Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Editor’s Choice
sensors-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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.

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
46 pages, 980 KiB  
Review
Dynamic Routings in Satellite Networks: An Overview
by Xiaoli Cao, Yitao Li, Xingzhong Xiong and Jun Wang
Sensors 2022, 22(12), 4552; https://doi.org/10.3390/s22124552 - 16 Jun 2022
Cited by 19 | Viewed by 7496
Abstract
The Satellite network is an important part of the global network. However, the complex architecture, changeable constellation topology, and frequent inter-satellite connection switching problems bring great challenges to the routing designs of satellite networks, making the study of the routing methods in satellite [...] Read more.
The Satellite network is an important part of the global network. However, the complex architecture, changeable constellation topology, and frequent inter-satellite connection switching problems bring great challenges to the routing designs of satellite networks, making the study of the routing methods in satellite networks a research hotspot. Therefore, this paper investigates the latest existing routing works to tackle the dynamic routing problems in satellite networks. The architecture and development of satellite networks are presented and analyzed first. Afterward, dynamic routing problems in satellite networks are analyzed in detail based on the time-varying network topology. According to the latest works, the advanced satellite network routing schemes, including single-layer and multi-layer dynamic routing are introduced and analyzed. In addition, the merits, shortcomings, and applications of these schemes are analyzed and summarized. Finally, potential technologies and future directions are discussed. Full article
(This article belongs to the Section Sensor Networks)
►▼ Show Figures

Figure 1

22 pages, 11698 KiB  
Article
Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks
by Dan Popescu, Mohamed El-khatib and Loretta Ichim
Sensors 2022, 22(12), 4399; https://doi.org/10.3390/s22124399 - 10 Jun 2022
Cited by 36 | Viewed by 8559
Abstract
Skin lesion detection and analysis are very important because skin cancer must be found in its early stages and treated immediately. Once installed in the body, skin cancer can easily spread to other body parts. Early detection would represent a very important aspect [...] Read more.
Skin lesion detection and analysis are very important because skin cancer must be found in its early stages and treated immediately. Once installed in the body, skin cancer can easily spread to other body parts. Early detection would represent a very important aspect since, by ensuring correct treatment, it could be curable. Thus, by taking all these issues into consideration, there is a need for highly accurate computer-aided systems to assist medical staff in the early detection of malignant skin lesions. In this paper, we propose a skin lesion classification system based on deep learning techniques and collective intelligence, which involves multiple convolutional neural networks, trained on the HAM10000 dataset, which is able to predict seven skin lesions including melanoma. The convolutional neural networks experimentally chosen, considering their performances, to implement the collective intelligence-based system for this purpose are: AlexNet, GoogLeNet, GoogLeNet-Places365, MobileNet-V2, Xception, ResNet-50, ResNet-101, InceptionResNet-V2 and DenseNet201. We then analyzed the performances of each of the above-mentioned convolutional neural networks to obtain a weight matrix whose elements are weights associated with neural networks and classes of lesions. Based on this matrix, a new decision matrix was used to build the multi-network ensemble system (Collective Intelligence-based System), combining each of individual neural network decision into a decision fusion module (Collective Decision Block). This module would then have the responsibility to take a final and more accurate decision related to the prediction based on the associated weights of each network output. The validation accuracy of the proposed system is about 3 percent better than that of the best performing individual network. Full article
(This article belongs to the Special Issue Image Processing and Pattern Recognition Based on Deep Learning)
►▼ Show Figures

Figure 1

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 - 7 Jun 2022
Cited by 28 | Viewed by 3958
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)
►▼ Show Figures

Figure 1

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 - 2 Jun 2022
Cited by 54 | Viewed by 12872
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)
►▼ Show Figures

Figure 1

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 30 | Viewed by 9368
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)
►▼ Show Figures

Figure 1

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 2425
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)
►▼ Show Figures

Graphical abstract

23 pages, 5724 KiB  
Article
Integration of Digital Twin, Machine-Learning and Industry 4.0 Tools for Anomaly Detection: An Application to a Food Plant
by Giovanni Paolo Tancredi, Giuseppe Vignali and Eleonora Bottani
Sensors 2022, 22(11), 4143; https://doi.org/10.3390/s22114143 - 30 May 2022
Cited by 18 | Viewed by 3182
Abstract
This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been [...] Read more.
This work describes a structured solution that integrates digital twin models, machine-learning algorithms, and Industry 4.0 technologies (Internet of Things in particular) with the ultimate aim of detecting the presence of anomalies in the functioning of industrial systems. The proposed solution has been designed to be suitable for implementation in industrial plants not directly designed for Industry 4.0 applications. More precisely, this manuscript delineates an approach for implementing three machine-learning algorithms into a digital twin environment and then applying them to a real plant. This paper is based on two previous studies in which the digital twin environment was first developed for the industrial plant under investigation, and then used for monitoring selected plant parameters. Findings from the previous studies are exploited in this work and advanced by implementing and testing the machine-learning algorithms. The results show that two out of the three machine-learning algorithms are effective enough in predicting anomalies, thus suggesting their implementation for enhancing the safety of employees working at industrial plants. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
►▼ Show Figures

Figure 1

17 pages, 5349 KiB  
Article
Large-Area and Low-Cost Force/Tactile Capacitive Sensor for Soft Robotic Applications
by Amir Pagoli, Frédéric Chapelle, Juan-Antonio Corrales-Ramon, Youcef Mezouar and Yuri Lapusta
Sensors 2022, 22(11), 4083; https://doi.org/10.3390/s22114083 - 27 May 2022
Cited by 21 | Viewed by 5306
Abstract
This paper presents a novel design and development of a low-cost and multi-touch sensor based on capacitive variations. This new sensor is very flexible and easy to fabricate, making it an appropriate choice for soft robot applications. Materials (conductive ink, silicone, and control [...] Read more.
This paper presents a novel design and development of a low-cost and multi-touch sensor based on capacitive variations. This new sensor is very flexible and easy to fabricate, making it an appropriate choice for soft robot applications. Materials (conductive ink, silicone, and control boards) used in this sensor are inexpensive and easily found in the market. The proposed sensor is made of a wafer of different layers, silicone layers with electrically conductive ink, and a pressure-sensitive conductive paper sheet. Previous approaches like e-skin can measure the contact point or pressure of conductive objects like the human body or finger, while the proposed design enables the sensor to detect the object’s contact point and the applied force without considering the material conductivity of the object. The sensor can detect five multi-touch points at the same time. A neural network architecture is used to calibrate the applied force with acceptable accuracy in the presence of noise, variation in gains, and non-linearity. The force measured in real time by a commercial precise force sensor (ATI) is mapped with the produced voltage obtained by changing the layers’ capacitance between two electrode layers. Finally, the soft robot gripper embedding the suggested tactile sensor is utilized to grasp an object with position and force feedback signals. Full article
(This article belongs to the Special Issue Soft Robotics and Sensors)
►▼ Show Figures

Figure 1

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 30 | Viewed by 5070
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))
►▼ Show Figures

Figure 1

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 10 | Viewed by 3404
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)
►▼ Show Figures

Figure 1

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 23 | Viewed by 4988
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)
►▼ Show Figures

Figure 1

22 pages, 3713 KiB  
Review
Recent Advances in the Use of Surface-Enhanced Raman Scattering for Illicit Drug Detection
by Shamim Azimi and Aristides Docoslis
Sensors 2022, 22(10), 3877; https://doi.org/10.3390/s22103877 - 20 May 2022
Cited by 26 | Viewed by 5266
Abstract
The rapid increase in illicit drug use and its adverse health effects and socio-economic consequences have reached alarming proportions in recent years. Surface-enhanced Raman scattering (SERS) has emerged as a highly sensitive analytical tool for the detection of low dosages of drugs in [...] Read more.
The rapid increase in illicit drug use and its adverse health effects and socio-economic consequences have reached alarming proportions in recent years. Surface-enhanced Raman scattering (SERS) has emerged as a highly sensitive analytical tool for the detection of low dosages of drugs in liquid and solid samples. In the present article, we review the state-of-the-art use of SERS for chemical analysis of illicit drugs in aqueous and complex biological samples, including saliva, urine, and blood. We also include a review of the types of SERS substrates used for this purpose, pointing out recent advancements in substrate fabrication towards quantitative and qualitative detection of illicit drugs. Finally, we conclude by providing our perspective on the field of SERS-based drug detection, including presently faced challenges. Overall, our review provides evidence of the strong potential of SERS to establish itself as both a laboratory and in situ analytical method for fast and sensitive drug detection and identification. Full article
(This article belongs to the Special Issue Surface-Enhanced Raman Scattering (SERS) for (Bio)chemical Sensing Applications)
►▼ Show Figures

Figure 1

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 3551
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)
►▼ Show Figures

Figure 1

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 28 | Viewed by 4110
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)
►▼ Show Figures

Figure 1

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 16 | Viewed by 5463
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)
►▼ Show Figures

Graphical abstract

21 pages, 3888 KiB  
Article
Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
by Ye-Eun Kim, Yea-Sul Kim and Hwankuk Kim
Sensors 2022, 22(10), 3819; https://doi.org/10.3390/s22103819 - 18 May 2022
Cited by 25 | Viewed by 5004
Abstract
The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine [...] Read more.
The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology. Full article
(This article belongs to the Collection Cryptography and Security in IoT and Sensor Networks)
►▼ Show Figures

Figure 1

11 pages, 1915 KiB  
Article
Self-Powered and Autonomous Vibrational Wake-Up System Based on Triboelectric Nanogenerators and MEMS Switch
by Yuan Lin, Youchao Qi, Jiaqi Wang, Guoxu Liu, Zhaozheng Wang, Junqing Zhao, Yi Lv, Zhi Zhang, Ning Tian, Mengbi Wang, Yuanfen Chen and Chi Zhang
Sensors 2022, 22(10), 3752; https://doi.org/10.3390/s22103752 - 14 May 2022
Cited by 17 | Viewed by 3128
Abstract
With the extensive application of wireless sensing nodes, the demand for sustainable energy in unattended environments is increasing. Here, we report a self-powered and autonomous vibrational wake-up system (SAVWS) based on triboelectric nanogenerators and micro-electromechanical system (MEMS) switches. The energy triboelectric nanogenerator (E-TENG) [...] Read more.
With the extensive application of wireless sensing nodes, the demand for sustainable energy in unattended environments is increasing. Here, we report a self-powered and autonomous vibrational wake-up system (SAVWS) based on triboelectric nanogenerators and micro-electromechanical system (MEMS) switches. The energy triboelectric nanogenerator (E-TENG) harvests vibration energy to power the wireless transmitter through a MEMS switch. The signal triboelectric nanogenerator (S-TENG) controls the state of the MEMS switch as a self-powered accelerometer and shows good linearity in the acceleration range of 1–4.5 m/s2 at 30 Hz with a sensitivity of about 14.6 V/(m/s2). When the acceleration increases, the S-TENG turns on the MEMS switch, and the wireless transmitter transmits an alarm signal with the energy from E-TENG, using only 0.64 mJ. Using TENGs simultaneously as an energy source and a sensor, the SAVWS provides a self-powered vibration monitoring solution for unattended environments and shows extensive applications and great promise in smart factories, autonomous driving, and the Internet of Things. Full article
(This article belongs to the Special Issue Integration of Sensing and Energy Supply)
►▼ Show Figures

Figure 1

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 19 | Viewed by 3928
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)
►▼ Show Figures

Figure 1

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 7 | Viewed by 3978
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)
►▼ Show Figures

Figure 1

18 pages, 4371 KiB  
Article
Multilevel Structural Components Detection and Segmentation toward Computer Vision-Based Bridge Inspection
by Weilei Yu and Mayuko Nishio
Sensors 2022, 22(9), 3502; https://doi.org/10.3390/s22093502 - 4 May 2022
Cited by 13 | Viewed by 2889
Abstract
Bridge inspection plays a critical role in mitigating the safety risks associated with bridge deterioration and decay. CV (computer vision) technology can facilitate bridge inspection by accurately automating the structural recognition tasks, especially useful in UAV (unmanned aerial vehicles)-assisted bridge inspections. This study [...] Read more.
Bridge inspection plays a critical role in mitigating the safety risks associated with bridge deterioration and decay. CV (computer vision) technology can facilitate bridge inspection by accurately automating the structural recognition tasks, especially useful in UAV (unmanned aerial vehicles)-assisted bridge inspections. This study proposed a framework for the multilevel inspection of bridges based on CV technology, and provided verification using CNN (convolution neural network) models. Using a long-distance dataset, recognition of the bridge type was performed using the Resnet50 network. The dataset was built using internet image captures of 1200 images of arched bridges, cable-stayed bridges and suspension bridges, and the network was trained and evaluated. A classification accuracy of 96.29% was obtained. The YOLOv3 model was used to recognize bridge components in medium-distance bridge images. A dataset was created from 300 images of girders and piers collected from the internet, and image argumentation techniques and the tuning of model hyperparameters were investigated. A detection accuracy of 93.55% for the girders and 82.64% for the piers was obtained. For close-distance bridge images, segmentation and recognition of bridge components were investigated using the instance segmentation algorithm of the Mask–RCNN model. A dataset containing 800 images of girders and bearings was created, and annotated based on Yokohama City bridge inspection image records data. The trained model showed an accuracy of 90.8% for the bounding box and 87.17% for the segmentation. This study also contributed to research on bridge image acquisition, computer vision model comparison, hyperparameter tuning, and optimization techniques. Full article
(This article belongs to the Section Sensing and Imaging)
►▼ Show Figures

Figure 1

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 21 | Viewed by 4499
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)
►▼ Show Figures

Figure 1

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 23 | Viewed by 5297
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)
►▼ Show Figures

Figure 1

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 25 | Viewed by 3321
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)
►▼ Show Figures

Figure 1

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 63 | Viewed by 9986
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)
►▼ Show Figures

Graphical abstract

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 44 | Viewed by 12267
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)
►▼ Show Figures

Figure 1

34 pages, 3720 KiB  
Review
Wireless Power Transfer in Wirelessly Powered Sensor Networks: A Review of Recent Progress
by S. M. Asiful Huda, Muhammad Yeasir Arafat and Sangman Moh
Sensors 2022, 22(8), 2952; https://doi.org/10.3390/s22082952 - 12 Apr 2022
Cited by 36 | Viewed by 6814
Abstract
With the emergence of the Internet of Things (IoT), billions of wireless devices, including sensors and wearable devices, are evolving under the IoT technology. The limited battery life of the sensor nodes remains a crucial implementation challenge to enable such a revolution, primarily [...] Read more.
With the emergence of the Internet of Things (IoT), billions of wireless devices, including sensors and wearable devices, are evolving under the IoT technology. The limited battery life of the sensor nodes remains a crucial implementation challenge to enable such a revolution, primarily because traditional battery replacement requires enormous human effort. Wirelessly powered sensor networks (WPSNs), which would eliminate the need for regular battery replacement and improve the overall lifetime of sensor nodes, are the most promising solution to efficiently address the limited battery life of the sensor nodes. In this study, an in-depth survey is conducted on the wireless power transfer (WPT) techniques through which sensor devices can harvest energy to avoid frequent node failures. Following a general overview of WPSNs, three wireless power transfer models are demonstrated, and their respective enabling techniques are discussed in light of the existing literature. Moreover, the existing WPT techniques are comprehensively reviewed in terms of critical design parameters and performance factors. Subsequently, crucial key performance-enhancing techniques for WPT in WPSNs are discussed. Finally, several challenges and future directions are presented for motivating further research on WPSNs. Full article
(This article belongs to the Section Sensor Networks)
►▼ Show Figures

Figure 1

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 - 9 Apr 2022
Cited by 28 | Viewed by 6864
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)
►▼ Show Figures

Figure 1

24 pages, 155272 KiB  
Article
LoRa Based IoT Platform for Remote Monitoring of Large-Scale Agriculture Farms in Chile
by Mohamed A. Ahmed, Jose Luis Gallardo, Marcos D. Zuniga, Manuel A. Pedraza, Gonzalo Carvajal, Nicolás Jara and Rodrigo Carvajal
Sensors 2022, 22(8), 2824; https://doi.org/10.3390/s22082824 - 7 Apr 2022
Cited by 24 | Viewed by 10587
Abstract
Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with [...] Read more.
Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack. Full article
(This article belongs to the Special Issue IoT for Smart Agriculture)
►▼ Show Figures

Figure 1

12 pages, 1112 KiB  
Article
Electropolymerized, Molecularly Imprinted Polymer on a Screen-Printed Electrode—A Simple, Fast, and Disposable Voltammetric Sensor for Trazodone
by Isabel Seguro, Patrícia Rebelo, João G. Pacheco and Cristina Delerue-Matos
Sensors 2022, 22(7), 2819; https://doi.org/10.3390/s22072819 - 6 Apr 2022
Cited by 14 | Viewed by 3016
Abstract
In recent years, analytical chemistry has been facing new challenges, particularly in developing low-cost, green, and easy-to-reproduce methods. In this work, a simple, reproducible, and low-cost electrochemical (voltammetric) molecularly imprinted polymer (MIP) sensor was designed specifically for the detection of trazodone (TZD). Trazodone [...] Read more.
In recent years, analytical chemistry has been facing new challenges, particularly in developing low-cost, green, and easy-to-reproduce methods. In this work, a simple, reproducible, and low-cost electrochemical (voltammetric) molecularly imprinted polymer (MIP) sensor was designed specifically for the detection of trazodone (TZD). Trazodone (TZD) is an antidepressant drug consumed worldwide since the 1970s. By combining electropolymerization (surface imprinting) with screen-printed electrodes (SPCEs), the sensor is easy to prepare, is environmentally friendly (uses small amounts of reagents), and can be used for in situ analysis through integration with small, portable devices. The MIP was obtained using cyclic voltammetry (CV), using 4-aminobenzoic acid (4-ABA) as the functional monomer in the presence of TZF molecules in 0.1 M HCl. Non-imprinted control was also constructed in the absence of TZD. Both polymers were characterized using CV, and TZD detection was performed with DPV using the oxidation of TZD. The polymerization conditions were studied and optimized. Comparing the TZD signal for MIP/SPCE and NIP/SPCE, an imprinting factor of 71 was estimated, indicating successful imprinting of the TZD molecules within the polymeric matrix. The analytical response was linear in the range of 5–80 µM, and an LOD of 1.6 µM was estimated. Selectivity was evaluated by testing the sensor for molecules with a similar structure to TZD, and the ability of MIP/SPCE to selectively bind to TZD was proven. The sensor was applied to spiked tap water samples and human serum with good recoveries and allowed for a fast analysis (around 30 min). Full article
(This article belongs to the Section Chemical Sensors)
►▼ Show Figures

Figure 1

19 pages, 6473 KiB  
Article
Two-Dimensional Phononic Crystal Based Sensor for Characterization of Mixtures and Heterogeneous Liquids
by Nikolay Mukhin, Mykhailo Kutia, Alexander Aman, Ulrike Steinmann and Ralf Lucklum
Sensors 2022, 22(7), 2816; https://doi.org/10.3390/s22072816 - 6 Apr 2022
Cited by 14 | Viewed by 3257
Abstract
We show new approaches to developing acoustic liquid sensors based on phononic crystals. The proposed phononic crystal integrates fluidic elements. A solid block with periodic cylindrical holes contains a defect—a liquid-filled cylindrical cavity. We pay attention to acoustic excitation and the readout of [...] Read more.
We show new approaches to developing acoustic liquid sensors based on phononic crystals. The proposed phononic crystal integrates fluidic elements. A solid block with periodic cylindrical holes contains a defect—a liquid-filled cylindrical cavity. We pay attention to acoustic excitation and the readout of the axisymmetric cylindrical resonator eigenmode of the liquid-filled defect in the middle of the phononic crystal structure. This mode solves the challenge of mechanical energy losses due to liquid viscosity. We also analyze the coupling effects between oscillations of liquid and solid systems and consider coupling issues between piezoelectric transducers and the liquid-filled cavity resonator. The numerical simulation of the propagation of acoustic waves through the phononic crystal sensor was carried out in COMSOL Multiphysics Software. The phononic crystal was made of stainless steel with mechanically drilled holes and was fabricated for experimental verification. We show that a tuning of the solid–liquid vibrational modes coupling is the key to an enhanced level of sensitivity to liquid properties. Besides (homogeneous) water–propanol mixtures, experimental studies were carried out on (disperse) water–fuel emulsions. Full article
(This article belongs to the Section Chemical Sensors)
►▼ Show Figures

Figure 1

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 - 2 Apr 2022
Cited by 19 | Viewed by 4181
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)
►▼ Show Figures

Figure 1

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 - 1 Apr 2022
Cited by 12 | Viewed by 3307
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)
►▼ Show Figures

Figure 1

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 2209
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)
►▼ Show Figures

Figure 1

44 pages, 726 KiB  
Review
Data Gathering Techniques in WSN: A Cross-Layer View
by Omer Gurewitz, Mark Shifrin and Efi Dvir
Sensors 2022, 22(7), 2650; https://doi.org/10.3390/s22072650 - 30 Mar 2022
Cited by 18 | Viewed by 5612
Abstract
Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural [...] Read more.
Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. Many novel solutions devised in the past do not scale well with the exponential growth in the population of the devices and need to be adapted, revised, or new innovative solutions are required to comply with this massive growth. Furthermore, recent technological advances present new opportunities which can be leveraged in this context. This paper provides a cross-layer perspective and review of data gathering in WSN and IoT networks. We provide some background and essential milestones that have laid the foundation of many subsequent solutions suggested over the years. We mainly concentrate on recent state-of-the-art research, which facilitates the scalable, energy-efficient, cost-effective, and human-friendly functionality of WSNs and the novel applications in the years to come. Full article
(This article belongs to the Special Issue Section “Sensor Networks”: 10th Anniversary)
►▼ Show Figures

Figure 1

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 14 | Viewed by 6622
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)
►▼ Show Figures

Figure 1

19 pages, 4704 KiB  
Article
Multi-ROI Spectral Approach for the Continuous Remote Cardio-Respiratory Monitoring from Mobile Device Built-In Cameras
by Nunzia Molinaro, Emiliano Schena, Sergio Silvestri and Carlo Massaroni
Sensors 2022, 22(7), 2539; https://doi.org/10.3390/s22072539 - 25 Mar 2022
Cited by 18 | Viewed by 2301
Abstract
Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via [...] Read more.
Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone’s camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min−1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances. Full article
(This article belongs to the Collection Biomedical Imaging and Sensing)
►▼ Show Figures

Figure 1

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 13 | Viewed by 6570
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)
►▼ Show Figures

Figure 1

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 3179
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)
►▼ Show Figures

Figure 1

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 59 | Viewed by 4799
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)
►▼ Show Figures

Figure 1

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 13 | Viewed by 2508
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)
►▼ Show Figures

Figure 1

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 40 | Viewed by 10591
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)
►▼ Show Figures

Figure 1

31 pages, 4235 KiB  
Review
Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?
by Tracy Warbrick
Sensors 2022, 22(6), 2262; https://doi.org/10.3390/s22062262 - 15 Mar 2022
Cited by 36 | Viewed by 10809
Abstract
Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to [...] Read more.
Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented. Full article
(This article belongs to the Special Issue Bridging Multimodal Neurodynamic Sensor Data)
►▼ Show Figures

Figure 1

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 - 9 Mar 2022
Cited by 16 | Viewed by 2215
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)
►▼ Show Figures

Figure 1

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 - 9 Mar 2022
Cited by 65 | Viewed by 11327
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)
►▼ Show Figures

Figure 1

21 pages, 9230 KiB  
Article
Tree Trunk Recognition in Orchard Autonomous Operations under Different Light Conditions Using a Thermal Camera and Faster R-CNN
by Ailian Jiang, Ryozo Noguchi and Tofael Ahamed
Sensors 2022, 22(5), 2065; https://doi.org/10.3390/s22052065 - 7 Mar 2022
Cited by 18 | Viewed by 4166
Abstract
In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to [...] Read more.
In an orchard automation process, a current challenge is to recognize natural landmarks and tree trunks to localize intelligent robots. To overcome low-light conditions and global navigation satellite system (GNSS) signal interruptions under a dense canopy, a thermal camera may be used to recognize tree trunks using a deep learning system. Therefore, the objective of this study was to use a thermal camera to detect tree trunks at different times of the day under low-light conditions using deep learning to allow robots to navigate. Thermal images were collected from the dense canopies of two types of orchards (conventional and joint training systems) under high-light (12–2 PM), low-light (5–6 PM), and no-light (7–8 PM) conditions in August and September 2021 (summertime) in Japan. The detection accuracy for a tree trunk was confirmed by the thermal camera, which observed an average error of 0.16 m for 5 m, 0.24 m for 15 m, and 0.3 m for 20 m distances under high-, low-, and no-light conditions, respectively, in different orientations of the thermal camera. Thermal imagery datasets were augmented to train, validate, and test using the Faster R-CNN deep learning model to detect tree trunks. A total of 12,876 images were used to train the model, 2318 images were used to validate the training process, and 1288 images were used to test the model. The mAP of the model was 0.8529 for validation and 0.8378 for the testing process. The average object detection time was 83 ms for images and 90 ms for videos with the thermal camera set at 11 FPS. The model was compared with the YOLO v3 with same number of datasets and training conditions. In the comparisons, Faster R-CNN achieved a higher accuracy than YOLO v3 in tree truck detection using the thermal camera. Therefore, the results showed that Faster R-CNN can be used to recognize objects using thermal images to enable robot navigation in orchards under different lighting conditions. Full article
(This article belongs to the Section Smart Agriculture)
►▼ Show Figures

Figure 1

18 pages, 26542 KiB  
Article
Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications
by Athanasios Douklias, Lazaros Karagiannidis, Fay Misichroni and Angelos Amditis
Sensors 2022, 22(5), 2049; https://doi.org/10.3390/s22052049 - 6 Mar 2022
Cited by 15 | Viewed by 7512
Abstract
Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is becoming more of a [...] Read more.
Visual sensing of the environment is crucial for flying an unmanned aerial vehicle (UAV) and is a centerpiece of many related applications. The ability to run computer vision and machine learning algorithms onboard an unmanned aerial system (UAS) is becoming more of a necessity in an effort to alleviate the communication burden of high-resolution video streaming, to provide flying aids, such as obstacle avoidance and automated landing, and to create autonomous machines. Thus, there is a growing interest on the part of many researchers in developing and validating solutions that are suitable for deployment on a UAV system by following the general trend of edge processing and airborne computing, which transforms UAVs from moving sensors into intelligent nodes that are capable of local processing. In this paper, we present, in a rigorous way, the design and implementation of a 12.85 kg UAV system equipped with the necessary computational power and sensors to serve as a testbed for image processing and machine learning applications, explain the rationale behind our decisions, highlight selected implementation details, and showcase the usefulness of our system by providing an example of how a sample computer vision application can be deployed on our platform. Full article
(This article belongs to the Special Issue UAV Imaging and Sensing)
►▼ Show Figures

Figure 1

16 pages, 4092 KiB  
Article
The Movesense Medical Sensor Chest Belt Device as Single Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional Validation Study
by Bruce Rogers, Marcelle Schaffarczyk, Martina Clauß, Laurent Mourot and Thomas Gronwald
Sensors 2022, 22(5), 2032; https://doi.org/10.3390/s22052032 - 5 Mar 2022
Cited by 19 | Viewed by 6529
Abstract
The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an [...] Read more.
The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson’s r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland–Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to −1.8 ms and 2.3 to −1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to −7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application. Full article
(This article belongs to the Special Issue Sensors for Heart Rate Monitoring)
►▼ Show Figures

Figure 1

11 pages, 1695 KiB  
Article
A Low-Cost Metamaterial Sensor Based on DS-CSRR for Material Characterization Applications
by Waseem Shahzad, Weidong Hu, Qasim Ali, Hamid Raza, Syed Muzahir Abbas and Leo P. Ligthart
Sensors 2022, 22(5), 2000; https://doi.org/10.3390/s22052000 - 4 Mar 2022
Cited by 29 | Viewed by 2743
Abstract
This paper presents a metamaterial sensor using a double slit complementary square ring resonator (DS-CSRR) that has been utilized for the measurement of dielectric materials, especially coal powder. The design is optimized for best performance of deep notch depth in transmission coefficient (Magnitude [...] Read more.
This paper presents a metamaterial sensor using a double slit complementary square ring resonator (DS-CSRR) that has been utilized for the measurement of dielectric materials, especially coal powder. The design is optimized for best performance of deep notch depth in transmission coefficient (Magnitude of S21). Sensitivity analysis of transmission coefficient with respect to structure dimensions has been carried out. Metamaterial properties of double negative permitivity and permeability were extracted from the S–parameters of this sensor. The optimized structure is fabricated using low cost FR-4 PCB board. Measured result shows resonance frequency of 4.75 GHz with a deep notch up to −41 dB. Simulated and measured results show good agreement in desired frequency band. For material characterization, first, two known materials are characterized using this metamaterial sensor. Their respective resonances and dielectric constants are known, so the transcendental equation of the sensor is formulated. Afterwards, the proposed sensor is used for dielectric measurement of two types of coal powder, i.e., Anthracite and Bituminous. The measured value of dielectric constant of Anthracite coal is 3.5 and of Bituminous coal is 2.52. This is a simple and effective nondestructive measurement technique for material testing applications. Full article
(This article belongs to the Special Issue Microwave Sensing and Applications)
►▼ Show Figures

Figure 1

15 pages, 5032 KiB  
Article
Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy
by Saverio Francini, Giovanni D’Amico, Elia Vangi, Costanza Borghi and Gherardo Chirici
Sensors 2022, 22(5), 2015; https://doi.org/10.3390/s22052015 - 4 Mar 2022
Cited by 45 | Viewed by 8181
Abstract
Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, [...] Read more.
Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
►▼ Show Figures

Graphical abstract

16 pages, 4470 KiB  
Article
A Dual-Mode 303-Megaframes-per-Second Charge-Domain Time-Compressive Computational CMOS Image Sensor
by Keiichiro Kagawa, Masaya Horio, Anh Ngoc Pham, Thoriq Ibrahim, Shin-ichiro Okihara, Tatsuki Furuhashi, Taishi Takasawa, Keita Yasutomi, Shoji Kawahito and Hajime Nagahara
Sensors 2022, 22(5), 1953; https://doi.org/10.3390/s22051953 - 2 Mar 2022
Cited by 13 | Viewed by 5591
Abstract
An ultra-high-speed computational CMOS image sensor with a burst frame rate of 303 megaframes per second, which is the fastest among the solid-state image sensors, to our knowledge, is demonstrated. This image sensor is compatible with ordinary single-aperture lenses and can operate in [...] Read more.
An ultra-high-speed computational CMOS image sensor with a burst frame rate of 303 megaframes per second, which is the fastest among the solid-state image sensors, to our knowledge, is demonstrated. This image sensor is compatible with ordinary single-aperture lenses and can operate in dual modes, such as single-event filming mode or multi-exposure imaging mode, by reconfiguring the number of exposure cycles. To realize this frame rate, the charge modulator drivers were adequately designed to suppress the peak driving current taking advantage of the operational constraint of the multi-tap charge modulator. The pixel array is composed of macropixels with 2 × 2 4-tap subpixels. Because temporal compressive sensing is performed in the charge domain without any analog circuit, ultrafast frame rates, small pixel size, low noise, and low power consumption are achieved. In the experiments, single-event imaging of plasma emission in laser processing and multi-exposure transient imaging of light reflections to extend the depth range and to decompose multiple reflections for time-of-flight (TOF) depth imaging with a compression ratio of 8× were demonstrated. Time-resolved images similar to those obtained by the direct-type TOF were reproduced in a single shot, while the charge modulator for the indirect TOF was utilized. Full article
(This article belongs to the Special Issue Recent Advances in CMOS Image Sensor)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 151-200 on page 4 of 15.

Go to page    first_page chevron_left 2 3 4 5 6 chevron_right last_page
Back to TopTop