UTSA Student Works
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Browsing UTSA Student Works by Department "Computer Science"
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Item A Programmable and Participatory Sensing Testbed using Micromobility Vehicles(UTSA Graduate School, 2024-04-02) Jadliwala, Murtuza; Prasad, Sushil K.; Griffin, Greg P.; Maiti, Anindya; Molina, Nico; Wijewickrama, Raveen; Ashan M.K., Buddhi; Trinh, Khoi V.; Najafian, Nima; Khan, Ubaidullah; Sakib, Nazmus; Duthie, Christina; Patel, Ahmer; Kumar, PriyankaWhat is ScooterLab? An NSF funded community research infrastructure initiative, currently under development at UTSA. This publicly-available micromobility testbed and crowd-sensing/crowd-sourcing infrastructure will provide researchers access to a community of riders and a fully operational fleet of customizable dockless e-scooters. Issues & challenges in micromobility: [Figure] Why ScooterLab? • Provides space for researchers to address multidisciplinary challenges • Bypasses commercial service providers who may be unwilling to share data for research • Offers more customizable sensors • Creates infrastructure necessary to collect diverse rider, mobility, and contextual data in realistic settings Broader impact: • Rider/pedestrian safety • Urban routing & infrastructure planning • Public policy • Transportation engineering • Data privacyItem Experiences in Delivering Online CS Teacher Professional Development(UTSA Graduate School, 2024-04-02) Wilde, Jina; Beltran, Emiliano; Zawatski, Michael J.; Fernandez, Amanda S.; Prasad, Priya V.; Yuen, Timothy T.This paper describes our team’s experience in designing and delivering the online teacher professional development (PD) program, Computer Science for San Antonio (CS4SA), aimed at empowering educators with computer science (CS) knowledge to increase Latinx participation in CS and STEM education within a large, urban predominantly Latinx school district in South Texas. This paper highlights the successes, challenges, and lessons learned while facilitating two cohorts of the CS PD through online platforms during the COVID-19 pandemic. As a result of this program, participants recognized the importance of integrating CS into their classroom and becoming advocates for the discipline at the high school level. Additionally, teachers, investigators, and other personnel learned important lessons for enhancing the program’s impact through collaboration with district administrators and refinement of the online learning experience.Item Semantic Segmentation for Materials Classification of Nuclear Fuels(2022-07-28) Mohanadhas, Daniel; Snyder, Chris; Fernandez, AmandaSemantic segmentation, the task of classifying objects in an image at a pixel level, has been done since 2012. While our method is not new, our application is. Unlike most tasks which are on clearly-defined objects, the dataset we attempt to label is like Perlin Noise: seemingly random but with clear patterns throughout. Additionally, we had a very small dataset to work with, but preliminary results show that approaches used on more standard applications also work well in this novel application.Item Strategic Freezing(2022-07-28) Seligman, Zachary; Patrick, David; Fernandez, AmandaConvolutional neural networks (CNNs) are notoriously data-intensive, requiring significantly large datasets for training accurately in an appropriate runtime. Recent approaches aiming to reduce this requirement focus on removal of low-quality samples in the data or unimportant filters, leaving a vast majority of the training set and model in tact. We propose Strategic Freezing, a new training strategy which strategically freezes features in order to maintain class retention. Preliminary results of our approach are demonstrated on the Imagenette dataset using ResNet34.