Advanced Signal Processing for Radiation Spectroscopy. Southern Innovation develops and markets world-leading pulse processing technologies for the rapid, accurate detection and measurement of radiation. The underlying real-time signal processing challenge relates to isolating often overlapping pulses, determining when each pulse arrived and the energy of each pulse. Recent advances in the computational power of digital signal processing boards makes it timely to develop innovative pulse process ....Advanced Signal Processing for Radiation Spectroscopy. Southern Innovation develops and markets world-leading pulse processing technologies for the rapid, accurate detection and measurement of radiation. The underlying real-time signal processing challenge relates to isolating often overlapping pulses, determining when each pulse arrived and the energy of each pulse. Recent advances in the computational power of digital signal processing boards makes it timely to develop innovative pulse processing algorithms based on optimal filtering of stochastic processes. It is expected that these algorithms will have widespread impact, both commercially for minerals exploration, materials analysis, medical imaging and security screening, and scientifically for improving the performance of synchrotrons and other equipment.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Dynamic substrates for surface-enhanced Raman scattering: piezoelectric actuated nanotextures with phase-locked signal processing. Surface-enhanced Raman scattering shows great promise for sensitive detection of a wide range of chemical and biological compounds. Novel electronic devices will be produced to actively tune the nanometre scale structures that generate the scattering signal, resulting in an improved fundamental understanding and control of the effect.