Robotics for zero-tillage agriculture. This project will develop small agricultural robots to increase broad-acre crop production and reduce environmental impact. These robots will have advanced navigation capability, will cooperate to cover large areas and resupply themselves, while causing less soil damage and applying herbicide more intelligently.
Lifelong robotic navigation using visual perception. Service robots are becoming a major part of our working and personal environments, in much the same way as personal computers already have. This project will develop new methods of practical and useful robot navigation that will enable Australia's industries and services to remain internationally competitive.
Discovery Early Career Researcher Award - Grant ID: DE120100995
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Visual navigation for sunny summer days and stormy winter nights. This project will develop innovative techniques for camera-based navigation that recognise locations under a wide range of environmental conditions caused by day-night cycles, weather and seasonal change. These techniques will enable the widespread use of cheap and lightweight cameras in robot and personal navigation systems.
Integrating Immunity And Genetics In Follicular Lymphoma To Establish A Prognostic Score Fit For The Modern Era
Funder
National Health and Medical Research Council
Funding Amount
$1,377,174.00
Summary
Follicular lymphoma (FL) is divided into early and advanced stages. Early stage FL is frequently cured, but there is no way to identify who will be cured and who won't. By contrast advanced stage FL is incurable. Our unique access to well-annotated clinical trial and population based cohorts allows us to perform a detailed biological comparison of early and advanced FL, to gain a deeper understanding of the impediments to eradicating the disease, and to predict outcome to conventional therapy.
Tuberculosis is one of the most threatening infectious diseases worldwide due to the low efficiency of the only licensed anti-tuberculosis vaccine, BCG. This project aims to interrogate two previously neglected immune mechanisms and their potential to enhance vaccine-induced immunity by incorporating these mechanisms into new genetically modified BCG strains. We will also investigate alternative BCG vaccination routes to generate long-lived immune cells that can rapidly control the infection.
Transport and innate immune properties of DNA in bacterial nano-sized vesicles. All types of living organisms release nano-sized membrane vesicles or “blebs” which they use for intercellular communication and transport of molecules. This project will determine how bacteria package DNA within these vesicles, how this DNA is transported into host cells and how it triggers immune responses in these cells.
Functional Dyspepsia: Characterisation Of The Immunopathology And Testing A Novel Therapeutic Strategy.
Funder
National Health and Medical Research Council
Funding Amount
$739,604.00
Summary
Dyspepsia, unexplained stomach discomfort and pain, is a common and costly problem; few effective treatments exist and the causes are unknown. We have found that the numbers of a type of immune cell, the eosinophil, are increased in the top of the small bowel in patients with dyspepsia. This study will explore the mechanisms that lead to increased eosinophils and then test the effectiveness of a treatment to suppress this overactive immune response which could rapidly change clinical practice.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Assuring dependability of complex adaptive multi-agent systems using time bands. As the complexity of computer-based systems rapidly increases, we need new methods for assuring their correct behaviour. This project will provide a means of relating behaviour at different timescales, enabling us to understand how the long-term behaviour of a system results from the short-term interactions between its components.