IEEE Sensors Journal最新文献

{"title":"Highly Sensitive and Accurate Cortisol Sensor Based on Long-Range Surface Plasmon Resonance","authors":"Virendra Kumar;Nitesh Kumar;Sarika Pal;Bela Goyal;Anuj K. Sharma;Yogendra Kumar Prajapati","doi":"10.1109/JSEN.2025.3555704","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555704","url":null,"abstract":"Cortisol is a stress hormone that can significantly control metabolism and immune system activities. Its higher and lower levels can cause Cushing’s syndrome and Addison’s disease, respectively. An accurate and highly sensitive detection of cortisol levels is crucial to monitor human mental and physical health. In this sequence, we propose and simulate a sensor design based on long-range surface plasmon resonance (LRSPR) for measuring cortisol concentrations in human saliva. The sensor’s structure includes a 2S2G prism, Cytop (1500 nm), silver (18 nm), bismuth titanate (12 nm), molybdenum ditelluride (<inline-formula> <tex-math>$2times 0.82$ </tex-math></inline-formula> nm), cysteamine (5 nm), and a sensing medium (SM). A detailed analysis of the simulation results related to proposed sensor design shows that it achieves a high angular figure of merit (FOMang.) of 276.3 RIU<inline-formula> <tex-math>${}^{-{1}}$ </tex-math></inline-formula>, detection accuracy (DA) of 25/°, imaging sensitivity (<inline-formula> <tex-math>${S} _{text {img.}}$ </tex-math></inline-formula>) of 13931 RIU<inline-formula> <tex-math>${}^{-{1}}$ </tex-math></inline-formula>, and an imaging figure of merit (IFOM) of 348275 (°RIU)<inline-formula> <tex-math>${}^{-{1}}$ </tex-math></inline-formula>. The comparison reveals that the proposed sensor significantly outperforms the conventional surface plasmon resonance (CSPR) sensor. The Finite Element Method (FEM) simulations further reveal that the proposed sensor design achieves a large penetration depth (PD) of 510.10 nm and a cortisol detection limit of 0.1765 ng/mL. The results demonstrate a significant improvement compared to the sensor reported in the literature. It shows the potential for noninvasive and accurate cortisol monitoring required for monitoring related health conditions.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17324-17331"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Radar System-Agnostic (RSA) Learning Architecture for Human Activity Recognition","authors":"Yipeng Ding;Ping Lv;Runjin Liu;Yiqun Peng;Minhao Ding","doi":"10.1109/JSEN.2025.3555573","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555573","url":null,"abstract":"In recent years, radar-based human activity recognition (HAR) has been widely applied across various fields. However, differences in radar setup, such as frequency band and waveform of transmitted signals, across various radar devices may lead to data incompatibility, limiting the collaborative capabilities of multiradar detection systems. To address this issue, this article proposes a radar system-agnostic (RSA) learning architecture for HAR. The framework enhances HAR performance by employing adversarial training of the gradient reversal layer (GRL) and the auxiliary classifier generative adversarial network (ACGAN), with the constraints of each module mutually reinforcing effectiveness. The proposed RSA architecture is evaluated through extensive experiments using radar datasets from three devices across different frequency bands, covering 11 types of human activities. The experimental results demonstrate that the algorithm performs well in single-domain and multidomain scenarios. In single-domain training, HAR accuracy improves by at least 1.5% over the baseline. Multidomain training significantly surpasses other methods, achieving approximately 97% accuracy with three domains. An ablation study further validates the contributions of the GRL and ACGAN components, confirming that their integration is essential for achieving optimal performance. These findings highlight the practicality and advantages of RSA for robust cross-radar frequency HAR.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18492-18502"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concentric Circular Nested Array Design Method for Acoustic Imaging Based on Differential Coarray Model","authors":"Zhiyuan Xie;Yan Yang;Junyan Zhang;Wenzhao Zhu;Zonglong Bai","doi":"10.1109/JSEN.2025.3555811","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555811","url":null,"abstract":"Acoustic imaging intuitively illustrates the locations of sound sources and has a wide range of applications, including fault detection. However, this technology requires a substantial number of microphones, which increases the complexity and cost of the data acquisition system. To reduce the number of microphones while maintaining the performance of acoustic imaging, this article introduces a concentric circular nested array (CCNA) design method. By extending the array using a differential coarray model, a virtual uniform concentric circular array is achieved. This virtual array preserves the advantages of the circular array structure while minimizing the number of required microphones. The performance of the proposed array is validated through simulation experiments. The simulation results demonstrate that the CCNA offers higher resolution, a greater dynamic range (DR), and improved positioning accuracy in acoustic imaging compared to other arrays. Finally, this article compares the acoustic imaging results of the CCNA with those of the uniform circular array (UCA) through experiments. The experimental results reveal that the positioning accuracy of the proposed CCNA surpasses that of the UCA, thereby confirming the superiority of the proposed design.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18106-18114"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Temperature Fiber Optic Sensor Performance for Heat Pipe Instrumentation","authors":"Christopher Balbier;Scout Bucks;Federico Scurti;Saya Lee","doi":"10.1109/JSEN.2025.3555932","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555932","url":null,"abstract":"Presented in this article are experimental results of an investigation on the performance of distributed fiber optic temperature sensors at temperatures up to <inline-formula> <tex-math>$800~^{circ }$ </tex-math></inline-formula>C. The experimental results produced in this work assess the performance of fiber optic temperature sensors for use in instrumenting liquid metal heat pipes. Distributed fiber optic temperature sensors are capable of providing high spatial and temporal resolution temperature measurements across a wide range of operating temperatures and conditions, making them intriguing candidates for many advanced nuclear reactor technologies. Tests were conducted at high temperature on the prolonged survivability, short-term performance, and high-temperature cycling effects of distributed optical fiber temperature sensors. A quartic fit of the spectral shift produced by the fiber sensors was developed to fit with thermocouple (TC) measurements of the experiment and was compared with fits available in literature. An upper limit of <inline-formula> <tex-math>$700~^{circ }$ </tex-math></inline-formula>C was established for the prolonged use of distributed fiber optic sensors. No significant hysteresis effects were observed when the fiber sensors were cycled at high temperatures. Distributed fiber optic temperature sensors were determined to be viable for instrumenting liquid metal heat pipes under limited operational conditions.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17117-17127"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Railway Wheel Flat Based on CBAM-Enhanced ResNet for Imbalanced Data","authors":"Wenjie Fu;Qixin He;Saisai Liu;Qibo Feng;Run Gao","doi":"10.1109/JSEN.2025.3555651","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555651","url":null,"abstract":"Wheel flat is a common fault during train operation, which seriously affects running safety. Deep learning flat detection methods can learn and identify flats automatically without relying on expert experience, which has attracted widespread attention. However, accurately detecting wheel flats remains challenging due to the strong interference signal components and the data imbalance from the lack of failure data. In this article, the convolutional block attention module-enhanced residual net (CBAM-enhanced ResNet) model is adopted for flat detection tasks to improve the robustness and the recognition ability of the model. To detect wheel flat for imbalanced data, a dataset expansion method based on wheel-rail dynamics simulation is proposed. In this method, the effects of wheel-flat lengths and the impact positions on flat signals were studied based on the developed vehicle-track coupled model. Then, new flat signals can be reconstructed by transforming the actual flat signal according to the obtained fitting relationships. Experiments were conducted to verify the effectiveness of the CBAM-enhanced ResNet model and the proposed dataset expansion method. The results show that the CBAM-enhanced ResNet model achieves better flat detection results than the ResNet model. After data expansion, the accuracy of both models was improved.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18268-18276"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Assisted Simultaneous Identification and Localization of Impacts on Metallic Structures Using Fiber Bragg Grating-Based Sensor","authors":"P. V. M. Vamsi;Srijith Kanakambaran","doi":"10.1109/JSEN.2025.3555710","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555710","url":null,"abstract":"Structural health monitoring plays a critical role in assessing the condition and performance of high-cost infrastructure. Impact monitoring is one of the crucial components of structural health monitoring. A fiber Bragg grating (FBG) sensor-based impact monitoring system has been demonstrated in this work, in which an FBG sensor bonded on a metallic plate picks up the vibration signals due to impacts caused by different materials. Time-domain and frequency-domain features extracted from the acquired data were fed to various machine learning models, and an accuracy of 88.25% was obtained using a random forest (RF) classifier for impact-type classification. Further, for simultaneous identification and localization of impacts, wavelet decomposition of the impact signals was performed to extract additional better features. Using all such features, impacts on the metallic plate were identified and localized at quadrant-level granularity with the highest accuracy of 92.25% using the soft voting classifier.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17128-17135"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotic Artistic Writing—Reproduction of Signs Drawn on the Surface of Plastically Deformable Materials","authors":"Xin He;Teresa Zielinska;Takafumi Matsumaru;Vibekananda Dutta","doi":"10.1109/JSEN.2025.3555571","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555571","url":null,"abstract":"Existing surface shaping methods focus on hard materials with stable physical properties. This means that the approaches developed are insufficient for shaping soft materials. The article describes a cheap method of reproducing plastic deformations using a robotic manipulator. The shape recording and reproduction system consists of two RGB-D cameras, two containers with kinetic sand, and a manipulator with 6 degrees of freedom (DOF). Volunteers create templates by drawing on the surface of kinetic sand with a wooden stylus or finger. Some pressure is exerted while writing. The resulting shapes recorded by the RGB-D camera have the form of ribbon ditches (grooves). The obtained point cloud is processed to create a sand deformation model. In the first stage, a dedicated local smoothing technique is used. Then, special algorithms are implemented to create a description of the main curvatures and key dimensions of recorded signs. A spline-based approach is used. The method allows for the representation of various shapes in a unified form. In the final stage, modulated sinusoidal functions define the robot’s trajectory. The effects of the robot’s operation are recorded to assess the reproduction quality. The point cloud structural similarity measure (Point SSIM) evaluates the results. Experimental research takes into account many different shapes. Copies of shapes created by humans and robots are compared with the originals. The outcomes show that the quality of reproduction achieved by humans and robots is comparable. The median-based curvature similarity measure obtained for the human was only 1.92% higher than the robot’s result, and the covariance-based geometric similarity measure was only 0.74% higher than the robot’s score. The system can be used to mass-produce souvenirs or special implants.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18090-18105"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seeing Through Dense Fog With an Intelligent Adaptive Dehazing System","authors":"Pengyun Chen;Ning Cao;Ziqin Xu;Shuang Cui;Shaohui Jin;Hao Liu;Mingliang Xu","doi":"10.1109/JSEN.2025.3555446","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555446","url":null,"abstract":"Most existing image dehazing methods are mainly suitable for synthetic datasets and often perform poorly in real-world, complex hazy scenarios. To address this issue, this article proposes an intelligent adaptive dehazing system (IADS) that integrates range-gated imaging with deep learning, combining image acquisition and restoration for enhanced dehazing performance. The range-gated imaging system reduces scattered light interference. However, our approach primarily focuses on enhancing image quality through advanced dehazing methods. Specifically, we introduce the MSCENAFormer dehazing network, which achieves high-quality reconstruction of target scenes in dense fog by effectively removing fog and improving visibility. The core modules of MSCENAFormer include the multiscale enhanced neighborhood attention (MSENA) module and the comprehensive attention refinement module (CARM). MSENA is designed to capture rich local information in harsh environment, improving the dehazing effect and enhancing image details. CARM integrates the local and global information to optimize the visual effect further. In addition, the adaptive feature mixing (AFM) module is used to fuse multiscale features for better performance. To validate the performance of our method, we utilize our lab-collected nonhomogeneous haze real dataset, O-ITDF, along with the public datasets NH-HAZE, NTIRE2021, and NTIRE2023. Experimental results demonstrate that our proposed MSCENAFormer outperforms many methods. We share our code at <uri>https://github.com/NingCao-zzu/MSCENAFormer</uri>.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17696-17705"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A MEMS IMU-Based Air-Propelled Positioning Ball for Small-Diameter Underground Pipeline Localization","authors":"Xiaoji Niu;Jundong Hu;Qijin Chen;Dong Zhao","doi":"10.1109/JSEN.2025.3555335","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555335","url":null,"abstract":"Accurate locating of small-diameter underground pipelines (typically 20–65 mm) is crucial for avoiding or minimizing the damage during urban construction and improving maintenance efficiency. However, conventional pipeline inspection Gauges (PIGs) equipped with high-grade inertial measurement units (IMUs), which are primarily designed for normal size pipeline, cannot access small diameter or tightly curved pipelines due to their large overall size and the large-sized high-precision IMUs they rely on. This limitation leaves a critical gap in accurately locating such pipelines, as no effective method currently exists for their precise positioning. To address this challenge, we propose a novel method using an air-propelled inertial positioning ball (IPB) integrated with a chip-level micro-electromechanical systems (MEMS) IMU, so as to make the device compact and lightweight enough to traverse the small-diameter pipelines. However, it comes with a serious problem that the MEMS IMU chip has large sensor errors leading to fast position drift and therefore can only keep positioning accuracy for several seconds. Unlike the conventional PIGs, which are typically pulled through pipelines by steel cables at a low speed (around 1 m/s), the lightweight IPB is designed to be propelled by airflow, allowing it to fly rapidly (around 10 m/s) through the pipeline. This significantly reduces the integration time of the MEMS IMU, thereby mitigating its cumulative errors effectively. Field tests conducted in a 48-m-long small-diameter pipe demonstrate the effectiveness of the IPB, with single-run positioning errors of 0.77 m in the transverse direction and 1.13 m in the height direction. By averaging the results from four independent runs, the maximum positioning errors were reduced to 0.36 m (0.75% of pipe length) in the transverse direction and 0.36 m (0.75% of pipe length) in the height direction. The proposed new approach provides a practical, efficient, and accurate solution for locating small-diameter and tightly curved underground pipelines, addressing a critical gap in underground pipeline surveying.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18257-18267"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended Object Observability Analysis With Range Extent Measurement Dimension Loss","authors":"Songyao Dou;Ying Chen;Yaobing Lu","doi":"10.1109/JSEN.2025.3555435","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555435","url":null,"abstract":"Extended object tracking, which can obtain rich information about the object’s shape, size, and orientation, has received widespread attention. The modeling method based on support functions (SFs) or extended Gaussian images (EGIs) needs the down-range measurement and cross-range measurement of the object to estimate the information about the object’s extent. Down-range and cross-range refer to the projection lengths of the object along and perpendicular to the radar line of sight (LOS), respectively. The down-range measurement can be generated from the high range resolution profile (HRRP). However, in some radar applications, limited by the resolution capability in cross-range, the cross-range measurements are unavailable. To track extended objects only using down-range measurements, this article analyzes the observability of the extended object in detail. The analysis results indicate that the observability of the extended object is affected by the dimensions of range extent measurement, and the required dimensions of the range extent measurement for extended object tracking vary under different motion modes. According to the analysis results, when the object performs coordinate turn motion, extended object tracking can be achieved using only down-range measurements. The results of simulation experiments confirm this conclusion. The analysis results presented in this article clarify the measurement conditions required for extended object tracking and expand the application scope of tracking algorithms based on the SF and EGI modeling.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17706-17716"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}