Founded from the real world and impacting the real world, the DT Lab and its former body are injecting impactful intelligence into a wide range of data services.

Our research

A Digital Twin for Managing the Power Resilience of Telecom Infrastructure

Aiming to improve the power resilience of the telecom infrastructure in the events of natural disasters, this project leverages advanced AI techniques and builds a dedicated digital twin platform that empowers us to facilitate data sharing, real-time risk predicative analysis, and situation-aware recommendations to sustain critical communication services.

Smart Flood and Storm Intelligence Sensing Initiative

Aiming to enhance community-based alerting systems for a safer and more resilient environment, we develop cutting-edge AI techniques and construct a flood intelligence digital twin that seamlessly integrates historical and real-time data, empowering us to provide high-resolution predictions of risks to critical infrastructure and high-risk community groups in real-time.

O-RAN Enabled QoE Enhancement with Digital Twin and Artificial Intelligence

We build a 5G digital twin model to investigate data extrapolation and prediction using ML/AI technologies for intelligent scheduling to manage radio resources to maximise QoE at per user and per application granularity.

Advanced smart city data analysis: advanced license plate recognition

In this project, researchers are developing advanced deep neural network models for accurate detection, recognition and analysis of real-world license plates from Indian and middle eastern countries, collaborating with the Department of Industry, Innovation and Science and iOmniscient Pty Ltd.

Medical body slice image segmentation and 3D reconstruction

Real human body slices are more complicated and contain more noise than traditional MR and CT images. In this project with Byker Digital Biotechnology, we are working to build a 3D model from real human body slices, overcoming substantial challenges in the accurate and automated annotation of large volumes of training data for deep-learning-based segmentation of tissues and organs toward 3D human body reconstruction.

 

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