RoboCrab: An integrative approach to the natural ecology of decision making. The project aims to analyse and model the sophisticated and context-dependent escape behaviour of fiddler crabs under both natural conditions and in controlled laboratory settings. A crucial problem for biology is to understand how animals can make adaptive decisions in natural, complex sensory environments; such understanding also has direct application to robotics. The project plans to examine the effects of eye stabi ....RoboCrab: An integrative approach to the natural ecology of decision making. The project aims to analyse and model the sophisticated and context-dependent escape behaviour of fiddler crabs under both natural conditions and in controlled laboratory settings. A crucial problem for biology is to understand how animals can make adaptive decisions in natural, complex sensory environments; such understanding also has direct application to robotics. The project plans to examine the effects of eye stabilisation and oscillation, record from key neural stages using naturalistic stimuli to derive precise algorithms, and integrate and test the results on a robot model – RoboCrab. This may provide new insight into the integration of low-level sensory input with behavioural decision making circuits and the evolution of escape behaviours.Read moreRead less
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
Tools, methodologies and reasoning support for developing companion-toy modules. This project investigates building of modules for an intelligent Toy which can be customised and adapted over time by add-on modules. Intelligent interactive toys are growing in popularity, and the ability for such a toy to develop over a prolonged lifetime, is both a sound business idea and a mechanism for extending the useful life of the Toy.
Antigen Selection In The MHC-restricted Cellular Immune Response
Funder
National Health and Medical Research Council
Funding Amount
$175,570.00
Summary
The body's white cells eliminate microorganisms through the actions of immune lymphocytes and other cells which conspire to kill and neutralise these unwanted guests. When microorganisms hide inside the cells of the body they are still detected by a set of T lymphocytes which have specific receptors for scrutinising the surface of cells for any changes which might signal an intracellular infection. The immune system is ever vigilant in its search for signs of infection which are generally appare ....The body's white cells eliminate microorganisms through the actions of immune lymphocytes and other cells which conspire to kill and neutralise these unwanted guests. When microorganisms hide inside the cells of the body they are still detected by a set of T lymphocytes which have specific receptors for scrutinising the surface of cells for any changes which might signal an intracellular infection. The immune system is ever vigilant in its search for signs of infection which are generally apparent when molecules called antigens are released by microorganisms and captured by the body's cells. This activates lymphocytes resulting in an immune response capable of eliminating the microorganisms. Scrutiny of the body's cells by lymphocytes occurs continuously even when there is no infection present in the body. Following infection of a cell, microbial antigens reveal the infection by their appearance on the cell surface where they are detected by the immune system's lymphocytes. This occurs through a mechanism called antigen presentation. During antigen presentation the proteins inside the cell, including those of any invading microorganism, are first degraded into shorter molecules called peptides. This event is called antigen processing. A fraction of the peptides created by antigen processing are captured by specialised receptors present on all cells. These receptors are called HLA or histocompatibility molecules. This project examines the molecular events which mediate the capture of peptide antigens by HLA molecules. The main focus is on those peptide antigens which elicit killer T cell responses by the immune system. A knowledge of how these peptides are selected for presentation and how they are captured and carried to the cell surface is fundamental to understanding immune responses to microorganisms, tumours, allergens, transplants and self tissues as in autoimmunity. Therefore the study is of great general relevance.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100203
Funder
Australian Research Council
Funding Amount
$385,000.00
Summary
Autonomous benthic observing system. This project seeks to improve our ability to monitor marine habitats and characterise their variability by enhancing the Integrated Marine Observing system (IMOS) Autonomous Underwater Vehicle (AUV) Facility. The new AUV infrastructure will reduce operating costs, increase robustness of the sampling effort and insure continued operation for the next decade.
Real-time signal processing and distributed robotic telescope networking for co-detection of gravitational waves and their optical counterparts. An international collaboration of scientists will employ a global network of telescopes and detectors to search for ripples in space-time. The project will use novel computational tools to study exotic phenomena in the distant Universe.