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{"title":"Energy Storage Design for Energy Harvesting Sensors","authors":"Daniel Monagle;Thomas C. Krause;Aaron W. Langham;Steven B. Leeb","doi":"10.1109/JSEN.2025.3573926","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573926","url":null,"abstract":"Energy harvesting sensors scavenge energy from their surroundings to power themselves without a battery or utility-connected power supply. Sensors that avoid batteries and bespoke power wire connections offer flexibility for avoiding complications in safety and infrastructure. Energy from the sensor’s environment often arrives intermittently or stochastically, complicating the sensor design process. The size of onboard energy storage becomes a critical design decision. Energy storage allows the harvesting system to accumulate energy over time that can later be consumed for sensor tasks. This article presents a modeling and design guide for sizing sensor energy storage. These guidelines balance the tension between cold-start time and steady-state endurance. Cold-start time and steady-state endurance, as a function of energy storage design parameters, are quantified and analyzed with respect to both deterministic and stochastic energy harvest profiles. Results are demonstrated using experimentally measured power consumption data from an industrial machine on a microgrid. Two practical sensor storage design examples demonstrate the design guide. Simulation results highlight the very restrictive storage unit design space over which both fast boot-up and sufficient endurance are satisfied for a notional sensor application. The negative effect of oversized storage on overall sensor <sc>on</small>-time over long time periods of thousands of hours is also demonstrated. These results emphasize the significant impact of storage unit start-up and maximum voltage threshold design choices and their ability to reduce a required storage capacitance by over an order of magnitude to meet the same application requirements.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24614-24625"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550236","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":"Marine Diesel Engine Bushing Fault Pattern Recognition Based on the Sensor Data and Improved Density Peaks Clustering Algorithm","authors":"Xiaonan Chang;Jing Xu;Zhenrui Zhang;Xinyang Sun;Bingwu Gao;Changwen Yang","doi":"10.1109/JSEN.2025.3574410","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574410","url":null,"abstract":"Bushing fault pattern recognition is crucial for extending the lifespan of marine diesel engines. Density peak clustering (DPC) is widely used as an unsupervised learning method for fault pattern recognition. However, the DPC algorithm faces the problems of uneven local density distribution of data and sensitivity to parameter selection when dealing with axial tile fault diagnosis. To address the above problems, this article introduces an unsupervised approach using the improved density peaks clustering (IAO-HDPC) for sensor data clustering and further applies this method to the data collected by the sensors for the bushing fault pattern recognition. Specifically, the proposed method first revises the allocation strategy of the clustering algorithm to address the issue of the DPC algorithm’s sensitivity to the local density of the data. Subsequently, the improved aquila optimizer (IAO) algorithm is employed to determine the optimal parameters for the clustering algorithm, thereby solving the challenge of parameter selection in the DPC algorithm. Finally, the experimental results demonstrate that the method achieves an average fault identification accuracy of 98%. Compared with the other four unsupervised algorithms, the proposed method achieves the best recognition results.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25338-25352"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550421","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":"Development of Molecularly Imprinted Surface Plasmon Resonance Sensor for the Detection of Metrafenone at Maximum Residue Levels","authors":"Kıvılcım Çaktü Güler;Ilgım Göktürk;Fatma Yılmaz;Fatma Kartal;Adil Denizli","doi":"10.1109/JSEN.2025.3574219","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574219","url":null,"abstract":"In this study, metrafenone (MET), a widely used pesticide, was detected in food, soil, and water samples using a molecularly imprinted surface plasmon resonance (MIP@SPR) sensor. For this purpose, MET-imprinted MET imprinted poly [2-hydroxyethyl methacrylate (HEMA)-N-methacryloyl-L-phenylalanine methyl ester (MAPA)] (MET-MIP) nanoparticles were synthesized and immobilized onto the surface of the sensors, thereby creating MET-specific recognition sites. The characterization of MET-MIP nanoparticles was performed using Nano zetasizer measurements and scanning electron microscopy (SEM). The surface characterization of the MIP@SPR sensors was conducted using atomic force microscopy (AFM), contact angle (CA) measurements, and Fourier-transform infrared spectroscopy with attenuated total reflectance (FTIR-ATR) analysis. Kinetic analyses were performed using the SPR system with MET solutions prepared in the concentration range of 0.01–10 mg/L. The obtained results indicated that the limit of detection (LOD) of the MIP@SPR sensor for MET detection was 0.0031 mg/L. For selectivity studies, novaluron was used as the competitor molecule. The MIP@SPR sensors exhibited a 19.92-fold higher selectivity for MET than novaluron. To evaluate the success of the imprinting process, nonimprinted poly [2-hydroxyethyl methacrylate (HEMA)-N-methacryloyl-L-phenylalanine methyl ester (MAPA)] (NIP) nanoparticles were synthesized, and the imprinting factor of the MIP@SPR sensor was calculated as 17.78. For real sample analysis, tomato samples were tested using the MIP@SPR sensor, and high-performance liquid chromatography (HPLC) analysis was conducted to confirm the presence of MET in the samples, thereby validating the results.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"23587-23593"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550553","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":"Thermal Reflow Transfer Printing of Ultra-Thin Metal Conductive Layer for Flexible Sensors on Fabric Substrate","authors":"Xueyan Chang;Enze Liu;Hui Liao;Zhimin Cai;Ying Yi;Xiaochi Liu;Yahua Yuan;Jian Sun","doi":"10.1109/JSEN.2025.3573715","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573715","url":null,"abstract":"Fabric substrates offer mechanical flexibility, air permeability, and textile compatibility, making them ideal for wearable sensors. However, their porous and irregular surfaces pose challenges for integrating ultra-thin, highly conductive metal layers, which are essential for electrical conductivity and signal transmission in flexible electronics. Conventional techniques struggle with precise deposition and uniform coverage on such substrates. Here, we present a thermal reflow transfer printing technique using a caramel-corn syrup mixture as a reflowable transfer medium. Under mild heating, this sugar-based stamp transitions into a rubbery state, enabling the metal layer to conform seamlessly to the fabric surface. Using this method, we successfully printed large-area 30 nm-thick ultra-thin gold electrodes onto fabric substrates, achieving low resistivity of <inline-formula> <tex-math>$6.0times 10^{-{8}}~Omega cdot $ </tex-math></inline-formula> m and outstanding mechanical flexibility under bending. As application demonstrations, we fabricated and tested humidity and pressure sensors using the transferred interdigitated gold electrodes on fabric substrates. The measurements confirm the good sensitivity, reliability, and stability of these sensors. The versatility of the transfer printing method, combined with the outstanding properties of the transferred metal layers, makes it a promising solution for the development of next-generation wearable sensing technologies.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"23615-23622"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550712","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":"Author's Guide for IEEE Electrical Insulation Magazine Technical Articles","authors":"","doi":"10.1109/MEI.2025.11023181","DOIUrl":"https://doi.org/10.1109/MEI.2025.11023181","url":null,"abstract":"","PeriodicalId":444,"journal":{"name":"IEEE Electrical Insulation Magazine","volume":"41 3","pages":"C3-C3"},"PeriodicalIF":2.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RTK-LIO: Tightly Coupled RTK/LiDAR/Inertial Navigation System Based on Optimization Approach","authors":"Rongtian Wang;Yuqi Zhang;Tao Li;Chao Wang;Qi Wu;Ling Pei;Wen-An Zhang","doi":"10.1109/JSEN.2025.3574472","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574472","url":null,"abstract":"Global navigation satellite system real-time kinematic (GNSS-RTK) serves as a vital tool for providing absolute positioning for autonomous systems. However, its performance suffers considerable degradation in urban canyon environments due to the well-known challenges caused by multipath effects and non-line-of-sight (NLOS). Light detection and ranging (LiDAR)/inertial odometry (LIO) offers high-precision local pose estimation in structured urban settings, but it tends to accumulate drift over time. Recognizing their complementary strengths, this article proposes an adaptive integration of GNSS-RTK with LIO to achieve continuous and precise global positioning for autonomous systems in urban environments. The raw data are modeled and optimized within the framework of a factor graph. At the same time, double-difference (DD) carrier phase and ambiguity are added to the estimated states. Finally, RTK-LIO is evaluated on public datasets. It greatly exceeds the benchmarks [LiDAR-inertial-GNSS odometry (LIGO), GNSS/LiDAR/IMU odometry (GLIO), and real-time kinematic positioning (RTK)] in both accuracy and smoothness. To benefit the community, the implementation is open-sourced at <uri>http://gitee.com/bryantaoli/rtk-lio</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"26220-26227"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557889","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":"An Electrochemical Sensor Based on a Facile Synthesis of Chitosan-Blend-Polyaniline Decorated COOH-MWCNT-Hollandite/ α -MnO2 Nanocomposites for Creatinine Detection","authors":"Kabyashree Hazarika;Jiten Chandra Dutta","doi":"10.1109/JSEN.2025.3574062","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574062","url":null,"abstract":"This article comprehensively reports novel synthesis of bio-compatible and sustainable nano-composite based electrochemical sensor comprising of natural and conducting polymers; chitosan (CHIT) and polyaniline (PANI), carboxylic acid functionalized multiwalled carbon-nanotubes (COOH-MWCNTs) and transition metal oxide; Hollandite &lt;inline-formula&gt; &lt;tex-math&gt;$left(alpha right.$ &lt;/tex-math&gt;&lt;/inline-formula&gt;-MnO2), an allotrope of manganese dioxide for creatinine (CRE) detection. A facile chemical solution methodology is utilized for the synthesis process. The work emphasizes on a novel anatomical approach that promises excellent CRE affinity. &lt;inline-formula&gt; &lt;tex-math&gt;$left(alpha right.$ &lt;/tex-math&gt;&lt;/inline-formula&gt;-MnO2) is ingeniously encapsulated within the nano-tubes of COOH-MWCNT &lt;inline-formula&gt; &lt;tex-math&gt;$left(alpha right.$ &lt;/tex-math&gt;&lt;/inline-formula&gt;-MnO2@COOH-MWCNT) and thereafter, blended polymers (CHIT-b-PANI) are decorated over &lt;inline-formula&gt; &lt;tex-math&gt;$left(alpha right.$ &lt;/tex-math&gt;&lt;/inline-formula&gt; -MnO2@COOH-MWCNT) conjugate. The final composite obtained is CHIT-b-PANI/&lt;inline-formula&gt; &lt;tex-math&gt;$alpha$ &lt;/tex-math&gt;&lt;/inline-formula&gt;-MnO2@COOH-MWCNT. Drop coating technique is implemented to deposit CHIT-b-PANI/MnO2@COOH-MWCNT on indium tin oxide (ITO) laminated glass plate. Powder X-ray diffraction (P-XRD), high resolution transmission electron microscopy (HR-TEM), field emission transmission electron microscopy (FETEM), field emission scanning electron microscopy (FESEM), scanning electron microscopy (SEM), atomic field microscopy (AFM), Fourier transform infrared spectroscopy (F-TIR), and UV-Visible Spectroscopy confirmed the successful anatomical morphology and chemical conformation of CHIT-b-PANI/&lt;inline-formula&gt; &lt;tex-math&gt;$alpha$ &lt;/tex-math&gt;&lt;/inline-formula&gt; -MnO2@COOH-MWCNT. The electro-catalytic affinity of CHIT-b-PANI/ &lt;inline-formula&gt; &lt;tex-math&gt;$alpha$ &lt;/tex-math&gt;&lt;/inline-formula&gt;-MnO2@COOH-MWCNT/ITO toward CRE is investigated in a three electrode arrangement with a buffer solution (0.1 M, pH 8) containing commercial CRE in different concentrations &lt;inline-formula&gt; &lt;tex-math&gt;$(1-334.46 mu mathrm{M})$ &lt;/tex-math&gt;&lt;/inline-formula&gt;. The developed CRE electrochemical sensor displayed excellent CRE-compatibility results exhibitin wider detection range, 1–243.48 &lt;inline-formula&gt; &lt;tex-math&gt;$mu mathrm{M}$ &lt;/tex-math&gt;&lt;/inline-formula&gt; (Regression coefficient, R2 = 0.9601), inflated sensitivity of 3204.02 &lt;inline-formula&gt; &lt;tex-math&gt;$mu mathrm{AmM}^{-1}$ &lt;/tex-math&gt;&lt;/inline-formula&gt; low LOD and LOQ of 1.02 and 3.12 &lt;inline-formula&gt; &lt;tex-math&gt;$mu mathrm{M}$ &lt;/tex-math&gt;&lt;/inline-formula&gt;. Superior temperature stability &lt;inline-formula&gt; &lt;tex-math&gt;$left(30^{circ} mathrm{C}-60^{circ} mathrm{C}right)$ &lt;/tex-math&gt;&lt;/inline-formula&gt;, excellent anti-interference characteristics (COV 3.75%); exorbitant reproducibility (COV 0.75%), highly acceptable repeatability (COV 1.32%) and broader storage stability of eight months (COV 5.34%) assured optimum ","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"23579-23586"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550590","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 Transactions on Dielectrics and Electrical Insulation","authors":"","doi":"10.1109/MEI.2025.11023206","DOIUrl":"https://doi.org/10.1109/MEI.2025.11023206","url":null,"abstract":"","PeriodicalId":444,"journal":{"name":"IEEE Electrical Insulation Magazine","volume":"41 3","pages":"57-59"},"PeriodicalIF":2.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11023206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mass Flow Rate Measurement of Gas-Liquid Two-Phase Flow Using Multi-Sensor Data Fusion and Soft Computing Model","authors":"Peng Suo;Jiangtao Sun;Shiying Shi;Fanghao Lu;Mengxian Shen;Xiaokai Zhang;Te Liang;Xiaolin Li;Zihan Zhu;Shijie Sun;Lijun Xu","doi":"10.1109/JSEN.2025.3574085","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574085","url":null,"abstract":"This article presents a novel method for measuring the mass flow rate of gas-liquid two-phase flow based on the multi-sensor data fusion and soft computing model. A multi-sensor system comprising a throat-extended Venturi tube (TEVT) and a dual-modality electrical sensor (DMES) has been developed for gas-liquid two-phase flow measurement. Soft computing models are employed to address the intricate non-linear mapping between the measurement data and flow parameters. Initially, flow regimes are identified based on the time-domain features of the multi-sensor data using a support vector machine (SVM). Subsequently, mass quality is derived from the multi-differential pressure fluctuations and the eigenvalue sequence of the normalized electrical matrices, employing a hybrid neural network comprising a convolution neural network and a deep neural network (DNN). Ultimately, gas/liquid over-reading (OR) is predicted via extreme gradient boosting (XGBoost) using multi-differential pressure ratios. The gas and liquid mass flow rates are subsequently derived from the preceding results. The proposed method addresses the issue that the parameters measurement of gas-liquid two-phase flow is significantly influenced by the flow regimes, and achieves accurate flow rate measurement under the diverse flow regimes. Experimental validation confirms the method’s effectiveness and superior performance compared to conventional approaches.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25314-25323"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550085","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":"Hand Gesture Recognition With Uncertainty Awareness via FMCW Radar Sensing and Deep Learning","authors":"The Tuan Trinh;Hien Vu Pham;Tien Dat Le;Minhuy Le","doi":"10.1109/JSEN.2025.3573743","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573743","url":null,"abstract":"Frequency-modulated continuous-wave (FMCW) radar is a promising device for hand gesture reconstruction in autonomous control systems. A few groundbreaking studies have achieved remarkable advancements in automated hand gesture recognition by combining FMCW radar systems with deep learning (DL) techniques. However, one limitation of these DL models is their inability to convey the uncertainty linked to the model’s predictions, which holds great significance in the context of autonomous control systems. This article introduces the solution to address the challenge by developing an uncertainty-aware deep convolutional neural network (CNN). The network uses a CNN model that incorporates advanced techniques including Monte Carlo dropout (MCD), deep ensemble learning (DEL), and spectral-normalized neural Gaussian process. Our novel network architecture aims to predict hand gestures efficiently while providing an estimation of the associated uncertainty in the model’s predictions. The proposed approach was evaluated on a dataset with ten gestures collected from ten volunteers. The model could predict gestures with an accuracy of over 99% which is superior and noise-resistant to the existing deterministic models. The dataset was available at <uri>https://github.com/thetuantrinh/Hand-Gesture-Recognition.git</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24517-24524"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550442","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}