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Sensors, Volume 25, Issue 9 (May-1 2025) – 77 articles

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19 pages, 1225 KiB  
Article
Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis
by Simone Mari, Giovanni Bucci, Fabrizio Ciancetta, Edoardo Fiorucci and Andrea Fioravanti
Sensors 2025, 25(9), 2710; https://doi.org/10.3390/s25092710 - 24 Apr 2025
Abstract
This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual [...] Read more.
This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contaminants critical for quality assurance. The system acquires high-resolution monochrome images under various lighting configurations, including natural light and infrared (IR) at 850 nm and 940 nm, with different angles of incidence. Four blob detection algorithms—adaptive thresholding, Laplacian of Gaussian (LoG), Difference of Gaussians (DoG), and Determinant of Hessian (DoH)—were implemented and evaluated based on their ability to detect surface impurities. Performance was assessed by comparing the total detected blob area before and after the cleaning process, providing a proxy for both sensitivity and false positive rate. Among the tested methods, adaptive thresholding under 30° natural light produced the best results, with a statistically significant z-score of +2.05 in the pre-wash phase and reduced false detections in post-wash conditions. The LoG and DoG methods were more prone to spurious detections, while DoH demonstrated intermediate performance but struggled with reflective surfaces. The proposed approach offers a cost-effective and scalable solution for real-time quality control in industrial environments, with the potential to improve process reliability and reduce waste due to surface defects. Full article
(This article belongs to the Special Issue Intelligent Industrial Process Control Systems: 2nd Edition)
18 pages, 1980 KiB  
Article
A Robust Multivariate Time Series Classification Approach Based on Topological Data Analysis for Channel Fault Tolerance
by Seong-Yeon Jeung and Jang-Woo Kwon
Sensors 2025, 25(9), 2709; https://doi.org/10.3390/s25092709 - 24 Apr 2025
Abstract
In this study, we propose a robust artificial intelligence (AI) model for vibration monitoring of rotating equipment to support reliable operation across various industries, including manufacturing, power plants, and aerospace. The reliability and completeness of sensor data are essential for early detection of [...] Read more.
In this study, we propose a robust artificial intelligence (AI) model for vibration monitoring of rotating equipment to support reliable operation across various industries, including manufacturing, power plants, and aerospace. The reliability and completeness of sensor data are essential for early detection of anomalies in equipment and for performing predictive maintenance. While AI-based predictive maintenance and condition-monitoring technologies have advanced in recent years, the issue of data loss caused by sensor failures remains a significant challenge that leads to performance degradation of AI models. In particular, for equipment utilizing multiple sensors, the complete loss of data from a single sensor significantly diminishes the predictive maintenance capability of AI models, thereby reducing their reliability. To address this issue, this study introduces topological data analysis (TDA) to develop a robust AI model. TDA analyzes the topological structure of sensor data to generate consistent feature vectors that capture the intrinsic characteristics of the data. This enables stable predictions even when certain channels of multi-sensor data are entirely missing. The proposed method demonstrates high performance resilience under conditions of partial sensor data loss, thereby contributing to enhanced reliability of AI-based predictive maintenance systems and the establishment of efficient maintenance strategies in the future. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
19 pages, 1974 KiB  
Article
MFBCE: A Multi-Focal Bionic Compound Eye for Distance Measurement
by Qiwei Liu, Xia Wang, Jiaan Xue, Shuaijun Lv and Ranfeng Wei
Sensors 2025, 25(9), 2708; https://doi.org/10.3390/s25092708 - 24 Apr 2025
Abstract
In response to the demand for small-size, high-precision, and real-time target distance measurement in platforms such as autonomous vehicles and drones, this paper investigates the multi-focal bionic compound eye (MFBCE) and its associated distance measurement algorithm. MFBCE was designed to integrate multiple lenses [...] Read more.
In response to the demand for small-size, high-precision, and real-time target distance measurement in platforms such as autonomous vehicles and drones, this paper investigates the multi-focal bionic compound eye (MFBCE) and its associated distance measurement algorithm. MFBCE was designed to integrate multiple lenses with different focal lengths and a CMOS array. Based on this system, a multi-eye distance measurement algorithm based on target detection was proposed. The algorithm derives the application of binocular distance measurement on cameras with different focal lengths, overcoming the limitation of traditional binocular algorithms that only work with identical cameras. By utilizing the multi-scale information obtained from multiple lenses with different focal lengths, the ranging accuracy of the MFBCE is improved. The telephoto lenses, with their narrow field of view, are beneficial for capturing detailed target information, while wide-angle lenses, with their larger field of view, are useful for acquiring information about the target’s environment. Experiments using the least squares method for ranging targets at 100 cm yielded a mean absolute error (MAE) of 1.05, approximately one-half of the binocular distance measurement algorithm. The proposed MFBCE demonstrates significant potential for applications in near-range obstacle avoidance, robotic grasping, and assisted driving. Full article
(This article belongs to the Section Biosensors)
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18 pages, 7042 KiB  
Article
The Aboveground Biomass Estimation of the Grain for Green Program Stands Using UAV-LiDAR and Sentinel-2 Data
by Gaoke Yueliang, Gentana Ge, Xiaosong Li, Cuicui Ji, Tiancan Wang, Tong Shen, Yubo Zhi, Chaochao Chen and Licheng Zhao
Sensors 2025, 25(9), 2707; https://doi.org/10.3390/s25092707 - 24 Apr 2025
Abstract
Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, [...] Read more.
Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, research in this area remains significantly limited. In this study, we developed the optimal tree height-diameter at breast height (DBH) growth models for major tree species and constructed a high-quality AGB sample dataset by integrating airborne LiDAR data and tree species information. Then, the AGB of the GGP stands was estimated using the Sentinel-2 data and the gradient boosting decision tree (GBDT) algorithm. The results showed that the AGB sample dataset constructed using the proposed approach exhibited strong consistency with field-measured data (R2 = 0.89). The GBDT-based AGB estimation model shows high accuracy, with an R2 of 0.96 and a root mean square error (RMSE) of 560 g/m2. Key variables such as tasseled cap greenness (TCG), visible-band difference vegetation index (VDVI), red-edge normalized difference vegetation index (RENDVI), and visible-band difference vegetation index (VDVI) were identified as highly important. This highlights that vegetation indices and tasseled cap transformation index information are key factors in estimating AGB. The AGB of major tree species in the new round of the GGP stands in Inner Mongolia ranged from 120 to 9253 g/m2, with mean values of 978 g/m2 for poplar, 622 g/m2 for Mongolian Scots pine, and 313 g/m2 for Chinese red pine species. This study offers a practical method for AGB estimation in GGP stands, contributing significantly to sustainable forest management and ecological conservation efforts. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
24 pages, 3951 KiB  
Article
Optimization of OPM-MEG Layouts with a Limited Number of Sensors
by Urban Marhl, Rok Hren, Tilmann Sander and Vojko Jazbinšek
Sensors 2025, 25(9), 2706; https://doi.org/10.3390/s25092706 - 24 Apr 2025
Abstract
Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture [...] Read more.
Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information. Full article
(This article belongs to the Collection Medical Applications of Sensor Systems and Devices)
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18 pages, 2715 KiB  
Article
Fabrication of ZnO Thin Films Doped with Na at Different Percentages for Sensing CO2 in Small Quantities at Room Temperature
by Marina Stramarkou, Achilleas Bardakas, Magdalini Krokida and Christos Tsamis
Sensors 2025, 25(9), 2705; https://doi.org/10.3390/s25092705 - 24 Apr 2025
Abstract
The objective of this study is the fabrication of sensors which can detect modifications in CO2 concentrations at room temperature, thus indicating the quality or microbial spoilage of food products when incorporated into food packaging. ZnO nanostructures are known for their ability [...] Read more.
The objective of this study is the fabrication of sensors which can detect modifications in CO2 concentrations at room temperature, thus indicating the quality or microbial spoilage of food products when incorporated into food packaging. ZnO nanostructures are known for their ability to detect organic gases; however, their effectiveness is limited to high temperatures (greater than 200 °C). To overcome this limitation, sodium (Na) doping is investigated as a way to enhance the sensing properties of ZnO films and lower the working temperature. In this study, undoped and Na-doped ZnO thin films were developed via the sol-gel method with different Na percentages (2.5, 5 and 7.5%) and were deposited via spin coating. The crystal structure, the morphology, and the surface topography of the developed films were characterized by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Atomic Force Microscopy (AFM), respectively. Furthermore, the response to CO2 was measured by varying its concentration up to 500 ppm at room temperature. All the developed films presented the characteristic diffraction peaks of the ZnO wurtzite hexagonal crystal structure. SEM revealed that the films consisted of densely packed grains, with an average particle size of 58 nm. Na doping increased the film thickness but reduced the surface roughness. Finally, the developed sensors demonstrated very good CO2 sensing properties, with the 2.5% Na-doped sensor having an enhanced sensing performance concerning sensitivity, response, and recovery times. This leads to the conclusion that Na-doped ZnO sensors could be used for the detection of microbial spoilage in food products at room temperature, making them suitable for smart food packaging applications. Full article
(This article belongs to the Section Chemical Sensors)
21 pages, 706 KiB  
Article
MUF-Net: A Novel Self-Attention Based Dual-Task Learning Approach for Automatic Left Ventricle Segmentation in Echocardiography
by Juan Lyu, Jinpeng Meng, Yu Zhang and Sai Ho Ling
Sensors 2025, 25(9), 2704; https://doi.org/10.3390/s25092704 - 24 Apr 2025
Abstract
Left ventricular ejection fraction (LVEF) is a critical indicator for assessing cardiac function and diagnosing heart disease. LVEF can be derived by estimating the left ventricular volume from end-systolic and end-diastolic frames through echocardiography segmentation. However, current algorithms either focus primarily on single-frame [...] Read more.
Left ventricular ejection fraction (LVEF) is a critical indicator for assessing cardiac function and diagnosing heart disease. LVEF can be derived by estimating the left ventricular volume from end-systolic and end-diastolic frames through echocardiography segmentation. However, current algorithms either focus primarily on single-frame segmentation, neglecting the temporal and spatial correlations between consecutive frames, or often fail to effectively address the inherent challenges posed by the low-contrast and fuzzy edges characteristic of echocardiography, thereby resulting in suboptimal segmentation outcomes. In this study, we propose a novel self-attention-based dual-task learning approach for automatic left ventricle segmentation. First, we introduce a multi-scale edge-attention U-Net to achieve supervised semantic segmentation of echocardiography. Second, an optical flow network is developed to capture the changes in the optical flow fields between frames in an unsupervised manner. These two tasks are then jointly trained using a temporal consistency mechanism to extract spatio-temporal features across frames. Experimental results demonstrate that our model outperforms existing segmentation methods. Our proposed method not only enhances the performance of semantic segmentation but also improves the consistency of segmentation between consecutive frames. Full article
(This article belongs to the Special Issue Transformer-Based Deep Learning in Medical Imaging and Healthy Sensors)
20 pages, 3004 KiB  
Article
An Evaluation of the Acoustic Activity Emitted in Fiber-Reinforced Concrete Under Flexure at Low Temperature
by Omar A. Kamel, Ahmed A. Abouhussien, Assem A. A. Hassan and Basem H. AbdelAleem
Sensors 2025, 25(9), 2703; https://doi.org/10.3390/s25092703 - 24 Apr 2025
Abstract
This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel [...] Read more.
This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (Sr) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, Sr, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article
(This article belongs to the Special Issue Novel Sensor Technologies for Civil Infrastructure Monitoring)
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17 pages, 14682 KiB  
Article
Research on Space Targets Simulation Modulation Algorithm Combined Global–Local Multi-Spectral Radiation Features
by Yu Zhang, Songzhou Yang, Zhipeng Wei, Jian Zhang, Bin Zhao, Dianwu Ren, Jingrui Sun, Lu Wang, Taiyang Ren, Dongpeng Yang and Guoyu Zhang
Sensors 2025, 25(9), 2702; https://doi.org/10.3390/s25092702 - 24 Apr 2025
Abstract
To solve the international problem of global–local radiation features simulation of multi-spectral space targets, this paper proposes a multi-spectral space target simulation modulation algorithm that can combine global–local spectral radiation features. An overall architecture of a series-parallel multi-source information fusion space target simulation [...] Read more.
To solve the international problem of global–local radiation features simulation of multi-spectral space targets, this paper proposes a multi-spectral space target simulation modulation algorithm that can combine global–local spectral radiation features. An overall architecture of a series-parallel multi-source information fusion space target simulation system (MITS) is constructed, and a global–local multi-spectral radiation feature modulation link is built. A multi-spectral feature modulation algorithm consisting of three modules, including optical engine non-uniformity compensation, global spectral radiant energy modulation, and local radiant grayscale modulation, is designed, and an experimental platform is built to verify the correctness and advancement of the proposed algorithm. The results indicate that the non-uniformity is better than 3.78%, the global simulation error is better than −4.56%, and the local simulation error is better than 4.25%. It is one of the few multi-spectral target simulation modulation algorithms worldwide that can combine the global whole and local details. It supports the performance test and technology iteration of multi-spectral optical loads. It helps to supplement the theoretical system of multi-spectral space target simulation and enhance the ground-based semi-physical simulation link of optical loads. Full article
(This article belongs to the Special Issue Advances in Optical Sensing, Instrumentation and Systems: 2nd Edition)
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15 pages, 3162 KiB  
Article
A Monocular Camera as an Operation Logger for Motorized Mobility Scooters: Visual Odometry Method to Estimate Steering and Throttle Angles
by Naoto Haraguchi, Yi Liu, Haruki Sugiyama, Kazunori Hase and Jun Suzurikawa
Sensors 2025, 25(9), 2701; https://doi.org/10.3390/s25092701 - 24 Apr 2025
Abstract
Motorized mobility scooters (MMSs) are vital assistive technology devices that facilitate independent living for older adults. In many cases, older adults with physical impairments operate MMSs without special licenses, increasing the risk of accidents caused by operational errors. Although sensing systems have been [...] Read more.
Motorized mobility scooters (MMSs) are vital assistive technology devices that facilitate independent living for older adults. In many cases, older adults with physical impairments operate MMSs without special licenses, increasing the risk of accidents caused by operational errors. Although sensing systems have been developed to record MMS operations and evaluate driving skills, they face challenges in clinical applications because of the complexity of installing inertial measurement units (IMUs). This study proposes a novel recording system for MMS operation that uses a compact single-lens camera and image processing. The system estimates steering and throttle angles during MMS operation using optical flow and template matching approaches. Estimation relies on road surface images captured by a single monocular camera, significantly reducing the complexity of the sensor setup. The proposed system successfully estimated the steering angle with comparable accuracy to existing approaches using IMUs. Estimation of the throttle angle was negatively affected by the inertia of the MMS body during acceleration and deceleration but demonstrated high accuracy during stable driving conditions. This method provides a fundamental computational technique for measuring MMS operations using camera images. With its simple setup, the proposed system enhances the usability of recording systems for evaluating MMS driving skills. Full article
(This article belongs to the Special Issue Sensors and Wearables for Rehabilitation)
13 pages, 3783 KiB  
Article
A Soft Capacitive Pressure Sensor Based on a Liquid Dielectric Layer
by Meng Zhang, Chengjie Qiu, Jianxiang Wang, Xuan Huang, Wu Zhang, Lip-Ket Chin and Wenli Shang
Sensors 2025, 25(9), 2700; https://doi.org/10.3390/s25092700 - 24 Apr 2025
Abstract
Soft electronic technology has broad application prospects in biomedical and wearable devices, among others, due to its flexibility, lightweight nature, and biocompatibility. Although various materials and structures have been proposed for pressure sensors based on soft electronic technology, most studies focus on a [...] Read more.
Soft electronic technology has broad application prospects in biomedical and wearable devices, among others, due to its flexibility, lightweight nature, and biocompatibility. Although various materials and structures have been proposed for pressure sensors based on soft electronic technology, most studies focus on a specific function with fixed sensitivity, lacking tunability to expand the operational range. In this work, we demonstrated a low-cost polydimethylsiloxane (PDMS)-based pressure sensor that can be easily fabricated by laser ablation and mature PDMS fabrication technology. We then employed a liquid solution to serve as the dielectric layer of the pressure sensor. By injecting different liquid solutions, the sensitivity of the capacitive pressure sensor can be easily adjusted. A 2.73-fold increase in sensitivity and excellent sensing linearity with a determination coefficient greater than 0.85 were achieved. The pressure sensor was applied to demonstrate material property measurements and Morse code adaptation. We foresee that the adjustable soft capacitive pressure sensor has extensive applications in wearable devices, material metrology, healthcare point-of-care devices, and other fields. Full article
(This article belongs to the Section Electronic Sensors)
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22 pages, 1194 KiB  
Article
Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy
by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du and Ziqing Han
Sensors 2025, 25(9), 2699; https://doi.org/10.3390/s25092699 - 24 Apr 2025
Abstract
White blood cell (WBC) classification plays a crucial role in hematopathology and clinical diagnostics. However, traditional methods are constrained by limited receptive fields and insufficient utilization of contextual information, which hinders classification performance. To address these limitations, this paper proposes an enhanced WBC [...] Read more.
White blood cell (WBC) classification plays a crucial role in hematopathology and clinical diagnostics. However, traditional methods are constrained by limited receptive fields and insufficient utilization of contextual information, which hinders classification performance. To address these limitations, this paper proposes an enhanced WBC classification algorithm, CCE-YOLOv7, which is built upon the YOLOv7 framework. The proposed method introduces four key innovations to enhance detection accuracy and model efficiency: (1) A novel Conv2Former (Convolutional Transformer) backbone was designed to combine the local pattern extraction capability of convolutional neural networks (CNNs) with the global contextual reasoning of transformers, thereby improving the expressiveness of feature representation. (2) The CARAFE (Content-Aware ReAssembly of Features) upsampling operator was adopted to replace conventional interpolation methods, thereby enhancing the spatial resolution and semantic richness of feature maps. (3) An Efficient Multi-scale Attention (EMA) module was introduced to refine multi-scale feature fusion, enabling the model to better focus on spatially relevant features critical for WBC classification. (4) Soft-NMS (Soft Non-Maximum Suppression) was used instead of traditional NMS to better preserve true positives in densely packed or overlapping cell scenarios, thereby reducing false positives and false negatives. Experimental validation was conducted on a WBC image dataset acquired using the Fourier ptychographic microscopy (FPM) system. The proposed CCE-YOLOv7 achieved a detection accuracy of 89.3%, showing a 7.8% improvement over the baseline YOLOv7. Furthermore, CCE-YOLOv7 reduced the number of parameters by 2 million and lowered computational complexity by 5.7 GFLOPs, offering an efficient and lightweight model suitable for real-time clinical applications. To further evaluate model effectiveness, comparative experiments were conducted with YOLOv8 and YOLOv11. CCE-YOLOv7 achieved a 4.1% higher detection accuracy than YOLOv8 while reducing computational cost by 2.4 GFLOPs. Compared with the more advanced YOLOv11, CCE-YOLOv7 maintained competitive accuracy (only 0.6% lower) while using significantly fewer parameters and 4.3 GFLOPs less in computation, highlighting its superior trade-off between accuracy and efficiency. These results demonstrate that CCE-YOLOv7 provides a robust, accurate, and computationally efficient solution for automated WBC classification, with significant clinical applicability. Full article
(This article belongs to the Section Biomedical Sensors)
27 pages, 1866 KiB  
Article
Smartphone-Based Deep Learning System for Detecting Ractopamine-Fed Pork Using Visual Classification Techniques
by Hong-Dar Lin, Mao-Quan He and Chou-Hsien Lin
Sensors 2025, 25(9), 2698; https://doi.org/10.3390/s25092698 - 24 Apr 2025
Abstract
Ractopamine, a beta-agonist used to enhance lean meat yield, poses health risks with excessive consumption. To comply with global trade policies, Taiwan permits imports of North American (USA. and Canadian) pork containing ractopamine, raising concerns over unclear labeling and potential misidentification as Taiwan [...] Read more.
Ractopamine, a beta-agonist used to enhance lean meat yield, poses health risks with excessive consumption. To comply with global trade policies, Taiwan permits imports of North American (USA. and Canadian) pork containing ractopamine, raising concerns over unclear labeling and potential misidentification as Taiwan pork. Given the high demand for pork, consumers need a reliable way to verify meat authenticity. To address this issue, this study proposes a smartphone-based visual detection system for meat cut and pork origin classification, extending to ractopamine detection. Consumers can use mobile devices in retail settings to analyze pork images and make informed decisions. The system employs a three-stage process: first, applying a black elliptical mask to extract the outer ROI (region of interest) for meat cut classification; then, using a black square mask to obtain the inner ROI for pork origin classification; and finally, determining ractopamine presence in North American pork. Experimental results demonstrate MobileNet’s superior accuracy and efficiency, achieving a 96% CR (classification rate) for meat cut classification, a 79.11% average CR and 90.25% F1 score for pork origin classification, and an 80.67% average CR and 80.56% F1 score for ractopamine detection. These findings confirm the system’s effectiveness in enhancing meat authenticity verification and market transparency. Full article
(This article belongs to the Special Issue Innovative Sensors and Embedded Sensor Systems for Food Analysis)
19 pages, 15307 KiB  
Article
FARVNet: A Fast and Accurate Range-View-Based Method for Semantic Segmentation of Point Clouds
by Chuang Chen, Lulu Zhao, Wenwu Guo, Xia Yuan, Shihan Tan, Jing Hu, Zhenyuan Yang, Shengjie Wang and Wenyi Ge
Sensors 2025, 25(9), 2697; https://doi.org/10.3390/s25092697 (registering DOI) - 24 Apr 2025
Abstract
Environmental perception systems provide foundational geospatial intelligence for precision mapping applications. Light Detection and Ranging (LiDAR) provides critical 3D point cloud data for environmental perception systems, yet efficiently processing unstructured point clouds while extracting semantically meaningful information remains a persistent challenge. This paper [...] Read more.
Environmental perception systems provide foundational geospatial intelligence for precision mapping applications. Light Detection and Ranging (LiDAR) provides critical 3D point cloud data for environmental perception systems, yet efficiently processing unstructured point clouds while extracting semantically meaningful information remains a persistent challenge. This paper presents FARVNet, a novel real-time Range-View (RV)-based semantic segmentation framework that explicitly models the intrinsic correlation between intensity features and spatial coordinates to enhance feature representation in point cloud analysis. Our architecture introduces three key innovations: First, the Geometric Field of View Reconstruction (GFVR) module rectifies spatial distortions and compensates for structural degradation induced during the spherical projection of 3D LiDAR point clouds onto 2D range images. Second, the Intensity Reconstruction (IR) module is employed to update the “Intensity Vanishing State” for zero-intensity points, including those from LiDAR acquisition limitations, thus enhancing the learning ability and robustness of the network. Third, the Adaptive Multi-Scale Feature Fusion (AMSFF) is applied to balance high-frequency and low-frequency features, augmenting the model expressiveness and generalization ability. Experimental evaluations demonstrate that FARVNet achieves state-of-the-art performance in single-sensor real-time segmentation tasks while maintaining computational efficiency suitable for environmental perception systems. Our method ensures high performance while balancing real-time capability, making it highly promising for LiDAR-based real-time applications. Full article
(This article belongs to the Section Radar Sensors)
27 pages, 3915 KiB  
Article
Enhancing Last-Mile Logistics: AI-Driven Fleet Optimization, Mixed Reality, and Large Language Model Assistants for Warehouse Operations
by Saverio Ieva, Ivano Bilenchi, Filippo Gramegna, Agnese Pinto, Floriano Scioscia, Michele Ruta and Giuseppe Loseto
Sensors 2025, 25(9), 2696; https://doi.org/10.3390/s25092696 - 24 Apr 2025
Abstract
Due to the rapid expansion of e-commerce and urbanization, Last-Mile Delivery (LMD) faces increasing challenges related to cost, timeliness, and sustainability. Artificial intelligence (AI) techniques are widely used to optimize fleet management, while augmented and mixed reality (AR/MR) technologies are being adopted to [...] Read more.
Due to the rapid expansion of e-commerce and urbanization, Last-Mile Delivery (LMD) faces increasing challenges related to cost, timeliness, and sustainability. Artificial intelligence (AI) techniques are widely used to optimize fleet management, while augmented and mixed reality (AR/MR) technologies are being adopted to enhance warehouse operations. However, existing approaches often treat these aspects in isolation, missing opportunities for optimization and operational efficiency gains through improved information visibility across different roles in the logistics workforce. This work proposes the adoption of novel technological solutions integrated in an LMD framework that combines AI-based optimization of shipment allocation and vehicle route planning with a knowledge graph (KG)-driven decision support system. Additionally, the paper discusses the exploitation of relevant recent tools, including large language model (LLM)-powered conversational assistants for managers and operators and MR-based headset interfaces supporting warehouse operators by providing real-time data and enabling direct interaction with the system through virtual contextual UI elements. The framework prioritizes the customizability of AI algorithms and real-time information sharing between stakeholders. An experiment with a system prototype in the Apulia region is presented to evaluate the feasibility of the system in a realistic logistics scenario, highlighting its potential to enhance coordination and efficiency in LMD operations. The results suggest the usefulness of the approach while also identifying benefits and challenges in real-world applications. Full article
(This article belongs to the Special Issue Sensors and Smart City)
22 pages, 9809 KiB  
Article
Research on the Design of an On-Line Lubrication System for Wire Ropes
by Fan Zhou, Yuemin Wang and Ruqing Gong
Sensors 2025, 25(9), 2695; https://doi.org/10.3390/s25092695 - 24 Apr 2025
Abstract
This study presents an on-line intelligent lubrication system utilizing specialty grease to address lubricant loss and uneven coating issues in traditional methods. Characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), the specialty grease demonstrates superior tribological performance, achieving a [...] Read more.
This study presents an on-line intelligent lubrication system utilizing specialty grease to address lubricant loss and uneven coating issues in traditional methods. Characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), the specialty grease demonstrates superior tribological performance, achieving a 46.7% reduction in the average friction coefficient and 33.3% smaller wear scar diameter under a 392 N load compared to conventional lubricants. The system features an automatic control vehicle design integrating heating, grease supply, lubrication-scraping mechanisms, and a dual closed-loop intelligent control system combining PID-based temperature regulation with machine vision. Experiments identified 50 °C as the optimal heating temperature. Kinematic modeling and grease consumption analysis guided greasing parameters optimization, validated through simulations and practical tests. Evaluated on a 20 m long, 36.5 mm diameter wire rope, the system achieved full coverage within 60 s, forming a uniform lubricant layer of 0.3–1.0 mm thickness (±0.15 mm deviation). It realizes the innovative application of high-adhesion lubricating grease, adaptive process control, and real-time thickness feedback technology, significantly improving the lubrication effect, reducing maintenance costs, and extending the lifespan of the wire rope. This provides intelligent lubrication technology support for the reliable operation of wire ropes in industrial fields. Full article
(This article belongs to the Section Industrial Sensors)
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15 pages, 4708 KiB  
Article
Spectral Correlation Demodulation Analysis for Fault Diagnosis of Planetary Gearboxes
by Xiaohui Duan, Rongzhou Lin and Zhipeng Feng
Sensors 2025, 25(9), 2694; https://doi.org/10.3390/s25092694 - 24 Apr 2025
Abstract
Planetary gearbox vibrations exhibit complex amplitude modulation (AM) and frequency modulation (FM) characteristics. The spectral correlation (SC) can reveal the cyclostationarity of rotating machinery signals, but previous studies have modeled gearbox signals as impulse response signals rather than AM–FM signals. This paper presents [...] Read more.
Planetary gearbox vibrations exhibit complex amplitude modulation (AM) and frequency modulation (FM) characteristics. The spectral correlation (SC) can reveal the cyclostationarity of rotating machinery signals, but previous studies have modeled gearbox signals as impulse response signals rather than AM–FM signals. This paper presents a fault diagnosis method for planetary gearboxes via SC based on the AM–FM model. Firstly, the theoretical expression for the spectral correlation characteristics of AM-FM signals is derived, showing that their demodulation features consist of discrete and grouped points rather than continuous vertical lines, and demonstrating the capability of SC to reveal the cyclostationarity of AM–FM signals. Then, the theoretical SC characteristics of planetary gearbox vibration signals under gear localized fault conditions are derived in closed form, providing theoretical guidance for fault diagnosis. The theoretical derivations are validated experimentally, and the localized faults on the ring, sun, and planet gears are successfully diagnosed. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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24 pages, 21210 KiB  
Article
A Novel Grouting Diffusion Monitoring System Based on ZigBee Wireless Sensor Network
by Xiangpeng Wang, Tingkai Wang, Jinyu Gao, Meng Yang, Fanqiang Lin and Yong Jia
Sensors 2025, 25(9), 2693; https://doi.org/10.3390/s25092693 - 24 Apr 2025
Abstract
Grouting technology is widely used in construction and civil engineering, where evaluating grouting effectiveness is crucial due to the uncertainty of subsurface conditions. Existing methods face drawbacks such as destructiveness, high cost, poor durability, and limited data collection. To address these issues, this [...] Read more.
Grouting technology is widely used in construction and civil engineering, where evaluating grouting effectiveness is crucial due to the uncertainty of subsurface conditions. Existing methods face drawbacks such as destructiveness, high cost, poor durability, and limited data collection. To address these issues, this paper proposes a novel wireless real-time monitoring system based on a ZigBee sensor network framework. The sensor system integrates a direct current method in geophysics with apparent resistivity measurement to assess grouting effectiveness in real time. It consists of multichannel data acquisition units with electrodes for sensing underground currents and a user control unit for centralized management and data processing. A system acquisition performance test confirmed that the differential input channel’s equivalent input noise of the ADC was only 175 μV and 188 μV, and the average error of the captured sine wave data was 4.51 mV and 4.19 mV, ensuring the voltage measurement accuracy of the data acquisition units. Stability testing of the equipment in road and construction environments showed an average RSD of 2.86% and 2.92%, respectively, indicating good stability of the measurements. ZigBee network performance tests in three simulated environments and a field test showed that the packet loss rate (PLR) was less than 2% from 0 to 50 m, ensuring network communication in grouting project scenarios. On-site experiments demonstrate that the system can simultaneously monitor multiple profiles and perform inversions in the grouting area, which can be assembled into 3D inversion images for evaluating grout diffusion, offering valuable insights for optimizing construction operations, and enhancing grouting efficiency. Full article
(This article belongs to the Topic Advances in Non-Destructive Testing Methods, 3rd Edition)
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19 pages, 865 KiB  
Systematic Review
A Comparative Study on the Integration of Eye-Tracking in Recommender Systems
by Osamah M. Al-Omair
Sensors 2025, 25(9), 2692; https://doi.org/10.3390/s25092692 - 24 Apr 2025
Abstract
This study investigated the integration of eye tracking technologies in recommender systems, focusing on their potential to enhance personalization, accuracy, and user engagement. Eye tracking metrics, including fixation duration and gaze patterns, provide a non-intrusive means of capturing real-time user preferences, which can [...] Read more.
This study investigated the integration of eye tracking technologies in recommender systems, focusing on their potential to enhance personalization, accuracy, and user engagement. Eye tracking metrics, including fixation duration and gaze patterns, provide a non-intrusive means of capturing real-time user preferences, which can lead to more effective recommendations. Through a comprehensive comparison of current studies, this paper synthesizes findings on the impact of eye tracking across application domains such as e-commerce and media. The results indicate notable improvements in recommendation accuracy with the use of gaze-based feedback. However, limitations persist, including reliance on controlled environments, limited sample diversity, and the high cost of specialized eye tracking equipment. To address these challenges, this paper proposes a structured framework that systematically integrates eye tracking data into real-time recommendation generation. The framework consists of an Eye Tracking Module, a Preferences Module, and a Recommender Module, creating an adaptive recommendation process that continuously refines user preferences based on implicit gaze-based interactions. This novel approach enhances the adaptability of recommender systems by minimizing reliance on static user profiles. Future research directions include the integration of additional behavioral indicators and the development of accessible eye tracking tools to broaden real-world impact. Eye tracking shows substantial promise in advancing recommender systems but requires further refinement to achieve practical, scalable applications across diverse contexts. Full article
(This article belongs to the Special Issue Multi-Modal Data Sensing and Processing)
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25 pages, 17637 KiB  
Article
Trinocular Vision-Driven Robotic Fertilization: Enhanced YOLOv8n for Precision Mulberry Growth Synchronization
by Ma Ming, Osama Elsherbiny and Jianmin Gao
Sensors 2025, 25(9), 2691; https://doi.org/10.3390/s25092691 - 24 Apr 2025
Abstract
This study focused on addressing the issue of delayed root system development in mulberry trees during aerosol cultivation, which is attributed to the asynchronous growth of branches and buds. To tackle this challenge, we propose an intelligent foliar fertilizer spraying system based on [...] Read more.
This study focused on addressing the issue of delayed root system development in mulberry trees during aerosol cultivation, which is attributed to the asynchronous growth of branches and buds. To tackle this challenge, we propose an intelligent foliar fertilizer spraying system based on deep learning. The system incorporates a parallel robotic arm spraying device and employs trinocular vision to capture image datasets of mulberry tree branches. After comparing YOLOv8n with other YOLO versions, we made several enhancements to the YOLOv8n model. These improvements included the introduction of the Asymptotic Feature Pyramid Network (AFPN), the optimization of feature extraction using the MSBlock module, the adoption of a dynamic ATSS label assignment strategy, and the replacement of the CIoU loss function with the Focal_XIoU loss function. Furthermore, an artificial neural network was utilized to calculate the coordinates of the robotic arm. The experimental results demonstrate that the enhanced YOLOv8n model achieved an average precision of 94.48%, representing a 6.05% improvement over the original model. Additionally, the prediction error for the robotic arm coordinates was maintained at ≤1.3%. This system effectively enables the precise location and directional fertilization of mulberry branches exhibiting lagging growth, thereby significantly promoting the synchronous development of mulberry seedlings. Full article
(This article belongs to the Section Sensors and Robotics)
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14 pages, 6527 KiB  
Article
Thickness-Tunable PDMS-Based SERS Sensing Substrates
by Diego P. Pacherrez Gallardo, Shu Kawamura, Ryo Shoji, Lina Yoshida and Binbin Weng
Sensors 2025, 25(9), 2690; https://doi.org/10.3390/s25092690 - 24 Apr 2025
Abstract
Surface-enhanced Raman scattering (SERS) spectroscopy is an ultra-sensitive analytical method with the powerful signal-molecule detection capability. Coupling with the polydimethylsiloxane (PDMS) material, SERS can be enabled on a polymeric substrate for fast-developing bio-compatible sensing applications. However, due to PDMS’s high viscosity, conventional PDMS-SERS [...] Read more.
Surface-enhanced Raman scattering (SERS) spectroscopy is an ultra-sensitive analytical method with the powerful signal-molecule detection capability. Coupling with the polydimethylsiloxane (PDMS) material, SERS can be enabled on a polymeric substrate for fast-developing bio-compatible sensing applications. However, due to PDMS’s high viscosity, conventional PDMS-SERS substrates are typically thick and stiff, limiting their freedom for engineering flexible micro/nano functioning devices. To address this issue, we propose to adopt a low viscosity decamethylcyclopentasiloxane (D5) solvent as a diluent solution. Via controlling the mixture ratio of D5 and PDMS and the spin-coating speed for deposition, this method resulted in a film of a well-defined thickness from sub-millimeter down to a 100 nm scale. Furthermore, thanks to the unsaturated Si-H chemical bonds in the PDMS curing agent, the PDMS film could effectively reduce the Ag+ ions to Ag nanoparticles (NPs) directly bonding onto the substrate surface uniformly. Via adjusting the size and density of the AgNPs through reaction temperature and time, strong SERS was achieved and verified using R6G with the detection limit down to 0.1 ppm, attributed to the AgNPs’ plasmonic enhancement effect. Full article
(This article belongs to the Special Issue Application of Nano-Materials Technology and Raman Spectroscopy in Biochemical Sensors)
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14 pages, 1004 KiB  
Article
Characteristics of Trunk Acceleration and Angular Velocity in Turning Movement in Post-Stroke Patients with High Risk of Falling
by Daiki Naito, Keita Honda, Yusuke Sekiguchi, Shin-Ichi Izumi and Satoru Ebihara
Sensors 2025, 25(9), 2689; https://doi.org/10.3390/s25092689 - 24 Apr 2025
Abstract
Although falls commonly occur in post-stroke patients during turning, the characteristics of trunk movement during turning in individuals at a high risk of falling remain unclear. The aim of this study was to clarify the characteristics of trunk translational and rotational movements during [...] Read more.
Although falls commonly occur in post-stroke patients during turning, the characteristics of trunk movement during turning in individuals at a high risk of falling remain unclear. The aim of this study was to clarify the characteristics of trunk translational and rotational movements during turning in post-stroke patients with a high risk of falling. Trunk acceleration and angular velocity were measured using the inertial measurement unit of an iPhone in the timed up and go test and compared among 13 post-stroke patients with a high risk of falling (age: 69.38 ± 12.44 years, Berg Balance Scale (BBS) < 45), 18 post-stroke patients with a low risk of falling (age: 71.22 ± 8.50 years, BBS ≥ 45), and 10 age-matched healthy controls (age: 65.90 ± 11.57). We examined the differences in trunk movement during turning between groups and the relationships between the BBS score and trunk movement. The high-risk group exhibited the longest completion time (χ2 = 31.21, p < 0.001) and the lowest maximum of trunk angular velocity along the vertical axis among groups (χ2 = 28.51, p < 0.001). Furthermore, the high-risk group showed a higher minimum (absolute value) of trunk angular velocity along the mediolateral axis compared to the low-risk group (χ2 = 9.80, p = 0.007). The maximum trunk angular velocity along the vertical axis (r = 0.66, p < 0.001) and the minimum trunk angular velocity along the mediolateral axis (r = 0.51, p = 0.003) were significantly correlated with the BBS score. We found that post-stroke patients with a high risk of falling exhibited slower trunk rotation angular velocity and faster trunk flexion angular velocity during turning compared to low-risk groups. Our findings suggest that despite the decrease during turning speed due to poor balance control, post-stroke patients with a high risk of falling exhibit a greater disturbance in the sagittal plane. Full article
(This article belongs to the Section Biomedical Sensors)
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16 pages, 4117 KiB  
Article
C2L3-Fusion: An Integrated 3D Object Detection Method for Autonomous Vehicles
by Thanh Binh Ngo, Long Ngo, Anh Vu Phi, Trung Thị Hoa Trang Nguyen, Andy Nguyen, Jason Brown and Asanka Perera
Sensors 2025, 25(9), 2688; https://doi.org/10.3390/s25092688 - 24 Apr 2025
Abstract
Accurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based 2D object detection using YOLOv8 and LiDAR data-based 3D object detection using PointPillars, hence named C2L3-Fusion [...] Read more.
Accurate 3D object detection is crucial for autonomous vehicles (AVs) to navigate safely in complex environments. This paper introduces a novel fusion framework that integrates Camera image-based 2D object detection using YOLOv8 and LiDAR data-based 3D object detection using PointPillars, hence named C2L3-Fusion. Unlike conventional fusion approaches, which often struggle with feature misalignment, C2L3-Fusion enhances spatial consistency and multi-level feature aggregation, significantly improving detection accuracy. Our method outperforms state-of-the-art approaches such as YoPi-CLOCs Fusion Network, standalone YOLOv8, and standalone PointPillars, achieving mean Average Precision (mAP) scores of 89.91% (easy), 79.26% (moderate), and 78.01% (hard) on the KITTI dataset. Successfully implemented on the Nvidia Jetson AGX Xavier embedded platform, C2L3-Fusion maintains real-time performance while enhancing robustness, making it highly suitable for self-driving vehicles. This paper details the methodology, mathematical formulations, algorithmic advancements, and real-world testing of C2L3-Fusion, offering a comprehensive solution for 3D object detection in autonomous navigation. Full article
(This article belongs to the Special Issue Control Systems, Vision Technology and Sensor Fusion for Unmanned Robotic Vehicles)
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16 pages, 8058 KiB  
Article
YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation
by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen and Chunmei Shi
Sensors 2025, 25(9), 2687; https://doi.org/10.3390/s25092687 - 24 Apr 2025
Abstract
The real-time monitoring of animal postures through computer vision techniques has become essential for modern precision livestock management. To overcome the limitations of current behavioral analysis systems in balancing computational efficiency and detection accuracy, this study develops an optimized deep learning framework named [...] Read more.
The real-time monitoring of animal postures through computer vision techniques has become essential for modern precision livestock management. To overcome the limitations of current behavioral analysis systems in balancing computational efficiency and detection accuracy, this study develops an optimized deep learning framework named YOLOv8-BCD specifically designed for ovine posture recognition. The proposed architecture employs a multi-level lightweight design incorporating enhanced feature fusion mechanisms and spatial-channel attention modules, effectively improving detection performance in complex farm environments with occlusions and variable lighting. Our methodology introduces three technical innovations: (1) Adaptive multi-scale feature aggregation through bidirectional cross-layer connections. (2) Context-aware attention weighting for critical region emphasis. (3) Streamlined detection head optimization for resource-constrained devices. The experimental dataset comprises 1476 annotated images capturing three characteristic postures (standing, lying, and side lying) under practical farming conditions. Comparative evaluations demonstrate significant improvements over baseline models, achieving 91.7% recognition accuracy with 389 FPS processing speed while maintaining 19.2% parameter reduction and 32.1% lower computational load compared to standard YOLOv8. This efficient solution provides technical support for automated health monitoring in intensive livestock production systems, showing practical potential for large-scale agricultural applications requiring real-time behavioral analysis. Full article
(This article belongs to the Section Smart Agriculture)
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14 pages, 6171 KiB  
Article
A Discrete Fourier Transform-Based Signal Processing Method for an Eddy Current Detection Sensor
by Songhua Huang, Maocheng Hong, Ge Lin, Bo Tang and Shaobin Shen
Sensors 2025, 25(9), 2686; https://doi.org/10.3390/s25092686 - 24 Apr 2025
Abstract
This paper presents a discrete Fourier transform (DFT)-based signal processing framework for eddy current non-destructive testing (NDT), aiming to enhance signal quality for precise defect characterization in critical nuclear components. By enforcing strict periodicity matching between sampling points and signal frequencies, the proposed [...] Read more.
This paper presents a discrete Fourier transform (DFT)-based signal processing framework for eddy current non-destructive testing (NDT), aiming to enhance signal quality for precise defect characterization in critical nuclear components. By enforcing strict periodicity matching between sampling points and signal frequencies, the proposed approach mitigates DFT spectrum leakage, validated via phase linearity analysis with errors of ≤0.07° across the 20 Hz–1 MHz frequency range. A high-resolution 24-bit analog-to-digital converter (ADC) hardware architecture eliminates complex analog balancing circuits, reducing system-wide noise by overcoming the limitations of traditional 16-bit ADCs. A 6 × 6 mm application-specific integrated circuit (ASIC) for array sensors enables three-dimensional (3D) defect visualization, complemented by Gaussian filtering to suppress vibration-induced noise. Our experimental results demonstrate that the digital method yields smoother signal waveforms and superior 3D defect imaging for nuclear power plant tubes, enhancing result interpretability. Field tests confirm stable performance, showcasing clear 3D defect distributions and improved inspection performance compared to conventional techniques. By integrating DFT signal processing, hardware optimization, and array sensing, this study introduces a robust framework for precise defect localization and characterization in nuclear components, addressing key challenges in eddy current NDT through systematic signal integrity enhancement and hardware innovation. Full article
(This article belongs to the Special Issue Selected Papers from the 10th International Electronic Conference on Sensors and Applications)
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17 pages, 4386 KiB  
Article
Advanced SPR-Based Biosensors for Potential Use in Cancer Detection: A Theoretical Approach
by Talia Tene, Fabian Arias Arias, Darío Fernando Guamán-Lozada, María Augusta Guadalupe Alcoser, Lala Gahramanli, Cristian Vacacela Gomez and Stefano Bellucci
Sensors 2025, 25(9), 2685; https://doi.org/10.3390/s25092685 - 24 Apr 2025
Abstract
This study presents a numerical investigation of surface plasmon resonance (SPR) sensors based on multilayer configurations incorporating BK7, silver, silicon nitride (Si3N4), and black phosphorus (BP). Using the transfer matrix method, the optical performance of four architectures was evaluated [...] Read more.
This study presents a numerical investigation of surface plasmon resonance (SPR) sensors based on multilayer configurations incorporating BK7, silver, silicon nitride (Si3N4), and black phosphorus (BP). Using the transfer matrix method, the optical performance of four architectures was evaluated under refractive index perturbations consistent with values reported in prior theoretical and experimental studies. The sensor response was characterized through metrics such as angular sensitivity, resonance shift, full width at half maximum, attenuation, and derived figures including detection accuracy and limit of detection. Parametric optimization was performed for the thickness of each functional layer to enhance sensing performance. Among all configurations, those incorporating both Si3N4 and BP demonstrated the highest angular sensitivity, reaching up to 394.46°/RIU. These enhancements were accompanied by increased attenuation and spectral broadening, revealing trade-offs in sensor design. The results, based entirely on numerical modeling, provide a comparative framework for guiding SPR sensor optimization under idealized optical conditions. Full article
(This article belongs to the Section Biomedical Sensors)
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24 pages, 2130 KiB  
Article
Enhanced Cloud Detection Using a Unified Multimodal Data Fusion Approach in Remote Images
by Yan Mo, Puhui Chen, Wanting Zhou and Wei Chen
Sensors 2025, 25(9), 2684; https://doi.org/10.3390/s25092684 - 24 Apr 2025
Abstract
Aiming at the complexity of network architecture design and the low computational efficiency caused by variations in the number of modalities in multimodal cloud detection tasks, this paper proposes an efficient and unified multimodal cloud detection model, M2Cloud, which can process any number [...] Read more.
Aiming at the complexity of network architecture design and the low computational efficiency caused by variations in the number of modalities in multimodal cloud detection tasks, this paper proposes an efficient and unified multimodal cloud detection model, M2Cloud, which can process any number of modal data. The core innovation of M2Cloud lies in its novel multimodal data fusion method. This method avoids architectural changes for new modalities, thereby significantly reducing incremental computing costs and enhancing overall efficiency. Furthermore, the designed multimodal data fusion module possesses strong generalization capabilities and can be seamlessly integrated into other network architectures in a plug-and-play manner, greatly enhancing the module’s practicality and flexibility. To address the challenge of unified multimodal feature extraction, we adopt two key strategies: (1) constructing feature extraction modules with shared but independent weights for each modality to preserve the inherent features of each modality; (2) utilizing cosine similarity to adaptively learn complementary features between different modalities, thereby reducing redundant information. Experimental results demonstrate that M2Cloud achieves or even surpasses the state-of-the-art (SOTA) performance on the public multimodal datasets WHUS2-CD and WHUS2-CD+, verifying its effectiveness in the unified multimodal cloud detection task. The research presented in this paper offers new insights and technical support for the field of multimodal data fusion and cloud detection, and holds significant theoretical and practical value. Full article
(This article belongs to the Section Remote Sensors)
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21 pages, 1480 KiB  
Article
LLM-Based Unknown Function Automated Modeling in Sensor-Driven Systems for Multi-Language Software Security Verification
by Liangjun Deng, Qi Zhong, Jingcheng Song, Hang Lei and Wenjuan Li
Sensors 2025, 25(9), 2683; https://doi.org/10.3390/s25092683 - 24 Apr 2025
Abstract
The rapid expansion of the Internet of Things (IoT) has made software security and reliability a critical concern. With multi-language programs running on edge computing, embedded systems, and sensors, each connected device represents a potential attack vector, threatening data integrity and privacy. Symbolic [...] Read more.
The rapid expansion of the Internet of Things (IoT) has made software security and reliability a critical concern. With multi-language programs running on edge computing, embedded systems, and sensors, each connected device represents a potential attack vector, threatening data integrity and privacy. Symbolic execution is a key technique for automated vulnerability detection. However, unknown function interfaces, such as sensor interactions, limit traditional concrete or concolic execution due to uncertain function returns and missing symbolic expressions. Compared with system simulation, the traditional method is to construct an interface abstraction layer for the symbolic execution engine to reduce the cost of simulation. Nevertheless, the disadvantage of this solution is that the manual modeling of these functions is very inefficient and requires professional developers to spend hundreds of hours. In order to improve efficiency, we propose an LLM-based automated approach for modeling unknown functions. By fine-tuning a 20-billion-parameter language model, it automatically generates function models based on annotations and function names. Our method improves symbolic execution efficiency, reducing reliance on manual modeling, which is a limitation of existing frameworks like KLEE. Experimental results primarily focus on comparing the usability, accuracy, and efficiency of LLM-generated models with human-written ones. Our approach was integrated into one verification platform project and applied to the verification of smart contracts with distributed edge computing characteristics. The application of this method directly reduces the manual modeling effort from a month to just a few minutes. This provides a foundational validation of our method’s feasibility, particularly in reducing modeling time while maintaining quality. This work is the first to integrate LLMs into formal verification, offering a scalable and automated verification solution for sensor-driven software, blockchain smart contracts, and WebAssembly systems, expanding the scope of secure IoT development. Full article
(This article belongs to the Special Issue Advanced Applications of WSNs and the IoT—2nd Edition)
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15 pages, 8651 KiB  
Article
Rotating Polarization Magnetometry
by Szymon Pustelny and Przemysław Włodarczyk
Sensors 2025, 25(9), 2682; https://doi.org/10.3390/s25092682 - 24 Apr 2025
Abstract
Precise magnetometry is vital in numerous scientific and technological applications. At the forefront of sensitivity, optical atomic magnetometry, particularly techniques utilizing nonlinear magneto-optical rotation (NMOR), enables ultraprecise measurements across a broad field range. Despite their potential, these techniques reportedly lose sensitivity in higher [...] Read more.
Precise magnetometry is vital in numerous scientific and technological applications. At the forefront of sensitivity, optical atomic magnetometry, particularly techniques utilizing nonlinear magneto-optical rotation (NMOR), enables ultraprecise measurements across a broad field range. Despite their potential, these techniques reportedly lose sensitivity in higher magnetic fields, which is attributed to the alignment-to-orientation conversion (AOC) process. In our study, we utilized light with continuously rotating linear polarization to avoid the AOC, which produced robust optical signals and achieving high magnetometric sensitivity over a dynamic range nearly three times greater than Earth’s magnetic field. We demonstrated that employing rotating polarization surpasses other NMOR techniques that use modulated light. Our findings also indicate that the previously observed signal deterioration was not due to the AOC, suggesting an alternative cause for this decline. Full article
(This article belongs to the Special Issue Atomic Magnetic Sensors)
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15 pages, 3355 KiB  
Article
Portable Measurement System for the Characterization of Capacitive Field-Effect Sensors
by Tobias Karschuck, Stefan Schmidt, Stefan Achtsnicht, Joey Ser, Ismail Bouarich, Georges Aboutass, Arshak Poghossian, Patrick H. Wagner and Michael J. Schöning
Sensors 2025, 25(9), 2681; https://doi.org/10.3390/s25092681 - 24 Apr 2025
Abstract
A user-friendly, portable, low-cost readout system for the on-site or point-of-care characterization of chemo- and biosensors based on an electrolyte–insulator–semiconductor capacitor (EISCAP) has been developed using a thumb-drive-sized commercial impedance analyzer. The system is controlled by a custom Python script and allows to [...] Read more.
A user-friendly, portable, low-cost readout system for the on-site or point-of-care characterization of chemo- and biosensors based on an electrolyte–insulator–semiconductor capacitor (EISCAP) has been developed using a thumb-drive-sized commercial impedance analyzer. The system is controlled by a custom Python script and allows to characterize EISCAP sensors with different methods (impedance spectra, capacitance-voltage, and constant-capacitance modes), which are selected in a user interface. The performance of the portable readout system was evaluated by pH measurements and the detection of the antibiotic penicillin, hereby using EISCAPs consisting of Al/p-Si/SiO2/Ta2O5 structures and compared to the results obtained with a stationary commercial impedance analyzer. Both the portable and the commercial systems provide very similar results with almost perfectly overlapping recorded EISCAP signals. The new portable system can accelerate the transition of EISCAP sensors from research laboratories to commercial end-user devices. Full article
(This article belongs to the Special Issue Sensors from Miniaturization of Analytical Instruments (2nd Edition))
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