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Selected Papers from the 10th International Electronic Conference on Sensors...
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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: closed (20 October 2024) | Viewed by 8393

Special Issue Editors

Prof. Dr. Stefano Mariani

E-Mail Website
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
Prof. Dr. Francisco Falcone

E-Mail Website
Guest Editor
Department of Electrical, Electronic and Communication Engineering & Institute for Smart Cities (ISC), Public University of Navarre, 31006 Pamplona, Spain
Interests: wireless networks; performance evaluation; distributed systems; context-aware environments; IoT; next-generation wireless systems
Special Issues, Collections and Topics in MDPI journals
Dr. Stefan Bosse

E-Mail Website
Guest Editor
1. Department Mathematics and Computer Science, University of Bremen, 28359 Bremen, Germany
2. Department Mechanical Engineering, University of Siegen, 57072 Siegen, Germany
Interests: artificial intelligence; machine learning; distributed systems; parallel systems; programming languages, self-organizing systems; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jean-Marc Laheurte

E-Mail Website
Guest Editor
Laboratory of Electronics, Systèmes de Communications and Microsystems, Université Gustave Eiffel, 77420 Marne-la-Vallée, France
Interests: antennas in matter; RFID technologies; RFID localization; body array antennas (BANs); 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.

Prof. 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

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  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

12 pages, 7826 KiB  
Communication
Novel MEMS Multisensor Chip for Aerodynamic Pressure Measurements
by Žarko Lazić, Milče M. Smiljanić, Dragan Tanasković, Milena Rašljić-Rafajilović, Katarina Cvetanović, Evgenija Milinković, Marko V. Bošković, Stevan Andrić, Ivana Jokić, Predrag Poljak and Miloš Frantlović
Sensors 2025, 25(3), 600; https://doi.org/10.3390/s25030600 - 21 Jan 2025
Viewed by 402
Abstract
The key equipment for performing aerodynamic testing of objects, such as road and railway vehicles, aircraft, and wind turbines, as well as stationary objects such as bridges and buildings, are multichannel pressure measurement instruments (pressure scanners). These instruments are typically based on arrays [...] Read more.
The key equipment for performing aerodynamic testing of objects, such as road and railway vehicles, aircraft, and wind turbines, as well as stationary objects such as bridges and buildings, are multichannel pressure measurement instruments (pressure scanners). These instruments are typically based on arrays of separate pressure sensors built in an enclosure that also contains temperature sensors used for temperature compensation. However, there are significant limitations to such a construction, especially when increasing requirements in terms of miniaturization, the number of pressure channels, and high measurement performance must be met at the same time. In this paper, we present the development and realization of an innovative MEMS multisensor chip, which is designed with the intention of overcoming these limitations. The chip has four MEMS piezoresistive pressure-sensing elements and two resistive temperature-sensing elements, which are all monolithically integrated, enabling better sensor matching and thermal coupling while providing a high number of pressure channels per unit area. The main steps of chip development are preliminary chip design, numerical simulations of the chip’s mechanical behavior when exposed to the measured pressure, final chip design, fabrication processes (photolithography, thermal oxidation, diffusion, layer deposition, micromachining, anodic bonding, and wafer dicing), and electrical testing. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Electronic Conference on Sensors and Applications)
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17 pages, 7212 KiB  
Article
Zigbee-Based Wireless Sensor Network of MEMS Accelerometers for Pavement Monitoring
by Nicky Andre Prabatama, Mai Lan Nguyen, Pierre Hornych, Stefano Mariani and Jean-Marc Laheurte
Sensors 2024, 24(19), 6487; https://doi.org/10.3390/s24196487 - 9 Oct 2024
Cited by 1 | Viewed by 3454
Abstract
In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of [...] Read more.
In this paper, we propose a wireless sensor network for pavement health monitoring exploiting the Zigbee technology. Accelerometers are adopted to measure local accelerations linked to pavement vibrations, which are then converted into displacements by a signal processing algorithm. Each device consists of an on-board unit buried in the roadway and a roadside unit. The on-board unit comprises a microcontroller, an accelerometer and a Zigbee module that transfers acceleration data wirelessly to the roadside unit. The roadside unit consists of a Raspberry Pi, a Zigbee module and a USB Zigbee adapter. Laboratory tests were conducted using a vibration table and with three different accelerometers, to assess the system capability. A typical displacement signal from a five-axle truck was applied to the vibration table with two different displacement peaks, allowing for two different vehicle speeds. The prototyped system was then encapsulated in PVC packaging, deployed and tested in a real-life road situation with a fatigue carousel featuring rotating truck axles. The laboratory and on-road measurements show that displacements can be estimated with an accuracy equivalent to that of a reference sensor. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Electronic Conference on Sensors and Applications)
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12 pages, 780 KiB  
Article
Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features
by Itaf Omar Joudeh, Ana-Maria Cretu and Stéphane Bouchard
Sensors 2024, 24(13), 4398; https://doi.org/10.3390/s24134398 - 7 Jul 2024
Cited by 1 | Viewed by 1760
Abstract
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual [...] Read more.
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into a virtual reality (VR) system that runs cognitive remediation exercises for people with mental health disorders. As such, the prediction of emotional states is essential to customize treatments for those individuals. We exploit the Remote Collaborative and Affective Interactions (RECOLA) database to predict arousal and valence values using machine learning techniques. RECOLA includes audio, video, and physiological recordings of interactions between human participants. To allow learners to focus on the most relevant data, features are extracted from raw data. Such features can be predesigned, learned, or extracted implicitly using deep learners. Our previous work on video recordings focused on predesigned and learned visual features. In this paper, we extend our work onto deep visual features. Our deep visual features are extracted using the MobileNet-v2 convolutional neural network (CNN) that we previously trained on RECOLA’s video frames of full/half faces. As the final purpose of our work is to integrate our solution into a practical VR application using head-mounted displays, we experimented with half faces as a proof of concept. The extracted deep features were then used to predict arousal and valence values via optimizable ensemble regression. We also fused the extracted visual features with the predesigned visual features and predicted arousal and valence values using the combined feature set. In an attempt to enhance our prediction performance, we further fused the predictions of the optimizable ensemble model with the predictions of the MobileNet-v2 model. After decision fusion, we achieved a root mean squared error (RMSE) of 0.1140, a Pearson’s correlation coefficient (PCC) of 0.8000, and a concordance correlation coefficient (CCC) of 0.7868 on arousal predictions. We achieved an RMSE of 0.0790, a PCC of 0.7904, and a CCC of 0.7645 on valence predictions. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Electronic Conference on Sensors and Applications)
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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 1429
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
(This article belongs to the Special Issue Selected Papers from the 10th International Electronic Conference on Sensors and Applications)
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