This research uses computational photography and machine learning to monitor electricity quality through the flickering patterns of everyday lights. By analyzing images captured in cities such as Kampala and Nairobi, the work offers a low-cost method for measuring voltage instability and improving power-grid planning in underserved communities lacking reliable electricity infrastructure.

This research improves RF and microwave power amplifiers by reducing signal distortion using analog predistortion. The approach enhances energy efficiency, signal quality, and reliability in wireless and satellite communication. By producing near-ideal signals, it supports future connectivity demands and contributes to greener, more efficient telecommunications infrastructure.

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.