IEEE Sensors Letters最新文献

Radar-Based Hand Gesture Recognition: Leveraging Raw ADC Data Through Independent Component Analysis and Spiking Neural Networks 基于雷达的手势识别：通过独立分量分析和脉冲神经网络利用原始ADC数据

IF 2.2

IEEE Sensors Letters Pub Date : 2026-04-01 Epub Date: 2026-03-04 DOI: 10.1109/LSENS.2026.3669063

Muhammad Arsalan;Muhammad Ghufran Janjua;Umm-e- Habiba;Avik Santra;Vadim Issakov

引用次数: 0

MultiCoil Eddy Current Sensor For Nondestructive Testing of Carbon Fiber-Reinforced Structures 用于碳纤维增强结构无损检测的多线圈涡流传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2026-04-01 Epub Date: 2026-03-18 DOI: 10.1109/LSENS.2026.3675605

Till Schulze;Dirk Hofmann;Johannes Mersch;Christian Pilz;Maren Rake;Martin Schulze;Chokri Cherif;Henning Heuer

{"title":"MultiCoil Eddy Current Sensor For Nondestructive Testing of Carbon Fiber-Reinforced Structures","authors":"Till Schulze;Dirk Hofmann;Johannes Mersch;Christian Pilz;Maren Rake;Martin Schulze;Chokri Cherif;Henning Heuer","doi":"10.1109/LSENS.2026.3675605","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3675605","url":null,"abstract":"Depth-resolved characterization of carbon fiber reinforced polymer (CFRP) laminates remains challenging due to signal superposition from multiple layers in conventional eddy current testing. This letter presents a novel multicoil sensor architecture that enables simultaneous measurements at different penetration depths. The sensor comprises one transmitter coil and four receiver coils. One concentric coil with the transmitter arranged around the receiver for shallow layer sensitivity, and three additional receivers arranged concentrically around each other but with an offset to the primary pair for enhanced depth discrimination. By exploiting the distinct electromagnetic coupling characteristics of each receiver configuration and combining this spatial diversity with frequency-multiplexed excitation, the sensor provides complementary information about layer-specific fiber orientations and defects. Experimental validation on symmetric multilayer CFRP laminates with deliberately introduced anomalies demonstrates the sensor's capability to distinguish features in individual layers that were previously indistinguishable with single-coil approaches.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11442951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737114","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

CEFD: A Novel Approach for the Analysis of Complex-Valued Signals CEFD：复值信号分析的新方法

IF 2.2

IEEE Sensors Letters Pub Date : 2026-04-01 Epub Date: 2026-03-10 DOI: 10.1109/LSENS.2026.3672568

Dhanhanjay Pachori;M. Sabarimalai Manikandan

{"title":"CEFD: A Novel Approach for the Analysis of Complex-Valued Signals","authors":"Dhanhanjay Pachori;M. Sabarimalai Manikandan","doi":"10.1109/LSENS.2026.3672568","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3672568","url":null,"abstract":"This letter presents the complex empirical Fourier decomposition (CEFD) method for analysis of complex-valued nonstationary signals encountered in modern sensor systems. Further, time–frequency representation (TFR) has been proposed by employing Hilbert spectral analysis on the intrinsic mode functions (IMFs) obtained from CEFD. Complex-valued signals commonly arise in sensing modalities, such as radar, wind monitoring, biomedical instrumentation, and vibration-based sensors, where accurate signal decomposition is important for reliable sensing and interpretation. The proposed methodology is validated using a synthetic signal and a real-world wind signal. Quantitative performance evaluation is carried out using the Rényi entropy of the TFR and the signal-to-reconstruction error of the IMFs obtained via CEFD. The results demonstrate that the proposed framework provides improved signal reconstruction, mode separation, and signal interpretability compared to existing methods, thereby enhancing sensor-level signal analysis and postacquisition processing. Due to the adaptability and effectiveness of CEFD, the proposed framework is well suited for real-life sensing applications involving complex-valued signals and contributes toward improved performance in intelligent sensor systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 4","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147606183","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

High-Sensitivity Dual-CSRR-Loaded SIW Sensor for Solid and Liquid Material Characterization at X-Band 用于x波段固体和液体材料表征的高灵敏度双csrr负载SIW传感器

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-04-06 DOI: 10.1109/LSENS.2026.3680969

Qiyu Yin;Teng Li

引用次数: 0

A Polyaniline-SDS Based Flexible Sensor for Glyphosate Detection in Aqueous Media 基于聚苯胺- sds的柔性传感器用于水介质中草甘膦的检测

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-03-26 DOI: 10.1109/LSENS.2026.3678171

Lazar Milić;Mitar Simić;Radmila Tomovska;Jadranka Blaževska-Gilev;Goran M. Stojanović

{"title":"A Polyaniline-SDS Based Flexible Sensor for Glyphosate Detection in Aqueous Media","authors":"Lazar Milić;Mitar Simić;Radmila Tomovska;Jadranka Blaževska-Gilev;Goran M. Stojanović","doi":"10.1109/LSENS.2026.3678171","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3678171","url":null,"abstract":"Glyphosate is one of the most widely used herbicides worldwide, and its extensive application has raised growing concerns regarding environmental persistence. Conventional analytical techniques for glyphosate detection, such as chromatographic and spectrometric methods, offer high accuracy but require complex instrumentation, extensive sample preparation, and skilled personnel, limiting their suitability for rapid and on-site monitoring. In this work, a flexible electrochemical sensor, based on a screen-printed electrode functionalized with a polyaniline/sodium dodecyl sulfate (PANI/SDS) composite is presented for glyphosate detection in aqueous media. Electrochemical characterization was performed using open-circuit potential, electrochemical impedance spectroscopy, and differential pulse voltammetry. The sensor exhibited a linear response to glyphosate concentrations in the range of 0.0013–0.02%wt, with a sensitivity of 134.0 μA/%wt. The detection mechanism was supported by mathematical modeling based on the Randles–Ševčík relation, confirming diffusion-controlled charge-transfer kinetics. Analytical figures of merit include a limit of detection of 6.76 <inline-formula><tex-math>$ cdot $</tex-math></inline-formula> 10<sup>−3</sup> %wt and a limit of quantification of 2.25 <inline-formula><tex-math>$ cdot $</tex-math></inline-formula> 10<sup>−2</sup> %wt. Selectivity was demonstrated against potassium. Owing to its simple fabrication, low-cost materials, and flexible format, the proposed PANI/SDS-based sensor represents a promising platform for scalable and field-deployable glyphosate monitoring in environmental applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11456819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665260","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

Effective IMU Sensing and Servo Actuation Delay Estimation for Inertially Stabilized Platform 惯性稳定平台的有效IMU传感与伺服驱动延迟估计

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-03-23 DOI: 10.1109/LSENS.2026.3676978

Yi Chiew Han

{"title":"Effective IMU Sensing and Servo Actuation Delay Estimation for Inertially Stabilized Platform","authors":"Yi Chiew Han","doi":"10.1109/LSENS.2026.3676978","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3676978","url":null,"abstract":"Inertially stabilized platforms (ISPs) rely on motion sensors and actuators to maintain output stability, but sensor measurement, signal processing, and actuator response introduce combined delay, causing compensating motion to lag behind disturbances and impairing stabilization performance. Although numerous studies model inertial measurement unit (IMU) behavior, servo dynamics, and stabilization control strategies, very few works directly quantify the effective delay in an ISP. This work provides a systematic experimental method to measure this delay using a combined IMU-servo setup and an optical motion-tracking reference. Three experiments were implemented: 1) baseline stabilization with direct servo commands to establish ideal zero sensor and processor delay performance and verify the absence of additional system delays; 2) active stabilization using real-time IMU measurements, which showed significant degradation compared to baseline, with significantly increased peak-to-peak displacement and prolonged deviations above 5°; and 3) predictive stabilization using future angle estimation, which restored near-baseline stability and allowed estimation of the effective sensor-actuator delay. The results demonstrate that even modest delays of tens of milliseconds can lead to severe instability, and that estimation of effective delay provides essential insight into hardware limitations and guides the design of both stabilization algorithms and low-latency ISP system.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665407","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

A Portable Multimodal Sensing System for Localized Air Quality and Respiratory Health Assessment 用于局部空气质量和呼吸健康评估的便携式多模态传感系统

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-03-26 DOI: 10.1109/LSENS.2026.3678147

Devansh Shah;Sarfraz Hussain

{"title":"A Portable Multimodal Sensing System for Localized Air Quality and Respiratory Health Assessment","authors":"Devansh Shah;Sarfraz Hussain","doi":"10.1109/LSENS.2026.3678147","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3678147","url":null,"abstract":"Air pollution exposure varies significantly across microenvironments and directly impacts respiratory health, yet conventional monitoring approaches rely on sparse fixed stations that provide city-level measurements without capturing individualized exposure conditions or physiological responses. This lack of personalized monitoring limits the ability to assess how localized pollution affects individual respiratory health in real time. To address this gap, this letter presents LungHero, a portable multimodal sensing system that integrates localized air quality measurement with physiological respiratory indicators for personalized exposure assessment. The system combines low-cost particulate matter and gas sensors with temperature–humidity sensing, pulse oximetry monitoring, and acoustic cough analysis. A weighted Air Quality Index (AQI) is computed from normalized environmental sensor readings, while cough signals captured through a mobile device are analyzed using Mel-spectrogram features and a convolutional neural network to assess respiratory severity. Experimental evaluation demonstrates stable air quality sensing, effective cough classification with 89.44% accuracy, and observable correlations between degraded air quality, reduced <inline-formula><tex-math>$SpO_{2}$</tex-math></inline-formula> levels, and adverse cough patterns. The results suggest that multimodal sensing at the personal scale can provide meaningful insights into respiratory health under polluted conditions, offering a practical alternative to traditional station-based monitoring systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796203","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

AI-Assisted Functionalized Microcantilever With Multilayer Simulation for Noninvasive Breath-Based Diabetes Screening 基于呼吸无创糖尿病筛查的人工智能辅助功能化微悬臂多层模拟

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-03-24 DOI: 10.1109/LSENS.2026.3677112

Navaneeth Bhaskar;Manisha Dale;Rutuja A Deshmukh;Vinayak Bairagi;Sharad T Jadhav

{"title":"AI-Assisted Functionalized Microcantilever With Multilayer Simulation for Noninvasive Breath-Based Diabetes Screening","authors":"Navaneeth Bhaskar;Manisha Dale;Rutuja A Deshmukh;Vinayak Bairagi;Sharad T Jadhav","doi":"10.1109/LSENS.2026.3677112","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3677112","url":null,"abstract":"Breath-based sensing has become a promising noninvasive method for early disease screening, especially for chronic metabolic disorders, such as diabetes mellitus. Although elevated breath acetone is a known marker of impaired glucose metabolism, current breath-based sensing methods are mostly based on single-biomarker sensing or multiple sensor arrays, so they suffer from poor robustness and complexity. In this letter, an artificial intelligence (AI) assisted functionalized microcantilever sensor with preliminary experimental validation for noninvasive breath-based screening for diabetes using volatile organic compound (VOC) fingerprint detection is proposed. The proposed sensor is based on a single microcantilever coated with multiple VOC-responsive functional layers and can selectively interact with host-related VOCs associated with diabetes. Adsorption-induced mass loading and surface stress variations are responsible for unique mechanical response patterns that act as disease-specific fingerprints. Analysis shows resonant frequency shifts of the order of tens of hertz and static deflections in the nanometer scale for different breath compositions of VOCs. Furthermore, AI-aided mechanical fingerprint classification achieved up to 96.8% accuracy under controlled evaluation conditions.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 5","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665276","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

Design and Optimization of a Compact Metacarpophalangeal Joint Angle Sensor for Robotic Finger 紧凑型机器人手指掌指关节角度传感器的设计与优化

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-03-23 DOI: 10.1109/LSENS.2026.3676891

Yuanfei Zhang;Tianqi Jiang;Jiahao Zhang;Wangyang Li;Fenglei Ni;Hong Liu

引用次数: 0

REDM-CDAR: Continuous Driver Action Recognition via Radar Energy Distribution Map REDM-CDAR：基于雷达能量分布图的驾驶员动作连续识别

IF 2.2

IEEE Sensors Letters Pub Date : 2026-03-01 Epub Date: 2026-03-02 DOI: 10.1109/LSENS.2026.3669010

Shufeng Gong;Yiming Fang;Zhiyue Guo;Xinzhuo Yang;Congwei Chen;Zhefu Wu

引用次数: 0