Plant based foods: Towards sustainable and acceptable meat analogues. The project aims to address the need for engineering plant-based food products to deliver a sensory experience akin to meat. The project expects to generate new knowledge on the structural drivers for emulating meat-like texture and taste within burger products. Expected outcomes of this project include new ingredients and food characterisation methodologies, including rheology and sensory, which can be employed in rational ....Plant based foods: Towards sustainable and acceptable meat analogues. The project aims to address the need for engineering plant-based food products to deliver a sensory experience akin to meat. The project expects to generate new knowledge on the structural drivers for emulating meat-like texture and taste within burger products. Expected outcomes of this project include new ingredients and food characterisation methodologies, including rheology and sensory, which can be employed in rational food structure design. This should provide significant benefits in enhancing the consumer acceptance of plant-based foods that is required to support the rapidly growing market opportunity for them and sustainable food production.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
Discovery Early Career Researcher Award - Grant ID: DE210101297
Funder
Australian Research Council
Funding Amount
$429,000.00
Summary
A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the e ....A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the emerging field of biological imaging and to deliver an integrated imaging platform for mapping various tissue microscopic components at the cellular level. Successful outcomes have the potential for commercialisation and will accelerate a range of fundamental science and engineering studies requiring imaging techniques.Read moreRead less
Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in bi ....Efficient learning from multiple brain imaging data sets. Brain imaging data analysis methods have proven to be very effective in the study of brain functions and the identification of brain disorders because they minimise the modelling assumptions on the underlying structure of the problem. Analysis of multiple brain imaging data sets, either of the same modality as in multitask or multisubject data sets or from different modalities as in the case of data fusion, is a challenging problem in biomedical image analysis. This project will lead to fundamental contributions as well as techniques that address both problems: extraction of relevant features information from multisubject brain imaging data sets of the same modality or from fusion of brain imaging data sets collected from multimodalities.Read moreRead less
Perceptually-motivated speech parameters for concurrent coding and noise-robust distributed recognition of human speech for mobile telephony systems. With speech being a simple and natural form of communication, speech recognition technology is being widely used in mobile phones. Nowadays, consumers can interact with remote systems via spoken words. This project will develop remote speech recognition with better accuracy and noise-robustness while using the existing mobile phone infrastructure.
Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make th ....Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make the speech and speaker recognition systems less sensitive to additive background noise and make them more useful in telecommunications and business.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102601
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Distribution-free system identification: building models from experimental data under minimal statistical assumptions. In fields with strict safety or quality requirements, such as production control, communication and navigation, there is a great need for methods that can build models with guaranteed performance. However, there is a lack of efficient solutions that can work under minimal assumptions on the disturbances; the project aims at developing such methods.
Joint modelling and recognition of linguistic and paralinguistic speech information. A new modelling framework will be developed exploiting interdependence between linguistic and paralinguistic cues to improve automatic recognition of emotion-related information. Applications in the high-tech industry include automatic routing of angry telephone customers or pre-suicidal crisis centre callers to specialist operators/clinicians.
Miniaturised biosensors with high selectivity . This project aims to develop a technological platform for the fabrication of miniaturised and flexible sensors that enable the quantitative detection of important bioactive compounds such as fatty acids and biogenic amines. By utilising multi-enzymatic reactions in solid phase and engineering task-specific inks, chemiresistive sensors will be printed seamlessly as a whole. The sensors will respond to complex target biomolecules via a series of enzy ....Miniaturised biosensors with high selectivity . This project aims to develop a technological platform for the fabrication of miniaturised and flexible sensors that enable the quantitative detection of important bioactive compounds such as fatty acids and biogenic amines. By utilising multi-enzymatic reactions in solid phase and engineering task-specific inks, chemiresistive sensors will be printed seamlessly as a whole. The sensors will respond to complex target biomolecules via a series of enzymatic reactions through which the analyte will convert to much simpler, reactive and hence measurable molecules. This project will enable to design miniaturised sensors for point-of-care detection of biomolecules that cannot be yet evaluated by the end users.Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less