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Intelligent MEMS Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (1 April 2023) | Viewed by 3512

Special Issue Editor


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Guest Editor
Durham School of Architectural Engineering and Construction, Mechanical Engineering Department (Courtesy appointment), University of Nebraska - Lincoln, 1110 S. 67th Street, Omaha, NE 68182-0816, USA
Interests: MEMS; neuromorphic sensors; IoT; smart building

Special Issue Information

Dear Colleagues,

Micro-electro-mechanical systems (MEMS) sensors have enabled simple and cheap data collection in smart devices in the internet of things (IoT) age. Emerging technologies, such as wearable devices and soft robots, require data collection for feedback control, and information processing aims to incorporate micro-sensors for this reason. However, traditional sensing methods, in which a MEMS sensor converts a physical quantity into an analog electrical signal, which is subsequently converted into digital; are likely insufficient for such technologies because of the very strict requirements of these technologies (power, space, and latency). Instead, a bio-inspired sensing scheme would be needed, at which information is pre-processed at, or near, the sensor node before being processed at the smart device’s “brain”. The more data is processed at away from the central-processors, the faster and less costly communication becomes; and the less information is needed to be processed at the computationally inefficient central processor.

Thus, there is a great need for new types of sensor technologies, specifically MEMS technologies, that performs computational processes along with sensing. Examples of these technologies include MEMS sensor networks, MEMS logic gates, and neuromorphic (neuro-inspired, neural network) MEMS. These technologies are ill-defined within the literature and would fit well under the umbrella of “Smart MEMS”.

Dr. Fadi Alsaleem
Guest Editor

Manuscript Submission Information

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Keywords

  • MEMS
  • Digital MEMS
  • sensor networks
  • reservoir computing
  • neuromorphic sensors
  • smart MEMS
  • IoT

Published Papers (1 paper)

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Research

15 pages, 3841 KiB  
Article
Colocalized Sensing and Intelligent Computing in Micro-Sensors
by Mohammad H Hasan, Ali Al-Ramini, Eihab Abdel-Rahman, Roozbeh Jafari and Fadi Alsaleem
Sensors 2020, 20(21), 6346; https://doi.org/10.3390/s20216346 - 06 Nov 2020
Cited by 13 | Viewed by 2455
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Intelligent MEMS Sensors)
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