From menda.eulenfeld at tamucc.edu  Wed Jun  3 12:00:13 2026
From: menda.eulenfeld at tamucc.edu (Eulenfeld, Menda)
Date: Wed, 3 Jun 2026 17:00:13 +0000
Subject: [cosc-grad-students-list] Ruben Dominguez Dissertation - Energy
 Harvesting and Autonomous Data Collection for Coastal Resilience Monitoring
Message-ID: <BYAPR06MB3814EDC0A810A1D21EB67D3E9C132@BYAPR06MB3814.namprd06.prod.outlook.com>

Title:
Energy Harvesting and Autonomous Data Collection for Coastal Resilience Monitoring

Abstract:
Monitoring coastal environments is vital for maintaining ecological balance, supporting sustainable development, preserving economic and cultural value, and ensuring human safety from natural hazards such as flooding, shoreline erosion, and storm surge. Effective monitoring enables risk assessment, early detection, and mitigation strategies that enhance coastal resilience. Common approaches for coastal monitoring include remote sensing, data buoys, autonomous vehicles, and fixed observation stations, which collectively provide valuable environmental insight for researchers and decision-makers. However, these systems are often constrained by limited power availability, high maintenance demands, and restricted mobility, which reduce their effectiveness in long-term and large-scale deployments. These limitations highlight the need for sustainable and adaptive monitoring solutions capable of operating in dynamic coastal environments. This dissertation presents the development of self-sustaining, energy-aware coastal sensing networks that address the challenges of power limitation, communication reliability, and data collection scalability. The proposed framework integrates energy harvesting (EH) sensor nodes with adaptive communication strategies and autonomous data collection mechanisms to enable persistent and resilient environmental monitoring. The system combines battery-less LoRa-based sensor nodes powered by ambient energy sources with a mobile gateway platform capable of autonomous navigation and data retrieval. The research addresses three key objectives: (1) enabling reliable communication and network recovery under intermittent power conditions, (2) optimizing energy utilization through adaptive scheduling and energy-aware operation, and (3) integrating autonomous mobile platforms for efficient and scalable data collection across distributed sensing regions. Collectively, these contributions establish a unified framework for sustainable coastal monitoring that reduces reliance on fixed infrastructure and frequent maintenance. The proposed system supports long-term environmental sensing, improves data availability in infrastructure-limited regions, and contributes to enhanced coastal resilience through scalable and energy-efficient monitoring technologies.

Location:
RFEB 108, June 9th, 10am-11am

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From menda.eulenfeld at tamucc.edu  Thu Jun  4 09:46:37 2026
From: menda.eulenfeld at tamucc.edu (Eulenfeld, Menda)
Date: Thu, 4 Jun 2026 14:46:37 +0000
Subject: [cosc-grad-students-list] RESEARCH DISSERTATION SEMINAR NOTICE -
 Syed Izzat Ullah
Message-ID: <BYAPR06MB38145A5B3F8E6B55F27A69909C102@BYAPR06MB3814.namprd06.prod.outlook.com>

RESEARCH DISSERTATION SEMINAR NOTICE
COMPUTER SCIENCE DOCTORAL PROGRAM
COLLEGE OF ENGINEERING AND COMPUTER SCIENCE
TEXAS A&M UNIVERSITY-CORPUS CHRISTI

SUBJECT:
Uncertainty-Aware Probabilistic Forecasting of Non-Cooperative Dynamic Obstacles for Safe Autonomous Aerial Navigation
SPEAKER:
Syed Izzat Ullah
DATE:
June 10, 2026
TIME:
12:00 p.m.
PLACE:
RFEB 108
ZOOM LINK:
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ABSTRACT
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, dynamic environments for applications such as urban air mobility, surveillance, emergency response, and package delivery. Safe autonomous navigation in airspace shared with non-cooperative dynamic obstacles, such as birds, unregistered drones, and other uninformed aerial agents, remains a fundamental challenge in aerial robotics. Existing motion planning methods are constrained by significant limitations that undermine their effectiveness in real-world scenarios. First, they often rely on linear or constant-velocity motion models that do not capture the nonlinear and context-dependent behavior of dynamic obstacles, which can lead to delayed or inadequate avoidance maneuvers. Second, most trajectory planning frameworks lack modularity, treating obstacle prediction and planning as separate processes, which hinders proactive decision-making and limits the integration of advanced prediction models. Third, the absence of standardized benchmarking infrastructure to characterize the platform-level uncertainties such as actuator nonlinearities, estimator drift, and battery-dependent thrust degradation on resource-constrained aerial systems, that affect the perception-prediction-planning pipeline.

This dissertation addresses these limitations through four contributions within a modular framework for uncertainty-aware UAV trajectory planning. First, POF+MADER integrates a decentralized Kalman filter-based Probabilistic Obstacle Filter with optimization-based trajectory planners, replacing the assumption of perfect obstacle trajectory knowledge with real-time uncertainty-aware forecasts embedded as dynamic constraints. Evaluation across 800 simulation runs yields a 38.75% improvement in success rate with a 0.8% increase in navigation time, and hardware experiments on Crazyflie 2.1 UAVs show a 25% reduction in collision rate under realistic sensing conditions. Second, SynTraG introduces a parametric trajectory generator that produces configurable three-dimensional corpora of non-cooperative aerial obstacle motion using randomized kinematic primitives with heteroscedastic noise, and releases 47,894 trajectories to address the lack of public training data in this domain. Building on this data foundation, the third contribution, AeroCast, combines a Pre-LN Transformer encoder with a Mixture Density Network output head to predict per-timestep Gaussian mixture distributions over future three-dimensional displacements. On a hybrid real-and-synthetic corpus of 90,116 sequences spanning nine motion categories, AeroCast reduces Average Displacement Error and Final Displacement Error by approximately 50% relative to the strongest recurrent baseline, achieves the lowest negative log-likelihood among all compared methods, and completes inference in 0.1 ms per sample. Finally, NanoBench introduces a multi-task benchmark for the Crazyflie 2.1 nano-quadrotor comprising over 170 flight trajectories with synchronized Vicon ground truth, raw IMU measurements, onboard EKF estimates, PID controller internals, and motor PWM commands at 100 Hz. NanoBench defines standardized protocols for system identification, controller benchmarking, and state estimation, and establishes the first open dataset to jointly expose actuator-level and estimator-level signals with millimeter-accurate external ground truth on a commercially available nano-scale platform.

These contributions show that uncertainty in autonomous UAV navigation must be addressed across the full pipeline rather than within any single module. In this dissertation, uncertainty is treated at the planning level through probabilistic trajectory generation, at the data level through synthetic corpus construction with realistic localization noise, at the prediction level through calibrated multi-modal forecasting, and at the platform level through benchmarked characterization of actuation and estimation effects on resource-constrained hardware.

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