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Colocalized Sensing and Intelligent Computing in Micro-Sensors
Mohammad H Hasan1, Ali Al-Ramini1, Eihab Abdel-Rahman2
1Mechanical and Materials Department, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Sensors (Basel, Switzerland)
|November 11, 2020
Summary
This study introduces a novel sensor-level reservoir computing (RC) method using microelectromechanical systems (MEMS) sensors. This approach achieves over 99% accuracy in signal classification tasks while reducing electronic components and enhancing noise resistance.
Area of Science:
- Sensor technology
- Nonlinear dynamics
- Machine learning hardware
Background:
- Reservoir computing (RC) typically requires complex electronic components for signal processing.
- Integrating computing directly at the sensor level offers potential for reduced hardware and increased efficiency.
- Microelectromechanical systems (MEMS) present a promising platform for colocalized sensing and computation.
Purpose of the Study:
- To develop and demonstrate a delay-based reservoir computing approach at the sensor level.
- To utilize microelectromechanical systems (MEMS) as the core component for colocalized sensing and computation.
- To evaluate the performance, efficiency, and robustness of the proposed MEMS-based RC system.
Main Methods:
- Implementation of a time-multiplexed bias for transient maintenance in a MEMS device.
- Using unmodulated electrical or environmental signals (e.g., acceleration) as input.
- Experimental evaluation using a classification task differentiating signal waveform profiles.
- Analysis of classification accuracy, virtual node probing rates, and noise resistance.
Main Results:
- Demonstrated successful reservoir computing using a single MEMS device, performing colocalized sensing and computing.
- Achieved over 99% classification accuracy for distinguishing between electrical and acceleration waveforms.
- Showcased the ability to operate at up to 4x slower virtual node probing rates, easing sampling requirements.
- Confirmed the noise-resistance capability of the MEMS-based RC scheme.
Conclusions:
- The proposed MEMS-based sensor-level reservoir computing approach is effective and highly accurate.
- This method significantly reduces the need for peripheral electronics compared to traditional RC systems.
- The system offers flexibility in probing rates and demonstrates robustness against noise, paving the way for efficient edge computing applications.
