Sensors

21 pages, 3130 KiB

Open AccessEditor’s ChoiceArticle

Large-Scale Indoor Camera Positioning Using Fiducial Markers

by Pablo García-Ruiz, Francisco J. Romero-Ramirez, Rafael Muñoz-Salinas, Manuel J. Marín-Jiménez and Rafael Medina-Carnicer

Sensors 2024, 24(13), 4303; https://doi.org/10.3390/s24134303 - 2 Jul 2024

Cited by 2 | Viewed by 2049

Abstract

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing [...] Read more.

Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology. Full article

(This article belongs to the Special Issue Sensor Fusion Applications for Navigation and Indoor Positioning)

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12 pages, 10429 KiB

Open AccessEditor’s ChoiceArticle

DATIV—Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors

by Gazi Hasanuzzaman, Tom Buchwald, Christoph Schunk, Christoph Egbers, Andreas Schröder and Uwe Hampel

Sensors 2024, 24(13), 4314; https://doi.org/10.3390/s24134314 - 2 Jul 2024

Viewed by 1464

Abstract

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, [...] Read more.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments. Full article

(This article belongs to the Special Issue Potentialities and Limits of Low-Cost Sensors for Air Quality Monitoring)

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15 pages, 5461 KiB

Open AccessEditor’s ChoiceArticle

Drone-Borne Magnetic Gradiometry in Archaeological Applications

by Filippo Accomando and Giovanni Florio

Sensors 2024, 24(13), 4270; https://doi.org/10.3390/s24134270 - 1 Jul 2024

Cited by 8 | Viewed by 2534

Abstract

The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors [...] Read more.

The use of magnetometers arranged in a gradiometer configuration offers a practical and widely used solution, particularly in archaeological applications where the sources of interest are generally shallow. Since magnetic anomalies due to archaeological remains often have low amplitudes, highly sensitive magnetic sensors are kept very close to the ground to reveal buried structures. However, the deployment of Unmanned Aerial Vehicles (UAVs) is increasingly becoming a reliable and valuable tool for the acquisition of magnetic data, providing uniform coverage of large areas and access to even very steep terrain, saving time and reducing risks. However, the application of a vertical gradiometer for drone-borne measurements is still challenging due to the instability of the system drone magnetometer in flight and noise issues due to the magnetic interference of the mobile platform or related to the oscillation of the suspended sensors. We present the implementation of a magnetic vertical gradiometer UAV system and its use in an archaeological area of Southern Italy. To reduce the magnetic and electromagnetic noise caused by the aircraft, the magnetometer was suspended 3m below the drone using ropes. A Continuous Wavelet Transform analysis of data collected in controlled tests confirmed that several characteristic power spectrum peaks occur at frequencies compatible with the magnetometer oscillations. This noise was then eliminated with a properly designed low-pass filter. The resulting drone-borne vertical gradient data compare very well with ground-based magnetic measurements collected in the same area and taken as a control dataset. Full article

(This article belongs to the Special Issue Advances on UAV-Based Sensing and Imaging)

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14 pages, 4820 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Micro-Raman Spectroscopy: A Variable Spectral Resolution Instrument Using Zoom Lens Technology

by Ivan Pavić, Nediljko Kaštelan, Arkadiusz Adamczyk and Mile Ivanda

Sensors 2024, 24(13), 4284; https://doi.org/10.3390/s24134284 - 1 Jul 2024

Viewed by 1605

Abstract

Raman spectroscopy is a powerful analytical technique based on the inelastic scattering of photons. Conventional macro-Raman spectrometers are suitable for mass analysis but often lack the spatial resolution required to accurately examine microscopic regions of interest. For this reason, the development of micro-Raman [...] Read more.

Raman spectroscopy is a powerful analytical technique based on the inelastic scattering of photons. Conventional macro-Raman spectrometers are suitable for mass analysis but often lack the spatial resolution required to accurately examine microscopic regions of interest. For this reason, the development of micro-Raman spectrometers has been driven forward. However, even with micro-Raman spectrometers, high resolution is required to gain better insight into materials that provide low-intensity Raman signals. Here, we show the development of a micro-Raman spectrometer with implemented zoom lens technology. We found that by replacing a second collimating mirror in the monochromator with a zoom lens, the spectral resolution could be continuously adjusted at different zoom factors, i.e., high resolution was achieved at a higher zoom factor and lower spectral resolution was achieved at a lower zoom factor. A quantitative analysis of a micro-Raman spectrometer was performed and the spectral resolution was analysed by FWHM using the Gaussian fit. Validation was also performed by comparing the results obtained with those of a high-grade laboratory Raman spectrometer. A quantitative analysis was also performed using the ANOVA method and by assessing the signal-to-noise ratio between the two systems. Full article

(This article belongs to the Special Issue High-Resolution Spectroscopy and Sensing)

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19 pages, 964 KiB

Open AccessEditor’s ChoiceArticle

Optimizing IoT Intrusion Detection Using Balanced Class Distribution, Feature Selection, and Ensemble Machine Learning Techniques

by Muhammad Bisri Musthafa, Samsul Huda, Yuta Kodera, Md. Arshad Ali, Shunsuke Araki, Jedidah Mwaura and Yasuyuki Nogami

Sensors 2024, 24(13), 4293; https://doi.org/10.3390/s24134293 - 1 Jul 2024

Cited by 9 | Viewed by 2975

Abstract

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial [...] Read more.

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs’ ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models’ performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of

96.92 %

and

99.77 %

and overfitting values of

0.33 %

and

0.04 %

on the two datasets, respectively. The model’s ROC is also shaped with a sharp bend, with AUC values of

0.9665

and

0.9971

for the UNSW-NB15 dataset and the NSL-KD dataset, respectively. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing—2nd Edition)

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19 pages, 7015 KiB

Open AccessEditor’s ChoiceArticle

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework

by Muhammad Farooq Siddique, Zahoor Ahmad, Niamat Ullah, Saif Ullah and Jong-Myon Kim

Sensors 2024, 24(12), 4009; https://doi.org/10.3390/s24124009 - 20 Jun 2024

Cited by 17 | Viewed by 4645

Abstract

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various [...] Read more.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

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47 pages, 3414 KiB

Open AccessEditor’s ChoiceReview

Obstacle Avoidance and Path Planning Methods for Autonomous Navigation of Mobile Robot

by Kornél Katona, Husam A. Neamah and Péter Korondi

Sensors 2024, 24(11), 3573; https://doi.org/10.3390/s24113573 - 1 Jun 2024

Cited by 26 | Viewed by 23040

Abstract

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without [...] Read more.

Path planning creates the shortest path from the source to the destination based on sensory information obtained from the environment. Within path planning, obstacle avoidance is a crucial task in robotics, as the autonomous operation of robots needs to reach their destination without collisions. Obstacle avoidance algorithms play a key role in robotics and autonomous vehicles. These algorithms enable robots to navigate their environment efficiently, minimizing the risk of collisions and safely avoiding obstacles. This article provides an overview of key obstacle avoidance algorithms, including classic techniques such as the Bug algorithm and Dijkstra’s algorithm, and newer developments like genetic algorithms and approaches based on neural networks. It analyzes in detail the advantages, limitations, and application areas of these algorithms and highlights current research directions in obstacle avoidance robotics. This article aims to provide comprehensive insight into the current state and prospects of obstacle avoidance algorithms in robotics applications. It also mentions the use of predictive methods and deep learning strategies. Full article

(This article belongs to the Special Issue Mobile Robots: Navigation, Control and Sensing)

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42 pages, 9029 KiB

Open AccessEditor’s ChoiceReview

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

by Khaled Osmani and Detlef Schulz

Sensors 2024, 24(10), 3064; https://doi.org/10.3390/s24103064 - 11 May 2024

Cited by 12 | Viewed by 9080

Abstract

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures [...] Read more.

The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 2095 KiB

Open AccessEditor’s ChoiceArticle

FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything

by Safouane El Ghazouali, Youssef Mhirit, Ali Oukhrid, Umberto Michelucci and Hichem Nouira

Sensors 2024, 24(9), 2889; https://doi.org/10.3390/s24092889 - 30 Apr 2024

Cited by 8 | Viewed by 4874

Abstract

In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline [...] Read more.

In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth maps, as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation. Full article

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

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22 pages, 679 KiB

Open AccessEditor’s ChoiceArticle

Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring

by Kulsoom S. Bughio, David M. Cook and Syed Afaq A. Shah

Sensors 2024, 24(9), 2804; https://doi.org/10.3390/s24092804 - 27 Apr 2024

Cited by 11 | Viewed by 2824

Abstract

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding [...] Read more.

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications. Full article

(This article belongs to the Section Internet of Things)

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32 pages, 2237 KiB

Open AccessEditor’s ChoiceReview

Smart Sensors and Smart Data for Precision Agriculture: A Review

by Abdellatif Soussi, Enrico Zero, Roberto Sacile, Daniele Trinchero and Marco Fossa

Sensors 2024, 24(8), 2647; https://doi.org/10.3390/s24082647 - 21 Apr 2024

Cited by 84 | Viewed by 43249

Abstract

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus [...] Read more.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies. Full article

(This article belongs to the Special Issue Feature Review Papers in Intelligent Sensors)

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17 pages, 2540 KiB

Open AccessEditor’s ChoiceArticle

Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control

by Vahid Mohammadi, Ramin Shahbad, Mojtaba Hosseini, Mohammad Hossein Gholampour, Saeed Shiry Ghidary, Farshid Najafi and Ahad Behboodi

Sensors 2024, 24(8), 2585; https://doi.org/10.3390/s24082585 - 18 Apr 2024

Cited by 10 | Viewed by 4055

Abstract

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic [...] Read more.

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive resistor (FSR) sensors with the average current of servomotors to enhance both the speed and accuracy of grasping. Therefore, we aim to create a grasping mechanism that is more dexterous than grippers and less complex than robotic hands. To achieve this goal, we designed a two-finger robotic hand with two degrees of freedom on each finger; an FSR was integrated into each fingertip to enable object categorization and the detection of the initial contact. Subsequently, servomotor currents were monitored continuously to implement impedance control and maintain the grasp of objects in a wide range of stiffness. The proposed hand categorized objects’ stiffness upon initial contact and exerted accurate force by fusing FSR and the motor currents. An experimental test was conducted using a Yale–CMU–Berkeley (YCB) object set consisted of a foam ball, an empty soda can, an apple, a glass cup, a plastic cup, and a small milk packet. The robotic hand successfully picked up these objects from a table and sat them down without inflicting any damage or dropping them midway. Our results represent a significant step forward in developing haptic robotic hands with advanced object perception and manipulation capabilities. Full article

(This article belongs to the Special Issue Advancements in Sensing Technologies and Control Mechanisms for Assistive Robotics: Enhancing Human–Robot Interaction and Assistance)

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21 pages, 12925 KiB

Open AccessEditor’s ChoiceArticle

Learning-Based Control of Autonomous Vehicles Using an Adaptive Neuro-Fuzzy Inference System and the Linear Matrix Inequality Approach

by Mohammad Sheikhsamad and Vicenç Puig

Sensors 2024, 24(8), 2551; https://doi.org/10.3390/s24082551 - 16 Apr 2024

Cited by 5 | Viewed by 2608

Abstract

This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified [...] Read more.

This paper proposes a learning-based control approach for autonomous vehicles. An explicit Takagi–Sugeno (TS) controller is learned using input and output data from a preexisting controller, employing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm. At the same time, the vehicle model is identified in the TS model form for closed-loop stability assessment using Lyapunov theory and LMIs. The proposed approach is applied to learn the control law from an MPC controller, thus avoiding the use of online optimization. This reduces the computational burden of the control loop and facilitates real-time implementation. Finally, the proposed approach is assessed through simulation using a small-scale autonomous racing car. Full article

(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)

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12 pages, 3876 KiB

Open AccessEditor’s ChoiceArticle

SPR and Double Resonance LPG Biosensors for Helicobacter pylori BabA Antigen Detection

by Georgi Dyankov, Tinko Eftimov, Evdokiya Hikova, Hristo Najdenski, Vesselin Kussovski, Petia Genova-Kalou, Vihar Mankov, Hristo Kisov, Petar Veselinov, Sanaz Shoar Ghaffari, Mila Kovacheva-Slavova, Borislav Vladimirov and Nikola Malinowski

Sensors 2024, 24(7), 2118; https://doi.org/10.3390/s24072118 - 26 Mar 2024

Cited by 6 | Viewed by 2410

Abstract

Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance [...] Read more.

Given the medical and social significance of Helicobacter pylori infection, timely and reliable diagnosis of the disease is required. The traditional invasive and non-invasive conventional diagnostic techniques have several limitations. Recently, opportunities for new diagnostic methods have appeared based on the recent advance in the study of H. pylori outer membrane proteins and their identified receptors. In the present study we assess the way in which outer membrane protein–cell receptor reactions are applicable in establishing a reliable diagnosis. Herein, as well as in other previous studies of ours, we explore the reliability of the binding reaction between the best characterized H. pylori adhesin BabA and its receptor, the blood antigen Le^b. For the purpose we developed surface plasmon resonance (SPR) and double resonance long period grating (DR LPG) biosensors based on the BabA–Le^b binding reaction for diagnosing H. pylori infection. In SPR detection, the sensitivity was estimated at 3000 CFU/mL—a much higher sensitivity than that of the RUT test. The DR LPG biosensor proved to be superior in terms of accuracy and sensitivity—concentrations as low as 10² CFU/mL were detected. Full article

(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)

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19 pages, 607 KiB

Open AccessEditor’s ChoiceArticle

Simplified Indoor Localization Using Bluetooth Beacons and Received Signal Strength Fingerprinting with Smartwatch

by Leana Bouse, Scott A. King and Tianxing Chu

Sensors 2024, 24(7), 2088; https://doi.org/10.3390/s24072088 - 25 Mar 2024

Cited by 11 | Viewed by 3703

Abstract

Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals [...] Read more.

Variations in Global Positioning Systems (GPSs) have been used for tracking users’ locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user’s room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently. Full article

(This article belongs to the Collection Sensors and Systems for Indoor Positioning)

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17 pages, 7103 KiB

Open AccessEditor’s ChoiceArticle

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

by Syed Danish Ali, Sameen Raut, Joseph Dahlen, Laurence Schimleck, Richard Bergman, Zhou Zhang and Vahid Nasir

Sensors 2024, 24(6), 1992; https://doi.org/10.3390/s24061992 - 21 Mar 2024

Cited by 9 | Viewed by 2626

Abstract

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep [...] Read more.

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R²) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs. Full article

(This article belongs to the Section Sensing and Imaging)

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22 pages, 3579 KiB

Open AccessEditor’s ChoiceArticle

Realistic 3D Phantoms for Validation of Microwave Sensing in Health Monitoring Applications

by Mariella Särestöniemi, Daljeet Singh, Rakshita Dessai, Charline Heredia, Sami Myllymäki and Teemu Myllylä

Sensors 2024, 24(6), 1975; https://doi.org/10.3390/s24061975 - 20 Mar 2024

Cited by 12 | Viewed by 3054

Abstract

The development of new medical-monitoring applications requires precise modeling of effects on the human body as well as the simulation and the emulation of realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable 3D human-body emulation [...] Read more.

The development of new medical-monitoring applications requires precise modeling of effects on the human body as well as the simulation and the emulation of realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable 3D human-body emulation platforms that could be used for evaluating emerging microwave-based medical monitoring/sensing applications such as the detection of brain tumors, strokes, and breast cancers, as well as for capsule endoscopy studies. New phantom recipes are developed for microwave ranges for phantom molds with realistic shapes. The second aim is to validate the feasibility and reliability of using the phantoms for practical scenarios with electromagnetic simulations using tissue-layer models and biomedical antennas. The third aim is to investigate the impact of the water temperature in the phantom-cooking phase on the dielectric properties of the stabilized phantom. The evaluations show that the dielectric properties of the developed phantoms correspond closely to those of real human tissue. The error in dielectric properties varies between 0.5–8%. In the practical-scenario simulations, the differences obtained with phantoms-based simulations in S21 parameters are 0.1–13 dB. However, the differences are smaller in the frequency ranges used for medical applications. Full article

(This article belongs to the Special Issue Microwave Sensing Systems)

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29 pages, 1070 KiB

Open AccessEditor’s ChoiceReview

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

by Robin Chataut, Mary Nankya and Robert Akl

Sensors 2024, 24(6), 1888; https://doi.org/10.3390/s24061888 - 15 Mar 2024

Cited by 72 | Viewed by 15408

Abstract

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks [...] Read more.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field. Full article

(This article belongs to the Special Issue Topical Advisory Panel Members’ Collection Series: 5G and Beyond Networks and Technologies)

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19 pages, 7676 KiB

Open AccessEditor’s ChoiceArticle

Condition Monitoring of Railway Bridges Using Vehicle Pitch to Detect Scour

by Claire McGeown, David Hester, Eugene J. OBrien, Chul-Woo Kim, Paul Fitzgerald and Vikram Pakrashi

Sensors 2024, 24(5), 1684; https://doi.org/10.3390/s24051684 - 5 Mar 2024

Cited by 5 | Viewed by 2209

Abstract

This study proposes the new condition monitoring concept of using features in the measured rotation, or ‘pitch’ signal, of a crossing vehicle as an indicator of the presence of foundation scour in a bridge. The concept is explored through two-dimensional vehicle–bridge interaction modelling, [...] Read more.

This study proposes the new condition monitoring concept of using features in the measured rotation, or ‘pitch’ signal, of a crossing vehicle as an indicator of the presence of foundation scour in a bridge. The concept is explored through two-dimensional vehicle–bridge interaction modelling, with a reduction in stiffness under a pier used to represent the effects of scour. A train consisting of three 10-degree-of-freedom carriages cross the model on a profiled train track, each train varying slightly in terms of mass and velocity. An analysis of the pitch of the train carriages can clearly identify when scour is present. The concept is further tested in a scaled laboratory experiment consisting of a tractor–trailer crossing a four-span simply supported bridge on piers. The foundation support is represented by four springs under each pier, which can be replaced with springs of a reduced stiffness to mimic the effect of scour. The laboratory model also consistently shows a divergence in vehicle pitch between healthy and scoured bridge states. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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18 pages, 969 KiB

Open AccessEditor’s ChoiceArticle

Wrist-Based Fall Detection: Towards Generalization across Datasets

by Vanilson Fula and Plinio Moreno

Sensors 2024, 24(5), 1679; https://doi.org/10.3390/s24051679 - 5 Mar 2024

Cited by 15 | Viewed by 4100

Abstract

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence [...] Read more.

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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22 pages, 4887 KiB

Open AccessEditor’s ChoiceArticle

Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

by Ibrahim Meftah, Junping Hu, Mohammed A. Asham, Asma Meftah, Li Zhen and Ruihuan Wu

Sensors 2024, 24(5), 1647; https://doi.org/10.3390/s24051647 - 3 Mar 2024

Cited by 13 | Viewed by 4537

Abstract

Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle [...] Read more.

Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces. Full article

(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving)

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39 pages, 19997 KiB

Open AccessEditor’s ChoiceReview

Recent Advances and Current Trends in Transmission Tomographic Diffraction Microscopy

by Nicolas Verrier, Matthieu Debailleul and Olivier Haeberlé

Sensors 2024, 24(5), 1594; https://doi.org/10.3390/s24051594 - 29 Feb 2024

Cited by 6 | Viewed by 3229

Abstract

Optical microscopy techniques are among the most used methods in biomedical sample characterization. In their more advanced realization, optical microscopes demonstrate resolution down to the nanometric scale. These methods rely on the use of fluorescent sample labeling in order to break the diffraction [...] Read more.

Optical microscopy techniques are among the most used methods in biomedical sample characterization. In their more advanced realization, optical microscopes demonstrate resolution down to the nanometric scale. These methods rely on the use of fluorescent sample labeling in order to break the diffraction limit. However, fluorescent molecules’ phototoxicity or photobleaching is not always compatible with the investigated samples. To overcome this limitation, quantitative phase imaging techniques have been proposed. Among these, holographic imaging has demonstrated its ability to image living microscopic samples without staining. However, for a 3D assessment of samples, tomographic acquisitions are needed. Tomographic Diffraction Microscopy (TDM) combines holographic acquisitions with tomographic reconstructions. Relying on a 3D synthetic aperture process, TDM allows for 3D quantitative measurements of the complex refractive index of the investigated sample. Since its initial proposition by Emil Wolf in 1969, the concept of TDM has found a lot of applications and has become one of the hot topics in biomedical imaging. This review focuses on recent achievements in TDM development. Current trends and perspectives of the technique are also discussed. Full article

(This article belongs to the Section Sensing and Imaging)

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19 pages, 4682 KiB

Open AccessEditor’s ChoiceReview

Effects of Visible Light on Gas Sensors: From Inorganic Resistors to Molecular Material-Based Heterojunctions

by Sujithkumar Ganesh Moorthy and Marcel Bouvet

Sensors 2024, 24(5), 1571; https://doi.org/10.3390/s24051571 - 29 Feb 2024

Cited by 12 | Viewed by 2353

Abstract

In the last two decades, many research works have been focused on enhancing the properties of gas sensors by utilising external triggers like temperature and light. Most interestingly, the light-activated gas sensors show promising results, particularly using visible light as an external trigger [...] Read more.

In the last two decades, many research works have been focused on enhancing the properties of gas sensors by utilising external triggers like temperature and light. Most interestingly, the light-activated gas sensors show promising results, particularly using visible light as an external trigger that lowers the power consumption as well as improves the stability, sensitivity and safety of the sensors. It effectively eliminates the possible damage to sensing material caused by high operating temperature or high energy light. This review summarises the effect of visible light illumination on both chemoresistors and heterostructure gas sensors based on inorganic and organic materials and provides a clear understanding of the involved phenomena. Finally, the fascinating concept of ambipolar gas sensors is presented, which utilised visible light as an external trigger for inversion in the nature of majority charge carriers in devices. This review should offer insight into the current technologies and offer a new perspective towards future development utilising visible light in light-assisted gas sensors. Full article

(This article belongs to the Special Issue Chemical Sensors—Recent Advances and Future Challenges 2023–2024)

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19 pages, 4631 KiB

Open AccessEditor’s ChoiceArticle

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images

by Lynn-Jade S. Jong, Jelmer G. C. Appelman, Henricus J. C. M. Sterenborg, Theo J. M. Ruers and Behdad Dashtbozorg

Sensors 2024, 24(5), 1567; https://doi.org/10.3390/s24051567 - 28 Feb 2024

Cited by 3 | Viewed by 2709

Abstract

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists [...] Read more.

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging. Full article

(This article belongs to the Special Issue Sensors for Hyperspectral Imaging: Technologies, Methods and Data Processing)

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14 pages, 2609 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG

by Naoya Kawamura, Wataru Sato, Koh Shimokawa, Tomohiro Fujita and Yasutomo Kawanishi

Sensors 2024, 24(5), 1536; https://doi.org/10.3390/s24051536 - 27 Feb 2024

Cited by 2 | Viewed by 2117

Abstract

Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate [...] Read more.

Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective–physiological association. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning for Sensor Systems)

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32 pages, 4052 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in Biosensors for Diagnosis of Autoimmune Diseases

by Ahlem Teniou, Amina Rhouati and Jean-Louis Marty

Sensors 2024, 24(5), 1510; https://doi.org/10.3390/s24051510 - 26 Feb 2024

Cited by 8 | Viewed by 6084

Abstract

Over the last decade, autoimmune diseases (ADs) have undergone a significant increase because of genetic and/or environmental factors; therefore, their simple and fast diagnosis is of high importance. The conventional diagnostic techniques for ADs require tedious sample preparation, sophisticated instruments, a dedicated laboratory, [...] Read more.

Over the last decade, autoimmune diseases (ADs) have undergone a significant increase because of genetic and/or environmental factors; therefore, their simple and fast diagnosis is of high importance. The conventional diagnostic techniques for ADs require tedious sample preparation, sophisticated instruments, a dedicated laboratory, and qualified personnel. For these reasons, biosensors could represent a useful alternative to these methods. Biosensors are considered to be promising tools that can be used in clinical analysis for an early diagnosis due to their high sensitivity, simplicity, low cost, possible miniaturization (POCT), and potential ability for real-time analysis. In this review, recently developed biosensors for the detection of autoimmune disease biomarkers are discussed. In the first part, we focus on the main AD biomarkers and the current methods of their detection. Then, we discuss the principles and different types of biosensors. Finally, we overview the characteristics of biosensors based on different bioreceptors reported in the literature. Full article

(This article belongs to the Special Issue Electrochemical DNA- and Aptasensors for the Detection of Low-Molecular Compounds)

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25 pages, 23803 KiB

Open AccessEditor’s ChoiceArticle

Scale Modular Test Platform for the Characterization of PD Measuring Systems Using HFCT Sensors

by Eduardo Arcones, Fernando Álvarez, Abderrahim Khamlichi and Fernando Garnacho

Sensors 2024, 24(5), 1363; https://doi.org/10.3390/s24051363 - 21 Feb 2024

Cited by 5 | Viewed by 1982

Abstract

Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high [...] Read more.

Today, online partial discharge (PD) measurements are common practice to assess the condition status of dielectrics in high-voltage (HV) electrical grids. However, when online PD measurements are carried out in electrical facilities, several disadvantages must be considered. Among the most important are high levels of changing electrical noise and interferences, signal phase couplings (cross-talk phenomena), and the simultaneous presence of various defects and difficulties in localizing and identifying them. In the last few decades, various PD-measuring systems have been developed to deal with these inconveniences and try to achieve the adequate supervision of electrical installations. In the state of the art, one of the main problems that electrical companies and technology developers face is the difficulty in characterizing the measuring system’s functionalities in laboratory setups or in real-world facilities, where simulated or real defects must be detected. This is mainly due to the complexity and costs that the laboratory setups entail and the fact that the facilities are permanently in service. Furthermore, in the latter scenario, owners cannot assign facilities to carry out the tests, which could cause irreversible damage. Additionally, with the aforementioned installations, a comparison of results over time in various locations is not possible, and noise conditions cannot be controlled to perform the characterizations in a correct way. To deal with the problems indicated, in this article, an affordable scale modular test platform that simulates an HV installation is presented, where real on-site PD measuring conditions are simulated and controlled. In this first development, the HV installation comprises a cable system connected at both ends to a gas-insulated substation (GIS). As the most common acquisition technique in online applications is based on the placement of high-frequency current transformer (HFCT) sensors in the grounding cables of facilities, the test platform is mainly adapted to carry out measurements with this type of sensor. The designed and developed test platform was validated to assess its features and the degree of convergence with a real installation, showing the convenience of its use for the appropriate and standardized characterization of PD-measuring systems. Full article

(This article belongs to the Special Issue Feature Papers in Physical Sensors 2023)

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23 pages, 3301 KiB

Open AccessEditor’s ChoiceArticle

Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks

by Miguel Rebollo, Jaime Andrés Rincon, Luís Hernández, Francisco Enguix and Carlos Carrascosa

Sensors 2024, 24(4), 1342; https://doi.org/10.3390/s24041342 - 19 Feb 2024

Cited by 5 | Viewed by 2117

Abstract

One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents [...] Read more.

One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents communicate with other agents to share their learning model as they are available to the wireless connection range. When deploying a set of agents, it is essential to study whether all the WANET agents will be reachable before the deployment. The paper proposes to explore it by generating a simulation close to the real world using a framework (FIVE) that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study. The paper also presents how and why the concept of artifact has been included in the above-mentioned framework as a way to highlight the importance of some devices used in the environment that have to be located in specific places to ensure the full connection of the system. This inclusion is the first step to allow Digital Twins to be modeled with this framework, now allowing a Digital Shadow of those devices. Full article

(This article belongs to the Special Issue Advances in Agents and Multiagent Systems for Sensor Applications)

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15 pages, 496 KiB

Open AccessEditor’s ChoiceReview

Anomaly Detection Methods in Autonomous Robotic Missions

by Shivoh Chirayil Nandakumar, Daniel Mitchell, Mustafa Suphi Erden, David Flynn and Theodore Lim

Sensors 2024, 24(4), 1330; https://doi.org/10.3390/s24041330 - 19 Feb 2024

Cited by 4 | Viewed by 5851

Abstract

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in [...] Read more.

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs. Full article

(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robotics: 2nd Edition)

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16 pages, 2705 KiB

Open AccessEditor’s ChoiceReview

Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review

by Ekta Singh Dahiya, Anubha Manju Kalra, Andrew Lowe and Gautam Anand

Sensors 2024, 24(4), 1318; https://doi.org/10.3390/s24041318 - 18 Feb 2024

Cited by 24 | Viewed by 11509

Abstract

In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, [...] Read more.

In the rapidly evolving landscape of continuous electrocardiogram (ECG) monitoring systems, there is a heightened demand for non-invasive sensors capable of measuring ECGs and detecting heart rate variability (HRV) in diverse populations, ranging from cardiovascular patients to sports enthusiasts. Challenges like device accuracy, patient privacy, signal noise, and long-term safety impede the use of wearable devices in clinical practice. This scoping review aims to assess the performance and safety of novel multi-channel, sensor-based biopotential wearable devices in adults. A comprehensive search strategy was employed on four databases, resulting in 143 records and the inclusion of 12 relevant studies. Most studies focused on healthy adult subjects (n = 6), with some examining controlled groups with atrial fibrillation (AF) (n = 3), long QT syndrome (n = 1), and sleep apnea (n = 1). The investigated bio-sensor devices included chest-worn belts (n = 2), wrist bands (n = 2), adhesive chest strips (n = 2), and wearable textile smart clothes (n = 4). The primary objective of the included studies was to evaluate device performance in terms of accuracy, signal quality, comparability, and visual assessment of ECGs. Safety findings, reported in five articles, indicated no major side effects for long-term/continuous monitoring, with only minor instances of skin irritation. Looking forward, there are ample opportunities to enhance and test these technologies across various physical activity intensities and clinical conditions. Full article

(This article belongs to the Special Issue Wearable Sensing of Medical Condition at Home Environment)

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40 pages, 7330 KiB

Open AccessEditor’s ChoiceReview

Non-Terrestrial Networks for Energy-Efficient Connectivity of Remote IoT Devices in the 6G Era: A Survey

by Stefanos Plastras, Dimitrios Tsoumatidis, Dimitrios N. Skoutas, Angelos Rouskas, Georgios Kormentzas and Charalabos Skianis

Sensors 2024, 24(4), 1227; https://doi.org/10.3390/s24041227 - 15 Feb 2024

Cited by 23 | Viewed by 6721

Abstract

The Internet of Things (IoT) is gaining popularity and market share, driven by its ability to connect devices and systems that were previously siloed, enabling new applications and services in a cost-efficient manner. Thus, the IoT fuels societal transformation and enables groundbreaking innovations [...] Read more.

The Internet of Things (IoT) is gaining popularity and market share, driven by its ability to connect devices and systems that were previously siloed, enabling new applications and services in a cost-efficient manner. Thus, the IoT fuels societal transformation and enables groundbreaking innovations like autonomous transport, robotic assistance, and remote healthcare solutions. However, when considering the Internet of Remote Things (IoRT), which refers to the expansion of IoT in remote and geographically isolated areas where neither terrestrial nor cellular networks are available, internet connectivity becomes a challenging issue. Non-Terrestrial Networks (NTNs) are increasingly gaining popularity as a solution to provide connectivity in remote areas due to the growing integration of satellites and Unmanned Aerial Vehicles (UAVs) with cellular networks. In this survey, we provide the technological framework for NTNs and Remote IoT, followed by a classification of the most recent scientific research on NTN-based IoRT systems. Therefore, we provide a comprehensive overview of the current state of research in IoRT and identify emerging research areas with high potential. In conclusion, we present and discuss 3GPP’s roadmap for NTN standardization, which aims to establish an energy-efficient IoRT environment in the 6G era. Full article

(This article belongs to the Special Issue Advances in Intelligent Sensors and IoT Solutions)

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12 pages, 3728 KiB

Open AccessEditor’s ChoiceArticle

A Novel Biosensor for the Detection of Glucose Concentration Using the Dual-Peak Long Period Grating in the Near- to Mid-Infrared

by Namita Sahoo, Bing Sun, Yidong Tan, Kaiming Zhou and Lin Zhang

Sensors 2024, 24(4), 1247; https://doi.org/10.3390/s24041247 - 15 Feb 2024

Cited by 2 | Viewed by 2435

Abstract

In this article, we demonstrate an improved efficient fibre sensor with a high sensitivity to measure glucose concentrations in the physiological range of human beings, operating in a broad spectral bandwidth from the near- to mid-infrared. The sensor consists of a dual-peak long [...] Read more.

In this article, we demonstrate an improved efficient fibre sensor with a high sensitivity to measure glucose concentrations in the physiological range of human beings, operating in a broad spectral bandwidth from the near- to mid-infrared. The sensor consists of a dual-peak long period grating (DPLPG) with a period of 150 μm inscribed in an optical fibre with a diameter of 80 μm. The investigation of sensing for refractive index results in a sensitivity of ~−885.7 nm/refractive index unit (RIU) and ~2008.6 nm/RIU in the range of 1.30–1.44. The glucose measurement is achieved by the immobilisation of a layer of enzyme of glucose oxidase (GOD) onto the fibre surface for the selective enhancement of sensitivity for glucose. The sensor can measure glucose concentrations with a maximum sensitivity of −36.25 nm/(mg/mL) in the range of 0.1–3.0 mg/mL. To the best of our knowledge, this is the highest sensitivity ever achieved for a measurement of glucose with a long period grating-based sensor, indicating its potential for many applications including pharmaceutical, biomedical and food industries. Full article

(This article belongs to the Special Issue Fiber Grating Sensors and Applications)

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30 pages, 1424 KiB

Open AccessEditor’s ChoiceReview

A Review of Sensing Technologies for Indoor Autonomous Mobile Robots

by Yu Liu, Shuting Wang, Yuanlong Xie, Tifan Xiong and Mingyuan Wu

Sensors 2024, 24(4), 1222; https://doi.org/10.3390/s24041222 - 14 Feb 2024

Cited by 23 | Viewed by 11666

Abstract

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding [...] Read more.

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments. Full article

(This article belongs to the Special Issue Advanced Sensing and Control Technologies for Autonomous Robots)

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17 pages, 3583 KiB

Open AccessEditor’s ChoiceArticle

Impact of Fatigue on Ergonomic Risk Scores and Foot Kinetics: A Field Study Employing Inertial and In-Shoe Plantar Pressure Measurement Devices

by Steven Simon, Jonas Dully, Carlo Dindorf, Eva Bartaguiz, Stephan Becker and Michael Fröhlich

Sensors 2024, 24(4), 1175; https://doi.org/10.3390/s24041175 - 10 Feb 2024

Cited by 2 | Viewed by 2697

Abstract

(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to [...] Read more.

(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to assess the influence of physical strain on the Borg CR-10 body map, ergonomic risk scores, and foot pressure in a real-world setting. (2) Methods: Twenty-four participants (seventeen men and seven women) were included in this field study. Inertial measurement units (IMUs) (n = 24) and in-shoe plantar pressure measurements (n = 18) captured the workload of production and office workers at the beginning of their work shift and three hours later, working without any break. In addition to the two 12 min motion capture processes, a Borg CR-10 body map and fatigue visual analog scale (VAS) were applied twice. Kinematic and kinetic data were processed using MATLAB and SPSS software, resulting in scores representing the relative distribution of the Rapid Upper Limb Assessment (RULA) and Computer-Assisted Recording and Long-Term Analysis of Musculoskeletal Load (CUELA), and in-shoe plantar pressure. (3) Results: Significant differences were observed between the two measurement times of physical exertion and fatigue, but not for ergonomic risk scores. Contrary to the hypothesis of the authors, there were no significant differences between the in-shoe plantar pressures. Significant differences were observed between the dominant and non-dominant sides for all kinetic variables. (4) Conclusions: The posture scores of RULA and CUELA and in-shoe plantar pressure side differences were a valuable basis for adapting one-sided requirements in the work process of the workers. Traditional observational methods must be adapted more sensitively to detect kinematic deviations at work. The results of this field study enhance our knowledge about the use and benefits of sensors for ergonomic risk assessments and interventions. Full article

(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Wearable Sensing Technologies for Biomechanical and Physiological Monitoring and Analysis)

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20 pages, 2864 KiB

Open AccessEditor’s ChoiceArticle

Low-Cost Optical Sensors for Soil Composition Monitoring

by Francisco Javier Diaz, Ali Ahmad, Lorena Parra, Sandra Sendra and Jaime Lloret

Sensors 2024, 24(4), 1140; https://doi.org/10.3390/s24041140 - 9 Feb 2024

Cited by 6 | Viewed by 4097

Abstract

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time [...] Read more.

Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification. Full article

(This article belongs to the Special Issue Application and Framework Development for Agriculture)

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12 pages, 2950 KiB

Open AccessEditor’s ChoiceArticle

First Acyclovir Determination Procedure via Electrochemically Activated Screen-Printed Carbon Electrode Coupled with Well-Conductive Base Electrolyte

by Katarzyna Tyszczuk-Rotko, Katarzyna Staniec, Damian Gorylewski and Aleksy Keller

Sensors 2024, 24(4), 1125; https://doi.org/10.3390/s24041125 - 8 Feb 2024

Cited by 3 | Viewed by 1796

Abstract

In this work, a new voltammetric procedure for acyclovir (ACY) trace-level determination has been described. For this purpose, an electrochemically activated screen-printed carbon electrode (aSPCE) coupled with well-conductive electrolyte (CH₃COONH₄, CH₃COOH and NH₄Cl) was used [...] Read more.

In this work, a new voltammetric procedure for acyclovir (ACY) trace-level determination has been described. For this purpose, an electrochemically activated screen-printed carbon electrode (aSPCE) coupled with well-conductive electrolyte (CH₃COONH₄, CH₃COOH and NH₄Cl) was used for the first time. A commercially available SPCE sensor was electrochemically activated by conducting cyclic voltammetry (CV) scans in 0.1 mol L⁻¹ NaOH solution and rinsed with deionized water before a series of measurements were taken. This treatment reduced the charge transfer resistance, increased the electrode active surface area and improved the kinetics of the electron transfer. The activation step and high conductivity of supporting electrolyte significantly improved the sensitivity of the procedure. The newly developed differential-pulse adsorptive stripping voltammetry (DPAdSV) procedure is characterized by having the lowest limit of detection among all voltammetric procedures currently described in the literature (0.12 nmol L⁻¹), a wide linear range of the calibration curve (0.5–50.0 and 50.0–1000.0 nmol L⁻¹) as well as extremely high sensitivity (90.24 nA nmol L⁻¹) and was successfully applied in the determination of acyclovir in commercially available pharmaceuticals. Full article

(This article belongs to the Special Issue Biosensors and Electrochemical Sensors)

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14 pages, 4337 KiB

Open AccessEditor’s ChoiceArticle

Double-Branch Multi-Scale Contextual Network: A Model for Multi-Scale Street Tree Segmentation in High-Resolution Remote Sensing Images

by Hongyang Zhang and Shuo Liu

Sensors 2024, 24(4), 1110; https://doi.org/10.3390/s24041110 - 8 Feb 2024

Cited by 7 | Viewed by 1805

Abstract

Street trees are of great importance to urban green spaces. Quick and accurate segmentation of street trees from high-resolution remote sensing images is of great significance in urban green space management. However, traditional segmentation methods can easily miss some targets because of the [...] Read more.

Street trees are of great importance to urban green spaces. Quick and accurate segmentation of street trees from high-resolution remote sensing images is of great significance in urban green space management. However, traditional segmentation methods can easily miss some targets because of the different sizes of street trees. To solve this problem, we propose the Double-Branch Multi-Scale Contextual Network (DB-MSC Net), which has two branches and a Multi-Scale Contextual (MSC) block in the encoder. The MSC block combines parallel dilated convolutional layers and transformer blocks to enhance the network’s multi-scale feature extraction ability. A channel attention mechanism (CAM) is added to the decoder to assign weights to features from RGB images and the normalized difference vegetation index (NDVI). We proposed a benchmark dataset to test the improvement of our network. Experimental research showed that the DB-MSC Net demonstrated good performance compared with typical methods like Unet, HRnet, SETR and recent methods. The overall accuracy (OA) was improved by at least 0.16% and the mean intersection over union was improved by at least 1.13%. The model’s segmentation accuracy meets the requirements of urban green space management. Full article

(This article belongs to the Special Issue AI-Driven Sensing for Image Processing and Recognition)

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47 pages, 21466 KiB

Open AccessEditor’s ChoiceReview

A Review of Recent Advances in Human-Motion Energy Harvesting Nanogenerators, Self-Powering Smart Sensors and Self-Charging Electronics

by Justyna Gołąbek and Michał Strankowski

Sensors 2024, 24(4), 1069; https://doi.org/10.3390/s24041069 - 6 Feb 2024

Cited by 19 | Viewed by 7029

Abstract

In recent years, portable and wearable personal electronic devices have rapidly developed with increasing mass production and rising energy consumption, creating an energy crisis. Using batteries and supercapacitors with limited lifespans and environmental hazards drives the need to find new, environmentally friendly, and [...] Read more.

In recent years, portable and wearable personal electronic devices have rapidly developed with increasing mass production and rising energy consumption, creating an energy crisis. Using batteries and supercapacitors with limited lifespans and environmental hazards drives the need to find new, environmentally friendly, and renewable sources. One idea is to harness the energy of human motion and convert it into electrical energy using energy harvesting devices—piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs) and hybrids. They are characterized by a wide variety of features, such as lightness, flexibility, low cost, richness of materials, and many more. These devices offer the opportunity to use new technologies such as IoT, AI or HMI and create smart self-powered sensors, actuators, and self-powered implantable/wearable devices. This review focuses on recent examples of PENGs, TENGs and hybrid devices for wearable and implantable self-powered systems. The basic mechanisms of operation, micro/nano-scale material selection and manufacturing processes of selected examples are discussed. Current challenges and the outlook for the future of the nanogenerators are also discussed. Full article

(This article belongs to the Special Issue Smart Wearable Health Monitoring Systems: Materials, Sensors, Nanogenerators and Self-Powered Applications)

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22 pages, 1628 KiB

Open AccessEditor’s ChoiceReview

E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities

by Kai Yang, Stuart A. McErlain-Naylor, Beckie Isaia, Andrew Callaway and Steve Beeby

Sensors 2024, 24(4), 1058; https://doi.org/10.3390/s24041058 - 6 Feb 2024

Cited by 22 | Viewed by 8903

Abstract

E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles [...] Read more.

E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles of wearable technologies in sport and fitness in monitoring movement and biosignals used to assess performance, reduce injury risk, and motivate training/exercise. The drivers of research in e-textiles are discussed after reviewing existing non-textile and textile-based commercial wearable products. Different sensing components/materials (e.g., inertial measurement units, electrodes for biosignals, piezoresistive sensors), manufacturing processes, and their applications in sports and fitness published in the literature were reviewed and discussed. Finally, the paper presents the current challenges of e-textiles to achieve practical applications at scale and future perspectives in e-textiles research and development. Full article

(This article belongs to the Section Wearables)

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24 pages, 35023 KiB

Open AccessEditor’s ChoiceArticle

Calibration of a Low-Cost Methane Sensor Using Machine Learning

by Hazel Louise Mitchell, Simon J. Cox and Hugh G. Lewis

Sensors 2024, 24(4), 1066; https://doi.org/10.3390/s24041066 - 6 Feb 2024

Cited by 5 | Viewed by 2659

Abstract

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection [...] Read more.

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0–200 ppm methane, 5–30 °C and 40–80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R² of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm. Full article

(This article belongs to the Special Issue Gas Sensors: Progress, Perspectives and Challenges)

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12 pages, 1401 KiB

Open AccessEditor’s ChoiceArticle

Optical Sensing Using Hybrid Multilayer Grating Metasurfaces with Customized Spectral Response

by Mahmoud H. Elshorbagy, Alexander Cuadrado and Javier Alda

Sensors 2024, 24(3), 1043; https://doi.org/10.3390/s24031043 - 5 Feb 2024

Cited by 1 | Viewed by 2585

Abstract

Customized metasurfaces allow for controlling optical responses in photonic and optoelectronic devices over a broad band. For sensing applications, the spectral response of an optical device can be narrowed to a few nanometers, which enhances its capabilities to detect environmental changes that shift [...] Read more.

Customized metasurfaces allow for controlling optical responses in photonic and optoelectronic devices over a broad band. For sensing applications, the spectral response of an optical device can be narrowed to a few nanometers, which enhances its capabilities to detect environmental changes that shift the spectral transmission or reflection. These nanophotonic elements are key for the new generation of plasmonic optical sensors with custom responses and custom modes of operation. In our design, the metallic top electrode of a hydrogenated amorphous silicon thin-film solar cell is combined with a metasurface fabricated as a hybrid dielectric multilayer grating. This arrangement generates a plasmonic resonance on top of the active layer of the cell, which enhances the optoelectronic response of the system over a very narrow spectral band. Then, the solar cell becomes a sensor with a response that is highly dependent on the optical properties of the medium on top of it. The maximum sensitivity and figure of merit (FOM) are S_B = 36,707 (mA/W)/RIU and ≈167 RIU⁻¹, respectively, for the 560 nm wavelength using TE polarization. The optical response and the high sensing performance of this device make it suitable for detecting very tiny changes in gas media. This is of great importance for monitoring air quality and thecomposition of gases in closed atmospheres. Full article

(This article belongs to the Special Issue Optical Sensing and Technologies)

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12 pages, 1871 KiB

Open AccessEditor’s ChoiceArticle

Optical Encryption Using Attention-Inserted Physics-Driven Single-Pixel Imaging

by Wen-Kai Yu, Shuo-Fei Wang and Ke-Qian Shang

Sensors 2024, 24(3), 1012; https://doi.org/10.3390/s24031012 - 4 Feb 2024

Cited by 5 | Viewed by 1858

Abstract

Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With [...] Read more.

Optical encryption based on single-pixel imaging (SPI) has made great advances with the introduction of deep learning. However, the use of deep neural networks usually requires a long training time, and the networks need to be retrained once the target scene changes. With this in mind, we propose an SPI encryption scheme based on an attention-inserted physics-driven neural network. Here, an attention module is used to encrypt the single-pixel measurement value sequences of two images, together with a sequence of cryptographic keys, into a one-dimensional ciphertext signal to complete image encryption. Then, the encrypted signal is fed into a physics-driven neural network for high-fidelity decoding (i.e., decryption). This scheme eliminates the need for pre-training the network and gives more freedom to spatial modulation. Both simulation and experimental results have demonstrated the feasibility and eavesdropping resistance of this scheme. Thus, it will lead SPI-based optical encryption closer to intelligent deep encryption. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 3541 KiB

Open AccessEditor’s ChoiceArticle

Development of a Low-Cost Distributed Computing Pipeline for High-Throughput Cotton Phenotyping

by Vaishnavi Thesma, Glen C. Rains and Javad Mohammadpour Velni

Sensors 2024, 24(3), 970; https://doi.org/10.3390/s24030970 - 2 Feb 2024

Cited by 5 | Viewed by 2037

Abstract

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem [...] Read more.

In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster’s distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture. Full article

(This article belongs to the Special Issue Sensor and AI Technologies in Intelligent Agriculture)

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48 pages, 1345 KiB

Open AccessEditor’s ChoiceArticle

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

by Tesfahunegn Minwuyelet Mengistu, Taewoon Kim and Jenn-Wei Lin

Sensors 2024, 24(3), 968; https://doi.org/10.3390/s24030968 - 1 Feb 2024

Cited by 28 | Viewed by 4315

Abstract

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play [...] Read more.

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements. Full article

(This article belongs to the Special Issue New Trends in Artificial Intelligence of Things)

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23 pages, 8527 KiB

Open AccessEditor’s ChoiceArticle

Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming

by Juan Felipe Jaramillo-Hernández, Vicente Julian, Cedric Marco-Detchart and Jaime Andrés Rincón

Sensors 2024, 24(3), 937; https://doi.org/10.3390/s24030937 - 31 Jan 2024

Cited by 9 | Viewed by 2813

Abstract

In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer [...] Read more.

In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method’s efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things. Full article

(This article belongs to the Special Issue Application and Framework Development for Agriculture)

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14 pages, 5506 KiB

Open AccessEditor’s ChoiceArticle

Enhanced Sensitivity in Optical Sensors through Self-Image Theory and Graphene Oxide Coating

by Cristina Cunha, Catarina Monteiro, António Vaz, Susana Silva, Orlando Frazão and Susana Novais

Sensors 2024, 24(3), 891; https://doi.org/10.3390/s24030891 - 30 Jan 2024

Cited by 4 | Viewed by 2933

Abstract

This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human [...] Read more.

This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human saliva. Prior to sensor fabrication, the theoretical self-image points were rigorously validated using Multiphysics COMSOL 6.0 software. Subsequently, the sensor was fabricated to a length corresponding to the second self-image point (29.12 mm) and coated with an 80 µm/mL graphene oxide film using the Layer-by-Layer technique. The sensor characterization in refractive index demonstrated a wavelength sensitivity of 200 ± 6 nm/RIU. Comparative evaluations of uncoated and graphene oxide-coated sensors applied to measure glucose in solutions ranging from 25 to 200 mg/dL showed an eightfold sensitivity improvement with one bilayer of Polyethyleneimine/graphene. The final graphene oxide-based sensor exhibited a sensitivity of 10.403 ± 0.004 pm/(mg/dL) and demonstrated stability with a low standard deviation of 0.46 pm/min and a maximum theoretical resolution of 1.90 mg/dL. Full article

(This article belongs to the Special Issue Advanced Optics and Photonics Technologies for Sensing Applications)

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14 pages, 2673 KiB

Open AccessEditor’s ChoiceArticle

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

by Salaheddin Hosseinzadeh, Moses Ashawa, Nsikak Owoh, Hadi Larijani and Krystyna Curtis

Sensors 2024, 24(3), 860; https://doi.org/10.3390/s24030860 - 29 Jan 2024

Cited by 6 | Viewed by 2934

Abstract

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression [...] Read more.

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks. Full article

(This article belongs to the Special Issue LoRa and LoRaWAN: Latest Advances in Applications, Systems, and Sensor Networks)

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39 pages, 7211 KiB

Open AccessEditor’s ChoiceArticle

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

by Ildar Rakhmatulin, Minh-Son Dao, Amir Nassibi and Danilo Mandic

Sensors 2024, 24(3), 877; https://doi.org/10.3390/s24030877 - 29 Jan 2024

Cited by 39 | Viewed by 13628

Abstract

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We [...] Read more.

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, and dimension reduction, among others. In addition, we conduct an in-depth analysis of well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, and combined architecture. This paper further offers a comprehensive evaluation of these architectures, covering accuracy metrics, hyperparameters, and an appendix that contains a table outlining the parameters of commonly used CNN architectures for feature extraction from EEG signals. Full article

(This article belongs to the Special Issue AI and Sensing Technology in Medicine and Public Health)

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28 pages, 13558 KiB

Open AccessEditor’s ChoiceArticle

Experimental Investigation for Monitoring Corrosion Using Plastic Optical Fiber Sensors

by Liang Hou, Shinichi Akutagawa, Yuki Tomoshige and Takashi Kimura

Sensors 2024, 24(3), 885; https://doi.org/10.3390/s24030885 - 29 Jan 2024

Cited by 2 | Viewed by 1689

Abstract

The timely and cost-effective identification of the onset of corrosion and its progress would be critical for effectively maintaining structural integrity. Consequently, a series of fundamental experiments were conducted to capture the corrosion process on a steel plate using a new type of [...] Read more.

The timely and cost-effective identification of the onset of corrosion and its progress would be critical for effectively maintaining structural integrity. Consequently, a series of fundamental experiments were conducted to capture the corrosion process on a steel plate using a new type of plastic optical fiber (POF) sensor. Electrolytic corrosion experiments were performed on a 5 mm thick steel plate immersed in an aqueous solution. The POF sensor installed on the upper side of the plate and directed downward detected the upward progression of the corrosion zone that formed on the underside of the plate. The results showed that the POF sensors could detect the onset of the upward-progressing corrosion front as it passed the 1 and 2 mm marks related to the thickness of the corroded zone. The POF sensors were designed to optically identify corrosion; therefore, the data obtained by these sensors could be processed using a newly developed graphic application software for smartphones and also identified by the naked eye. This method offered an easy and cost-effective solution for verifying the corrosion state of structural components. Full article

(This article belongs to the Special Issue Specialty Optical Fiber-Based Sensors)

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17 pages, 1263 KiB

Open AccessEditor’s ChoiceArticle

Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications

by Jihun Lee, Hun Kim and Jaewoo So

Sensors 2024, 24(3), 837; https://doi.org/10.3390/s24030837 - 27 Jan 2024

Cited by 1 | Viewed by 2285

Abstract

The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, [...] Read more.

The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation: 2nd Edition)

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