Greg P. Timms

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Online automated quality assessment is critical to determine a sensor's fitness for purpose in real-time applications. A Dynamic Bayesian Network (DBN) framework is proposed to produce probabilistic quality assessments and represent the uncertainty of sequentially correlated sensor readings. This is a novel framework to represent the causes, quality state(More)
Numerous sources of uncertainty are associated with the data acquisition process in marine sensor networks. It is thus required to assure that the data quality of sensors is fit for the intended purpose. We propose a supervised learning framework to infer the quality of sensor observations online. A problem with using supervised classification in quality(More)
The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is(More)
Shellfish farms need to be closed from harvesting when the water body is contaminated to avoid a serious health hazard. We have designed a sensor network framework to monitor a number of water quality variables to check the health of shellfish farms and predict closure if hazardous. Because of the uncertainty associated with the data acquisition process, a(More)
This research study focused on automatic sensor data annotation and visualisation of dynamic weather data acquired from a large sensor network. The aim was to develop a data visualisation method for CSIRO's South Esk hydrological sensor web to evaluate the overall network performance and provide visual data quality assessment. The visual data quality(More)