Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100049
Funder
Australian Research Council
Funding Amount
$600,000.00
Summary
Establishment of the Australian data archive: an integrated research facility for the social sciences and humanities. The Australian data archive will enable Australia's leading researchers to address complex social, economic and environmental problems, leading to the development of evidence based policy. The archive will have an open access policy which will ensure that the general public, media and government and non-government agencies are able to examine the data used by researchers to arriv ....Establishment of the Australian data archive: an integrated research facility for the social sciences and humanities. The Australian data archive will enable Australia's leading researchers to address complex social, economic and environmental problems, leading to the development of evidence based policy. The archive will have an open access policy which will ensure that the general public, media and government and non-government agencies are able to examine the data used by researchers to arrive at their conclusions.Read moreRead less
TraitCapture: Genomic modelling for plant phenomics under environmental stress. This project aims to develop software to integrate new hyper-spectral and 3D growth models of plant phenomics with population genomics to identify heritable developmental traits across varied environments. Genome wide association studies aim to then be used to identify causal genes. Functional structural plant models incorporating genetic variation will be used to predict growth under simulated stress environments. ....TraitCapture: Genomic modelling for plant phenomics under environmental stress. This project aims to develop software to integrate new hyper-spectral and 3D growth models of plant phenomics with population genomics to identify heritable developmental traits across varied environments. Genome wide association studies aim to then be used to identify causal genes. Functional structural plant models incorporating genetic variation will be used to predict growth under simulated stress environments. The research team unites international industry, the Australian Plant Phenomics Facility, and university statistical geneticists. TraitCapture software will use open standards applicable to both controlled and field environments enabling plant breeders to pre-select adaptive traits to increase crop productivity under environmental stress.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102948
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Interactive computer vision for image interpretation. This project aims at pushing forward the fundamental research in interactive computer vision. The outcome of this project will enable reliable and efficient solutions to real world image interpretation tasks, such as medical image analysis, financial document processing, and impact evaluation from natural disasters.
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less