Avionics and UAV Innovation Award

Partnered by: Boeing

School: School of Electrical Engineering and Computer Science

Recognising outstanding innovation in avionics, autonomous systems and unmanned aerial vehicles, demonstrating technical excellence and the potential to advance aerospace applications.

Deep Learning-Based Multi-Channel Speech Enhancement for Low Signal-to-Noise Ratio Scenario

by  Yifei Wei

Supervisor(s): Jihui (Aimee) Zhang

Speech enhancement for drone audition is highly challenging due to the strong rotor noise. This project develops a deep learning–based multi-channel speech enhancement framework that effectively suppresses strong drone rotor noise in extremely low signal-to-noise ratio conditions.

Language-Guided Drone Simulator for Emergency Search and Rescue

by  Jack Ham, Sahil Singh, Oliver Hosking, Ian Buchanan, Aryan Kamath

Supervisor(s): Ethan Jones, Julia Drugova, Thilina Halloluwa, Mashhuda Glencross, Jason Weigel

Our project integrated multiple different AI tools to allow humans to easily interact with drones. You can use a text to speech feature to tell the drone what you want it to do, another algorithm will transfer these human instructions into actions for the drone to execute. You can also activate an object detection model to help you find the humans in this search and rescue setting.

Robust FPGA-centric CNN visual localisation for GPS-denied UAVs

by  Max Gadd

Supervisor(s): Matthew D'Souza

Global Positioning System (GPS) signals can be obstructed, interfered with, or deliberately denied, leaving small unmanned aerial vehicles (UAVs) without a reliable global reference. Visual localisation provides a camera-based alternative, but performance degrades under illumination, seasonal, and viewpoint changes. This project targets realtime, onboard, absolute localisation from a single image.