IEEE Sensors Journal最新文献

{"title":"Dynamic Magnetic Field Compensation Based on Real-Time Signal Separation for Array Optically Pumped Magnetometers","authors":"Yaqiong Niu;Longsheng Cheng;Xingshen Hou;Chaofeng Ye","doi":"10.1109/JSEN.2025.3543684","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543684","url":null,"abstract":"Optically pumped magnetometers (OPMs) operating in the spin-exchange relaxation-free (SERF) regime achieve excellent sensitivity in environments with zero magnetic fields. Therefore, it is essential to suppress the environment’s magnetic field. As the environment’s magnetic field is not constant, it is necessary to compensate the magnetic field dynamically. This article presents a method of compensating the environment magnetic field dynamically for array OPMs based on real-time signal separation. The OPMs serve as the signal measurement sensors, as well as the quasi-static environment magnetic field monitoring sensors. The OPMs are placed orthogonal to each other to monitor the three components of the magnetic field. The output signals are separated and reconstructed using a fast wavelet transform. The low-frequency component is utilized to calculate the compensation current and the high-frequency component is recorded as the measurement result. This method does not require any additional sensors for magnetic field compensation, resulting in a simplified setting and low cost. The experimental results show that the method can suppress the quasi-static magnetic field fluctuations to less than one percent in real time. The magnetic field fluctuation is controlled within 10, 10, and 1 pT for the three axes. For array sensors, the effect of the magnetic field gradient can be reduced by utilizing internal coils in each OPM. The magnetic signal of a human heart is measured, which verifies the feasibility of the method for ultraweak biomagnetic field measurement.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10913-10921"},"PeriodicalIF":4.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761488","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":"An Ultrafast-Transient-Response Boost Converter With V-Cubed-Controlled Techniques Suitable for Low-Voltage Solar Cells in Wireless Sensor Networks","authors":"Jiann-Jong Chen;Yuh-Shyan Hwang;Guan-Zhi Lin;Hung-Wei Chiu","doi":"10.1109/JSEN.2025.3544719","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544719","url":null,"abstract":"This article proposed a V-cubed adaptive-on-time-controlled boost converter with zero-current-detection (ZCD) techniques suitable for low-voltage solar cells in wireless sensor networks. This converter employs V-cubed adaptive-on-time control (AOTC) and incorporates a zero-current detector to enhance efficiency under light loads. The design and implementation are carried out using the T18HVG2 (TSMC 0.18-<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m HV 1P6M) process, with a chip area of <inline-formula> <tex-math>$1.067times 1.073$ </tex-math></inline-formula> mm2. The input voltage range is 0.5–1.2 V, the output voltage is 1.5–2.0 V, and the load current range is 10–150 mA. The transient response times are 1 and <inline-formula> <tex-math>$0.8~mu $ </tex-math></inline-formula>s, achieving a peak efficiency of 94.2% at a load current of 130 mA. The major contributions of this article are: 1) we proposed a V-cubed adaptive-on-time-controlled boost converter with ZCD techniques; 2) the proposed converter has been simulated with SIMPLIS and HSPICE to verify the function well; and 3) the performance of the proposed converter is better than others’ work. In a dc–dc boost converter, a voltage-cubed control strategy is used, which usually means adjusting the control parameters in the control loop according to the cube of the output voltage. The application of this method can bring some advantages, which are mainly reflected in the following aspects: 1) improved efficiency; 2) improved dynamic response; 3) reduced control noise and oscillation; 4) improved load regulation; and 5) extended component life. In summary, the application of voltage cubic control in dc–dc boost converters can improve efficiency, stability, and dynamic response, giving it obvious advantages in applications requiring high performance and high stability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12286-12293"},"PeriodicalIF":4.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748872","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":"Wideband Fan-Beam Lens Antenna for IR-UWB Through-the-Wall Human Detection Radar","authors":"Soyeong Lee;Daeyeong Yoon;Kyuhwan Hwang;Heesu Wang;Kyung-Hwan Park;Yong Bae Park","doi":"10.1109/JSEN.2025.3543804","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543804","url":null,"abstract":"This article presents a fan-beam lens antenna design for impulse radio ultrawideband (IR-UWB) through-the-wall human detection radar applications operating in the 1–8 GHz. The proposed fan-beam lens antenna integrates a dielectric lens, designed by adjusting the lengths of dielectric slabs and air layers, with a Vivaldi antenna to achieve a fan-beam pattern characterized by a narrow beamwidth in the E-plane while maintaining a wide beamwidth in the H-plane. The antenna demonstrates excellent time-domain characteristics with a high fidelity factor exceeding 0.93, low reflection signal ringing, and maintained narrow beamwidth in the E-plane for far-field pulse radiation patterns. Through experimental validation, the antenna achieved SNR improvement due to concentrated signal propagation, enabling stable human detection at distances beyond 5 m through walls. The wide H-plane beamwidth allows effective detection of subjects in various postures, including lying positions. The proposed antenna design shows promising potential for various applications including disaster site search and rescue, building surveillance, fall detection in care facilities, noncontact vital sign monitoring, and smart home occupancy detection systems.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11246-11257"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748989","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 High-Performance Gas Sensor Employing a Spacer-Based Cavity With Distributed Bragg Reflectors","authors":"Michal Gryga;Jakub Chylek;Dalibor Ciprian;Pavel Pokorny;Tomas Fort;Jaroslav Sobota;Petr Hlubina","doi":"10.1109/JSEN.2025.3543877","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543877","url":null,"abstract":"A spacer-based cavity with distributed Bragg reflectors (DBRs) is employed in high-performance gas sensing utilizing wavelength interrogation. The DBR is formed by a 1-D photonic crystal (1DPhC) comprising six bilayers of TiO2/SiO2 with a termination layer of TiO2, and the spacer is a Ti layer deposited partially on one of the DBRs. The reflectance spectra of the sensor are measured in visible and near-infrared spectral regions at a near-normal incidence of light for humid air, and narrow dips in the 1DPhC bandgap due to the cavity modes are resolved. Two thicknesses of the spacer are considered, and it is revealed that the spacer thickness change is accompanied by nearly fivefold sensitivity enhancement. The sensitivity to relative humidity (RH) of humid air, a figure of merit (FOM), and the limit of detection reached 0.444 nm/%RH, 0.178%RH−1, and 0.022%RH, respectively. The theoretical analysis showed that the RH response of the sensor is due to the porous termination layer of the 1DPhC, and the strong dependence of the sensor sensitivity and FOM on both the spacer thickness and the position of the resonance dip in the bandgap was confirmed. A sensor employing a spacer-based cavity with DBRs thus represents an effective alternative with advantages such as sensing at normal incidence of light with high sensitivity and FOM.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10992-11000"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748940","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":"Smart Road Studs With Magnetic Sensors for Multilane Traffic Volume Detection","authors":"Yanli Sun;Wei Quan;Hua Wang;Yimeng Feng;Xiaolong Ma;Hao Li;Jiayu Sun;Jixuan Cheng","doi":"10.1109/JSEN.2025.3543945","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543945","url":null,"abstract":"Traffic detection is essential in intelligent transportation systems. Magnetic sensors, valued for their compactness, low cost, and robustness to interference, show promise as traffic detectors. Yet their current vehicle detection abilities fall short of widespread deployment. This study addresses this gap by integrating magnetic sensors into smart road studs (SRSs), enabling adaptive sensing and control capabilities. Conventional detection algorithms for roadside or center-lane placement are unsuitable for sensors on lane markings. This article introduces a multilane traffic volume detection algorithm tailored for the SRS network. A multiscale convolutional neural network (MSCNN) module based on 1-D convolution is first designed to automatically extract multiscale features from individual signals. Then, the C-Transformer (C-Trans) and S-Transformer (S-Trans) encoding modules, built on Transformer architecture, are employed to capture both intrasignal and spatial intersensor correlations. By merging multiscale, correlation-based, and manually extracted features, the proposed method facilitates multilane vehicle detection. To further refine accuracy and underscore the role of key sensor nodes, a single-sensor vehicle detection approach is integrated, with the Dempster-Shafer (D-S) theory used for result fusion. Experimental results demonstrate that the proposed approach achieves multilane traffic volume detection with an error rate of approximately 1.6%, outperforming current methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11737-11748"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761444","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 Two-Stage Framework With Ore-Detect and Segment Anything Model for Ore Particle Segmentation and Size Measurement","authors":"Fei Li;Xiaoyan Liu;Zongping Li","doi":"10.1109/JSEN.2025.3543918","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543918","url":null,"abstract":"This article proposes a novel detection-driven method for ore particle (OP) segmentation, specifically designed to enable precise measurement of particle size distribution (PSD), a critical factor in mineral processing. Accurate segmentation is essential for calculating particle size, which directly contributes to the evaluation of PSD and ensures effective ore quality control and processing efficiency. Conventional segmentation methods often struggle to generalize across diverse ore types, irregular particle geometries, and complex spatial arrangements, limiting their robustness and adaptability in real-world applications. The proposed method overcomes these challenges by first using Ore-Detect, a detection module that efficiently localizes OPs, followed by a refined segmentation process using the segment anything model (SAM) to precisely delineate particle boundaries. This two-stage approach ensures high accuracy in particle segmentation and size measurement, even in the presence of occlusions and significant size variations. Experimental results demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance across key metrics: mean intersection over union (mIoU) of 85.31%, precision of 88.81%, recall of 87.12%, <inline-formula> <tex-math>$F1$ </tex-math></inline-formula>-score of 87.95%, average precision (AP) of 94.06%, boundary displacement error (BDE) of 6.98, and object-level accuracy (OLA) of 89.51%. Furthermore, the framework achieves a PSD measurement error within 5%. In terms of computational efficiency, the proposed method processes 100 ore images of <inline-formula> <tex-math>$512times 512$ </tex-math></inline-formula> resolution in 11.27 s, demonstrating its ability to meet real-time industrial requirements.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11722-11736"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761557","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":"An Efficient Radar-Based Gesture Recognition Method Using Enhanced GMM and Hybrid SNN","authors":"Yifan Wu;Li Wu;Taiyang Hu;Zelong Xiao;Mengxuan Xiao;Lei Li","doi":"10.1109/JSEN.2025.3543875","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543875","url":null,"abstract":"This article proposes an energy-efficient and high-accuracy gesture recognition framework to address the challenges of high computational complexity and interference susceptibility in conventional radar-based gesture recognition methods. First, an enhanced Gaussian mixture model (GMM) with an optimized learning rate is introduced to improve anti-interference performance by exploiting spatial and velocity differences between gesture signals and target-like interference. Furthermore, a novel spiking neural network (SNN) architecture is proposed, combining a 2-D convolutional neural-network (2D-CNN) for spatial feature extraction with a long short-term memory (LSTM) network for capturing, long-term temporal dependencies. This hybrid architecture effectively integrates short-term and long-term temporal dynamics to enhance recognition accuracy. Additionally, spike-timing-dependent plasticity (STDP) is incorporated to address the non-differentiability of spike-based data, thereby improving the network’s feature learning capabilities. To evaluate the proposed approach, a radar-based gesture dataset comprising seven gesture categories was constructed using a 60-GHz frequency-modulated continuous wave (FMCW) radar system. Experimental results demonstrate a recognition accuracy of 99.28%, alongside computational complexity and power consumption have better performance than the existing competitive methods, suiting power and resource-constrained environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12511-12524"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748887","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":"Error Modulation Scheme for Dual RINS Based on Device Time-Sequence Fusion","authors":"Haoyu Bu;Feng Zha;Hongyang He;Chenyang Zhang;Quanchao Zhang","doi":"10.1109/JSEN.2025.3544437","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544437","url":null,"abstract":"The rotational inertial navigation system (RINS) mitigates system errors through periodic rotation, with the effectiveness of compensation contingent upon the inertial measurement unit (IMU) rotation scheme employed. In conventional rotational modulation, the rotation of the IMU introduces additional errors resulting from rotational angular motion coupled with scale factor and installation error, particularly exacerbating asymmetric scale factor error. An optimal rotation scheme should eliminate inertial sensor errors without introducing new ones. Building upon the maximum rotatable angle of the IMU, a rotation modulation scheme based on time-sequence fusion (TSF) of dual RINS is proposed for the traditional single-axis two-position rotation–stop (with identical stopping durations) and single-axis four-position rotation–stop (with identical stopping durations only at symmetric positions) schemes. The proposed scheme ensures that the dual RINS is precisely timed to rotate and stop, utilizing solely the stop data derived from TSF for calculation. While modulating constant error, the rotation-induced coupling errors are effectively mitigated, and the oscillations in attitude and velocity are reduced. Simulation results show that the system positioning error based on TSF is reduced from 6.13 n mile/72 h for a single system to 1.48 n mile/72 h. Experimental results indicate that, relative to the single system, TSF reduces the latitude error root-mean-square error (RMSE) by 64.44% and the longitude error RMSE by 62.89% in 36 h.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10839-10851"},"PeriodicalIF":4.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761589","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":"Real-Time Diagnosis of Abrupt and Incipient Faults in IMU Using a Lightweight CNN-Transformer Hybrid Model","authors":"Jia Song;Zhipeng Chen;Wenling Li","doi":"10.1109/JSEN.2025.3543588","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543588","url":null,"abstract":"The fault diagnosis is crucial for improving the reliability and safety of industrial sensors. Diagnosing faults in inertial measurement units (IMUs) is particularly challenging due to the complex nature of abrupt and incipient faults, which require the accurate and rapid diagnosis. This article presents a hybrid model that combines convolutional neural networks (CNNs) and Transformer encoder architectures. The CNN component effectively extracts local fault features, while the Transformer encoder captures long-range dependencies in time-series data, enabling the precise and rapid IMU fault diagnosis. To meet the autonomous and real-time operational demands of IMU fault diagnosis, the knowledge distillation is applied to develop a lightweight version of the model. This optimization facilitates efficient deployment on resource-limited hardware, maintaining the original model’s accuracy and rapid processing speed. The effectiveness of the proposed approach is validated through comprehensive comparisons with other models, demonstrating the superior diagnostic accuracy, low fault diagnosis delay, and suitability for real-time applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12496-12510"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748865","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":"Deep Learning-Enabled Noninvasive Human ECG and Long-Term Heart Rate Variability Monitoring and Matching With Sleep Stages Based on an Optical Fiber Sensor System","authors":"Haochun Gao;Qing Wang;Ke Li;Jing Zhou;Xiang Wang;Changyuan Yu","doi":"10.1109/JSEN.2025.3543869","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543869","url":null,"abstract":"Optical fiber sensors, known for their small size, lightweight, and resistance to electronic interference, are widely used in various applications, including medical vital signs monitoring and the Internet of Medical Things (IoMT). They are particularly useful in recording electrocardiogram (ECG) signals and measuring heart rate variability (HRV), which provides insights into the autonomic nervous system activity. HRV, affected by sleep quality, can be used to assess sleep stages. Monitoring ECG and HRV during sleep can identify sleep disruptions and guide interventions to improve sleep quality. However, existing optical fiber sensors for vital signs monitoring have some drawbacks, including signal loss, sensitivity to light source intensity changes, signal distortion during long-distance transmission, and high manufacturing and maintenance costs. They also may produce missing or anomalous data due to sensor failures, transmission and storage issues, or other unforeseen factors. Conventional monitoring methods can be uncomfortable for long-term daily ECG and HRV monitoring. To address these problems, we propose a novel optical fiber sensor based on a fiber interferometer and a robust semi-supervised framework, termed ensemble bidirectional long short-term memory with attention framework (EBLA), which is composed of complete ensemble empirical mode decomposition with adaptive noise network (CEDANN) and graph-based semi-supervised classification model (GSCM) modules, can reconstruct and analyze raw ECG signals, extract temporal and spatial features, and match the relationship between HRV and sleep stages. The framework also applies external knowledge of acquired signals for graph modeling for the first time, revealing data relationships and better understanding the global sample structure from the raw ECG signal. The average root-mean-square error (RMSE) and mean absolute error (MAE) of experiments reach 1.711 and 1.196, respectively, demonstrating its feasibility and effectiveness, exhibiting a better effect and its superior to the state-of-the-art approaches. This work has the potential to promote smart healthcare monitoring and the application of optical fiber sensing.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11131-11147"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748933","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}