Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cardiovascular digital twins using a Windkessel physics informed neural network.

NPJ digital medicine·2026
Same author

ArterialNet: Reconstructing Arterial Blood Pressure Waveform With Wearable Pulsatile Signals, a Cohort-Aware Approach.

IEEE open journal of engineering in medicine and biology·2026
Same author

Estimation of Blood Pressure Response to Physiological Maneuvers in Hypertensive Patients using Bioimpedance.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Reconstruction of Aortic Waveforms from Peripheral Data using Physics Informed Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Tracking Limb Movement in Preterm Infants Using an Inertial Measurement Bracelet.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Resonant Drive Techniques for Electrostatic Microelectromechanical Systems (MEMS): A Comparative Study.

Sensors (Basel, Switzerland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 1, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.6K

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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
MEMSMEMS accelerometercolocalized sensing and computingneuromorphic computingreservoir computing

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

957
Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
08:12

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

13.1K

Related Experiment Videos

Last Updated: Dec 1, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.6K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

957
Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing
08:12

Synthesis and Operation of Fluorescent-core Microcavities for Refractometric Sensing

Published on: March 13, 2013

13.1K

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.