@inproceedings{c820cf62e86445b3b469cf8dce177f6e,
title = "Hardware AI-empowered Ultrasensitive Detection",
abstract = "This work proposed a universal platform for ultra-sensitive detection, which integrates sensory data acquisition and spectral feature extraction into a single machine learning (ML) hardware. We fabricated and tested the sensing platform in glucose detection tasks, reaching 5 orders of magnitude higher sensitivity compared to the state-of-the-art. This technology requires no bulky spectral measuring devices such as a spectrum analyzer but a standard off-the-shelf camera to achieve real-time detection of the glucose concentration.",
author = "Qizhou Wang and Ning Li and Zhao He and Lopez, {Arturo Burguete} and Maksim Makarenko and Fei Xiang and Andrea Fratalocchi",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Machine Learning in Photonics 2024 ; Conference date: 08-04-2024 Through 12-04-2024",
year = "2024",
doi = "10.1117/12.3009163",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Francesco Ferranti and Hedayati, {Mehdi Keshavarz} and Andrea Fratalocchi",
booktitle = "Machine Learning in Photonics",
address = "United States",
}