IEEE Sensors Letters最新文献_第3页

Monitoring of Wire Breaks in PCCPs Based on Fiber Michelson Interferometer and DAS 基于光纤迈克尔逊干涉仪和DAS的pccp断线监测

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-15 DOI: 10.1109/LSENS.2026.3734254

Guoheng Qi;Kunbi Zhu;Wenzhu Huang;Wentao Zhang

{"title":"Monitoring of Wire Breaks in PCCPs Based on Fiber Michelson Interferometer and DAS","authors":"Guoheng Qi;Kunbi Zhu;Wenzhu Huang;Wentao Zhang","doi":"10.1109/LSENS.2026.3734254","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3734254","url":null,"abstract":"Prestressed concrete cylinder pipes (PCCPs) are an important lifeline project in the city, but the fracture of steel wires in the pipeline structure in the process of use will bring great risks to the operation of the pipeline. Recently, distributed acoustic sensing (DAS) has become one of the solutions to monitor wire breaks by virtue of the advantages of long–distance and intensive observation, but the measurement frequency band and sensing distance of conventional DAS are often mutually restricted. In this study, we propose a novel PCCP wire break monitoring scheme via deep integration of fiber Michelson interferometer and DAS—combining them at both the sensing and data levels. At the sensing level, MI captures high-frequency signals while DAS collects position-related information. At the data level, we use Kurtosis and wavelet detail coefficient (cD3) energy to form feature vectors to detect the wire break signals from the interferometer data and adopt weighted Pearson correlation for localization. Experiment results confirm successful capture of wire break signals with a broad frequency range (100 Hz–44 kHz), including previously unreported 20–44 kHz high-frequency components. The results show that wire break signals are expected to be distinguished by Kurtosis and wavelet energy characteristics and localized by feature vectors matching method.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"3506604-3506604"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Garment-Integrated Silver-Based Interdigitated Capacitive Pressure Sensor Fabricated by Microdispensing for Elbow Motion Detection 基于微点胶的服装集成银基交叉数字电容压力传感器用于肘部运动检测

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-31 DOI: 10.1109/LSENS.2026.3728876

Maria Papaefstathiou;Mohamed Elgendi;Carlo Menon

{"title":"Garment-Integrated Silver-Based Interdigitated Capacitive Pressure Sensor Fabricated by Microdispensing for Elbow Motion Detection","authors":"Maria Papaefstathiou;Mohamed Elgendi;Carlo Menon","doi":"10.1109/LSENS.2026.3728876","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3728876","url":null,"abstract":"Garment-integrated wearable sensor devices are becoming essential for daily motion monitoring. The direct ink writing (DIW) technique offers a promising solution by enabling direct printing onto flexible substrates that can be seamlessly integrated into garments. Unlike conventional printing methods that require multiple layers or masking steps, the proposed approach enables rapid, precise dispensing through controlled deposition of conductive inks onto the substrate. This letter presents a thermoplastic polyurethane (TPU)-based silver (Ag) ink formulation for DIW fabrication of a flexible capacitive sensor, ensuring strong interfacial bonding with the TPU substrate and enabling seamless integration into garments. The developed sensor demonstrated stable and reproducible capacitance responses under repeated mechanical loads, achieving a sensitivity of 0.343 <inline-formula><tex-math>$mathrm{kPa^{-1}}$</tex-math></inline-formula> and pressure sensing up to 20 kPa and a high durability of 5500 loading/unloading cycles. As a proof of concept, the sensor was heat-transferred onto a textile sleeve for elbow motion detection. It functioned as a pressure sensor by detecting touch through capacitance changes. Tests in two wearable scenarios with five separate attempts, with 20 repetitions each showed consistent, reliable performance (<italic>p</i> = 0.357 > 0.05).","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6010604-6010607"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11673090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mechanically Isolating and Self-Monitoring Low-Cost Phononic Connectors for Amorphous Wearable Bioelectronics 用于非晶可穿戴生物电子学的机械隔离和自我监测低成本声子连接器

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-16 DOI: 10.1109/LSENS.2026.3734690

Alessandro Ascani Orsini;Daniel Guo;Haoyu Xu;Yijun Lai;Chang Liu;Mengke Wang;Sung-Min Cho;Nitish Thakor

{"title":"Mechanically Isolating and Self-Monitoring Low-Cost Phononic Connectors for Amorphous Wearable Bioelectronics","authors":"Alessandro Ascani Orsini;Daniel Guo;Haoyu Xu;Yijun Lai;Chang Liu;Mengke Wang;Sung-Min Cho;Nitish Thakor","doi":"10.1109/LSENS.2026.3734690","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3734690","url":null,"abstract":"Wearable bioelectronic systems typically employ uniform flexible interconnects that mechanically couple distributed electrodes, allowing motion at one site to perturb contact pressure and signal quality elsewhere. This work introduces a connector inspired by phononics that reduces force transmission between electrodes while self-monitoring deformation for artifact labeling. By spatially varying the pitch of serpentine interconnects, the connector redistributes strain and mechanically isolates adjacent sensing sites. Embedded strain sensors provide a direct electrical readout of connector deformation and enable identification and timing of motion-induced disturbances at their mechanical origin. Mechanical testing showed reduced effective stiffness (7× lower spring constant), localized strain, and a shifted transmissibility band (1–3 Hz) that decreased force transfer to the electrode, while synchronized strain-gauge signals with electrophysiological recordings aligned with and predicted motion-induced disturbances (<italic>p</i> < 0.005). Together, these findings indicate the phononic design influences recording stability while allowing mechanical disturbances to be measured rather than inferred.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6500304-6500304"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Battery-Free Wireless Electrochemical pH Sensor for Intraoral Monitoring 用于口腔内监测的无电池无线电化学pH传感器

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-14 DOI: 10.1109/LSENS.2026.3724375

Rong Fu;Xinyu Zhang;Ziliang Zhang;Leixin Ouyang;Gabriel Tien-Min Chu;Mark Ming-Cheng Cheng

引用次数: 0

Raycasting-Based Stereo-to-CAD Registration for Industrial Inspection 基于射线铸造的工业检测立体- cad配准

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-08 DOI: 10.1109/LSENS.2026.3731486

Adrian Kaune;Lennart Hinz;Eduard Reithmeier

引用次数: 0

A Machine Learning-Based Classification of Urine Urea Using CSVM Model Trained on PCA Reduced EIS Data Obtained From Disposable Biosensor 基于一次性生物传感器PCA约简EIS数据训练的CSVM模型尿液尿素机器学习分类

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-08 DOI: 10.1109/LSENS.2026.3731848

Saransh Chourey;Asmita Bose;Avishek Adhikary

{"title":"A Machine Learning-Based Classification of Urine Urea Using CSVM Model Trained on PCA Reduced EIS Data Obtained From Disposable Biosensor","authors":"Saransh Chourey;Asmita Bose;Avishek Adhikary","doi":"10.1109/LSENS.2026.3731848","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3731848","url":null,"abstract":"This letter presents a novel urease-polymethyle methyle acrelate (PMMA) coated paper-based disposable sensor and a cubic support vector machine (CSVM)-based sensing for detecting urea in a droplet of 0.3 mL artificial urine samples. The proposed CSVM model takes first nine principal components (PC) of the phase spectrum of sensor’s bioimpedance spectroscopy (BIS) data. The PC counts, the phase feature, and the CSVM model are all are determined based on the performance index variation in two 32 model×PC counts grid matrices. The performance index is a fivefold cross validated training accuracy for classifying eight concentration classes (0.5<inline-formula><tex-math>$-$</tex-math></inline-formula>4.0 g/dL) averaged over 15 iterations. Proposed CSVM-principal component analysis-BIS sensing topology achieves an average training accuracy 97.9%<inline-formula><tex-math>$pm$</tex-math></inline-formula>0.3% on 1656 unaugmented training data and test accuracy 93.7% on 184 held-out test data. The novelty of the work is to design a sensor, which is more accurate, faster responsive, covers full range of human urine, and requires less analyte than conventional ones. Its selectivity study shows that it is 45%<inline-formula><tex-math>$-$</tex-math></inline-formula>57% more selective than any other major interfering compounds. This establish the proposed work as a proof-of-concept for the future development of an inexpensive portable urea detection meter for urine, blood, farm water, and other complex media.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6011004-6011004"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Background-Calibrated Wi-Fi Sensing for Semantic Space-State Separation 用于语义空间-状态分离的背景校准Wi-Fi传感

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-11 DOI: 10.1109/LSENS.2026.3733363

Shunsuke Uehashi;Ayaka Sata;Shigenori Tani

{"title":"Background-Calibrated Wi-Fi Sensing for Semantic Space-State Separation","authors":"Shunsuke Uehashi;Ayaka Sata;Shigenori Tani","doi":"10.1109/LSENS.2026.3733363","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3733363","url":null,"abstract":"Wi-Fi channel state information (CSI) is promising for device-free sensing, but deployment is difficult when task-specific labeled calibration and high-dimensional processing are required. This letter presents a background-calibrated, label-free framework requiring only a short empty-room CSI interval for initialization. A dynamic cue from short-term variation and a static cue from deviation from the empty-space profile are jointly interpreted: low/low, low/high, high/high, and high/low cue combinations correspond to empty, static occupied, active occupied, and inconsistent states, respectively. Experiments with ESP32-based 2.4-GHz, 20-MHz Wi-Fi nodes in two meeting rooms achieved macro F1-scores of 0.933 and 0.943. A motion-only ablation cannot represent static occupancy (F1 = 0 in both rooms), whereas the proposed formulation achieved static-state F1-scores of 0.894 and 0.903. In addition, the semantic state was preserved down to latent dimension 8 (7.75× compression) with fixed pretrained autoencoders, while performance degraded sharply at dimension 4 and below. These results support interpretable three-state sensing using background-only calibration without task-state labels.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"5505804-5505804"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Attention Guided Two-Stream Graph Convolutional Network for Improved Multilabel Retinal Disease Diagnosis 自注意引导的双流图卷积网络用于改进的多标签视网膜疾病诊断

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-09 DOI: 10.1109/LSENS.2026.3732284

Jatin;Moordhan Songade;Deepak Ranjan Nayak;Dinesh Kumar Tyagi;Tapan Kumar Gandhi

{"title":"Self-Attention Guided Two-Stream Graph Convolutional Network for Improved Multilabel Retinal Disease Diagnosis","authors":"Jatin;Moordhan Songade;Deepak Ranjan Nayak;Dinesh Kumar Tyagi;Tapan Kumar Gandhi","doi":"10.1109/LSENS.2026.3732284","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3732284","url":null,"abstract":"Retinal disorders are the leading cause of partial and permanent vision loss across the globe, making early diagnosis and timely intervention paramount for effective treatment planning. Recent deep learning methods for retinal disease diagnosis from fundus images acquired using optical imaging sensors primarily focus on single-disease detection, despite the fact that a fundus image may contain multiple diseases. However, the multilabel retinal disease classification task remains challenging due to high interclass similarity, large variations in lesion shape and size, variations in sensor-acquired image quality, and class imbalance. While limited attempts have been made to this end, they remain limited in effectively capturing the complex relationships among diseases. In addition, they fall short of extracting salient and richer visual features and their relationships, resulting in suboptimal performance. To address these issues, we propose a self-attention guided two-stream graph convolutional network (SAT-GCNet), a unified framework that seamlessly models visual features and disease relationships via graph-based learning for effective multilabel retinal disease diagnosis. Specifically, in the first stream, we employ multiple CNN backbones to capture diverse visual patterns from fundus images and subsequently introduce a self-attention guided adaptive GCN (SAA-GCN) block to model feature correlations. While in the second stream, domain-specific label embeddings are generated and used in the SAA-GCN block to effectively capture disease correlations and incorporate rich semantic information, thereby guiding the model for better prediction. Extensive experimental evaluations on two benchmark datasets, OIA-ODIR, and MuReD, demonstrate that SAT-GCNet achieves superior performance compared to current state-of-the-art approaches, while also providing enhanced reliability.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6010804-6010804"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Load Type for Passive Magnetic Energy Harvesters Equipped With Saturation-Delaying Capacitor Under High Primary Currents 高一次电流条件下具有延迟饱和电容的无源磁能采集器的最佳负载类型

IF 2.4

IEEE Sensors Letters Pub Date : 2026-09-01 Epub Date: 2026-07-16 DOI: 10.1109/LSENS.2026.3713992

Alon Kuperman

引用次数: 0

High-Resolution Two-Target Indoor Tracking Using Conv-TasNet on Ceiling-Mounted PIR Analog Signals 基于顶装PIR模拟信号的高分辨率双目标室内跟踪

IF 2.4

IEEE Sensors Letters Pub Date : 2026-09-01 Epub Date: 2026-08-18 DOI: 10.1109/LSENS.2026.3724819

Ming-Hung Kuo;Chih-Yu Wen

{"title":"High-Resolution Two-Target Indoor Tracking Using Conv-TasNet on Ceiling-Mounted PIR Analog Signals","authors":"Ming-Hung Kuo;Chih-Yu Wen","doi":"10.1109/LSENS.2026.3724819","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3724819","url":null,"abstract":"Passive infrared (PIR) sensors are ideal for indoor positioning due to their low cost, low power consumption, and privacy preservation. However, existing PIR systems primarily target single-occupant scenarios; separating mixed analog signals from co-occurring individuals remains a critical challenge. This study introduces a novel framework utilizing Conv-TasNet—originally designed for audio source separation—to isolate overlapping PIR signals from two targets. To resolve postseparation permutation ambiguity, we implement a joint Mel-frequency cepstral coefficients (MFCC) and XGBoost classification model. Experimental evaluation using a ceiling-mounted PIR system (3.3 × 3.3 <inline-formula><tex-math>$text{m}^{mathbf{2}}$</tex-math></inline-formula>) demonstrates that the MFCC-XGBoost model achieves 73% tracking accuracy and a 0.39 m path error. This reduces localization error by 42% compared to state-of-the-art multitarget systems, while maintaining an inference latency of just 0.07 s. These results prove that advanced algorithms can achieve precise, real-time, two-target tracking using cost-effective hardware, offering a practical, and privacy-preserving localization solution.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 9","pages":"6009604-6009604"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0