Professor Andy Dong; Dr Massimo Garbuio; Professor Dan Lovallo
The project seeks to provide theoretical insight and empirical evidence on the behavioural determinants of companies and executives that lead to market-creating growth. The question of how to support economic growth through innovation has drawn much attention and debate because Australian companies underperform in delivering value creation from new innovations. This project plans to test a model of growth based upon companies’ changes to routines and resource configurations as they create actions to prove a new hypothesis, rather than to confirm what markets already know. The model will involve policies that executives may find beneficial in building up their companies’ capabilities in sensing and seizing options for growth before the market logics of those options have been proven elsewhere. Developing such capabilities could boost the rate of market-creating innovations arising from Australian research.
Professor Dagan Feng; Dr Jinman Kim; Professor Michael Fulham; Associate Professor Stefan Eberl
The project aims to develop a framework to provide users with the interactive access to information that is necessary for the best collaborative decision-making. Visual analytics theory is becoming increasing valuable for managing ‘big data’ because it can provide interactive and intuitive understanding of the rich information embedded within complex data and decision support systems. There are, however, fundamental challenges that currently prevent visual analytics from being routinely applied to multi-disciplinary collaboration, which is now ‘the norm’ to solve large complicated problems where there is significant social impact. This project aims to address these challenges and improve visual analytics theory by developing a biomedical visual image analytics framework that enables interactive information retrieval of multidisciplinary databases.
Dr Vincent Gramoli; Professor Alan Fekete; Professor Rachid Guerraoui
The project intends to improve data structures to reduce the bottleneck effect caused by multiple processor cores. The hardware used for a typical server platform has increasing numbers of processor cores. This growing number of cores creates a bottleneck effect when accessing the data that are structured in the shared memory of these servers. These contended data structures limit the server performance and new algorithms are necessary. The project proposes to relax traditional consistency criteria to provide high concurrency and to leverage optimistic executions that proceed concurrently but may roll back depending on the conflicts with other concurrent executions they encounter. The concurrent data structures would allow application performance to scale with higher numbers of hardware cores.
Professor Seok-Hee Hong; Professor Peter Eades; Professor Dr Hiroshi Nagamochi
This project aims to develop new efficient algorithms to enable analysts to visually understand complex data and detect anomalies or patterns. It aims to develop visualisation algorithms for sparse non-planar graphs arising from real-world networks. Specifically, the project plans to investigate structural properties of sparse non-planar topological graphs such as k-planar graphs, k-skew graphs, and k-quasi-planar graphs, and design efficient testing algorithms, embedding algorithms, and drawing algorithms. These algorithms will be evaluated with real-world social networks and biological networks. New insights into the mathematical interplay between combinatorial and geometric structures would provide a theoretical foundation for a new generation of complex network visualisation methods with potential applications in social networks, systems biology, health informatics, finance and security.
Associate Professor Craig Jin; Dr Nicolas Epain; Associate Professor Alexis Glaunes; Professor Augusto Sarti; Mr Anthony Tew
The project aim is to allow the general listener to enjoy high-fidelity 3-D sound over headphones. Such 3-D audio is of paramount importance when inter-personal communication requires situational awareness, (eg search and rescue, fire-fighting, and air traffic control). To achieve this, the project aims to address one of the toughest problems in audio signal processing: deriving high-fidelity 3-D audio headphone filters from photos and/or 3D scans of ears. The project plans to address fundamental research questions in statistical shape and data analysis and to perceptually evaluate the 3-D audio methods developed.
Professor Eduardo Nebot; Dr Fabio Ramos
This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.