Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Improved management of marine habitats by learning from historical change. This project aims to greatly improve the cost-effectiveness of actions to protect and restore shallow subtidal marine habitats by quantifying the severity and distribution of recent human impacts. Environmental change will be quantified as the difference between contemporary and historical assemblages encompassing thousands of invertebrate species, and by reading historical chronicles coded by mollusc shells layered in se ....Improved management of marine habitats by learning from historical change. This project aims to greatly improve the cost-effectiveness of actions to protect and restore shallow subtidal marine habitats by quantifying the severity and distribution of recent human impacts. Environmental change will be quantified as the difference between contemporary and historical assemblages encompassing thousands of invertebrate species, and by reading historical chronicles coded by mollusc shells layered in sediments. The roles of different stressors (warming, dredging, eutrophication, introduced species, sediment runoff) will be distinguished. Expected outcomes include continental-scale understanding of factors that facilitate ecosystem decline and recovery, and of sites and species traits most affected by ongoing threats.Read moreRead less
Functional characterisation of contaminant-nanoparticle associations. Nanoparticles present in the environment modify the movement and toxicity of contaminants. This project targets key gaps that hinder the ability to predict the fate and behaviour of environmental contaminants; this will lead to the optimisation of legislative framework and the management/remediation of contaminated sites (for example, mine sites, landfills).
Robotic investigation of water optical properties in the Southern Ocean. The project aims to improve our understanding of light–matter interactions in the waters of the Southern Ocean (SO), in particular the role of phytoplankton and associated material of biological origin. Phytoplankton are the energy source for the food web and a critical component of carbon cycling in the SO. However, their dynamics in the SO cannot be quantified using satellite observations because bio-optical data processi ....Robotic investigation of water optical properties in the Southern Ocean. The project aims to improve our understanding of light–matter interactions in the waters of the Southern Ocean (SO), in particular the role of phytoplankton and associated material of biological origin. Phytoplankton are the energy source for the food web and a critical component of carbon cycling in the SO. However, their dynamics in the SO cannot be quantified using satellite observations because bio-optical data processing algorithms perform poorly due to a lack of field data. This project seeks to remedy this by improving understanding of SO bio-optics, and by providing novel algorithms of known uncertainty, based on in situ data.Read moreRead less
High resolution health assessment of Antarctic plants as climate changes. Declines in terrestrial ecosystem health as a result of a drying climate have been observed in some areas of East Antarctica. This project aims to determine if such changes are widespread. Since mosses, the dominant plants of Antarctica, preserve a record of past climate down their shoots they can be used as surrogates to study how both ecosystems and climate are changing at remote polar sites. Outcomes will include improv ....High resolution health assessment of Antarctic plants as climate changes. Declines in terrestrial ecosystem health as a result of a drying climate have been observed in some areas of East Antarctica. This project aims to determine if such changes are widespread. Since mosses, the dominant plants of Antarctica, preserve a record of past climate down their shoots they can be used as surrogates to study how both ecosystems and climate are changing at remote polar sites. Outcomes will include improved climate data for Antarctica, enabling more robust analysis of regional climate change, and development of ultrahigh-resolution techniques capable of non-destructively monitoring Antarctic ecosystem health. This research will advance ecosystem science and inform best practice in management of Antarctic biodiversity.Read moreRead less
Ring constructions and algorithms for enhancing performance of BCH codes. BCH codes form a major class of codes used in modern communication systems. The aim of this project is to enhance the efficiency of this class of codes by combining them in constructions enabling correction of deletion and insertion errors, and develop efficient implementations of encoding and decoding algorithms incorporating soft decision methods for enhanced error correction. Significance of the project is explained by ....Ring constructions and algorithms for enhancing performance of BCH codes. BCH codes form a major class of codes used in modern communication systems. The aim of this project is to enhance the efficiency of this class of codes by combining them in constructions enabling correction of deletion and insertion errors, and develop efficient implementations of encoding and decoding algorithms incorporating soft decision methods for enhanced error correction. Significance of the project is explained by the role of fast, secure and reliable communications in modern information and communication technology. Expected outcomes include new efficient algorithms and commercial modules available for symbolic computation systems with applications in telecommunications industry.
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Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less