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Special Issue "Selected Papers from the 4th International Electronic Conference on Sensors and Applications"

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

Deadline for manuscript submissions: 31 May 2018

Special Issue Editors

Guest Editor
Dr. Stefano Mariani

Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
Website | E-Mail
Phone: +39-0223994279
Fax: +39-0223994300
Interests: MEMS; structural sensors; Kalman filtering
Guest Editor
Dr. Francesco Ciucci

Mechanical and Aerospace Engineering & Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology Clear Water Bay Kowloon, Hong Kong
Website | E-Mail
Fax: +852 2358 1543
Interests: solid state ionics; fuel cells; lithium batteries; chemical sensors
Guest Editor
Dr. Dirk Lehmhus

ISIS Sensorial Materials Scientific Centre, University of Bremen, 28359 Bremen, Germany
Website | E-Mail
Interests: porous and cellular metals; metal foams; syntactic foams; metal matrix syntactic foams; metal matrix composites; powder metallurgy; powder technology; finite element analysis; integrated computational materials engineering (ICME); smart structures; sensor integration; sensorial materials; structural health monitoring (SHM)
Guest Editor
Dr. Thomas B. Messervey

CEO and Co-Founder, Research to Market Solution s.r.l., Pavia, Italy
Website | E-Mail
Interests: using sensor data to make better engineering decisions across design, assessment, maintenance, inspections, and energy management to include machine learning
Guest Editor
Dr. Alberto Vallan

Politecnico di Torino, Department of Electronics and Telecommunications, corso Duca degli Abruzzi, 24, I-10129, Torino, Italy
Website | E-Mail
Interests: Fiber Optical Sensors, measurement science and technology, uncertainty evaluation
Guest Editor
Dr. Stefan Bosse

University of Bremen, Department of Mathematics and Computer Science, 28359 Bremen, Germany
Website1 | Website2 | E-Mail
Interests: distributed computing; sensor networks; sensorial materials; Internet-of-Things; cloud computing; agent-based computing; multi-agent systems; agent platforms; machine learning; self-organizing systems; embedded systems

Special Issue Information

Dear Colleagues,

This Special Issue comprises selected papers from the Proceedings of the 4th International Electronic Conference on Sensors and Applications, held 15–30 November 2017 on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 4th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Selected papers which attracted the most interest on the web, or that provided a particularly innovative contribution, have been gathered for publication. These papers have been subjected to peer review and are published with the aim of rapid and wide dissemination of research results, developments and applications. We hope this Conference Series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications.

Dr. Stefano Mariani
Dr. Francesco Ciucci
Dr. Dirk Lehmhus
Dr. Thomas B. Messervey
Dr. Alberto Vallan
Dr. Stefan Bosse
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 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. Sensors 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 1800 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

  • biosensors
  • chemical sensors
  • physical sensors
  • sensor networks
  • applications
  • Smart Cities
  • Smart Sensing Systems and Structures

Published Papers (4 papers)

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Research

Open AccessArticle Integrated Method for Personal Thermal Comfort Assessment and Optimization through Users’ Feedback, IoT and Machine Learning: A Case Study
Sensors 2018, 18(5), 1602; https://doi.org/10.3390/s18051602
Received: 12 April 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 17 May 2018
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Abstract
Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV
[...] Read more.
Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones. Following this perspective, the paper describes the results of an in-field investigation of thermal conditions through the use of nearable and wearable solutions, parametric models and machine learning techniques. The aim of the research is the exploration of the reliability of IoT-based solutions combined with advanced algorithms, in order to create a replicable framework for the assessment and improvement of user thermal satisfaction. For this purpose, an experimental test in real offices was carried out involving eight workers. Parametric models are applied for the assessment of thermal comfort; IoT solutions are used to monitor the environmental variables and the users’ parameters; the machine learning CART method allows to predict the users’ profile and the thermal comfort perception respect to the indoor environment. Full article
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Open AccessArticle Radio-Frequency Localization of Multiple Partial Discharges Sources with Two Receivers
Sensors 2018, 18(5), 1410; https://doi.org/10.3390/s18051410
Received: 3 April 2018 / Revised: 27 April 2018 / Accepted: 28 April 2018 / Published: 3 May 2018
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Abstract
Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied
[...] Read more.
Spatial localization of emitting sources is especially interesting in different fields of application. The focus of an earthquake, the determination of cracks in solid structures, or the position of bones inside a body are some examples of the use of multilateration techniques applied to acoustic and vibratory signals. Radar, GPS and wireless sensors networks location are based on radiofrequency emissions and the techniques are the same as in the case of acoustic emissions. This paper is focused on the determination of the position of sources of partial discharges in electrical insulation for maintenance based on the condition of the electrical equipment. The use of this phenomenon is a mere example of the capabilities of the proposed method but it is very representative because the emission can be electromagnetic in the VHF and UHF ranges or acoustic. This paper presents a method to locate more than one source in space with only two receivers, one of them in a fixed position and the other describing a circumference around the first one. The signals arriving from the different sources to the antennas are first separated using a classification technique based on their spectral components. Then, the individualized time differences of arrival (TDOA) from the sources collected at different angles describe a function, angle versus TDOA, that has all the geometric information needed to locate the source. The paper will show how to derive these functions for any source analytically with the position of the source as unknown parameters. Then, it will be demonstrated that it is possible to fit the curve with experimental measurements of the TDOA to obtain the parameters of the position of each source. Finally, the technique is extended to the localization of the emitter in three dimensions. Full article
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Open AccessArticle Detection of Anomalous Noise Events on Low-Capacity Acoustic Nodes for Dynamic Road Traffic Noise Mapping within an Hybrid WASN
Sensors 2018, 18(4), 1272; https://doi.org/10.3390/s18041272
Received: 13 March 2018 / Revised: 17 April 2018 / Accepted: 18 April 2018 / Published: 20 April 2018
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Abstract
One of the main aspects affecting the quality of life of people living in urban and suburban areas is the continuous exposure to high road traffic noise (RTN) levels. Nowadays, thanks to Wireless Acoustic Sensor Networks (WASN) noise in Smart Cities has started
[...] Read more.
One of the main aspects affecting the quality of life of people living in urban and suburban areas is the continuous exposure to high road traffic noise (RTN) levels. Nowadays, thanks to Wireless Acoustic Sensor Networks (WASN) noise in Smart Cities has started to be automatically mapped. To obtain a reliable picture of the RTN, those anomalous noise events (ANE) unrelated to road traffic (sirens, horns, people, etc.) should be removed from the noise map computation by means of an Anomalous Noise Event Detector (ANED). In Hybrid WASNs, with master-slave architecture, ANED should be implemented in both high-capacity (Hi-Cap) and low-capacity (Lo-Cap) sensors, following the same principle to obtain consistent results. This work presents an ANED version to run in real-time on μ Controller-based Lo-Cap sensors of a hybrid WASN, discriminating RTN from ANE through their Mel-based spectral energy differences. The experiments, considering 9 h and 8 min of real-life acoustic data from both urban and suburban environments, show the feasibility of the proposal both in terms of computational load and in classification accuracy. Specifically, the ANED Lo-Cap requires around 1 6 of the computational load of the ANED Hi-Cap, while classification accuracies are slightly lower (around 10%). However, preliminary analyses show that these results could be improved in around 4% in the future by means of considering optimal frequency selection. Full article
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Open AccessArticle An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures
Sensors 2018, 18(3), 831; https://doi.org/10.3390/s18030831
Received: 7 February 2018 / Revised: 6 March 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and
[...] Read more.
The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications. Full article
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