Meta-neural-network for real-time and passive deep-learning-based object recognition
The authors present a passive meta-neural-network for real-time recognition of objects by analysis of acoustic scattering. It consists of unit cells termed meta-neurons, mimicking an analogous neural network for classical waves, and is shown to recognise handwritten digits and misaligned orbital-ang...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2020-12-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-020-19693-x |
Summary: | The authors present a passive meta-neural-network for real-time recognition of objects by analysis of acoustic scattering. It consists of unit cells termed meta-neurons, mimicking an analogous neural network for classical waves, and is shown to recognise handwritten digits and misaligned orbital-angular-momentum vortices. |
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ISSN: | 2041-1723 |