Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100206
Funder
Australian Research Council
Funding Amount
$423,154.00
Summary
Pain: Open to interpretation? This project aims to determine how pain interpretation drives pain experience, using rigorous state-of-the-art lab research. This project expects to generate new knowledge about the psychological mechanisms maintaining pain experience and avoidance behaviour, using novel techniques to measure interpretation of pain sensations. Expected outcomes include the development of an evidence-based psychological model of pain interpretation, enhanced capacity to build interna ....Pain: Open to interpretation? This project aims to determine how pain interpretation drives pain experience, using rigorous state-of-the-art lab research. This project expects to generate new knowledge about the psychological mechanisms maintaining pain experience and avoidance behaviour, using novel techniques to measure interpretation of pain sensations. Expected outcomes include the development of an evidence-based psychological model of pain interpretation, enhanced capacity to build international collaborations, and ecologically valid methods for measuring pain interpretation. This research forms a solid platform for further translational research, to build novel, scalable interventions to improve outcomes for the one in five Australians living with chronic pain.Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100978
Funder
Australian Research Council
Funding Amount
$406,818.00
Summary
A new Iron Age! Making Iron complexes fit for C-C cross-coupling catalysis. This project aims to develop new iron catalysts as alternatives to the expensive and increasingly rare noble metals currently used in C-C bond forming reactions, the most important single-step in the fine-chemicals sector.
This project expects to create a flexible yet robust framework by introducing a hemilabile ligand into the backbone of the iron complex to control the number of vacant coordination sites.
Expected outc ....A new Iron Age! Making Iron complexes fit for C-C cross-coupling catalysis. This project aims to develop new iron catalysts as alternatives to the expensive and increasingly rare noble metals currently used in C-C bond forming reactions, the most important single-step in the fine-chemicals sector.
This project expects to create a flexible yet robust framework by introducing a hemilabile ligand into the backbone of the iron complex to control the number of vacant coordination sites.
Expected outcomes of this project are 1) iron complexes able to catalyse biaryl couplings from sustainable substrates and 2) knowledge on structure-property relationships of iron-based catalytic processes.
Australia will benefit by applying its own resources and help preserving the valuable noble metals for processes relying on them.Read moreRead less
Redesigning Landcare policy to better coordinate across landholders. This project aims to study how landscape-sensitive economic incentives and social norms can be leveraged to enhance the short- and long-term effectiveness of conservation programs. It will yield new knowledge for innovative designs in conservation contracting that is urgently needed to address worsening environmental threats in Australia and worldwide. In collaboration with Nobel laureate Vernon Smith’s team, new methods and pr ....Redesigning Landcare policy to better coordinate across landholders. This project aims to study how landscape-sensitive economic incentives and social norms can be leveraged to enhance the short- and long-term effectiveness of conservation programs. It will yield new knowledge for innovative designs in conservation contracting that is urgently needed to address worsening environmental threats in Australia and worldwide. In collaboration with Nobel laureate Vernon Smith’s team, new methods and protocols will improve our ability to generate better data and better understand how social and incentive mechanisms can constructively interact to facilitate collaborative environmental action. Results will help make the achievement of environmental targets and the use of public funds more cost-effective. Read moreRead less
Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and e ....Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health. This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.Read moreRead less
Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes. This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with thei ....Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes. This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with their environment, and the simulation of spatiotemporal deformations in anatomical organs. Benefits include a better understanding of growth processes, predictive models of how degenerative diseases progress and a computational framework that will assist in designing proper mitigation and intervention strategies.Read moreRead less
Tensor and Hypergraph Methods in Fitting Visual Data. This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.
Mid-Career Industry Fellowships - Grant ID: IM230100157
Funder
Australian Research Council
Funding Amount
$788,572.00
Summary
Improving Australian iron ore comminution for green steel production. Decarbonisation of the iron ore and steel industry will involve the design of new mineral processing approaches to make the Australian iron ore amenable to green steel production. Energy-efficient ore crushing for optimal ore grades production is key to the development and economics of green steel.
This fellowship project, with embedded industry experts, aims at better understanding the fragmentation mechanics of Pilbara iron ....Improving Australian iron ore comminution for green steel production. Decarbonisation of the iron ore and steel industry will involve the design of new mineral processing approaches to make the Australian iron ore amenable to green steel production. Energy-efficient ore crushing for optimal ore grades production is key to the development and economics of green steel.
This fellowship project, with embedded industry experts, aims at better understanding the fragmentation mechanics of Pilbara iron ore. It will exploit micro-computed tomography coupled with advanced mechanical testing to offer transformative characterisation methods of ore comminution. The project outcomes will help develop new technologies and optimal production paths to realise a higher-grade iron ore needed for a decarbonised steel industry.
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Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed ....Collision Avoidance in Shipping Lanes via Intelligent Sensor Data Fusion . This project aims to develop an online maritime traffic monitoring system for reliable collision/contact avoidance that exploits complementary data from high-resolution airborne sensors and surface vessel sensors. Our approach is based on optimal scheduling and fusion of the sensor data and possibly other sources of data to construct a comprehensive dynamic picture of maritime traffic, in real-time. Moreover, the proposed methodology enables quantification of confidence in the predictions. This will provide ship owners, directly to their vessels and/or at the fleet management centres, information such as weather reports, reliable collision/no-collision warnings and avoidance strategies, on-the-fly. Read moreRead less