We report magnetic anomaly detection of the Boryeong undersea tunnel using a drone-mounted all-optical pulsed atomic magnetometer. The optically pumped rubidium atomic magnetometer, with a single-beam configuration, measures the scalar magnetic field using a free-induction-decay readout. The sensor head has an outer volume of 112 mL and a mass of approximately 100 g. The baseline noise floor measured inside a magnetic shield is 1.98 pT per square root hertz from 5 to 100 Hz. Residual magnetic anomaly maps obtained at flight altitudes of 50, 100, and 150 m show an elongated anomaly pattern consistent with the tunnel location identified from the map data. Line profiles show bipolar magnetic anomalies with zero-crossing positions that remain close at the three flight altitudes, while the peak-to-peak amplitude decreases and the peak-to-peak separation increases with altitude. For the survey line with the largest magnetic anomaly amplitude, the peak-to-peak amplitudes at 50, 100, and 150 m are 225, 93, and 48 nT, respectively. The altitude dependence of the peak-to-peak amplitude is described by an inverse-square model and gives apparent source depths with a mean value of 71 m, comparable to the known maximum tunnel depth of approximately 80 m below sea level.
{"title":"Magnetic Anomaly Detection of an Undersea Tunnel Using a Drone-Mounted All-Optical Pulsed Atomic Magnetometer","authors":"Hyeonjae Kim;Sin Hyuk Yim;Sangkyung Lee;Eun-Seok Bang;Sang Rae Yeo;Yun Jeong Kim;Taek Jeong;Younghoon Lim","doi":"10.1109/JSEN.2026.3729190","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3729190","url":null,"abstract":"We report magnetic anomaly detection of the Boryeong undersea tunnel using a drone-mounted all-optical pulsed atomic magnetometer. The optically pumped rubidium atomic magnetometer, with a single-beam configuration, measures the scalar magnetic field using a free-induction-decay readout. The sensor head has an outer volume of 112 mL and a mass of approximately 100 g. The baseline noise floor measured inside a magnetic shield is 1.98 pT per square root hertz from 5 to 100 Hz. Residual magnetic anomaly maps obtained at flight altitudes of 50, 100, and 150 m show an elongated anomaly pattern consistent with the tunnel location identified from the map data. Line profiles show bipolar magnetic anomalies with zero-crossing positions that remain close at the three flight altitudes, while the peak-to-peak amplitude decreases and the peak-to-peak separation increases with altitude. For the survey line with the largest magnetic anomaly amplitude, the peak-to-peak amplitudes at 50, 100, and 150 m are 225, 93, and 48 nT, respectively. The altitude dependence of the peak-to-peak amplitude is described by an inverse-square model and gives apparent source depths with a mean value of 71 m, comparable to the known maximum tunnel depth of approximately 80 m below sea level.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30274-30281"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11679131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-15Epub Date: 2026-08-20DOI: 10.1109/JSEN.2026.3720592
Donghan Liu;Gongyue Zhang;Haoran Zheng;Yixuan Sheng;Honghai Liu
A-mode ultrasound (AUS) sensing has emerged as a promising modality for human–machine interaction (HMI), owing to its high signal-to-noise ratio (SNR), compact form factor, and ability to capture deep muscle deformation. This study presents a unified spatiotemporal decoding architecture with transfer learning for cross-subject hand gesture recognition (HGR) using wearable AUS. A commercially available four-channel AUS system was employed to noninvasively collect forearm echo signals corresponding to static, dynamic, and in-hand manipulation gestures. A convolutional block attention module (CBAM)–long short-term memory (CBAM–LSTM) network was constructed to model spatial and temporal dependencies, where CBAM enhances discriminative spatial feature extraction, and LSTM captures time-varying muscle dynamics. A fine-tuning strategy enables efficient adaptation to unseen users with limited calibration data. Under a leave-one-subject-out protocol, the proposed architecture achieved average accuracies of 86.75%, 95.6%, and 87.3% for static, dynamic, and in-hand gestures, respectively. In addition, within-subject evaluation yielded macro-F1-scores above 97%, indicating strong intrasubject consistency. These results validate AUS as a robust and scalable sensing modality for muscle-activity-based gesture recognition, with promising potential for rehabilitation training and intelligent prosthetic control.
{"title":"Multicategory Cross-Subject Hand Gesture Recognition Based on Spatiotemporal Decoding via A-Mode Ultrasound Sensing","authors":"Donghan Liu;Gongyue Zhang;Haoran Zheng;Yixuan Sheng;Honghai Liu","doi":"10.1109/JSEN.2026.3720592","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3720592","url":null,"abstract":"A-mode ultrasound (AUS) sensing has emerged as a promising modality for human–machine interaction (HMI), owing to its high signal-to-noise ratio (SNR), compact form factor, and ability to capture deep muscle deformation. This study presents a unified spatiotemporal decoding architecture with transfer learning for cross-subject hand gesture recognition (HGR) using wearable AUS. A commercially available four-channel AUS system was employed to noninvasively collect forearm echo signals corresponding to static, dynamic, and in-hand manipulation gestures. A convolutional block attention module (CBAM)–long short-term memory (CBAM–LSTM) network was constructed to model spatial and temporal dependencies, where CBAM enhances discriminative spatial feature extraction, and LSTM captures time-varying muscle dynamics. A fine-tuning strategy enables efficient adaptation to unseen users with limited calibration data. Under a leave-one-subject-out protocol, the proposed architecture achieved average accuracies of 86.75%, 95.6%, and 87.3% for static, dynamic, and in-hand gestures, respectively. In addition, within-subject evaluation yielded macro-<italic>F</i>1-scores above 97%, indicating strong intrasubject consistency. These results validate AUS as a robust and scalable sensing modality for muscle-activity-based gesture recognition, with promising potential for rehabilitation training and intelligent prosthetic control.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31521-31531"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149175994","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}
This article proposes the time–frequency separable convolutional compression network (TFSCCN) as a novel system architecture for sound event localization and detection (SELD). TFSCCN employs 1-D convolution kernels of varying dimensions to separately extract features from time and frequency components. Additionally, it reduces the number of model parameters by carefully managing the increase or decrease of channel counts within the neural network. The model also integrates multihead self-attention (MHSA) to capture both global and local information in time-series features and utilizes dual-branch tracking technology to effectively locate and detect overlapping sound events, whether they belong to the same class or different classes.
{"title":"Advancing Sound Event Localization and Detection: A Multibranch Time--Frequency Separable Convolutional Compression Network Approach","authors":"Wenny Ramadha Putri;Shih-Tsung Yang;Bach-Tung Pham;Po-Cheng Chan;An Dang;Shang-Kuan Chen;Chung-I Huang;Kuo-Chen Li;Shih-Lun Chen;Pao-Chi Chang;Jia-Ching Wang","doi":"10.1109/JSEN.2026.3724896","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3724896","url":null,"abstract":"This article proposes the time–frequency separable convolutional compression network (TFSCCN) as a novel system architecture for sound event localization and detection (SELD). TFSCCN employs 1-D convolution kernels of varying dimensions to separately extract features from time and frequency components. Additionally, it reduces the number of model parameters by carefully managing the increase or decrease of channel counts within the neural network. The model also integrates multihead self-attention (MHSA) to capture both global and local information in time-series features and utilizes dual-branch tracking technology to effectively locate and detect overlapping sound events, whether they belong to the same class or different classes.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31278-31287"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176050","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}
Gyroscopes are essential elements in navigation, consumer electronics, robotics, and aerospace applications. Most micro-electromechanical systems (MEMS) implementations rely on capacitive sensing mechanisms, which limit the dimensional scaling to the micrometer scale. In this work, we introduce a MEMS-like two-degree-of-freedom (2-DOF) gyroscope that exploits the rectification functionality of magnetic tunnel junctions (MTJs) as its readout mechanism and as the transducer of the mechanical dynamics. Experimentally characterized MTJs have been used to calibrate and perform an experiment-informed design of the magneto-mechanical model combining micromagnetic theory with 2-DOF mechanical equations. We demonstrated that the output is linear with angular rate, and that the proposed device is able to extract the angular rate in dynamic cases exploiting a homodyne demodulation approach. The results open a path toward a compact, complementary metal-oxide semiconductor (CMOS)-compatible readout pathway that relaxes reliance on tight capacitive gaps and motivates multiphysics designs of the device.
{"title":"A Magneto-Mechanical Gyroscope With Spintronic Readout","authors":"Andrea Meo;Francesca Garescì;Pedro Bossi Núñez;Maddalena Fiorentino;Teresa Natale;Ludovico Dindelli;Victor Lopez Dominguez;Banibrato Sinha;Riccardo Tomasello;Francesco Dell'Olio;Pedram Khalili Amiri;Mario Carpentieri;Giovanni Finocchio","doi":"10.1109/JSEN.2026.3728166","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3728166","url":null,"abstract":"Gyroscopes are essential elements in navigation, consumer electronics, robotics, and aerospace applications. Most micro-electromechanical systems (MEMS) implementations rely on capacitive sensing mechanisms, which limit the dimensional scaling to the micrometer scale. In this work, we introduce a MEMS-like two-degree-of-freedom (2-DOF) gyroscope that exploits the rectification functionality of magnetic tunnel junctions (MTJs) as its readout mechanism and as the transducer of the mechanical dynamics. Experimentally characterized MTJs have been used to calibrate and perform an experiment-informed design of the magneto-mechanical model combining micromagnetic theory with 2-DOF mechanical equations. We demonstrated that the output is linear with angular rate, and that the proposed device is able to extract the angular rate in dynamic cases exploiting a homodyne demodulation approach. The results open a path toward a compact, complementary metal-oxide semiconductor (CMOS)-compatible readout pathway that relaxes reliance on tight capacitive gaps and motivates multiphysics designs of the device.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30182-30192"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11683337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distributed multitarget tracking (DMTT) enables multiple sensor nodes with heterogeneous and limited fields of view (FoVs) to cooperatively estimate target trajectories through network-wide fusion. However, when FoVs are occluded, local tracks may become fragmented and inconsistently labeled, and these local identity errors can propagate through distributed fusion. To address these issues, this article proposes consistent fusion by associated groups algorithm with a generated label mechanism (CFAG-GLM), a track-level network-wide fusion framework that integrates associated-group fusion with a generated label mechanism. Internode track association and intragroup state fusion produce consistent fused estimates across nodes, while a unified global-label update maintains persistent fused identities across scans. Within this process, a rebirth relation based on temporal and kinematic consistency reconnects reinitialized local tracks to existing global identities after occlusion-induced interruptions. Comparisons with density-level and track-level fusion baselines, together with a no-rebirth ablation, show that CFAG-GLM preserves network-wide consistency while reducing trajectory fragmentation and identity discontinuities in the tested occluded-FoV scenarios. In the two occluded-FoV scenarios, CFAG-GLM achieves the lowest OSPA${}^{text {(}{2}text {)}}$ and LP switching errors among the compared methods while retaining runtime comparable to the track-level baselines. Relative to CFAG, it reduces the occlusion-induced OSPA${}^{text {(}{2}text {)}}$ degradation by 69.2% and 65.7%, respectively, and the corresponding LP total-error degradation by 42.9% and 43.3%, respectively.
{"title":"Network-Wide Consistent Fusion for Distributed Multitarget Tracking Under Limited and Occluded Fields of View","authors":"Chengping Ma;Yunxiao Peng;Shuai Yuan;Ci Wang;Xiaoxiao Zhuo;Liang Tang;ChenFeng Xu;Yu Zhao","doi":"10.1109/JSEN.2026.3730854","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3730854","url":null,"abstract":"Distributed multitarget tracking (DMTT) enables multiple sensor nodes with heterogeneous and limited fields of view (FoVs) to cooperatively estimate target trajectories through network-wide fusion. However, when FoVs are occluded, local tracks may become fragmented and inconsistently labeled, and these local identity errors can propagate through distributed fusion. To address these issues, this article proposes consistent fusion by associated groups algorithm with a generated label mechanism (CFAG-GLM), a track-level network-wide fusion framework that integrates associated-group fusion with a generated label mechanism. Internode track association and intragroup state fusion produce consistent fused estimates across nodes, while a unified global-label update maintains persistent fused identities across scans. Within this process, a rebirth relation based on temporal and kinematic consistency reconnects reinitialized local tracks to existing global identities after occlusion-induced interruptions. Comparisons with density-level and track-level fusion baselines, together with a no-rebirth ablation, show that CFAG-GLM preserves network-wide consistency while reducing trajectory fragmentation and identity discontinuities in the tested occluded-FoV scenarios. In the two occluded-FoV scenarios, CFAG-GLM achieves the lowest OSPA<inline-formula> <tex-math>${}^{text {(}{2}text {)}}$ </tex-math></inline-formula> and LP switching errors among the compared methods while retaining runtime comparable to the track-level baselines. Relative to CFAG, it reduces the occlusion-induced OSPA<inline-formula> <tex-math>${}^{text {(}{2}text {)}}$ </tex-math></inline-formula> degradation by 69.2% and 65.7%, respectively, and the corresponding LP total-error degradation by 42.9% and 43.3%, respectively.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31633-31647"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176090","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}
Pub Date : 2026-10-15Epub Date: 2026-08-31DOI: 10.1109/JSEN.2026.3726886
Yalin Wang;Yitao Chen;Chenliang Liu;Yue Cao;Cao Song
Online prediction of key quality variables is essential for the real-time optimization and stable operation of complex industrial processes. However, with the increasing scale of modern industrial flowsheets, production systems are usually organized into multiple interconnected subprocesses. These subprocesses exhibit relatively independent local dynamic behaviors due to different equipment units and operating mechanisms while remaining globally coupled through material transfer and energy exchange. Such local independence and global interdependence make it difficult for existing soft sensor models to comprehensively characterize multihierarchical spatiotemporal dependencies, thereby posing significant challenges for accurate quality prediction. To address these issues, this article proposes a novel multihierarchical spatial–temporal dependence-aware graph convolutional network (MHSTA-GCN) for online quality prediction in large-scale industrial processes. In particular, an integrated graph topology learning method is designed by combining correlation-based prior knowledge with adaptive parameter learning to construct coupled spatial relationships among variables, thereby enhancing structural reliability while uncovering latent dynamic dependencies. Subsequently, a mix-hop graph convolutional network (GCN) is developed to selectively propagate critical information through multihop operations and is incorporated into the spatial–temporal dependence-aware feature extraction module to capture multiscale sequential patterns and dynamic variations, thereby modeling complex spatiotemporal interactions. Furthermore, a multihierarchical feature extraction framework is introduced to jointly exploit global and local process information, enabling a comprehensive multihierarchical feature representation. Extensive experiments on two real-world large-scale industrial process datasets validate the effectiveness of the proposed method.
{"title":"Learning Multihierarchical Spatiotemporal Dependence of Complex Industrial Flowsheets and Its Soft Sensor Applications","authors":"Yalin Wang;Yitao Chen;Chenliang Liu;Yue Cao;Cao Song","doi":"10.1109/JSEN.2026.3726886","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3726886","url":null,"abstract":"Online prediction of key quality variables is essential for the real-time optimization and stable operation of complex industrial processes. However, with the increasing scale of modern industrial flowsheets, production systems are usually organized into multiple interconnected subprocesses. These subprocesses exhibit relatively independent local dynamic behaviors due to different equipment units and operating mechanisms while remaining globally coupled through material transfer and energy exchange. Such local independence and global interdependence make it difficult for existing soft sensor models to comprehensively characterize multihierarchical spatiotemporal dependencies, thereby posing significant challenges for accurate quality prediction. To address these issues, this article proposes a novel multihierarchical spatial–temporal dependence-aware graph convolutional network (MHSTA-GCN) for online quality prediction in large-scale industrial processes. In particular, an integrated graph topology learning method is designed by combining correlation-based prior knowledge with adaptive parameter learning to construct coupled spatial relationships among variables, thereby enhancing structural reliability while uncovering latent dynamic dependencies. Subsequently, a mix-hop graph convolutional network (GCN) is developed to selectively propagate critical information through multihop operations and is incorporated into the spatial–temporal dependence-aware feature extraction module to capture multiscale sequential patterns and dynamic variations, thereby modeling complex spatiotemporal interactions. Furthermore, a multihierarchical feature extraction framework is introduced to jointly exploit global and local process information, enabling a comprehensive multihierarchical feature representation. Extensive experiments on two real-world large-scale industrial process datasets validate the effectiveness of the proposed method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30813-30827"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176189","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}
Pub Date : 2026-10-15Epub Date: 2026-09-11DOI: 10.1109/JSEN.2026.3731085
Hanlin Ge;Haiwen Yuan;Haibin Yuan;Jianxun Lv;Yuxin Deng;Cong Chen;Chengxin Liu
Accurate monitoring of ground-level synthetic electric fields beneath ultrahigh voltage (UHV) direct current (DC) transmission lines is critical for assessing the electromagnetic environment. As the primary instruments for this task, field mill sensors deployed in outdoor environments are susceptible to ambient temperature fluctuations, particularly in regions with significant diurnal temperature variations. This sensitivity induces substantial measurement drift, which severely constrains the reliability of long-term electric field monitoring under complex operating conditions. This article investigates the mechanism of measurement errors caused by temperature-induced modulation frequency drift, and proposes a dual-mode adaptive control strategy based on a novel circuit topology to achieve automatic regulation of the modulation frequency. The experimental results demonstrate that, compared with conventional methods, the proposed strategy effectively compensates for measurement errors of the field mill sensor across a temperature range from $- 30~^{circ }$ C to $70~^{circ }$ C. Specifically, the coefficient of variation (CV) of sensitivity was reduced to 0.64%, and the maximum linearity error was suppressed to 0.28%. This work provides a reliable solution for accurate ground-level synthetic electric field measurement in complex outdoor environments, offering promising potential for engineering applications.
准确监测特高压（UHV）直流（DC）输电线路下的地面合成电场对于评估电磁环境至关重要。作为这项任务的主要仪器，部署在室外环境中的现场磨机传感器容易受到环境温度波动的影响，特别是在昼夜温度变化较大的地区。这种灵敏度导致了大量的测量漂移，严重制约了复杂工作条件下长期电场监测的可靠性。本文研究了温度诱导调制频率漂移引起测量误差的机理，提出了一种基于新型电路拓扑结构的双模自适应控制策略，实现了调制频率的自动调节。实验结果表明，与传统方法相比，该方法能有效补偿现场磨机传感器在$- 30~ $ {circ}$ C ~ $70~ $ {circ}$ C温度范围内的测量误差，灵敏度变异系数（CV）降至0.64%，最大线性误差降至0.28%。该研究为复杂室外环境下地面合成电场的精确测量提供了可靠的解决方案，具有广阔的工程应用前景。
{"title":"Dual-Mode Adaptive Control Strategy for Modulation Frequency Drift Compensation in Field Mill Sensors","authors":"Hanlin Ge;Haiwen Yuan;Haibin Yuan;Jianxun Lv;Yuxin Deng;Cong Chen;Chengxin Liu","doi":"10.1109/JSEN.2026.3731085","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3731085","url":null,"abstract":"Accurate monitoring of ground-level synthetic electric fields beneath ultrahigh voltage (UHV) direct current (DC) transmission lines is critical for assessing the electromagnetic environment. As the primary instruments for this task, field mill sensors deployed in outdoor environments are susceptible to ambient temperature fluctuations, particularly in regions with significant diurnal temperature variations. This sensitivity induces substantial measurement drift, which severely constrains the reliability of long-term electric field monitoring under complex operating conditions. This article investigates the mechanism of measurement errors caused by temperature-induced modulation frequency drift, and proposes a dual-mode adaptive control strategy based on a novel circuit topology to achieve automatic regulation of the modulation frequency. The experimental results demonstrate that, compared with conventional methods, the proposed strategy effectively compensates for measurement errors of the field mill sensor across a temperature range from <inline-formula> <tex-math>$- 30~^{circ }$ </tex-math></inline-formula>C to <inline-formula> <tex-math>$70~^{circ }$ </tex-math></inline-formula>C. Specifically, the coefficient of variation (CV) of sensitivity was reduced to 0.64%, and the maximum linearity error was suppressed to 0.28%. This work provides a reliable solution for accurate ground-level synthetic electric field measurement in complex outdoor environments, offering promising potential for engineering applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31461-31472"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176236","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}
Pub Date : 2026-10-15Epub Date: 2026-08-28DOI: 10.1109/JSEN.2026.3726629
Jelena Kršić;Mirjana G. Stojanović;Marjan N. Miletić;Vladimir A. Atanasoski;Marija D. Ivanović;Aleksandra Maluckov;Jovana Petrović
Heart rate (HR) is a valuable and widely used indicator of cardiac health. Electrocardiographic (ECG) recordings are the medical gold standard for HR estimation. However, the growing demand for mobile cardiac monitoring prompted investigation of other sensing modalities. Photoplethysmographs (PPGs) and accelerometers (ACCs) have been heavily investigated and made their way to the market. However, direct comparisons of different sensing modalities, besides the individual benchmarking to ECG, needed for their proper placement in the diagnostic and monitoring landscape are scarce. The reasons vary from the challenges with sensor synchronization to a small range of HRs probed at rest. In this article, we exploit the open SensSmartTech dataset recorded by a multimodal device before and immediately after activity, with the HR span of over 100 beats per minute (bpm), to directly compare the HR estimation accuracy from the carotid PPG, brachial PPG, chest-mounted ACC, and phonocardiograph (PCG), with an ECG as a reference. The HR was estimated using peak-finding methods, preceded by envelope detection in fast chest waveforms. ACC and PCG delivered HR with root mean square error (RMSE) below the 5-bpm threshold recommended by the American National Standard for cardiac monitors, HR meters and alarms. Slightly larger RMSEs, in the range of 6–9 bpm, obtained from PPG are attributed to the motion artifacts. The results show that, within the bounds of this controlled comparison, the detection of fast events spectrally distinct from the respiration and body movements renders higher HR accuracy than much slower arterial pulses measured in a comparable measurement configuration and filtered by similar algorithms.
{"title":"Heart Rate Estimation by Different Sensing Modalities Under Resting and Postexercise-Recovery Conditions","authors":"Jelena Kršić;Mirjana G. Stojanović;Marjan N. Miletić;Vladimir A. Atanasoski;Marija D. Ivanović;Aleksandra Maluckov;Jovana Petrović","doi":"10.1109/JSEN.2026.3726629","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3726629","url":null,"abstract":"Heart rate (HR) is a valuable and widely used indicator of cardiac health. Electrocardiographic (ECG) recordings are the medical gold standard for HR estimation. However, the growing demand for mobile cardiac monitoring prompted investigation of other sensing modalities. Photoplethysmographs (PPGs) and accelerometers (ACCs) have been heavily investigated and made their way to the market. However, direct comparisons of different sensing modalities, besides the individual benchmarking to ECG, needed for their proper placement in the diagnostic and monitoring landscape are scarce. The reasons vary from the challenges with sensor synchronization to a small range of HRs probed at rest. In this article, we exploit the open SensSmartTech dataset recorded by a multimodal device before and immediately after activity, with the HR span of over 100 beats per minute (bpm), to directly compare the HR estimation accuracy from the carotid PPG, brachial PPG, chest-mounted ACC, and phonocardiograph (PCG), with an ECG as a reference. The HR was estimated using peak-finding methods, preceded by envelope detection in fast chest waveforms. ACC and PCG delivered HR with root mean square error (RMSE) below the 5-bpm threshold recommended by the American National Standard for cardiac monitors, HR meters and alarms. Slightly larger RMSEs, in the range of 6–9 bpm, obtained from PPG are attributed to the motion artifacts. The results show that, within the bounds of this controlled comparison, the detection of fast events spectrally distinct from the respiration and body movements renders higher HR accuracy than much slower arterial pulses measured in a comparable measurement configuration and filtered by similar algorithms.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31562-31574"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176250","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}
Fiber-optic magnetometers often find it difficult to balance vector discrimination and environmental stability, in particular because of cross-sensitivity to temperature and strain. In this work, a compact vector magnetic-field sensor based on a symmetric seven-core fiber (SCF) Michelson interferometer (MI) is proposed and demonstrated. The sensor combines a fan-in/fan-out (FIFO) device with a sensor head coated with a magnetic-force-driven composite to form a differential sensing structure. This design exploits the consistent spatial properties of the multicore fiber (MCF) and supports a push–pull strain sensing mechanism that doubles the sensitivity while suppressing common-mode thermal noise. The sensor achieves a highly linear vector response ($text {R}{,}^{{2}}gt {0}.{98}$ ) with a maximum sensitivity of 0.485 nm/mT and a figure-of-eight angular dependence that provides directional discrimination of the magnetic field. The differential configuration also shows good short-term stability under zero-field conditions, with a wavelength fluctuation standard deviation of 0.179 nm over 45 min. With its compact structure, simple configuration, and stable directional response, the sensor shows potential for magnetic-field monitoring and orientation-related sensing applications.
{"title":"Vector Magnetic-Field Sensing via Differential Interferometry in a Seven-Core Fiber","authors":"Qingyu Wei;Yucheng Yao;Yalin Gao;Xuchen Hua;Zhiyong Zhao;Ming Tang","doi":"10.1109/JSEN.2026.3730097","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3730097","url":null,"abstract":"Fiber-optic magnetometers often find it difficult to balance vector discrimination and environmental stability, in particular because of cross-sensitivity to temperature and strain. In this work, a compact vector magnetic-field sensor based on a symmetric seven-core fiber (SCF) Michelson interferometer (MI) is proposed and demonstrated. The sensor combines a fan-in/fan-out (FIFO) device with a sensor head coated with a magnetic-force-driven composite to form a differential sensing structure. This design exploits the consistent spatial properties of the multicore fiber (MCF) and supports a push–pull strain sensing mechanism that doubles the sensitivity while suppressing common-mode thermal noise. The sensor achieves a highly linear vector response (<inline-formula> <tex-math>$text {R}{,}^{{2}}gt {0}.{98}$ </tex-math></inline-formula>) with a maximum sensitivity of 0.485 nm/mT and a figure-of-eight angular dependence that provides directional discrimination of the magnetic field. The differential configuration also shows good short-term stability under zero-field conditions, with a wavelength fluctuation standard deviation of 0.179 nm over 45 min. With its compact structure, simple configuration, and stable directional response, the sensor shows potential for magnetic-field monitoring and orientation-related sensing applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30503-30510"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176268","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}
Accurate flow rate monitoring is essential for quantitative analysis and precise fluid manipulation in microfluidic systems. However, achieving high-sensitivity in situ flow monitoring remains challenging due to the extremely weak pressure generated by microflows and the stringent miniaturization requirements of microfluidic systems. In this work, an ultrasoft diaphragm-based fiber-tip Fabry–Pérot sensor is proposed for direct in-channel microflow monitoring. The sensor is fabricated by two-photon polymerization (TPP) using a heterogeneous structure composed of a rigid supporting frame and a compliant diaphragm, enabling an effective balance between structural stability and sensitivity. The sensing performance is further enhanced through systematic optimization of the diaphragm geometry. Experimental results demonstrate a sensitivity of −0.4629 nm/($mu $ L/min) over a flow rate range of 5–$25~mu $ L/min. The fiber-tip configuration provides an ultracompact and easily integrated sensing solution. The proposed sensor shows strong potential for in situ microflow monitoring in microfluidic and biomedical applications.
{"title":"An Ultrasoft-Diaphragm Fiber-Optic Flow Sensor for In Situ Monitoring in Microfluidic Chips","authors":"Xiaoting Wu;Zhuo Ren;Xin Lv;Xuhao Ji;Xi Hu;Zhihai Liu;Yu Zhang","doi":"10.1109/JSEN.2026.3729334","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3729334","url":null,"abstract":"Accurate flow rate monitoring is essential for quantitative analysis and precise fluid manipulation in microfluidic systems. However, achieving high-sensitivity in situ flow monitoring remains challenging due to the extremely weak pressure generated by microflows and the stringent miniaturization requirements of microfluidic systems. In this work, an ultrasoft diaphragm-based fiber-tip Fabry–Pérot sensor is proposed for direct in-channel microflow monitoring. The sensor is fabricated by two-photon polymerization (TPP) using a heterogeneous structure composed of a rigid supporting frame and a compliant diaphragm, enabling an effective balance between structural stability and sensitivity. The sensing performance is further enhanced through systematic optimization of the diaphragm geometry. Experimental results demonstrate a sensitivity of −0.4629 nm/(<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>L/min) over a flow rate range of 5–<inline-formula> <tex-math>$25~mu $ </tex-math></inline-formula>L/min. The fiber-tip configuration provides an ultracompact and easily integrated sensing solution. The proposed sensor shows strong potential for in situ microflow monitoring in microfluidic and biomedical applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30495-30502"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176269","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}
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