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Colocalized Sensing and Intelligent Computing in Micro-Sensors

1
Mechanical and Materials Department, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
2
Systems Design Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada
3
Electrical and Computer Engineering Department, Texas A&M University, College Station, TX 77843, USA
4
Durham School of Architectural Engineering and Construction, University of Nebraska–Lincoln, Omaha, NE 68182, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6346; https://doi.org/10.3390/s20216346
Received: 3 September 2020 / Revised: 29 October 2020 / Accepted: 3 November 2020 / Published: 6 November 2020
(This article belongs to the Special Issue Intelligent MEMS Sensors)
This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme. View Full-Text
Keywords: MEMS; reservoir computing; colocalized sensing and computing; neuromorphic computing; MEMS accelerometer MEMS; reservoir computing; colocalized sensing and computing; neuromorphic computing; MEMS accelerometer
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MDPI and ACS Style

H Hasan, M.; Al-Ramini, A.; Abdel-Rahman, E.; Jafari, R.; Alsaleem, F. Colocalized Sensing and Intelligent Computing in Micro-Sensors. Sensors 2020, 20, 6346. https://doi.org/10.3390/s20216346

AMA Style

H Hasan M, Al-Ramini A, Abdel-Rahman E, Jafari R, Alsaleem F. Colocalized Sensing and Intelligent Computing in Micro-Sensors. Sensors. 2020; 20(21):6346. https://doi.org/10.3390/s20216346

Chicago/Turabian Style

H Hasan, Mohammad, Ali Al-Ramini, Eihab Abdel-Rahman, Roozbeh Jafari, and Fadi Alsaleem. 2020. "Colocalized Sensing and Intelligent Computing in Micro-Sensors" Sensors 20, no. 21: 6346. https://doi.org/10.3390/s20216346

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