Special Research Initiatives - Grant ID: SR0354753
Funder
Australian Research Council
Funding Amount
$10,000.00
Summary
MESH: amalgamating innovative teams of cross-disciplinary collaborators for creativity in Media-arts, E-culture, Science and Humanities. MESH is a cross-disciplinary network that amalgamates a national array of sub-networks of research in digital arts, ICT and cross-cultural and policy negotiation. It boosts Australia's existing cross-disiciplinary strengths in Media-arts, E-culture, Science and Humanities by encouraging existing digital sub-networks to grow together via well-brokered communic ....MESH: amalgamating innovative teams of cross-disciplinary collaborators for creativity in Media-arts, E-culture, Science and Humanities. MESH is a cross-disciplinary network that amalgamates a national array of sub-networks of research in digital arts, ICT and cross-cultural and policy negotiation. It boosts Australia's existing cross-disiciplinary strengths in Media-arts, E-culture, Science and Humanities by encouraging existing digital sub-networks to grow together via well-brokered communications and demonstrations online and on-location. Progressively, MESH participants will discover existing harmonies whilst also inventing new languages and protocols leading to breakthroughs in cross-disciplinary collaboration and innovation. MESH encourages a 'paradigm shift' in digital research, realising the extraordinary potential that is ready but latent across Australia's arts and sciences.Read moreRead less
Analysis of Polynomial Phase Signals with Missing Observations. Many non-stationary signals in radar, physics and communications can be modelled as polynomial phase signals. These signals are often incomplete due to missing observations from intermittent sensor failures, outliers, receiver errors, periodic interference and inaccessibility of data. The aim of this project is to develop robust and computationally efficient methods for recovering such signals from small data sets when there is a la ....Analysis of Polynomial Phase Signals with Missing Observations. Many non-stationary signals in radar, physics and communications can be modelled as polynomial phase signals. These signals are often incomplete due to missing observations from intermittent sensor failures, outliers, receiver errors, periodic interference and inaccessibility of data. The aim of this project is to develop robust and computationally efficient methods for recovering such signals from small data sets when there is a large proportion of missing observations. This will contribute to a conceptual advancement in the field of signal processing and will provide new methods for use in applications such as radar, astrophysics, seismology, vibration analysis and communications.Read moreRead less