IEEE Sensors Journal最新文献

{"title":"A Polarization-Insensitive Dual-Band Metamaterial Absorber for High-Performance Microwave Liquid Sensing","authors":"Priyanshi Mantri;Pankaj Srivastava;Biswabandhu Jana;Pinku Ranjan","doi":"10.1109/JSEN.2026.3731421","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3731421","url":null,"abstract":"A compact dual-band metamaterial absorber (MTM-A) for microwave sensing is proposed and numerically analyzed over 4–14 GHz. The unit cell consists of a metallic resonator and a continuous metallic ground plane, separated by an FR-4 epoxy substrate with a relative permittivity of 4.3. The optimized design exhibits near-unity absorption at 5.57 and 11.09 GHz, with stable performance under variation of polarization angle and oblique incidence under both polarization modes. The sensing performance is evaluated using a sample layer (SL) whose dielectric constant is varied from 1.8 to 3.6, resulting in a clear frequency shift. The sensor achieves sensitivities of 0.57 GHz/RIU and 1.4 GHz/RIU along with corresponding figure of merit (FOM) values as <inline-formula> <tex-math>$14.98~{mathbf {RIU}}^{-mathbf {1}}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$37.61~{mathbf {RIU}}^{-mathbf {1}}$ </tex-math></inline-formula> at the respective resonance frequencies. Furthermore, the enhanced quality factors for both the resonance bands indicate sharp resonance characteristics and enhanced detection accuracy. These results confirm the suitability of the proposed metamaterial (MTM)-A for microwave liquid sensing applications, including vegetable oil and petroleum-based hydrocarbon liquids.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30540-30547"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176005","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":"3-D Au/ZnO Hierarchical Array Engineering: Unraveling Four-Stage Interfacial Adsorption Mechanism for Robust Self-Cleaning SERS With Ultrahigh Reproducibility","authors":"Zhaocheng Shao;Yupeng Zhao;Jianyuan Huang;Haozhan Sun;Shuhua Wei;Jing Zhang;Anjie Ming","doi":"10.1109/JSEN.2026.3725839","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3725839","url":null,"abstract":"Unpredictable variation of surface-enhanced Raman scattering (SERS) intensity, caused by uncontrolled interfacial adsorption, severely limits quantitative applications. To address this issue, the evolution of analyte adsorption states was systematically investigated on a 3-D Au nanoparticles/ZnO nanoflowers (Au NPs/ZnO NFs) substrate. By correlating Raman signal fluctuations with finite element method (FEM) simulations, a four-stage interfacial adsorption model consisting of direct adsorption, indirect adsorption, saturation, and crystallization was established. Spatial volume integral evaluations reveal that during initial adsorption, capillary forces drive molecules to form an optimal monolayer within extreme nanogaps, thereby maximizing the utilization of hot spots and triggering a rapid signal surge. Conversely, excessive volume induces thick crystallization, leading to severe optical shielding and a global collapse of the localized electric field. Guided by this framework, the optimal linear dynamic range was defined, overcoming false-negative interference. Furthermore, the substrate achieved a <inline-formula> <tex-math>$10^{-{8}}$ </tex-math></inline-formula> M limit of detection for Rhodamine 6G (R6G) and exceptional 15-min photocatalytic self-cleaning. This study presents a high-performance, reusable SERS platform and establishes a universal physical framework that maps macroscopic sample volume to microscale adsorption dynamics, providing crucial guidelines for maximizing linearity and defining accurate dynamic ranges in practical SERS detection protocols.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30033-30042"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176010","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":"Diffuse Reflectance Spectroscopy for Optical Identification of Dental Materials Toward Minimally Invasive Treatment","authors":"Jindřich Charvát;Aleš Procházka","doi":"10.1109/JSEN.2026.3727480","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3727480","url":null,"abstract":"Diffuse reflectance spectroscopy (DRS) is a broadband optical technique (400–1600 nm) with demonstrated potential for caries detection. However, its capability to identify residual restorative materials has not yet been systematically investigated. Current visual–tactile examination achieves only about 60% sensitivity, and no existing method provides reliable real-time guidance. This in vitro study is the first to evaluate DRS as a tool for the automated discrimination of dental restorative materials from sound tooth structure. Fifty extracted human premolars and molars, together with standardized specimens of four materials (composite resin, universal composite, resin-modified glass ionomer cement (RIVA LC), and self-adhesive resin cement), were measured using a prototype fiber-optic DRS probe. In total, 1080 spectral records were collected across six classes (four materials, dentin, and enamel). A two-layer neural network classifier achieved an accuracy of 96.3% (cross-validation (CV) error: 0.046), outperforming support vector machine (SVM) (87.0%), Bayesian (88.0%), and k-nearest neighbor (90.7%) models. The results demonstrate that each material produces a distinct spectral fingerprint that is clearly separable from healthy dental hard tissues. Spectral features of the resin-modified glass ionomer dental cement are the closest to those of the healthy tooth dentin. In clinical practice, a DRS probe could provide objective, operator-independent, real-time feedback, enabling precise removal of existing restorations while preserving sound dentin, as well as targeted localization of obturation remnants during endodontic retreatment. Future work will focus on in vivo validation under intraoral conditions, probe miniaturization for endodontic applications, and the development of a comprehensive spectral reference database to support routine clinical use.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31575-31582"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11677364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176024","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":"Research on PCB Surface Small Defects Detection Method Based on Improved YOLOv11","authors":"Rongxiao Liu;Zhengyang Fu;Quanhai Wang","doi":"10.1109/JSEN.2026.3726479","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3726479","url":null,"abstract":"The automated detection of minute defects on the surfaces of the printed circuit board (PCB) is crucial for ensuring the quality and reliability of electronic products. However, the current detection methods frequently encounter significant issues with fine feature loss during downsampling, making it challenging to accurately identify small targets against complex background textures. To tackle these challenges, this article introduces the YOLO-DFA detection model, which is based on an enhanced version of YOLOv11. This model features two innovative core modules: frequency-domain decoupled space-to-depth (SPD) convolution (DF-SPD Conv) and channel refinement attention (CRA). The DF-SPD module effectively decouples high-frequency defects from low-frequency backgrounds by employing spatial lossless rearrangement and an asymmetric dual-stream architecture that utilizes channel segmentation, thereby mitigating feature loss at the fundamental architectural level. Furthermore, the CRA module purifies defective signals by eliminating redundant noise in complex environments, enhancing feature interactions across channel dimensions and employing a multidimensional feature recalibration mechanism. Experimental results demonstrate that YOLO-DFA performs exceptionally well on the DeepPCB dataset across several metrics, achieving an <inline-formula> <tex-math>$text {mAP}_{{50}}$ </tex-math></inline-formula> value of 97.5%, which surpasses the YOLOv11 baseline by 2.9%. Furthermore, maintaining a robust inference speed of 22.6 frames per second (FPS) on our evaluation platform, the proposed method demonstrates a favorable balance between detection accuracy and computational efficiency, providing strong potential for real-time PCB inspection applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30790-30803"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176040","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":"Dual-Domain Joint Similarity and Bayesian Inference for Large-Scale Wi-Fi Localization","authors":"Xiaofan Song;Pu Ge;Zhizhong Zhang;Lilan Liu","doi":"10.1109/JSEN.2026.3727783","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3727783","url":null,"abstract":"Driven by the demand for indoor digital twins and large-scale location-based services (LBSs) in 6G-enabled intelligent spaces, low-cost and wide-coverage Wi-Fi fingerprinting has emerged as a pivotal technology. However, with the expansion of spatial scale and the increasing complexity of topology, traditional Wi-Fi fingerprinting is susceptible to received signal strength (RSS) nonlinearity and random noise. This weakens the spatial correlation between RSS similarity and physical proximity, exacerbates fingerprint ambiguity, increases search overhead, and ultimately degrades localization performance. Therefore, we propose a coarse-to-fine localization method based on joint similarity that simultaneously captures region-level spatial topology and intraregion signal statistics. By integrating stable RSS ranking structures with physical proximity, the method identifies coherent spatial regions and selects highly informative access point (AP) subsets to construct compact fingerprints for coarse localization. Fine localization is achieved through Bayesian inference using Gaussian likelihood models within each region. Simulations and real-world experiments validate the effectiveness of the proposed method. On the measured dataset, it reduces mean error by 36.5% and online latency by 60.6% relative to the respective best competing state-of-the-art (SOTA) methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30907-30920"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176206","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":"N-Type SnO2-Based Trace-Level Fe(III) Ion Sensor for Drinking Water Quality Monitoring","authors":"Sanjay Singh;Sampurna Mukherjee;Sayan Dey","doi":"10.1109/JSEN.2026.3729478","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3729478","url":null,"abstract":"Iron(III) contamination in potable water systems represents a critical environmental and public health concern due to its widespread prevalence and associated toxicity. This study details the development of a highly efficient, cost-effective conductometric sensor based on nanostructured n-type <inline-formula> <tex-math>$text {SnO}_{{2}}$ </tex-math></inline-formula> for rapid, trace-level quantification of Fe(III) ions in aqueous environments. The fabricated device exhibits exceptional analytical performance, achieving a peak current response ratio (<inline-formula> <tex-math>$text {I}_{text {Fe(III)}}/text {I}_{text {Bare}}$ </tex-math></inline-formula>) of approximately 403 at a 10-ppm Fe(III) concentration, alongside an ultralow limit of detection (LOD) of 0.001 ppm experimentally. Furthermore, the sensor demonstrates rapid dynamic kinetics, characterized by response and recovery times of 4.82 and 12.01 s, respectively. It also maintains robust long-term stability over a 31-day period with a marginal performance deviation of <inline-formula> <tex-math>$pm {2}.{23}%$ </tex-math></inline-formula>. The platform exhibits stringent selectivity for Fe(III) against a background of ubiquitous interfering ions typical of drinking water, combined with outstanding device-to-device reproducibility, attributed to surface adsorption-desorption, yielding a maximum error of only 0.891%. Mechanistically, the enhanced sensing paradigm is attributed to a surface adsorption–desorption process. This phenomenon is governed by exponential defect-induced injection and depletion of charge carriers, which are fundamentally facilitated by abundant oxygen vacancies within the <inline-formula> <tex-math>$text {SnO}_{{2}}$ </tex-math></inline-formula> lattice. In summary, the integration of profound sensitivity, rapid response kinetics, strict reproducibility, and extended operational stability positions this <inline-formula> <tex-math>$text {SnO}_{{2}}$ </tex-math></inline-formula>-based chemiresistive platform as a highly viable architecture for the real-time, continuous monitoring of iron pollutants in aquatic ecosystems and water infrastructure.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30094-30098"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176207","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":"S3E-Det: A Strict Spatial–Spectral Equivariant Detector for Multispectral Object Detection","authors":"Peng Zhang;Tingfa Xu;Jinrui Sui;Yuyang Tian;Jianan Li","doi":"10.1109/JSEN.2026.3726270","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3726270","url":null,"abstract":"Multispectral object detection (MOD) requires detectors to exploit material-dependent spectral responses while maintaining consistent localization under geometric variations. Existing MOD detectors learn discriminative spectral representations, yet lack a unified mechanism that preserves spectral order, adapts spectral–context fusion across pyramid levels, and regulates geometric responses under rotation. To address these limitations, we propose S³E-Det, a strict spatial–spectral equivariant detector that couples ordered spectral modeling with transformation-consistent prediction. SOSC imposes local order constraints on input-derived spectral features through first-order variation, while CDEF adapts spectral–context aggregation across pyramid levels. A compact GAH reconciles invariant and equivariant output properties within a unified prediction stage. Across MODA, HOD3K, and DroneVehicle, S³E-Det combines high detection accuracy with a compact parameter footprint and reduced rotation-induced variation.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30778-30789"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176209","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 0.6 V Complementary Self-Referenced R-2R SAR-Based Temperature Sensor","authors":"Hui Zhou;Yi Wang;Bowen Fan;Fen Qin;Guanghan Zhang;Yunbei Si;Hu Li;Shuang Xie","doi":"10.1109/JSEN.2026.3729248","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3729248","url":null,"abstract":"This article presents a 0.6 V complementary self-referenced R-2R successive approximation register (SAR) temperature sensor for very large scale integration (VLSI) thermal management. With a digital-like architecture that uses only a comparator as its active analog block, the proposed design reuses complementary R-2R resistor ladders to implement temperature sensing and SAR quantization simultaneously. By virtue of the mutual referencing between the positive and negative temperature coefficient ladders, it requires no high-precision or high-frequency external reference clock or voltage biasing. Compared with the single-sided counterpart, this architecture nearly doubles the effective thermal sensitivity and achieves a low supply sensitivity of <inline-formula> <tex-math>$0.74~^{circ } text {C/ {V}}$ </tex-math></inline-formula>. To ensure reliable operation under sub-1 V supply, a low-voltage dynamic comparator integrated with a partial feedback bulk adaptive floating inverter amplifier (PFBAFIA), output offset storage (OOS), and a body-driven latch is proposed in this work. Benefiting from the digital-like structure, the chip area, energy consumption, and conversion time of the sensor scale linearly with the number of bits. The proposed design is verified by postlayout simulations in 55 nm CMOS technology: it covers a wide temperature range from <inline-formula> <tex-math>$- 40~^{circ } text {C}$ </tex-math></inline-formula> to <inline-formula> <tex-math>$125~^{circ } text {C}$ </tex-math></inline-formula>, and achieves a <inline-formula> <tex-math>${3}sigma $ </tex-math></inline-formula> inaccuracy of <inline-formula> <tex-math>$pm 0.45~^{circ } $ </tex-math></inline-formula>C after two-point calibration. Meanwhile, it features a single conversion energy of only 0.7 nJ and a resolution figure of merit (RFoM) of 15.1 pJ-K².","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31413-31421"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176226","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":"ST-DGAT: A Fault-Characteristic Frequency-Guided Dynamic Graph Attention Transformer for Bearing Remaining Useful Life Prediction","authors":"Jingjing Cao;Yuhao Zhang;Lu Qian","doi":"10.1109/JSEN.2026.3730240","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3730240","url":null,"abstract":"Accurate remaining useful life (RUL) prediction of rolling bearings remains challenging because fault-related spectral components evolve across frequency and time. This article proposes a spatio-temporal dynamic graph attention transformer (ST-DGAT) that embeds bearing kinematics into frequency-graph learning. A logarithmic continuous wavelet transform (CWT) representation combines fault-characteristic anchors with complementary background frequencies. Frequency positional embedding (FPE) preserves node identity, while adaptive dual-channel fusion integrates complementary horizontal and vertical information. A learnable degradation prototype (LDP) produces sample-dependent continuous gates, and mechanism-consistent edge residuals bias graph interactions toward kinematic relations while retaining communication among all node pairs. Global bottleneck injection (GBI) supplies graph-level context, and a Transformer models temporal evolution. The method is validated on two real run-to-failure datasets. Comprehensive ablation and local-bandwidth sensitivity studies support the design, while comparisons with recent approaches demonstrate competitive overall prediction accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"31062-31073"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176245","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":"TriGS-SLAM: Tristate Visibility Modeling for Robust 3DGS-SLAM in Dynamic Scenes Based on RGB-D Sensor","authors":"Yong Zhou;Yongchuan Zhang;Jun Xia;Yiduo Bai;Yong He","doi":"10.1109/JSEN.2026.3719476","DOIUrl":"https://doi.org/10.1109/JSEN.2026.3719476","url":null,"abstract":"Simultaneous localization and mapping (SLAM) systems based on 3-D Gaussian splatting (3DGS) have garnered widespread attention due to their real-time high-fidelity rendering capabilities. However, in dynamic scenes, the interpretation of Gaussian visibility at the boundaries of dynamic occlusions is still unstable, often manifesting as boundary flickering, ghosting, residual artifacts, and geometric interpretation errors, which further interfere with pose estimation and map updates. The reason is that dynamic occlusion is not a simple binary switching process, but a continuous transition from fully visible to completely invisible. Traditional binary mask processing causes structural errors in information utilization for continuous occlusion changes, whereas relying solely on continuous opacity to handle discrete visibility switching can easily lead to boundary flickering and trailing effects. To this end, we propose a three-state visibility modeling method driven by multisource evidence for dynamic scenes captured by red-green-blue-depth (RGB-D) sensors. This method explicitly introduces a partial visibility intermediate state between the traditional binary states of visible and invisible, thereby transforming dynamic occlusion handling from a traditional post hoc residual-correction strategy into a visibility interpretation mechanism oriented toward the generative process. On this basis, we further use the three-state posterior as a soft constraint weight to uniformly regulate rendering, pose optimization, and mapping, enabling the system to form more stable and consistent observation interpretations in dynamic boundaries and occluded areas. Experimental results show that the proposed method can effectively alleviate boundary flickering and occlusion artifacts, and outperform existing state-of-the-art methods in terms of pose tracking accuracy and map reconstruction quality on real-world dynamic-scene datasets.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 20","pages":"30703-30720"},"PeriodicalIF":4.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176265","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}