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Selected Papers from the 10th International Electronic Conference on Sensors and Applications

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 20 October 2024 | Viewed by 924

Special Issue Editors


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Guest Editor
Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
Interests: MEMS; smart materials; micromechanics; machine learning-driven materials modeling
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Department of Electrical, Electronic and Communication Engineering & Institute for Smart Cities (ISC), Public University of Navarre, 31006 Pamplona, Spain
2. School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico
Interests: wireless networks; performance evaluation; distributed systems; context-aware environments; IoT; next-generation wireless systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

E-Mail Website
Guest Editor
Laboratory of Electronics, SYstèmes de COmmunications and Microsystems, Université Gustave Eiffel, Champs-sur-Marne, France
Interests: antennas in matter; RFID technologies; RFID localization; body array antennas (BANs) and channel modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is going to comprise extended and expanded versions of proceedings papers from the 10th International Electronic Conference on Sensors and Applications(https://sciforum.net/event/ecsa-10), hold on 15–30 November 2023 on https://ecsa-10.sciforum.net/. In this 10th edition of the e-conference, contributors are invited to provide papers and presentations from the fields of sensors and applications at large. Selected papers that will attract the most interest on the web, or that will provide particularly innovative contributions, are going to be gathered for publication. These papers will be subjected to peer review and possibly published with the aim of the rapid and wide dissemination of research results, developments, and applications. We hope that this conference series will grow further in the future and become recognized as a new way and venue through which to (electronically) present new developments related to the fields of sensors and their applications.

Dr. Stefano Mariani
Prof. Dr. Francisco Falcone
Dr. Stefan Bosse
Prof. Dr. Jean-Marc Laheurte
Guest Editors

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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • chemo- and biosensors
  • physical sensors
  • sensor networks, the IoT, and structural health monitoring
  • sensor data analytics
  • sensors and artificial intelligence
  • smart agriculture sensors
  • materials for sensing applications
  • electronic sensors, devices, and systems
  • wearable sensors and healthcare applications
  • robotics, sensors, and Industry 4.0

Published Papers (1 paper)

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Research

18 pages, 8498 KiB  
Article
3D Indoor Position Estimation Based on a UDU Factorization Extended Kalman Filter Structure Using Beacon Distance and Inertial Measurement Unit Data
by Tolga Bodrumlu and Fikret Caliskan
Sensors 2024, 24(10), 3048; https://doi.org/10.3390/s24103048 - 11 May 2024
Viewed by 355
Abstract
The development of the GPS (Global Positioning System) and related advances have made it possible to conceive of an outdoor positioning system with great accuracy; however, for indoor positioning, more efficient, reliable, and cost-effective technology is required. There are a variety of techniques [...] Read more.
The development of the GPS (Global Positioning System) and related advances have made it possible to conceive of an outdoor positioning system with great accuracy; however, for indoor positioning, more efficient, reliable, and cost-effective technology is required. There are a variety of techniques utilized for indoor positioning, such as those that are Wi-Fi, Bluetooth, infrared, ultrasound, magnetic, and visual-marker-based. This work aims to design an accurate position estimation algorithm by combining raw distance data from ultrasonic sensors (Marvelmind Beacon) and acceleration data from an inertial measurement unit (IMU), utilizing the extended Kalman filter (EKF) with UDU factorization (expressed as the product of a triangular, a diagonal, and the transpose of the triangular matrix) approach. Initially, a position estimate is calculated through the use of a recursive least squares (RLS) method with a trilateration algorithm, utilizing raw distance data. This solution is then combined with acceleration data collected from the Marvelmind sensor, resulting in a position solution akin to that of the GPS. The data were initially collected via the ROS (Robot Operating System) platform and then via the Pixhawk development card, with tests conducted using a combination of four fixed and one moving Marvelmind sensors, as well as three fixed and one moving sensors. The designed algorithm is found to produce accurate results for position estimation, and is subsequently implemented on an embedded development card (Pixhawk). The tests showed that the designed algorithm gives accurate results with centimeter precision. Furthermore, test results have shown that the UDU-EKF structure integrated into the embedded system is faster than the classical EKF. Full article
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