently inverse and ill-posed nature. Despite advancements in traditional methods and supervised learning approaches, particularly in medical imaging such as Medical Resonance Imaging (MRI) scans, the reliance on paired datasets and known noise distributions remains a practical hurdle. Recent progress in noise statistical independence theory and diffusion models has revitalized research interest, offering promising avenues for unsupervised denoising. However, existing methods often yield overly smoothed results or introduce hallucinated structures, limiting their clinical applicability. This thesis tackles the core challenge of progressing towards unsupervised denoising of MRI scans. It aims to retain intricate details without smoothing or introducing artificial structures, thus ensuring the production of high-quality MRI images. The thesis makes a three-fold contribution: Firstly, it presents a detailed analysis of traditional techniques, early machine learning algorithms for denoising, and new statistical-based models, with an extensive evaluation study on self-supervised denoising methods highlighting their limitations. Secondly, it conducts an evaluation study on an emerging class of diffusion-based denoising methods, accompanied by additional empirical findings and discussions on their effectiveness and limitations, proposing solutions to enhance their utility. Lastly, it introduces a novel approach, Unsupervised Multi-stage Ensemble Deep Learning with diffusion models for denoising MRI scans (MEDL). Leveraging diffusion models, this approach operates independently of signal or noise priors and incorporates weighted rescaling of multi-stage reconstructions to balance over-smoothing and hallucination tendencies. Evaluation using benchmark datasets demonstrates an average gain of 1dB and 2% in PSNR and SSIM metrics, respectively, over existing approaches.
ContributorsVora, Sahil (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Zhou, Yuxiang (Committee member) / Arizona State University (Publisher)
Created2024
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
Objective: It’s not well understood how youth perceive existing fruit and vegetable (FV) marketing materials available in schools. This ancillary study sought to assess the acceptability of FV marketing materials freely available to schools among adolescents in grades 6-12.
Methods: Middle and high school adolescents (n=40; 50% female; 52.5% Hispanic) in the Phoenix, AZ area were asked to rank marketing materials (n=35) from favorite to least favorite in four categories: table tents, medium posters, large posters and announcements. Favorites were determined by showing participants two items at a time and having them choose which they preferred; items were displayed to each adolescent in a random order. Adolescents participated in a 20-30 minute interview on their favorite items in each category based on acceptance/attractiveness, comprehension, relevance, motivation and uniqueness of the materials. A content analysis was performed on top rated marketing materials. Top rated marketing materials were determined by the number of times the advertisement was ranked first in its category.
Results: An analysis of the design features of the items indicated that most participants (84%) preferred marketing materials with more than 4 color groups. Participant preference of advertisement length and word count was varied. A total of 5 themes and 20 subthemes emerged when participants discussed their favorite FV advertisements. Themes included: likes (e.g., colors, length, FV shown), dislikes (e.g., length, FV shown), health information (e.g., vitamin shown), comprehension (e.g., doesn’t recognize FV), and social aspects (e.g., peer opinion). Peer opinion often influenced participant opinion on marketing materials. Participants often said peers wouldn’t like the advertisements shown: “…kids my age think that vegetables are not good, and they like food more than vegetables.” Fruits and vegetable pictured as well as the information in the marketing materials also influenced adolescent preference.
Conclusion: Students preferred advertisements with more color and strong visual aspects. Word count had minimal influence on their opinions of the marketing materials, while information mentioned and peer opinion did have a positive effect. Further research needs to be done to determine if there is a link between adolescent preferences on FV marketing materials and FV consumption habits.
Methods: Middle and high school adolescents (n=40; 50% female; 52.5% Hispanic) in the Phoenix, AZ area were asked to rank marketing materials (n=35) from favorite to least favorite in four categories: table tents, medium posters, large posters and announcements. Favorites were determined by showing participants two items at a time and having them choose which they preferred; items were displayed to each adolescent in a random order. Adolescents participated in a 20-30 minute interview on their favorite items in each category based on acceptance/attractiveness, comprehension, relevance, motivation and uniqueness of the materials. A content analysis was performed on top rated marketing materials. Top rated marketing materials were determined by the number of times the advertisement was ranked first in its category.
Results: An analysis of the design features of the items indicated that most participants (84%) preferred marketing materials with more than 4 color groups. Participant preference of advertisement length and word count was varied. A total of 5 themes and 20 subthemes emerged when participants discussed their favorite FV advertisements. Themes included: likes (e.g., colors, length, FV shown), dislikes (e.g., length, FV shown), health information (e.g., vitamin shown), comprehension (e.g., doesn’t recognize FV), and social aspects (e.g., peer opinion). Peer opinion often influenced participant opinion on marketing materials. Participants often said peers wouldn’t like the advertisements shown: “…kids my age think that vegetables are not good, and they like food more than vegetables.” Fruits and vegetable pictured as well as the information in the marketing materials also influenced adolescent preference.
Conclusion: Students preferred advertisements with more color and strong visual aspects. Word count had minimal influence on their opinions of the marketing materials, while information mentioned and peer opinion did have a positive effect. Further research needs to be done to determine if there is a link between adolescent preferences on FV marketing materials and FV consumption habits.
ContributorsPisano, Sydney Alexis (Author) / Bruening, Meg (Thesis advisor) / Adams, Marc (Committee member) / Grgich, Traci (Committee member) / Arizona State University (Publisher)
Created2019
Description
College students are a niche of young adults, characterized by abnormal sleeping habits and inactive lifestyles. Many students entering college are as young as 18 years old and graduate by 22 years old, a window of time in which their bones are still accruing mineral. The purpose of this cross-sectional study was to determine whether sleep patterns and physical activity observed in college students (N= 52) 18-25 years old at Arizona State University influenced bone biomarkers, osteocalcin (OC) and N-terminal telopeptide of type 1 collagen (NTX-1) concentrations. Students completed various dietary and health history questionnaires including the International Physical Activity Questionnaire short form. Students wore an actigraphy watch for 7 consecutive nights to record sleep events including total sleep time, sleep onset latency and wake after sleep onset. Total sleep time had a significant, negative correlation with OC (r = -0.298, p-value =0.036) while sleep onset latency had a significant, positive correlation with NTX-1 serum concentration (r = 0.293, p-value = 0.037). Despite correlational findings, only sleep percent was found to be significant (beta coefficient = 0.271 p-value = 0.788) among all the sleep components assessed, after adjusting for gender, race, BMI and calcium intake in multivariate regression models. Physical activity alone was not associated with either bone biomarker. Physical activity*sleep onset latency interactions were significantly correlated with osteocalcin (r = 0.308, p-value =0.006) and NTX-1 (r = 0.286, p-value = 0.042) serum concentrations. Sleep percent*physical activity interactions were significantly correlated with osteocalcin (r = 0.280, p-value = 0.049) but not with NTX-1 serum concentrations. Interaction effects were no longer significant after adjusting for covariates in the regression models. While sleep percent was a significant component in the regression model for NTX-1, it was not clinically significant. Overall, sleep patterns and physical activity did not explain OC and NTX-1 serum concentrations in college students 18-25 years old. Future studies may need to consider objective physical activity devices including accelerometers to measure activity levels. At this time, college students should review sleep and physical activity recommendations to ensure optimal healthy habits are practiced.
ContributorsMahmood, Tara Nabil (Author) / Whisner, Corrie (Thesis advisor) / Dickinson, Jared (Committee member) / Petrov, Megan (Committee member) / Adams, Marc (Committee member) / Arizona State University (Publisher)
Created2019
Description
Background In the United States (US), first-year university students typically live on campus and purchase a meal plan. In general, meal plans allow the student a set number of meals per week or semester, or unlimited meals. Understanding how students’ use their meal plan, and barriers and facilitators to meal plan use, may help decrease nutrition-related issues.
Methods First-year students’ meal plan and residence information was provided by a large, public, southwestern university for the 2015-2016 academic year. A subset of students (n=619) self-reported their food security status. Logistic generalized estimating equations (GEEs) were used to determine if meal plan purchase and use were associated with food insecurity. Linear GEEs were used to examine several potential reasons for lower meal plan use. Logistic and Linear GEEs were used to determine similarities in meal plan purchase and use for a total of 599 roommate pairs (n=1186 students), and 557 floormates.
Results Students did not use all of the meals available to them; 7% of students did not use their meal plan for an entire month. After controlling for socioeconomic factors, compared to students on unlimited meal plans, students on the cheapest meal plan were more likely to report food insecurity (OR=2.2, 95% CI=1.2, 4.1). In Fall, 26% of students on unlimited meal plans reported food insecurity. Students on the 180 meals/semester meal plan who used fewer meals were more likely to report food insecurity (OR=0.9, 95% CI=0.8, 1.0); after gender stratification this was only evident for males. Students’ meal plan use was lower if the student worked a job (β=-1.3, 95% CI=-2.3, -0.3) and higher when their roommate used their meal plan frequently (β=0.09, 99% CI=0.04, 0.14). Roommates on the same meal plan (OR=1.56, 99% CI=1.28, 1.89) were more likely to use their meals together.
Discussion This study suggests that determining why students are not using their meal plan may be key to minimizing the prevalence of food insecurity on college campuses, and that strategic roommate assignments may result in students’ using their meal plan more frequently. Students’ meal plan information provides objective insights into students’ university transition.
Methods First-year students’ meal plan and residence information was provided by a large, public, southwestern university for the 2015-2016 academic year. A subset of students (n=619) self-reported their food security status. Logistic generalized estimating equations (GEEs) were used to determine if meal plan purchase and use were associated with food insecurity. Linear GEEs were used to examine several potential reasons for lower meal plan use. Logistic and Linear GEEs were used to determine similarities in meal plan purchase and use for a total of 599 roommate pairs (n=1186 students), and 557 floormates.
Results Students did not use all of the meals available to them; 7% of students did not use their meal plan for an entire month. After controlling for socioeconomic factors, compared to students on unlimited meal plans, students on the cheapest meal plan were more likely to report food insecurity (OR=2.2, 95% CI=1.2, 4.1). In Fall, 26% of students on unlimited meal plans reported food insecurity. Students on the 180 meals/semester meal plan who used fewer meals were more likely to report food insecurity (OR=0.9, 95% CI=0.8, 1.0); after gender stratification this was only evident for males. Students’ meal plan use was lower if the student worked a job (β=-1.3, 95% CI=-2.3, -0.3) and higher when their roommate used their meal plan frequently (β=0.09, 99% CI=0.04, 0.14). Roommates on the same meal plan (OR=1.56, 99% CI=1.28, 1.89) were more likely to use their meals together.
Discussion This study suggests that determining why students are not using their meal plan may be key to minimizing the prevalence of food insecurity on college campuses, and that strategic roommate assignments may result in students’ using their meal plan more frequently. Students’ meal plan information provides objective insights into students’ university transition.
Contributorsvan Woerden, Irene (Author) / Bruening, Meg (Thesis advisor) / Hruschka, Daniel (Committee member) / Schaefer, David (Committee member) / Vega-Lopez, Sonia (Committee member) / Adams, Marc (Committee member) / Arizona State University (Publisher)
Created2019