Randomized resonant metamaterials for single-sensor identification of elastic vibrations

Area of Science:

• Acoustics
• Materials Science
• Signal Processing

Background:

• Vibrations contain valuable physical information across diverse scientific and engineering fields.
• Traditional multi-source vibration identification necessitates extensive sensor networks and complex hardware.
• Compressive sensing offers a method to reduce sensing requirements by encoding physical fields, but its application to vibration data was unexplored.

Purpose of the Study:

• To propose a novel method for single-sensor compressed identification of elastic vibrations.
• To introduce a randomized resonant metamaterial capable of encoding spatial vibration information.
• To demonstrate the feasibility of reconstructing spatial vibration data using a compressive sensing framework.

Main Methods:

• Development of a randomized resonant metamaterial with randomly coupled local resonators.
• Utilizing disordered effective masses in local resonators to achieve uncorrelated vibration transmissions.
• Applying a compressive sensing framework for the reconstruction of encoded spatial vibration information.

Main Results:

• The randomized resonant metamaterial successfully encodes spatial vibration information through uncorrelated transmissions.
• Demonstrated reconstruction of spatial vibration data from single-sensor measurements.
• The proposed metamaterial exhibits reconfigurability while maintaining effective sensing performance.

Conclusions:

• Presents a new paradigm for single-sensor vibration sensing through vibration transmission encoding.
• The randomized resonant metamaterial offers a pathway to significantly simpler sensing devices.
• Potential applications extend beyond vibration sensing to encoding other forms of physical information.