Data Management Technologies for the Magnetic Resonance Imaging e-Research Grid. Howard Florey Institute researchers will collaborate with SGI's file-systems engineering team. Substantial benefits are expected from the development of techniques to support centralized and distributed processing medical image datasets. Issues requiring research include file space allocation algorithms and caching strategies. The proposed rapid database access technologies aim at solving these problems in the medic ....Data Management Technologies for the Magnetic Resonance Imaging e-Research Grid. Howard Florey Institute researchers will collaborate with SGI's file-systems engineering team. Substantial benefits are expected from the development of techniques to support centralized and distributed processing medical image datasets. Issues requiring research include file space allocation algorithms and caching strategies. The proposed rapid database access technologies aim at solving these problems in the medical imaging research context. The project attempts to 'improve data management for existing and new business applications'. This enhanced sharing of information will improve critical mass therefore fostering national and international collaboration. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101379
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
Towards Transferable Visual Understanding in the Real World. This project aims to investigate how to improve the transferability of visual understanding algorithm and system in the real-world applications. This project expects to innovate and advance knowledge in the fields of visual transfer learning and generalizable visual representation learning. Expected outcomes of this project include techniques and algorithms to make the visual understanding system robust to diverse real-world scenarios. ....Towards Transferable Visual Understanding in the Real World. This project aims to investigate how to improve the transferability of visual understanding algorithm and system in the real-world applications. This project expects to innovate and advance knowledge in the fields of visual transfer learning and generalizable visual representation learning. Expected outcomes of this project include techniques and algorithms to make the visual understanding system robust to diverse real-world scenarios. This project should provide significant benefits, such as improving the robustness and safety of autonomous vehicles in transportation area, and reducing the cost of destructive data collection for intelligent fault detection in advanced manufacturing area.Read moreRead less