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Toward remote and secure authentication: Disambiguation of magnetic microwire signatures using neural networks

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Abstract

Secure and high-throughput authentication systems require materials with uniquely identifiable responses that can be remotely detected and rapidly disambiguated. To this end, complex electromagnetic responses from arrangements of amorphous ferromagnetic microwires were analyzed using machine learning. These novel materials deliver maximal spectral dispersion when the frequency of incident electromagnetic radiation matches the microwire resonance. Utilizing data obtained from 225 unique microwire arrangements, a neural network reproduced the response distribution of unseen data to a confidence level of 90%, with a mean square error less than 0.01. This favorable performance affirms the potential of magnetic microwires for use in tags for secure article surveillance systems.

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Data availability

The authors will not make data used for training the neural network models available since they are part of an IP disclosure.

Code availability

The code used for analysis will be made available on request.

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Funding

This study was funded by Northeastern University, NSF D-ISN 2039945, Fulbright España, I-Link A20074 (CSIC), Spanish Ministry of Science and Innovation RTI2018-095856-B-C21 and Comunidad de Madrid NANOMAGCOST S2018/NMT-4321.

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Authors and Affiliations

  1. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

    Akshar Varma & Ravi Sundaram

  2. Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, USA

    Xiaoyu Zhang & Laura H. Lewis

  3. Department of Chemical Engineering, Northeastern University, Boston, MA, USA

    Brian Lejeune & Laura H. Lewis

  4. Department of Materials Physics, Complutense University of Madrid, Madrid, Spain

    Laura Cebada Almagro & Pilar Marin

  5. Instituto de Ciencia de Materiales de Madrid, Cantoblanco, Madrid, Spain

    Rafael P. del Real

  6. Instituto de Magnetismo Aplicado, Universidad Complutense de Madrid-ADIF, Madrid, Spain

    Rafael P. del Real & Pilar Marin

  7. The George J. Kostas Research Institute for Homeland Security, Northeastern University, Burlington, MA, USA

    Ogheneyunume Fitchorova & Laura H. Lewis

  8. KRI at Northeastern University, LLC, Burlington, MA, USA

    Ogheneyunume Fitchorova

  9. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA

    Ogheneyunume Fitchorova

Authors
  1. Akshar Varma
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  2. Xiaoyu Zhang
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  4. Laura Cebada Almagro
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  7. Ogheneyunume Fitchorova
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  8. Laura H. Lewis
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  9. Ravi Sundaram
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Correspondence to Akshar Varma.

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Varma, A., Zhang, X., Lejeune, B. et al. Toward remote and secure authentication: Disambiguation of magnetic microwire signatures using neural networks. MRS Communications 13, 16–20 (2023). https://doi.org/10.1557/s43579-022-00302-5

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  • DOI: https://doi.org/10.1557/s43579-022-00302-5

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