arXiv subjects

Anthony Griffin

Publications and source records attributed to Anthony Griffin.

3 recordsLinked to original sources

arXiv2026

The 256-antenna Coherent All-Sky Monitor

Radio astronomy is uniquely coupled to exponential trends in computation because the optics (cross-correlation, beamforming, and imaging) and spectrometry (i.e. channelization) can now be done digitally. Inexpensive analog-to-digital converters (ADCs) can sample signals from large numbers of antennas and graphics processing units (GPUs) allow us to coherently process wide-field radio data in real time, motivating large-$N$ aperture arrays at moderate cost. We describe the 256-antenna Coherent All-Sky Monitor (CASM-256), a dense aperture array operating at 375-500\,MHz, currently being deployed at the Owens Valley Radio Observatory (OVRO) in Big Pine, California. The large field-of-view (FoV$\sim10^4$\,deg$^2$) and point-source sensitivity of CASM-256 will allow it to detect local Universe fast radio bursts (FRBs). The nearby sample is ideal for unveiling the physical origin of FRBs, measuring the baryonic content of nearby galaxy halos, and discovering prompt multi-wavelength and multi-messenger counterparts to FRBs. CASM will search for fast transients in the Milky Way such as FRB analogs, pulsar giant pulses, and the new source class known as long-period radio transients. We describe the instrument and present on-sky data from the first two dozen antennas, including an operational real-time GPU based FRB search pipeline. We emphasize the scalability of the concept and describe paths to a future CASM array with tens of thousands of antennas that could detect one million FRBs.

astro-ph.IM↗

arXiv2020

Towards Detection of Sheep Onboard a UAV

In this work we consider the task of detecting sheep onboard an unmanned aerial vehicle (UAV) flying at an altitude of 80 m. At this height, the sheep are relatively small, only about 15 pixels across. Although deep learning strategies have gained enormous popularity in the last decade and are now extensively used for object detection in many fields, state-of-the-art detectors perform poorly in the case of smaller objects. We develop a novel dataset of UAV imagery of sheep and consider a variety of object detectors to determine which is the most suitable for our task in terms of both accuracy and speed. Our findings indicate that a UNet detector using the weighted Hausdorff distance as a loss function during training is an excellent option for detection of sheep onboard a UAV.

cs.CV↗

arXiv2012

Post-Nonlinear Sparse Component Analysis Using Single-Source Zones and Functional Data Clustering

In this paper, we introduce a general extension of linear sparse component analysis (SCA) approaches to postnonlinear (PNL) mixtures. In particular, and contrary to the state-of-art methods, our approaches use a weak sparsity source assumption: we look for tiny temporal zones where only one source is active. We investigate two nonlinear single-source confidence measures, using the mutual information and a local linear tangent space approximation (LTSA). For this latter measure, we derive two extensions of linear single-source measures, respectively based on correlation (LTSA-correlation) and eigenvalues (LTSA-PCA). A second novelty of our approach consists of applying functional data clustering techniques to the scattered observations in the above single-source zones, thus allowing us to accurately estimate them.We first study a classical approach using a B-spline approximation, and then two approaches which locally approximate the nonlinear functions as lines. Finally, we extend our PNL methods to more general nonlinear mixtures. Combining single-source zones and functional data clustering allows us to tackle speech signals, which has never been performed by other PNL-SCA methods. We investigate the performance of our approaches with simulated PNL mixtures of real speech signals. Both the mutual information and the LTSA-correlation measures are better-suited to detecting single-source zones than the LTSA-PCA measure. We also find local-linear-approximation-based clustering approaches to be more flexible and more accurate than the B-spline one.

cs.IT↗