Meta-neural-network for real-time and passive deep-learning-based object recognition

Jingkai Weng, Yujiang Ding, Chengbo Hu, Xue-Feng Zhu, Bin Liang (), Jing Yang and Jianchun Cheng ()
Additional contact information
Jingkai Weng: Collaborative Innovation Center of Advanced Microstructures, Nanjing University
Yujiang Ding: Collaborative Innovation Center of Advanced Microstructures, Nanjing University
Chengbo Hu: Collaborative Innovation Center of Advanced Microstructures, Nanjing University
Xue-Feng Zhu: Huazhong University of Science and Technology
Bin Liang: Collaborative Innovation Center of Advanced Microstructures, Nanjing University
Jing Yang: Collaborative Innovation Center of Advanced Microstructures, Nanjing University
Jianchun Cheng: Collaborative Innovation Center of Advanced Microstructures, Nanjing University

Nature Communications, 2020, vol. 11, issue 1, 1-8

Abstract: Abstract Analyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the “intelligence” to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-19693-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19693-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-19693-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().