Sensors

17 pages, 425 KiB

Open AccessEditor’s ChoiceArticle

Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare

by Francisco J. Jaime, Antonio Muñoz, Francisco Rodríguez-Gómez and Antonio Jerez-Calero

Sensors 2023, 23(21), 8944; https://doi.org/10.3390/s23218944 - 3 Nov 2023

Cited by 18 | Viewed by 3643

Abstract

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings [...] Read more.

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection. Full article

(This article belongs to the Special Issue IoT Cybersecurity)

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19 pages, 6979 KiB

Open AccessEditor’s ChoiceReview

Spectrum Sensing, Clustering Algorithms, and Energy-Harvesting Technology for Cognitive-Radio-Based Internet-of-Things Networks

by Xavier Fernando and George Lăzăroiu

Sensors 2023, 23(18), 7792; https://doi.org/10.3390/s23187792 - 11 Sep 2023

Cited by 45 | Viewed by 4914

Abstract

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the [...] Read more.

The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange. Full article

(This article belongs to the Special Issue Spectrum Sensing for Wireless Communication Systems)

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20 pages, 2433 KiB

Open AccessEditor’s ChoiceReview

Trends in Single-Molecule Total Internal Reflection Fluorescence Imaging and Their Biological Applications with Lab-on-a-Chip Technology

by Louis Colson, Youngeun Kwon, Soobin Nam, Avinashi Bhandari, Nolberto Martinez Maya, Ying Lu and Yongmin Cho

Sensors 2023, 23(18), 7691; https://doi.org/10.3390/s23187691 - 6 Sep 2023

Cited by 6 | Viewed by 3026

Abstract

Single-molecule imaging technologies, especially those based on fluorescence, have been developed to probe both the equilibrium and dynamic properties of biomolecules at the single-molecular and quantitative levels. In this review, we provide an overview of the state-of-the-art advancements in single-molecule fluorescence imaging techniques. [...] Read more.

Single-molecule imaging technologies, especially those based on fluorescence, have been developed to probe both the equilibrium and dynamic properties of biomolecules at the single-molecular and quantitative levels. In this review, we provide an overview of the state-of-the-art advancements in single-molecule fluorescence imaging techniques. We systematically explore the advanced implementations of in vitro single-molecule imaging techniques using total internal reflection fluorescence (TIRF) microscopy, which is widely accessible. This includes discussions on sample preparation, passivation techniques, data collection and analysis, and biological applications. Furthermore, we delve into the compatibility of microfluidic technology for single-molecule fluorescence imaging, highlighting its potential benefits and challenges. Finally, we summarize the current challenges and prospects of fluorescence-based single-molecule imaging techniques, paving the way for further advancements in this rapidly evolving field. Full article

(This article belongs to the Special Issue Molecular Imaging and Sensing: Design, Development, and Applications)

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Graphical abstract

38 pages, 3819 KiB

Open AccessEditor’s ChoiceReview

Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review

by Nikolaos Peladarinos, Dimitrios Piromalis, Vasileios Cheimaras, Efthymios Tserepas, Radu Adrian Munteanu and Panagiotis Papageorgas

Sensors 2023, 23(16), 7128; https://doi.org/10.3390/s23167128 - 11 Aug 2023

Cited by 28 | Viewed by 11472

Abstract

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica [...] Read more.

Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain. Full article

(This article belongs to the Special Issue IoT for Smart Agriculture)

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16 pages, 9326 KiB

Open AccessEditor’s ChoiceArticle

3D-Printed Graphene Nanoplatelets/Polymer Foams for Low/Medium-Pressure Sensors

by Marco Fortunato, Luca Pacitto, Nicola Pesce and Alessio Tamburrano

Sensors 2023, 23(16), 7054; https://doi.org/10.3390/s23167054 - 9 Aug 2023

Cited by 1 | Viewed by 1405

Abstract

The increasing interest in wearable devices for health monitoring, illness prevention, and human motion detection has driven research towards developing novel and cost-effective solutions for highly sensitive flexible sensors. The objective of this work is to develop innovative piezoresistive pressure sensors utilizing two [...] Read more.

The increasing interest in wearable devices for health monitoring, illness prevention, and human motion detection has driven research towards developing novel and cost-effective solutions for highly sensitive flexible sensors. The objective of this work is to develop innovative piezoresistive pressure sensors utilizing two types of 3D porous flexible open-cell foams: Grid and triply periodic minimal surface structures. These foams will be produced through a procedure involving the 3D printing of sacrificial templates, followed by infiltration with various low-viscosity polymers, leaching, and ultimately coating the pores with graphene nanoplatelets (GNPs). Additive manufacturing enables precise control over the shape and dimensions of the structure by manipulating geometric parameters during the design phase. This control extends to the piezoresistive response of the sensors, which is achieved by infiltrating the foams with varying concentrations of a colloidal suspension of GNPs. To examine the morphology of the produced materials, field emission scanning electron microscopy (FE-SEM) is employed, while mechanical and piezoresistive behavior are investigated through quasi-static uniaxial compression tests. The results obtained indicate that the optimized grid-based structure sensors, manufactured using the commercial polymer Solaris, exhibit the highest sensitivity compared to other tested samples. These sensors demonstrate a maximum sensitivity of 0.088 kPa⁻¹ for pressures below 10 kPa, increasing to 0.24 kPa⁻¹ for pressures of 80 kPa. Furthermore, the developed sensors are successfully applied to measure heartbeats both before and after aerobic activity, showcasing their excellent sensitivity within the typical pressure range exerted by the heartbeat, which typically falls between 10 and 20 kPa. Full article

(This article belongs to the Special Issue Graphene-Based Strain and Pressure Sensors)

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30 pages, 10953 KiB

Open AccessEditor’s ChoiceReview

A Review on Acoustic Emission Testing for Structural Health Monitoring of Polymer-Based Composites

by Noor Ghadarah and David Ayre

Sensors 2023, 23(15), 6945; https://doi.org/10.3390/s23156945 - 4 Aug 2023

Cited by 17 | Viewed by 4376

Abstract

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as [...] Read more.

Acoustic emission (AE) has received increased interest as a structural health monitoring (SHM) technique for various materials, including laminated polymer composites. Piezoelectric sensors, including PZT (piezoelectric ceramic) and PVDF (piezoelectric polymer), can monitor AE in materials. The thickness of the piezoelectric sensors (as low as 28 µm—PVDF) allows embedding the sensors within the laminated composite, creating a smart material. Incorporating piezoelectric sensors within composites has several benefits but presents numerous difficulties and challenges. This paper provides an overview of acoustic emission testing, concluding with a discussion on embedding piezoelectric AE sensors within fibre-polymer composites. Various aspects are covered, including the underlying AE principles in fibre-based composites, factors that influence the reliability and accuracy of AE measurements, methods to artificially induce acoustic emission, and the correlation between AE events and damage in polymer composites. Full article

(This article belongs to the Section Electronic Sensors)

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29 pages, 12357 KiB

Open AccessEditor’s ChoiceReview

A Review on Electrospun Nanofiber Composites for an Efficient Electrochemical Sensor Applications

by Ramkumar Vanaraj, Bharathi Arumugam, Gopiraman Mayakrishnan, Ick Soo Kim and Seong Cheol Kim

Sensors 2023, 23(15), 6705; https://doi.org/10.3390/s23156705 - 26 Jul 2023

Cited by 2 | Viewed by 1893

Abstract

The present review article discusses the elementary concepts of the sensor mechanism and various types of materials used for sensor applications. The electrospinning method is the most comfortable method to prepare the device-like structure by means of forming from the fiber structure. Though [...] Read more.

The present review article discusses the elementary concepts of the sensor mechanism and various types of materials used for sensor applications. The electrospinning method is the most comfortable method to prepare the device-like structure by means of forming from the fiber structure. Though there are various materials available for sensors, the important factor is to incorporate the functional group on the surface of the materials. The post-modification sanction enhances the efficiency of the sensor materials. This article also describes the various types of materials applied to chemical and biosensor applications. The chemical sensor parts include acetone, ethanol, ammonia, and CO₂, H₂O₂, and NO₂ molecules; meanwhile, the biosensor takes on glucose, uric acid, and cholesterol molecules. The above materials have to be sensed for a healthier lifestyle for humans and other living organisms. The prescribed review articles give a detailed report on the Electrospun materials for sensor applications. Full article

(This article belongs to the Special Issue Electrospun Composite Nanofibers: Sensing and Biosensing Applications)

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27 pages, 8928 KiB

Open AccessEditor’s ChoiceReview

Current Sensor Integration Issues with Wide-Bandgap Power Converters

by Ali Parsa Sirat and Babak Parkhideh

Sensors 2023, 23(14), 6481; https://doi.org/10.3390/s23146481 - 18 Jul 2023

Cited by 12 | Viewed by 3626

Abstract

Precise current sensing is essential for several power electronics’ protection, control, and reliability mechanisms. Even so, WBG power converters will likely struggle to develop a single current-sensing scheme to measure various types of currents due to the limited space and size of these [...] Read more.

Precise current sensing is essential for several power electronics’ protection, control, and reliability mechanisms. Even so, WBG power converters will likely struggle to develop a single current-sensing scheme to measure various types of currents due to the limited space and size of these devices, the required high sensing speed, and the high electromagnetic interference (EMI) emissions they cause. Analysis of existing current sensors was conducted in such terms with the objective of understanding the challenges associated with their integration into WBG power converters. Since each of these requirements has different design tradeoffs, it is challenging to consider one specific method of current sensing to be perfect for all situations; thus, the possibility of developing novel methods to improve the performance of these single-scheme current sensors is further explored. Full article

(This article belongs to the Special Issue Wide Bandgap Power Integrated Circuits and Sensors)

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34 pages, 4264 KiB

Open AccessEditor’s ChoiceReview

Advances in MIMO Antenna Design for 5G: A Comprehensive Review

by Tej Raj, Ranjan Mishra, Pradeep Kumar and Ankush Kapoor

Sensors 2023, 23(14), 6329; https://doi.org/10.3390/s23146329 - 12 Jul 2023

Cited by 26 | Viewed by 10921

Abstract

Multiple-input multiple-output (MIMO) technology has emerged as a highly promising solution for wireless communication, offering an opportunity to overcome the limitations of traffic capacity in high-speed broadband wireless network access. By utilizing multiple antennas at both the transmitting and receiving ends, the MIMO [...] Read more.

Multiple-input multiple-output (MIMO) technology has emerged as a highly promising solution for wireless communication, offering an opportunity to overcome the limitations of traffic capacity in high-speed broadband wireless network access. By utilizing multiple antennas at both the transmitting and receiving ends, the MIMO system enhances the efficiency and performance of wireless communication systems. This manuscript specifies a comprehensive review of MIMO antenna design approaches for fifth generation (5G) and beyond. With an introductory glimpse of cellular generation and the frequency spectrum for 5G, profound key enabling technologies for 5G mobile communication are presented. A detailed analysis of MIMO performance parameters in terms of envelope correlation coefficient (ECC), total active reflection coefficient (TARC), mean effective gain (MEG), and isolation is presented along with the advantages of MIMO technology over conventional SISO systems. MIMO is characterized and the performance is compared based on wideband/ultra-wideband, multiband/reconfigurable, circular polarized wideband/circular polarized ultra-wideband/circular polarized multiband, and reconfigurable categories. The design approaches of MIMO antennas for various 5G bands are discussed. It is subsequently enriched with the detailed studies of wideband (WB)/ultra-wideband (UWB), multiband, and circular polarized MIMO antennas with different design techniques. A good MIMO antenna system should be well decoupled among different ports to enhance its performance, and hence isolation among different ports is a crucial factor in designing high-performance MIMO antennas. A summary of design approaches with improved isolation is presented. The manuscript summarizes the various MIMO antenna design aspects for NR FR-1 (new radio frequency range) and NR FR-2, which will benefit researchers in the field of 5G and forthcoming cellular generations. Full article

(This article belongs to the Section Physical Sensors)

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28 pages, 11710 KiB

Open AccessEditor’s ChoiceArticle

A Wireless Sensor Network for Residential Building Energy and Indoor Environmental Quality Monitoring: Design, Instrumentation, Data Analysis and Feedback

by Mathieu Bourdeau, Julien Waeytens, Nedia Aouani, Philippe Basset and Elyes Nefzaoui

Sensors 2023, 23(12), 5580; https://doi.org/10.3390/s23125580 - 14 Jun 2023

Cited by 10 | Viewed by 2642

Abstract

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building [...] Read more.

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors’ technological choices, implementation, calibration, and maintenance. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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20 pages, 431 KiB

Open AccessEditor’s ChoiceArticle

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

by Yazeed Alotaibi and Mohammad Ilyas

Sensors 2023, 23(12), 5568; https://doi.org/10.3390/s23125568 - 14 Jun 2023

Cited by 26 | Viewed by 4431

Abstract

The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of [...] Read more.

The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency. In order to improve the efficiency of the IDS, a binary classification of normal and abnormal IoT traffic is constructed to enhance the IDS performance. Our method employs various supervised ML algorithms and ensemble classifiers. The proposed model was trained on TON-IoT network traffic datasets. Four of the trained ML-supervised models have achieved the highest accurate outcomes; Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor. These four classifiers are fed to two ensemble approaches: voting and stacking. The ensemble approaches were evaluated using the evaluation metrics and compared for their efficacy on this classification problem. The accuracy of the ensemble classifiers was higher than that of the individual models. This improvement can be attributed to ensemble learning strategies that leverage diverse learning mechanisms with varying capabilities. By combining these strategies, we were able to enhance the reliability of our predictions while reducing the occurrence of classification errors. The experimental results show that the framework can improve the efficiency of the Intrusion Detection System, achieving an accuracy rate of 0.9863. Full article

(This article belongs to the Special Issue IoT Network Security)

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38 pages, 24263 KiB

Open AccessEditor’s ChoiceReview

Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges

by Francisco J. Tovar-Lopez

Sensors 2023, 23(12), 5406; https://doi.org/10.3390/s23125406 - 7 Jun 2023

Cited by 36 | Viewed by 10564

Abstract

Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, [...] Read more.

Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, they have played a crucial role in assessing air, water, and soil quality, as well as ensured food safety. Despite notable progress, numerous challenges persist. This review article addresses recent developments in micro- and nanotechnology-enabled sensors for biomedical and environmental challenges, focusing on enhancing basic sensing techniques through micro/nanotechnology. Additionally, it explores the applications of these sensors in addressing current challenges in both biomedical and environmental domains. The article concludes by emphasizing the need for further research to expand the detection capabilities of sensors/devices, enhance sensitivity and selectivity, integrate wireless communication and energy-harvesting technologies, and optimize sample preparation, material selection, and automated components for sensor design, fabrication, and characterization. Full article

(This article belongs to the Special Issue Advances in Micro- and Nano- Sensors/Devices for Environmental and Biomedical Applications)

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24 pages, 2207 KiB

Open AccessEditor’s ChoiceArticle

Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques

by Davide Astolfi, Fabrizio De Caro and Alfredo Vaccaro

Sensors 2023, 23(12), 5376; https://doi.org/10.3390/s23125376 - 6 Jun 2023

Cited by 11 | Viewed by 2687

Abstract

The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to [...] Read more.

The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the literature. Initially, a sequential feature selection approach is employed to minimize the root-mean-square error between measurements and model estimates. Subsequently, Shapley coefficients are computed for the selected input variables to estimate their contribution towards explaining the average error. Two real-world data sets, representing wind turbines with different technologies, are discussed to illustrate the application of the proposed method. The experimental results of this study validate the effectiveness of the proposed methodology in detecting hidden anomalies. The methodology successfully identifies a new set of highly explanatory variables linked to the mechanical or electrical control of the rotor and blade pitch, which have not been previously explored in the literature. These findings highlight the novel insights provided by the methodology in uncovering crucial variables that significantly contribute to anomaly detection. Full article

(This article belongs to the Special Issue AI-Assisted Condition Monitoring and Fault Diagnosis)

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16 pages, 5168 KiB

Open AccessEditor’s ChoiceArticle

A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles

by Simone Carone, Giovanni Pappalettera, Caterina Casavola, Simone De Carolis and Leonardo Soria

Sensors 2023, 23(11), 5345; https://doi.org/10.3390/s23115345 - 5 Jun 2023

Cited by 8 | Viewed by 1888

Abstract

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with [...] Read more.

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%. Full article

(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)

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33 pages, 4823 KiB

Open AccessEditor’s ChoiceArticle

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio

by Panagiotis T. Karfakis, Micael S. Couceiro and David Portugal

Sensors 2023, 23(11), 5354; https://doi.org/10.3390/s23115354 - 5 Jun 2023

Cited by 6 | Viewed by 3796

Abstract

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited [...] Read more.

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable. Full article

(This article belongs to the Special Issue Sensor Based Perception for Field Robotics)

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15 pages, 4845 KiB

Open AccessEditor’s ChoiceArticle

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

by Shenglin Li and Hwan-Sik Yoon

Sensors 2023, 23(10), 4888; https://doi.org/10.3390/s23104888 - 19 May 2023

Cited by 7 | Viewed by 6291

Abstract

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to [...] Read more.

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems Based on Sensor Fusion)

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29 pages, 2565 KiB

Open AccessEditor’s ChoiceReview

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

by Luca Neri, Matt T. Oberdier, Kirsten C. J. van Abeelen, Luca Menghini, Ethan Tumarkin, Hemantkumar Tripathi, Sujai Jaipalli, Alessandro Orro, Nazareno Paolocci, Ilaria Gallelli, Massimo Dall’Olio, Amir Beker, Richard T. Carrick, Claudio Borghi and Henry R. Halperin

Sensors 2023, 23(10), 4805; https://doi.org/10.3390/s23104805 - 16 May 2023

Cited by 19 | Viewed by 6292

Abstract

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices [...] Read more.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data. Full article

(This article belongs to the Special Issue ECG Signal Processing Techniques and Applications)

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35 pages, 3792 KiB

Open AccessEditor’s ChoiceReview

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

by Inyeong Bae and Jungpyo Hong

Sensors 2023, 23(10), 4643; https://doi.org/10.3390/s23104643 - 10 May 2023

Cited by 22 | Viewed by 10982

Abstract

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including [...] Read more.

With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development. Full article

(This article belongs to the Special Issue Intelligent Sound Measurement Sensor and System 2022)

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26 pages, 1275 KiB

Open AccessEditor’s ChoiceReview

Oxygen Sensor-Based Respirometry and the Landscape of Microbial Testing Methods as Applicable to Food and Beverage Matrices

by Dmitri B. Papkovsky and Joseph P. Kerry

Sensors 2023, 23(9), 4519; https://doi.org/10.3390/s23094519 - 6 May 2023

Cited by 10 | Viewed by 2812

Abstract

The current status of microbiological testing methods for the determination of viable bacteria in complex sample matrices, such as food samples, is the focus of this review. Established methods for the enumeration of microorganisms, particularly, the ‘gold standard’ agar plating method for the [...] Read more.

The current status of microbiological testing methods for the determination of viable bacteria in complex sample matrices, such as food samples, is the focus of this review. Established methods for the enumeration of microorganisms, particularly, the ‘gold standard’ agar plating method for the determination of total aerobic viable counts (TVC), bioluminescent detection of total ATP, selective molecular methods (immunoassays, DNA/RNA amplification, sequencing) and instrumental methods (flow cytometry, Raman spectroscopy, mass spectrometry, calorimetry), are analyzed and compared with emerging oxygen sensor-based respirometry techniques. The basic principles of optical O₂ sensing and respirometry and the primary materials, detection modes and assay formats employed are described. The existing platforms for bacterial cell respirometry are then described, and examples of particular assays are provided, including the use of rapid TVC tests of food samples and swabs, the toxicological screening and profiling of cells and antimicrobial sterility testing. Overall, O₂ sensor-based respirometry and TVC assays have high application potential in the food industry and related areas. They detect viable bacteria via their growth and respiration; the assay is fast (time to result is 2–8 h and dependent on TVC load), operates with complex samples (crude homogenates of food samples) in a simple mix-and-measure format, has low set-up and instrumentation costs and is inexpensive and portable. Full article

(This article belongs to the Special Issue Optical Sensing Methods for Microorganism Identification)

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66 pages, 52190 KiB

Open AccessEditor’s ChoiceReview

Ultraviolet Photodetectors: From Photocathodes to Low-Dimensional Solids

by Antoni Rogalski, Zbigniew Bielecki, Janusz Mikołajczyk and Jacek Wojtas

Sensors 2023, 23(9), 4452; https://doi.org/10.3390/s23094452 - 2 May 2023

Cited by 18 | Viewed by 6332

Abstract

The paper presents the long-term evolution and recent development of ultraviolet photodetectors. First, the general theory of ultraviolet (UV) photodetectors is briefly described. Then the different types of detectors are presented, starting with the older photoemission detectors through photomultipliers and image intensifiers. More [...] Read more.

The paper presents the long-term evolution and recent development of ultraviolet photodetectors. First, the general theory of ultraviolet (UV) photodetectors is briefly described. Then the different types of detectors are presented, starting with the older photoemission detectors through photomultipliers and image intensifiers. More attention is paid to silicon and different types of wide band gap semiconductor photodetectors such as AlGaN, SiC-based, and diamond detectors. Additionally, Ga₂O₃ is considered a promising material for solar-blind photodetectors due to its excellent electrical properties and a large bandgap energy. The last part of the paper deals with new UV photodetector concepts inspired by new device architectures based on low-dimensional solid materials. It is shown that the evolution of the architecture has shifted device performance toward higher sensitivity, higher frequency response, lower noise, and higher gain-bandwidth products. Full article

(This article belongs to the Special Issue Towards Optoelectronic Technology: From Basic Research to Applications)

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27 pages, 929 KiB

Open AccessEditor’s ChoiceReview

Low-Cost Water Quality Sensors for IoT: A Systematic Review

by Edson Tavares de Camargo, Fabio Alexandre Spanhol, Juliano Scholz Slongo, Marcos Vinicius Rocha da Silva, Jaqueline Pazinato, Adriana Vechai de Lima Lobo, Fábio Rizental Coutinho, Felipe Walter Dafico Pfrimer, Cleber Antonio Lindino, Marcio Seiji Oyamada and Leila Droprinchinski Martins

Sensors 2023, 23(9), 4424; https://doi.org/10.3390/s23094424 - 30 Apr 2023

Cited by 27 | Viewed by 9520

Abstract

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to [...] Read more.

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices. Full article

(This article belongs to the Section Internet of Things)

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26 pages, 10190 KiB

Open AccessEditor’s ChoiceArticle

An Unmanned Surface Vehicle (USV): Development of an Autonomous Boat with a Sensor Integration System for Bathymetric Surveys

by Fernando Sotelo-Torres, Laura V. Alvarez and Robert C. Roberts

Sensors 2023, 23(9), 4420; https://doi.org/10.3390/s23094420 - 30 Apr 2023

Cited by 16 | Viewed by 15306

Abstract

A reliable yet economical unmanned surface vehicle (USV) has been developed for the bathymetric surveying of lakes. The system combines an autonomous navigation framework, environmental sensors, and a multibeam echosounder to collect submerged topography, temperature, and wind speed and monitor the vehicle’s status [...] Read more.

A reliable yet economical unmanned surface vehicle (USV) has been developed for the bathymetric surveying of lakes. The system combines an autonomous navigation framework, environmental sensors, and a multibeam echosounder to collect submerged topography, temperature, and wind speed and monitor the vehicle’s status during prescribed path-planning missions. The main objective of this research is to provide a methodological framework to build an autonomous boat with independent decision-making, efficient control, and long-range navigation capabilities. Integration of sensors with navigation control enabled the automatization of position, orientation, and velocity. A solar power integration was also tested to control the duration of the autonomous missions. The results of the solar power compared favorably with those of the standard LiPO battery system. Extended and autonomous missions were achieved with the developed platform, which can also evaluate the danger level, weather circumstances, and energy consumption through real-time data analysis. With all the incorporated sensors and controls, this USV can make self-governing decisions and improve its safety. A technical evaluation of the proposed vehicle was conducted as a measurable metric of the reliability and robustness of the prototype. Overall, a reliable, economic, and self-powered autonomous system has been designed and built to retrieve bathymetric surveys as a first step to developing intelligent reconnaissance systems that combine field robotics with machine learning to make decisions and adapt to unknown environments. Full article

(This article belongs to the Special Issue Hydrographic Systems and Sensors)

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22 pages, 4697 KiB

Open AccessEditor’s ChoiceReview

Advances in Electrochemical Biosensor Technologies for the Detection of Nucleic Acid Breast Cancer Biomarkers

by Ana-Maria Chiorcea-Paquim

Sensors 2023, 23(8), 4128; https://doi.org/10.3390/s23084128 - 20 Apr 2023

Cited by 15 | Viewed by 3827

Abstract

Breast cancer is the second leading cause of cancer deaths in women worldwide; therefore, there is an increased need for the discovery, development, optimization, and quantification of diagnostic biomarkers that can improve the disease diagnosis, prognosis, and therapeutic outcome. Circulating cell-free nucleic acids [...] Read more.

Breast cancer is the second leading cause of cancer deaths in women worldwide; therefore, there is an increased need for the discovery, development, optimization, and quantification of diagnostic biomarkers that can improve the disease diagnosis, prognosis, and therapeutic outcome. Circulating cell-free nucleic acids biomarkers such as microRNAs (miRNAs) and breast cancer susceptibility gene 1 (BRCA1) allow the characterization of the genetic features and screening breast cancer patients. Electrochemical biosensors offer excellent platforms for the detection of breast cancer biomarkers due to their high sensitivity and selectivity, low cost, use of small analyte volumes, and easy miniaturization. In this context, this article provides an exhaustive review concerning the electrochemical methods of characterization and quantification of different miRNAs and BRCA1 breast cancer biomarkers using electrochemical DNA biosensors based on the detection of hybridization events between a DNA or peptide nucleic acid probe and the target nucleic acid sequence. The fabrication approaches, the biosensors architectures, the signal amplification strategies, the detection techniques, and the key performance parameters, such as the linearity range and the limit of detection, were discussed. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Electrochemical Sensors)

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36 pages, 17376 KiB

Open AccessEditor’s ChoiceArticle

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study

by Nastassia Vysotskaya, Christoph Will, Lorenzo Servadei, Noah Maul, Christian Mandl, Merlin Nau, Jens Harnisch and Andreas Maier

Sensors 2023, 23(8), 4111; https://doi.org/10.3390/s23084111 - 19 Apr 2023

Cited by 7 | Viewed by 5226

Abstract

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable [...] Read more.

Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of

9.2 \pm 8.3

mmHg (mean error ± standard deviation) and a diastolic error of

7.7 \pm 5.7

mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors Technologies for Healthcare Monitoring)

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18 pages, 5780 KiB

Open AccessEditor’s ChoiceArticle

Crowdsourced Indoor Positioning with Scalable WiFi Augmentation

by Yinhuan Dong, Guoxiong He, Tughrul Arslan, Yunjie Yang and Yingda Ma

Sensors 2023, 23(8), 4095; https://doi.org/10.3390/s23084095 - 19 Apr 2023

Cited by 7 | Viewed by 1908

Abstract

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, [...] Read more.

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark. Full article

(This article belongs to the Special Issue Multi-Sensor Positioning for Navigation in Smart Cities)

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17 pages, 4842 KiB

Open AccessEditor’s ChoiceArticle

FSVM: A Few-Shot Threat Detection Method for X-ray Security Images

by Cheng Fang, Jiayue Liu, Ping Han, Mingrui Chen and Dayu Liao

Sensors 2023, 23(8), 4069; https://doi.org/10.3390/s23084069 - 18 Apr 2023

Cited by 8 | Viewed by 2408

Abstract

In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint [...] Read more.

In recent years, automatic detection of threats in X-ray baggage has become important in security inspection. However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items. In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only a small number of labeled samples. Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers. A combined loss function utilizing SVM loss is also created as the additional constraint. We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions. Experimental results show that compared with four common few-shot detection models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels). Full article

(This article belongs to the Special Issue Artificial Intelligence in Computer Vision: Methods and Applications)

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16 pages, 2865 KiB

Open AccessEditor’s ChoiceArticle

A New Design to Rayleigh Wave EMAT Based on Spatial Pulse Compression

by Chuanliu Jiang, Zhichao Li, Zeyang Zhang and Shujuan Wang

Sensors 2023, 23(8), 3943; https://doi.org/10.3390/s23083943 - 13 Apr 2023

Cited by 12 | Viewed by 2413

Abstract

The main disadvantage of the electromagnetic acoustic transducer (EMAT) is low energy-conversion efficiency and low signal-to-noise ratio (SNR). This problem can be improved by pulse compression technology in the time domain. In this paper, a new coil structure with unequal spacing was proposed [...] Read more.

The main disadvantage of the electromagnetic acoustic transducer (EMAT) is low energy-conversion efficiency and low signal-to-noise ratio (SNR). This problem can be improved by pulse compression technology in the time domain. In this paper, a new coil structure with unequal spacing was proposed for a Rayleigh wave EMAT (RW-EMAT) to replace the conventional meander line coil with equal spacing, which allows the signal to be compressed in the spatial domain. Linear and nonlinear wavelength modulations were analyzed to design the unequal spacing coil. Based on this, the performance of the new coil structure was analyzed by the autocorrelation function. Finite element simulation and experiments proved the feasibility of the spatial pulse compression coil. The experimental results show that the received signal amplitude is increased by 2.3~2.6 times, the signal with a width of 20 μs could be compressed into a δ-like pulse of less than 0.25 μs and the SNR is increased by 7.1–10.1 dB. These indicate that the proposed new RW-EMAT can effectively enhance the strength, time resolution and SNR of the received signal. Full article

(This article belongs to the Special Issue Development, Investigation and Application of Acoustic Sensors: Part II)

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28 pages, 1981 KiB

Open AccessEditor’s ChoiceReview

Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions

by Urvish Trivedi, Dimitrios Menychtas, Redwan Alqasemi and Rajiv Dubey

Sensors 2023, 23(8), 3912; https://doi.org/10.3390/s23083912 - 12 Apr 2023

Cited by 6 | Viewed by 4237

Abstract

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as [...] Read more.

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as humans. The complexity of the human body has led researchers to create a framework for robot motion planning to recreate those motions in robotic systems using various redundancy resolution methods. This study conducts a thorough analysis of the relevant literature to provide a detailed exploration of the different redundancy resolution methodologies used in motion generation for mimicking human motion. The studies are investigated and categorized according to the study methodology and various redundancy resolution methods. An examination of the literature revealed a strong trend toward formulating intrinsic strategies that govern human movement through machine learning and artificial intelligence. Subsequently, the paper critically evaluates the existing approaches and highlights their limitations. It also identifies the potential research areas that hold promise for future investigations. Full article

(This article belongs to the Section Sensors and Robotics)

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32 pages, 959 KiB

Open AccessEditor’s ChoiceReview

Smart Transportation: An Overview of Technologies and Applications

by Damilola Oladimeji, Khushi Gupta, Nuri Alperen Kose, Kubra Gundogan, Linqiang Ge and Fan Liang

Sensors 2023, 23(8), 3880; https://doi.org/10.3390/s23083880 - 11 Apr 2023

Cited by 79 | Viewed by 57317

Abstract

As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects [...] Read more.

As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices. Full article

(This article belongs to the Special Issue Evolution of IoT and IIoT: Opportunities, Challenges, and Applications)

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17 pages, 4248 KiB

Open AccessEditor’s ChoiceArticle

An Automotive LiDAR Performance Test Method in Dynamic Driving Conditions

by Jewoo Park, Jihyuk Cho, Seungjoo Lee, Seokhwan Bak and Yonghwi Kim

Sensors 2023, 23(8), 3892; https://doi.org/10.3390/s23083892 - 11 Apr 2023

Cited by 8 | Viewed by 5229

Abstract

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high level of autonomous driving functions, as well as a standard Advanced Driver Assistance System (ADAS). LiDAR capabilities and signal repeatabilities under extreme weather conditions are of utmost concern in [...] Read more.

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high level of autonomous driving functions, as well as a standard Advanced Driver Assistance System (ADAS). LiDAR capabilities and signal repeatabilities under extreme weather conditions are of utmost concern in terms of the redundancy design of automotive sensor systems. In this paper, we demonstrate a performance test method for automotive LiDAR sensors that can be utilized in dynamic test scenarios. In order to measure the performance of a LiDAR sensor in a dynamic test scenario, we propose a spatio-temporal point segmentation algorithm that can separate a LiDAR signal of moving reference targets (car, square target, etc.), using an unsupervised clustering method. An automotive-graded LiDAR sensor is evaluated in four harsh environmental simulations, based on time-series environmental data of real road fleets in the USA, and four vehicle-level tests with dynamic test cases are conducted. Our test results showed that the performance of LiDAR sensors may be degraded, due to several environmental factors, such as sunlight, reflectivity of an object, cover contamination, and so on. Full article

(This article belongs to the Section Vehicular Sensing)

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23 pages, 3078 KiB

Open AccessEditor’s ChoiceReview

Review of Zinc Oxide Piezoelectric Nanogenerators: Piezoelectric Properties, Composite Structures and Power Output

by Neelesh Bhadwal, Ridha Ben Mrad and Kamran Behdinan

Sensors 2023, 23(8), 3859; https://doi.org/10.3390/s23083859 - 10 Apr 2023

Cited by 25 | Viewed by 7041

Abstract

Lead-containing piezoelectric materials typically show the highest energy conversion efficiencies, but due to their toxicity they will be limited in future applications. In their bulk form, the piezoelectric properties of lead-free piezoelectric materials are significantly lower than lead-containing materials. However, the piezoelectric properties [...] Read more.

Lead-containing piezoelectric materials typically show the highest energy conversion efficiencies, but due to their toxicity they will be limited in future applications. In their bulk form, the piezoelectric properties of lead-free piezoelectric materials are significantly lower than lead-containing materials. However, the piezoelectric properties of lead-free piezoelectric materials at the nano scale can be significantly larger than the bulk scale. This review looks at the suitability of ZnO nanostructures as candidate lead-free piezoelectric materials for use in piezoelectric nanogenerators (PENGs) based on their piezoelectric properties. Of the papers reviewed, Neodymium-doped ZnO nanorods (NRs) have a comparable piezoelectric strain constant to bulk lead-based piezoelectric materials and hence are good candidates for PENGs. Piezoelectric energy harvesters typically have low power outputs and an improvement in their power density is needed. This review systematically reviews the different composite structures of ZnO PENGs to determine the effect of composite structure on power output. State-of-the-art techniques to increase the power output of PENGs are presented. Of the PENGs reviewed, the highest power output belonged to a vertically aligned ZnO nanowire (NWs) PENG (1-3 nanowire composite) with a power output of 45.87 μW/cm² under finger tapping. Future directions of research and challenges are discussed. Full article

(This article belongs to the Special Issue MEMS Sensors and Actuators 2022–2023)

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31 pages, 6976 KiB

Open AccessEditor’s ChoiceReview

Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning

by Chiranjivi Neupane, Maisa Pereira, Anand Koirala and Kerry B. Walsh

Sensors 2023, 23(8), 3868; https://doi.org/10.3390/s23083868 - 10 Apr 2023

Cited by 16 | Viewed by 5973

Abstract

Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. This shift is now [...] Read more.

Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. This shift is now occurring for size assessment of fruit on trees, i.e., in the orchard. This review focuses on: (i) allometric relationships between fruit weight and lineal dimensions; (ii) measurement of fruit lineal dimensions with traditional tools; (iii) measurement of fruit lineal dimensions with machine vision, with attention to the issues of depth measurement and recognition of occluded fruit; (iv) sampling strategies; and (v) forward prediction of fruit size (at harvest). Commercially available capability for in-orchard fruit sizing is summarized, and further developments of in-orchard fruit sizing by machine vision are anticipated. Full article

(This article belongs to the Section Smart Agriculture)

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21 pages, 13877 KiB

Open AccessEditor’s ChoiceArticle

Recognition and Counting of Apples in a Dynamic State Using a 3D Camera and Deep Learning Algorithms for Robotic Harvesting Systems

by R. M. Rasika D. Abeyrathna, Victor Massaki Nakaguchi, Arkar Minn and Tofael Ahamed

Sensors 2023, 23(8), 3810; https://doi.org/10.3390/s23083810 - 7 Apr 2023

Cited by 16 | Viewed by 3985

Abstract

Recognition and 3D positional estimation of apples during harvesting from a robotic platform in a moving vehicle are still challenging. Fruit clusters, branches, foliage, low resolution, and different illuminations are unavoidable and cause errors in different environmental conditions. Therefore, this research aimed to [...] Read more.

Recognition and 3D positional estimation of apples during harvesting from a robotic platform in a moving vehicle are still challenging. Fruit clusters, branches, foliage, low resolution, and different illuminations are unavoidable and cause errors in different environmental conditions. Therefore, this research aimed to develop a recognition system based on training datasets from an augmented, complex apple orchard. The recognition system was evaluated using deep learning algorithms established from a convolutional neural network (CNN). The dynamic accuracy of the modern artificial neural networks involving 3D coordinates for deploying robotic arms at different forward-moving speeds from an experimental vehicle was investigated to compare the recognition and tracking localization accuracy. In this study, a Realsense D455 RGB-D camera was selected to acquire 3D coordinates of each detected and counted apple attached to artificial trees placed in the field to propose a specially designed structure for ease of robotic harvesting. A 3D camera, YOLO (You Only Look Once), YOLOv4, YOLOv5, YOLOv7, and EfficienDet state-of-the-art models were utilized for object detection. The Deep SORT algorithm was employed for tracking and counting detected apples using perpendicular, 15°, and 30° orientations. The 3D coordinates were obtained for each tracked apple when the on-board camera in the vehicle passed the reference line and was set in the middle of the image frame. To optimize harvesting at three different speeds (0.052 ms⁻¹, 0.069 ms⁻¹, and 0.098 ms⁻¹), the accuracy of 3D coordinates was compared for three forward-moving speeds and three camera angles (15°, 30°, and 90°). The mean average precision ([email protected]) values of YOLOv4, YOLOv5, YOLOv7, and EfficientDet were 0.84, 0.86, 0.905, and 0.775, respectively. The lowest root mean square error (RMSE) was 1.54 cm for the apples detected by EfficientDet at a 15° orientation and a speed of 0.098 ms⁻¹. In terms of counting apples, YOLOv5 and YOLOv7 showed a higher number of detections in outdoor dynamic conditions, achieving a counting accuracy of 86.6%. We concluded that the EfficientDet deep learning algorithm at a 15° orientation in 3D coordinates can be employed for further robotic arm development while harvesting apples in a specially designed orchard. Full article

(This article belongs to the Special Issue 3D Reconstruction with RGB-D Cameras and Multi-sensors)

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25 pages, 4252 KiB

Open AccessEditor’s ChoiceReview

Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality

by Ananya Hazarika and Mehdi Rahmati

Sensors 2023, 23(7), 3682; https://doi.org/10.3390/s23073682 - 2 Apr 2023

Cited by 25 | Viewed by 9313

Abstract

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of [...] Read more.

Augmented reality and virtual reality technologies are witnessing an evolutionary change in the 5G and Beyond (5GB) network due to their promising ability to enable an immersive and interactive environment by coupling the virtual world with the real one. However, the requirement of low-latency connectivity, which is defined as the end-to-end delay between the action and the reaction, is very crucial to leverage these technologies for a high-quality immersive experience. This paper provides a comprehensive survey and detailed insight into various advantageous approaches from the hardware and software perspectives, as well as the integration of 5G technology, towards 5GB, in enabling a low-latency environment for AR and VR applications. The contribution of 5GB systems as an outcome of several cutting-edge technologies, such as massive multiple-input, multiple-output (mMIMO) and millimeter wave (mmWave), along with the utilization of artificial intelligence (AI) and machine learning (ML) techniques towards an ultra-low-latency communication system, is also discussed in this paper. The potential of using a visible-light communications (VLC)-guided beam through a learning algorithm for a futuristic, evolved immersive experience of augmented and virtual reality with the ultra-low-latency transmission of multi-sensory tracking information with an optimal scheduling policy is discussed in this paper. Full article

(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)

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37 pages, 5055 KiB

Open AccessEditor’s ChoiceReview

A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

by Pengsu Mao, Haoran Li and Zhibin Yu

Sensors 2023, 23(7), 3673; https://doi.org/10.3390/s23073673 - 31 Mar 2023

Cited by 19 | Viewed by 6809

Abstract

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already [...] Read more.

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect’s input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development. Full article

(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))

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15 pages, 3868 KiB

Open AccessEditor’s ChoiceArticle

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

by Shenglin Li and Hwan-Sik Yoon

Sensors 2023, 23(7), 3661; https://doi.org/10.3390/s23073661 - 31 Mar 2023

Cited by 4 | Viewed by 4584

Abstract

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors [...] Read more.

Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections. Full article

(This article belongs to the Special Issue Applications of Machine Learning in Automotive Engineering)

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12 pages, 2256 KiB

Open AccessEditor’s ChoiceArticle

Towards the Use of Individual Fluorescent Nanoparticles as Ratiometric Sensors: Spectral Robustness of Ultrabright Nanoporous Silica Nanoparticles

by Mahshid Iraniparast, Berney Peng and Igor Sokolov

Sensors 2023, 23(7), 3471; https://doi.org/10.3390/s23073471 - 26 Mar 2023

Cited by 3 | Viewed by 1889

Abstract

Here we address an important roadblock that prevents the use of bright fluorescent nanoparticles as individual ratiometric sensors: the possible variation of fluorescence spectra between individual nanoparticles. Ratiometric measurements using florescent dyes have shown their utility in measuring the spatial distribution of temperature, [...] Read more.

Here we address an important roadblock that prevents the use of bright fluorescent nanoparticles as individual ratiometric sensors: the possible variation of fluorescence spectra between individual nanoparticles. Ratiometric measurements using florescent dyes have shown their utility in measuring the spatial distribution of temperature, acidity, and concentration of various ions. However, the dyes have a serious limitation in their use as sensors; namely, their fluorescent spectra can change due to interactions with the surrounding dye. Encapsulation of the d, e in a porous material can solve this issue. Recently, we demonstrated the use of ultrabright nanoporous silica nanoparticles (UNSNP) to measure temperature and acidity. The particles have at least two kinds of encapsulated dyes. Ultrahigh brightness of the particles allows measuring of the signal of interest at the single particle level. However, it raises the problem of spectral variation between particles, which is impossible to control at the nanoscale. Here, we study spectral variations between the UNSNP which have two different encapsulated dyes: rhodamine R6G and RB. The dyes can be used to measure temperature. We synthesized these particles using three different ratios of the dyes. We measured the spectra of individual nanoparticles and compared them with simulations. We observed a rather small variation of fluorescence spectra between individual UNSNP, and the spectra were in very good agreement with the results of our simulations. Thus, one can conclude that individual UNSNP can be used as effective ratiometric sensors. Full article

(This article belongs to the Section Sensor Materials)

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27 pages, 10065 KiB

Open AccessEditor’s ChoiceReview

Advances in Multicore Fiber Interferometric Sensors

by Yucheng Yao, Zhiyong Zhao and Ming Tang

Sensors 2023, 23(7), 3436; https://doi.org/10.3390/s23073436 - 24 Mar 2023

Cited by 13 | Viewed by 3593

Abstract

In this paper, a review of multicore fiber interferometric sensors is given. Due to the specificity of fiber structure, i.e., multiple cores integrated into only one fiber cladding, multicore fiber (MCF) interferometric sensors exhibit many desirable characteristics compared with traditional fiber interferometric sensors [...] Read more.

In this paper, a review of multicore fiber interferometric sensors is given. Due to the specificity of fiber structure, i.e., multiple cores integrated into only one fiber cladding, multicore fiber (MCF) interferometric sensors exhibit many desirable characteristics compared with traditional fiber interferometric sensors based on single-core fibers, such as structural and functional diversity, high integration, space-division multiplexing capacity, etc. Thanks to the unique advantages, e.g., simple fabrication, compact size, and good robustness, MCF interferometric sensors have been developed to measure various physical and chemical parameters such as temperature, strain, curvature, refractive index, vibration, flow, torsion, etc., among which the extraordinary vector-bending sensing has also been extensively studied by making use of the differential responses between different cores of MCFs. In this paper, different types of MCF interferometric sensors and recent developments are comprehensively reviewed. The basic configurations and operating principles are introduced for each interferometric structure, and, eventually, the performances of various MCF interferometric sensors for different applications are compared, including curvature sensing, vibration sensing, temperature sensing, and refractive index sensing. Full article

(This article belongs to the Special Issue State-of-the-Art Optical Sensors Technology in China)

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23 pages, 18382 KiB

Open AccessEditor’s ChoiceArticle

A Concurrent Framework for Constrained Inverse Kinematics of Minimally Invasive Surgical Robots

by Jacinto Colan, Ana Davila, Khusniddin Fozilov and Yasuhisa Hasegawa

Sensors 2023, 23(6), 3328; https://doi.org/10.3390/s23063328 - 22 Mar 2023

Cited by 14 | Viewed by 3138

Abstract

Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot’s [...] Read more.

Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot’s motion and the accuracy of its movements. In particular, the inverse kinematics (IK) problem is critical for robot-assisted minimally invasive surgery (RMIS), where satisfying the remote center of motion (RCM) constraint is essential to prevent tissue damage at the incision point. Several IK strategies have been proposed for RMIS, including classical inverse Jacobian IK and optimization-based approaches. However, these methods have limitations and perform differently depending on the kinematic configuration. To address these challenges, we propose a novel concurrent IK framework that combines the strengths of both approaches and explicitly incorporates RCM constraints and joint limits into the optimization process. In this paper, we present the design and implementation of concurrent inverse kinematics solvers, as well as experimental validation in both simulation and real-world scenarios. Concurrent IK solvers outperform single-method solvers, achieving a 100% solve rate and reducing the IK solving time by up to 85% for an endoscope positioning task and 37% for a tool pose control task. In particular, the combination of an iterative inverse Jacobian method with a hierarchical quadratic programming method showed the highest average solve rate and lowest computation time in real-world experiments. Our results demonstrate that concurrent IK solving provides a novel and effective solution to the constrained IK problem in RMIS applications. Full article

(This article belongs to the Special Issue Sensor-Based Surgical and Medical Robotics: Challenges and Opportunities)

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14 pages, 8451 KiB

Open AccessEditor’s ChoiceArticle

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study

by Ghena Hammour and Danilo P. Mandic

Sensors 2023, 23(6), 3319; https://doi.org/10.3390/s23063319 - 21 Mar 2023

Cited by 11 | Viewed by 11699

Abstract

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 [...] Read more.

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring. Full article

(This article belongs to the Section Wearables)

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40 pages, 6479 KiB

Open AccessEditor’s ChoiceReview

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

by Kareem Eltouny, Mohamed Gomaa and Xiao Liang

Sensors 2023, 23(6), 3290; https://doi.org/10.3390/s23063290 - 20 Mar 2023

Cited by 35 | Viewed by 8658

Abstract

Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the [...] Read more.

Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods. Full article

(This article belongs to the Special Issue Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience)

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15 pages, 9351 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Study on the Effects of Spray Coating Methods and Substrates on Polyurethane/Carbon Nanofiber Sensors

by Mounika Chowdary Karlapudi, Mostafa Vahdani, Sheyda Mirjalali Bandari, Shuhua Peng and Shuying Wu

Sensors 2023, 23(6), 3245; https://doi.org/10.3390/s23063245 - 19 Mar 2023

Cited by 9 | Viewed by 2672

Abstract

Thermoplastic polyurethane (TPU) has been widely used as the elastic polymer substrate to be combined with conductive nanomaterials to develop stretchable strain sensors for a variety of applications such as health monitoring, smart robotics, and e-skins. However, little research has been reported on [...] Read more.

Thermoplastic polyurethane (TPU) has been widely used as the elastic polymer substrate to be combined with conductive nanomaterials to develop stretchable strain sensors for a variety of applications such as health monitoring, smart robotics, and e-skins. However, little research has been reported on the effects of deposition methods and the form of TPU on their sensing performance. This study intends to design and fabricate a durable, stretchable sensor based on composites of thermoplastic polyurethane and carbon nanofibers (CNFs) by systematically investigating the influences of TPU substrates (i.e., either electrospun nanofibers or solid thin film) and spray coating methods (i.e., either air-spray or electro-spray). It is found that the sensors with electro-sprayed CNFs conductive sensing layers generally show a higher sensitivity, while the influence of the substrate is not significant and there is no clear and consistent trend. The sensor composed of a TPU solid thin film with electro-sprayed CNFs exhibits an optimal performance with a high sensitivity (gauge factor ~28.2) in a strain range of 0–80%, a high stretchability of up to 184%, and excellent durability. The potential application of these sensors in detecting body motions has been demonstrated, including finger and wrist-joint movements, by using a wooden hand. Full article

(This article belongs to the Special Issue Use of Smart Wearable Sensors and AI Methods in Providing P4 Medicine)

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19 pages, 10645 KiB

Open AccessEditor’s ChoiceArticle

Pipeline Leakage Detection Using Acoustic Emission and Machine Learning Algorithms

by Niamat Ullah, Zahoor Ahmed and Jong-Myon Kim

Sensors 2023, 23(6), 3226; https://doi.org/10.3390/s23063226 - 17 Mar 2023

Cited by 31 | Viewed by 7559

Abstract

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak [...] Read more.

Pipelines play a significant role in liquid and gas resource distribution. Pipeline leaks, however, result in severe consequences, such as wasted resources, risks to community health, distribution downtime, and economic loss. An efficient autonomous leakage detection system is clearly required. The recent leak diagnosis capability of acoustic emission (AE) technology has been well demonstrated. This article proposes a machine learning-based platform for leakage detection for various pinhole-sized leaks using the AE sensor channel information. Statistical measures, such as kurtosis, skewness, mean value, mean square, root mean square (RMS), peak value, standard deviation, entropy, and frequency spectrum features, were extracted from the AE signal as features to train the machine learning models. An adaptive threshold-based sliding window approach was used to retain the properties of both bursts and continuous-type emissions. First, we collected three AE sensor datasets and extracted 11 time domain and 14 frequency domain features for a one-second window for each AE sensor data category. The measurements and their associated statistics were transformed into feature vectors. Subsequently, these feature data were utilized for training and evaluating supervised machine learning models to detect leaks and pinhole-sized leaks. Several widely known classifiers, such as neural networks, decision trees, random forests, and k-nearest neighbors, were evaluated using the four datasets regarding water and gas leakages at different pressures and pinhole leak sizes. We achieved an exceptional overall classification accuracy of 99%, providing reliable and effective results that are suitable for the implementation of the proposed platform. Full article

(This article belongs to the Special Issue Sensors and Intelligent Data Processing for Condition Monitoring and Fault Diagnosis)

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15 pages, 3960 KiB

Open AccessEditor’s ChoiceArticle

A Wearable Insole System to Measure Plantar Pressure and Shear for People with Diabetes

by Jinghua Tang, Dan L. Bader, David Moser, Daniel J. Parker, Saeed Forghany, Christopher J. Nester and Liudi Jiang

Sensors 2023, 23(6), 3126; https://doi.org/10.3390/s23063126 - 15 Mar 2023

Cited by 15 | Viewed by 6553

Abstract

Pressure coupled with shear stresses are the critical external factors for diabetic foot ulceration assessment and prevention. To date, a wearable system capable of measuring in-shoe multi-directional stresses for out-of-lab analysis has been elusive. The lack of an insole system capable of measuring [...] Read more.

Pressure coupled with shear stresses are the critical external factors for diabetic foot ulceration assessment and prevention. To date, a wearable system capable of measuring in-shoe multi-directional stresses for out-of-lab analysis has been elusive. The lack of an insole system capable of measuring plantar pressure and shear hinders the development of an effective foot ulcer prevention solution that could be potentially used in a daily living environment. This study reports the development of a first-of-its-kind sensorised insole system and its evaluation in laboratory settings and on human participants, indicating its potential as a wearable technology to be used in real-world applications. Laboratory evaluation revealed that the linearity error and accuracy error of the sensorised insole system were up to 3% and 5%, respectively. When evaluated on a healthy participant, change in footwear resulted in approximately 20%, 75% and 82% change in pressure, medial–lateral and anterior–posterior shear stress, respectively. When evaluated on diabetic participants, no notable difference in peak plantar pressure, as a result of wearing the sensorised insole, was measured. The preliminary results showed that the performance of the sensorised insole system is comparable to previously reported research devices. The system has adequate sensitivity to assist footwear assessment relevant to foot ulcer prevention and is safe to use for people with diabetes. The reported insole system presents the potential to help assess diabetic foot ulceration risk in a daily living environment underpinned by wearable pressure and shear sensing technologies. Full article

(This article belongs to the Special Issue Saving Limbs and Lives-Sensing and Monitoring Technologies for the Diabetic Foot)

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22 pages, 2419 KiB

Open AccessEditor’s ChoiceReview

An Extended AI-Experience: Industry 5.0 in Creative Product Innovation

by Amy Grech, Jörn Mehnen and Andrew Wodehouse

Sensors 2023, 23(6), 3009; https://doi.org/10.3390/s23063009 - 10 Mar 2023

Cited by 12 | Viewed by 4444

Abstract

Creativity plays a significant role in competitive product ideation. With the increasing emergence of Virtual Reality (VR) and Artificial Intelligence (AI) technologies, the link between such technologies and product ideation is explored in this research to assist and augment creative scenarios in the [...] Read more.

Creativity plays a significant role in competitive product ideation. With the increasing emergence of Virtual Reality (VR) and Artificial Intelligence (AI) technologies, the link between such technologies and product ideation is explored in this research to assist and augment creative scenarios in the engineering field. A bibliographic analysis is performed to review relevant fields and their relationships. This is followed by a review of current challenges in group ideation and state-of-the-art technologies with the aim of addressing them in this study. This knowledge is applied to the transformation of current ideation scenarios into a virtual environment using AI. The aim is to augment designers’ creative experiences, a core value of Industry 5.0 that focuses on human-centricity, social and ecological benefits. For the first time, this research reclaims brainstorming as a challenging and inspiring activity where participants are fully engaged through a combination of AI and VR technologies. This activity is enhanced through three key areas: facilitation, stimulation, and immersion. These areas are integrated through intelligent team moderation, enhanced communication techniques, and access to multi-sensory stimuli during the collaborative creative process, therefore providing a platform for future research into Industry 5.0 and smart product development. Full article

(This article belongs to the Special Issue Human-Centred Smart Manufacturing - Industry 5.0)

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15 pages, 32729 KiB

Open AccessEditor’s ChoiceArticle

Colorimetric and Fluorescent Sensing of Copper Ions in Water through o-Phenylenediamine-Derived Carbon Dots

by Roberto Pizzoferrato, Ramanand Bisauriya, Simonetta Antonaroli, Marcello Cabibbo and Artur J. Moro

Sensors 2023, 23(6), 3029; https://doi.org/10.3390/s23063029 - 10 Mar 2023

Cited by 16 | Viewed by 2451

Abstract

Fluorescent nitrogen and sulfur co-doped carbon dots (NSCDs) were synthesized using a simple one-step hydrothermal method starting from o-phenylenediamine (OPD) and ammonium sulfide. The prepared NSCDs presented a selective dual optical response to Cu(II) in water through the arising of an absorption band [...] Read more.

Fluorescent nitrogen and sulfur co-doped carbon dots (NSCDs) were synthesized using a simple one-step hydrothermal method starting from o-phenylenediamine (OPD) and ammonium sulfide. The prepared NSCDs presented a selective dual optical response to Cu(II) in water through the arising of an absorption band at 660 nm and simultaneous fluorescence enhancement at 564 nm. The first effect was attributed to formation of cuprammonium complexes through coordination with amino functional groups of NSCDs. Alternatively, fluorescence enhancement can be explained by the oxidation of residual OPD bound to NSCDs. Both absorbance and fluorescence showed a linear increase with an increase of Cu(II) concentration in the range 1–100 µM, with the lowest detection limit of 100 nM and 1 µM, respectively. NSCDs were successfully incorporated in a hydrogel agarose matrix for easier handling and application to sensing. The formation of cuprammonium complexes was strongly hampered in an agarose matrix while oxidation of OPD was still effective. As a result, color variations could be perceived both under white light and UV light for concentrations as low as 10 µM. Since these color changes were similarly perceived in tap and lake water samples, the present method could be a promising candidate for simple, cost-effective visual monitoring of copper onsite. Full article

(This article belongs to the Collection Optical Chemical Sensors: Design and Applications)

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18 pages, 3601 KiB

Open AccessEditor’s ChoiceArticle

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

by Bach-Tung Pham, Phuong Thi Le, Tzu-Chiang Tai, Yi-Chiung Hsu, Yung-Hui Li and Jia-Ching Wang

Sensors 2023, 23(6), 2993; https://doi.org/10.3390/s23062993 - 9 Mar 2023

Cited by 10 | Viewed by 5876

Abstract

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on [...] Read more.

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset. Full article

(This article belongs to the Special Issue Sensors and Signal Processing for Biomedical Application)

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29 pages, 947 KiB

Open AccessEditor’s ChoiceReview

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

by Marianne Boyer, Laurent Bouyer, Jean-Sébastien Roy and Alexandre Campeau-Lecours

Sensors 2023, 23(6), 2927; https://doi.org/10.3390/s23062927 - 8 Mar 2023

Cited by 24 | Viewed by 10103

Abstract

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and [...] Read more.

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application. Full article

(This article belongs to the Special Issue EMG Sensors and Signal Processing Technologies)

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17 pages, 17089 KiB

Open AccessEditor’s ChoiceArticle

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain

by Liangliang Li, Ming Lv, Zhenhong Jia and Hongbing Ma

Sensors 2023, 23(6), 2888; https://doi.org/10.3390/s23062888 - 7 Mar 2023

Cited by 17 | Viewed by 1870

Abstract

Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel [...] Read more.

Multi-focus image fusion plays an important role in the application of computer vision. In the process of image fusion, there may be blurring and information loss, so it is our goal to obtain high-definition and information-rich fusion images. In this paper, a novel multi-focus image fusion method via local energy and sparse representation in the shearlet domain is proposed. The source images are decomposed into low- and high-frequency sub-bands according to the shearlet transform. The low-frequency sub-bands are fused by sparse representation, and the high-frequency sub-bands are fused by local energy. The inverse shearlet transform is used to reconstruct the fused image. The Lytro dataset with 20 pairs of images is used to verify the proposed method, and 8 state-of-the-art fusion methods and 8 metrics are used for comparison. According to the experimental results, our method can generate good performance for multi-focus image fusion. Full article

(This article belongs to the Section Sensing and Imaging)

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24 pages, 783 KiB

Open AccessEditor’s ChoiceReview

Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions

by Mohammed Ayalew Belay, Sindre Stenen Blakseth, Adil Rasheed and Pierluigi Salvo Rossi

Sensors 2023, 23(5), 2844; https://doi.org/10.3390/s23052844 - 6 Mar 2023

Cited by 21 | Viewed by 15119

Abstract

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of [...] Read more.

The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted. Full article

(This article belongs to the Special Issue Signal Processing and AI in Sensor Networks and IoT)

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