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Editor’s Choice Articles

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

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24 pages, 5922 KB  
Article
Close-Range Coordination to Enhance Constant Distance Spacing Policies in Oversaturated Traffic Systems
by Kay Massow, Niko Pfeifer, Fabian Ketzler and Ilja Radusch
Sensors 2024, 24(15), 4865; https://doi.org/10.3390/s24154865 - 26 Jul 2024
Cited by 1 | Viewed by 976
Abstract
In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance [...] Read more.
In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance traffic capacity, they suffer from notable limitations regarding string stability and diminished safety margins at high velocities. In our previous work, we proposed applying CDG in specific scenarios, such as starting platoons at signalized intersections, where traffic throughput is critical and safety requirements can be met due to relatively low speeds. We demonstrated the substantial potential of CDG to increase the capacity of signalized intersections under oversaturated conditions. However, our study also revealed potential performance drops of CDG in dense traffic networks. To address these issues, we propose close-range coordination between vehicles to (1) limit platoon length, (2) create gaps for merging, and (3) avoid entering intersections when there is a high likelihood of stopping within the intersection area. In this paper, we extend our previous work by implementing these three measures. We successfully evaluate their positive impact on CDG’s performance in entire traffic systems through large-scale traffic simulations involving several thousand vehicles, thereby affirming our earlier hypothesis Full article
(This article belongs to the Section Vehicular Sensing)
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20 pages, 7336 KB  
Article
Spectral Features Analysis for Print Quality Prediction in Additive Manufacturing: An Acoustics-Based Approach
by Michael Olowe, Michael Ogunsanya, Brian Best, Yousef Hanif, Saurabh Bajaj, Varalakshmi Vakkalagadda, Olukayode Fatoki and Salil Desai
Sensors 2024, 24(15), 4864; https://doi.org/10.3390/s24154864 - 26 Jul 2024
Cited by 9 | Viewed by 1770
Abstract
Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on [...] Read more.
Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic–Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques. Full article
(This article belongs to the Collection Sensors and Sensing Technology for Industry 4.0)
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13 pages, 3500 KB  
Article
Electrochemical Detection of Ammonia in Water Using NiCu Carbonate Hydroxide-Modified Carbon Cloth Electrodes: A Simple Sensing Method
by Guangfeng Zhou, Guanda Wang, Xing Zhao, Dong He, Chun Zhao and Hui Suo
Sensors 2024, 24(15), 4824; https://doi.org/10.3390/s24154824 - 25 Jul 2024
Cited by 5 | Viewed by 2028
Abstract
Excessive ammonia nitrogen can potentially compromise the safety of drinking water. Therefore, developing a rapid and simple detection method for ammonia nitrogen in drinking water is of great importance. Nickel–copper hydroxides exhibit strong catalytic capabilities and are widely applied in ammonia nitrogen oxidation. [...] Read more.
Excessive ammonia nitrogen can potentially compromise the safety of drinking water. Therefore, developing a rapid and simple detection method for ammonia nitrogen in drinking water is of great importance. Nickel–copper hydroxides exhibit strong catalytic capabilities and are widely applied in ammonia nitrogen oxidation. In this study, a self-supported electrode made of nickel–copper carbonate hydroxide was synthesized on a carbon cloth collector via a straightforward one-step hydrothermal method for rapid ammonia nitrogen detection in water. It exhibits sensitivities of 3.9 μA μM−1 cm−2 and 3.13 μA μM−1 cm−2 within linear ranges of 1 μM to 100 μM and 100 μM to 400 μM, respectively, using a simple and rapid i-t method. The detection limit is as low as 0.62 μM, highlighting its excellent anti-interference properties against various anions and cations. The methodology’s simplicity and effectiveness suggest broad applicability in water quality monitoring and environmental protection, particularly due to its significant cost-effectiveness. Full article
(This article belongs to the Special Issue Electrochemical Sensors for Detection and Analysis)
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29 pages, 2313 KB  
Article
Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
by Carlos Lester Duenas Santos, Ahmad Mohamad Mezher, Juan Pablo Astudillo León, Julian Cardenas Barrera, Eduardo Castillo Guerra and Julian Meng
Sensors 2024, 24(15), 4818; https://doi.org/10.3390/s24154818 - 25 Jul 2024
Cited by 4 | Viewed by 3128
Abstract
Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh [...] Read more.
Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols. Full article
(This article belongs to the Special Issue Advanced Communication and Computing Technologies for Smart Grid)
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14 pages, 6763 KB  
Article
Mo-Doped LaFeO3 Gas Sensors with Enhanced Sensing Performance for Triethylamine Gas
by Chenyu Shen, Hongjian Liang, Ziyue Zhao, Suyi Guo, Yuxiang Chen, Zhenquan Tan, Xue-Zhi Song and Xiaofeng Wang
Sensors 2024, 24(15), 4851; https://doi.org/10.3390/s24154851 - 25 Jul 2024
Cited by 11 | Viewed by 2061
Abstract
Triethylamine is a common volatile organic compound (VOC) that plays an important role in areas such as organic solvents, chemical industries, dyestuffs, and leather treatments. However, exposure to triethylamine atmosphere can pose a serious threat to human health. In this study, gas-sensing semiconductor [...] Read more.
Triethylamine is a common volatile organic compound (VOC) that plays an important role in areas such as organic solvents, chemical industries, dyestuffs, and leather treatments. However, exposure to triethylamine atmosphere can pose a serious threat to human health. In this study, gas-sensing semiconductor materials of LaFeO3 nano materials with different Mo-doping ratios were synthesized by the sol–gel method. The crystal structures, micro morphologies, and surface states of the prepared samples were characterized by XRD, SEM, and XPS, respectively. The gas-sensing tests showed that the Mo doping enhanced the gas-sensing performance of LaFeO3. Especially, the 4% Mo-doped LaFeO3 exhibited the highest response towards triethylamine (TEA) gas, a value approximately 11 times greater than that of pure LaFeO3. Meantime, the 4% Mo-doped LaFeO3 sensor showed a remarkably robust linear correlation between the response and the concentration (R2 = 0.99736). In addition, the selectivity, stability, response/recovery time, and moisture-proof properties were evaluated. Finally, the gas-sensing mechanism is discussed. This study provides an idea for exploring a new type of efficient and low-cost metal-doped LaFeO3 sensor to monitor the concentration of triethylamine gas for the purpose of safeguarding human health and safety. Full article
(This article belongs to the Special Issue Recent Advancements in Olfaction and Electronic Nose)
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18 pages, 1535 KB  
Article
Acquisition and Analysis of Facial Electromyographic Signals for Emotion Recognition
by Marcin Kołodziej, Andrzej Majkowski and Marcin Jurczak
Sensors 2024, 24(15), 4785; https://doi.org/10.3390/s24154785 - 24 Jul 2024
Cited by 2 | Viewed by 3282
Abstract
The objective of the article is to recognize users’ emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals [...] Read more.
The objective of the article is to recognize users’ emotions by classifying facial electromyographic (EMG) signals. A biomedical signal amplifier, equipped with eight active electrodes positioned in accordance with the Facial Action Coding System, was used to record the EMG signals. These signals were registered during a procedure where users acted out various emotions: joy, sadness, surprise, disgust, anger, fear, and neutral. Recordings were made for 16 users. The mean power of the EMG signals formed the feature set. We utilized these features to train and evaluate various classifiers. In the subject-dependent model, the average classification accuracies were 96.3% for KNN, 94.9% for SVM with a linear kernel, 94.6% for SVM with a cubic kernel, and 93.8% for LDA. In the subject-independent model, the classification results varied depending on the tested user, ranging from 91.4% to 48.6% for the KNN classifier, with an average accuracy of 67.5%. The SVM with a cubic kernel performed slightly worse, achieving an average accuracy of 59.1%, followed by the SVM with a linear kernel at 53.9%, and the LDA classifier at 41.2%. Additionally, the study identified the most effective electrodes for distinguishing between pairs of emotions. Full article
(This article belongs to the Special Issue Signal Processing for Sensing Technologies in Biomedical Engineering Applications: 3rd Edition)
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21 pages, 8219 KB  
Article
An Improved Fire and Smoke Detection Method Based on YOLOv8n for Smart Factories
by Ziyang Zhang, Lingye Tan and Tiong Lee Kong Robert
Sensors 2024, 24(15), 4786; https://doi.org/10.3390/s24154786 - 24 Jul 2024
Cited by 8 | Viewed by 2968
Abstract
Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. [...] Read more.
Factories play a crucial role in economic and social development. However, fire disasters in factories greatly threaten both human lives and properties. Previous studies about fire detection using deep learning mostly focused on wildfire detection and ignored the fires that happened in factories. In addition, lots of studies focus on fire detection, while smoke, the important derivative of a fire disaster, is not detected by such algorithms. To better help smart factories monitor fire disasters, this paper proposes an improved fire and smoke detection method based on YOLOv8n. To ensure the quality of the algorithm and training process, a self-made dataset including more than 5000 images and their corresponding labels is created. Then, nine advanced algorithms are selected and tested on the dataset. YOLOv8n exhibits the best detection results in terms of accuracy and detection speed. ConNeXtV2 is then inserted into the backbone to enhance inter-channel feature competition. RepBlock and SimConv are selected to replace the original Conv and improve computational ability and memory bandwidth. For the loss function, CIoU is replaced by MPDIoU to ensure an efficient and accurate bounding box. Ablation tests show that our improved algorithm achieves better performance in all four metrics reflecting accuracy: precision, recall, F1, and mAP@50. Compared with the original model, whose four metrics are approximately 90%, the modified algorithm achieves above 95%. mAP@50 in particular reaches 95.6%, exhibiting an improvement of approximately 4.5%. Although complexity improves, the requirements of real-time fire and smoke monitoring are satisfied. Full article
(This article belongs to the Collection 3D Human-Computer Interaction Imaging and Sensing)
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31 pages, 766 KB  
Review
A Review of Patient Bed Sensors for Monitoring of Vital Signs
by Michaela Recmanik, Radek Martinek, Jan Nedoma, Rene Jaros, Mariusz Pelc, Radovan Hajovsky, Jan Velicka, Martin Pies, Marta Sevcakova and Aleksandra Kawala-Sterniuk
Sensors 2024, 24(15), 4767; https://doi.org/10.3390/s24154767 - 23 Jul 2024
Cited by 8 | Viewed by 7918
Abstract
The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs [...] Read more.
The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods. Full article
(This article belongs to the Special Issue Biomedical Data Analysis and Sensing in Human-Machine Interaction: 2nd Edition)
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22 pages, 2371 KB  
Article
Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis
by Rabé Andersson, Javier Bermejo-García, Rafael Agujetas, Mikael Cronhjort and José Chilo
Sensors 2024, 24(15), 4769; https://doi.org/10.3390/s24154769 - 23 Jul 2024
Cited by 4 | Viewed by 5275
Abstract
Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. [...] Read more.
Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems. Full article
(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)
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20 pages, 5427 KB  
Article
Development of a High-Sensitivity Millimeter-Wave Radar Imaging System for Non-Destructive Testing
by Hironaru Murakami, Taiga Fukuda, Hiroshi Otera, Hiroyuki Kamo and Akito Miyoshi
Sensors 2024, 24(15), 4781; https://doi.org/10.3390/s24154781 - 23 Jul 2024
Viewed by 2970
Abstract
There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is [...] Read more.
There is an urgent need to develop non-destructive testing (NDT) methods for infrastructure facilities and residences, etc., where human lives are at stake, to prevent collapse due to aging or natural disasters such as earthquakes before they occur. In such inspections, it is desirable to develop a remote, non-contact, non-destructive inspection method that can inspect cracks as small as 0.1 mm on the surface of a structure and damage inside and on the surface of the structure that cannot be seen by the human eye with high sensitivity, while ensuring the safety of the engineers inspecting the structure. Based on this perspective, we developed a radar module (absolute gain of the transmitting antenna: 13.5 dB; absolute gain of the receiving antenna: 14.5 dB) with very high directivity and minimal loss in the signal transmission path between the radar chip and the array antenna, using our previously developed technology. A single-input, multiple-output (SIMO) synthetic aperture radar (SAR) imaging system was developed using this module. As a result of various performance evaluations using this system, we were able to demonstrate that this system has a performance that fully satisfies the abovementioned indices. First, the SNR in millimeter-wave (MM-wave) imaging was improved by 5.4 dB compared to the previously constructed imaging system using the IWR1443BOOST EVM, even though the measured distance was 2.66 times longer. As a specific example of the results of measurements on infrastructure facilities, the system successfully observed cracks as small as 0.1 mm in concrete materials hidden under glass fiber-reinforced tape and black acrylic paint. In this case, measurements were also made from a distance of about 3 m to meet the remote observation requirements, but the radar module with its high-directivity and high-gain antenna proved to be more sensitive in detecting crack structures than measurements made from a distance of 780 mm. In order to estimate the penetration length of MM waves into concrete, an experiment was conducted to measure the penetration of MM waves through a thin concrete slab with a thickness of 3.7 mm. As a result, Λexp = 6.0 mm was obtained as the attenuation distance of MM waves in the concrete slab used. In addition, transmission measurement experiments using a composite material consisting of ceramic tiles and fireproof board, which is a component of a house, and experiments using composite plywood, which is used as a general housing construction material in Japan, succeeded in making perspective observations of defects in the internal structure, etc., which are invisible to the human eye. Full article
(This article belongs to the Special Issue Radar Imaging, Communications and Sensing)
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29 pages, 5115 KB  
Article
Open-Vocabulary Predictive World Models from Sensor Observations
by Robin Karlsson, Ruslan Asfandiyarov, Alexander Carballo, Keisuke Fujii, Kento Ohtani and Kazuya Takeda
Sensors 2024, 24(14), 4735; https://doi.org/10.3390/s24144735 - 21 Jul 2024
Viewed by 1692
Abstract
Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from [...] Read more.
Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots. Full article
(This article belongs to the Collection Robotics and 3D Computer Vision)
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23 pages, 15975 KB  
Article
Integrating the Capsule-like Smart Aggregate-Based EMI Technique with Deep Learning for Stress Assessment in Concrete
by Quoc-Bao Ta, Quang-Quang Pham, Ngoc-Lan Pham and Jeong-Tae Kim
Sensors 2024, 24(14), 4738; https://doi.org/10.3390/s24144738 - 21 Jul 2024
Cited by 9 | Viewed by 1964
Abstract
This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor [...] Read more.
This study presents a concrete stress monitoring method utilizing 1D CNN deep learning of raw electromechanical impedance (EMI) signals measured with a capsule-like smart aggregate (CSA) sensor. Firstly, the CSA-based EMI measurement technique is presented by depicting a prototype of the CSA sensor and a 2 degrees of freedom (2 DOFs) EMI model for the CSA sensor embedded in a concrete cylinder. Secondly, the 1D CNN deep regression model is designed to adapt raw EMI responses from the CSA sensor for estimating concrete stresses. Thirdly, a CSA-embedded cylindrical concrete structure is experimented with to acquire EMI responses under various compressive loading levels. Finally, the feasibility and robustness of the 1D CNN model are evaluated for noise-contaminated EMI data and untrained stress EMI cases. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)
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24 pages, 1845 KB  
Review
Unveiling Colorectal Cancer Biomarkers: Harnessing Biosensor Technology for Volatile Organic Compound Detection
by Rebecca Golfinopoulou, Kyriaki Hatziagapiou, Sophie Mavrikou and Spyridon Kintzios
Sensors 2024, 24(14), 4712; https://doi.org/10.3390/s24144712 - 20 Jul 2024
Cited by 4 | Viewed by 2946
Abstract
Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such [...] Read more.
Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring. The present review is a comprehensive presentation of the current state-of-the-art VOC-based CRC diagnostics, with a specific focus on recent advancements in biosensor design and application. Among them, breath-based chromatography pattern analysis and sampling techniques are overviewed, along with nanoparticle-based optical and electrochemical biosensor approaches. Limitations of the currently available technologies are also discussed with an outlook for improvement in combination with big data analytics and advanced instrumentation, as well as expanding the scope and specificity of CRC-related volatile biomarkers. Full article
(This article belongs to the Special Issue Innovative Sensors and IoT for AI-Enabled Smart Healthcare)
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31 pages, 61407 KB  
Article
Deformation Estimation of Textureless Objects from a Single Image
by Sahand Eivazi Adli, Joshua K. Pickard, Ganyun Sun and Rickey Dubay
Sensors 2024, 24(14), 4707; https://doi.org/10.3390/s24144707 - 20 Jul 2024
Viewed by 1280
Abstract
Deformations introduced during the production of plastic components degrade the accuracy of their 3D geometric information, a critical aspect of object inspection processes. This phenomenon is prevalent among primary plastic products from manufacturers. This work proposes a solution for the deformation estimation of [...] Read more.
Deformations introduced during the production of plastic components degrade the accuracy of their 3D geometric information, a critical aspect of object inspection processes. This phenomenon is prevalent among primary plastic products from manufacturers. This work proposes a solution for the deformation estimation of textureless plastic objects using only a single RGB image. This solution encompasses a unique image dataset of five deformed parts, a novel method for generating mesh labels, sequential deformation, and a training model based on graph convolution. The proposed sequential deformation method outperforms the prevalent chamfer distance algorithm in generating precise mesh labels. The training model projects object vertices into features extracted from the input image, and then, predicts vertex location offsets based on the projected features. The predicted meshes using these offsets achieve a sub-millimeter accuracy on synthetic images and approximately 2.0 mm on real images. Full article
(This article belongs to the Section Sensing and Imaging)
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29 pages, 22874 KB  
Article
Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps
by João Martins, Sara M. Cerqueira, André Whiteman Catarino, Alexandre Ferreira da Silva, Ana M. Rocha, Jorge Vale, Miguel Ângelo and Cristina P. Santos
Sensors 2024, 24(14), 4717; https://doi.org/10.3390/s24144717 - 20 Jul 2024
Cited by 4 | Viewed by 4046
Abstract
Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head [...] Read more.
Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut’s Elitex fiber outperformed Shieldex’s fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm. Full article
(This article belongs to the Special Issue Applications of Smart Sensing Textiles for Assessment and Assistance of Motion)
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24 pages, 8548 KB  
Article
The Identification of Manure Spreading on Bare Soil through the Development of Multispectral Indices from Sentinel-2 Data: The Emilia-Romagna Region (Italy) Case Study
by Marco Dubbini, Maria Belluzzo, Villiam Zanni Bertelli, Alessandro Pirola, Antonella Tornato and Cinzia Alessandrini
Sensors 2024, 24(14), 4687; https://doi.org/10.3390/s24144687 - 19 Jul 2024
Cited by 1 | Viewed by 1685
Abstract
Satellite remote sensing is currently an established, effective, and constantly used tool and methodology for monitoring agriculture and fertilisation. At the same time, in recent years, the need for the detection of livestock manure and digestate spreading on the soil is emerging, and [...] Read more.
Satellite remote sensing is currently an established, effective, and constantly used tool and methodology for monitoring agriculture and fertilisation. At the same time, in recent years, the need for the detection of livestock manure and digestate spreading on the soil is emerging, and the development of spectral indices and classification processes based on satellite multispectral data acquisitions is growing. However, the application of such indicators is still underutilised and, given the polluting impact of livestock manure and digestate on soil, groundwater, and air, an in-depth study is needed to improve the monitoring of this practice. Additionally, this paper aims at exposing a new spectral index capable of detecting the land affected by livestock manure and digestate spreading. This indicator was created by studying the spectral response of bare soil and livestock manure and digestate, using Copernicus Sentinel-2 MSI satellite acquisitions and ancillary datasets (e.g., soil moisture, precipitation, regional thematic maps). In particular, time series of multispectral satellite acquisitions and ancillary data were analysed, covering a survey period of 13 months between February 2022 and February 2023. As no previous indications on fertilisation practices are available, the proposed approach consists of investigating a broad-spectrum area, without investigations of specific test sites. A large area of approximately 236,344 hectares covering three provinces of the Emilia-Romagna Region (Italy) was therefore examined. A series of ground truth points were also collected for assessing accuracy by filling in the confusion matrix. Based on the definition of the spectral index, a value of the latter greater than three provides the most conservative threshold for detecting livestock manure and digestate spreading with an accuracy of 62.53%. Such results are robust to variations in the spectral response of the soil. On the basis of these very encouraging results, it is considered plausible that the proposed index could improve the techniques for detecting the spreading of livestock manure and digestate on bare ground, classifying the areas themselves with a notable saving of energy compared to the current investigation methodologies directly on the ground. Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing in Geospatial Monitoring)
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13 pages, 5070 KB  
Article
Pollen-Modified Flat Silk Cocoon Pressure Sensors for Wearable Applications
by Shengnan Wang, Yujia Wang, Yi Wang, Jiaqi Liu, Fan Liu, Fangyin Dai, Jiashen Li and Zhi Li
Sensors 2024, 24(14), 4698; https://doi.org/10.3390/s24144698 - 19 Jul 2024
Cited by 2 | Viewed by 1432
Abstract
Microstructures have been proved as crucial factors for the sensing performance of flexible pressure sensors. In this study, polypyrrole (PPy)/sunflower pollen (SFP) (P/SFP) was prepared via the in situ growth of PPy on the surface of degreased SFP with a sea urchin-like microstructure; [...] Read more.
Microstructures have been proved as crucial factors for the sensing performance of flexible pressure sensors. In this study, polypyrrole (PPy)/sunflower pollen (SFP) (P/SFP) was prepared via the in situ growth of PPy on the surface of degreased SFP with a sea urchin-like microstructure; then, these P/SFP microspheres were sprayed onto a flat silk cocoon (FSC) to prepare a sensing layer P/SFP-FSC. PPy-FSC (P-FSC) was prepared as an electrode layer through the in situ polymerization of PPy on the FSC surface. The sensing layer P/SFP-FSC was placed between two P-FSC electrode layers to assemble a P/SFP-FSC pressure sensor together with a fork finger electrode. With 6 mg/cm2 of optimized sprayed P/SFP microspheres, the prepared flexible pressure sensor has a sensitivity of up to 0.128 KPa−1 in the range of 0–13.18 KPa and up to 0.13 KPa−1 in the range of 13.18–30.65 KPa, a fast response/recovery time (90 ms/80 ms), and a minimum detection limit as low as 40 Pa. This fabricated flexible P/SFP-FSC sensor can monitor human motion and can also be used for the encrypted transmission of important information via Morse code. In conclusion, the developed flexible P/SFP-FSC pressure sensor based on microstructure modification in this study shows good application prospects in the field of human–computer interaction and wearable electronic devices. Full article
(This article belongs to the Special Issue Functional Polymers and Fibers: Sensing Materials and Applications)
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13 pages, 4244 KB  
Article
Correction of Thin Cirrus Absorption Effects in Landsat 8 Thermal Infrared Sensor Images Using the Operational Land Imager Cirrus Band on the Same Satellite Platform
by Bo-Cai Gao, Rong-Rong Li, Yun Yang and Martha Anderson
Sensors 2024, 24(14), 4697; https://doi.org/10.3390/s24144697 - 19 Jul 2024
Cited by 2 | Viewed by 1257
Abstract
Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help [...] Read more.
Data from the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) instruments onboard the Landsat 8 and Landsat 9 satellite platforms are subject to contamination by cloud cover, with cirrus contributions being the most difficult to detect and mask. To help address this issue, a cirrus detection channel (Band 9) centered within the 1.375-μm water vapor absorption region was implemented on OLI, with a spatial resolution of 30 m. However, this band has not yet been fully utilized in the Collection 2 Landsat 8/9 Level 2 surface temperature data products that are publicly released by U.S. Geological Survey (USGS). The temperature products are generated with a single-channel algorithm. During the surface temperature retrievals, the effects of absorption of infrared radiation originating from the warmer earth’s surfaces by ice clouds, typically located in the upper portion of the troposphere and re-emitting at much lower temperatures (approximately 220 K), are not taken into consideration. Through an analysis of sample Level 1 TOA and Level 2 surface data products, we have found that thin cirrus cloud features present in the Level 1 1.375-μm band images are directly propagated down to the Level 2 surface data products. The surface temperature errors resulting from thin cirrus contamination can be 10 K or larger. Previously, we reported an empirical and effective technique for removing thin cirrus scattering effects in OLI images, making use of the correlations between the 1.375-μm band image and images of any other OLI bands located in the 0.4–2.5 μm solar spectral region. In this article, we describe a variation of this technique that can be applied to the thermal bands, using the correlations between the Level 1 1.375-μm band image and the 11-μm BT image for the effective removal of thin cirrus absorption effects. Our results from three data sets acquired over spatially uniform water surfaces and over non-uniform land/water boundary areas suggest that if the cirrus-removed TOA 11-μm band BT images are used for the retrieval of the Level 2 surface temperature (ST) data products, the errors resulting from thin cirrus contaminations in the products can be reduced to about 1 K for spatially diffused cirrus scenes. Full article
(This article belongs to the Section Remote Sensors)
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20 pages, 560 KB  
Article
Deep Learning Soft-Decision GNSS Multipath Detection and Mitigation
by Fernando Nunes and Fernando Sousa
Sensors 2024, 24(14), 4663; https://doi.org/10.3390/s24144663 - 18 Jul 2024
Cited by 2 | Viewed by 2839
Abstract
A technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of [...] Read more.
A technique is proposed to detect the presence of the multipath effect in Global Navigation Satellite Signal (GNSS) signals using a convolutional neural network (CNN) as the building block. The network is trained and validated, for a wide range of C/N0 values, with a realistic dataset constituted by the synthetic noisy outputs of a 2D grid of correlators associated with different Doppler frequencies and code delays (time-domain dataset). Multipath-disturbed signals are generated in agreement with the various scenarios encompassed by the adopted multipath model. It was found that pre-processing the outputs of the correlators grid with the two-dimensional Discrete Fourier Transform (frequency-domain dataset) enables the CNN to improve the accuracy relative to the time-domain dataset. Depending on the kind of CNN outputs, two strategies can then be devised to solve the equation of navigation: either remove the disturbed signal from the equation (hard decision) or process the pseudoranges with a weighted least-squares algorithm, where the entries of the weighting matrix are computed using the analog outputs of the neural network (soft decision). Full article
(This article belongs to the Special Issue Advances in Signal Processing for GNSS and Complementary PNT Technologies)
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22 pages, 8212 KB  
Article
Inkjet Printing Magnetostrictive Materials for Structural Health Monitoring of Carbon Fibre-Reinforced Polymer Composite
by Nisar Ahmed, Patrick J. Smith and Nicola A. Morley
Sensors 2024, 24(14), 4657; https://doi.org/10.3390/s24144657 - 18 Jul 2024
Cited by 4 | Viewed by 1767
Abstract
Inkjet printing of magnetic materials has increased in recent years, as it has the potential to improve research in smart, functional materials. Magnetostriction is an inherent property of magnetic materials which allows strain or magnetic fields to be detected. This makes it very [...] Read more.
Inkjet printing of magnetic materials has increased in recent years, as it has the potential to improve research in smart, functional materials. Magnetostriction is an inherent property of magnetic materials which allows strain or magnetic fields to be detected. This makes it very attractive for sensors in the area of structural health monitoring by detecting internal strains in carbon fibre-reinforced polymer (CFRP) composite. Inkjet printing offers design flexibility for these sensors to influence the magnetic response to the strain. This allows the sensor to be tailored to suit the location of defects in the CFRP. This research has looked into the viability of printable soft magnetic materials for structural health monitoring (SHM) of CFRP. Magnetite and nickel ink dispersions were selected to print using the JetLab 4 drop-on-demand technique. The printability of both inks was tested by selecting substrate, viscosity and solvent evaporation. Clogging was found to be an issue for both ink dispersions. Sonicating and adjusting the jetting parameters helped in distributing the nanoparticles. We found that magnetite nanoparticles were ideal as a sensor as there is more than double increase in saturation magnetisation by 49 Am2/kg and more than quadruple reduction of coercive field of 5.34 kA/m than nickel. The coil design was found to be the most sensitive to the field as a function of strain, where the gradient was around 80% higher than other sensor designs. Additive layering of 10, 20 and 30 layers of a magnetite square patch was investigated, and it was found that the 20-layered magnetite print had an improved field response to strain while maintaining excellent print resolution. SHM of CFRP was performed by inducing a strain via bending and it was found that the magnetite coil detected a change in field as the strain was applied. Full article
(This article belongs to the Special Issue Challenges and Future Trends of Magnetic Sensors)
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16 pages, 15074 KB  
Article
Study of a Narrow Fabric-Based E-Textile System—From Research to Field Tests
by Paula Veske-Lepp, Bjorn Vandecasteele, Filip Thielemans, Vera De Glas, Severine Delaplace, Bart Allaert, Kurt Dewulf, Annick Depré and Frederick Bossuyt
Sensors 2024, 24(14), 4624; https://doi.org/10.3390/s24144624 - 17 Jul 2024
Cited by 4 | Viewed by 1791
Abstract
Electronic textiles (e-textiles) are a branch of wearable technology based on integrating smart systems into textile materials creating different possibilities, transforming industries, and improving individuals’ quality of life. E-textiles hold vast potential, particularly for use in personal protective equipment (PPE) by embedding sensors [...] Read more.
Electronic textiles (e-textiles) are a branch of wearable technology based on integrating smart systems into textile materials creating different possibilities, transforming industries, and improving individuals’ quality of life. E-textiles hold vast potential, particularly for use in personal protective equipment (PPE) by embedding sensors and smart technologies into garments, thus significantly enhancing safety and performance. Although this branch of research has been active for several decades now, only a few products have made it to the market. Achieving durability, reliability, user acceptance, sustainability, and integration into current manufacturing processes remains challenging. High levels of reliability and user acceptance are critical for technical textiles, such as those used in PPE. While studies address washing reliability and field tests, they often overlook end user preferences regarding smart textiles. This paper presents a narrow fabric-based e-textile system co-developed by engineers, garment and textiles’ manufacturers, and firefighters. It highlights material choices and integration methods, and evaluates the system’s reliability, sustainability, and user experience, providing comprehensive insights into developing and analyzing e-textile products, particularly in the PPE field. Full article
(This article belongs to the Special Issue Challenges, Opportunities, Innovations and Applications in Electronic Textiles: E-Textiles 2023)
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16 pages, 15204 KB  
Article
E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing
by Fulin Cai, Teresa Wu and Fleming Y. M. Lure
Sensors 2024, 24(14), 4620; https://doi.org/10.3390/s24144620 - 17 Jul 2024
Cited by 3 | Viewed by 1392
Abstract
Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power [...] Read more.
Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time–frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer’s disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet’s superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models. Full article
(This article belongs to the Section Radar Sensors)
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13 pages, 1665 KB  
Article
Advanced Techniques for Liver Fibrosis Detection: Spectral Photoacoustic Imaging and Superpixel Photoacoustic Unmixing Analysis for Collagen Tracking
by Laith R. Sultan, Valeria Grasso, Jithin Jose, Maryam Al-Hasani, Mrigendra B. Karmacharya and Chandra M. Sehgal
Sensors 2024, 24(14), 4617; https://doi.org/10.3390/s24144617 - 17 Jul 2024
Cited by 3 | Viewed by 2464
Abstract
Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of [...] Read more.
Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of spectral photoacoustic imaging (sPAI) to monitor collagen development in liver fibrosis. Utilizing a novel data-driven superpixel photoacoustic unmixing (SPAX) framework, we aimed to distinguish collagen presence and evaluate its correlation with fibrosis progression. We employed an established diethylnitrosamine (DEN) model in rats to study liver fibrosis over various time points. Our results revealed a significant correlation between increased collagen photoacoustic signal intensity and advanced fibrosis stages. Collagen abundance maps displayed dynamic changes throughout fibrosis progression. These findings underscore the potential of sPAI for the noninvasive monitoring of collagen dynamics and fibrosis severity assessment. This research advances the development of noninvasive diagnostic tools and personalized management strategies for liver fibrosis. Full article
(This article belongs to the Section Biomedical Sensors)
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21 pages, 5203 KB  
Article
How Do We Calibrate a Battery Electric Vehicle Model Based on Controller Area Network Bus Data?
by Dávid Tollner, Ádám Nyerges, Mahmoud Said Jneid, Attila Geleta and Máté Zöldy
Sensors 2024, 24(14), 4637; https://doi.org/10.3390/s24144637 - 17 Jul 2024
Cited by 4 | Viewed by 2472
Abstract
Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the [...] Read more.
Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that. Full article
(This article belongs to the Special Issue Intelligent Sensors for Smart and Autonomous Vehicles)
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12 pages, 2311 KB  
Article
Explore Ultrasonic-Induced Mechanoluminescent Solutions towards Realising Remote Structural Health Monitoring
by Marilyne Philibert and Kui Yao
Sensors 2024, 24(14), 4595; https://doi.org/10.3390/s24144595 - 16 Jul 2024
Viewed by 3362
Abstract
Ultrasonic guided waves, which are often generated and detected by piezoelectric transducers, are well established to monitor engineering structures. Wireless solutions are sought to eliminate cumbersome wire installation. This work proposes a method for remote ultrasonic-based structural health monitoring (SHM) using mechanoluminescence (ML). [...] Read more.
Ultrasonic guided waves, which are often generated and detected by piezoelectric transducers, are well established to monitor engineering structures. Wireless solutions are sought to eliminate cumbersome wire installation. This work proposes a method for remote ultrasonic-based structural health monitoring (SHM) using mechanoluminescence (ML). Propagating guided waves transmitted by a piezoelectric transducer attached to a structure induce elastic deformation that can be captured by elastico-ML. An ML coating composed of copper-doped zinc sulfide (ZnS:Cu) particles embedded in PVDF on a thin aluminium plate can be used to achieve the elastico-ML for the remote sensing of propagating guided waves. The simulation and experimental results indicated that a very high voltage would be required to reach the threshold pressure applied to the ML particles, which is about 1.5 MPa for ZnS particles. The high voltage was estimated to be 214 Vpp for surface waves and 750 Vpp for Lamb waves for the studied configuration. Several possible technical solutions are suggested for achieving ultrasonic-induced ML for future remote SHM systems. Full article
(This article belongs to the Special Issue Sensors in Civil Structural Health Monitoring)
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17 pages, 5453 KB  
Article
Breathing Monitoring in Soccer: Part I—Validity of Commercial Wearable Sensors
by Lorenzo Innocenti, Chiara Romano, Giuseppe Greco, Stefano Nuccio, Alessio Bellini, Federico Mari, Sergio Silvestri, Emiliano Schena, Massimo Sacchetti, Carlo Massaroni and Andrea Nicolò
Sensors 2024, 24(14), 4571; https://doi.org/10.3390/s24144571 - 15 Jul 2024
Cited by 3 | Viewed by 2206
Abstract
Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with [...] Read more.
Growing evidence suggests that respiratory frequency (fR) is a valid marker of effort during high-intensity exercise, including sports of an intermittent nature, like soccer. However, very few attempts have been made so far to monitor fR in soccer with unobtrusive devices. This study assessed the validity of three strain-based commercial wearable devices measuring fR during soccer-specific movements. On two separate visits to the soccer pitch, 15 players performed a 30 min validation protocol wearing either a ComfTech® (CT) vest or a BioharnessTM (BH) 3.0 strap and a Tyme WearTM (TW) vest. fR was extracted from the respiratory waveform of the three commercial devices with custom-made algorithms and compared with that recorded with a reference face mask. The fR time course of the commercial devices generally resembled that of the reference system. The mean absolute percentage error was, on average, 7.03% for CT, 8.65% for TW, and 14.60% for BH for the breath-by-breath comparison and 1.85% for CT, 3.27% for TW, and 7.30% for BH when comparison with the reference system was made in 30 s windows. Despite the challenging measurement scenario, our findings show that some of the currently available wearable sensors are indeed suitable to unobtrusively measure fR in soccer. Full article
(This article belongs to the Special Issue Sensor Technologies in Sports and Exercise)
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28 pages, 3061 KB  
Article
BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks
by Khadija Begum, Md Ariful Islam Mozumder, Moon-Il Joo and Hee-Cheol Kim
Sensors 2024, 24(14), 4591; https://doi.org/10.3390/s24144591 - 15 Jul 2024
Cited by 20 | Viewed by 4816
Abstract
The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have [...] Read more.
The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback–Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks. Full article
(This article belongs to the Special Issue Intelligent Solutions for Cybersecurity)
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15 pages, 5344 KB  
Article
Grating Structures for Silver-Based Surface Plasmon Resonance Sensors with Adjustable Excitation Angle
by Pongsak Sarapukdee, Dirk Schulz and Stefan Palzer
Sensors 2024, 24(14), 4538; https://doi.org/10.3390/s24144538 - 13 Jul 2024
Cited by 1 | Viewed by 2442
Abstract
Silver-based grating structures offer means for implementing low-cost, efficient grating couplers for use in surface plasmon resonance (SPR) sensors. One-dimensional grating structures with a fixed periodicity are confined to operate effectively within a single planar orientation. However, two-dimensional grating structures as well as [...] Read more.
Silver-based grating structures offer means for implementing low-cost, efficient grating couplers for use in surface plasmon resonance (SPR) sensors. One-dimensional grating structures with a fixed periodicity are confined to operate effectively within a single planar orientation. However, two-dimensional grating structures as well as grating structures with variable periodicity allow for the plasmon excitation angle to be seamlessly adjusted. This study demonstrates silver-based grating designs that allow for the plasmon excitation angle to be adjusted via rotation or beam position. The flexible angle adjustment opens up the possibility of developing SPR sensor designs with an expanded dynamic range and increased flexibility in sensing applications. The results demonstrate that efficient coupling into two diffraction orders is possible, which ultimately leads to an excitation angle range from 16° to 40° by rotating a single structure. The findings suggest a promising direction for the development of versatile and adaptable SPR sensing platforms with enhanced performance characteristics. Full article
(This article belongs to the Section Biosensors)
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25 pages, 1870 KB  
Review
Technical Principles and Clinical Applications of Electrical Impedance Tomography in Pulmonary Monitoring
by Ziqiang Cui, Xinyan Liu, Hantao Qu and Huaxiang Wang
Sensors 2024, 24(14), 4539; https://doi.org/10.3390/s24144539 - 13 Jul 2024
Cited by 7 | Viewed by 3634
Abstract
Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and [...] Read more.
Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications. Full article
(This article belongs to the Section Sensing and Imaging)
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31 pages, 4981 KB  
Review
Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications
by Cristina Origlia, David O. Rodriguez-Duarte, Jorge A. Tobon Vasquez, Jean-Charles Bolomey and Francesca Vipiana
Sensors 2024, 24(14), 4515; https://doi.org/10.3390/s24144515 - 12 Jul 2024
Cited by 23 | Viewed by 7323
Abstract
Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous [...] Read more.
Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1–15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives. Full article
(This article belongs to the Special Issue Microwave Sensing Systems)
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17 pages, 3815 KB  
Article
Site-Independent Mapping of Clay Content in Vineyard Soils via Mobile Proximal Gamma-Ray Spectrometry and Machine Learning Calibrations
by Ralf Wehrle and Stefan Pätzold
Sensors 2024, 24(14), 4528; https://doi.org/10.3390/s24144528 - 12 Jul 2024
Cited by 3 | Viewed by 1429
Abstract
Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious [...] Read more.
Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious and expensive. Gamma-ray spectrometry (GS) is a suitable tool for predicting clay content in precision agriculture when locally calibrated, but it has scarcely been tested site-independently and in vineyards. This study evaluated GS to predict clay content with a site-independent calibration and four machine learning algorithms (Support Vector Machines, Random Forest, k-Nearest Neighbors, and Bayesian regulated neuronal networks) in eight vineyards from four German vine-growing regions. Clay content in the studied soils ranged from 62 to 647 g kg−1. The Random Forest calibration was most suitable. Test set evaluation revealed good model performance for the entire dataset with RPIQ = 4.64, RMSEP = 56.7 g kg−1, and R2 = 0.87; however, prediction quality varied between the sites. Overall, GS with the Random Forest model calibration was appropriate to predict the clay content and its spatial distribution, even for heterogeneous geopedological settings and in individual plots. Therefore, GS is considered a valuable tool for soil mapping in vineyards, where clay content and product quality are closely linked. Full article
(This article belongs to the Special Issue Precision Agriculture and Sensor Systems—2nd Edition)
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16 pages, 4847 KB  
Article
Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions
by Yujia Zhang, Xingwang Tang, Sichuan Xu and Chuanyu Sun
Sensors 2024, 24(14), 4451; https://doi.org/10.3390/s24144451 - 10 Jul 2024
Cited by 14 | Viewed by 2576
Abstract
Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions [...] Read more.
Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks. Utilizing deep learning methods, we estimated the SOH of PEMFCs under dynamic operating conditions and investigated the performance of long short-term memory networks (LSTM), gated recurrent units (GRU), temporal convolutional networks (TCN), and transformer models for SOH estimation tasks. We also explored the impact of different sampling intervals and training set proportions on the predictive performance of these models. The results indicated that shorter sampling intervals and higher training set proportions significantly improve prediction accuracy. The study also highlighted the challenges posed by the presence of cracks. Cracks cause more frequent and intense voltage fluctuations, making it more difficult for the models to accurately capture the dynamic behavior of PEMFCs, thereby increasing prediction errors. However, under crack-free conditions, due to more stable voltage output, all models showed improved predictive performance. Finally, this study underscores the effectiveness of deep learning models in estimating the SOH of PEMFCs and provides insights into optimizing sampling and training strategies to enhance prediction accuracy. The findings make a significant contribution to the development of more reliable and efficient PEMFC systems for sustainable energy applications. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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62 pages, 10336 KB  
Review
Efficient Integration of Ultra-low Power Techniques and Energy Harvesting in Self-Sufficient Devices: A Comprehensive Overview of Current Progress and Future Directions
by Rocco Citroni, Fabio Mangini and Fabrizio Frezza
Sensors 2024, 24(14), 4471; https://doi.org/10.3390/s24144471 - 10 Jul 2024
Cited by 21 | Viewed by 2889
Abstract
Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly [...] Read more.
Compact, energy-efficient, and autonomous wireless sensor nodes offer incredible versatility for various applications across different environments. Although these devices transmit and receive real-time data, efficient energy storage (ES) is crucial for their operation, especially in remote or hard-to-reach locations. Rechargeable batteries are commonly used, although they often have limited storage capacity. To address this, ultra-low-power design techniques (ULPDT) can be implemented to reduce energy consumption and prolong battery life. The Energy Harvesting Technique (EHT) enables perpetual operation in an eco-friendly manner, but may not fully replace batteries due to its intermittent nature and limited power generation. To ensure uninterrupted power supply, devices such as ES and power management unit (PMU) are needed. This review focuses on the importance of minimizing power consumption and maximizing energy efficiency to improve the autonomy and longevity of these sensor nodes. It examines current advancements, challenges, and future direction in ULPDT, ES, PMU, wireless communication protocols, and EHT to develop and implement robust and eco-friendly technology solutions for practical and long-lasting use in real-world scenarios. Full article
(This article belongs to the Special Issue Feature Review Papers in the ‘Sensor Networks’ Section 2024–2025)
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27 pages, 4700 KB  
Review
Polarimetric Imaging for Robot Perception: A Review
by Camille Taglione, Carlos Mateo and Christophe Stolz
Sensors 2024, 24(14), 4440; https://doi.org/10.3390/s24144440 - 9 Jul 2024
Cited by 3 | Viewed by 3710
Abstract
In recent years, the integration of polarimetric imaging into robotic perception systems has increased significantly, driven by the accessibility of affordable polarimetric sensors. This technology complements traditional color imaging by capturing and analyzing the polarization characteristics of light. This additional information provides robots [...] Read more.
In recent years, the integration of polarimetric imaging into robotic perception systems has increased significantly, driven by the accessibility of affordable polarimetric sensors. This technology complements traditional color imaging by capturing and analyzing the polarization characteristics of light. This additional information provides robots with valuable insights into object shape, material composition, and other properties, ultimately enabling more robust manipulation tasks. This review aims to provide a comprehensive analysis of the principles behind polarimetric imaging and its diverse applications within the field of robotic perception. By exploiting the polarization state of light, polarimetric imaging offers promising solutions to three key challenges in robot vision: Surface segmentation; depth estimation through polarization patterns; and 3D reconstruction using polarimetric data. This review emphasizes the practical value of polarimetric imaging in robotics by demonstrating its effectiveness in addressing real-world challenges. We then explore potential applications of this technology not only within the core robotics field but also in related areas. Through a comparative analysis, our goal is to elucidate the strengths and limitations of polarimetric imaging techniques. This analysis will contribute to a deeper understanding of its broad applicability across various domains within and beyond robotics. Full article
(This article belongs to the Section Sensors and Robotics)
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14 pages, 9017 KB  
Article
Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology
by Santiago Salamanca, Pilar Merchán, Alejandro Espacio, Emiliano Pérez and María José Merchán
Sensors 2024, 24(13), 4390; https://doi.org/10.3390/s24134390 - 6 Jul 2024
Cited by 4 | Viewed by 2085
Abstract
This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it [...] Read more.
This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings. Full article
(This article belongs to the Special Issue Advances in Multispectral Sensing, Imaging Techniques and Computational Applications in Cultural Heritage)
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20 pages, 7374 KB  
Article
Piezoelectric Transducers: Complete Electromechanical Model with Parameter Extraction
by Michael L. Isaf and Gabriel A. Rincón-Mora
Sensors 2024, 24(13), 4367; https://doi.org/10.3390/s24134367 - 5 Jul 2024
Cited by 2 | Viewed by 4699
Abstract
This paper presents a complete electromechanical (EM) model of piezoelectric transducers (PTs) independent of high or low coupling assumptions, vibration conditions, and geometry. The PT’s spring stiffness is modeled as part of the domain coupling transformer, and the piezoelectric EM coupling coefficient is [...] Read more.
This paper presents a complete electromechanical (EM) model of piezoelectric transducers (PTs) independent of high or low coupling assumptions, vibration conditions, and geometry. The PT’s spring stiffness is modeled as part of the domain coupling transformer, and the piezoelectric EM coupling coefficient is modeled explicitly as a split inductor transformer. This separates the coupling coefficient from the coefficient used for conversion between mechanical and electrical domains, providing a more insightful understanding of the energy transfers occurring within a PT and allowing for analysis not previously possible. This also illustrates the role the PT’s spring plays in EM energy conversion. The model is analyzed and discussed from a circuits and energy harvesting perspective. Coupling between domains and how loading affects coupled energy are examined. Moreover, simple methods for experimentally extracting model parameters, including the coupling coefficient, are provided to empower engineers to quickly and easily integrate PTs in SPICE simulations for the rapid and improved development of PT interface circuits. The model and parameter extractions are validated by comparing them to the measured response of a physical cantilever-style PT excited by regular and irregular vibrations. In most cases, less than a 5–10% error between measured and simulated responses is observed. Full article
(This article belongs to the Special Issue Piezoelectric Energy Harvesting System)
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15 pages, 4789 KB  
Article
Planar Hall Effect Magnetic Sensors with Extended Field Range
by Daniel Lahav, Moty Schultz, Shai Amrusi, Asaf Grosz and Lior Klein
Sensors 2024, 24(13), 4384; https://doi.org/10.3390/s24134384 - 5 Jul 2024
Cited by 2 | Viewed by 2875
Abstract
The magnetic field range in which a magnetic sensor operates is an important consideration for many applications. Elliptical planar Hall effect (EPHE) sensors exhibit outstanding equivalent magnetic noise (EMN) on the order of pT/Hz, which makes them promising for many [...] Read more.
The magnetic field range in which a magnetic sensor operates is an important consideration for many applications. Elliptical planar Hall effect (EPHE) sensors exhibit outstanding equivalent magnetic noise (EMN) on the order of pT/Hz, which makes them promising for many applications. Unfortunately, the current field range in which EPHE sensors with pT/Hz EMN can operate is sub-mT, which limits their potential use. Here, we fabricate EPHE sensors with an increased field range and measure their EMN. The larger field range is obtained by increasing the uniaxial shape-induced anisotropy parallel to the long axis of the ellipse. We present measurements of EPHE sensors with magnetic anisotropy which ranges between 12 Oe and 120 Oe and show that their EMN at 10 Hz changes from 800 pT/Hz to 56 nT/Hz. Furthermore, we show that the EPHE sensors behave effectively as single magnetic domains with negligible hysteresis. We discuss the potential use of EPHE sensors with extended field range and compare them with sensors that are widely used in such applications. Full article
(This article belongs to the Special Issue Magnetic Field Sensing and Measurement Techniques)
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19 pages, 4157 KB  
Review
Artificial Intelligence-Based Microfluidic Platform for Detecting Contaminants in Water: A Review
by Yihao Zhang, Jiaxuan Li, Yu Zhou, Xu Zhang and Xianhua Liu
Sensors 2024, 24(13), 4350; https://doi.org/10.3390/s24134350 - 4 Jul 2024
Cited by 14 | Viewed by 4383
Abstract
Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as [...] Read more.
Water pollution greatly impacts humans and ecosystems, so a series of policies have been enacted to control it. The first step in performing pollution control is to detect contaminants in the water. Various methods have been proposed for water quality testing, such as spectroscopy, chromatography, and electrochemical techniques. However, traditional testing methods require the utilization of laboratory equipment, which is large and not suitable for real-time testing in the field. Microfluidic devices can overcome the limitations of traditional testing instruments and have become an efficient and convenient tool for water quality analysis. At the same time, artificial intelligence is an ideal means of recognizing, classifying, and predicting data obtained from microfluidic systems. Microfluidic devices based on artificial intelligence and machine learning are being developed with great significance for the next generation of water quality monitoring systems. This review begins with a brief introduction to the algorithms involved in artificial intelligence and the materials used in the fabrication and detection techniques of microfluidic platforms. Then, the latest research development of combining the two for pollutant detection in water bodies, including heavy metals, pesticides, micro- and nanoplastics, and microalgae, is mainly introduced. Finally, the challenges encountered and the future directions of detection methods based on industrial intelligence and microfluidic chips are discussed. Full article
(This article belongs to the Collection Microfluidic Sensors)
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22 pages, 18896 KB  
Article
Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing
by Luyang Liu, Hiroki Nishikawa, Jinjia Zhou, Ittetsu Taniguchi and Takao Onoye
Sensors 2024, 24(13), 4348; https://doi.org/10.3390/s24134348 - 4 Jul 2024
Cited by 3 | Viewed by 1930
Abstract
Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost [...] Read more.
Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. Full article
(This article belongs to the Special Issue Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes: 2nd Edition)
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27 pages, 1078 KB  
Review
Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis
by João N. D. Fernandes, Vitor E. M. Cardoso, Alberto Comesaña-Campos and Alberto Pinheira
Sensors 2024, 24(13), 4355; https://doi.org/10.3390/s24134355 - 4 Jul 2024
Cited by 14 | Viewed by 6780
Abstract
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in [...] Read more.
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. Full article
(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)
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9 pages, 545 KB  
Article
Do Elite Basketball Players Maintain Peak External Demands throughout the Entire Game?
by Hugo Salazar, Filip Ujakovic, Jernej Plesa, Alberto Lorenzo and Enrique Alonso-Pérez-Chao
Sensors 2024, 24(13), 4318; https://doi.org/10.3390/s24134318 - 3 Jul 2024
Cited by 4 | Viewed by 2266
Abstract
Consideration of workload intensity and peak demands across different periods of basketball games contributes to understanding the external physical requirements of elite basketball players. Therefore, the aim of this study was to investigate the average intensity and peak demands encountered by players throughout [...] Read more.
Consideration of workload intensity and peak demands across different periods of basketball games contributes to understanding the external physical requirements of elite basketball players. Therefore, the aim of this study was to investigate the average intensity and peak demands encountered by players throughout game quarters. PlayerLoad per minute and PlayerLoad at three different time samples (30 s, 1 min, and 3 min) were used as workload metrics. A total of 14 professional elite male basketball players were monitored during 30 official games to investigate this. A linear mixed model and Cohen’s d were employed to identify significant differences and quantify the effect sizes among game quarters. The results showed a significant, moderate effect in PlayerLoad per minute between Q1 vs. Q4, and a small effect between Q2 and Q3 vs. Q4. Furthermore, a small to moderate decline was observed in external peak values for PlayerLoad across game quarters. Specifically,, a significant decrease was found for the 3 min time window between Q1 and other quarters. The findings from the present study suggest that professional basketball players tend to experience fatigue or reduced physical output as the game progresses. Full article
(This article belongs to the Special Issue Sensors for Performance Analysis in Team Sports)
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21 pages, 3130 KB  
Article
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 2333
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 KB  
Article
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 1750
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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20 pages, 1252 KB  
Article
Distinguishing between Wheat Grains Infested by Four Fusarium Species by Measuring with a Low-Cost Electronic Nose
by Piotr Borowik, Miłosz Tkaczyk, Przemysław Pluta, Adam Okorski, Marcin Stocki, Rafał Tarakowski and Tomasz Oszako
Sensors 2024, 24(13), 4312; https://doi.org/10.3390/s24134312 - 2 Jul 2024
Cited by 4 | Viewed by 2151
Abstract
An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: [...] Read more.
An electronic device based on the detection of volatile substances was developed in response to the need to distinguish between fungal infestations in food and was applied to wheat grains. The most common pathogens belong to the fungi of the genus Fusarium: F. avenaceum, F. langsethiae, F. poae, and F. sporotrichioides. The electronic nose prototype is a low-cost device based on commercially available TGS series sensors from Figaro Corp. Two types of gas sensors that respond to the perturbation are used to collect signals useful for discriminating between the samples under study. First, an electronic nose detects the transient response of the sensors to a change in operating conditions from clean air to the presence of the gas being measured. A simple gas chamber was used to create a sudden change in gas composition near the sensors. An inexpensive pneumatic system consisting of a pump and a carbon filter was used to supply the system with clean air. It was also used to clean the sensors between measurement cycles. The second function of the electronic nose is to detect the response of the sensor to temperature disturbances of the sensor heater in the presence of the gas to be measured. It has been shown that features extracted from the transient response of the sensor to perturbations by modulating the temperature of the sensor heater resulted in better classification performance than when the machine learning model was built from features extracted from the response of the sensor in the gas adsorption phase. By combining features from both phases of the sensor response, a further improvement in classification performance was achieved. The E-nose enabled the differentiation of F. poae from the other fungal species tested with excellent performance. The overall classification rate using the Support Vector Machine model reached 70 per cent between the four fungal categories tested. Full article
(This article belongs to the Special Issue Gas Recognition in E-Nose System)
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15 pages, 5461 KB  
Article
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 10 | Viewed by 3018
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 KB  
Article
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 1744
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 KB  
Article
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 13 | Viewed by 3465
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 KB  
Article
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 5320
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 KB  
Review
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 40 | Viewed by 27145
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 KB  
Review
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 14 | Viewed by 11066
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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