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Aaron R. Hawkins
Researcher at Brigham Young University
Publications - 367
Citations - 6739
Aaron R. Hawkins is an academic researcher from Brigham Young University. The author has contributed to research in topics: Waveguide (optics) & Optofluidics. The author has an hindex of 44, co-authored 355 publications receiving 6220 citations. Previous affiliations of Aaron R. Hawkins include Cornell University & University of California, Santa Barbara.
Papers
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Proceedings ArticleDOI
High longevity rubidium packaging method suitable for integrated optics
Matthieu Giraud-Carrier,John F. Hulbert,Thomas Wall,Aaron R. Hawkins,Jennifer A. Black,Holger Schmidt +5 more
TL;DR: In this paper, a construction approach to long-lasting rubidium (Rb) atomic vapor cells compatible with integrated photonic platforms is presented, where Indium solder seals did not exhibit any decrease in optical atomic density after being held at 95 °C for thirty days.
Proceedings ArticleDOI
A Fast Linear Reconstruction Method for Scanning Impedance Imaging
TL;DR: A fast linear model is derived and applied to the impedance image reconstruction of scanning impedance imaging, which leads to a calibrated approximation of the exact impedance distribution rather than a relative one from the original simplified linear method.
Journal ArticleDOI
Effects of Post-Etch Microstructures on the Optical Transmittance of Silica Ridge Waveguides
TL;DR: Of the tested RIE processes, one can be suggested for silica waveguides, which results in the lowest optical loss and coincidently has the fastest etch rate.
Planar Resistive Electrode Ion Traps: A New Tool for Planetary Atmosphere Analysis
Y. Peng,M. Wang,Ivan W. Miller,Brett J. Hansen,Z. Zhang,Samuel E. Tolley,Aaron R. Hawkins,J. Radebaugh,M. L. Lee,Daniel E. Austin +9 more
Journal ArticleDOI
Machine learning at the edge for AI-enabled multiplexed pathogen detection
TL;DR: In this paper , a robust target identification scheme that utilizes a deep neural network (DNN) for multiplex detection of single particles and molecular biomarkers is presented. But the model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board).