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...

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Autores principales:	Jingkai Weng, Yujiang Ding, Chengbo Hu, Xue-Feng Zhu, Bin Liang, Jing Yang, Jianchun Cheng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b152561300274e6da7804a83cb216ab4
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id	oai:doaj.org-article:b152561300274e6da7804a83cb216ab4
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spelling	oai:doaj.org-article:b152561300274e6da7804a83cb216ab42021-12-02T13:27:23ZMeta-neural-network for real-time and passive deep-learning-based object recognition10.1038/s41467-020-19693-x2041-1723https://doaj.org/article/b152561300274e6da7804a83cb216ab42020-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19693-xhttps://doaj.org/toc/2041-1723The 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.Jingkai WengYujiang DingChengbo HuXue-Feng ZhuBin LiangJing YangJianchun ChengNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jingkai Weng Yujiang Ding Chengbo Hu Xue-Feng Zhu Bin Liang Jing Yang Jianchun Cheng Meta-neural-network for real-time and passive deep-learning-based object recognition
description	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.
format	article
author	Jingkai Weng Yujiang Ding Chengbo Hu Xue-Feng Zhu Bin Liang Jing Yang Jianchun Cheng
author_facet	Jingkai Weng Yujiang Ding Chengbo Hu Xue-Feng Zhu Bin Liang Jing Yang Jianchun Cheng
author_sort	Jingkai Weng
title	Meta-neural-network for real-time and passive deep-learning-based object recognition
title_short	Meta-neural-network for real-time and passive deep-learning-based object recognition
title_full	Meta-neural-network for real-time and passive deep-learning-based object recognition
title_fullStr	Meta-neural-network for real-time and passive deep-learning-based object recognition
title_full_unstemmed	Meta-neural-network for real-time and passive deep-learning-based object recognition
title_sort	meta-neural-network for real-time and passive deep-learning-based object recognition
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/b152561300274e6da7804a83cb216ab4
work_keys_str_mv	AT jingkaiweng metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition AT yujiangding metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition AT chengbohu metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition AT xuefengzhu metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition AT binliang metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition AT jingyang metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition AT jianchuncheng metaneuralnetworkforrealtimeandpassivedeeplearningbasedobjectrecognition
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Meta-neural-network for real-time and passive deep-learning-based object recognition

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