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The Ngc 3109 Satellite System: The First Systematic Resolved Search For Dwarf Galaxies Around An Smc-Mass Host, Amandine Doliva-Dolinsky, Burçin Mutlu-Pakdil, Denija Crnojević, Dhayaa Anbajagane, Jeffrey L. Carlin, Jonah Medoff, David J. Sand, Erik Tollerud, Sungsoon Lim, Paul Bennet, Alex Drlica-Wagner, Catherine E. Fielder, Jonathan R. Hargis, Kai Herron, Laura Congreve Hunter, Michael G. Jones, Aaron J. Romanowsky, For Full Author List, See Comments Below Aug 2025

The Ngc 3109 Satellite System: The First Systematic Resolved Search For Dwarf Galaxies Around An Smc-Mass Host, Amandine Doliva-Dolinsky, Burçin Mutlu-Pakdil, Denija Crnojević, Dhayaa Anbajagane, Jeffrey L. Carlin, Jonah Medoff, David J. Sand, Erik Tollerud, Sungsoon Lim, Paul Bennet, Alex Drlica-Wagner, Catherine E. Fielder, Jonathan R. Hargis, Kai Herron, Laura Congreve Hunter, Michael G. Jones, Aaron J. Romanowsky, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

We report the results of the deepest search to date for dwarf galaxies around NGC 3109, a barred spiral galaxy with a mass similar to that of the Small Magellanic Cloud (SMC), using a semiautomated search method. Using the Dark Energy Camera, we survey a region covering a projected distance of ∼70 kpc of NGC 3109 (D = 1.3 Mpc, Rvir ∼ 90 kpc, M ∼ 108M*) as part of the MADCASH and DELVE-DEEP programs. We introduce a newly developed semiresolved search method, used alongside a resolved search, to identify crowded dwarf galaxies around NGC 3109. Using both …

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Latticeanalytics: Strut-Level Visualization And Inspection Of Additively Manufactured Lattice Structures, Haichao Miao, Saurabh Narain, Vuthea Chheang, Garrett Hooten, Raiyan Seede, Pavol Klacansky, Kaila Morgen Bertsch, Gabe Guss, Brian Giera, Peer Timo Bremer Aug 2025

Latticeanalytics: Strut-Level Visualization And Inspection Of Additively Manufactured Lattice Structures, Haichao Miao, Saurabh Narain, Vuthea Chheang, Garrett Hooten, Raiyan Seede, Pavol Klacansky, Kaila Morgen Bertsch, Gabe Guss, Brian Giera, Peer Timo Bremer

Faculty Research, Scholarly, and Creative Activity

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. We introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. …

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Coloring Outside The Lines: Factors Facilitating Divergent Thinking, Michelle Baca Reinke Aug 2025

Coloring Outside The Lines: Factors Facilitating Divergent Thinking, Michelle Baca Reinke

Master's Theses

Divergent thinking involves producing a multitude of unique and diverse ideas. The ability to “color outside the lines” is crucial to business, education, and social domains. The flexibility, originality, and fluency dimensions of divergent thinking are commonly assessed using the Alternative Uses Task, which involves generating uses for common objects. Prior research suggests that high identification (first-person perspective) with schema-inconsistent experiences increases flexibility, while semantic density (degree of conceptual associations) impacts originality and fluency inversely. However, their combined effects across multiple divergent thinking dimensions remain unexplored. The present research investigated the interactive effects of identification (high, low), schema consistency (consistent, …

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Evaluating Demand-Responsive Scheduling In Public Transportation Service Offerings To Retirement Communities, Steven D. Silver Aug 2025

Evaluating Demand-Responsive Scheduling In Public Transportation Service Offerings To Retirement Communities, Steven D. Silver

Mineta Transportation Institute

This project addresses alternatives to fixed schedule local transportation service offerings to members of retirement communities. The project design measures the effects of the contrast between fixed schedule (Fixed) service and demand-responsive services (DRT) in retirement communities in Santa Clara County that are closely matched in demographic profiles and geographic location. The research team investigates the effects of scheduling alternatives in a diary format with a within-person variable of four recent trips. The measure of public transportation usage under the Fixed schedule and DRT service offerings is supplemented by a measure of subjective well-being (STS) for each trip—in other words, …

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Understanding Residential Development In A High-Quality Transit Area (Hqta): An Application Of Deep Learning, Dohyung Kim Aug 2025

Understanding Residential Development In A High-Quality Transit Area (Hqta): An Application Of Deep Learning, Dohyung Kim

Mineta Transportation Institute

Considerable research addresses the dynamics between land use and public transportation, including the impacts of transit systems on urban development and vice versa. However, this research has underestimated the heterogeneity of contributing factors to urban developments near transit by property type: vacant lot and occupied property. That is, what drives development in vacant lots differs from what drives development in already-occupied property. A lack of comprehensive data that differentiates development records based on the type of property is one explanation for this research gap. This project fills this research gap by employing a deep learning algorithm and separately examining the …

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Developing An Integrated Risk Assessment Framework To Quantify The Resilience Of Critical Railway Infrastructure: Characterization Of Risks And Hazards To The U.S. Rail Network Through Empirical Data And Past Events, Haizhong Wang, Brian M. Staes, Benyamin Ghoreishi Aug 2025

Developing An Integrated Risk Assessment Framework To Quantify The Resilience Of Critical Railway Infrastructure: Characterization Of Risks And Hazards To The U.S. Rail Network Through Empirical Data And Past Events, Haizhong Wang, Brian M. Staes, Benyamin Ghoreishi

Mineta Transportation Institute

To improve understanding of the impact of natural hazards on the U.S. railway system, this project developed a comprehensive, geospatially precise dataset that maps historical hazard events to specific rail segments. The project provides insight into when, where, and how much damage natural hazards have inflicted on rail infrastructure across the country. While a rail-specific hazard database did not previously exist, this research combines data from the Geological Survey (USGS) Flood Events Database, Hydrologic Unit Codes (HUC), the National Centers for Environmental Information (NCEI) from the National Oceanic and Atmospheric Administration (NOAA), and the National Weather Service (NWS) forecast zones. …

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Roadside Asset Extraction From Mobile Lidar Point Cloud, Yushin Ahn, Riadh Munjy, Stephen Choi Aug 2025

Roadside Asset Extraction From Mobile Lidar Point Cloud, Yushin Ahn, Riadh Munjy, Stephen Choi

Mineta Transportation Institute

Mobile LiDAR systems are powerful tools that help us map roads and their surroundings in 3D with great speed and precision. The data provided by these systems support urban planning efforts, digital mapping, transportation infrastructure maintenance, and more. This report presents a comprehensive workflow for roadside asset extraction using Mobile Terrestrial Laser Scanning (MTLS) data, focusing on road lane detection, cross-section slope analysis, and point cloud classification. Roadside asset extraction is the identification and classification of roadside features like signs and poles. The dataset, acquired using a high-resolution mobile LiDAR system, contains over 5.7 billion points (pieces of data) across …

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Analyzing Sports Motion Using Calculus, Joshua Vidal, Maryam Khazaei Pool Aug 2025

Analyzing Sports Motion Using Calculus, Joshua Vidal, Maryam Khazaei Pool

Faculty Research, Scholarly, and Creative Activity

Calculus helps analyze sports motion by modeling a ball's vertical path as a quadratic fuction, capturing initial velocity, gravity, and height. Differentiating this function reveals velocity and acceleration, key to finding peak height, time to peak, and airtime. Using Python, we can accurately calculate and visualize these motions. Linear algebra further connects player skills like strength, technique, and accuracy to kick distance, enabling performance prediction and comparision.

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A Systematic Review Of Machine Learning In Groundwater Monitoring, Mrunmayee Dhapre, Shrikant Jadhav, Debanjana Das, Jehanzeb Khan, Youngsoo Kim, Sen Chiao, Thomas Danielson Aug 2025

A Systematic Review Of Machine Learning In Groundwater Monitoring, Mrunmayee Dhapre, Shrikant Jadhav, Debanjana Das, Jehanzeb Khan, Youngsoo Kim, Sen Chiao, Thomas Danielson

Faculty Research, Scholarly, and Creative Activity

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This …

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Machine Learning-Guided Field Site Selection For River Classification, Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan Aug 2025

Machine Learning-Guided Field Site Selection For River Classification, Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

Faculty Research, Scholarly, and Creative Activity

Sufficient abundance and variety of field site sampling are crucial for obtaining an accurate reach-scale river classification of a regional stream network in support of scientific research and river management. However, many studies still randomly select field sites or only visit accessible streams. This leads to an inadequate exploration of stream characteristics, resulting in incomplete or inaccurate classification. Machine learning has been recognized for discovering and extracting streams’ geomorphic patterns efficiently and accurately from data, but its application in field site sampling design is still in its infancy. This study developed a general and practical field site selection framework by …

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Dynamic Mode Decomposition Of Geostrophically Balanced Motions From Swot Cal/Val In The Separated Gulf Stream, Takaya Uchida, Badarvada Yadidya, Karl E. Lapo, Xiaobiao Xu, Jeffrey J. Early, Brian K. Arbic, Dimitris Menemenlis, Luna Hiron, Eric P. Chassignet, Jay F. Shriver, Maarten C. Buijsman Aug 2025

Dynamic Mode Decomposition Of Geostrophically Balanced Motions From Swot Cal/Val In The Separated Gulf Stream, Takaya Uchida, Badarvada Yadidya, Karl E. Lapo, Xiaobiao Xu, Jeffrey J. Early, Brian K. Arbic, Dimitris Menemenlis, Luna Hiron, Eric P. Chassignet, Jay F. Shriver, Maarten C. Buijsman

Faculty Research, Scholarly, and Creative Activity

The decomposition of oceanic flow into its geostrophically balanced and unbalanced motions carries theoretical and practical significance for the oceanographic community. These two motions have distinct dynamical characteristics and affect the transport of tracers differently from one another. The launch of the Surface Water and Ocean Topography (SWOT) satellite provides a prime opportunity to diagnose the surface balanced and unbalanced motions on a global scale at an unprecedented spatial resolution. Here, we apply dynamic-mode decomposition (DMD), a linear-algebraic data-driven method, to tidally-forced idealized and realistic numerical simulations at submesoscale-permitting resolution and one-day-repeat SWOT observations of sea-surface height (SSH) in the …

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Seeing Bias Through Eye Movements: Investigating Empathy And Engagement With Authentic And Altered Refugee Imagery, Reem Albaghli, Akshay Sunil Gurnaney, Nada Attar Jul 2025

Seeing Bias Through Eye Movements: Investigating Empathy And Engagement With Authentic And Altered Refugee Imagery, Reem Albaghli, Akshay Sunil Gurnaney, Nada Attar

Faculty Research, Scholarly, and Creative Activity

This study investigates perceptual biases in responses to Syrian and Ukrainian refugee images using human-computer interaction (HCI) techniques. We hypothesized that media portrayals might shape empathy and influence both biases and donation behavior. Participants viewed both authentic and face-swapped images, with eye-tracking data capturing pupil size and decision-making patterns. In Experiment 1, participants' eye-tracking data and donation behavior indicated higher engagement and donations for authentic images, suggesting a preference for unaltered images without ethnicity-based bias. Experiment 2 explored this further by presenting cropped refugee faces alongside neutral or intellectual art, asking participants to choose the art that best matched each …

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Perceptions Of Department Chairs And Program Directors Of Ethnic Studies In The California State University System: A Convergent Parallel Mixed Methods Study, Christopher Jarrett Cox Jul 2025

Perceptions Of Department Chairs And Program Directors Of Ethnic Studies In The California State University System: A Convergent Parallel Mixed Methods Study, Christopher Jarrett Cox

Dissertations

The purpose of this dissertation study is to explore the conditions of Ethnic Studies departments and programs in the California State University (CSU) system nine years after the release of the CSU Ethnic Studies Task Force report released in 2016. Using a convergent parallel mixed methods analysis, this dissertation revisits several questions from the original CSU survey to assess the effectiveness of Ethnic Studies units in the CSU. Respondents to the survey included 13 of the 58 recognized department chairs and program directors for Ethnic Studies units among the 23 campuses of the CSU system, and interview participants included 5 …

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Cost-Effective Automated Uhi Mapping With Ai: A Case Study Of A Scalable Framework For Climate Equity In San José, California, Martin Alvarez Lopez Jul 2025

Cost-Effective Automated Uhi Mapping With Ai: A Case Study Of A Scalable Framework For Climate Equity In San José, California, Martin Alvarez Lopez

Master's Theses

Urban areas experience the Urban Heat Island (UHI) effect, with higher temperatures than rural areas, disproportionately impacting low-income communities. Mapping UHIs is a process that usually requires significant amount of human resources, and is not scalable. The lack of accurate and detailed UHI maps makes it difficult for decision makers to design effective mitigation strategies. In this work we introduce a cost-effective, scalable, and universally applicable UHI mapping framework that leverages open-source data and AI-driven feature extraction from remote sensing imagery. Using various causative factors such as city characteristics, anthropogenic heat, city canyons, and meteorological variables, we create UHI maps …

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Stead: Spatio-Temporal Efficient Anomaly Detection For Time And Compute Sensitive Applications, Andrew Gao Jul 2025

Stead: Spatio-Temporal Efficient Anomaly Detection For Time And Compute Sensitive Applications, Andrew Gao

Master's Theses

This paper presents a new method for anomaly detection in automated systems with time and compute sensitive requirements, with unparalleled efficiency. As these systems become increasingly popular, ensuring their safety has become more important than ever. Therefore, this paper focuses on how to quickly and effectively detect various anomalies in the aforementioned systems, with the goal of making them safer and more effective. Many detection systems have been developed with great success under spatial contexts; however, there is still significant room for improvement when it comes to temporal context. While there is substantial work regarding this task, there is minimal …

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How Effective Are Engineered And Non-Engineered Riparian Forest Restoration Projects In Providing Salmonid-Quality Habitat In The San Francisco Bay Area?, Alexander Ivanov Jul 2025

How Effective Are Engineered And Non-Engineered Riparian Forest Restoration Projects In Providing Salmonid-Quality Habitat In The San Francisco Bay Area?, Alexander Ivanov

Master's Theses

Streams and their riparian zones in the San Francisco Bay Area have been degraded, severely harming salmonid populations in the region. Ecological restoration is a common approach to return stream function. Such restoration projects vary widely in intensity, from low-impact vegetation removal/planting to highly engineered projects involving structures and grading. This research investigated the effectiveness of streams restored using engineered versus non-engineered solutions in supporting salmonid habitat viability in San Francisco Bay Area streams. Fourteen streams, seven from each of the two types of restoration, were sampled in the wet and dry season in 2024, three times each season. Data …

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Quantization On Graph Neural Networks For Image Classification, Rithik Reddy Katpally Jul 2025

Quantization On Graph Neural Networks For Image Classification, Rithik Reddy Katpally

Master's Theses

Quantization has become a key approach for reducing storage and computational demands of deep neural networks while maintaining high accuracy. Although 8-bit quantization is well-established for convolutional architectures such as ResNet50 and MobileNetV2, its application to graph-based vision models remains underexplored. In this work, we extend quantization-aware training to Vision Graph Neural Networks (ViGs) and conduct comparisons with quantized CNNs on the CIFAR-100 dataset. To ensure parity, all models have same training hyperparameters such as learning rate, batch size, optimizer, number of epochs. We used numerous techniques to preserve performance for low-bit precision. First, Pauta Quantization clips activation outliers based …

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Generalizability Of Existing Ai Models For Automatic Disfluency Detection In Children's Speech, Caryn W. C. Iwakiri Jul 2025

Generalizability Of Existing Ai Models For Automatic Disfluency Detection In Children's Speech, Caryn W. C. Iwakiri

Master's Theses

Achieving consensus in identifying disfluencies and diagnosing developmental stuttering has been a significant challenge for many clinicians, particularly in young bilingual children, who tend to show increased disfluencies. Artificial intelligence (AI) has the potential to recognize more complex yet systematic patterns that might not be easily observable by even trained clinicians. It also has the potential to bring consistency in the measurement of stuttered speech. This study compares the performance of four AI models, their performance on detecting disfluencies including both stutter-like-disfluencies (SLD) and typical disfluencies (TD) in monolingual and bilingual children's data, then explores whether increasing the quantity of …

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Quantum Algorithm Emulation Using Fpgas, Samuel Petruescu Jul 2025

Quantum Algorithm Emulation Using Fpgas, Samuel Petruescu

Master's Theses

Field Programmable Gate Arrays (FPGAs) have been used in most of the physics sub-fields for various unique purposes. This includes particle physics, quantum optics, and, more recently, quantum computing. FPGAs boast many benefits over previous experimental and computational setups. They are versatile, easy to program, and cost-effective, leading to an understandable desire to incorporate them into the new field of quantum computing. While FPGAs have been used to help control the readout and control of physical qubits, they can also be a good tool for improving algorithm simulations, which is the focus of this paper. Different algorithms have different computational …

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Superconducting Quantum Chip Design And Simulation, James F. Saslow Jul 2025

Superconducting Quantum Chip Design And Simulation, James F. Saslow

Master's Theses

This work presents a comprehensive analytical and simulation-based study of superconducting quantum chip architectures, spanning both 2D planar and 3D-integrated designs. We first design and simulate a planar quantum chip layout using Qiskit Metal, which is imported into Ansys HFSS (High Frequency Structure Simulator) to construct a 3D model and perform electromagnetic simulations. A detailed analysis using Driven Modal, Eigenmode, Magnetostatic, and Q3D Capacitance Extraction solvers ensures our simulations accurately reflect physical behavior. The results are validated against analytical models from microwave engineering. pyEPR (Python Energy Participation Ratio) is used to extract key quantum metrics, including eigenfrequencies, quality factors, decoherence …

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Fluid-Structure Interaction Comsol Model For Unsteady Flow Through A 3d Bi-Leaflet Mechanical Heart Valve, Aya Zaky Jul 2025

Fluid-Structure Interaction Comsol Model For Unsteady Flow Through A 3d Bi-Leaflet Mechanical Heart Valve, Aya Zaky

Master's Theses

In this study, we developed a 3D Fluid-Structure Interaction (FSI) model using COMSOL© to analyze the unsteady flow through a bi-leaflet mechanical heart valve. The model incorporates sufficient complexity to test various factors, such as leaflet angle rotation, material selection for rigid components, fluid properties, and different simulation modes. Simultaneously, the model maintains a simplified structure and is developed using a single software platform, facilitating ease of modification and expansion by other researchers or developers. The mechanical heart valve (MHV) design was created using St. Jude’s geometric parameters as a reference. The input and output pressure waveforms are derived from …

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Using Facial Recognition For Selective Pose Detection, William J. Parker Jul 2025

Using Facial Recognition For Selective Pose Detection, William J. Parker

Master's Theses

Pose detection involves locating and identifying key body points for all individuals within a frame. This enables the ability to convert the pose into a digital format, which can then be recorded and analyzed for a variety of purposes. Advancements in the field have already opened applications in areas such as digital fitness coaches, fall detection, and virtual reality. Existing approaches primarily focus on tracking all detected individuals, which limits the practical applications when attempting to analyze a single or specific subject when there are other people in frame. Previous work has discussed integrating identification, but these approaches use identification …

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Sirilla: Predicting Traffic Flow Via Stacked Decentralized Federated Learning, Andrew Selvia Jul 2025

Sirilla: Predicting Traffic Flow Via Stacked Decentralized Federated Learning, Andrew Selvia

Master's Theses

Billions of people today rely on traffic predictions to optimize their travels. Digital mapping services deliver accurate predictions by learning from vast troves of historical data. Impressive as these systems are, their assumptions do not always apply. They depend on an endless flow of sensitive user data to a central authority, a stable Internet connection, and trustworthiness on both sides of the traditional client-server model. This thesis explores a novel architecture which bucks those assumptions. In the proposed model, traffic data remains on edge devices which individually train models via federated learning. Beyond the obvious privacy benefits, this architecture enables …

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"Are You My Counselor?" A Call To Action How Latinx General Counselors Can Support Latinx Student Retention Rates At California Community College Hispanic-Serving Institutions, Valentín García Cabrera Jul 2025

"Are You My Counselor?" A Call To Action How Latinx General Counselors Can Support Latinx Student Retention Rates At California Community College Hispanic-Serving Institutions, Valentín García Cabrera

Dissertations

Latinx students comprise the largest population within the California Community College (CCC) system, but experience disproportionately lower retention rates. This dissertation used a phenomenological qualitative design and testimonios collected from Latinx general counselors to explore factors influencing Latinx student retention. Sixteen tenured Latinx general counselors from ten Northern California Hispanic-serving community colleges served as participants. Guided by two research questions, the study explored counselors’ perspectives on the factors influencing Latinx community college students’ retention and the counseling strategies, services, and resources that General Counseling Departments should provide to support Latinx student retention. Grounded in Latinx Critical Race Theory, the study …

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Contracts, Communication, And Contextual Integrity: A Case Study About Student Data Use In Higher Education, Joseph Fessehaye Jul 2025

Contracts, Communication, And Contextual Integrity: A Case Study About Student Data Use In Higher Education, Joseph Fessehaye

Dissertations

This case study examines the contractual agreements between the University of Missouri System and the California State University System with the Educational Advisory Board (EAB), focusing on how these contracts align with institutional student data privacy policies. The research utilizes a Policy Document Analysis approach and explores both universities’ strategic plans, student data privacy guidelines, and the third-party agreements with EAB, using a Contextual Integrity framework to analyze how student data is managed and protected. Through the analysis of these documents, this dissertation investigates whether the flow of student data within the context of these contracts meets institutional privacy standards …

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Cultivating Belonging: Empowering Hispanic Parents In The U.S. Education System, Kalpana Galagali Jul 2025

Cultivating Belonging: Empowering Hispanic Parents In The U.S. Education System, Kalpana Galagali

Dissertations

This qualitative study explores the experiences of Hispanic families at Bay Area Middle School and the strategies I employed to create a sense of belonging and support for both students and their families. Key strategies included fostering effective communication through Spanish interpreters, celebrating cultural traditions, and organizing inclusive events like the annual cultural fair. Parents expressed gratitude for the personal attention their children received and noted how these initiatives positively impacted their sense of belonging. The findings highlight that cultural integration and community-building efforts significantly enhanced family engagement, transforming perceptions of the school environment from feelings of disconnection to appreciation …

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Early Care And Education Systems In The United States Of America: Voices Of The Past, Present, And Future, Timothy Harper Jul 2025

Early Care And Education Systems In The United States Of America: Voices Of The Past, Present, And Future, Timothy Harper

Dissertations

This mixed-methods study centers the voices, knowledge, and leadership of Black and Indigenous women working in Early Care and Education (ECE) while tracing how racism, sexism, and classism have shaped the field from colonization to the present day. Twenty in-depth interviews with sixteen Black and Indigenous educators across the United States are paired with secondary analyses of national workforce data to create a multilayered portrait of their professional realities. Grounded in Black feminist thought and Indigenous desire-based inquiry, the analysis surfaces six inter-locking themes: (a) enduring structural barriers that depress wages and limit advancement, (b) the day-to-day resilience that sustains …

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Lessons From The 2023 Ieee Autonomous Drone Chase Challenge, Luigi Raphael I. Dy, Kristoffer B. Borgen, John H. Mott, Yung Hsiang Lu, Li Yu Lin, Zhangpeng Yang, James M. Goppert, Jakub Tomczak, Stefano Roccella, Andrea Vannini, Zhiwei Dong Jul 2025

Lessons From The 2023 Ieee Autonomous Drone Chase Challenge, Luigi Raphael I. Dy, Kristoffer B. Borgen, John H. Mott, Yung Hsiang Lu, Li Yu Lin, Zhangpeng Yang, James M. Goppert, Jakub Tomczak, Stefano Roccella, Andrea Vannini, Zhiwei Dong

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

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Indirect Excitons And Many-Body Interactions In Ingaas Double Quantum Wells, Christopher L. Smallwood, Rachel Owen, Matthew W. Day, Takeshi Suzuki, Rohan Singh, Travis M. Autry, Smriti Bhalerao, Fauzia Jabeen, Steven T. Cundiff Jul 2025

Indirect Excitons And Many-Body Interactions In Ingaas Double Quantum Wells, Christopher L. Smallwood, Rachel Owen, Matthew W. Day, Takeshi Suzuki, Rohan Singh, Travis M. Autry, Smriti Bhalerao, Fauzia Jabeen, Steven T. Cundiff

Faculty Research, Scholarly, and Creative Activity

Spatially indirect excitons in semiconductor quantum wells are relevant to basic research and device applications because they exhibit enhanced tunability, delocalized wave functions, and potentially longer lifetimes relative to direct excitons. Here we investigate the properties of indirect excitons and their coupling interactions with direct excitons in asymmetric InGaAs double quantum wells using optical multidimensional coherent spectroscopy and photoluminescence excitation spectroscopy. Analyses of the spectra confirm a strong influence of many-body effects and reveal that excited-state zero-quantum coherences between direct and indirect excitons in the quantum wells dephase faster than the much-higher-energy single-quantum coherences between excitonic excited states and ground …

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Advancing The Upper Class E Traffic Management (Etm) Concept: Results Of Nasa’S First Etm Collaborative Evaluation With Industry Partners, Deborah L. Bakowski, Connie L. Brasil, Kevin Christian Garzon Galindo, Abhinay Tiwari, Paul U. Lee, Conrad Dang-Gabriel, Jeffrey Homola, Mark Evans Jul 2025

Advancing The Upper Class E Traffic Management (Etm) Concept: Results Of Nasa’S First Etm Collaborative Evaluation With Industry Partners, Deborah L. Bakowski, Connie L. Brasil, Kevin Christian Garzon Galindo, Abhinay Tiwari, Paul U. Lee, Conrad Dang-Gabriel, Jeffrey Homola, Mark Evans

Faculty Research, Scholarly, and Creative Activity

Innovative aircraft design and emerging technologies are enabling new types of uncrewed, high-altitude long endurance (HALE) vehicles, including fixed-wing HALE vehicles and stratospheric balloons to operate above 60,000 feet. However, due to a variety of factors including the diverse performance characteristics and extended mission duration of these new vehicles, the Air Traffic Management system faces challenges in meeting the needs of this high-altitude environment. To address these challenges, the Higher Airspace (HA)/Upper Class E Traffic Management (ETM) concept proposes using a scalable, cooperative approach tailored for high-altitude operations. To evaluate the feasibility of the HA/ETM concept, NASA Ames Research Center …

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