Why this Cluster exists
The ISCAS cluster exists to concentrate research on the areas of Electronics, Sensors and Digital Systems. The aim is to demonstrate a world-class capability for UniSC supported by the maturity of the engineering programs, particularly electrical and mechatronic at Moreton Bay.
The ISCAS cluster aims to:
- Target projects and collaborations in Digital Economy Research
- Focus research efforts on developing new innovative technologies for Australian industries in areas such as agriculture, aquaculture, defence, health, communications, transportation, robotics, smart grid, coastal monitoring, forestry, fisheries, farms and services in urban and regional Australia
- Operate in close collaboration with world class research centres
- Integrate research activities and outcomes into teaching
- Provide mentorship of ECRs and supervision of HDR/PhD students
Our work
The cluster members have established, and continue to establish, a reputation in their respective research fields. Some of their work is detailed below.
Li-minn Ang |
Prof. Ang has an international reputation in computer, electrical and systems engineering including IoT, intelligent systems, machine learning, visual information processing, embedded and reconfigurable computing systems (FPGA). |
David Alonso-Caneiro |
Dr Alonso-Caneiro has strong expertise in the areas of image processing, computer vision, and applied medical image analysis and devices. |
Dariusz Alterman |
Dr Alterman has significant expertise in civil, mechanical and structural engineering, artificial intelligence. He is a Fellow of IEAust. |
Christophe Gerber |
A/Prof. Gerber has strong expertise in sensors and remote sensing. He is part of the committee equipping the new MBC buildings with sensors. |
Umer Izhar |
Dr Izhar focuses on research in the areas of sensor design and integration, mechatronics, and robotics. |
Adrian McCallum |
Dr McCallum has strong expertise in sensors and systems for geotechnical engineering. |
Selvan Pather |
A/Prof. Pather has strong expertise in integrating sensors, electronics with |
Sajeeb Saha |
Dr Saha has strong expertise in the areas of sensing, renewable energy systems and storage. |
Sanjeev Srivastava |
A/Prof. Srivastava is working on projects funded by national and international agencies where he is using geospatial data collected from remote sensing satellites and drones. |
Mingzhong Wang |
Dr Wang is internationally recognised for his expertise in machine learning and data-driven systems. He has contributed as a program committee member and reviewer for esteemed AI/ML conferences and journals. |
Phil Yeoh |
Dr Yeoh has an international reputation in the areas of wireless communications, signal processing, and blockchains with applications in IoT, sensor networks, drones and information security. |
Our impact
Establishing the ISCAS cluster is a strategic initiative of the School of Science, Technology and Engineering at UniSC. These are just some of the projects in the ISCAS cluster.
AI and signal processing models for extraction of ENF signals for video geo-localisation and timestamping from smartphones
The objective of this project is to investigate the feasibility of developing techniques for an automated software tool to be able to utilise AI, machine learning and signal processing techniques to be effective for extracting ENF (electric network frequency) from video data captured from smartphones. The methodology will extend the approaches to be able to accommodate video data captured in different environments and lighting conditions, sources and compression ratios. We formulate the approach for ENF extraction and matching and validate the work on real-world datasets. The project is funded by DSTG.
Multimodal narrative extraction: Concept graph learning modelling and classification of scientific articles
The objective of this project is to investigate the feasibility of developing techniques for an automated tool to be able to rapidly classify and organize the vast amounts of textual and multimodal data for improved decision-making. The project aims to develop techniques to utilize concept graph learning (CGL) modelling for the organization and classification of scientific articles. We formulate the CGL approach for learning and modelling and validate the work on articles from real-world datasets. The final part of the project gives discussions and recommendations for the applicability of the techniques towards multimodal datasets. The project is funded by ONI and DSTG.
Optimal sizing of solar and storage for commercial buildings with EV charging infrastructure including local control algorithms and optimization for integration into a Virtual Power Plant (VPP)
This project endeavours to formulate an optimal algorithm for the sizing and energy management of solar PV and BESSs tailored for commercial buildings equipped with EV charging infrastructure. The primary objective is to maximize the utilization of energy harvested from solar PV systems. By addressing the intermittency challenges posed by varying weather conditions, fluctuating load demands, and EV charging, the designed solar PV and BESSs aim to ensure consistent energy availability. Furthermore, the study delves into the potential of virtually interlinking solar PV and BESSs across diverse commercial buildings. This coordinated energy management approach aspires to enable these interconnected systems to function collectively as a Virtual Power Plant (VPP). The project is funded by UniSC LAUNCH.
Developing autonomous aquaculture monitoring system
Diseases such as the white spot syndrome virus (WSSV) have a high mortality rate and spread quickly through the crustacean population in aquaculture settings. This research aims to develop an autonomous system for monitoring and intervention in aquaculture settings. The proposed system will help decrease crustacean mortality on the farms due to the disease. The project will particularly look at applying machine learning techniques to identify prawns in real-time and to inform on the morphological aspects of the prawns. Another aim of this project is to work towards developing soft actuators (mainly for underwater operation) to handle crustacean population. The project is funded by UniSC LAUNCH.
Brain-computer-interface assisted recovery from traumatic injuries
Some of the most vulnerable areas of the human body that can be affected in a traumatic road accident are the spinal cord and the brain. As a result of this, paralysis can occur when these areas are injured. The innovative machine that this project proposes will be used to understand which brain signals can be used to control the movement of an arm when paralysis has occurred. This can be achieved by reading voltage levels that are emitted from the brain using a Brain Computer Interface, while the arm is being moved with an anti-gravity robotic arm assisting. This is a student-led project funded by Bionics QLD under a student prize award.
In the news
We have lift-off: UniSC aims to deliver electric flying taxi
Commuters could be catching a taxi in the sky from the Sunshine Coast to Brisbane by 2030, if a new student project goes to plan.
Research partnership to focus on new transport technologies
A new research partnership between USC and the Queensland Government will help prepare the state for futuristic transport that uses emerging technologies to better connect people and goods in more sustainable, efficient and convenient ways.