Machine Learning for Fracture Risk Assessment from Simple Radiography. This project aims to develop a novel, reliable, low-cost system to detect poor bone health and assess fracture risk to help to prevent and manage osteoporosis-related fractures. Currently, osteoporosis-related fractures cost our health system millions of dollars annually and costs are increasing with our ageing population. Early detection of poor bone health will improve the effectiveness of preventive measures and ease this ....Machine Learning for Fracture Risk Assessment from Simple Radiography. This project aims to develop a novel, reliable, low-cost system to detect poor bone health and assess fracture risk to help to prevent and manage osteoporosis-related fractures. Currently, osteoporosis-related fractures cost our health system millions of dollars annually and costs are increasing with our ageing population. Early detection of poor bone health will improve the effectiveness of preventive measures and ease this burden. Current methods include unreliable, crude clinical and visual guides that suggest osteoporosis screening. The project plans to develop a novel system by applying machine learning algorithms to radiology data which is commonly captured for diagnosing other conditions.Read moreRead less
Developing key vision technology for automation of aquaculture factory. This project aims to investigate structural, coloured textural, and hyperspectral analysis approaches to achieve automated lobster molt-cycle staging and classification to the level required for commercial production. High labour cost, water contamination, and disease transmission are major barriers in Australian bay lobster aquaculture inhibiting its large scale production. Automation of the production process and reducing ....Developing key vision technology for automation of aquaculture factory. This project aims to investigate structural, coloured textural, and hyperspectral analysis approaches to achieve automated lobster molt-cycle staging and classification to the level required for commercial production. High labour cost, water contamination, and disease transmission are major barriers in Australian bay lobster aquaculture inhibiting its large scale production. Automation of the production process and reducing the human contact with animals are of high priority in the development of this Australian-led emerging industry. The project aims to develop technology to bring this world- first aquaculture factory to large scale production, and create new export opportunities for lobsters and production systems.Read moreRead less
Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the developmen ....Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the development of a cutting-edge technique to develop new intelligent applications in a decentralised and privacy-sensitive scenario. This game-changing research will advance current data mining and artificial intelligence research from centralised intelligence to decentralised intelligence with a collaboration network.Read moreRead less
Enhanced Through-Wall Imaging using Bayesian Compressive Sensing. The aim of this project is to develop radar imaging techniques which enable us to 'see' objects behind walls and opaque materials. The major intended breakthrough is the ability to image objects behind walls and inside buildings or enclosed structures without accessing the scene. Novel signal and image processing algorithms, based on Bayesian compressive sensing, will be developed to enhance image quality and resolution, improve s ....Enhanced Through-Wall Imaging using Bayesian Compressive Sensing. The aim of this project is to develop radar imaging techniques which enable us to 'see' objects behind walls and opaque materials. The major intended breakthrough is the ability to image objects behind walls and inside buildings or enclosed structures without accessing the scene. Novel signal and image processing algorithms, based on Bayesian compressive sensing, will be developed to enhance image quality and resolution, improve speed of operation, and reduce the cost and time of data acquisition and processing. Many applications are expected to benefit from this research including search and rescue, surveillance, security, and defence. The research outcomes are expected to enhance the capabilities of the Australian armed forces, counter-terrorism, police and law-enforcement agencies.Read moreRead less
Active Segmentation for Cooperative Mobile Robots in Outdoor Environments. The objective of this project is to develop a principled understanding of how to improve the segmentation of three-dimensional range data in a cooperative manner by judiciously choosing future sensor viewpoints of a team of mobile robots. The viewpoint of the robot affects both the density of three-dimensional data points and the areas that are unobservable due to occlusions. Project outcomes will open up a whole new appr ....Active Segmentation for Cooperative Mobile Robots in Outdoor Environments. The objective of this project is to develop a principled understanding of how to improve the segmentation of three-dimensional range data in a cooperative manner by judiciously choosing future sensor viewpoints of a team of mobile robots. The viewpoint of the robot affects both the density of three-dimensional data points and the areas that are unobservable due to occlusions. Project outcomes will open up a whole new approach to the process of autonomously gathering information about objects in outdoor environments, will synergise with existing classification and motion planning methods, and will support Australia's continued role as a leader in field robotics research.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH210100030
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub in Intelligent Robotic Systems for Real-Time Asset Management. This hub aims to transform the way assets and infrastructure are managed by developing new capabilities for intelligent robotic systems for inspection, monitoring, and maintenance. The hub expects to generate new knowledge in robotics and associated fields including sensing, planning, data processing, and machine learning using interdisciplinary approaches and tight collaboration between academia and industry. The ex ....ARC Research Hub in Intelligent Robotic Systems for Real-Time Asset Management. This hub aims to transform the way assets and infrastructure are managed by developing new capabilities for intelligent robotic systems for inspection, monitoring, and maintenance. The hub expects to generate new knowledge in robotics and associated fields including sensing, planning, data processing, and machine learning using interdisciplinary approaches and tight collaboration between academia and industry. The expected outcomes are robots with the ability to autonomously collect data for integration into a digital twin that provides a real-time representation of the true state of a physical asset. The benefits include both improved asset management and establishing Australia as a leading manufacturer of advanced robotic systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100964
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerg ....Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will help to safeguard Australian business, industry, and society from cyberfraud, online rumour-mongering, and financial loss.Read moreRead less
Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project ....Deep Pattern Mining for Brain Graph Analysis: A Data Mining Perspective. This project brings together experts in the fields of data mining and cognitive neuroscience. This project aims to develop new data analytics tools, algorithms, and models to combine complex multi-source neuroimage brain data and non-imaging data, to explore the interplays among these different data structures and identify novel functional patterns from complex brain graph structures. The research undertaken in this project expects to provide practical data analysis approaches and establish the theoretical foundations for data mining with multiple sources of brain data.Read moreRead less
Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin ....Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less