Discovery Early Career Researcher Award - Grant ID: DE230100477
Funder
Australian Research Council
Funding Amount
$421,554.00
Summary
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and ....Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and protect privacy in AI applications. The anticipated outcomes should provide significant benefits to a wide range of applications, such as providing timely alerts to the media, government organisations, and the industry about misleading fake visual data, and preventing financial crimes on synthetic identity fraud.Read moreRead less
A Machine Learning Framework for Concrete Workability Estimation . Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons fo ....A Machine Learning Framework for Concrete Workability Estimation . Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons for rejection at construction sites, resulting in significant costs, waste, and delays. Multimodal data sources will be used to provide a reliable workability estimate in real time, enabling construction teams to identify and rectify workability issues in transit while continuously monitoring the adjustments effects.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100168
Funder
Australian Research Council
Funding Amount
$413,847.00
Summary
Self-Supervised Sequential Biomedical Image-Omics. This project aims to develop a self-supervised sequential biomedical image-omics model to uncover the underlying biological processes e.g., normal or abnormal. Sequential biomedical images are state-of-the-art imaging modalities which allow to depict changes in progression to the human body. New self-supervised machine learning algorithms are proposed to derive features from heterogenous and unlabelled sequential images. These derived features w ....Self-Supervised Sequential Biomedical Image-Omics. This project aims to develop a self-supervised sequential biomedical image-omics model to uncover the underlying biological processes e.g., normal or abnormal. Sequential biomedical images are state-of-the-art imaging modalities which allow to depict changes in progression to the human body. New self-supervised machine learning algorithms are proposed to derive features from heterogenous and unlabelled sequential images. These derived features will then be used to characterise the morphological and functional changes, which provide opportunities to increase understanding of progression of diseases of individual subject. The outcome from this project will provide new insights into system biology with potential future benefits in healthcare.Read moreRead less
Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global com ....Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global competitiveness. Expected outcomes of this project will help to break the communication barriers between the Deaf and hearing population. This should provide significant benefits to Deaf communities through enhanced communication and improved quality-of-life, leading to a fair, more inclusive and resilient Australian society.Read moreRead less
Collaborative Sensing and Learning for Maritime Situational Awareness. We aim to demonstrate coordinated autonomous sensing of naval assets in dynamic maritime environments, reducing the operational load required to deliver a high quality maritime situational awareness. A realistic simulation based approach will help us develop novel artificial intelligence technology including: self-adaptive strategies for dynamic asset allocation, embedded smart sensing capabilities for naval observation syste ....Collaborative Sensing and Learning for Maritime Situational Awareness. We aim to demonstrate coordinated autonomous sensing of naval assets in dynamic maritime environments, reducing the operational load required to deliver a high quality maritime situational awareness. A realistic simulation based approach will help us develop novel artificial intelligence technology including: self-adaptive strategies for dynamic asset allocation, embedded smart sensing capabilities for naval observation systems and novel approaches to continuous collaborative learning from multi-spectral media. In addition to the emerging partnership between participants, the project will advance sovereign capability to develop maritime intelligence gathering technology for the Royal Australian Navy to underpin stability in our region. Read moreRead less
Gesture-controlled interaction to enrich information access. This project is a study of gestural computing (enabled by sensors such as pressure mats, infra-red sensors and video tracking) which aims to move away from desk-bound, restrictive computing environments and towards computing that is more integral to the building structure and space itself. Linking gesture controllers and information sonification delivers a unique bridge between data and human interaction. Enriching the capacity to acce ....Gesture-controlled interaction to enrich information access. This project is a study of gestural computing (enabled by sensors such as pressure mats, infra-red sensors and video tracking) which aims to move away from desk-bound, restrictive computing environments and towards computing that is more integral to the building structure and space itself. Linking gesture controllers and information sonification delivers a unique bridge between data and human interaction. Enriching the capacity to access information in dense workplace environments is central to improved efficiency across the Australian workforce. Greater accuracy and enhanced techniques for controlling information in visually-overloaded work environments contribute to Australia's competitive leadership in a global marketplace.Read moreRead less
Quality of Service in 3G Wireless Systems with Hybrid Networks. The increasing demand of providing Internet-based services over the mobile handsets has led to the transition from circuit-based 2G systems to packet-based network architectures in the emerging 3G systems. A major technical challenge for this paradigm shift is maintaining quality of services (QoS) for the existing data and voice and future Internet services such as streaming multimedia over such packet switching networks. This proje ....Quality of Service in 3G Wireless Systems with Hybrid Networks. The increasing demand of providing Internet-based services over the mobile handsets has led to the transition from circuit-based 2G systems to packet-based network architectures in the emerging 3G systems. A major technical challenge for this paradigm shift is maintaining quality of services (QoS) for the existing data and voice and future Internet services such as streaming multimedia over such packet switching networks. This project aims to investigate end-to-end QoS solutions in the 3G systems. Australian mobile Internet and voice carriers will significantly benefit from this technology.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0347194
Funder
Australian Research Council
Funding Amount
$411,000.00
Summary
Interactive Television Audience Research Laboratory. Interactive Television is a rapidly emerging platform for global media and e-commerce that is poised to dramatically transform the role of television in society. In collaboration with a range of university and industry partners, Murdoch University aims to establish Australia's first dedicated public research laboratory for assessing consumer motivation, evaluating program usability and theorising audience response to Interactive Television app ....Interactive Television Audience Research Laboratory. Interactive Television is a rapidly emerging platform for global media and e-commerce that is poised to dramatically transform the role of television in society. In collaboration with a range of university and industry partners, Murdoch University aims to establish Australia's first dedicated public research laboratory for assessing consumer motivation, evaluating program usability and theorising audience response to Interactive Television applications. The laboratory will feature specialised testing equipment designed to emulate real-world digital broadcasting environments, enabling rich data on viewing behaviour to be collected and analysed. As an independent facility, the laboratory will provide an invaluable resource for academic and industry research.Read moreRead less
Automatic Machine Learning with Imperfect Data for Video Analysis . This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be construct ....Automatic Machine Learning with Imperfect Data for Video Analysis . This project aims to propose new algorithms and technologies for constructing an efficient video analysis system, which will be aligned with Australia’s science and research priorities. Specifically, during this project, a novel network structure search method based on auto machine learning will be proposed, an unsupervised domain adaptation algorithm will be developed, and a generative data augmentation method will be constructed. All of these will construct a stable and efficient deep neural network, which is able to process large size videos captured from real scenarios in high efficiencies. Various fields, such as health care service and cybersecurity, will benefit hugely from this project.Read moreRead less