Helicopters operate low and slow in cluttered environments where wires, towers, poles, cranes, and terrain are the leading causes of controlled-flight-into-obstacle accidents. A large amount of obstacle information already exists in open-source geospatial databases and open imagery sources, but it is fragmented, inconsistently formatted, and not directly consumable by airborne avionics. This project asks whether those open sources can be aggregated and normalized into a single obstacle database in a standard, avionics-consumable format (target: ARINC 815), and whether a vision system can both validate the data against real imagery and visually augment (overlay) detected obstacles onto that imagery to improve pilot obstacle awareness and detection.

Scope
1. Work with UA engineers (avionics data, vision/imaging, and software) to understand the ARINC 815 format and how airborne systems consume it, plus the accuracy, integrity, and update requirements that matter for flight use.
2. Survey and evaluate available open-source obstacle and geospatial databases and open imagery sources for coverage, accuracy, currency, and licensing; determine what is usable and where gaps exist.
3. Design a pipeline that ingests these open sources, deconflicts and normalizes them, and outputs a consumable obstacle database in ARINC 815 format.
4. Validate the dataset using Google Maps / open aerial and street-level imagery to confirm obstacle presence and attributes.
5. Develop a prototype vision system to detect obstacles in imagery, correlate detections against the database, and artificially paint/overlay the known obstacles onto the image to demonstrate improved detection and situational awareness.
6. Test with sample geographies supplied or agreed with UA, measure detection performance and data quality, and iterate.
7. Develop a plan (including cost, data-licensing, and scalability considerations, and a path toward the data integrity/certification questions UA would need to address) to move from prototype to a usable tool.
8. Present results in a video conference and PowerPoint presentation to a group of UA engineers.

Additional Resources
UA will supply guidance on the ARINC 815 format and consumption requirements and can provide sample areas or reference data as agreed.

Skills Requested
Software engineering, data engineering/ETL, GIS and geospatial data, computer vision and image analysis, machine learning, data analysis and visualization. Bonus knowledge: avionics/aviation data standards, remote sensing/photogrammetry, systems engineering, statistics.

Benefits to Society
Better obstacle detection for rotorcraft saves lives by protecting crews and the public during emergency medical services (EMS/air ambulance), search and rescue missions, law enforcement activities, firefighting, and utility/infrastructure operations, where low-altitude flight near wires and towers carries the highest risk.

Objective:
Develop a modular motorcycle rider assistance system that improves rider safety, awareness, and communication through an integrated heads-up display, audio system, rider networking, and intelligent sensing while remaining compatible with existing motorcycles and helmets.
Scope:
Design and integrate a smart motorcycle helmet system using both COTS and custom-built components to deliver critical rider information—navigation, speed, hazard alerts, and enhanced situational awareness—alongside comfort features such as noise cancellation, music, and phone connectivity, all accessed through a voice controlled heads-up display.

The primary objective of this project is for the student team to design, build, and validate a prototype automated bond-tooling lubrication and inventory management system suitable for real-world industrial manufacturing applications. The system shall provide a controlled and repeatable method of applying lubricant while maintaining traceability and inventory records for each piece of bond tooling. The solution should be capable of identifying individual tools, tracking their usage history, maintaining inventory status, and supporting calibration scheduling based on predetermined usage metrics such as heat-cycle counts or production cycles.

A secondary objective is to develop an automated inspection capability that evaluates bond-tooling surfaces after use. This inspection system should utilize vision-based or other sensing technologies to identify wear, damage, contamination, or other defects that could negatively affect product quality. The inspection results should be recorded and integrated into the overall tooling tracking system to support preventive maintenance and quality assurance initiatives.

Integration with Kahr Bearing's existing Ignition SCADA platform is a key project requirement. The proposed system shall include a real-time Human-Machine Interface (HMI) that allows operators to monitor bond-tooling inventory, review usage history, schedule calibration events, and access maintenance records. The HMI should provide traceability by linking tooling usage data to work orders, part numbers, batch numbers, and other relevant production information. A PLC from a Kahr-approved vendor shall serve as the primary controller for system operation and communication with the SCADA platform.

To ensure compliance with company safety standards, the project will also include the design and construction of a dedicated PTFE release-agent application enclosure. This enclosure must provide adequate ventilation and fume extraction to protect operators while maintaining a safe working environment. The team shall develop maintenance procedures and documentation for the enclosure to ensure it continues to operate within company requirements throughout its service life.

Senphonix is commercializing SleeveSense™, a continuous vital signs monitoring platform for inpatient and outpatient use. In the inpatient setting, SleeveSense communicates wirelessly via LoRa (Long Range) radio technology using a single Senphonix-provided LoRa radio deployed per hospital floor or unit. The LoRa radio has its own dedicated secure cellular connection to the Senphonix cloud, operating independently of the hospital’s network and requiring only a standard 110V outlet. Each patient bed is equipped with a Powercast bedside RF charging unit operating at 915 MHz; the LoRa radio and all Powercast units on the floor share the same frequency band and must coexist reliably.

A reliable LoRa link between the sleeve and the radio is essential for continuous, uninterrupted vital signs monitoring. However, real hospital environments present significant RF challenges: metal bed frames, medical equipment, thick concrete and drywall partitions, elevator shafts, and adjacent patient rooms all affect signal propagation in ways that standard free-space models do not capture. Senphonix deploys one LoRa radio per hospital floor or unit, relying on the long-range characteristics of LoRa to cover an entire ward from a single radio. Senphonix currently lacks empirical data on actual signal coverage, interference patterns, and whether a single radio can reliably serve all patient beds on a floor under real-world hospital configurations. The number of radios required per floor may vary depending on floor plan geometry and RF environment, and this study will establish the empirical basis for that determination.

This capstone project will systematically characterize LoRa signal propagation in a simulated and, where possible, actual hospital environment, and deliver a validated deployment specification that Senphonix's installation team can use to configure each clinical site correctly before the first patient is fitted.
Beyond characterizing coverage with the current hardware, Senphonix sees a parallel opportunity this project may pursue: to develop an improved, hospital-optimized antenna for the LoRa/cellular radio that maximizes in-building coverage and link reliability, and (if the measurement data supports it) to explore the preliminary design of a new LoRa/cellular radio purpose-built for Senphonix in place of the current off-the-shelf unit. Coverage characterization (Objectives 1–8) remains the core, must-complete deliverable; the antenna and device design work described below is a stretch objective to be scoped with the team based on progress and available time.

What if yesterday's plastic waste became tomorrow's fuel?

This project will design, build, and demonstrate a working pyrolysis reactor prototype capable of converting shredded plastic waste into separated liquid fuel products and recoverable process gases. The student team will develop the reactor, heating system, condensation train, fuel collection tanks, controls, and safety systems required to prove the process at prototype scale.

The final demonstration must show continuous conversion of representative plastic waste into usable fuel fractions, document product yields and operating performance, and identify the engineering requirements for scale-up.

The goal is not only to prove that waste plastic can be transformed into valuable products, but to establish a practical pathway from a university prototype to a deployable commercial system.

Design, fabricate, and validate a functional prototype of a robotic rattlesnake intended for controlled rattlesnake avoidance training for dogs.
The prototype should incorporate:
-Biometric snake locomotion
-Defensive coiling posture
-Realistic tail rattling sound and motion
-Wireless control
-Programmable training behaviors
-Replaceable silicone exterior
-Modular construction for easy maintenance
-Rattlesnake scent module (using commercially available training scents)

This project will support Acron Aviation’s new Automatic Dependent Surveillance – Broadcast In (ADS-B In) application, SURF-A (SURFace Alerting). This application, which runs on the aircraft’s collision avoidance radio (TCAS), provides situational awareness and alerting for potential conflicts in taxi, takeoff, and landing phases of flight. Data will be recorded any time a SURF-A safety threshold for the aircraft is crossed. When incidents or events happen in the field, airline customers will reach out to Acron to understand their data from the encounter: whether certain alerts were valid, or should be considered nuisance alerts. This process supports and improves safety of operation for the airlines and their passengers.

This project will create a tool to quickly analyze SURF-A data from real-world aircraft encounters. The Acron Customer Support Team will need to analyze events efficiently to provide timely responses to customers, and the tool will provide Customer Support experience and guidance for how the SURF-A function should or should not operate. Additionally, the Acron engineering team needs to be able to quickly analyze events to determine whether design changes are required. The tool will be used by Acron engineering and support only (not by the airline).

Acron has a partially developed tool, but has new requirements and several new, desired capabilities to add. The tool will be able to input various data recording types, processes the data by checking it against a defined set of requirements, and then provide an analysis output confirming that the event either did or did not meet performance measurements and output requirements.

Aircraft engine icing is one of the major safety-of-flight concerns in aviation industry. Number of engine icing events are reported each year, emphasizing the importance of continued research, testing, and risk mitigation. As modern aircraft engines are developed to be lighter, more fuel efficient and cost competitive, their designs may become more susceptible to engine icing risks. To mitigate these risks, icing wind tunnel testing plays a critical role in evaluating engine performance and safety under icing conditions. A successful icing wind tunnel test requires a clear understanding of particle size distribution, particle shape, and particle density. The particles used in testing may include steam-condensed droplets, atomized water spray, and snow or ice crystals.

To develop the capability to characterize icing particle size distribution, particle shape, and particle density, the test setup will incorporate multiple measurement systems. The proposed setup includes a
1) Particle Image Velocimetry (PIV) system to measure particle size, shape, and spatial distribution
2) Wire-resistance-based system to measure particle density
3) Flow-collection device with a particle trap.
Each of these systems can operate independently; however, when used together, they provide a means to calibrate and validate the overall measurement approach. The PIV and wire-resistance measurement data will require software-based post-processing to convert the raw data into meaningful results, including particle size distribution, particle shape, and particle density metrics.

As human spaceflight transitions to long-duration missions (e.g., Artemis Orion, Lunar Gateway, Lunar South Pole Station, and eventual Mars exploration), crews must manage acute medical and surgical crises without the possibility of immediate evacuation. Current space habitats lack dedicated, highly integrated clinical workspaces capable of quantifying the exact medical consumables, power, and environmental resources required to manage these off-world emergencies.

Biosphere 2 has created a space analog habitat in the original Test Module called SAM (Space Analog for Moon and Mars). It is the only analog habitat on North America that can perform closed loop environmental control tests. The Smart Surgical Bay (SSB) project will deliver a physical, modular, and data-instrumented medical/surgical bed platform designed for the spatially constrained SAM habitat. This system will serve as a high-fidelity, closed-loop simulation and education asset that passively and actively captures data regarding patient vitals, medical supply logistics, waste generation, and habitat environmental impacts during simulated medical and surgical procedures.

As human spaceflight transitions toward long-duration missions, medical crews will increasingly manage emergencies without immediate evacuation capabilities. Surgeons or providers will need to keep their cognitive skills up to date with oral quizzing in the same manner as general surgery oral boards. Building on last year’s AI General Surgery Mock Oral Board (MOB) project's foundation (https://boards-trainer.ai/home/), this project will refine the existing AI mock oral board system to eliminate hinting while streamlining case construction and grading. It will integrate an ad-supported and self-sustaining online clinical collaboration network for peer case submission/review. It will create a just-in-time (JIT) training module that cross-references existing surgical resources and vetted mock oral cases with real-time disease-specific queries. The platform will support dual deployment: a cloud-hosted environment for terrestrial residency programs and an edge-computing variant optimized for high-performance laptops, ensuring autonomous functionality during spaceflight or analog exercises where communication latency is prohibitive.