Special Issue "Inertial MEMS Devices"

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A:Physics".

Deadline for manuscript submissions: 15 October 2020.

Special Issue Editor

Prof. Edmond Cretu
Website
Guest Editor
Department of Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall Vancouver, BC V6T 1Z4, Canada
Interests: MEMS (sensors and actuators); microsystems; accelerometers; gyroscopes; feedback control; reduced order modelling; alternative microfabrication; ultrasonic transducers

Special Issue Information

Dear Colleagues,

Despite being considered one of the most mature applications of micro-electromechanical systems (MEMS), inertial sensors still have a steady growth rate, with their range of applications extending from the initial automotive market to smartphones and wearable sensors for various body monitoring functions. Recent advances in this context range from alternative microfabrication technologies, beyond silicon, for low-cost wearable sensors, to new operating principles that lead to higher sensitivity of MEMS inertial sensors while maintaining the downscaling trend. Such new techniques exploit new electromechanical interactions at microscale, such as operation on the stability border, parametric amplification or mode localized sensing, pushing the inertial sensing limits set by the thermomechanical and electronic noise sources. The higher sensitivity and stable operation targets, leading, for instance, to demanding sensors like gravimeters, require in most cases an integration with electronic feedback loops—the trend here is to emphasize digital control such as sigma-delta or sliding mode techniques, for an easy and robust integration with other electronic subsystems. At the application level, many new directions lead to a structured data fusion of several sensing channels for the reconstruction of multiple degrees-of-freedom (DoFs), for instance, through Kalman filtering and its various extensions.

We intend therefore to cover in this Special Issue some of these exciting topics, through papers addressing a wide range of inertial sensors research avenues, including, but not limited to:

  • modern microfabrication technologies for inertial MEMS sensors;
  • Advanced sensing alternatives for high-sensitivity or robust inertial sensing;
  • Modelling and simulation (information flow or energy flow) of inertial MEMS sensors;
  • Specific packaging solutions for long term robust operation in a varying environment;
  • Modern feedback control architectures dedicated to inertial sensors;
  • Low-power readout electronics dedicated to inertial sensors;
  • Self-calibrating techniques for a guaranteed accuracy in-the-field;
  • Innovative applications of inertial sensors.

Prof. Edmond Cretu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • inertial sensors
  • MEMS
  • accelerometer
  • gyroscope
  • inertial measurement unit
  • microfabrication
  • Kalman filtering
  • reduced order macromodelling

Published Papers (1 paper)

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Research

Open AccessArticle
A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment
Micromachines 2020, 11(7), 642; https://doi.org/10.3390/mi11070642 - 29 Jun 2020
Abstract
Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading [...] Read more.
Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems. Full article
(This article belongs to the Special Issue Inertial MEMS Devices)
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