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Selected Papers from the 9th International Electronic Conference on Sensors and Applications

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 8668

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

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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 will comprise extended and expanded versions of proceedings papers from the 9th International Electronic Conference on Sensors and Applications, which is to be held on 1–15 November 2022 on sciforum.net. In this 9th edition of the e-conference, contributors are invited to provide papers and presentations from the field of sensors and applications at large. Selected papers that will attract the most interest on the web, or that will provide a particularly innovative contribution, will be gathered for publication. These papers will be subjected to peer review and could possibly be published with the aim of 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 by which to (electronically) present novel developments related to the field 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 network and IoT
  • remote sensing
  • sensor data analytics
  • applications

Published Papers (4 papers)

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Research

17 pages, 5199 KiB  
Article
Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet
by Mohammad Mohiuddin, Md. Saiful Islam, Shirajul Islam, Md. Sipon Miah and Ming-Bo Niu
Sensors 2023, 23(18), 7764; https://doi.org/10.3390/s23187764 - 8 Sep 2023
Cited by 9 | Viewed by 1621
Abstract
The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In [...] Read more.
The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification’s success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults. Full article
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10 pages, 1790 KiB  
Article
Sugar Detection in Aqueous Solution Using an SMS Fiber Device
by Nailea Mar-Abundis, Yadira Aracely Fuentes-Rubio, René Fernando Domínguez-Cruz and José Rafael Guzmán-Sepúlveda
Sensors 2023, 23(14), 6289; https://doi.org/10.3390/s23146289 - 11 Jul 2023
Cited by 2 | Viewed by 1429
Abstract
We report on the fabrication and testing of a fiber optics sensor based on multimodal interference effects, which aims at the detection of different types of sweeteners dissolved in water. The device, which has a simple structure, commonly known as the SMS configuration, [...] Read more.
We report on the fabrication and testing of a fiber optics sensor based on multimodal interference effects, which aims at the detection of different types of sweeteners dissolved in water. The device, which has a simple structure, commonly known as the SMS configuration, is built by splicing a segment of commercial-grade, coreless multimode fiber (NC-MMF) between two standard single-mode fibers (SMFs). In this configuration, the evanescent field traveling outside the core of the NC-MMF allows the sensing of the refractive index of the surrounding media, making it possible to detect different levels of sugar concentration. The optical sensor was tested with aqueous solutions of glucose, fructose, and sucrose in the concentration range from 0 wt% to 20 wt% at room temperature. The proposed device exhibits a linear response with a sensitivity of 0.1835 nm/wt% for sucrose, 0.1687 nm/wt% for fructose, and 0.1694 nm/wt% for glucose, respectively, with a sensing resolution of around 0.5 wt%. Finally, we show that, despite having similar concentration behavior, some degree of discrimination between the different sugars can be achieved by assessing their thermo-optical response. Full article
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19 pages, 2005 KiB  
Article
Prediction of Continuous Emotional Measures through Physiological and Visual Data
by Itaf Omar Joudeh, Ana-Maria Cretu, Stéphane Bouchard and Synthia Guimond
Sensors 2023, 23(12), 5613; https://doi.org/10.3390/s23125613 - 15 Jun 2023
Cited by 3 | Viewed by 1730
Abstract
The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust [...] Read more.
The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments. Full article
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9 pages, 2928 KiB  
Communication
A Monitoring System for Carbon Dioxide in Honeybee Hives: An Indicator of Colony Health
by Martin Bencsik, Adam McVeigh, Costas Tsakonas, Tarun Kumar, Luke Chamberlain and Michael I. Newton
Sensors 2023, 23(7), 3588; https://doi.org/10.3390/s23073588 - 29 Mar 2023
Cited by 3 | Viewed by 2581
Abstract
Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO2, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR [...] Read more.
Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO2, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR sensors placed in the crown boards and queen excluders of honeybee colonies. The same sensors also provide relative humidity and temperature data. Several months of data have been recorded from four different hives. The mass change measurements, from hive scales, when foragers leave the hive were compared with the data from the gas sensors. Our data suggest that it is possible to estimate the colony size from the change in measured CO2, however no such link with the humidity is observed. Data are presented showing the CO2 decreasing over many weeks as a colony dies. Full article
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