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Editor’s Choice Articles

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

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16 pages, 377 KiB  
Article
Security at the Edge for Resource-Limited IoT Devices
by Daniele Canavese, Luca Mannella, Leonardo Regano and Cataldo Basile
Sensors 2024, 24(2), 590; https://doi.org/10.3390/s24020590 - 17 Jan 2024
Cited by 23 | Viewed by 6812
Abstract
The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, [...] Read more.
The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy’s modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems. Full article
(This article belongs to the Special Issue Emerging IoT Technologies for Smart Environments, 3rd Edition)
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27 pages, 5246 KiB  
Article
On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications
by Aswin K. Ramasubramanian, Marios Kazasidis, Barry Fay and Nikolaos Papakostas
Sensors 2024, 24(2), 578; https://doi.org/10.3390/s24020578 - 17 Jan 2024
Cited by 6 | Viewed by 2397
Abstract
Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that [...] Read more.
Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human–robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors’ performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator’s poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance. Full article
(This article belongs to the Special Issue Multi-sensor for Human Activity Recognition: 2nd Edition)
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14 pages, 1087 KiB  
Article
Wearable Sensor to Monitor Quality of Upper Limb Task Practice for Stroke Survivors at Home
by Na Jin Seo, Kristen Coupland, Christian Finetto and Gabrielle Scronce
Sensors 2024, 24(2), 554; https://doi.org/10.3390/s24020554 - 16 Jan 2024
Cited by 4 | Viewed by 3149
Abstract
Many stroke survivors experience persistent upper extremity impairment that limits performance in activities of daily living. Upper limb recovery requires high repetitions of task-specific practice. Stroke survivors are often prescribed task practices at home to supplement rehabilitation therapy. A poor quality of task [...] Read more.
Many stroke survivors experience persistent upper extremity impairment that limits performance in activities of daily living. Upper limb recovery requires high repetitions of task-specific practice. Stroke survivors are often prescribed task practices at home to supplement rehabilitation therapy. A poor quality of task practices, such as the use of compensatory movement patterns, results in maladaptive neuroplasticity and suboptimal motor recovery. There currently lacks a tool for the remote monitoring of movement quality of stroke survivors’ task practices at home. The objective of this study was to evaluate the feasibility of classifying movement quality at home using a wearable IMU. Nineteen stroke survivors wore an IMU sensor on the paretic wrist and performed four functional upper limb tasks in the lab and later at home while videorecording themselves. The lab data served as reference data to classify home movement quality using dynamic time warping. Incorrect and correct movement quality was labeled by a therapist. The home task practice movement quality was classified with an accuracy of 92% and F1 score of 0.95 for all tasks combined. Movement types contributing to misclassification were further investigated. The results support the feasibility of a home movement quality monitoring system to assist with upper limb rehabilitation post stroke. Full article
(This article belongs to the Special Issue Smart Sensing Technology for Human Activity Recognition)
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20 pages, 58464 KiB  
Article
Comparison of Shoulder Range of Motion Quantified with Mobile Phone Video-Based Skeletal Tracking and 3D Motion Capture—Preliminary Study
by Wolbert van den Hoorn, Maxence Lavaill, Kenneth Cutbush, Ashish Gupta and Graham Kerr
Sensors 2024, 24(2), 534; https://doi.org/10.3390/s24020534 - 15 Jan 2024
Cited by 5 | Viewed by 2816
Abstract
Background: The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Methods: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation [...] Read more.
Background: The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Methods: Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple’s vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis. Results: We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements. Conclusions: Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation)
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33 pages, 2529 KiB  
Review
Wearable Sensors as a Preoperative Assessment Tool: A Review
by Aron Syversen, Alexios Dosis, David Jayne and Zhiqiang Zhang
Sensors 2024, 24(2), 482; https://doi.org/10.3390/s24020482 - 12 Jan 2024
Cited by 12 | Viewed by 3723
Abstract
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not [...] Read more.
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool. Full article
(This article belongs to the Special Issue Biomedical Electronics and Wearable Systems)
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13 pages, 4679 KiB  
Article
Flexible Strain Sensors Based on Bionic Parallel Vein-like Structures for Human Motion Monitoring
by Boshuo Yin, Furong Liu, Qingyuan Chen, Ming Liu and Feiying Wang
Sensors 2024, 24(2), 468; https://doi.org/10.3390/s24020468 - 12 Jan 2024
Cited by 5 | Viewed by 1929
Abstract
In recent years, strain sensors have penetrated various fields. The capability of sensors to convert physical signals into electrical signals is of great importance in healthcare. However, it is still challenging to obtain sensors with high sensitivity, large operating range and low cost. [...] Read more.
In recent years, strain sensors have penetrated various fields. The capability of sensors to convert physical signals into electrical signals is of great importance in healthcare. However, it is still challenging to obtain sensors with high sensitivity, large operating range and low cost. In this paper, a stretchable strain sensor made of a double-layer conductive network, including a biomimetic multilayer graphene-Ecoflex (MLG-Ecoflex) substrate and a multilayer graphene-carbon nanotube (MLG-CNT) composite up-layer was developed. The combined action of the two layers led to an excellent performance with an operating range of up to 580% as well as a high sensitivity (gauge factor (GFmax) of 1517.94). In addition, a pressure sensor was further designed using the bionic vein-like structure with a multi-layer stacking of MLG-Ecoflex/MLG-CNT/MLG-Ecoflex to obtain a relatively high deformation along the direction of thickness. The device presented a high sensing performance (up to a sensitivity of 0.344 kPa−1) capable of monitoring small movements of the human body such as vocalizations and gestures. The good performance of the sensors together with a simple fabrication procedure (flip-molding) make it of potential use for some applications, for example human health monitoring and other areas of human interaction. Full article
(This article belongs to the Special Issue Flexible Wearable Sensors)
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13 pages, 20072 KiB  
Article
An IoT System for Air Pollution Monitoring with Safe Data Transmission
by Janusz Bobulski, Sabina Szymoniak and Kamila Pasternak
Sensors 2024, 24(2), 445; https://doi.org/10.3390/s24020445 - 11 Jan 2024
Cited by 13 | Viewed by 12379
Abstract
Air pollution has become a global issue due to rapid urbanization and industrialization. Bad air quality is Europe’s most significant environmental health risk, causing serious health problems. External air pollution is not the only issue; internal air pollution is just as severe and [...] Read more.
Air pollution has become a global issue due to rapid urbanization and industrialization. Bad air quality is Europe’s most significant environmental health risk, causing serious health problems. External air pollution is not the only issue; internal air pollution is just as severe and can also lead to adverse health outcomes. IoT is a practical approach for monitoring and publishing real-time air quality information. Numerous IoT-based air quality monitoring systems have been proposed using micro-sensors for data collection. These systems are designed for outdoor air quality monitoring. They use sensors to measure air quality parameters such as CO2, CO, PM10, NO2, temperature, and humidity. The data are acquired with a set of sensors placed on an electric car. They are then sent to the server. Users can subscribe to the list and receive information about local pollution. This system allows real-time localized air quality monitoring and sending data to customers. The work additionally presents a secure data transmission protocol ensuring system security. This protocol provides system-wide attack resiliency and interception, which is what existing solutions do not offer. Full article
(This article belongs to the Special Issue Sensors and Pattern Recognition Methods for Security and Industrial Applications (SPR-SIA)—2nd Edition)
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20 pages, 5668 KiB  
Article
An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors
by Qi Liu, Yongchao Yu, Boon Siew Han and Wei Zhou
Sensors 2024, 24(2), 443; https://doi.org/10.3390/s24020443 - 11 Jan 2024
Cited by 5 | Viewed by 2262
Abstract
The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white [...] Read more.
The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectral subtraction (ISS) method is proposed to enhance its denoising performance. In the ISS method, a reference noise signal measured by the same sensing system without working loads is considered the estimated noise, the same sliding window is used to divide the power spectrums of the measured and reference noise signals into multiple frequency bands, and the formula of spectral subtraction in the standard SS method is modified. A simulation analysis and an experiment are executed by using simulated signals and establishing a vibration test rig based on the FBG sensor, respectively. The statistical results demonstrate the effectiveness and feasibility of the ISS method in simultaneously eliminating white Gaussian noise and multifrequency interference while well maintaining the useful information components. Full article
(This article belongs to the Section Optical Sensors)
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18 pages, 1047 KiB  
Review
Recent Trends in Chemical Sensors for Detecting Toxic Materials
by Yeonhong Kim, Yangwon Jeon, Minyoung Na, Soon-Jin Hwang and Youngdae Yoon
Sensors 2024, 24(2), 431; https://doi.org/10.3390/s24020431 - 10 Jan 2024
Cited by 15 | Viewed by 6468
Abstract
Industrial development has led to the widespread production of toxic materials, including carcinogenic, mutagenic, and toxic chemicals. Even with strict management and control measures, such materials still pose threats to human health. Therefore, convenient chemical sensors are required for toxic chemical monitoring, such [...] Read more.
Industrial development has led to the widespread production of toxic materials, including carcinogenic, mutagenic, and toxic chemicals. Even with strict management and control measures, such materials still pose threats to human health. Therefore, convenient chemical sensors are required for toxic chemical monitoring, such as optical, electrochemical, nanomaterial-based, and biological-system-based sensors. Many existing and new chemical sensors have been developed, as well as new methods based on novel technologies for detecting toxic materials. The emergence of material sciences and advanced technologies for fabrication and signal-transducing processes has led to substantial improvements in the sensing elements for target recognition and signal-transducing elements for reporting interactions between targets and sensing elements. Many excellent reviews have effectively summarized the general principles and applications of different types of chemical sensors. Therefore, this review focuses on chemical sensor advancements in terms of the sensing and signal-transducing elements, as well as more recent achievements in chemical sensors for toxic material detection. We also discuss recent trends in biosensors for the detection of toxic materials. Full article
(This article belongs to the Special Issue Chemical Sensors for Toxic Chemical Detection)
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30 pages, 7796 KiB  
Article
Configuration Investigation, Structure Design and Deployment Dynamics of Rigid-Reflector Spaceborne Antenna with Deviation-Angle Panel
by Guodong Tan, Kaiqi Liu, Xuechao Duan, Qunbiao Wang, Dan Zhang, Dongwu Yang and Dingchao Niu
Sensors 2024, 24(2), 385; https://doi.org/10.3390/s24020385 - 8 Jan 2024
Cited by 1 | Viewed by 2579
Abstract
Rigid-reflector spaceborne antennas (RRSAs) are well-suited for high-frequency application scenarios due to their high surface accuracy. However, the low stowing efficiency of RRSAs limits the aperture diameters and further deteriorates the electromagnetic (EM) performances in terms of gain, resolution and sensitivity. After conducting [...] Read more.
Rigid-reflector spaceborne antennas (RRSAs) are well-suited for high-frequency application scenarios due to their high surface accuracy. However, the low stowing efficiency of RRSAs limits the aperture diameters and further deteriorates the electromagnetic (EM) performances in terms of gain, resolution and sensitivity. After conducting systematic feature analysis with respect to several typical RRSAs, we propose a novel type of RRSA to solve the aforementioned problems. Inspired by the pose adjustment process for a higher stowing efficiency of traditional RRSAs, we also propose a new segmentation scheme of a reflective surface consisting of a deviation-angle panel that facilitates a higher stowing efficiency. Based on this scheme, its corresponding folded configuration is implemented by combining Euler’s rotation theorem and the idea of parameter identification. In addition, we also compare the stowing efficiency of different schemes to verify the high stowing efficiency of the configuration. Finally, we perform mechanism/structure design and deployment dynamics to demonstrate that the antenna can be successfully deployed and exhibits excellent deployment quality. The results suggest that the proposed antenna possesses higher stowing efficiency than that of the same kind, with a stable deployment and interference-free process. Full article
(This article belongs to the Special Issue Novel Antennas for Wireless Communication and Intelligent Sensing)
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37 pages, 625 KiB  
Review
Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions
by Easa Alalwany and Imad Mahgoub
Sensors 2024, 24(2), 368; https://doi.org/10.3390/s24020368 - 8 Jan 2024
Cited by 38 | Viewed by 6771
Abstract
The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and [...] Read more.
The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement. Full article
(This article belongs to the Section Sensor Networks)
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20 pages, 1936 KiB  
Article
BLE-Based Indoor Localization: Analysis of Some Solutions for Performance Improvement
by Filippo Milano, Helbert da Rocha, Marco Laracca, Luigi Ferrigno, António Espírito Santo, José Salvado and Vincenzo Paciello
Sensors 2024, 24(2), 376; https://doi.org/10.3390/s24020376 - 8 Jan 2024
Cited by 12 | Viewed by 4914
Abstract
This paper addresses indoor localization using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, adopting the Received Signal Strength Indicator (RSSI) for the distance estimation. Different solutions have been proposed in the scientific literature to improve the performance of this [...] Read more.
This paper addresses indoor localization using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, adopting the Received Signal Strength Indicator (RSSI) for the distance estimation. Different solutions have been proposed in the scientific literature to improve the performance of this localization technology, but a detailed performance comparison of these solutions is still missing. The aim of this work is to make an experimental analysis combining different solutions for the performance improvement of BLE-based indoor localization, identifying the most effective one. The considered solutions involve different RSSI signals’ conditioning, the use of anchor–tag distance estimation techniques, as well as approaches for estimating the unknown tag position. An experimental campaign was executed in a complex indoor environment, characterized by the continuous presence in the movement of working staff and numerous obstacles. The exploitation of multichannel transmission using RSSI signal aggregation techniques showed the greater performance improvement of the localization system, reducing the positioning error (from 1.5 m to about 1 m). The other examined solutions have shown a lesser impact in the performance improvement with a decrease or an increase in the positioning errors, depending on the considered combination of the adopted solutions. Full article
(This article belongs to the Special Issue Indoor and Outdoor Sensor Networks for Positioning and Localization)
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17 pages, 425 KiB  
Article
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 45 | Viewed by 6279
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  
Review
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 75 | Viewed by 6613
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  
Review
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 7 | Viewed by 4675
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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38 pages, 3819 KiB  
Review
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 77 | Viewed by 19235
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  
Article
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 3 | Viewed by 1912
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  
Review
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 32 | Viewed by 7162
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  
Review
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 4 | Viewed by 2731
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  
Review
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 18 | Viewed by 4975
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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20 pages, 431 KiB  
Article
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 49 | Viewed by 6535
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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28 pages, 11710 KiB  
Article
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 15 | Viewed by 3643
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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38 pages, 24263 KiB  
Review
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 77 | Viewed by 15635
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  
Article
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 23 | Viewed by 3813
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  
Article
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 10 | Viewed by 2609
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  
Article
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 11 | Viewed by 4924
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  
Article
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 11 | Viewed by 9175
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  
Review
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 44 | Viewed by 10471
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  
Review
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 42 | Viewed by 16577
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  
Review
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 13 | Viewed by 3750
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  
Review
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 39 | Viewed by 9972
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  
Review
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 41 | Viewed by 19125
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  
Article
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 28 | Viewed by 18349
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  
Review
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 23 | Viewed by 5227
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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18 pages, 5780 KiB  
Article
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 12 | Viewed by 2598
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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36 pages, 17376 KiB  
Article
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 16 | Viewed by 7025
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±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±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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16 pages, 2865 KiB  
Article
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 14 | Viewed by 3042
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  
Review
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 5872
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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17 pages, 4248 KiB  
Article
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 15 | Viewed by 6899
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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32 pages, 959 KiB  
Review
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 164 | Viewed by 83316
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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31 pages, 6976 KiB  
Review
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 23 | Viewed by 8534
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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23 pages, 3078 KiB  
Review
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 51 | Viewed by 10950
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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21 pages, 13877 KiB  
Article
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 32 | Viewed by 5924
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 (mAP@0.5) 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  
Review
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 61 | Viewed by 13709
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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15 pages, 3868 KiB  
Article
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 9 | Viewed by 5829
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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37 pages, 5055 KiB  
Review
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 39 | Viewed by 13604
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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12 pages, 2256 KiB  
Article
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 4 | Viewed by 2255
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  
Review
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 23 | Viewed by 4821
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  
Article
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 18 | Viewed by 4183
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  
Article
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 18 | Viewed by 14532
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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