Breathing and snoring sound analysis in sleep apnea. About 800,000 Australians suffer from the disease sleep Apnoea (OSA) which has snoring as its earliest symptom. We develop electronics and snore processing algorithms to classify snorers into OSA-positive and OSA-negative classes, based on advanced technology derived from speech recognition systems.
Discovery Early Career Researcher Award - Grant ID: DE190101174
Funder
Australian Research Council
Funding Amount
$395,000.00
Summary
Building a mechanical quantum memory from superfluid helium. This project aims to implement a quantum computer memory module which can be integrated in a future hybrid quantum computer, where the advantages of different quantum platforms are combined. The memory module will be achieved by coupling a superconducting qubit to the surface vibrations of superfluid helium and exchanging quantum information between the qubit and helium. By simulating chemical reactions, the project expects to find cle ....Building a mechanical quantum memory from superfluid helium. This project aims to implement a quantum computer memory module which can be integrated in a future hybrid quantum computer, where the advantages of different quantum platforms are combined. The memory module will be achieved by coupling a superconducting qubit to the surface vibrations of superfluid helium and exchanging quantum information between the qubit and helium. By simulating chemical reactions, the project expects to find cleaner alternatives of current industrial processes, reducing environmental impact. The outcomes should provide significant benefits for testing the validity of quantum mechanics, and by contributing to the realisation of a quantum computer, contribute to broad socio-economic benefits.Read moreRead less
Creating a smart city through internet of things. This project will deliver smart new ways of urban monitoring using ubiquitous sensing and data analysis for city management and sustainability. It will deliver researcher training, global clientele for local technology and a platform for local industry growth.
Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than ai ....Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world’s largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.Read moreRead less