Research areas summary
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RF wireless systems:
Software defined radio architecture
Cognitive radios for satellites
Radio access network architecture
UAV channel modelling
Celllular-to-UAV communications
Physical layer security
Antenna design and fabrication
Wireless RF signal propagation in hybrid environments (Terrestrial, Satellite, UAV, maritime, etc...)
Direction of arrival estimation
Communication theory
Information theory
Satellite and space systems:
Satellite communications systems
Satellite Q/V band channel modelling
Satellite constellation optimization
Optical / THz inter-satellite links
Synthetic aperture radar systems and interference
Hyper spectral imaging for remote sensing
Space situational awareness
Optical wireless communications
Underwater optical wireless communications and propagation
Optical camera communications
Vehicular optical wireless communications
Hybrid optical / THz systems
Power System Stability & Renewable Energy Integration
Investigating grid stability challenges, microgrid control strategies, and renewable power generation integration
Machine Learning for Materials Science & Engineering
Optimizing functional materials for clean energy, environmental sustainability, and advanced manufacturing applications.
Deep learning techniques for condition assessment of stormwater pipe network
Digital Material Passport
Semiconductor and Electronic Devices
Wide-bandgap Semiconductor Devices
Electronic Materials and Thin-film Deposition
Material Characterization
Neuromorphic and Bio-inspired Electronic Devices
Sensors and Photodetectors
High-power and Energy-efficient Device Technologies
Advanced Electronic Devices
Vacuum Nanoelectronics
Resistive Switching Devices
Machine learning and Signal Processing:
AI-enabled software defined radios
AI-enabled wireless channel prediction and adaptation
Signal processing for autonomous systems
Radio localization
Biomedical signal processing
Radio spectrum occupancy modelling and spectrum classification
Deep learning for device authentication and fraud detection
Deep learning for radar signal classification
AI-enabled Machine vision and SLAM applications (slam simultaneous localization and mapping)
Digital Intelligence in Buildings and Construction
Artificial Intelligence
Building Information Modelling (BIM)
Internet of Things (IoT)
Asset Management
Lifecycle Assessments
Solar Building Envelope
Nanomaterials and Environmental Technologies
Chemical Sensing
Nanomaterials
Pollution Control
Resource Recovery
Satellite communications
Digital connectivity has become the foundation of prosperity and an essential need for functioning societies. Despite this dependence, limitation on Internet access remains a prevalent issue, largely hinging on socioeconomic and geographic factors. A promising solution to attain global access equality is based on integrated terrestrial-satellite networks that rely on low Earth orbit (LEO) mega constellations. While the benefits of LEO constellations complement the shortcomings of terrestrial networks, their incorporation impacts the network design, adding complexity and challenges. Our research aim at developing new methods and techniques for satellite systems including:
Cognitive radio (dynamic spectrum access) for terrestrial and non-terrestrial networks (satellite / UAV)
Hybrid satellite-terrestrial communication systems development
Interference localization and mitigation
Satellite antenna design and beamforming
Machine learning onboard the satellite
Signal (radio source / transmitter) localization
Orbital determination and tracking
Network latency analysis and improvement
Satellite constellation optimization
Satellite Q/V band radio channel modelling
Optical / THz inter-satellite links
Synthetic Aperture Radar
Synthetic Aperture Radar (SAR) technology offers numerous benefits over optical imaging systems used for Earth observation. SAR can create high-resolution radar images of the Earth's surface, even in adverse weather conditions and independent of the sunlight. It is also capable of penetrating through clouds, fog, and vegetation, making it ideal for mapping and monitoring the Earth's terrain and infrastructure. SAR can thus be used in a variety of applications, including remote sensing, environmental monitoring, and disaster management. The unique benefit of SAR imaging is its ability to detect the smallest changes in the environment over time, allowing for long-term monitoring and assessment of natural and artificial changes to the landscape. Such advantages of SAR are only possible because it relies on the radio frequency spectrum, instead of the optical spectrum, to construct the reflectivity image of the observed scene. Another important difference to optical imagery is that SAR systems are active sensors, which means that they need to illuminate their target swath (region) by generating a relatively high-power signal and then listening to the very weak reflections bouncing back from the ground. The topics that we are actively working on are:
SAR signal interference detention and mitigation
RF-level spaceborne SAR signal emulation.
Passive spaceborne SAR
SAR radar design and implementation
Wireless optical communications
Traditionally, “wireless communication” normally refers to the systems and technologies using the radio frequency (RF) range. However, the RF range nowadays faces the congestion issue due to the scarcity of spectrum resources. On the other hand, optical wireless communications explore the electromagnetic spectrum in the optical frequency range, where hundreds of terahertz bandwidth are available, offering the possibility to support ultra-high-speed wireless communications. Optical wireless communications are also immune to conventional electromagnetic interference (EMI) and can be used in RF-hostile environments, providing a promising solution for beyond-5G. Our research focuses on advanced technologies in optical wireless communications, such as optical MIMO, advanced modulation and signal processing (both conventional and machine learning based), and silicon-based photonic integration of key devices and transceivers. Our work targets at various use-case scenarios, including data-centre networks, mobile fronthaul / backhaul, last-meter indoor connectivity, vehicular communications, underwater communications, and satellite communications and networks.
Optoelectronics neuromorphics
This capability relies on an ability to create autonomous sensing and decision-making hardware systems. These can be designed to interface to a broad range of satellite systems with a goal to create secure, efficient and smart operations while maintaining structural and mechanical integrity. We will build on key cross-disciplinary RMIT strengths in advanced materials, nanoelectronics, photonics, quantum systems and machine learning to develop a world-leading capability to service the needs of the Australian defence and space sector. The theme envisions to develop technologies that capture, analyse, process and communicate information coupled with decision making abilities using faster, lighter and smarter systems. Generating the information will rely on a multitude of sensing/detection capabilities and artificial vision catering across the electromagnetic spectrum. These technologies are required to ensure autonomous manoeuvrability, automated data analysis and maintaining satellite health via remote satellite health monitoring systems for informed decision making. The suite of technologies will open new avenues for navigation in extra-terrestrial environments thereby enhancing our capability to understand our planet and the universe in a more holistic manner. Our research in this area includes:
Novel materials for hyperspectral imaging (in the VIS and IR spectrum)
Optoelectronics neuromorphics for spaceborne AI processing
Novel sensors for space situational awareness
Low power AI processing
Reconfigurable Intelligent Surfaces
Using Reconfigurable Intelligent Surface (RIS), smart wireless networks can transmit information without generating new signals but recycling the incoming signal. However, as an emerging technology, fundamental analysis – in terms of rate, reliability, and efficiency – is needed to understand the performance of RIS-empowered wireless networks. Expected outcomes include new communication-theoretic models and the enabling technologies to realise them in practice. These smart environments have the potential to offer “greener” and more "seamless wireless connectivity" for the future wireless network.
Simultaneous Wireless Sensing and Communication
To investigate sensing, localisation and communication strategies to improve the performance of modern tactical radio networks. Such networks face all of the well-known design challenges of mobile ad-hoc networks (MANETs) but with added complication of a contested and adversarial operating environment. By exploiting the power of radio nodes to sense the radio spectrum, as well as to communicate over it, a distributed network of nodes can create a detailed picture of the surrounding radio-frequency (RF) environment: the nodes can work together to map the “RF weather”. Under this theme we design advanced sensing and localisation methods to accurately map the RF spectrum, and then exploit this map in communication system design.
AI for training efficacy and skill retention
This theme uses AI to optimize the training process of people. Using neural network models, we research physiological monitoring of training efficacy and skill retention in trainees. It creates a real-time physiological monitoring system to assess training progress and provide diagnostic feedback to trainees and instructors. It determines the link between task-specific performance, cognitive workload, and training efficacy. The research outcomes will be applied to identify the retention of skills in trainees and the need for re-training. Application examples include control tower personnel and pilot training, sports training, and customer service training
AI-aided human-machine decision-making
The project creates a distributed decision-making system of neural network units. The units will work on independent tasks based on different data modalities and labels. The outcomes from the units will support the final decision-making process. No arbitrary rules will be applied; the final decision will be made based on the information flow through the system and algebraic connections between the units. Application examples include multimodal medical diagnosis, physiological monitoring, multimodal cyber security, and person identification. A similar approach will also be used to model to predict the preferences of individuals or groups of people. It will determine how individuals choose among different alternatives and what drives their preferences. Factors contributing to the decision-making processes underlying how choices and preferences are made will be investigated. The model will provide an instrument to forecast public and personal demands. Application examples include areas such as planning, advertising, entertainment, and retailing.
Neuromorphic Sensing for Bio-Inspired Computing
This project explores the design and development of bio-inspired sensors for neuromorphic computing. The focus is on creating efficient, low-power sensory systems that mimic biological processes, integrating advanced materials to enhance the functionality of artificial neural networks in applications like robotics and health monitoring.
Ga₂O₃ Wide-Bandgap Semiconductor Devices for UVC Detection and High-Power Applications
This project focuses on developing Ga₂O₃-based devices for both UVC detection and high-power applications. Ga₂O₃’s wide bandgap makes it suitable for efficient UVC photodetectors and high-voltage devices. Research includes optimizing material properties, device fabrication, and performance testing under various conditions to enhance the sensitivity and stability of UVC detectors. Project 2: Neuromorphic Sensing for Bio-Inspired Computing This project explores the design and development of bio-inspired sensors for neuromorphic computing. The focus is on creating efficient, low-power sensory systems that mimic biological processes, integrating advanced materials to enhance the functionality of artificial neural networks in applications like robotics and health monitoring.
Vacuum Electronics is Back to Future - empty state electronics
This project aims to investigate nano-vacuum channel electronic devices and circuits operating at high frequencies with high mechanical strength by exploring novel designs, materials, and nanofabrication methods. This concept re-imagines a century-old vacuum tube at the nanoscale to create virtual-vacuum or empty-state, enabling scattering-free carrier transport. New knowledge in the field of Empty-State Electronics will be created by harnessing the best of vacuum electron devices and advanced fabrication techniques. The expected outcomes are a new class of fundamental electronic components and circuits which operate at high frequencies, are power-efficient, and radiation-immune. Beyond high-speed benefits, the discovery of electron-beam triggered can be applied to Space technology.
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