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Acoustic metamaterial for subwavelength edge detection

Author

Listed:
  • Miguel Molerón

    (Swiss Federal Institute of Technology (ETH))

  • Chiara Daraio

    (Swiss Federal Institute of Technology (ETH)
    California Institute of Technology)

Abstract

Metamaterials have demonstrated the possibility to produce super-resolved images by restoring propagative and evanescent waves. However, for efficient information transfer, for example, in compressed sensing, it is often desirable to visualize only the fast spatial variations of the wave field (carried by evanescent waves), as the one created by edges or small details. Image processing edge detection algorithms perform such operation, but they add time and complexity to the imaging process. Here we present an acoustic metamaterial that transmits only components of the acoustic field that are approximately equal to or smaller than the operating wavelength. The metamaterial converts evanescent waves into propagative waves exciting trapped resonances, and it uses periodicity to attenuate the propagative components. This approach achieves resolutions ∼5 times smaller than the operating wavelength and makes it possible to visualize independently edges aligned along different directions.

Suggested Citation

  • Miguel Molerón & Chiara Daraio, 2015. "Acoustic metamaterial for subwavelength edge detection," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms9037
    DOI: 10.1038/ncomms9037
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    Cited by:

    1. Chong Li & Xinxin Liao & Zhi-Ke Peng & Guang Meng & Qingbo He, 2023. "Highly sensitive and broadband meta-mechanoreceptor via mechanical frequency-division multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Yurou Jia & Suying Zhang & Xuan Zhang & Houyou Long & Caibin Xu & Yechao Bai & Ying Cheng & Dajian Wu & Mingxi Deng & Cheng-Wei Qiu & Xiaojun Liu, 2024. "Compact meta-differentiator for achieving isotropically high-contrast ultrasonic imaging," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Andrea Bacigalupo & Giorgio Gnecco & Marco Lepidi & Luigi Gambarotta, 2020. "Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 630-653, December.

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