Artificial Intelligence I

3:00 PM – Wearable Biomonitoring for Resource Management in Lunar EVA
Lucas Liang, Peak Manopchantaroj, Allan Garcia, Victor Garcia, Zeiler Randall-Reed, and Yogananda Isukapalli, University of Santa Barbara

Lunar extravehicular activities (EVAs) are long missions in which astronauts perform various tasks under extreme conditions with limited consumables. To mitigate the risk of depleting critical consumable resources, we propose a wearable biomonitoring system that measures, analyzes, and predicts physiological signals, including heart rate, blood oxygen saturation, respiration rate, body temperature, step rate, and arm swing rate. Alongside barometric data, these metrics are collected by a suite of sensors integrated with a Nordic nRF52840 microcontroller that filters the information and transmits it via low-energy Bluetooth to a MongoDB database. Preliminary data is labeled and used to train a machine learning (ML) model. This model can then analyze new data, classify the activity, and make predictions about pure oxygen consumption. Finally, all data is sent to a desktop application that displays calculated biomarker data, predictions, and feedback. This system promotes autonomy by offering insights into physical strain and enabling data-driven decisions during EVA tasks.

3:20 PM – Dynamic Spectrum Management: Traditional Approaches vs. AI- Driven Real-Time Allocation in Military Systems
Farzad Moazzami, Morgan State University

Efficient spectrum management is critical to address the growing demands of wireless communications, particularly in congested and contested environments such as military operations. Traditional spectrum management methods, based on fixed allocations and centralized control, have proven insufficient due to underutilization, inefficiency, and limited adaptability. This paper presents a comprehensive comparison between traditional and AI-driven approaches to Dynamic Spectrum Management (DSM), with emphasis on real-time adaptability and interference mitigation. We review classical allocation models, early cognitive radio techniques, and recent AI-based advances in spectrum sensing, allocation, and interference management. The core contribution of this work is introduction of a novel methodology for AI-driven real-time spectrum allocation in military systems, integrating supervised learning for spectrum prediction and reinforcement learning for autonomous decision-making. We propose specific AI-based strategies, including multi-agent coordination, proactive interference prediction, adaptive power control, and adversarial robust learning, to enhance resilience against both unintentional interference and hostile jamming. This research strongly suggests that AI-driven DSM can significantly improve spectrum utilization, reduce interference, and provide mission-critical reliability in military communication systems, while offering a roadmap for broader adoption in civilian and regulatory contexts.

3:40 PM – Exploring the Integration of Machine Learning and Animatronic Attention
Chayse Joseph Inniss, Cole Garolis-Bechtold, David Sepulveda, Icarus Newton, Isabella de Sousa Proulx, Lucas Tuan, Lucia Alday, Makena Dundon, Nathaniel Bidwell, Richard Posthuma, Tyler Bunnow, and Dr. Michael Marcellin,University of Arizona

Advancements in Artificial Intelligence (AI) are transforming the themed entertainment industry by enhancing animatronic engagement and immersion. Traditional animatronics rely on pre-programmed sequences or human operators, limiting their responsiveness to guests. Exploring AI and camera tracking can enable animatronics to interact dynamically with large crowds and adjust behavior based on real-time audience actions. By integrating telemetry, sensors, and machine learning, animatronics can focus their attention and respond more naturally to human movements and sounds. Adaptive systems like these create a more immersive and lifelike experience, reducing the need for human control and improving overall guest satisfaction.

4:00 PM – AI Driven Security: Using AI to Explore Security and Privacy Risks
Kamal Wada Ringim Sabo, Dr. Abirami Radhakrishnan, Dr. Dessa David, Dr. Jigish Zaveri, and Dr. Farzad Moazzami, Morgan State University

With the rapid advancement of artificial intelligence (AI), cybersecurity and data privacy have become pressing concerns in modern IT systems. Traditional defense mechanisms struggle to keep pace in addressing the scale, complexity, and speed of increasingly sophisticated and emerging threats. This paper addresses the challenges that existing security and privacy frameworks face by keeping pace with these evolving threats. This drives the need to understand how AI can contribute to identifying, analyzing, and mitigating security and privacy risks. The literature highlights the transformative potential of AI in this domain. From adversarial testing and privacy policy automation, training simulations to generation of synthetic data for privacy-preserving analysis, AI has emerged as an essential tool in advancing security resilience in complex digital environments. As a result, this research investigates the role of AI in addressing security and privacy risks in Information technology (IT) and Industrial Control Systems (ICS).