Tomasz Bednarz

The University of New South Wales, UNSW Art & Design, Faculty Member

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Cloud Computing for High Performance Image Analysis on a National Infrastructure

by Alex Khassapov and Tomasz Bednarz

2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013

ABSTRACT Cloud computing services offer highly reliable, scalable and efficient solutions with a ... more ABSTRACT Cloud computing services offer highly reliable, scalable and efficient solutions with a large pool of easily accessible, virtualized resources. They are becoming an increasingly prevalent delivery model. We have developed a cloud-based image analysis toolbox to provide a wide user base with easy access to the software tools we have developed over the last decade. The toolbox is provided as a service on an Australian national cloud infrastructure. The design and implementation of the cloud-based service are presented, including its architecture, key components and some image analysis and visualization examples showing the capabilities of the service for biomedical image analysis.

Affective and Effective Visualisation: Communicating Science to Non-Expert Users

by Phil Gough, Tomasz Bednarz, and Phillip Gough

This paper outlines Non-Expert User Visualisation (NEUVis), a mode of information visualisation c... more This paper outlines Non-Expert User Visualisation (NEUVis), a mode of information visualisation commonly practiced by artists and designers. NEUVis, a wicked problem, accounts for design constraints related to the affective (or emotional) response by the user. This contrasts NEUVis from the tame, but complex, problems associated with scientific visualisation. Examples of scientific visu-alisation and NEUVis show how the challenge of NEUVis can be overcome by collaboration between artists/designers and scientists. Current Research at the Design Lab at The University of Sydney into NEUVis aims to map the different levels of cognitive and emotional response of different modes of visualisation.

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Neural Network Based Approach for Malicious Node Detection in Wireless Sensor Networks

by Octavian Bass and Tomasz Bednarz

Even if their resources in terms of energy, memory, computational power and bandwidth are strictl... more Even if their resources in terms of energy, memory, computational power and bandwidth are strictly limited, sensor networks have proved their huge viability in the real world, being just a matter of time until this kind of networks will be standardized and used broadly in the field. One of the important problems that are related to the use of wireless sensor networks in harsh environments is the gap in their security. This paper provides a solution to discover malicious nodes in wireless sensor networks using an on-line neural network predictor based on past and present values obtained from neighboring nodes. This solution can be also a way to discover the malfunctioning nodes that were not a subject of an attack. Being localized on the base station level, our algorithm is suitable even for large-scale sensor networks.

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