8 citations · 16 across the 9 of their papers we have counts for
6 papers · 1 filter
Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson +2
Imminent radio telescope observatories provide massive data rates making deep learning based processing appealing while simultaneously demanding real-time performance at low-energy…
Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
Nicholas J Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI tr…
Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson +1
Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spikin…
Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency In…
Supervised Radio Frequency Interference Detection with SNNs
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio tele…
A Bibliometric Review of Neuromorphic Computing and Spiking Neural Networks
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Neuromorphic computing and spiking neural networks aim to leverage biological inspiration to achieve greater energy efficiency and computational power beyond traditional von Neuman…