Journal Description
Sensors
Sensors
is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB) and International Society for the Measurement of Physical Behaviour (ISMPB) are affiliated with Sensors and their members receive a discount on the article processing charges.
- Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
- Journal Rank: JCR - Q2 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Testimonials: See what our editors and authors say about Sensors.
- Companion journals for Sensors include: Chips, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
CIRF: Coupled Image Reconstruction and Fusion Strategy for Deep Learning Based Multi-Modal Image Fusion
Sensors 2024, 24(11), 3545; https://doi.org/10.3390/s24113545 (registering DOI) - 30 May 2024
Abstract
Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods
[...] Read more.
Multi-modal medical image fusion (MMIF) is crucial for disease diagnosis and treatment because the images reconstructed from signals collected by different sensors can provide complementary information. In recent years, deep learning (DL) based methods have been widely used in MMIF. However, these methods often adopt a serial fusion strategy without feature decomposition, causing error accumulation and confusion of characteristics across different scales. To address these issues, we have proposed the Coupled Image Reconstruction and Fusion (CIRF) strategy. Our method parallels the image fusion and reconstruction branches which are linked by a common encoder. Firstly, CIRF uses the lightweight encoder to extract base and detail features, respectively, through the Vision Transformer (ViT) and the Convolutional Neural Network (CNN) branches, where the two branches interact to supplement information. Then, two types of features are fused separately via different blocks and finally decoded into fusion results. In the loss function, both the supervised loss from the reconstruction branch and the unsupervised loss from the fusion branch are included. As a whole, CIRF increases its expressivity by adding multi-task learning and feature decomposition. Additionally, we have also explored the impact of image masking on the network’s feature extraction ability and validated the generalization capability of the model. Through experiments on three datasets, it has been demonstrated both subjectively and objectively, that the images fused by CIRF exhibit appropriate brightness and smooth edge transition with more competitive evaluation metrics than those fused by several other traditional and DL-based methods.
Full article
(This article belongs to the Special Issue Multi-Sensor Fusion in Medical Imaging, Diagnosis and Therapy)
Open AccessArticle
Application of Independent Component Analysis and Nelder–Mead Particle Swarm Optimization Algorithm in Non-Contact Blood Pressure Estimation
by
Te-Jen Su, Wei-Hong Lin, Qian-Yi Zhuang, Ya-Chung Hung, Wen-Rong Yang, Bo-Jun He and Shih-Ming Wang
Sensors 2024, 24(11), 3544; https://doi.org/10.3390/s24113544 (registering DOI) - 30 May 2024
Abstract
In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to
[...] Read more.
In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder–Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.
Full article
(This article belongs to the Special Issue Intelligent Medical Sensors and Applications)
►▼
Show Figures
Figure 1
Open AccessArticle
Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP
by
Dohyun Kim, Yuwon Kim, Jinseok Park, Hojin Choi, Hokyoung Ryu, Martin Loeser and Kyoungwon Seo
Sensors 2024, 24(11), 3543; https://doi.org/10.3390/s24113543 (registering DOI) - 30 May 2024
Abstract
Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer’s disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer’s disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools
[...] Read more.
Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer’s disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer’s disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.
Full article
(This article belongs to the Section Biomedical Sensors)
Open AccessReview
Ultrasound and Photoacoustic Imaging for the Guidance of Laser Ablation Procedures
by
Samuel John, Yan Yan, Shirin Abbasi and Mohammad Mehrmohammadi
Sensors 2024, 24(11), 3542; https://doi.org/10.3390/s24113542 (registering DOI) - 30 May 2024
Abstract
The accuracy and efficacy of laser ablation procedures depend on the accurate placement of the laser applicator within the diseased tissue, monitoring the real-time temperature during the ablation procedure, and mapping the extent of the ablated region. Ultrasound (US) imaging has been widely
[...] Read more.
The accuracy and efficacy of laser ablation procedures depend on the accurate placement of the laser applicator within the diseased tissue, monitoring the real-time temperature during the ablation procedure, and mapping the extent of the ablated region. Ultrasound (US) imaging has been widely used to guide ablation procedures. While US imaging offers significant advantages for guiding ablation procedures, its limitations include low imaging contrast, angular dependency, and limited ability to monitor the temperature. Photoacoustic (PA) imaging is a relatively new imaging modality that inherits the advantages of US imaging and offers enhanced capabilities for laser-guided ablations, such as accurate, angle-independent tracking of ablation catheters, the potential for quantitative thermometry, and monitoring thermal lesion formation. This work provides an overview of ultrasound-guided procedures and how different US-related artifacts limit their utility, followed by introducing PA as complementary to US as a solution to address the existing limitations and improve ablation outcomes. Furthermore, we highlight the integration of PA-driven features into existing US-guided laser ablation systems, along with their limitations and future outlooks. Integrated US/PA-guided laser ablation procedures can lead to safer and more precise treatment outcomes.
Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Advanced Sensing and Imaging Technologies)
Open AccessArticle
Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition
by
Liangliang Cheng, Justin de Groot, Kun Xie, Yanxin Si and Xiaodong Han
Sensors 2024, 24(11), 3541; https://doi.org/10.3390/s24113541 - 30 May 2024
Abstract
Accelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations. With rapid advancements in computer vision and deep learning, research into tracking the motion of individual pixels
[...] Read more.
Accelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations. With rapid advancements in computer vision and deep learning, research into tracking the motion of individual pixels with vision cameras has increased. The recently developed CoTracker, a transformer-based model, has demonstrated excellence in motion tracking, yet its performance in measuring structural vibrations has not been fully explored. This paper investigates the efficacy of the CoTracker model in extracting full-field structural vibrations using cameras. It is initially applied to capture the dense point movements in video sequences of a cantilever beam recorded using a high-speed camera. Subsequently, modal analysis using delay-embedding dynamic mode decomposition (DMD) is conducted to extract modal parameters including natural frequencies, damping ratios, and mode shapes. The results, benchmarked against those from a reference accelerometer and the Finite Element Method (FEM) result, demonstrate CoTracker’s high potential for general applicability in structural vibration measurements.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features
by
Xiaoyu Li, Baozhu Jia, Zhiqiang Liao and Xin Wang
Sensors 2024, 24(11), 3540; https://doi.org/10.3390/s24113540 (registering DOI) - 30 May 2024
Abstract
In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed.
[...] Read more.
In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.
Full article
(This article belongs to the Section Physical Sensors)
►▼
Show Figures
Figure 1
Open AccessReview
A Review of Image Sensors Used in Near-Infrared and Shortwave Infrared Fluorescence Imaging
by
Banghe Zhu and Henry Jonathan
Sensors 2024, 24(11), 3539; https://doi.org/10.3390/s24113539 - 30 May 2024
Abstract
To translate near-infrared (NIR) and shortwave infrared (SWIR) fluorescence imaging into the clinic, the paired imaging device needs to detect trace doses of fluorescent imaging agents. Except for the filtration scheme and excitation light source, the image sensor used will finally determine the
[...] Read more.
To translate near-infrared (NIR) and shortwave infrared (SWIR) fluorescence imaging into the clinic, the paired imaging device needs to detect trace doses of fluorescent imaging agents. Except for the filtration scheme and excitation light source, the image sensor used will finally determine the detection limitations of NIR and SWIR fluorescence imaging systems. In this review, we investigate the current state-of-the-art image sensors used in NIR and SWIR fluorescence imaging systems and discuss the advantages and limitations of their characteristics, such as readout architecture and noise factors. Finally, the imaging performance of these image sensors is evaluated and compared.
Full article
(This article belongs to the Special Issue Recent Advances in Fluorescence Sensing and Imaging)
Open AccessArticle
Three-Dimensional Segmentation of Equine Paranasal Sinuses in Multidetector Computed Tomography Datasets: Preliminary Morphometric Assessment Assisted with Clustering Analysis
by
Marta Borowska, Paweł Lipowicz, Kristina Daunoravičienė, Bernard Turek, Tomasz Jasiński, Jolanta Pauk and Małgorzata Domino
Sensors 2024, 24(11), 3538; https://doi.org/10.3390/s24113538 (registering DOI) - 30 May 2024
Abstract
The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering
[...] Read more.
The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2–25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.
Full article
(This article belongs to the Special Issue Challenges and Future Trends of 3D Image Sensing, Visualization, and Processing)
Open AccessArticle
Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images
by
Ziwei Song, Feifei Ding, Weiwei Wu, Zhuhuang Zhou and Shuicai Wu
Sensors 2024, 24(11), 3537; https://doi.org/10.3390/s24113537 - 30 May 2024
Abstract
Objective: Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors’ puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on
[...] Read more.
Objective: Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors’ puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on CT images is designed and implemented. Methods: The system mainly includes three modules: image segmentation and three-dimensional reconstruction, automatic surgical path planning, and image information management. Through organ segmentation and three- dimensional reconstruction based on CT images, the personalized abdominal spatial anatomical structure of patients is obtained, which is convenient for surgical path planning. The weighted summation method based on clinical constraints and the concept of Pareto optimality are used to solve the multi-objective optimization problem, screen the optimal needle entry path, and realize the automatic planning of the thermal ablation path. The image information database was established to store the information related to the surgical path. Results: In the discussion with clinicians, more than 78% of the paths generated by the planning system were considered to be effective, and the efficiency of system path planning is higher than doctors’ planning efficiency. Conclusion: After improvement, the system can be used for the planning of the thermal ablation path of a liver tumor and has certain clinical application value.
Full article
(This article belongs to the Special Issue Biomedical Engineering and Biotechnology Systems)
Open AccessArticle
Multivariable Iterative Learning Control Design for Precision Control of Flexible Feed Drives
by
Yulin Wang and Tesheng Hsiao
Sensors 2024, 24(11), 3536; https://doi.org/10.3390/s24113536 - 30 May 2024
Abstract
Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position
[...] Read more.
Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment.
Full article
(This article belongs to the Topic Industrial Control Systems)
►▼
Show Figures
Graphical abstract
Open AccessArticle
Hybrid Vibration Sensor for Equipment Monitoring and Diagnostics
by
Ivan V. Bryakin, Igor V. Bochkarev, Vadim R. Khramshin and Vadim R. Gasiyarov
Sensors 2024, 24(11), 3535; https://doi.org/10.3390/s24113535 - 30 May 2024
Abstract
Vibration diagnostics based on vibroacoustic signal data belong to the most common ways to monitor the technical condition of equipment and technical structures. The paper considers the general issues of vibration-based diagnostics and shows that in general, it is required to monitor both
[...] Read more.
Vibration diagnostics based on vibroacoustic signal data belong to the most common ways to monitor the technical condition of equipment and technical structures. The paper considers the general issues of vibration-based diagnostics and shows that in general, it is required to monitor both axial and torsional oscillations, as well as the inclination angle, occurring during the operation of various technical objects. To comprehensively monitor these parameters, a hybrid vibration sensor is proposed, simultaneously implementing three operating modes: recording linear displacements of the vibrating object; recording the rotation angle of the object at its torsional oscillations; recording the object angular deviation from the vertical component of the natural local geomagnetic field, i.e., the inclinometer mode. The proposed hybrid sensor design is described, and a theoretical analysis of the sensor’s operation in each of the aforementioned operating modes is performed. The authors show that in the inclinometer mode the sensor actually operates as a fluxgate meter. Generalizing the results of the sensor’s operation simultaneously in all three operating modes, an equation for the total output data signal has been obtained, which allows for obtaining the required information on the current values of linear displacements and rotation and inclination angles by selectively filtering it with respective three filters tuned to specific frequencies. The experimental studies of the proposed hybrid vibration sensor confirmed its ability to record various vibrational disturbances and changes in the inclination angle of the monitored object.
Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
by
Sina Rezaei, Angelina Maier and Hossein Arefi
Sensors 2024, 24(11), 3534; https://doi.org/10.3390/s24113534 - 30 May 2024
Abstract
Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera
[...] Read more.
Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Planning Socially Expressive Mobile Robot Trajectories
by
Philip Scales, Olivier Aycard and Véronique Aubergé
Sensors 2024, 24(11), 3533; https://doi.org/10.3390/s24113533 - 30 May 2024
Abstract
Many mobile robotics applications require robots to navigate around humans who may interpret the robot’s motion in terms of social attitudes and intentions. It is essential to understand which aspects of the robot’s motion are related to such perceptions so that we may
[...] Read more.
Many mobile robotics applications require robots to navigate around humans who may interpret the robot’s motion in terms of social attitudes and intentions. It is essential to understand which aspects of the robot’s motion are related to such perceptions so that we may design appropriate navigation algorithms. Current works in social navigation tend to strive towards a single ideal style of motion defined with respect to concepts such as comfort, naturalness, or legibility. These algorithms cannot be configured to alter trajectory features to control the social interpretations made by humans. In this work, we firstly present logistic regression models based on perception experiments linking human perceptions to a corpus of linear velocity profiles, establishing that various trajectory features impact human social perception of the robot. Secondly, we formulate a trajectory planning problem in the form of a constrained optimization, using novel constraints that can be selectively applied to shape the trajectory such that it generates the desired social perception. We demonstrate the ability of the proposed algorithm to accurately change each of the features of the generated trajectories based on the selected constraints, enabling subtle variations in the robot’s motion to be consistently applied. By controlling the trajectories to induce different social perceptions, we provide a tool to better tailor the robot’s actions to its role and deployment context to enhance acceptability.
Full article
(This article belongs to the Special Issue Applications of Intelligent Robots: Sensing, Interaction, Navigation and Control Systems)
►▼
Show Figures
Figure 1
Open AccessArticle
Designing High Performance Carbon/ZnSn(OH)6-Based Humidity Sensors
by
Min Zhang, Hongguang Jia, Shuying Wang and Zhenya Zhang
Sensors 2024, 24(11), 3532; https://doi.org/10.3390/s24113532 - 30 May 2024
Abstract
In this work, pure phase and carbon/ZnSn(OH)6 samples were synthesized by a hydrothermal method. The composite sample’s structure, morphology, and functional groups were investigated by X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy. Subsequently, ZnSn(OH)6
[...] Read more.
In this work, pure phase and carbon/ZnSn(OH)6 samples were synthesized by a hydrothermal method. The composite sample’s structure, morphology, and functional groups were investigated by X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, and Fourier transform infrared spectroscopy. Subsequently, ZnSn(OH)6 samples were modified with different carbon contents, and their humidity-sensing properties were investigated. The introduction of carbon increased the specific surface area of pure ZnSn(OH)6 samples, thus significantly improving the sensors’ humidity sensing response. The C10-ZnSn(OH)6 sensor exhibited a high response, up to three orders of magnitude, a humidity hysteresisof 13.5%, a fast response time of 3.2 s, and a recovery time of 24.4 s. The humidity sensor’s possible humidity sensing mechanism was also analyzed using the AC complex impedance puissance method with a simulated equivalent circuit. These results revealed that ZnSn(OH)6 can effectively detect ambient humidity and that the introduction of carbon significantly improves its humidity-sensing performance. The study provides an effective strategy for understanding and designing ZnSn(OH)6-based humidity sensors.
Full article
(This article belongs to the Collection Gas Sensors)
►▼
Show Figures
Figure 1
Open AccessReview
Bibliometric Analysis of Weather Radar Research from 1945 to 2024: Formations, Developments, and Trends
by
Yin Liu
Sensors 2024, 24(11), 3531; https://doi.org/10.3390/s24113531 - 30 May 2024
Abstract
In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar
[...] Read more.
In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar research from 1945 to 2024, employing scientometric methods to investigate 13,981 publications from the Web of Science (WoS) core collection database. This study aims to unravel, for the first time, the foundational structures shaping the knowledge domain of weather radar over an 80-year period, exploring general features, collaboration, co-citation, and keyword co-occurrence. Key findings reveal a significant surge in both publications and citations post-1990, peaking in 2022 with 1083 publications and 13832 citations, signaling sustained growth and interest in the field after a period of stagnation. The United States, China, and European countries emerge as key drivers of weather radar research, with robust international collaboration playing a pivotal role in the field’s rapid evolution. Analysis uncovers 30 distinct co-citation clusters, showcasing the progression of weather radar knowledge structures. Notably, deep learning emerges as a dynamic cluster, garnering attention and yielding substantial outcomes in contemporary research efforts. Over eight decades, the focus of weather radar investigations has transitioned from hardware and software enhancements to Artificial Intelligence (AI) technology integration and multifunctional applications across diverse scenarios. This study identifies four key areas for future research: leveraging AI technology, advancing all-weather observation techniques, enhancing system refinement, and fostering networked collaborative observation technologies. This research endeavors to support academics by offering an in-depth comprehension of the progression of weather radar research. The findings can be a valuable resource for scholars in efficiently locating pertinent publications and journals. Furthermore, policymakers can rely on the insights gleaned from this study as a well-organized reference point.
Full article
(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)
►▼
Show Figures
Figure 1
Open AccessArticle
A Recommendation System for Prosumers Based on Large Language Models
by
Simona-Vasilica Oprea and Adela Bâra
Sensors 2024, 24(11), 3530; https://doi.org/10.3390/s24113530 - 30 May 2024
Abstract
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min
[...] Read more.
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home’s comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers.
Full article
(This article belongs to the Special Issue IoT and Big Data Analytics for Smart Cities)
►▼
Show Figures
Figure 1
Open AccessArticle
Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map
by
Zhihao Su, Afzan Adam, Mohammad Faidzul Nasrudin and Anton Satria Prabuwono
Sensors 2024, 24(11), 3529; https://doi.org/10.3390/s24113529 - 30 May 2024
Abstract
Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods.
[...] Read more.
Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a “box map” based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a “box map”. In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research.
Full article
(This article belongs to the Special Issue Image Processing and Analysis for Object Detection: 2nd Edition)
►▼
Show Figures
Figure 1
Open AccessArticle
Study on Sensing Urine Concentrations in Water Using a Microwave Sensor Based on Hilbert Structure
by
Rusul Khalid Abdulsattar, Musab T. S. Al-Kaltakchi, Iulia Andreea Mocanu, Amer Abbood Al-Behadili and Zaid A. Abdu Hassain
Sensors 2024, 24(11), 3528; https://doi.org/10.3390/s24113528 - 30 May 2024
Abstract
In this study, a two-port network-based microwave sensor for liquid characterization is presented. The suggested sensor is built as a miniature microwave resonator using the third iteration of Hilbert’s fractal architecture. The suggested structure is used with the T-resonator to raise the sensor
[...] Read more.
In this study, a two-port network-based microwave sensor for liquid characterization is presented. The suggested sensor is built as a miniature microwave resonator using the third iteration of Hilbert’s fractal architecture. The suggested structure is used with the T-resonator to raise the sensor quality factor. The suggested sensor is printed on a FR4 substrate and has a footprint of . Analytically, a theoretical investigation is made to clarify how the suggested sensor might function. The suggested sensor is created and put to the test in an experiment. Later, two pans to contain the urine Sample Under Test (SUT) are printed on the sensor. Before loading the SUT, it is discovered that the suggested structure’s frequency resonance is 0.46 GHz. An 18 MHz frequency shift is added to the initial resonance after the pans are printed. They monitor the S-parameters in terms of S12 regarding the change in water content in the urine samples, allowing for the sensing component to be completed. As a result, 10 different samples with varying urine percentages are added to the suggested sensor to evaluate its ability to detect the presence of urine. Finally, it is discovered that the suggested process’ measurements and corresponding simulated outcomes agreed quite well.
Full article
(This article belongs to the Section Physical Sensors)
►▼
Show Figures
Figure 1
Open AccessArticle
Instrumental Evaluation of the Effects of Vertebral Consolidation Surgery on Trunk Muscle Activations and Co-Activations in Patients with Multiple Myeloma: Preliminary Results
by
Barbara Montante, Benedetta Zampa, Luca Balestreri, Rosanna Ciancia, Giorgia Chini, Alberto Ranavolo, Maurizio Rupolo, Zimi Sawacha, Martina Urbani, Tiwana Varrecchia and Mariagrazia Michieli
Sensors 2024, 24(11), 3527; https://doi.org/10.3390/s24113527 - 30 May 2024
Abstract
Multiple myeloma (MM) patients complain of pain and stiffness limiting motility. To determine if patients can benefit from vertebroplasty, we assessed muscle activation and co-activation before and after surgery. Five patients with MM and five healthy controls performed sitting-to-standing and lifting tasks. Patients
[...] Read more.
Multiple myeloma (MM) patients complain of pain and stiffness limiting motility. To determine if patients can benefit from vertebroplasty, we assessed muscle activation and co-activation before and after surgery. Five patients with MM and five healthy controls performed sitting-to-standing and lifting tasks. Patients performed the task before and one month after surgery. Surface electromyography (sEMG) was recorded bilaterally over the erector spinae longissimus and rectus abdominis superior muscles to evaluate the trunk muscle activation and co-activation and their mean, maximum, and full width at half maximum were evaluated. Statistical analyses were performed to compare MM patients before and after the surgery, MM and healthy controls and to investigate any correlations between the muscle’s parameters and the severity of pain in patients. The results reveal increased activations and co-activations after vertebroplasty as well as in comparison with healthy controls suggesting how MM patients try to control the trunk before and after vertebroplasty surgery. The findings confirm the beneficial effects of vertebral consolidation on the pain experienced by the patient, despite an overall increase in trunk muscle activation and co-activation. Therefore, it is important to provide patients with rehabilitation treatment early after surgery to facilitate the CNS to correctly stabilize the spine without overloading it with excessive co-activations.
Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation)
►▼
Show Figures
Figure 1
Open AccessReview
Implantable Pressure-Sensing Devices for Monitoring Abdominal Aortic Aneurysms in Post-Endovascular Aneurysm Repair
by
Nuno P. Silva, Bilal Amin, Eoghan Dunne, Niamh Hynes, Martin O’Halloran and Adnan Elahi
Sensors 2024, 24(11), 3526; https://doi.org/10.3390/s24113526 - 30 May 2024
Abstract
Over the past two decades, there has been extensive research into surveillance methods for the post-endovascular repair of abdominal aortic aneurysms, highlighting the importance of these technologies in supplementing or even replacing conventional image-screening modalities. This review aims to provide an overview of
[...] Read more.
Over the past two decades, there has been extensive research into surveillance methods for the post-endovascular repair of abdominal aortic aneurysms, highlighting the importance of these technologies in supplementing or even replacing conventional image-screening modalities. This review aims to provide an overview of the current status of alternative surveillance solutions for endovascular aneurysm repair, while also identifying potential aneurysm features that could be used to develop novel monitoring technologies. It offers a comprehensive review of these recent clinical advances, comparing new and standard clinical practices. After introducing the clinical understanding of abdominal aortic aneurysms and exploring current treatment procedures, the paper discusses the current surveillance methods for endovascular repair, contrasting them with recent pressure-sensing technologies. The literature on three commercial pressure-sensing devices for post-endovascular repair surveillance is analyzed. Various pre-clinical and clinical studies assessing the safety and efficacy of these devices are reviewed, providing a comparative summary of their outcomes. The review of the results from pre-clinical and clinical studies suggests a consistent trend of decreased blood pressure in the excluded aneurysm sac post-repair. However, despite successful pressure readings from the aneurysm sac, no strong link has been established to translate these measurements into the presence or absence of endoleaks. Furthermore, the results do not allow for a conclusive determination of ongoing aneurysm sac growth. Consequently, a strong clinical need persists for monitoring endoleaks and aneurysm growth following endovascular repair.
Full article
(This article belongs to the Special Issue Feature Review Papers in the Biomedical Sensors Section)
►▼
Show Figures
Figure 1
Journal Menu
► ▼ Journal Menu-
- Sensors Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal Browser-
arrow_forward_ios
Forthcoming issue
arrow_forward_ios Current issue - Vol. 24 (2024)
- Vol. 23 (2023)
- Vol. 22 (2022)
- Vol. 21 (2021)
- Vol. 20 (2020)
- Vol. 19 (2019)
- Vol. 18 (2018)
- Vol. 17 (2017)
- Vol. 16 (2016)
- Vol. 15 (2015)
- Vol. 14 (2014)
- Vol. 13 (2013)
- Vol. 12 (2012)
- Vol. 11 (2011)
- Vol. 10 (2010)
- Vol. 9 (2009)
- Vol. 8 (2008)
- Vol. 7 (2007)
- Vol. 6 (2006)
- Vol. 5 (2005)
- Vol. 4 (2004)
- Vol. 3 (2003)
- Vol. 2 (2002)
- Vol. 1 (2001)
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Energies, JMSE, Safety, Sensors, Processes
Safety, Reliability and Effectiveness of Internal Combustion Engines
Topic Editors: Leszek Chybowski, Jarosław Myśków, Przemysław Kowalak, Andrzej JakubowskiDeadline: 31 May 2024
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Topic in
Applied Sciences, Energies, Machines, Sensors, Vehicles
Vehicle Dynamics and Control
Topic Editors: Peter Gaspar, Junnian WangDeadline: 30 June 2024
Topic in
Acoustics, Environments, Remote Sensing, Sensors, Vehicles
Environmental Noise Prediction, Measurement and Control
Topic Editors: Bowen Hou, Jinhan MoDeadline: 20 July 2024
Conferences
Special Issues
Special Issue in
Sensors
Selected Papers from 20th World Conference on Non-Destructive Testing (WCNDT 2024)
Guest Editor: Seunghee ParkDeadline: 31 May 2024
Special Issue in
Sensors
Novel Sensors and Algorithms for Outdoor Mobile Robot
Guest Editors: Levente Tamás, Andras MajdikDeadline: 20 June 2024
Special Issue in
Sensors
Deep Learning Methods for Human Activity Recognition and Emotion Detection
Guest Editor: Mario Munoz-OrganeroDeadline: 30 June 2024
Special Issue in
Sensors
Detection and Measurement of Radioactive Noble Gases
Guest Editor: Dobromir PressyanovDeadline: 20 July 2024
Topical Collections
Topical Collection in
Sensors
Robotic and Sensor Technologies in Environmental Exploration and Monitoring
Collection Editors: Jacopo Aguzzi, Corrado Costa, Sergio Stefanni, Valerio Funari
Topical Collection in
Sensors
Microfluidic Sensors
Collection Editors: Sabina Merlo, Klaus Stefan Drese