IEEE Sensors Journal最新文献

{"title":"IEEE Sensors Council","authors":"","doi":"10.1109/JSEN.2025.3585795","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3585795","url":null,"abstract":"","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"C3-C3"},"PeriodicalIF":4.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634731","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}

{"title":"Guest Editorial Special Issue on Machine Learning for Radio Frequency Sensing","authors":"Avik Santra;Ashish Pandharipande;Pu Perry Wang;George Shaker;Bhavani Shankar Mysore;Guido Dolmans;Yan Chen;Negin Shariati Moghadam","doi":"10.1109/JSEN.2025.3573462","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573462","url":null,"abstract":"","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"23163-23163"},"PeriodicalIF":4.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11071932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557899","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}

{"title":"Identifying the Respiratory Sound Based on Single-Channel Separation and Hyperdimensional Computing","authors":"Jie Zheng;Yixuan Wang;Jinglong Niu;Yan Shi;Fei Xie","doi":"10.1109/JSEN.2025.3557909","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3557909","url":null,"abstract":"In intensive care units (ICUs), efficient respiratory management, particularly sputum suction in weakened patients, is critical. Traditional stethoscope-based methods for respiratory sound analysis in tracheal sputum assessment are time-consuming and often struggle to differentiate between cardiac and respiratory sounds, affecting sputum detection accuracy. To address these issues, we propose identifying respiratory sound based on single-channel separation and hyperdimensional computing (IRS-SSHC). Specifically, the proposed method first employs an encoder-decoder framework to effectively separate heart and respiratory sounds in the time domain. Then, it segments respiratory sounds using short-duration energy, where each segment is represented by a 1024-D vector space. Next, it utilizes light gradient boosting machine (LightGBM) based on the vector space for classification. Experimental results show that the classification ACC of IRS-SSHC is 97.9%, which outperforms existing methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24626-24633"},"PeriodicalIF":4.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550219","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":"IEEE Sensors Council","authors":"","doi":"10.1109/JSEN.2025.3580308","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3580308","url":null,"abstract":"","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"C3-C3"},"PeriodicalIF":4.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11069378","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557830","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}

{"title":"Crosstalk Suppression in the OFDR System Using a Dual-Wavelength wFBG Array","authors":"Xinao Jia;Xiaoyan Wen;Haifei Lv;Min Li;Shuo Deng;Ming-Yu Li","doi":"10.1109/JSEN.2025.3577200","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3577200","url":null,"abstract":"In this article, a dual-wavelength weak reflectivity fiber Bragg grating (wFBG) array was developed to suppress crosstalk in a wFBG-based distributed optical frequency-domain reflectometer system. In the newly developed array, two kinds of wFBG fabricated with different Bragg central wavelengths were arranged in an alternating pattern to replace the traditional identical wFBG. Crosstalk in the dual-wavelength array was theoretically simulated and experimentally measured and taken for comparative analysis with the identical array. Simulation and experiments indicate that the dual-wavelength array exhibits reduced crosstalk peak number and intensity, both of which facilitates demodulation and analysis of wFBG sensing. The signal-to-noise ratio (SNR) of the dual-wavelength array stands at 28.98 dB, which is 6.52 dB increased compared with the identical array. Lateral pressure sensing tests further prove the advantage of crosstalk suppression of the dual wFBG array. Lateral pressure sensitivity of the dual-wavelength array was measured to be 7.714 nm/<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula> with a pressure accuracy of <inline-formula> <tex-math>$pm 6.09~mu varepsilon $ </tex-math></inline-formula>, both of which exceed the performance of the identical array (7.438 nm/<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula> of lateral pressure sensitivity and <inline-formula> <tex-math>$pm 33.5~mu varepsilon $ </tex-math></inline-formula> of pressure test accuracy). Due to its simple structure, obvious crosstalk suppression, as well as SNR enhancement, the proposed dual-wavelength wFBG array would have extensive application prospects in distributed optical sensing fields.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"26663-26670"},"PeriodicalIF":4.3,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634864","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 Novel Centrally Symmetric Spiral-Nested Piezoelectric Energy Harvesting System and Its Management Circuit","authors":"Yuxuan Liu;Debo Wang;Licheng Deng","doi":"10.1109/JSEN.2025.3578615","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578615","url":null,"abstract":"To realize high power density, low-frequency, multidirectional energy harvesting, and efficient energy storage management, a piezoelectric energy harvesting system comprising a centrally symmetric spiral-nested piezoelectric energy harvester (CSS-PEH) and an energy management circuit is proposed in this work. The harvester features a doubly clamped outer beam and a nested dual-spiral inner beam, while the management circuit integrates a self-powered parallel synchronized switch harvesting on inductor (SP-PSSHI) rectification circuit, BQ25570 IC, and supercapacitor. A three-degree-of-freedom (2-DOF) lumped parameter model and an electromechanical coupling model are established to analyze the frequency response and output characteristics. The measured results show that the CSS-PHE has resonant frequencies at 12.8 and 17.2 Hz, with corresponding open-circuit voltages of 73.5 and 38.7 V. Meanwhile, the SP-PSSHI rectified series circuit achieved maximum output powers of 6.23 mW with the normalized power density (NPD) of <inline-formula> <tex-math>$32.4~mu $ </tex-math></inline-formula>W<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>g<inline-formula> <tex-math>${}^{-{2}} cdot $ </tex-math></inline-formula>mm<inline-formula> <tex-math>${}^{-{3}}$ </tex-math></inline-formula>; the SP-PSSHI rectified parallel circuit delivered the maximum output power of 3.21 mW with the NPD of <inline-formula> <tex-math>$21.6~mu $ </tex-math></inline-formula>W<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>g<inline-formula> <tex-math>${}^{-{2}}cdot $ </tex-math></inline-formula>mm<inline-formula> <tex-math>${}^{-{3}}$ </tex-math></inline-formula>. The energy management circuit enables supercapacitor charging up to 4.05 V while maintaining stable 3.3-V dc output. Furthermore, the CSS-PEH demonstrates multidirectional energy harvesting capability. Therefore, this piezoelectric energy harvesting system provides stable power supply solutions for many fields, such as intelligent transportation energy harvesting, wireless sensor networks, and so on.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"26520-26529"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634656","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":"Cooperative Spectrum Sensing With DeGAN and KANCNN for Nonorthogonal Multiple Access","authors":"Mingqian Yan;Yonghua Wang;Quanbin Liang;Tinghui Xu","doi":"10.1109/JSEN.2025.3578359","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578359","url":null,"abstract":"Nonorthogonal multiple access (NOMA) technology achieves higher communication throughput compared with orthogonal multiple access, but it also introduces significant challenges for spectrum sensing (SS), particularly in accurately detecting channels occupied by multiple users in complex environments. To address these challenges and enhance SS capabilities in power-domain NOMA, this article proposes a novel deep learning-based algorithm that integrates a denoising generative adversarial network (DeGAN) for effective noise reduction. The DeGAN module employs a joint loss function to effectively suppress noise while preserving critical time–frequency features of the signals. Subsequently, the power spectral density of the denoised signals is extracted and utilized as a feature for sample classification through a model that integrates the Kolmogorov–Arnold networks–convolutional neural network (KANCNN). Comparative results demonstrate that the DeGAN–KANCNN algorithm surpasses other methods in detection accuracy and interference resistance in challenging environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27265-27277"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634740","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":"Flow Characterization in Inclined Intermittent Flow Using Improved U-Net and Particle Image Velocimetry","authors":"Ting Xue;Zeyang Hao;Yan Wu","doi":"10.1109/JSEN.2025.3578715","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578715","url":null,"abstract":"Gas-liquid intermittent flow is of great engineering significance for the design and optimization of pipeline systems in complex terrains, while current research on the flow characteristics influenced by pipe inclination angle remains insufficient. In this study, the flow characteristics of intermittent flow in horizontal, 5° and 10° inclined pipelines are systematically analyzed by combining the improved deep learning model with particle image velocimetry (PIV) technology. First, the improved U-Net model integrating the convolutional block attention module (CBAM) is employed to achieve high-precision segmentation of the gas-liquid phase. Experimental results show that the improved model achieves 98.83% pixel accuracy (PA) and 97.01% mean intersection over union (MIoU) in phase segmentation tasks, which surpasses benchmark models, including DeepLabV3 and HRNet. By comparing the three inclined configurations, the pipe inclination angle is found to significantly alter the flow structure by increasing the axial gravitational component, which manifests in reduced length of elongated bubbles, decreased thickness of liquid film, and enhanced asymmetry in flow velocity distribution. Furthermore, the increase of the inclination angle will trigger the flow regime transition to unstable slug flow. The flow pattern transition boundary is established based on the mixed Froude number (Fr), revealing that the critical Fr value for the transition from slug flow to plug flow in the 10° inclined pipe decreased by 22% compared to the horizontal pipe. Research results provide essential parameter references for flow stability prediction and numerical simulation in pipeline design across complex terrains.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27278-27287"},"PeriodicalIF":4.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634673","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":"Research on Multimodal Fusion Perception Technology for Autonomous Sweeping Vehicle","authors":"Yang Zhang;Bo Yang;Wukun Lei;Xiaofei Pei","doi":"10.1109/JSEN.2025.3578375","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3578375","url":null,"abstract":"This article proposes a multimodal fusion framework to address the challenges of detecting and tracking specialized vehicles and dynamic targets in complex industrial park environments. The framework integrates LiDAR, a monocular Camera, and an inertial navigation system (INS) to achieve precise obstacle perception and stable tracking through dynamic region of interest (ROI) cropping, optimized point cloud clustering, target detection, and multimodal perception fusion. First, a path-aware dynamic ROI cropping method and a multiregion density-aware seed point cloud ground segmentation approach are introduced to improve adaptability and point cloud processing efficiency. Second, a two-stage refinement strategy method is proposed to enhance target clustering accuracy. Furthermore, by combining the 2-D detection network, a multimodal perception fusion module, and a multiobject tracking (MOT) strategy, the framework significantly improves fusion efficiency and matching accuracy. Field tests demonstrate that the framework achieves excellent performance, with static object localization deviations below 0.8 m and reliable state estimation for dynamic targets. On a custom dataset, the monocular Camera achieves 91.67% accuracy for specialized vehicles, while the fusion framework exhibits strong adaptability and reliability in complex scenarios.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 14","pages":"27743-27753"},"PeriodicalIF":4.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634758","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":"IEEE Sensors Council","authors":"","doi":"10.1109/JSEN.2025.3575674","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3575674","url":null,"abstract":"","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 12","pages":"C3-C3"},"PeriodicalIF":4.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299309","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}