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Special Issue "Sensors: 20th Anniversary"

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

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Prof. Dr. Vittorio M.N. Passaro
E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica e dell'Informazione (Department of Electrical and Information Engineering), Politecnico di Bari, Via Edoardo Orabona n. 4, 70125 Bari, Italy
Interests: optoelectronic technologies; photonic devices and sensors; nanophotonic integrated sensors; non linear integrated optics; microelectronic and nanoelectronic technologies
Special Issues and Collections in MDPI journals
Prof. Dr. Leonhard Reindl
E-Mail Website
Guest Editor
Albert-Ludwigs-University of Freiburg, Faculty of Engineering, Department of Microsystems Engineering - IMTEK, Laboratory for Electrical Instrumentation, Georges-Koehler-Allee 106; room 04-014, D-79110 Freiburg, Germany
Interests: surface acoustic wave components (SAW) sensors; energy self-sufficient sensors for WSNS; autonomous microsystems; energy harvesting; indoor localization and monitoring systems
Dr. Alexander Star
E-Mail Website
Guest Editor
Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, PA 15260, USA
Interests: nanosensors; carbon nanotubes and graphene; nanoparticles; nanotoxicology; drug delivery
Special Issues and Collections in MDPI journals
Prof. Dr. Eduard Llobet *
E-Mail Website
Guest Editor
MINOS-EMaS, Universitat Rovira i Virgili, 43007 Tarragona, Spain
Interests: gas sensors employing nanosized metal oxides and carbon nanomaterials integrated in ceramics, MEMS or flexible polymeric transducers; nanomaterial synthesis using CVD or VPT and surface functionalization via grafting of functional groups or molecules or substitutional doping; development of gas sensing applications in environment, security
* Section 'Chemical Sensors'
Special Issues and Collections in MDPI journals
Prof. Dr. Guillermo Villanueva
E-Mail Website
Guest Editor
École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, 1015 Lausanne, Switzerland
Interests: MEMS; NEMS; piezoelectric transduction; resonators; nonlinearity; 2D materials
Special Issues and Collections in MDPI journals
Prof. Dr. Mehmet Rasit Yuce
E-Mail Website
Guest Editor
Electrical and Computer Systems Engineering, Monash University, Clayton Melbourne, VIC 3800, Australia
Interests: wearable devices; IoT sensors; bioelectronics; IC circuits; wireless body area networks; MEMs design; biomedial circuits; RF electronics; energy harvesting; sensor/sensor interface circuits and low-power circuits for emerging technologies in wireless communications, such as UWB technology and the Internet of Things (IoT)
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues, 

In 2020, we are celebrating the 20th anniversary of the journal Sensors (ISSN 1424-8220). Since 2001, when the inaugural issue of Sensors was launched, we have already published more than 25,000 papers from more than 85,000 authors. More than 32,000 reviewers have submitted at least one review report. Our sincerest thanks go to our readers, innumerable authors, anonymous peer reviewers, editors, and all the people working in some way for the journal who have joined their efforts for years. These highlights would not have occurred without your participation.

To mark this significant milestone, a Special Issue entitled “Sensors: 20th Anniversary” is being launched. This Special Issue includes high-quality papers under the broad scope of Sensors. We would like to invite you to contribute an original research paper or a comprehensive review article on a trendy or hot topic for peer-review and possible publication.

Prof. Dr. Vittorio M.N. Passaro
Prof. Dr. Leonhard M. Reindl
Prof. Dr. Assefa M. Melesse
Prof. Dr. Alexander Star
Prof. Dr. Eduard Llobet
Dr. Guillermo Villanueva
Prof. Dr. Mehmet Rasit Yuce
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 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 2200 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.

Published Papers (40 papers)

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Research

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Article
Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting
Sensors 2021, 21(8), 2748; https://doi.org/10.3390/s21082748 - 13 Apr 2021
Cited by 3 | Viewed by 848
Abstract
Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification [...] Read more.
Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Robust Principal Component Thermography for Defect Detection in Composites
Sensors 2021, 21(8), 2682; https://doi.org/10.3390/s21082682 - 10 Apr 2021
Cited by 1 | Viewed by 706
Abstract
Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have [...] Read more.
Pulsed Thermography (PT) data are usually affected by noise and as such most of the research effort in the last few years has been directed towards the development of advanced signal processing methods to improve defect detection. Among the numerous techniques that have been proposed, principal component thermography (PCT)—based on principal component analysis (PCA)—is one of the most effective in terms of defect contrast enhancement and data compression. However, it is well-known that PCA can be significantly affected in the presence of corrupted data (e.g., noise and outliers). Robust PCA (RPCA) has been recently proposed as an alternative statistical method that handles noisy data more properly by decomposing the input data into a low-rank matrix and a sparse matrix. We propose to process PT data by RPCA instead of PCA in order to improve defect detectability. The performance of the resulting approach, Robust Principal Component Thermography (RPCT)—based on RPCA, was evaluated with respect to PCT—based on PCA, using a CFRP sample containing artificially produced defects. We compared results quantitatively based on two metrics, Contrast-to-Noise Ratio (CNR), for defect detection capabilities, and the Jaccard similarity coefficient, for defect segmentation potential. CNR results were on average 40% higher for RPCT than for PCT, and the Jaccard index was slightly higher for RPCT (0.7395) than for PCT (0.7010). In terms of computational time, however, PCT was 11.5 times faster than RPCT. Further investigations are needed to assess RPCT performance on a wider range of materials and to optimize computational time. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Miniaturized Hybrid Frequency Reader for Contactless Measurement Scenarios Using Resonant Surface Acoustic Wave Sensors
Sensors 2021, 21(7), 2367; https://doi.org/10.3390/s21072367 - 29 Mar 2021
Viewed by 701
Abstract
Due to higher automation and predictive maintenance, it becomes more and more important to acquire as many data as possible during industrial processes. However, many scenarios require remote sensing since either moving parts would result in wear and tear of cables or harsh [...] Read more.
Due to higher automation and predictive maintenance, it becomes more and more important to acquire as many data as possible during industrial processes. However, many scenarios require remote sensing since either moving parts would result in wear and tear of cables or harsh environments prevent a wired connection. In the last few years, resonant surface acoustic wave (SAW) sensors have promised the possibility to be interrogable wirelessly which showed very good results in first studies. Therefore, the sensor’s resonance frequency shifts due to a changed measurand and thus has to be determined. However, up to now frequency reader systems showed several drawbacks like high costs or insufficient accuracy that blocked the way for a widespread usage of this approach in the mass market. Hence, this article presents a miniaturized and low cost six-port based frequency reader for SAW resonators in the 2.45 GHz ISM band that does not require an external calculation unit. It is shown that it can be either used to evaluate the scenario or measure the frequency directly with an amplitude or phase measurement, respectively. The performance of the system, including the hardware and embedded software, is finally shown by wired and contactless torque measurements. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning
Sensors 2021, 21(6), 2240; https://doi.org/10.3390/s21062240 - 23 Mar 2021
Viewed by 972
Abstract
This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the [...] Read more.
This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and it is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed for our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Drone Secure Communication Protocol for Future Sensitive Applications in Military Zone
Sensors 2021, 21(6), 2057; https://doi.org/10.3390/s21062057 - 15 Mar 2021
Cited by 2 | Viewed by 1085
Abstract
Unmanned Aerial Vehicle (UAV) plays a paramount role in various fields, such as military, aerospace, reconnaissance, agriculture, and many more. The development and implementation of these devices have become vital in terms of usability and reachability. Unfortunately, as they become widespread and their [...] Read more.
Unmanned Aerial Vehicle (UAV) plays a paramount role in various fields, such as military, aerospace, reconnaissance, agriculture, and many more. The development and implementation of these devices have become vital in terms of usability and reachability. Unfortunately, as they become widespread and their demand grows, they are becoming more and more vulnerable to several security attacks, including, but not limited to, jamming, information leakage, and spoofing. In order to cope with such attacks and security threats, a proper design of robust security protocols is indispensable. Although several pieces of research have been carried out with this regard, there are still research gaps, particularly concerning UAV-to-UAV secure communication, support for perfect forward secrecy, and provision of non-repudiation. Especially in a military scenario, it is essential to solve these gaps. In this paper, we studied the security prerequisites of the UAV communication protocol, specifically in the military setting. More importantly, a security protocol (with two sub-protocols), that serves in securing the communication between UAVs, and between a UAV and a Ground Control Station, is proposed. This protocol, apart from the common security requirements, achieves perfect forward secrecy and non-repudiation, which are essential to a secure military communication. The proposed protocol is formally and thoroughly verified by using the BAN-logic (Burrow-Abadi-Needham logic) and Scyther tool, followed by performance evaluation and implementation of the protocol on a real UAV. From the security and performance evaluation, it is indicated that the proposed protocol is superior compared to other related protocols while meeting confidentiality, integrity, mutual authentication, non-repudiation, perfect forward secrecy, perfect backward secrecy, response to DoS (Denial of Service) attacks, man-in-the-middle protection, and D2D (Drone-to-Drone) security. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series
Sensors 2021, 21(6), 1954; https://doi.org/10.3390/s21061954 - 10 Mar 2021
Cited by 3 | Viewed by 641
Abstract
Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling [...] Read more.
Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μμs, which is below the maximum prediction horizon, therefore demonstrating the algorithm’s promise in real-time high-rate applications. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015–2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform
Sensors 2021, 21(5), 1884; https://doi.org/10.3390/s21051884 - 08 Mar 2021
Cited by 2 | Viewed by 723
Abstract
Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the [...] Read more.
Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015–2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of “Amathus” and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Are the Kinetics and Kinematics of the Surf Pop-Up Related to the Anthropometric Characteristics of the Surfer?
Sensors 2021, 21(5), 1783; https://doi.org/10.3390/s21051783 - 04 Mar 2021
Viewed by 819
Abstract
The surf pop-up is a unique and challenging skill, critical to successful surfing. Hypothesizing that anthropometric characteristics of surfers influence the pop-up performance, we aimed to measure kinematics and ground-reaction forces (GRF) during a simulated pop-up motion, and to relate these variables with [...] Read more.
The surf pop-up is a unique and challenging skill, critical to successful surfing. Hypothesizing that anthropometric characteristics of surfers influence the pop-up performance, we aimed to measure kinematics and ground-reaction forces (GRF) during a simulated pop-up motion, and to relate these variables with anthropometric characteristics. Twenty-three male surfers (age: 28.4 ± 10.1 years old; body mass: 68.3 ± 10.8 kg; height: 1.73 ± 0.07 m; time of practice: 12.4 ± 8.9 years; arm-span: 1.75 ± 8.9 m) perform a simulated pop-up in the laboratory, while GRF and 3D motion-capture data were acquired. The duration of the pop-up was 1.20 ± 0.19 s (60% push-up and 40% reaching/landing phase). During the push-up, the hands were placed 0.46 ± 0.05 m apart and generated a relative total peak-force of 0.99 ± 0.10 N/Weight, with symmetrical impulse of 0.30 ± 0.05 N·s/Weight for the dominant and 0.29 ± 0.07 N·s/Weight for the nondominant hand. Elbow angles were not different during the peak force application (110 ± 18° vs. 112 ± 18°, respectively) of the push-up phase. During the landing phase, the feet were placed 0.63 ± 0.10 m apart and generated a relative peak force of 1.63 ± 0.18 N/Weight. The impact force during landing was applied unevenly between the rear foot (28%) and the front foot (72%). In conclusion, most anthropometric-related variables showed no relationship with performance variables, with the exception of an inverse relationship between muscle mass and pop-up total duration. We also observed no differences in upper- and lower-body kinematics between the dominant vs. nondominant hands and among surfers who preferred a regular vs. “goofy-foot” stance. Finally, the force profiles between hands were similar and symmetric, while the lower extremities during the reaching phase were different, with the front foot applying greater force than that of the rear foot. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Fabrication of Carbon Disulfide Added Colloidal Gold Colorimetric Sensor for the Rapid and On-Site Detection of Biogenic Amines
Sensors 2021, 21(5), 1738; https://doi.org/10.3390/s21051738 - 03 Mar 2021
Viewed by 750
Abstract
Meat is often wasted due to the perceived concerns of its shelf life and preservation. Specifically, in meat formation, biogenic amines (BAs) are the major agents to spoil them. Herein, we have developed a carbon disulfide (CS2) added colloidal gold nanoparticles-based [...] Read more.
Meat is often wasted due to the perceived concerns of its shelf life and preservation. Specifically, in meat formation, biogenic amines (BAs) are the major agents to spoil them. Herein, we have developed a carbon disulfide (CS2) added colloidal gold nanoparticles-based colorimetric sensor for the rapid and on-site detection of biogenic amines. Transmission electron microscopy is used to observe the morphological changes in colloidal gold nanoparticles and aggregation behavior of CS2 added to the colloidal gold nanoparticles’ solution. Raman spectroscopic analysis is further used to characterize the peaks of CS2, Cad and CS2-Cad molecules. Absorption spectroscopy is used to estimate the colorimetric differences and diffuse reflectance spectra of the samples. The sensing analysis is performed systematically in the presence and absence of CS2. CS2 added colloidal gold nanoparticles colorimetric sensor detected the BAs with a limit of detection (LOD) value of 50.00 µM. Furthermore, the developed sensor has shown an LOD of 50.00 µM for the detection of multiple BAs at a single time. The observed differences in the colorimetric and absorption signals indicate that the structure of BAs is converted to the dithiocarbamate (DTC)-BA molecule, due to the chemical reactions between the amine groups of BAs and CS2. Significantly, the developed colorimetric sensor offers distinct features such as facile fabrication approach, on-site sensing strategy, rapid analysis, visual detection, cost-effective, possibility of mass production, availability to detect multiple BAs at a single time and appreciable sensitivity. The developed sensor can be effectively used as a promising and alternative on-site tool for the estimation of BAs. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Cantilever-Based Sensor Utilizing a Diffractive Optical Element with High Sensitivity to Relative Humidity
Sensors 2021, 21(5), 1673; https://doi.org/10.3390/s21051673 - 01 Mar 2021
Cited by 1 | Viewed by 626
Abstract
High-sensitivity and simple, low-cost readout are desirable features for sensors independent of the application area. Micro-cantilever sensors use the deflection induced by the analyte presence to achieve high-sensitivity but possess complex electronic readouts. Current holographic sensors probe the analyte presence by measuring changes [...] Read more.
High-sensitivity and simple, low-cost readout are desirable features for sensors independent of the application area. Micro-cantilever sensors use the deflection induced by the analyte presence to achieve high-sensitivity but possess complex electronic readouts. Current holographic sensors probe the analyte presence by measuring changes in their optical properties, have a simpler low-cost readout, but their sensitivity can be further improved. Here, the two working principles were combined to obtain a new hybrid sensor with enhanced sensitivity. The diffractive element, a holographically patterned thin photopolymer layer, was placed on a polymer (polydimethylsiloxane) layer forming a bi-layer macro-cantilever. The different responses of the layers to analyte presence lead to cantilever deflection. The sensitivity and detection limits were evaluated by measuring the variation in cantilever deflection and diffraction efficiency with relative humidity. It was observed that the sensitivity is tunable by controlling the spatial frequency of the photopolymer gratings and the cantilever thickness. The sensor deflection was also visible to the naked eye, making it a simple, user-friendly device. The hybrid sensor diffraction efficiency response to the target analyte had an increased sensitivity (10-fold when compared with the cantilever or holographic modes operating independently), requiring a minimum upturn in the readout complexity. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Communication
An Integrated Evanescent Field Sensor for the Simultaneous Measurement of Layer Refractive Index and Thickness
Sensors 2021, 21(5), 1628; https://doi.org/10.3390/s21051628 - 26 Feb 2021
Viewed by 655
Abstract
A novel integrated sensor for the simultaneous measurement of layer refractive index and thickness based on evanescent fields is proposed. The theoretical limits for the accuracy of the sensor were examined for the example of a TiO2 layer. The influence of production [...] Read more.
A novel integrated sensor for the simultaneous measurement of layer refractive index and thickness based on evanescent fields is proposed. The theoretical limits for the accuracy of the sensor were examined for the example of a TiO2 layer. The influence of production tolerance on the accuracy was evaluated. In the experimental part of this work, a sensor chip containing nanowire and nanorib waveguides realized in silicon on insulator technology was used to demonstrate the detection of refractive index and thickness of a TiO2 atomic layer deposition (ALD) layer. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Image Segmentation Using Encoder-Decoder with Deformable Convolutions
Sensors 2021, 21(5), 1570; https://doi.org/10.3390/s21051570 - 24 Feb 2021
Cited by 1 | Viewed by 718
Abstract
Image segmentation is an essential step in image analysis that brings meaning to the pixels in the image. Nevertheless, it is also a difficult task due to the lack of a general suited approach to this problem and the use of real-life pictures [...] Read more.
Image segmentation is an essential step in image analysis that brings meaning to the pixels in the image. Nevertheless, it is also a difficult task due to the lack of a general suited approach to this problem and the use of real-life pictures that can suffer from noise or object obstruction. This paper proposes an architecture for semantic segmentation using a convolutional neural network based on the Xception model, which was previously used for classification. Different experiments were made in order to find the best performances of the model (e.g., different resolution and depth of the network and data augmentation techniques were applied). Additionally, the network was improved by adding a deformable convolution module. The proposed architecture obtained a 76.8 mean IoU on the Pascal VOC 2012 dataset and 58.1 on the Cityscapes dataset. It outperforms SegNet and U-Net networks, both networks having considerably more parameters and also a higher inference time. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks
Sensors 2021, 21(4), 1528; https://doi.org/10.3390/s21041528 - 23 Feb 2021
Cited by 2 | Viewed by 995
Abstract
Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. [...] Read more.
Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
The Design and Simulation of a 16-Sensors Plantar Pressure Insole Layout for Different Applications: From Sports to Clinics, a Pilot Study
Sensors 2021, 21(4), 1450; https://doi.org/10.3390/s21041450 - 19 Feb 2021
Cited by 2 | Viewed by 1413
Abstract
The quantification of plantar pressure distribution is widely done in the diagnosis of lower limbs deformities, gait analysis, footwear design, and sport applications. To date, a number of pressure insole layouts have been proposed, with different configurations according to their applications. The goal [...] Read more.
The quantification of plantar pressure distribution is widely done in the diagnosis of lower limbs deformities, gait analysis, footwear design, and sport applications. To date, a number of pressure insole layouts have been proposed, with different configurations according to their applications. The goal of this study is to assess the validity of a 16-sensors (1.5 × 1.5 cm) pressure insole to detect plantar pressure distribution during different tasks in the clinic and sport domains. The data of 39 healthy adults, acquired with a Pedar-X® system (Novel GmbH, Munich, Germany) during walking, weight lifting, and drop landing, were used to simulate the insole. The sensors were distributed by considering the location of the peak pressure on all trials: 4 on the hindfoot, 3 on the midfoot, and 9 on the forefoot. The following variables were computed with both systems and compared by estimating the Root Mean Square Error (RMSE): Peak/Mean Pressure, Ground Reaction Force (GRF), Center of Pressure (COP), the distance between COP and the origin, the Contact Area. The lowest (0.61%) and highest (82.4%) RMSE values were detected during gait on the medial-lateral COP and the GRF, respectively. This approach could be used for testing different layouts on various applications prior to production. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
The Long-Lasting Story of One Sensor Development: From Novel Ionophore Design toward the Sensor Selectivity Modeling and Lifetime Improvement
Sensors 2021, 21(4), 1401; https://doi.org/10.3390/s21041401 - 17 Feb 2021
Cited by 1 | Viewed by 532
Abstract
The metalloporphyrin ligand bearing incorporated anion-exchanger fragment, 5-[4-(3-trimethylammonium)propyloxyphenyl]-10,15,20-triphenylporphyrinate of Co(II) chloride, CoTPP-N, has been tested as anion-selective ionophore in PVC-based solvent polymeric membrane sensors. A plausible sensor working mechanism includes the axial coordination of the target anion on ionophore metal center followed by [...] Read more.
The metalloporphyrin ligand bearing incorporated anion-exchanger fragment, 5-[4-(3-trimethylammonium)propyloxyphenyl]-10,15,20-triphenylporphyrinate of Co(II) chloride, CoTPP-N, has been tested as anion-selective ionophore in PVC-based solvent polymeric membrane sensors. A plausible sensor working mechanism includes the axial coordination of the target anion on ionophore metal center followed by the formed complex aggregation with the second ionophore molecule through positively charged anion-exchanger fragment. The UV-visible spectroscopic studies in solution have revealed that the analyte concentration increase induces the J-type porphyrin aggregation. Polymeric membranes doped with CoTPP-N showed close to the theoretical Nernstian response toward nitrite ion, preferably coordinated by the ionophore, and were dependent on the presence of additional membrane-active components (lipophilic ionic sites and ionophore) in the membrane phase. The resulting selectivity was a subject of specific interaction and/or steric factors. Moreover, it was demonstrated theoretically and confirmed experimentally that the selection of a proper ratio of ionophore and anionic additive can optimize the sensor selectivity and lifetime. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Ultrasonic-Based Environmental Perception for Mobile 5G-Oriented XR Applications
Sensors 2021, 21(4), 1329; https://doi.org/10.3390/s21041329 - 13 Feb 2021
Cited by 1 | Viewed by 997
Abstract
One of the sectors that is expected to significantly benefit from 5G network deployment is eXtended Reality (XR). Besides the very high bandwidth, reliability, and Quality of Service (QoS) to be delivered to end users, XR also requires accurate environmental perception for safety [...] Read more.
One of the sectors that is expected to significantly benefit from 5G network deployment is eXtended Reality (XR). Besides the very high bandwidth, reliability, and Quality of Service (QoS) to be delivered to end users, XR also requires accurate environmental perception for safety reasons: this is fundamental when a user, wearing XR equipment, is immersed in a “virtual” world, but moves in a “real” environment. To overcome this limitation (especially when using low-cost XR equipments, such as cardboards worn by the end user), it is possible to exploit the potentialities offered by Internet of Things (IoT) nodes with sensing/actuating capabilities. In this paper, we rely on ultrasonic sensor-based IoT systems to perceive the surrounding environment and to provide “side information” to XR systems, then performing a preliminary experimental characterization campaign with different ultrasonic IoT system configurations worn by the end user. The combination of the information flows associated with XR and IoT components is enabled by 5G technology. An illustrative experimental scenario, relative to a “Tourism 4.0” IoT-aided VR application deployed by Vodafone in Milan, Italy, is presented. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Industrial Control under Non-Ideal Measurements: Data-Based Signal Processing as an Alternative to Controller Retuning
Sensors 2021, 21(4), 1237; https://doi.org/10.3390/s21041237 - 10 Feb 2021
Viewed by 511
Abstract
Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay [...] Read more.
Industrial environments are characterised by the non-lineal and highly complex processes they perform. Different control strategies are considered to assure that these processes are correctly performed. Nevertheless, these strategies are sensible to noise-corrupted and delayed measurements. For that reason, denoising techniques and delay correction methodologies should be considered but, most of these techniques require a complex design and optimisation process as a function of the scenario where they are applied. To alleviate this, a complete data-based approach devoted to denoising and correcting the delay of measurements is proposed here with a two-fold objective: simplify the solution design process and achieve its decoupling from the considered control strategy as well as from the scenario. Here it corresponds to a Wastewater Treatment Plant (WWTP). However, the proposed solution can be adopted at any industrial environment since neither an optimization nor a design focused on the scenario is required, only pairs of input and output data. Results show that a minimum Root Mean Squared Error (RMSE) improvement of a 63.87% is achieved when the new proposed data-based denoising approach is considered. In addition, the whole system performance show that similar and even better results are obtained when compared to scenario-optimised methodologies. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Altitude Measurement-Based Optimization of the Landing Process of UAVs
Sensors 2021, 21(4), 1151; https://doi.org/10.3390/s21041151 - 06 Feb 2021
Cited by 2 | Viewed by 711
Abstract
The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, [...] Read more.
The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Making Historical Gyroscopes Alive—2D and 3D Preservations by Sensor Fusion and Open Data Access
Sensors 2021, 21(3), 957; https://doi.org/10.3390/s21030957 - 01 Feb 2021
Cited by 1 | Viewed by 1039
Abstract
The preservation of cultural heritage assets of all kind is an important task for modern civilizations. This also includes tools and instruments that have been used in the previous decades and centuries. Along with the industrial revolution 200 years ago, mechanical and electrical [...] Read more.
The preservation of cultural heritage assets of all kind is an important task for modern civilizations. This also includes tools and instruments that have been used in the previous decades and centuries. Along with the industrial revolution 200 years ago, mechanical and electrical technologies emerged, together with optical instruments. In the meantime, it is not only museums who showcase these developments, but also companies, universities, and private institutions. Gyroscopes are fascinating instruments with a history dating back 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition towards their sustainable future. Here, the systems are digitized in 2D, 2.5D, and 3D and are made available for a worldwide community using open access platforms. The technologies being used are computed tomography, computer vision, endoscopy, and photogrammetry. We present a novel workflow for combining voxel representations and colored point clouds, to create digital twins of the physical objects with 0.1 mm precision. This has not yet been investigated and is therefore pioneering work. Advantages and disadvantages are discussed and suggested work for the near future is outlined in this new and challenging field of tech heritage digitization. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation
Sensors 2021, 21(2), 601; https://doi.org/10.3390/s21020601 - 16 Jan 2021
Cited by 2 | Viewed by 883
Abstract
The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed [...] Read more.
The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Communication
Hybrid Carbon Microfibers-Graphite Fillers for Piezoresistive Cementitious Composites
Sensors 2021, 21(2), 518; https://doi.org/10.3390/s21020518 - 13 Jan 2021
Cited by 3 | Viewed by 890
Abstract
Multifunctional structural materials are very promising in the field of engineering. Particularly, their strain sensing ability draws much attention for structural health monitoring applications. Generally, strain sensing materials are produced by adding a certain amount of conductive fillers, around the so-called “percolation threshold”, [...] Read more.
Multifunctional structural materials are very promising in the field of engineering. Particularly, their strain sensing ability draws much attention for structural health monitoring applications. Generally, strain sensing materials are produced by adding a certain amount of conductive fillers, around the so-called “percolation threshold”, to the cement or composite matrix. Recently, graphite has been found to be a suitable filler for strain sensing. However, graphite requires high amounts of doping to reach percolation threshold. In order to decrease the amount of inclusions, this paper proposes cementitious materials doped with new hybrid carbon inclusions, i.e., graphite and carbon microfibers. Carbon microfibers having higher aspect ratio than graphite accelerate the percolation threshold of the graphite particles without incurring into dispersion issues. The resistivity and strain sensitivity of different fibers’ compositions are investigated. The electromechanical tests reveal that, when combined, carbon microfibers and graphite hybrid fillers reach to percolation faster and exhibit higher gauge factors and enhanced linearity. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
5G-Enabled Autonomous Driving Demonstration with a V2X Scenario-in-the-Loop Approach
Sensors 2020, 20(24), 7344; https://doi.org/10.3390/s20247344 - 21 Dec 2020
Cited by 4 | Viewed by 1149
Abstract
Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, [...] Read more.
Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities—including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios—on the M86 highway and the ZalaZONE proving ground in Hungary—for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
Video-Tachometer Methodology for Wind Turbine Rotor Speed Measurement
Sensors 2020, 20(24), 7314; https://doi.org/10.3390/s20247314 - 19 Dec 2020
Viewed by 646
Abstract
The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. [...] Read more.
The measurement of the rotational speed of rotating machinery is typically performed based on mechanical adherence; for example, in encoders. Nevertheless, it can be of interest in various types of applications to develop contactless vision-based methodologies to measure the speed of rotating machinery. In particular, contactless rotor speed measurement methods have several potential applications for wind turbine technology, in the context of non-intrusive condition monitoring approaches. The present study is devoted exactly to this problem: a ground level video-tachometer measurement technique and an image analysis algorithm for wind turbine rotor speed estimation are proposed. The methodology is based on the comparison between a reference frame and each frame of the video through the covariance matrix: a covariance time series is thus obtained, from which the rotational speed is estimated by passing to the frequency domain through the spectrogram. This procedure guarantees the robustness of the rotational speed estimation, despite the intrinsic non-stationarity of the system and the possible signal disturbances. The method is tested and discussed based on two experimental environments with different characteristics: the former is a small wind turbine model (with a 0.45 m rotor diameter) in the wind tunnel facility of the University of Perugia, whose critical aspect is the high rotational speed (up to the order of 1500 RPM). The latter test case is a wind turbine with a 44 m rotor diameter which is part of an industrial wind farm: in this case, the critical point regards the fact that measurements are acquired in uncontrolled conditions. It is shown that the method is robust enough to overcome the critical aspects of both test cases and to provide reliable rotational speed estimates. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events
Sensors 2020, 20(21), 6225; https://doi.org/10.3390/s20216225 - 31 Oct 2020
Cited by 2 | Viewed by 1581
Abstract
During the few last years, indoor and outdoor Air Quality Monitoring (AQM) has gained a lot of interest among the scientific community due to its direct relation with human health. The Internet of Things (IoT) and, especially, Wireless Sensor Networks (WSN) have given [...] Read more.
During the few last years, indoor and outdoor Air Quality Monitoring (AQM) has gained a lot of interest among the scientific community due to its direct relation with human health. The Internet of Things (IoT) and, especially, Wireless Sensor Networks (WSN) have given rise to the development of wireless AQM portable systems. This paper presents the development of a LoRa (short for long-range) based sensor network for AQM and gas leakage events detection. The combination of both a commercial gas sensor and a resistance measurement channel for graphene chemoresistive sensors allows both the calculation of an Air Quality Index based on the concentration of reducing species such as volatile organic compounds (VOCs) and CO, and it also makes possible the detection of NO2, which is an important air pollutant. The graphene sensor tested with the LoRa nodes developed allows the detection of NO2 pollution in just 5 min as well as enables monitoring sudden changes in the background level of this pollutant in the atmosphere. The capability of the system of detecting both reducing and oxidizing pollutant agents, alongside its low-cost, low-power, and real-time monitoring features, makes this a solution suitable to be used in wireless AQM and early warning systems. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Article
A Low-Cost On-Street Parking Management System Based on Bluetooth Beacons
Sensors 2020, 20(16), 4559; https://doi.org/10.3390/s20164559 - 14 Aug 2020
Cited by 4 | Viewed by 1115
Abstract
In recent years, many city governments around the world have begun to use information and communication technology to increase the management efficiency of on-street parking. Among various experimental smart parking projects, deployment of wireless magnetic sensors and smart parking meters are quite common. [...] Read more.
In recent years, many city governments around the world have begun to use information and communication technology to increase the management efficiency of on-street parking. Among various experimental smart parking projects, deployment of wireless magnetic sensors and smart parking meters are quite common. However, using wireless magnetic sensors can only detect the occupancy of parking spaces without the knowledge of who are currently using these parking spaces; human labor is still needed to issue the parking bills. In contrast, smart parking meters based on image recognition can detect the occupancy of parking spaces along with the license plate numbers, but the cost of deploying smart parking meters is relatively high. In this research, we investigate the feasibility of building an on-street parking management system mainly based on low-cost Bluetooth beacons. Specifically, beacon transmitters are installed in the vehicles, and beacon receivers are deployed along the roadside parking spaces. By processing the received beacon signals using Kalman filter, our system can detect the occupancy of parking spaces as well as the identification of the vehicles. Although distance estimation using the received signal strength is not accurate, our experiments show that it suffices for correct detection of parking occupancy. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review

Jump to: Research, Other

Review
Semantic Data Mining in Ubiquitous Sensing: A Survey
Sensors 2021, 21(13), 4322; https://doi.org/10.3390/s21134322 - 24 Jun 2021
Viewed by 491
Abstract
Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models [...] Read more.
Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review
Sensors 2021, 21(9), 2929; https://doi.org/10.3390/s21092929 - 22 Apr 2021
Cited by 2 | Viewed by 1041
Abstract
This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed [...] Read more.
This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Metal-Oxide Based Nanomaterials: Synthesis, Characterization and Their Applications in Electrical and Electrochemical Sensors
Sensors 2021, 21(7), 2494; https://doi.org/10.3390/s21072494 - 03 Apr 2021
Cited by 5 | Viewed by 1025
Abstract
Pure, mixed and doped metal oxides (MOX) have attracted great interest for the development of electrical and electrochemical sensors since they are cheaper, faster, easier to operate and capable of online analysis and real-time identification. This review focuses on highly sensitive chemoresistive type [...] Read more.
Pure, mixed and doped metal oxides (MOX) have attracted great interest for the development of electrical and electrochemical sensors since they are cheaper, faster, easier to operate and capable of online analysis and real-time identification. This review focuses on highly sensitive chemoresistive type sensors based on doped-SnO2, RhO, ZnO-Ca, Smx-CoFe2−xO4 semiconductors used to detect toxic gases (H2, CO, NO2) and volatile organic compounds (VOCs) (e.g., acetone, ethanol) in monitoring of gaseous markers in the breath of patients with specific pathologies and for environmental pollution control. Interesting results about the monitoring of biochemical substances as dopamine, epinephrine, serotonin and glucose have been also reported using electrochemical sensors based on hybrid MOX nanocomposite modified glassy carbon and screen-printed carbon electrodes. The fundamental sensing mechanisms and commercial limitations of the MOX-based electrical and electrochemical sensors are discussed providing research directions to bridge the existing gap between new sensing concepts and real-world analytical applications. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Advances in Plasmonic Sensing at the NIR—A Review
Sensors 2021, 21(6), 2111; https://doi.org/10.3390/s21062111 - 17 Mar 2021
Cited by 1 | Viewed by 1259
Abstract
Surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) are among the most common and powerful label-free refractive index-based biosensing techniques available nowadays. Focusing on LSPR sensors, their performance is highly dependent on the size, shape, and nature of the nanomaterial employed. [...] Read more.
Surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) are among the most common and powerful label-free refractive index-based biosensing techniques available nowadays. Focusing on LSPR sensors, their performance is highly dependent on the size, shape, and nature of the nanomaterial employed. Indeed, the tailoring of those parameters allows the development of LSPR sensors with a tunable wavelength range between the ultra-violet (UV) and near infra-red (NIR). Furthermore, dealing with LSPR along optical fiber technology, with their low attenuation coefficients at NIR, allow for the possibility to create ultra-sensitive and long-range sensing networks to be deployed in a variety of both biological and chemical sensors. This work provides a detailed review of the key science underpinning such systems as well as recent progress in the development of several LSPR-based biosensors in the NIR wavelengths, including an overview of the LSPR phenomena along recent developments in the field of nanomaterials and nanostructure development towards NIR sensing. The review ends with a consideration of key advances in terms of nanostructure characteristics for LSPR sensing and prospects for future research and advances in this field. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Fiber Bragg Grating Wavelength Drift in Long-Term High Temperature Annealing
Sensors 2021, 21(4), 1454; https://doi.org/10.3390/s21041454 - 19 Feb 2021
Cited by 2 | Viewed by 933
Abstract
High-temperature-resistant fiber Bragg gratings (FBGs) are the main competitors to thermocouples as sensors in applications for high temperature environments defined as being in the 600–1200 °C temperature range. Due to their small size, capacity to be multiplexed into high density distributed sensor arrays [...] Read more.
High-temperature-resistant fiber Bragg gratings (FBGs) are the main competitors to thermocouples as sensors in applications for high temperature environments defined as being in the 600–1200 °C temperature range. Due to their small size, capacity to be multiplexed into high density distributed sensor arrays and survivability in extreme ambient temperatures, they could provide the essential sensing support that is needed in high temperature processes. While capable of providing reliable sensing information in the short term, their long-term functionality is affected by the drift of the characteristic Bragg wavelength or resonance that is used to derive the temperature. A number of physical processes have been proposed as the cause of the high temperature wavelength drift but there is yet no credible description of this process. In this paper we review the literature related to the long-term wavelength drift of FBGs at high temperature and provide our recent results of more than 4000 h of high temperature testing in the 900–1000 °C range. We identify the major components of the high temperature wavelength drift and we propose mechanisms that could be causing them. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
The Art of Designing Remote IoT Devices—Technologies and Strategies for a Long Battery Life
Sensors 2021, 21(3), 913; https://doi.org/10.3390/s21030913 - 29 Jan 2021
Cited by 1 | Viewed by 1195
Abstract
Long-range wireless connectivity technologies for sensors and actuators open the door for a variety of new Internet of Things (IoT) applications. These technologies can be deployed to establish new monitoring capabilities and enhance efficiency of services in a rich diversity of domains. Low [...] Read more.
Long-range wireless connectivity technologies for sensors and actuators open the door for a variety of new Internet of Things (IoT) applications. These technologies can be deployed to establish new monitoring capabilities and enhance efficiency of services in a rich diversity of domains. Low energy consumption is essential to enable battery-powered IoT nodes with a long autonomy. This paper explains the challenges posed by combining low-power and long-range connectivity. An energy breakdown demonstrates the dominance of transmit and sleep energy. The principles for achieving both low-power and wide-area are outlined, and the landscape of available networking technologies that are suited to connect remote IoT nodes is sketched. The typical anatomy of such a node is presented, and the subsystems are zoomed into. The art of designing remote IoT devices requires an application-oriented approach, where a meticulous design and smart operation are essential to grant a long battery life. In particular we demonstrate the importance of strategies such as “think before you talk” and “race to sleep”. As maintenance of IoT nodes is often cumbersome due to being deployed at hard to reach places, extending the battery life of these devices is critical. Moreover, the environmental impact of batteries further demonstrates the need for a longer battery life in order to reduce the number of batteries used. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Fiber Optic Gas Sensors Based on Lossy Mode Resonances and Sensing Materials Used Therefor: A Comprehensive Review
Sensors 2021, 21(3), 731; https://doi.org/10.3390/s21030731 - 22 Jan 2021
Cited by 7 | Viewed by 1092
Abstract
Pollution in cities induces harmful effects on human health, which continuously increases the global demand of gas sensors for air quality control and monitoring. In the same manner, the industrial sector requests new gas sensors for their productive processes. Moreover, the association between [...] Read more.
Pollution in cities induces harmful effects on human health, which continuously increases the global demand of gas sensors for air quality control and monitoring. In the same manner, the industrial sector requests new gas sensors for their productive processes. Moreover, the association between exhaled gases and a wide range of diseases or health conditions opens the door for new diagnostic applications. The large number of applications for gas sensors has permitted the development of multiple sensing technologies. Among them, optical fiber gas sensors enable their utilization in remote locations, confined spaces or hostile environments as well as corrosive or explosive atmospheres. Particularly, Lossy Mode Resonance (LMR)-based optical fiber sensors employ the traditional metal oxides used for gas sensing purposes for the generation of the resonances. Some research has been conducted on the development of LMR-based optical fiber gas sensors; however, they have not been fully exploited yet and offer optimal possibilities for improvement. This review gives the reader a complete overview of the works focused on the utilization of LMR-based optical fiber sensors for gas sensing applications, summarizing the materials used for the development of these sensors as well as the fabrication procedures and the performance of these devices. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures
Sensors 2021, 21(2), 637; https://doi.org/10.3390/s21020637 - 18 Jan 2021
Cited by 1 | Viewed by 1153
Abstract
Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, [...] Read more.
Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges
Sensors 2021, 21(2), 546; https://doi.org/10.3390/s21020546 - 14 Jan 2021
Cited by 2 | Viewed by 1358
Abstract
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, [...] Read more.
(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops
Sensors 2021, 21(1), 171; https://doi.org/10.3390/s21010171 - 29 Dec 2020
Cited by 1 | Viewed by 1443
Abstract
In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies [...] Read more.
In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Review
Advances in Optical Biosensors and Sensors Using Nanoporous Anodic Alumina
Sensors 2020, 20(18), 5068; https://doi.org/10.3390/s20185068 - 07 Sep 2020
Cited by 3 | Viewed by 1354
Abstract
This review paper focuses on recent progress in optical biosensors using self-ordered nanoporous anodic alumina. We present the fabrication of self-ordered nanoporous anodic alumina, surface functionalization, and optical sensor applications. We show that self-ordered nanoporous anodic alumina has good potential for use in [...] Read more.
This review paper focuses on recent progress in optical biosensors using self-ordered nanoporous anodic alumina. We present the fabrication of self-ordered nanoporous anodic alumina, surface functionalization, and optical sensor applications. We show that self-ordered nanoporous anodic alumina has good potential for use in the fabrication of antibody-based (immunosensor), aptamer-based (aptasensor), gene-based (genosensor), peptide-based, and enzyme-based optical biosensors. The fabricated optical biosensors presented high sensitivity and selectivity. In addition, we also showed that the performance of the biosensors and the self-ordered nanoporous anodic alumina can be used for assessing biomolecules, heavy ions, and gas molecules. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Perspective
Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques
Sensors 2021, 21(10), 3532; https://doi.org/10.3390/s21103532 - 19 May 2021
Viewed by 949
Abstract
Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying [...] Read more.
Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying and quantifying MPs. Although standards exist for MP sample collection and preparation, methods of MP analysis vary considerably and produce data with a broad range of data content and quality. The need for extensive analysis-specific sample preparation in current technology approaches has hindered the emergence of a single technique which can operate on aqueous samples in the field, rather than on dried laboratory preparations. In this perspective, we consider MP measurement technologies with a focus on both their eventual field-deployability and their respective data products (e.g., MP particle count, size, and/or polymer type). We present preliminary demonstrations of several prospective MP measurement techniques, with an eye towards developing a solution or solutions that can transition from the laboratory to the field. Specifically, experimental results are presented from multiple prototype systems that measure various physical properties of MPs: pyrolysis-differential mobility spectroscopy, short-wave infrared imaging, aqueous Nile Red labeling and counting, acoustophoresis, ultrasound, impedance spectroscopy, and dielectrophoresis. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Perspective
Inorganic Thermoelectric Fibers: A Review of Materials, Fabrication Methods, and Applications
Sensors 2021, 21(10), 3437; https://doi.org/10.3390/s21103437 - 14 May 2021
Viewed by 792
Abstract
Thermoelectric technology can directly harvest the waste heat into electricity, which is a promising field of green and sustainable energy. In this aspect, flexible thermoelectrics (FTE) such as wearable fabrics, smart biosensing, and biomedical electronics offer a variety of applications. Since the nanofibers [...] Read more.
Thermoelectric technology can directly harvest the waste heat into electricity, which is a promising field of green and sustainable energy. In this aspect, flexible thermoelectrics (FTE) such as wearable fabrics, smart biosensing, and biomedical electronics offer a variety of applications. Since the nanofibers are one of the important constructions of FTE, inorganic thermoelectric fibers are focused on here due to their excellent thermoelectric performance and acceptable flexibility. Additionally, measurement and microstructure characterizations for various thermoelectric fibers (Bi-Sb-Te, Ag2Te, PbTe, SnSe and NaCo2O4) made by different fabrication methods, such as electrospinning, two-step anodization process, solution-phase deposition method, focused ion beam, and self-heated 3ω method, are detailed. This review further illustrates that some techniques, such as thermal drawing method, result in high performance of fiber-based thermoelectric properties, which can emerge in wearable devices and smart electronics in the near future. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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Perspective
Molecular Recognition: Perspective and a New Approach
Sensors 2021, 21(8), 2757; https://doi.org/10.3390/s21082757 - 14 Apr 2021
Viewed by 534
Abstract
This perspective presents an overview of approaches to the preparation of molecular recognition agents for chemical sensing. These approaches include chemical synthesis, using catalysts from biological systems, partitioning, aptamers, antibodies and molecularly imprinted polymers. The latter three approaches are general in that they [...] Read more.
This perspective presents an overview of approaches to the preparation of molecular recognition agents for chemical sensing. These approaches include chemical synthesis, using catalysts from biological systems, partitioning, aptamers, antibodies and molecularly imprinted polymers. The latter three approaches are general in that they can be applied with a large number of analytes, both proteins and smaller molecules like drugs and hormones. Aptamers and antibodies bind analytes rapidly while molecularly imprinted polymers bind much more slowly. Most molecularly imprinted polymers, formed by polymerizing in the presence of a template, contain a high level of covalent crosslinker that causes the polymer to form a separate phase. This results in a material that is rigid with low affinity for analyte and slow binding kinetics. Our approach to templating is to use predominantly or exclusively noncovalent crosslinks. This results in soluble templated polymers that bind analyte rapidly with high affinity. The biggest challenge of this approach is that the chains are tangled when the templated polymer is dissolved in water, blocking access to binding sites. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
Letter
Time-Frequency Spectral Signature of Limb Movements and Height Estimation Using Micro-Doppler Millimeter-Wave Radar
Sensors 2020, 20(17), 4660; https://doi.org/10.3390/s20174660 - 19 Aug 2020
Cited by 1 | Viewed by 728
Abstract
We present a technique for the identification of human and animal movement and height using a low power millimeter-wave radar. The detection was based on the transmission of a continuous wave and heterodyning the received signal reflected from the target to obtain micro-Doppler [...] Read more.
We present a technique for the identification of human and animal movement and height using a low power millimeter-wave radar. The detection was based on the transmission of a continuous wave and heterodyning the received signal reflected from the target to obtain micro-Doppler shifts associated with the target structure and motion. The algorithm enabled the extraction of target signatures from typical gestures and differentiated between humans, animals, and other ‘still’ objects. Analytical expressions were derived using a pendulum model to characterize the micro-Doppler frequency shifts due to the periodic motion of the human and animal limbs. The algorithm was demonstrated using millimeter-wave radar operating in the W-band. We employed a time–frequency distribution to analyze the detected signal and classify the type of targets. Full article
(This article belongs to the Special Issue Sensors: 20th Anniversary)
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