ArticleNature communications2020
Randomized resonant metamaterials for single-sensor identification of elastic vibrations.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Single-Sensor Vibration Sensing in Anisotropic Composites Enabled by Disordered Metasurfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Physical Intelligence in Small-Scale Robots and Machines.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Embodying Multifunctional Mechano-Intelligence in and Through Phononic Metastructures Harnessing Physical Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A wave-confining metasphere beamforming acoustic sensor for superior human-machine voice interaction.Science advances · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Vibrations carry a wealth of useful physical information in various fields. Identifying the multi-source vibration information generally requires a large number of sensors and complex hardware. Compressive sensing has been shown to be able to bypass the traditional sensing requirements by encoding spatial physical fields, but how to encode vibration information remains unexplored. Here we propose a randomized resonant metamaterial with randomly coupled local resonators for single-sensor compressed identification of elastic vibrations. The disordered effective masses of local resonators lead to highly uncorrelated vibration transmissions, and the spatial vibration information can thus be physically encoded. We demonstrate that the spatial vibration information can be reconstructed via a compressive sensing framework, and this metamaterial can be reconfigured while maintaining desirable performance. This randomized resonant metamaterial presents a new perspective for single-sensor vibration sensing via vibration transmission encoding, and potentially offers an approach to simpler sensing devices for many other physical information.
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Registered trials
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