This research addresses excessive false alarms in hospital medical devices, which burden staff and distress patients. By detecting and filtering noisy data, the proposed system prevents false alerts while preserving true ones. Early results show complete removal of false alarms, improving efficiency, patient experience, and clinical response in healthcare settings.

This research explores quantum radar signal processing, using quantum entanglement to improve detection by better separating signal from noise. It demonstrates that quantum radars are experimentally viable and mathematically comparable to conventional systems, with potential advantages. Applications include low-power, safe technologies such as medical imaging and interference-free sensing.

A biomedical engineering team developed a handheld device that measures newborn heart rate in under 10 seconds—far faster than current tools. Using a novel sensor and real-time algorithms, it improves clinicians’ ability to intervene within the critical first minute after birth. Clinical trials are complete, the device is patented, and commercialization is underway.

This research seeks to reduce the energy consumption of 4G and 5G networks—currently about 3% of global usage—by identifying the factors that drive it. By modelling how elements like signal noise affect energy demands in antennas and processing hardware, the project aims to guide the design of more efficient, sustainable mobile networks.