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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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15 pages, 7710 KiB  
Article
A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments
by Ahmed Elamin, Nader Abdelaziz and Ahmed El-Rabbany
Sensors 2022, 22(24), 9908; https://doi.org/10.3390/s22249908 - 16 Dec 2022
Cited by 14 | Viewed by 3143
Abstract
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying [...] Read more.
Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%. Full article
(This article belongs to the Section Navigation and Positioning)
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25 pages, 3483 KiB  
Article
A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5
by Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov and Jinsoo Cho
Sensors 2022, 22(23), 9384; https://doi.org/10.3390/s22239384 - 01 Dec 2022
Cited by 29 | Viewed by 5693
Abstract
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural [...] Read more.
Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network’s backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset. Full article
(This article belongs to the Special Issue Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments 2022-2023)
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38 pages, 1958 KiB  
Article
E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures
by Athanasia Zlatintsi, Panagiotis P. Filntisis, Christos Garoufis, Niki Efthymiou, Petros Maragos, Andreas Menychtas, Ilias Maglogiannis, Panayiotis Tsanakas, Thomas Sounapoglou, Emmanouil Kalisperakis, Thomas Karantinos, Marina Lazaridi, Vasiliki Garyfalli, Asimakis Mantas, Leonidas Mantonakis and Nikolaos Smyrnis
Sensors 2022, 22(19), 7544; https://doi.org/10.3390/s22197544 - 05 Oct 2022
Cited by 11 | Viewed by 2787
Abstract
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this [...] Read more.
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses. Full article
(This article belongs to the Special Issue AI for Biomedical Sensing and Imaging)
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25 pages, 4775 KiB  
Review
Optical Fiber Sensors and Sensing Networks: Overview of the Main Principles and Applications
by Cristiano Pendão and Ivo Silva
Sensors 2022, 22(19), 7554; https://doi.org/10.3390/s22197554 - 05 Oct 2022
Cited by 44 | Viewed by 7448
Abstract
Optical fiber sensors present several advantages in relation to other types of sensors. These advantages are essentially related to the optical fiber properties, i.e., small, lightweight, resistant to high temperatures and pressure, electromagnetically passive, among others. Sensing is achieved by exploring the properties [...] Read more.
Optical fiber sensors present several advantages in relation to other types of sensors. These advantages are essentially related to the optical fiber properties, i.e., small, lightweight, resistant to high temperatures and pressure, electromagnetically passive, among others. Sensing is achieved by exploring the properties of light to obtain measurements of parameters, such as temperature, strain, or angular velocity. In addition, optical fiber sensors can be used to form an Optical Fiber Sensing Network (OFSN) allowing manufacturers to create versatile monitoring solutions with several applications, e.g., periodic monitoring along extensive distances (kilometers), in extreme or hazardous environments, inside structures and engines, in clothes, and for health monitoring and assistance. Most of the literature available on this subject focuses on a specific field of optical sensing applications and details their principles of operation. This paper presents a more broad overview, providing the reader with a literature review that describes the main principles of optical sensing and highlights the versatility, advantages, and different real-world applications of optical sensing. Moreover, it includes an overview and discussion of a less common architecture, where optical sensing and Wireless Sensor Networks (WSNs) are integrated to harness the benefits of both worlds. Full article
(This article belongs to the Special Issue Optical Fiber Technology and Sensors)
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24 pages, 11672 KiB  
Article
The Use of Soil Moisture and Pore-Water Pressure Sensors for the Interpretation of Landslide Behavior in Small-Scale Physical Models
by Josip Peranić, Nina Čeh and Željko Arbanas
Sensors 2022, 22(19), 7337; https://doi.org/10.3390/s22197337 - 27 Sep 2022
Cited by 8 | Viewed by 3187
Abstract
This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation [...] Read more.
This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions”, and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models. Full article
(This article belongs to the Special Issue Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters Managements)
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49 pages, 19636 KiB  
Review
Sensing with Femtosecond Laser Filamentation
by Pengfei Qi, Wenqi Qian, Lanjun Guo, Jiayun Xue, Nan Zhang, Yuezheng Wang, Zhi Zhang, Zeliang Zhang, Lie Lin, Changlin Sun, Liguo Zhu and Weiwei Liu
Sensors 2022, 22(18), 7076; https://doi.org/10.3390/s22187076 - 19 Sep 2022
Cited by 18 | Viewed by 5697
Abstract
Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 1013–1014 W/cm2 with a large distance ranging from meter to kilometers [...] Read more.
Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 1013–1014 W/cm2 with a large distance ranging from meter to kilometers can effectively ionize, break, and excite the molecules and fragments, resulting in characteristic fingerprint emissions, which provide a great opportunity for investigating strong-field molecules interaction in complicated environments, especially remote sensing. Additionally, the ultrastrong intensity inside the filament can damage almost all the detectors and ignite various intricate higher order nonlinear optical effects. These extreme physical conditions and complicated phenomena make the sensing and controlling of filamentation challenging. This paper mainly focuses on recent research advances in sensing with femtosecond laser filamentation, including fundamental physics, sensing and manipulating methods, typical filament-based sensing techniques and application scenarios, opportunities, and challenges toward the filament-based remote sensing under different complicated conditions. Full article
(This article belongs to the Special Issue Sensing with Femtosecond Laser Filamentation)
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26 pages, 7233 KiB  
Article
Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming
by Kristina Dineva and Tatiana Atanasova
Sensors 2022, 22(17), 6566; https://doi.org/10.3390/s22176566 - 31 Aug 2022
Cited by 16 | Viewed by 3310
Abstract
Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide [...] Read more.
Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm’s data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system. Full article
(This article belongs to the Special Issue Ubiquitous Sensing and Intelligent Systems)
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31 pages, 5515 KiB  
Article
UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application
by Yun Sun, Herman Fesenko, Vyacheslav Kharchenko, Luo Zhong, Ihor Kliushnikov, Oleg Illiashenko, Olga Morozova and Anatoliy Sachenko
Sensors 2022, 22(17), 6444; https://doi.org/10.3390/s22176444 - 26 Aug 2022
Cited by 17 | Viewed by 2506
Abstract
This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of [...] Read more.
This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components—sensors, means of communication, processing, and presentation—in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles. Full article
(This article belongs to the Special Issue Complex Data Processing Systems and Computing Algorithms: New Concepts and Applications)
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15 pages, 1451 KiB  
Article
Miniterm, a Novel Virtual Sensor for Predictive Maintenance for the Industry 4.0 Era
by Eduardo Garcia, Nicolás Montés, Javier Llopis and Antonio Lacasa
Sensors 2022, 22(16), 6222; https://doi.org/10.3390/s22166222 - 19 Aug 2022
Cited by 15 | Viewed by 2008
Abstract
This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to [...] Read more.
This article introduces a novel virtual sensor for predictive maintenance called mini-term. A mini-term can be defined as the time it takes for a part of the machine to do its job. Being a technical sub-cycle time, its function has been linked to production. However, when a machine or component gets deteriorated, the mini-term also suffers deterioration, allowing it to be a multifunctional indicator for the prediction of machine failures as well as measurement of production. Currently, in Industry 4.0, one of the handicaps is Big Data and Data Analysis. However, in the case of predictive maintenance, the need to install sensors in the machines means that when the proposed scientific solutions reach the industry, they cannot be carried out massively due to the high cost this entails. The advantage introduced by the mini-term is that it can be implemented in an easy and simple way in pre-installed systems since you only need to program a timer in the PLC or PC that controls the line/machine in the production line, allowing, according to the authors’ knowledge, to build industrial Big Data on predictive maintenance for the first time, which is called Miniterm 4.0. This article shows evidence of the important improvements generated by the use of Miniterm 4.0 in a factory. At the end of the paper we show the evolution of TAV (Technical availability), Mean Time To Repair (MTTR), EM (Number of Work order (Emergency Orders/line Stop)) and OM (Labour hours in EM) showing a very important improvement as the number of mini-terms was increased and the Miniterm 4.0 system became more reliable. In particular, TAV is increased by 15%, OM is reduced in 5000 orders, MTTR is reduced in 2 h and there are produced 3000 orders less than when mini-terms did not exist. At the end of the article we discuss the benefits and limitations of the mini-terms and we show the conclusions and future works. Full article
(This article belongs to the Special Issue Machine Health Monitoring and Fault Diagnosis Techniques)
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13 pages, 4936 KiB  
Article
Research on the Time Drift Stability of Differential Inductive Displacement Sensors with Frequency Output
by Xiaolong Lu, Guiyun Tian, Zongwen Wang, Wentao Li, Dehua Yang, Haoran Li, You Wang, Jijun Ni and Yong Zhang
Sensors 2022, 22(16), 6234; https://doi.org/10.3390/s22166234 - 19 Aug 2022
Cited by 16 | Viewed by 2010
Abstract
An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital [...] Read more.
An edge displacement sensor is one of the key technologies for building large segmented mirror astronomical optical telescopes. A digital interface is one novel approach for sensor technologies, digital transformation and the Internet of Things (IoT) in particular. Frequency output sensors and inductance-to-digital converter (LDC) demonstrated significant advantages in comparison with conventional sensors with analog-to-digital converter (ADC) interfaces. In order for the differential inductive frequency output displacement (DIFOD) sensor to meet the high-stability requirements of segmented mirror astronomical telescopes, it is important to understand the factors for time drift of the sensor. This paper focuses on the investigation of key factors of sensor structure and material, signal conditioning and interface, and fixtures for time drift to permanently installed applications. First, the measurement principle and probe structural characteristics of the sensor are analyzed. Then, two kinds of signal conditioning and digitalization methods using resonance circuits and LDC chips are implemented and compared. Finally, the time drift stability experiments are performed on the sensors with different signal conditioning methods and fixtures under controlled temperature. Experimental results show that the magnetic shield ring effectively improves the sensitivity and quality factor of the sensors, the time drift stability of the sensor using the signal conditioning based on resonance circuits is better than that of the sensors using LDC chips, and the root mean square (RMS) of the sensor time drift meets the requirement of 0.01 μm/24 h. This study will help further development of high-stability of frequency output sensors and IoT-based systems for scaled-up applications in the future. Full article
(This article belongs to the Section Electronic Sensors)
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11 pages, 2525 KiB  
Article
Tunable Diode Laser Absorption Spectroscopy Based Temperature Measurement with a Single Diode Laser Near 1.4 μm
by Xiaonan Liu and Yufei Ma
Sensors 2022, 22(16), 6095; https://doi.org/10.3390/s22166095 - 15 Aug 2022
Cited by 59 | Viewed by 3380
Abstract
The rapidly changing and wide dynamic range of combustion temperature in scramjet engines presents a major challenge to existing test techniques. Tunable diode laser absorption spectroscopy (TDLAS) based temperature measurement has the advantages of high sensitivity, fast response, and compact structure. In this [...] Read more.
The rapidly changing and wide dynamic range of combustion temperature in scramjet engines presents a major challenge to existing test techniques. Tunable diode laser absorption spectroscopy (TDLAS) based temperature measurement has the advantages of high sensitivity, fast response, and compact structure. In this invited paper, a temperature measurement method based on the TDLAS technique with a single diode laser was demonstrated. A continuous-wave (CW), distributed feedback (DFB) diode laser with an emission wavelength near 1.4 μm was used for temperature measurement, which could cover two water vapor (H2O) absorption lines located at 7153.749 cm−1 and 7154.354 cm−1 simultaneously. The output wavelength of the diode laser was calibrated according to the two absorption peaks in the time domain. Using this strategy, the TDLAS system has the advantageous of immunization to laser wavelength shift, simple system structure, reduced cost, and increased system robustness. The line intensity of the two target absorption lines under room temperature was about one-thousandth of that under high temperature, which avoided the measuring error caused by H2O in the environment. The system was tested on a McKenna flat flame burner and a scramjet model engine, respectively. It was found that, compared to the results measured by CARS technique and theoretical calculation, this TDLAS system had less than 4% temperature error when the McKenna flat flame burner was used. When a scramjet model engine was adopted, the measured results showed that such TDLAS system had an excellent dynamic range and fast response. The TDLAS system reported here could be used in real engine in the future. Full article
(This article belongs to the Special Issue State-of-the-Art Optical Sensors Technology in China)
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20 pages, 11596 KiB  
Article
Electroactive Biofilms of Activated Sludge Microorganisms on a Nanostructured Surface as the Basis for a Highly Sensitive Biochemical Oxygen Demand Biosensor
by Saniyat Kurbanalieva, Vyacheslav Arlyapov, Anna Kharkova, Roman Perchikov, Olga Kamanina, Pavel Melnikov, Nadezhda Popova, Andrey Machulin, Sergey Tarasov, Evgeniya Saverina, Anatoly Vereshchagin and Anatoly Reshetilov
Sensors 2022, 22(16), 6049; https://doi.org/10.3390/s22166049 - 12 Aug 2022
Cited by 10 | Viewed by 2516
Abstract
The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with [...] Read more.
The possibility of the developing a biochemical oxygen demand (BOD) biosensor based on electroactive biofilms of activated sludge grown on the surface of a graphite-paste electrode modified with carbon nanotubes was studied. A complex of microscopic methods controlled biofilm formation: optical microscopy with phase contrast, scanning electron microscopy, and laser confocal microscopy. The features of charge transfer in the obtained electroactive biofilms were studied using the methods of cyclic voltammetry and electrochemical impedance spectroscopy. The rate constant of the interaction of microorganisms with the extracellular electron carrier (0.79 ± 0.03 dm3(g s)−1) and the heterogeneous rate constant of electron transfer (0.34 ± 0.02 cm s−1) were determined using the cyclic voltammetry method. These results revealed that the modification of the carbon nanotubes’ (CNT) electrode surface makes it possible to create electroactive biofilms. An analysis of the metrological and analytical characteristics of the created biosensors showed that the lower limit of the biosensor based on an electroactive biofilm of activated sludge is 0.41 mgO2/dm3, which makes it possible to analyze almost any water sample. Analysis of 12 surface water samples showed a high correlation (R2 = 0.99) with the results of the standard method for determining biochemical oxygen demand. Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2022)
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33 pages, 19332 KiB  
Article
Smart Strawberry Farming Using Edge Computing and IoT
by Mateus Cruz, Samuel Mafra, Eduardo Teixeira and Felipe Figueiredo
Sensors 2022, 22(15), 5866; https://doi.org/10.3390/s22155866 - 05 Aug 2022
Cited by 20 | Viewed by 5226
Abstract
Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to [...] Read more.
Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed. Full article
(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)
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23 pages, 3338 KiB  
Article
Design, Implementation and Experimental Investigation of a Pedestrian Street Crossing Assistance System Based on Visible Light Communications
by Alin-Mihai Căilean, Cătălin Beguni, Sebastian-Andrei Avătămăniței, Mihai Dimian and Valentin Popa
Sensors 2022, 22(15), 5481; https://doi.org/10.3390/s22155481 - 22 Jul 2022
Cited by 10 | Viewed by 3563
Abstract
In urban areas, pedestrians are the road users category that is the most exposed to road accident fatalities. In this context, the present article proposes a totally new architecture, which aims to increase the safety of pedestrians on the crosswalk. The first component [...] Read more.
In urban areas, pedestrians are the road users category that is the most exposed to road accident fatalities. In this context, the present article proposes a totally new architecture, which aims to increase the safety of pedestrians on the crosswalk. The first component of the design is a pedestrian detection system, which identifies the user’s presence in the region of the crosswalk and determines the future street crossing action possibility or the presence of a pedestrian engaged in street crossing. The second component of the system is the visible light communications part, which is used to transmit this information toward the approaching vehicles. The proposed architecture has been implemented at a regular scale and experimentally evaluated in outdoor conditions. The experimental results showed a 100% overall pedestrian detection rate. On the other hand, the VLC system showed a communication distance between 5 and 40 m when using a standard LED light crosswalk sign as a VLC emitter, while maintaining a bit error ratio between 10−7 and 10−5. These results demonstrate the fact that the VLC technology is now able to be used in real applications, making the transition from a high potential technology to a confirmed technology. As far as we know, this is the first article presenting such a pedestrian street crossing assistance system. Full article
(This article belongs to the Special Issue Automotive Visible Light Communications (AutoVLC))
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17 pages, 8338 KiB  
Article
Non-Destructive Testing Using Eddy Current Sensors for Defect Detection in Additively Manufactured Titanium and Stainless-Steel Parts
by Heba E. Farag, Ehsan Toyserkani and Mir Behrad Khamesee
Sensors 2022, 22(14), 5440; https://doi.org/10.3390/s22145440 - 21 Jul 2022
Cited by 23 | Viewed by 5258
Abstract
In this study, different eddy-current based probe designs (absolute and commercial reflection) are used to detect artificial defects with different sizes and at different depths in parts composed of stainless-steel (316) and titanium (TI-64) made by Laser Additive Manufacturing (LAM). The measured defect [...] Read more.
In this study, different eddy-current based probe designs (absolute and commercial reflection) are used to detect artificial defects with different sizes and at different depths in parts composed of stainless-steel (316) and titanium (TI-64) made by Laser Additive Manufacturing (LAM). The measured defect signal value using the probes is in the range of (20–200) millivolts. Both probes can detect subsurface defects on stainless-steel samples with average surface roughness of 11.6 µm and titanium samples with average surface roughness of 8.7 µm. It is found the signal reading can be improved by adding a coating layer made of thin paper to the bottom of the probes. The layer will decrease the surface roughness effect and smooth out the detected defect signal from any ripples. The smallest subsurface artificial defect size detected by both probes is an artificially made notch with 0.07 mm width and 25 mm length. In addition, both probes detected subsurface artificial blind holes in the range of 0.17 mm–0.3 mm radius. Results show that the absolute probe is more suitable to detect cracks and incomplete fusion holes, whereas the reflection probe is more suitable to detect small diameter blind holes. The setup can be used for defect detection during the additive manufacturing process once the melt pool is solidified. Full article
(This article belongs to the Special Issue Integrated Circuits and Technologies for Real-Time Sensing)
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21 pages, 15242 KiB  
Review
Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review
by Mohammad Abrar Shakil Sejan, Md Habibur Rahman, Beom-Sik Shin, Ji-Hye Oh, Young-Hwan You and Hyoung-Kyu Song
Sensors 2022, 22(14), 5405; https://doi.org/10.3390/s22145405 - 20 Jul 2022
Cited by 35 | Viewed by 6731
Abstract
An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. [...] Read more.
An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication. Full article
(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G Communication and Beyond)
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13 pages, 54802 KiB  
Article
Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios
by Pei-Fen Tsai, Chia-Hung Liao and Shyan-Ming Yuan
Sensors 2022, 22(14), 5351; https://doi.org/10.3390/s22145351 - 18 Jul 2022
Cited by 14 | Viewed by 6458
Abstract
In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with [...] Read more.
In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with the National Fire Protection Association (NFPA) 1801 standards as input to the YOLOv4 model for real-time object detection. The model trained with a single Nvidia GeForce 2070 can achieve >95% precision for the location of people in a low-visibility smoky scenario with 30.1 frames per second (FPS). This real-time result can be reported to control centers as useful information to help provide timely rescue and provide protection to firefighters before entering dangerous smoky fire situations. Full article
(This article belongs to the Special Issue Sensing with Infrared and Terahertz Technologies)
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24 pages, 2427 KiB  
Review
Monocular Depth Estimation Using Deep Learning: A Review
by Armin Masoumian, Hatem A. Rashwan, Julián Cristiano, M. Salman Asif and Domenec Puig
Sensors 2022, 22(14), 5353; https://doi.org/10.3390/s22145353 - 18 Jul 2022
Cited by 42 | Viewed by 14805
Abstract
In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in [...] Read more.
In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research. Full article
(This article belongs to the Section Intelligent Sensors)
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17 pages, 3482 KiB  
Article
Development of Wireless Sensor Network for Environment Monitoring and Its Implementation Using SSAIL Technology
by Shathya Duobiene, Karolis Ratautas, Romualdas Trusovas, Paulius Ragulis, Gediminas Šlekas, Rimantas Simniškis and Gediminas Račiukaitis
Sensors 2022, 22(14), 5343; https://doi.org/10.3390/s22145343 - 18 Jul 2022
Cited by 17 | Viewed by 4516
Abstract
The Internet of Things (IoT) technology and its applications are turning real-world things into smart objects, integrating everything under a common infrastructure to manage performance through a software application and offering upgrades with integrated web servers in a timely manner. Quality of life, [...] Read more.
The Internet of Things (IoT) technology and its applications are turning real-world things into smart objects, integrating everything under a common infrastructure to manage performance through a software application and offering upgrades with integrated web servers in a timely manner. Quality of life, the green economy, and pollution management in society require comprehensive environmental monitoring systems with easy-to-use features and maintenance. This research suggests implementing a wireless sensor network with embedded sensor nodes manufactured using the Selective Surface Activation Induced by Laser technology. Such technology allows the integration of electrical circuits with free-form plastic sensor housing. In this work, a low-cost asynchronous web server for monitoring temperature and humidity sensors connected to the ESP32 Wi-Fi module has been developed. Data from sensor nodes across the facility are collected and displayed in real-time charts on a web server. Multiple web clients on the same network can access the sensor data. The energy to the sensor nodes could be powered by harvesting energy from surrounding sources of electromagnetic radiation. This automated and self-powered system monitors environmental and climatic factors, helps with timely action, and benefits sensor design by allowing antenna and rf-circuit formation on various plastics, even on the body of the device itself. It also provides greater flexibility in hardware modification and rapid large-scale deployment. Full article
(This article belongs to the Special Issue Use Wireless Sensor Networks for Environmental Applications)
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16 pages, 2339 KiB  
Review
Single-Molecule Surface-Enhanced Raman Spectroscopy
by Yuxuan Qiu, Cuifang Kuang, Xu Liu and Longhua Tang
Sensors 2022, 22(13), 4889; https://doi.org/10.3390/s22134889 - 29 Jun 2022
Cited by 26 | Viewed by 5360
Abstract
Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to detect single molecules in a non-invasive, label-free manner with high-throughput. SM-SERS can detect chemical information of single molecules without statistical averaging and has wide application in chemical analysis, nanoelectronics, biochemical sensing, etc. Recently, a [...] Read more.
Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to detect single molecules in a non-invasive, label-free manner with high-throughput. SM-SERS can detect chemical information of single molecules without statistical averaging and has wide application in chemical analysis, nanoelectronics, biochemical sensing, etc. Recently, a series of unprecedented advances have been realized in science and application by SM-SERS, which has attracted the interest of various fields. In this review, we first elucidate the key concepts of SM-SERS, including enhancement factor (EF), spectral fluctuation, and experimental evidence of single-molecule events. Next, we systematically discuss advanced implementations of SM-SERS, including substrates with ultra-high EF and reproducibility, strategies to improve the probability of molecules being localized in hotspots, and nonmetallic and hybrid substrates. Then, several examples for the application of SM-SERS are proposed, including catalysis, nanoelectronics, and sensing. Finally, we summarize the challenges and future of SM-SERS. We hope this literature review will inspire the interest of researchers in more fields. Full article
(This article belongs to the Special Issue Molecular Opto-Electronic Sensing Devices and Techniques)
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29 pages, 3929 KiB  
Review
Electrochemical (Bio)Sensors Based on Covalent Organic Frameworks (COFs)
by Emiliano Martínez-Periñán, Marcos Martínez-Fernández, José L. Segura and Encarnación Lorenzo
Sensors 2022, 22(13), 4758; https://doi.org/10.3390/s22134758 - 23 Jun 2022
Cited by 23 | Viewed by 4380
Abstract
Covalent organic frameworks (COFs) are defined as crystalline organic polymers with programmable topological architectures using properly predesigned building blocks precursors. Since the development of the first COF in 2005, many works are emerging using this kind of material for different applications, such as [...] Read more.
Covalent organic frameworks (COFs) are defined as crystalline organic polymers with programmable topological architectures using properly predesigned building blocks precursors. Since the development of the first COF in 2005, many works are emerging using this kind of material for different applications, such as the development of electrochemical sensors and biosensors. COF shows superb characteristics, such as tuneable pore size and structure, permanent porosity, high surface area, thermal stability, and low density. Apart from these special properties, COF’s electrochemical behaviour can be modulated using electroactive building blocks. Furthermore, the great variety of functional groups that can be inserted in their structures makes them interesting materials to be conjugated with biological recognition elements, such as antibodies, enzymes, DNA probe, aptamer, etc. Moreover, the possibility of linking them with other special nanomaterials opens a wide range of possibilities to develop new electrochemical sensors and biosensors. Full article
(This article belongs to the Special Issue Game Changer Nanomaterials: A New Concept for Biosensing Applications)
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19 pages, 14831 KiB  
Article
The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning
by Jiachen Yang, Jingfei Ni, Yang Li, Jiabao Wen and Desheng Chen
Sensors 2022, 22(12), 4316; https://doi.org/10.3390/s22124316 - 07 Jun 2022
Cited by 24 | Viewed by 3339
Abstract
Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural [...] Read more.
Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning and IoT in Intelligent System)
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26 pages, 1592 KiB  
Review
A Review of Mobile Mapping Systems: From Sensors to Applications
by Mostafa Elhashash, Hessah Albanwan and Rongjun Qin
Sensors 2022, 22(11), 4262; https://doi.org/10.3390/s22114262 - 02 Jun 2022
Cited by 40 | Viewed by 10675
Abstract
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors, advances in computational resources, [...] Read more.
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors, advances in computational resources, the maturity of mapping algorithms, and the need for accurate and on-demand geographic information system (GIS) data and digital maps. Many MMSs combine hybrid sensors to provide a more informative, robust, and stable solution by complementing each other. In this paper, we presented a comprehensive review of the modern MMSs by focusing on: (1) the types of sensors and platforms, discussing their capabilities and limitations and providing a comprehensive overview of recent MMS technologies available in the market; (2) highlighting the general workflow to process MMS data; (3) identifying different use cases of mobile mapping technology by reviewing some of the common applications; and (4) presenting a discussion on the benefits and challenges and sharing our views on potential research directions. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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23 pages, 9451 KiB  
Article
Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS
by Jianwei Zhao, Shengyi Liu and Jinyu Li
Sensors 2022, 22(11), 4172; https://doi.org/10.3390/s22114172 - 31 May 2022
Cited by 19 | Viewed by 7342
Abstract
Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based [...] Read more.
Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Machine-Learning-Based Localization)
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15 pages, 2607 KiB  
Article
A Wavelength Modulation Spectroscopy-Based Methane Flux Sensor for Quantification of Venting Sources at Oil and Gas Sites
by Simon A. Festa-Bianchet, Scott P. Seymour, David R. Tyner and Matthew R. Johnson
Sensors 2022, 22(11), 4175; https://doi.org/10.3390/s22114175 - 31 May 2022
Cited by 5 | Viewed by 2044
Abstract
An optical sensor employing tunable diode laser absorption spectroscopy with wavelength modulation and 2f harmonic detection was designed, prototyped, and tested for applications in quantifying methane emissions from vent sources in the oil and gas sector. The methane absorption line at 6026.23 [...] Read more.
An optical sensor employing tunable diode laser absorption spectroscopy with wavelength modulation and 2f harmonic detection was designed, prototyped, and tested for applications in quantifying methane emissions from vent sources in the oil and gas sector. The methane absorption line at 6026.23 cm–1 (1659.41 nm) was used to measure both flow velocity and methane volume fraction, enabling direct measurement of the methane emission rate. Two configurations of the sensor were designed, tested, and compared; the first used a fully fiber-coupled cell with multimode fibers to re-collimate the laser beams, while the second used directly irradiated photodetectors protected by Zener barriers. Importantly, both configurations were designed to enable measurements within regulated Class I / Zone 0 hazardous locations, in which explosive gases are expected during normal operations. Controlled flows with methane volume fractions of 0 to 100% and a velocity range of 0 to 4 m/s were used to characterize sensor performance at a 1 Hz sampling rate. The measurement error in the methane volume fraction was less than 10,000 ppm (1%) across the studied range for both configurations. The short-term velocity measurement error with pure methane was <0.3 m/s with a standard deviation of 0.14 m/s for the fiber-coupled configuration and <0.15 m/s with a standard deviation of 0.07 m/s for the directly irradiated detector configuration. However, modal noise in the multimode fibers of the first configuration contributed to an unstable performance that was highly sensitive to mechanical disturbances. The second configuration showed good potential for an industrial sensor, successfully quantifying methane flow rates up to 11 kg/h within ±2.1 kg/h at 95% confidence over a range of methane fractions from 25–100%, and as low as ±0.85 kg/h in scenarios where the source methane fraction is initially unknown within this range and otherwise invariant. Full article
(This article belongs to the Section Environmental Sensing)
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15 pages, 5000 KiB  
Article
A Low-Cost AI Buoy System for Monitoring Water Quality at Offshore Aquaculture Cages
by Hoang-Yang Lu, Chih-Yung Cheng, Shyi-Chyi Cheng, Yu-Hao Cheng, Wen-Chen Lo, Wei-Lin Jiang, Fan-Hua Nan, Shun-Hsyung Chang and Naomi A. Ubina
Sensors 2022, 22(11), 4078; https://doi.org/10.3390/s22114078 - 27 May 2022
Cited by 24 | Viewed by 4054
Abstract
The ocean resources have been rapidly depleted in the recent decade, and the complementary role of aquaculture to food security has become more critical than ever before. Water quality is one of the key factors in determining the success of aquaculture and real-time [...] Read more.
The ocean resources have been rapidly depleted in the recent decade, and the complementary role of aquaculture to food security has become more critical than ever before. Water quality is one of the key factors in determining the success of aquaculture and real-time water quality monitoring is an important process for aquaculture. This paper proposes a low-cost and easy-to-build artificial intelligence (AI) buoy system that autonomously measures the related water quality data and instantly forwards them via wireless channels to the shore server. Furthermore, the data provide aquaculture staff with real-time water quality information and also assists server-side AI programs in implementing machine learning techniques to further provide short-term water quality predictions. In particular, we aim to provide a low-cost design by combining simple electronic devices and server-side AI programs for the proposed buoy system to measure water velocity. As a result, the cost for the practical implementation is approximately USD 2015 only to facilitate the proposed AI buoy system to measure the real-time data of dissolved oxygen, salinity, water temperature, and velocity. In addition, the AI buoy system also offers short-term estimations of water temperature and velocity, with mean square errors of 0.021 °C and 0.92 cm/s, respectively. Furthermore, we replaced the use of expensive current meters with a flow sensor tube of only USD 100 to measure water velocity. Full article
(This article belongs to the Special Issue Selected Papers from the 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2021))
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29 pages, 27963 KiB  
Article
Analysis of Magnetic Field Measurements for Indoor Positioning
by Guanglie Ouyang and Karim Abed-Meraim
Sensors 2022, 22(11), 4014; https://doi.org/10.3390/s22114014 - 25 May 2022
Cited by 9 | Viewed by 2832
Abstract
Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic [...] Read more.
Infrastructure-free magnetic fields are ubiquitous and have attracted tremendous interest in magnetic field-based indoor positioning. However, magnetic field-based indoor positioning applications face challenges such as low discernibility, heterogeneous devices, and interference from ferromagnetic materials. This paper first analyzes the statistical characteristics of magnetic field (MF) measurements from heterogeneous smartphones. It demonstrates that, in the absence of disturbances, the MF measurements in indoor environments follow a Gaussian distribution with temporal stability and spatial discernibility. It shows the fluctuations in magnetic field intensity caused by the rotation of a smartphone around the Z-axis. Secondly, it suggests that the RLOWESS method can be used to eliminate magnetic field anomalies, using magnetometer calibration to ensure consistent MF measurements in heterogeneous smartphones. Thirdly, it tests the magnetic field positioning performance of homogeneous and heterogeneous devices using different machine learning methods. Finally, it summarizes the feasibility/limitations of using only MF measurement for indoor positioning. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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32 pages, 7918 KiB  
Review
Non-Hermitian Sensing in Photonics and Electronics: A Review
by Martino De Carlo, Francesco De Leonardis, Richard A. Soref, Luigi Colatorti and Vittorio M. N. Passaro
Sensors 2022, 22(11), 3977; https://doi.org/10.3390/s22113977 - 24 May 2022
Cited by 20 | Viewed by 4396
Abstract
Recently, non-Hermitian Hamiltonians have gained a lot of interest, especially in optics and electronics. In particular, the existence of real eigenvalues of non-Hermitian systems has opened a wide set of possibilities, especially, but not only, for sensing applications, exploiting the physics of exceptional [...] Read more.
Recently, non-Hermitian Hamiltonians have gained a lot of interest, especially in optics and electronics. In particular, the existence of real eigenvalues of non-Hermitian systems has opened a wide set of possibilities, especially, but not only, for sensing applications, exploiting the physics of exceptional points. In particular, the square root dependence of the eigenvalue splitting on different design parameters, exhibited by 2 × 2 non-Hermitian Hamiltonian matrices at the exceptional point, paved the way to the integration of high-performance sensors. The square root dependence of the eigenfrequencies on the design parameters is the reason for a theoretically infinite sensitivity in the proximity of the exceptional point. Recently, higher-order exceptional points have demonstrated the possibility of achieving the nth root dependence of the eigenfrequency splitting on perturbations. However, the exceptional sensitivity to external parameters is, at the same time, the major drawback of non-Hermitian configurations, leading to the high influence of noise. In this review, the basic principles of PT-symmetric and anti-PT-symmetric Hamiltonians will be shown, both in photonics and in electronics. The influence of noise on non-Hermitian configurations will be investigated and the newest solutions to overcome these problems will be illustrated. Finally, an overview of the newest outstanding results in sensing applications of non-Hermitian photonics and electronics will be provided. Full article
(This article belongs to the Special Issue Silicon Photonics: A Theme Issue in Honor of Professor Richard A. Soref)
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18 pages, 4488 KiB  
Article
Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
by Shadi Ghiasi, Tingting Zhu, Ping Lu, Jannis Hagenah, Phan Nguyen Quoc Khanh, Nguyen Van Hao, Vital Consortium, Louise Thwaites and David A. Clifton
Sensors 2022, 22(10), 3866; https://doi.org/10.3390/s22103866 - 19 May 2022
Cited by 12 | Viewed by 3113
Abstract
Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning [...] Read more.
Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis. Full article
(This article belongs to the Special Issue Signal Processing in Biomedical Sensor Systems)
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18 pages, 2474 KiB  
Article
A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions
by Pedro Andrade, Ivanovitch Silva, Marianne Silva, Thommas Flores, Jordão Cassiano and Daniel G. Costa
Sensors 2022, 22(10), 3838; https://doi.org/10.3390/s22103838 - 19 May 2022
Cited by 24 | Viewed by 3444
Abstract
Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy [...] Read more.
Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO2 and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO2 emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal’s feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO2/km. Full article
(This article belongs to the Special Issue Intelligent Sensors for Smart City)
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14 pages, 2957 KiB  
Article
Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
by Tim Everett, Trey Taylor, Dong-Kyeong Lee and Dennis M. Akos
Sensors 2022, 22(10), 3825; https://doi.org/10.3390/s22103825 - 18 May 2022
Cited by 12 | Viewed by 4583
Abstract
The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone’s position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in [...] Read more.
The Google Smartphone Decimeter Challenge (GSDC) was a competition held in 2021, where data from a variety of instruments useful for determining a phone’s position (signals from GPS satellites, accelerometer readings, gyroscope readings, etc.) using Android smartphones were provided to be processed/assessed in regard to the most accurate determination of the longitude and latitude of user positions. One of the tools that can be utilized to process the GNSS measurements is RTKLIB. RTKLIB is an open-source GNSS processing software tool that can be used with the GNSS measurements, including code, carrier, and doppler measurements, to provide real-time kinematic (RTK), precise point positioning (PPP), and post-processed kinematic (PPK) solutions. In the GSDC, we focused on the PPK capabilities of RTKLIB, as the challenge only required post-processing of past data. Although PPK positioning is expected to provide sub-meter level accuracies, the lower quality of the Android measurements compared to geodetic receivers makes this performance difficult to achieve consistently. Another latent issue is that the original RTKLIB created by Tomoji Takasu is aimed at commercial GNSS receivers rather than smartphones. Therefore, the performance of the original RTKLIB for the GSDC is limited. Consequently, adjustments to both the code-base and the default settings are suggested. When implemented, these changes allowed RTKLIB processing to score 5th place, based on the performance submissions of the prior GSDC competition. Detailed information on what was changed, and the steps to replicate the final results, are presented in the paper. Moreover, the updated code-base, with all the implemented changes, is provided in the public repository. This paper outlines a procedure to optimize the use of RTKLIB for Android smartphone measurements, highlighting the changes needed given the low-quality measurements from the mobile phone platform (relative to the survey grade GNSS receiver), which can be used as a basis point for further optimization for future GSDC competitions. Full article
(This article belongs to the Special Issue Precise Positioning with Smartphones)
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13 pages, 1322 KiB  
Article
Facial Expression Recognition Based on Squeeze Vision Transformer
by Sangwon Kim, Jaeyeal Nam and Byoung Chul Ko
Sensors 2022, 22(10), 3729; https://doi.org/10.3390/s22103729 - 13 May 2022
Cited by 14 | Viewed by 3204
Abstract
In recent image classification approaches, a vision transformer (ViT) has shown an excellent performance beyond that of a convolutional neural network. A ViT achieves a high classification for natural images because it properly preserves the global image features. Conversely, a ViT still has [...] Read more.
In recent image classification approaches, a vision transformer (ViT) has shown an excellent performance beyond that of a convolutional neural network. A ViT achieves a high classification for natural images because it properly preserves the global image features. Conversely, a ViT still has many limitations in facial expression recognition (FER), which requires the detection of subtle changes in expression, because it can lose the local features of the image. Therefore, in this paper, we propose Squeeze ViT, a method for reducing the computational complexity by reducing the number of feature dimensions while increasing the FER performance by concurrently combining global and local features. To measure the FER performance of Squeeze ViT, experiments were conducted on lab-controlled FER datasets and a wild FER dataset. Through comparative experiments with previous state-of-the-art approaches, we proved that the proposed method achieves an excellent performance on both types of datasets. Full article
(This article belongs to the Special Issue Sensors-Based Human Action and Emotion Recognition)
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19 pages, 6069 KiB  
Article
Design of a Deployable Helix Antenna at L-Band for a 1-Unit CubeSat: From Theoretical Analysis to Flight Model Results
by Lara Fernandez, Marco Sobrino, Joan Adria Ruiz-de-Azua, Anna Calveras and Adriano Camps
Sensors 2022, 22(10), 3633; https://doi.org/10.3390/s22103633 - 10 May 2022
Cited by 6 | Viewed by 3481
Abstract
The 3Cat-4 mission aims at demonstrating the capabilities of a CubeSat to perform Earth Observation (EO) by integrating a combined GNSS-R and Microwave Radiometer payload into a 1-Unit CubeSat. One of the greatest challenges is the design of an antenna that respects [...] Read more.
The 3Cat-4 mission aims at demonstrating the capabilities of a CubeSat to perform Earth Observation (EO) by integrating a combined GNSS-R and Microwave Radiometer payload into a 1-Unit CubeSat. One of the greatest challenges is the design of an antenna that respects the 1-Unit CubeSat envelope while operating at the different frequency bands: Global Positioning System (GPS) L1 and Galileo E1 band (1575 MHz), GPS L2 band (1227 MHz), and the microwave radiometry band (1400–1427 MHz). Moreover, it requires between 8 and 12 dB of directivity depending on the band whilst providing at least 10 dB of front-to-back lobe ratio in L1 and L2 GPS bands. After a trade-off analysis on the type of antenna that could be used, a helix antenna was found to be the most suitable option to comply with the requirements, since it can be stowed during launch and deployed once in orbit. This article presents the antenna design from a radiation performance point of view starting with a theoretical analysis, then presenting the numerical simulations, the measurements in an Engineering Model (EM), and finally the final design and performance of the Flight Model (FM). Full article
(This article belongs to the Special Issue Antennas for Integrated Sensors Systems)
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10 pages, 1294 KiB  
Brief Report
Wearable Immersive Virtual Reality Device for Promoting Physical Activity in Parkinson’s Disease Patients
by Pablo Campo-Prieto, José Mª Cancela-Carral and Gustavo Rodríguez-Fuentes
Sensors 2022, 22(9), 3302; https://doi.org/10.3390/s22093302 - 26 Apr 2022
Cited by 13 | Viewed by 3883
Abstract
Parkinson’s disease (PD) is a neurological disorder that usually appears in the 6th decade of life and affects up to 2% of older people (65 years and older). Its therapeutic management is complex and includes not only pharmacological therapies but also physiotherapy. Exercise [...] Read more.
Parkinson’s disease (PD) is a neurological disorder that usually appears in the 6th decade of life and affects up to 2% of older people (65 years and older). Its therapeutic management is complex and includes not only pharmacological therapies but also physiotherapy. Exercise therapies have shown good results in disease management in terms of rehabilitation and/or maintenance of physical and functional capacities, which is important in PD. Virtual reality (VR) could promote physical activity in this population. We explore whether a commercial wearable head-mounted display (HMD) and the selected VR exergame could be suitable for people with mild–moderate PD. In all, 32 patients (78.1% men; 71.50 ± 11.80 years) were a part of the study. Outcomes were evaluated using the Simulator Sickness Questionnaire (SSQ), the System Usability Scale (SUS), the Game Experience Questionnaire (GEQ post-game module), an ad hoc satisfaction questionnaire, and perceived effort. A total of 60 sessions were completed safely (without adverse effects (no SSQ symptoms) and with low scores in the negative experiences of the GEQ (0.01–0.09/4)), satisfaction opinions were positive (88% considered the training “good” or “very good”), and the average usability of the wearable HMD was good (75.16/100). Our outcomes support the feasibility of a boxing exergame combined with a wearable commercial HMD as a suitable physical activity for PD and its applicability in different environments due to its safety, usability, low cost, and small size. Future research is needed focusing on postural instability, because it seems to be a symptom that could have an impact on the success of exergaming programs aimed at PD. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications)
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26 pages, 5275 KiB  
Review
Colorimetric Paper-Based Sensors against Cancer Biomarkers
by Mariana C. C. G. Carneiro, Ligia R. Rodrigues, Felismina T. C. Moreira and Maria Goreti F. Sales
Sensors 2022, 22(9), 3221; https://doi.org/10.3390/s22093221 - 22 Apr 2022
Cited by 19 | Viewed by 4492
Abstract
Cancer is a major cause of mortality and morbidity worldwide. Detection and quantification of cancer biomarkers plays a critical role in cancer early diagnosis, screening, and treatment. Clinicians, particularly in developing countries, deal with high costs and limited resources for diagnostic systems. Using [...] Read more.
Cancer is a major cause of mortality and morbidity worldwide. Detection and quantification of cancer biomarkers plays a critical role in cancer early diagnosis, screening, and treatment. Clinicians, particularly in developing countries, deal with high costs and limited resources for diagnostic systems. Using low-cost substrates to develop sensor devices could be very helpful. The interest in paper-based sensors with colorimetric detection increased exponentially in the last decade as they meet the criteria for point-of-care (PoC) devices. Cellulose and different nanomaterials have been used as substrate and colorimetric probes, respectively, for these types of devices in their different designs as spot tests, lateral-flow assays, dipsticks, and microfluidic paper-based devices (μPADs), offering low-cost and disposable devices. However, the main challenge with these devices is their low sensitivity and lack of efficiency in performing quantitative measurements. This review includes an overview of the use of paper for the development of sensing devices focusing on colorimetric detection and their application to cancer biomarkers. We highlight recent works reporting the use of paper in the development of colorimetric sensors for cancer biomarkers, such as proteins, nucleic acids, and others. Finally, we discuss the main advantages of these types of devices and highlight their major pitfalls. Full article
(This article belongs to the Special Issue Paper-Based Biosensing Platforms)
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21 pages, 4623 KiB  
Article
Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors
by Sean Givnan, Carl Chalmers, Paul Fergus, Sandra Ortega-Martorell and Tom Whalley
Sensors 2022, 22(9), 3166; https://doi.org/10.3390/s22093166 - 20 Apr 2022
Cited by 21 | Viewed by 2724
Abstract
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly [...] Read more.
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a machine learning (ML) approach to model normal working operations and detect anomalies. The approach extracts key features from signals representing a known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system where green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure. Full article
(This article belongs to the Section Intelligent Sensors)
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35 pages, 7799 KiB  
Article
Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions
by Andreas Holzinger, Anna Saranti, Alessa Angerschmid, Carl Orge Retzlaff, Andreas Gronauer, Vladimir Pejakovic, Francisco Medel-Jimenez, Theresa Krexner, Christoph Gollob and Karl Stampfer
Sensors 2022, 22(8), 3043; https://doi.org/10.3390/s22083043 - 15 Apr 2022
Cited by 42 | Viewed by 8421
Abstract
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent [...] Read more.
The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Automated Decision Making and Trusted Autonomy)
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21 pages, 1245 KiB  
Review
Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review
by Zhining Shi, Christopher W. K. Chow, Rolando Fabris, Jixue Liu and Bo Jin
Sensors 2022, 22(8), 2987; https://doi.org/10.3390/s22082987 - 13 Apr 2022
Cited by 33 | Viewed by 10243
Abstract
Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require [...] Read more.
Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring. This review summarizes the applications of online UV-Vis spectrophotometers for drinking water quality management in the last two decades. Water quality measurements can be performed directly using the built-in generic algorithms of the online UV-Vis instruments, including absorbance at 254 nm (UV254), colour, dissolved organic carbon (DOC), total organic carbon (TOC), turbidity and nitrate. To enhance the usability of this technique by providing a higher level of operations intelligence, the UV-Vis spectra combined with chemometrics approach offers simplicity, flexibility and applicability. The use of anomaly detection and an early warning was also discussed for drinking water quality monitoring at the source or in the distribution system. As most of the online UV-Vis instruments studies in the drinking water field were conducted at the laboratory- and pilot-scale, future work is needed for industrial-scale evaluation with ab appropriate validation methodology. Issues and potential solutions associated with online instruments for water quality monitoring have been provided. Current technique development outcomes indicate that future research and development work is needed for the integration of early warnings and real-time water treatment process control systems using the online UV-Vis spectrophotometers as part of the water quality management system. Full article
(This article belongs to the Section Chemical Sensors)
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19 pages, 1029 KiB  
Review
Surface Plasmon Resonance (SPR) Spectroscopy and Photonic Integrated Circuit (PIC) Biosensors: A Comparative Review
by Patrick Steglich, Giulia Lecci and Andreas Mai
Sensors 2022, 22(8), 2901; https://doi.org/10.3390/s22082901 - 09 Apr 2022
Cited by 17 | Viewed by 5950
Abstract
Label-free direct-optical biosensors such as surface-plasmon resonance (SPR) spectroscopy has become a gold standard in biochemical analytics in centralized laboratories. Biosensors based on photonic integrated circuits (PIC) are based on the same physical sensing mechanism: evanescent field sensing. PIC-based biosensors can play an [...] Read more.
Label-free direct-optical biosensors such as surface-plasmon resonance (SPR) spectroscopy has become a gold standard in biochemical analytics in centralized laboratories. Biosensors based on photonic integrated circuits (PIC) are based on the same physical sensing mechanism: evanescent field sensing. PIC-based biosensors can play an important role in healthcare, especially for point-of-care diagnostics, if challenges for a transfer from research laboratory to industrial applications can be overcome. Research is at this threshold, which presents a great opportunity for innovative on-site analyses in the health and environmental sectors. A deeper understanding of the innovative PIC technology is possible by comparing it with the well-established SPR spectroscopy. In this work, we shortly introduce both technologies and reveal similarities and differences. Further, we review some latest advances and compare both technologies in terms of surface functionalization and sensor performance. Full article
(This article belongs to the Special Issue Advances in Silicon Photonic Sensors)
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15 pages, 2631 KiB  
Article
An Aptasensor Based on a Flexible Screen-Printed Silver Electrode for the Rapid Detection of Chlorpyrifos
by A. K. M. Sarwar Inam, Martina Aurora Costa Angeli, Ali Douaki, Bajramshahe Shkodra, Paolo Lugli and Luisa Petti
Sensors 2022, 22(7), 2754; https://doi.org/10.3390/s22072754 - 02 Apr 2022
Cited by 18 | Viewed by 3641
Abstract
In this work, we propose a novel disposable flexible and screen-printed electrochemical aptamer-based sensor (aptasensor) for the rapid detection of chlorpyrifos (CPF). To optimize the process, various characterization procedures were employed, including Fourier transform infrared spectroscopy (FT-IR), electrochemical impedance spectroscopy (EIS), and cyclic [...] Read more.
In this work, we propose a novel disposable flexible and screen-printed electrochemical aptamer-based sensor (aptasensor) for the rapid detection of chlorpyrifos (CPF). To optimize the process, various characterization procedures were employed, including Fourier transform infrared spectroscopy (FT-IR), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV). Initially, the aptasensor was optimized in terms of electrolyte pH, aptamer concentration, and incubation time for chlorpyrifos. Under optimal conditions, the aptasensor showed a wide linear range from 1 to 105 ng/mL with a calculated limit of detection as low as 0.097 ng/mL and sensitivity of 600.9 µA/ng. Additionally, the selectivity of the aptasensor was assessed by identifying any interference from other pesticides, which were found to be negligible (with a maximum standard deviation of 0.31 mA). Further, the stability of the sample was assessed over time, where the reported device showed high stability over a period of two weeks at 4 °C. As the last step, the ability of the aptasensor to detect chlorpyrifos in actual samples was evaluated by testing it on banana and grape extracts. As a result, the device demonstrated sufficient recovery rates, which indicate that it can find application in the food industry. Full article
(This article belongs to the Special Issue Electrochemical Sensors in the Food Industry)
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14 pages, 2703 KiB  
Article
Nanoporous Cauliflower-like Pd-Loaded Functionalized Carbon Nanotubes as an Enzyme-Free Electrocatalyst for Glucose Sensing at Neutral pH: Mechanism Study
by Abdelghani Ghanam, Naoufel Haddour, Hasna Mohammadi, Aziz Amine, Andrei Sabac and François Buret
Sensors 2022, 22(7), 2706; https://doi.org/10.3390/s22072706 - 01 Apr 2022
Cited by 12 | Viewed by 2951
Abstract
In this work, we propose a novel functionalized carbon nanotube (f-CNT) supporting nanoporous cauliflower-like Pd nanostructures (PdNS) as an enzyme-free interface for glucose electrooxidation reaction (GOR) in a neutral medium (pH 7.4). The novelty resides in preparing the PdNS/f-CNT biomimetic nanocatalyst using a [...] Read more.
In this work, we propose a novel functionalized carbon nanotube (f-CNT) supporting nanoporous cauliflower-like Pd nanostructures (PdNS) as an enzyme-free interface for glucose electrooxidation reaction (GOR) in a neutral medium (pH 7.4). The novelty resides in preparing the PdNS/f-CNT biomimetic nanocatalyst using a cost-effective and straightforward method, which consists of drop-casting well-dispersed f-CNTs over the Screen-printed carbon electrode (SPCE) surface, followed by the electrodeposition of PdNS. Several parameters affecting the morphology, structure, and catalytic properties toward the GOR of the PdNS catalyst, such as the PdCl2 precursor concentration and electrodeposition conditions, were investigated during this work. The electrochemical behavior of the PdNS/f-CNT/SPCE toward GOR was investigated through Cyclic Voltammetry (CV), Linear Sweep Voltammetry (LSV), and amperometry. There was also a good correlation between the morphology, structure, and electrocatalytic activity of the PdNS electrocatalyst. Furthermore, the LSV response and potential-pH diagram for the palladium–water system have enabled the proposal for a mechanism of this GOR. The proposed mechanism would be beneficial, as the basis, to achieve the highest catalytic activity by selecting the suitable potential range. Under the optimal conditions, the PdNS/f-CNT/SPCE-based biomimetic sensor presented a wide linear range (1–41 mM) with a sensitivity of 9.3 µA cm−2 mM−1 and a detection limit of 95 µM (S/N = 3) toward glucose at a detection potential of +300 mV vs. a saturated calomel electrode. Furthermore, because of the fascinating features such as fast response, low cost, reusability, and poison-free characteristics, the as-proposed electrocatalyst could be of great interest in both detection systems (glucose sensors) and direct glucose fuel cells. Full article
(This article belongs to the Special Issue Game Changer Nanomaterials: A New Concept for Biosensing Applications)
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24 pages, 9920 KiB  
Article
Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment
by Daniel Neunteufel, Stefan Grebien and Holger Arthaber
Sensors 2022, 22(7), 2663; https://doi.org/10.3390/s22072663 - 30 Mar 2022
Cited by 8 | Viewed by 1879
Abstract
The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time [...] Read more.
The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time of flight (ToF) measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates time difference of arrival (TDoA) and AoA measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on software-defined radio (SDR) platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ISM band at 2.4 GHz. We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node RSS measurements using a simple clustering approach. In the considered scenario, an overall positioning root-mean-square error (RMSE) of 2.19 m is achieved. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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17 pages, 3229 KiB  
Article
Performance Evaluation of a Smart Bed Technology against Polysomnography
by Farzad Siyahjani, Gary Garcia Molina, Shawn Barr and Faisal Mushtaq
Sensors 2022, 22(7), 2605; https://doi.org/10.3390/s22072605 - 29 Mar 2022
Cited by 12 | Viewed by 5783
Abstract
The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) [...] Read more.
The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions. Full article
(This article belongs to the Section Intelligent Sensors)
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18 pages, 4196 KiB  
Review
Liquid Metal Based Nano-Composites for Printable Stretchable Electronics
by Dan Xu, Jinwei Cao, Fei Liu, Shengbo Zou, Wenjuan Lei, Yuanzhao Wu, Yiwei Liu, Jie Shang and Run-Wei Li
Sensors 2022, 22(7), 2516; https://doi.org/10.3390/s22072516 - 25 Mar 2022
Cited by 10 | Viewed by 5871
Abstract
Liquid metal (LM) has attracted prominent attention for stretchable and elastic electronics applications due to its exceptional fluidity and conductivity at room temperature. Despite progress in this field, a great disparity remains between material fabrication and practical applications on account of the high [...] Read more.
Liquid metal (LM) has attracted prominent attention for stretchable and elastic electronics applications due to its exceptional fluidity and conductivity at room temperature. Despite progress in this field, a great disparity remains between material fabrication and practical applications on account of the high surface tension and unavoidable oxidation of LM. Here, the composition and nanolization of liquid metal can be envisioned as effective solutions to the processibility–performance dilemma caused by high surface tension. This review aims to summarize the strategies for the fabrication, processing, and application of LM-based nano-composites. The intrinsic mechanism and superiority of the composition method will further extend the capabilities of printable ink. Recent applications of LM-based nano-composites in printing are also provided to guide the large-scale production of stretchable electronics. Full article
(This article belongs to the Special Issue Flexible Sensitive Magnetic/Electronic Materials and Sensors)
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16 pages, 3065 KiB  
Article
Tuning the Sensing Properties of N and S Co-Doped Carbon Dots for Colorimetric Detection of Copper and Cobalt in Water
by Ramanand Bisauriya, Simonetta Antonaroli, Matteo Ardini, Francesco Angelucci, Antonella Ricci and Roberto Pizzoferrato
Sensors 2022, 22(7), 2487; https://doi.org/10.3390/s22072487 - 24 Mar 2022
Cited by 14 | Viewed by 2752
Abstract
In this study, nitrogen and sulfur co-doped carbon dots (NS-CDs) were investigated for the detection of heavy metals in water through absorption-based colorimetric response. NS-CDs were synthesized by a simple one-pot hydrothermal method and characterized by TEM, STEM-coupled with energy dispersive X-ray analysis, [...] Read more.
In this study, nitrogen and sulfur co-doped carbon dots (NS-CDs) were investigated for the detection of heavy metals in water through absorption-based colorimetric response. NS-CDs were synthesized by a simple one-pot hydrothermal method and characterized by TEM, STEM-coupled with energy dispersive X-ray analysis, NMR, and IR spectroscopy. Addition of Cu(II) ions to NS-CD aqueous solutions gave origin to a distinct absorption band at 660 nm which was attributed to the formation of cuprammonium complexes through coordination with amino functional groups of NS-CDs. Absorbance increased linearly with Cu(II) concentration in the range 1–100 µM and enabled a limit of detection of 200 nM. No response was observed with the other tested metals, including Fe(III) which, however, appreciably decreased sensitivity to copper. Increase of pH of the NS-CD solution up to 9.5 greatly reduced this interference effect and enhanced the response to Cu(II), thus confirming the different nature of the two interactions. In addition, a concurrent response to Co(II) appeared in a different spectral region, thus suggesting the possibility of dual-species multiple sensitivity. The present method neither requires any other reagents nor any previous assay treatment and thus can be a promising candidate for low-cost monitoring of copper onsite and by unskilled personnel. Full article
(This article belongs to the Collection Optical Chemical Sensors: Design and Applications)
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28 pages, 3180 KiB  
Article
Blockchain-Based Security Mechanisms for IoMT Edge Networks in IoMT-Based Healthcare Monitoring Systems
by Filippos Pelekoudas-Oikonomou, Georgios Zachos, Maria Papaioannou, Marcus de Ree, José C. Ribeiro, Georgios Mantas and Jonathan Rodriguez
Sensors 2022, 22(7), 2449; https://doi.org/10.3390/s22072449 - 22 Mar 2022
Cited by 49 | Viewed by 4105
Abstract
Despite the significant benefits that the rise of Internet of Medical Things (IoMT) can bring into citizens’ quality of life by enabling IoMT-based healthcare monitoring systems, there is an urgent need for novel security mechanisms to address the pressing security challenges of IoMT [...] Read more.
Despite the significant benefits that the rise of Internet of Medical Things (IoMT) can bring into citizens’ quality of life by enabling IoMT-based healthcare monitoring systems, there is an urgent need for novel security mechanisms to address the pressing security challenges of IoMT edge networks in an effective and efficient manner before they gain the trust of all involved stakeholders and reach their full potential in the market of next generation IoMT-based healthcare monitoring systems. In this context, blockchain technology has been foreseen by the industry and research community as a disruptive technology that can be integrated into novel security solutions for IoMT edge networks, as it can play a significant role in securing IoMT devices and resisting unauthorized access during data transmission (i.e., tamper-proof transmission of medical data). However, despite the fact that several blockchain-based security mechanisms have already been proposed in the literature for different types of IoT edge networks, there is a lack of blockchain-based security mechanisms for IoMT edge networks, and thus more effort is required to be put on the design and development of security mechanisms relying on blockchain technology for such networks. Towards this direction, the first step is the comprehensive understanding of the following two types of blockchain-based security mechanisms: (a) the very few existing ones specifically designed for IoMT edge networks, and (b) those designed for other types of IoT networks but could be possibly adopted in IoMT edge networks due to similar capabilities and technical characteristics. Therefore, in this paper, we review the state-of-the-art of the above two types of blockchain-based security mechanisms in order to provide a foundation for organizing research efforts towards the design and development of reliable blockchain-based countermeasures, addressing the pressing security challenges of IoMT edge networks in an effective and efficient manner. Full article
(This article belongs to the Special Issue Blockchain for Internet of Things Applications)
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19 pages, 7281 KiB  
Article
Research on the Applicability of Vibration Signals for Real-Time Train and Track Condition Monitoring
by Ireneusz Celiński, Rafał Burdzik, Jakub Młyńczak and Maciej Kłaczyński
Sensors 2022, 22(6), 2368; https://doi.org/10.3390/s22062368 - 18 Mar 2022
Cited by 10 | Viewed by 2119
Abstract
The purpose of this research was to analyze the possibilities for the application of vibration signals in real-time train and track control. Proper experiments must be performed for the validation of the methods. Research on vibration in the context of transport must entail [...] Read more.
The purpose of this research was to analyze the possibilities for the application of vibration signals in real-time train and track control. Proper experiments must be performed for the validation of the methods. Research on vibration in the context of transport must entail many of the different nonlinear dynamic forces that may occur while driving. Therefore, the paper addresses two research cases. The developed application contains the identification of movement and dynamics and the evaluation of the technical state of the rail track. The statistics and resultant vector methods are presented. The paper presents other useful metrics to describe the dynamical properties of the driving train. The angle of the resultant horizontal and vertical accelerations is defined for the evaluation of the current position of cabin. It is calculated as an inverse tangent function of current longitudinal and transverse, longitudinal and vertical, transverse, and vertical accelerations. Additionally, the resultant vectors of accelerations are calculated. Full article
(This article belongs to the Special Issue Advanced Sensing and Machine Learning Techniques in Process Monitoring and Fault Diagnosis)
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20 pages, 4708 KiB  
Review
Sensors and Instruments for Brix Measurement: A Review
by Swapna A. Jaywant, Harshpreet Singh and Khalid Mahmood Arif
Sensors 2022, 22(6), 2290; https://doi.org/10.3390/s22062290 - 16 Mar 2022
Cited by 29 | Viewed by 8694
Abstract
Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis [...] Read more.
Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis of the agricultural products and alcoholic beverages. The Brix monitoring of fruit and vegetables by destructive methods includes sensory assessment involving sensory panels, instruments such as refractometer, hydrometer, and liquid chromatography. However, these techniques are manual, time-consuming, and most importantly, the fruits or vegetables are damaged during testing. On the other hand, the traditional sample-based methods involve manual sample collection of the liquid from the tank in fruit/vegetable juice making and in wineries or breweries. Labour ineffectiveness can be a significant drawback of such methods. This review presents recent developments in different destructive and nondestructive Brix measurement techniques focused on fruits, vegetables, and beverages. It is concluded that while there exist a variety of methods and instruments for Brix measurement, traits such as promptness and low cost of analysis, minimal sample preparation, and environmental friendliness are still among the prime requirements of the industry. Full article
(This article belongs to the Section Smart Agriculture)
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15 pages, 2252 KiB  
Article
Effect of the Dynamic Response of a Side-Wall Pressure Measurement System on Determining the Pressure Step Signal in a Shock Tube Using a Time-of-Flight Method
by Andrej Svete, Francisco Javier Hernández Castro and Jože Kutin
Sensors 2022, 22(6), 2103; https://doi.org/10.3390/s22062103 - 09 Mar 2022
Cited by 16 | Viewed by 1913
Abstract
Technological progress demands accurate measurements of rapidly changing pressures. This, in turn, requires the use of dynamically calibrated pressure meters. The shock tube enables the dynamic characterization by applying an almost ideal pressure step change to the pressure sensor under calibration. This paper [...] Read more.
Technological progress demands accurate measurements of rapidly changing pressures. This, in turn, requires the use of dynamically calibrated pressure meters. The shock tube enables the dynamic characterization by applying an almost ideal pressure step change to the pressure sensor under calibration. This paper evaluates the effect of the dynamic response of a side-wall pressure measurement system on the detection of shock wave passage times over the side-wall pressure sensors installed along the shock tube. Furthermore, it evaluates this effect on the reference pressure step signal determined at the end-wall of the driven section using a time-of-flight method. To determine the errors in the detection of the shock front passage times over the centers of the side-wall sensors, a physical model for simulating the dynamic response of the complete measurement chain to the passage of the shock wave was developed. Due to the fact that the use of the physical model requires information about the effective diameter of the pressure sensor, special attention was paid to determining the effective diameter of the side-wall pressure sensors installed along the shock tube. The results show that the relative systematic errors in the pressure step amplitude at the end-wall of the shock tube due to the errors in the detection of the shock front passage times over the side-wall pressure sensors are less than 0.0003%. On the other hand, the systematic errors in the phase lag of the end-wall pressure signal in the calibration frequency range appropriate for high-frequency dynamic pressure applications are up to a few tens of degrees. Since the target phase measurement uncertainty of the pressure sensors used in high-frequency dynamic pressure applications is only a few degrees, the corrections for the systematic errors in the detection of the shock front passage times over the side-wall pressure sensors with the use of the developed physical dynamic model are, therefore, necessary when performing dynamic calibrations of pressure sensors with a shock tube. Full article
(This article belongs to the Special Issue Metrology of Shock Waves)
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39 pages, 1357 KiB  
Review
Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
by Marco Esposito, Lorenzo Palma, Alberto Belli, Luisiana Sabbatini and Paola Pierleoni
Sensors 2022, 22(6), 2124; https://doi.org/10.3390/s22062124 - 09 Mar 2022
Cited by 52 | Viewed by 9135
Abstract
Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have [...] Read more.
Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures. Full article
(This article belongs to the Special Issue Sensors Application on Early Warning System)
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