Colocalized Sensing and Intelligent Computing in Micro-Sensors

Authors

mohammad H. Hasan, University of Nebraska–LincolnFollow
Ali Al-Ramini, University of Nebraska- LincolnFollow
Eihab Abdel-Rahman, University of WaterlooFollow
Roozbeh Jafari, Texas A&M University, College StationFollow
Fadi Alsaleem, University of Nebraska–LincolnFollow

Document Type

Article

Date of this Version

2020

Citation

Sensors 2020, 20, 6346; doi:10.3390/s20216346

Comments

2020 by the authors.

Abstract

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.