IEEE Sensors Letters最新文献

Subsynchronous Oscillation Detection Using PMU Measurements: Application to Iberian Peninsula Blackout 使用PMU测量的次同步振荡检测：在伊比利亚半岛停电中的应用

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-16 DOI: 10.1109/LSENS.2026.3734603

Hafiz Ahmed

{"title":"Subsynchronous Oscillation Detection Using PMU Measurements: Application to Iberian Peninsula Blackout","authors":"Hafiz Ahmed","doi":"10.1109/LSENS.2026.3734603","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3734603","url":null,"abstract":"Prior to April 2025 blackout, Iberian and continental European power grids experienced unusual sustained subsynchronous oscillations (SSSOs) at an exceptionally low frequency (0.2 Hz) following a sudden <inline-formula><tex-math>$sim$</tex-math></inline-formula>15 GW generation loss. To prevent future cascading failures and enhance network operators’ situational awareness, rapid detection of these low-frequency SSSOs is crucial. This letter addresses this challenge by developing a hybrid SSSO detection method that leverages phasor measurement unit data. The proposed approach combines empirical mode decomposition to extract oscillatory modes from low-rate synchrophasor data with supervised classifiers [decision tree (DT), k-nearest neighbors (kNNs), support vector machine (SVM), and artificial neural network (ANN)] for SSSO event identification. Among these, ANN and SVM classifiers achieved similar performance (supported by DeLong and McNemar tests for statistical significance) and outperforming the counterparts DT and kNN. Validation on unseen European datasets demonstrates strong generalizability. This approach provides a practical early-warning tool for SSSO events, contributing to future blackout prevention.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6011704-6011704"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131265","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

SPAD-Based Keyframe Mining by Self-Supervised Quality Ranking 基于spad的自监督质量排序关键帧挖掘

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-18 DOI: 10.1109/LSENS.2026.3735401

Jui-Huang Tsai;Yi-Ching Hung;Yun-Chiao Chen;Jia-Yu Lin;Hsi-Hao Huang;Chen-Yi Lee

{"title":"SPAD-Based Keyframe Mining by Self-Supervised Quality Ranking","authors":"Jui-Huang Tsai;Yi-Ching Hung;Yun-Chiao Chen;Jia-Yu Lin;Hsi-Hao Huang;Chen-Yi Lee","doi":"10.1109/LSENS.2026.3735401","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3735401","url":null,"abstract":"Single-photon avalanche diode (SPAD) arrays produce dense kilohertz streams of photon-count frames, yet under high-speed capture, most frames are invalid: the submillisecond exposure often catches a moving target just entering or leaving the field of view—or no target at all—and blindly feeding such blank or truncated frames misleads downstream task models. We address keyframe mining from high-speed photon-count streams: frame-level validity filtering before transmission. We present a controlled physical benchmark that reproduces these entry–exit dynamics—digit targets on a five-blade chopper at 520 Hz (31 200 r/min), captured by a <inline-formula><tex-math>$32times 32$</tex-math></inline-formula> SPAD array at 2.5 kHz—and Nano Gatekeeper, a self-supervised Siamese ranking network trained on physics-based degradation synthesis without manual labels. With 271 k parameters, it attains 97.91% mean average precision on real captures in which only 382 of 1059 annotated frames (36.1%) are valid, runs at 2501 frames per second (FPS) on an NVIDIA Corporation (NVIDIA) Jetson Orin Nano, and boosts downstream classification accuracy over existing baselines from 30% to 80% at a Top-1% transmission budget and from 31% to 61% at Top-10%.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6011604-6011604"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131262","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

Despeckling-Based Augmentation for Noise-Aware SONAR Image Classification on Edge Devices 基于去噪的边缘设备声纳图像分类增强方法

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-02 DOI: 10.1109/LSENS.2026.3730033

Swapna Pillai;Siddharth Singh Savner;Aurélien Bonvard;Philippe Courmontagne;Sujit Kumar Sahoo

{"title":"Despeckling-Based Augmentation for Noise-Aware SONAR Image Classification on Edge Devices","authors":"Swapna Pillai;Siddharth Singh Savner;Aurélien Bonvard;Philippe Courmontagne;Sujit Kumar Sahoo","doi":"10.1109/LSENS.2026.3730033","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3730033","url":null,"abstract":"Real-time sound navigation and ranging (SONAR) image classification on embedded platforms is critical for autonomous underwater monitoring and inspection. However, developing reliable classifiers for such deployments remains challenging due to severe speckle noise, limited annotated training data, and strict computational constraints on edge devices. To address these challenges, we propose a noise-aware training strategy for SONAR image classification that generates additional training samples by jointly exploiting noisy and despeckled image representations. By exposing the classifier to intermediate noise conditions between noisy and despeckled observations, the proposed strategy effectively enlarges the training distribution and promotes stable feature learning across varying noise levels. This formulation improves robustness in data-scarce SONAR imaging scenarios without increasing inference complexity. Experimental results on the 18-class Marine Debris Turntable Dataset improve MobileNet accuracy from 72.5% to 89.8% and SqueezeNet accuracy from 72.4% to 86.4%. The resulting lightweight classification pipeline is deployed on a Raspberry Pi 5 using open neural network exchange (ONNX) Runtime and INT8 quantization, achieving inference latencies as low as 2.29 ms while reducing model size by up to 74.8%. The proposed method further achieves 100% classification accuracy on the 12-class Marine Debris configuration, demonstrating its effectiveness for robust SONAR image classification on resource-constrained edge devices.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6012004-6012004"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133767","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 Towing-Tank Validation of a Distributed Pressure Sensor System for AUV Speed and Drift-Angle Estimation: A Preliminary Comparative Study 用于水下航行器速度和漂角估计的分布式压力传感器系统的设计和拖舱验证：初步对比研究

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-24 DOI: 10.1109/LSENS.2026.3726934

Jin-Yeong Park;Sung-Hoon Byun;So Young Sung;Jungwon Lee;Aeri Cho;Hyeon Kyu Yoon;Ji-Hye Kim

{"title":"Design and Towing-Tank Validation of a Distributed Pressure Sensor System for AUV Speed and Drift-Angle Estimation: A Preliminary Comparative Study","authors":"Jin-Yeong Park;Sung-Hoon Byun;So Young Sung;Jungwon Lee;Aeri Cho;Hyeon Kyu Yoon;Ji-Hye Kim","doi":"10.1109/LSENS.2026.3726934","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3726934","url":null,"abstract":"We present a distributed pressure sensor system (DPSS) that estimates the incoming flow speed <inline-formula><tex-math>$U$</tex-math></inline-formula> and drift angle <inline-formula><tex-math>$alpha$</tex-math></inline-formula> from multiple surface pressure sensors that mimic an artificial lateral line. The system integrates 21 pressure sensors with an embedded data-acquisition unit housed inside a pressure vessel whose profile replicates a real commercial autonomous underwater vehicle head. In total, 29 towing-tank runs were conducted over a range of flow speeds (<inline-formula><tex-math>$U$</tex-math></inline-formula>) and drift angles (<inline-formula><tex-math>$alpha$</tex-math></inline-formula>). Three inverse methods are developed and compared on the resulting dataset: first, an analytical pressure-coefficient <inline-formula><tex-math>$C_{p}(theta)$</tex-math></inline-formula> model with weighted least squares based on the Bernoulli equation (Phase 1); second, a 2-D arc-length <inline-formula><tex-math>$C_{p}$</tex-math></inline-formula> table with graph-Laplacian pressure-Poisson regularization (Phase 2); and third, a physics-informed neural network inverse map, evaluated by cross-validation (Phase 3). All three methods deliver useful estimates in the mid- and high-speed regime (<inline-formula><tex-math>$Ugeq 0.5$</tex-math></inline-formula> m/s), whereas the low-speed degradation observed at <inline-formula><tex-math>$U = 0.3$</tex-math></inline-formula> m/s is attributable to the sensor-resolution-limited signal-to-noise ratio (SNR) (<inline-formula><tex-math>$U_{min }approx 0.35$</tex-math></inline-formula> m/s), not to the inverse methods themselves.","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":"149133879","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

Rapid Prediabetes Screening With a VOC Nanosensor: A Novel Saturation-Onset Prediction-Driven Classifier 用挥发性有机化合物纳米传感器快速筛选前驱糖尿病：一种新的饱和开始预测驱动分类器

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-17 DOI: 10.1109/LSENS.2026.3735086

Tanushree Banerjee;Sakyajit Bhattacharya;Souradeep Roy;Prasanta K. Guha;Paramita Kar Choudhury;Avik Ghose;Arpan Pal;Sanjay Kimbahune

引用次数: 0

Design of a Digital AGC System for Spaceborne DTP 星载DTP数字AGC系统设计

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-17 DOI: 10.1109/LSENS.2026.3735187

Xiaoyu Zhang;Lu Zhang;Tingyang Chen;Jianxiong Zhang;Zekun Wang;Jingyu Zhang;Ning Zhang

{"title":"Design of a Digital AGC System for Spaceborne DTP","authors":"Xiaoyu Zhang;Lu Zhang;Tingyang Chen;Jianxiong Zhang;Zekun Wang;Jingyu Zhang;Ning Zhang","doi":"10.1109/LSENS.2026.3735187","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3735187","url":null,"abstract":"This letter presents a digital automatic gain control (AGC) architecture developed for the spaceborne digital transparent processor (DTP). The DTP is a multichannel array platform wherein each processing board supports a limited number of transmit/receive channels, achieving large-scale multisensor functionality through coordinated operation across multiple chips and boards. In such distributed sensor systems, AGC plays a critical role in maintaining channel gain stability and preserving a high dynamic range. Conventional digital AGC approaches are typically confined to single-chip or single-board implementations. Consequently, they encounter significant difficulties when required to coordinate wideband digital beamforming and multichannel digital channelization across physically separated processing boards. To overcome this limitation, a cross-board AGC architecture based on a distributed sensing and centralized control paradigm is proposed. In this framework, signal power estimation is performed by the distributed processing nodes across the multiboard sensor array system. A dedicated interface control field-programmable gate array (FPGA) aggregates the telemetry data through periodic polling, and a central microcontroller executes the global control algorithm to compute precise gain adjustment commands. These commands are subsequently disseminated via the FPGA, thereby enabling consistent gain calibration across all channels of the distributed sensor system. The proposed methodology has been validated through both simulation and hardware testing. The results confirm its scalability, real-time performance, and engineering feasibility for large-scale multichannel sensor systems.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"6012104-6012104"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130954","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

Electrochemical Impedance-Based Evaluation of Antibiotic Susceptibility in Bacterial Cells and Biofilms Grown on Gold Electrodes 基于电化学阻抗的细菌细胞和金电极生物膜抗生素敏感性评价

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-21 DOI: 10.1109/LSENS.2026.3726045

Walusha Esther Cardozo;Rhea Patel

{"title":"Electrochemical Impedance-Based Evaluation of Antibiotic Susceptibility in Bacterial Cells and Biofilms Grown on Gold Electrodes","authors":"Walusha Esther Cardozo;Rhea Patel","doi":"10.1109/LSENS.2026.3726045","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3726045","url":null,"abstract":"Catastrophic problem of antibiotic resistance is prevalent across the globe triggered by bacterial survival mechanisms. These biofilms are found in hospital acquired infections, such as catheters, valve, and implants, attributed to the ability of the exopolysaccharide layer in biofilm to confer antibiotic resistance. While traditional lab tests take 24–48 h to identify biofilms, impedance can detect in early adhesion stages, and decreasing time required for diagnosis. The rationale of this project focuses on biofilm's ability to develop as a diagnostic device in the detection and monitoring of bacterial cells. A hypothesis emphasizes speed and accuracy using real-time electrochemical impedance spectroscopy on gold screen-printed electrodes to determine minimum inhibition concentration is 0.001mg/ml and minimum biofilm eradication concentration is 5 mg/ml. Biofilm-forming bacterial cells using the electrochemical technique of impedance. This is a nonfaradic, label-free antimicrobial testing method that rapidly tracks impedance changes, or biofilm resistance during antibiotic exposure by analyzing impedance plots and peak variations across various antibiotic ranges. The outcome focuses on developing a superior technique for monitoring biofilm maturation and the efficiency of antibiotics against antibiotic-resistant biofilms.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"4503804-4503804"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131087","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

Planar CELC Microstrip Sensor for Noninvasive Dielectric Characterisation of Solids and Liquids 用于固体和液体非侵入介质表征的平面CELC微带传感器

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-08-26 DOI: 10.1109/LSENS.2026.3727885

Vishnupriya V;Sujith Raman;Laila D

{"title":"Planar CELC Microstrip Sensor for Noninvasive Dielectric Characterisation of Solids and Liquids","authors":"Vishnupriya V;Sujith Raman;Laila D","doi":"10.1109/LSENS.2026.3727885","DOIUrl":"https://doi.org/10.1109/LSENS.2026.3727885","url":null,"abstract":"This letter presents a high sensitivity microwave dielectric sensor based on a butterfly shaped resonant unit cell integrated on a microstrip transmission line, with a coaligned complementary electric <italic>LC</i> (CELC) resonator etched in to the ground plane. Unlike conventional split ring resonators (SRR) or straight stub configurations that probe the material under test (MUT) from a single plane, the proposed bilateral architecture simultaneously concentrates electric field energy in the MUT region from both the top and bottom planes, thereby maximizing the effective dielectric filling factor and sensing sensitivity. The sensor resonates at 3.66 GHz with a measured loaded Q factor of 9.6, demonstrating a maximum sensitivity of 10% and 4% for liquid and solid samples respectively, making it suitable for quality monitoring with simple single layer fabrication. The bi-anisotropic response inherent to SRRs is suppressed by the symmetric CELC topology, providing a clean, permittivity dominated resonance shift. Fabricated on FR4 with an overall footprint of 50 × 30 mm<sup>2</sup>, the sensor is validated using reference liquid and solid samples and benchmarked against recently published designs. The superior sensitivity facilitates precise quality assessment. This capability offers significant potential for real time applications in environmental applications, agriculture and food safety. The proposed method is experimentally validated by adulterating coconut oil with palm oil, enabling accurate quantification of the percentage of adulteration.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"10 10","pages":"3506404-3506404"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131286","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

Dynamic Mode Decomposition of Photoacoustic Sensor Data for Blood Classification 用于血液分类的光声传感器数据动态模态分解

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-10 DOI: 10.1109/LSENS.2026.3732943

Roberto G. Ramírez-Chavarría;Argelia Pérez-Pacheco;Marco P. Colín-García;Rosa M. Quispe-Siccha

引用次数: 0

A Radar-Acoustic Sensor Fusion Unobtrusive System for Imaging High-Impact Regions Inside Opaque Rotating Ball Mills 一种用于不透明旋转球磨机内部高冲击区域成像的雷达-声传感器融合非干扰系统

IF 2.4

IEEE Sensors Letters Pub Date : 2026-10-01 Epub Date: 2026-09-09 DOI: 10.1109/LSENS.2026.3732435

Raj Rakshit;Mayukh Biswas;Chirabrata Bhaumik;Arun Kumar Majumder

引用次数: 0