Spring 2024
Spring 2024 Muhammad Rashid Best Project Award winners.
Spring 2024 First Place
Unmanned Aerial Vehicle Detection System (UAVDS) and Remote ID Tracking System (RIDTS)
Team Members
Bill Fine, Juan Rivera Padilla, Devin Searcy
Mentor
Dr. Tarek Youssef Elsayed
Abstract
The Unmanned Aerial Vehicle Detection System (UAVDS) and Remote ID Tracking System (RIDTS), developed by UWF students, are two innovative solutions for detecting and identifying UAVs. These systems employ a dual approach to detect UAVs within a given area efficiently. The UAVDS utilizes advanced machine learning algorithms to monitor specific UAV radio frequency (RF) protocols, delivering a precise detection probability to the user. Meanwhile, the RIDTS focuses on capturing Remote ID signals emitted by UAVs, as recent Federal Aviation Administration (FAA) guidelines mandated. Both systems boast impressive capabilities, providing detection results within a remarkable three-second timeframe. Together, they form a robust defense against unauthorized UAV activity, ensuring enhanced safety and security in airspace monitoring.
Spring 2024 Second Place #1
Precision Localization System Using UWF Radio Technology
Team Members
Kevin Tan, John Cobb, Dillon Holder, and Alexander Phan
Mentor
Dr. Tarek Youssef Elsayed
Abstract
GPS systems have predominantly tracked objects. But GPS systems have a limitation- they aren’t very accurate, and they don’t function correctly indoors. Ultra Wideband radio waves don’t have these issues and perform more accurately and reliably than Bluetooth connections. Our localization system uses at least one “tag” that initiates a signal to at least three “anchors” to range distance data between them. This data is used to calculate the location of the tag. A single room and the immediate adjacent hallway outside are mapped and measured using centimeters, and more rooms can be added modularly if desired. The tag can be put on any object desired to be tracked, and this is displayed and calculated within the user interface, which is visually shown. The three anchors actively used for calculation appear green, while the idle one is red. The anchors outside of the active room are grey. This system can support up to 32 tags per room.
Spring 2024 Second Place #2
Traffic Monitoring and Alert System
Team Members
Matthew Hirst, Samer Mando, Katie Wilcox, Ryder Swan
Mentor
Dr. Bhuvana Ramachandran
Abstract
The project statement was given by Florida Power & Light (FPL) to UWF students with a critical goal: to enhance the safety of workers in the Maintenance of Traffic (MOT) zones through a student-developed project. This urgency stemmed from an incident where workers were nearly struck by an oncoming vehicle, highlighting the need for enhanced protective measures. Our team responded by designing a system with four key components—a sensor, an alert mechanism, a control panel, and a robust physical setup—using sophisticated engineering techniques to address these challenges. We aimed to significantly boost safety measures around MOT zones, reducing the risk of similar incidents.
We equipped the system with advanced technology, including Doppler/FMCW radar boards (OPS 243 & OPS 241), a Raspberry Pi4 for data processing, PLA filament, and an ABS shroud for physical housing, complemented by a custom-made PCB for seamless connectivity. The heart of our system lies in an algorithm that processes data on vehicle speed, acceleration, and direction to pinpoint potential hazards. This algorithm calculates the average speed of approaching vehicles and their necessary deceleration to navigate past the MOT zone safely. It ensures that if a vehicle’s speed and trajectory pose a threat—based on proximity and acceleration—the system triggers an alert, giving workers at least one second of advance notice, with auto-adjustments based on real-time traffic conditions.
The results of this project have been highly promising. Our integrated sensor and control system effectively communicates with the alert mechanism, signaling workers when danger is imminent. The algorithm’s ability to preemptively detect risky driving behaviors substantially increases worker safety. This project fulfilled its initial objectives and provided the team with valuable experience in applying engineering principles to real-world safety challenges. The team hopes this project can inspire future safety initiatives and be used as a prototype to increase workers' safety in utility construction zones. The team is grateful for its collaboration with FPL and looks forward to this project's positive impact on worker safety.
