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Detecting Synthetic Opioids with NQR Spectroscopy and Complex-Valued Signal Denoising
Amber Day · Natalie Klein · Michael Malone · Harris Mason · Sinead Williamson

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in Women in Machine Learning »

Dangerous synthetic opioids (e.g., fentanyl) are currently synthesized abroad and shipped into the United States illegally via international mail. They are largely responsible for the overdose crisis in the United States, which has been declared a public health emergency. One factor contributing to the influx of these drugs is the low risk of detection when mailed in small quantities. The goal of our research is to slow the passage of synthetic opioids into the United States by developing a technology capable of detecting them in unopened packages using Nuclear Quadrupole Resonance (NQR) spectroscopy along with complex-valued and real-valued neural networks for signal denoising and classification to improve detection.

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Author Information

Amber Day (University of Texas at Austin)
Natalie Klein (Los Alamos National Laboratory)
Michael Malone (Los Alamos National Laboratory)
Harris Mason (Los Alamos National Laboratory)
Sinead Williamson (University of Texas at Austin)

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