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1 August 2021 Diffractive network-based single-pixel machine vision
Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Xurong Li, Muhammed Veli, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118040A (2021) https://doi.org/10.1117/12.2594415
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
We report a single-pixel machine vision framework based on deep learning-designed diffractive surfaces to perform a desired machine learning task. The object within the input field-of-view is illuminated with a broadband light source and the subsequent diffractive surfaces are trained to encode the spatial information of the object features onto the power spectrum of the diffracted light that is collected by a single-pixel detector in a single-shot. We experimentally demonstrated the all-optical inference capabilities of this single-pixel machine vision platform by classifying handwritten digits using 3D-printed diffractive layers and a plasmonic nanoantenna-based time-domain spectroscopy setup operating at THz wavelengths.
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Jingxi Li, Deniz Mengu, Nezih T. Yardimci, Yi Luo, Xurong Li, Muhammed Veli, Yair Rivenson, Mona Jarrahi, and Aydogan Ozcan "Diffractive network-based single-pixel machine vision", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118040A (1 August 2021); https://doi.org/10.1117/12.2594415
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KEYWORDS
Machine vision
Machine learning
Light sources
Neural networks
Plasmonics
Scene classification
Sensors
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