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Sensitivity-Enhanced Displacement Sensing System Based on a Microwave Photonic Filter Incorporating a Figure-Eight Loop Interferometer 基于8字形环路干涉仪微波光子滤波器的灵敏度增强位移传感系统

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-04 DOI: 10.1109/JSEN.2025.3574575

Jianming Zhou;Guangying Wang;Jinying Fan;Junkai Zhang;Jiejun Zhang;Jianping Yao

{"title":"Sensitivity-Enhanced Displacement Sensing System Based on a Microwave Photonic Filter Incorporating a Figure-Eight Loop Interferometer","authors":"Jianming Zhou;Guangying Wang;Jinying Fan;Junkai Zhang;Jiejun Zhang;Jianping Yao","doi":"10.1109/JSEN.2025.3574575","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574575","url":null,"abstract":"This work introduces a novel displacement sensing system with enhanced sensitivity and resolution by integrating a figure-eight loop interferometer (FELI) into a microwave photonic (MWP) filter (MPF) architecture. Unlike conventional Mach-Zehnder interferometer (MZI)-based fiber sensors that rely on optical spectrum analyzers (OSAs) with limited resolution (~0.02 nm), the proposed system leverages the distinct interferometric properties of the FELI and the high spectral resolution of MPF-based electrical demodulation. This innovation enables substantial performance improvement. The FELI-based MPF sensor achieves a displacement sensitivity of 0.207 MHz/<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m, which is nearly twice that of the MZI-based MPF counterpart (0.113 MHz/<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m). Analysis indicates a theoretical displacement resolution as fine as 4.83 pm, with practical accuracy reaching <inline-formula> <tex-math>$0.3~mu $ </tex-math></inline-formula>m, far surpassing the 7.168 mm resolution achievable via typical optical wavelength demodulation. This four-order-of-magnitude improvement highlights the FELI-MPF system as a breakthrough platform for ultrahigh-resolution sensing, offering strong potential for compact, high-performance sensing in biometrics, on-chip diagnostics, and IoT applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24115-24121"},"PeriodicalIF":4.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557741","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}

引用次数: 0

Remaining Useful Life Estimation for Key Components of Manufacturing Equipment With Individual Differences 具有个体差异的制造设备关键部件剩余使用寿命估算

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-04 DOI: 10.1109/JSEN.2025.3574756

Qi Wu;Baokang Zhang;Tao Li;Yaowei Wang;Wen-An Zhang

{"title":"Remaining Useful Life Estimation for Key Components of Manufacturing Equipment With Individual Differences","authors":"Qi Wu;Baokang Zhang;Tao Li;Yaowei Wang;Wen-An Zhang","doi":"10.1109/JSEN.2025.3574756","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574756","url":null,"abstract":"Methods of accurate and reliable remaining useful life (RUL) estimation play a vital role in reducing proportion of both overuse and underuse of key components during the machining process, and have important engineering significance for ensuring machining accuracy. However, stochastic process model-based methods usually have limitations in modeling such complex machining processes due to the lack of nonlinear processing capabilities, and data-driven methods are insufficient to quantify the prediction uncertainty of degradation processes. In addition, owing to the complexity of the structure, materials, and usage environment, there will inevitably be a distinct degradation trajectory among different instances of the same type of key component. Therefore, a data-model interaction mechanism is proposed to compensate for the shortcomings of both composite degradation index (CDI) construction and stochastic degradation modeling so as to improve the accuracy of RUL estimation. Specifically, k-nearest neighbor (KNN) is employed to establish a topology for capturing the interdependencies among sensor nodes. Then, the CDI for characterizing the degradation trends of key components is constructed by a graph Kolmogorov-Arnold network (GKAN), which enhances the nonlinear characterization ability of the method. Moreover, the drift coefficients within the stochastic degradation model are characterized as random variables following a Gaussian distribution, aiming to quantify the inherent individual variability during the modeling of the stochastic degradation process. More importantly, the data of the intermediate degradation process are taken into account by the improved stochastic degradation model, allowing for greater flexibility in the initial degradation level. Finally, the effectiveness and superiority of the proposed method are verified by the PHM2010 and PHM2012 datasets.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"26228-26240"},"PeriodicalIF":4.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557728","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}

引用次数: 0

Development of a Single-System Dual-Emission Ratiometric Fluorescent Probe for Efficient Ethanol Sensing 高效乙醇传感单系统双发射比荧光探针的研制

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-04 DOI: 10.1109/JSEN.2025.3573695

Yingwei Zhou;Fupeng Liu;Qiang Shi;Mengli Li;Bing Yang;Muhammad Imran;Fengzhe Cui;Zhaogang Nie

{"title":"Development of a Single-System Dual-Emission Ratiometric Fluorescent Probe for Efficient Ethanol Sensing","authors":"Yingwei Zhou;Fupeng Liu;Qiang Shi;Mengli Li;Bing Yang;Muhammad Imran;Fengzhe Cui;Zhaogang Nie","doi":"10.1109/JSEN.2025.3573695","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3573695","url":null,"abstract":"Determining the alcoholic strength is pivotal for quality control in the brewing industry; hence, developing a sensitive and accurate method for detecting alcoholic strength is of great importance. This study reported the preparation of a single-system dual-emission fluorescent probe via the solvothermal method, using tea extract as a reaction precursor. The probe exhibited two distinct emission bands with varying properties, where ethanol enhances aggregation-induced emission (AIE) in the blue region, while water promotes aggregation-caused quenching (ACQ) in the red region. This enables the probe to generate distinct fluorescence (FL) responses to different ethanol concentrations present in the alcohol sample. This is the first application of single-system dual-emission carbon dots (CDs), synthesized from biomass materials, for the detection of alcoholic strength. The probe demonstrated a strong positive linear correlation (<inline-formula> <tex-math>${R}^{{2}} =0.9901$ </tex-math></inline-formula>) with ethanol concentrations ranging from 10 vol.% to 70 vol.%, achieving a low detection limit of only 0.063 vol.%. Compared to previously reported probes, this one exhibited a broader linear range and a significantly lower limit of detection. The probe, furthermore, showcased high accuracy (errors <1 vol.%) and reproducibility relative standard deviation ((RSD) <0.6%) in actual samples detection, indicating its potential for alcoholic strength determination in liquor companies for practical application.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25879-25886"},"PeriodicalIF":4.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550628","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}

引用次数: 0

MOA-YOLO: An Accurate, Real-Time, and Lightweight YOLOv10-Based Algorithm for Deep-Sea Fish Detection MOA-YOLO：一种精确、实时、轻量级的基于yolov10的深海鱼类检测算法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-04 DOI: 10.1109/JSEN.2025.3574723

Zhenyu Hu;Qi Chen

{"title":"MOA-YOLO: An Accurate, Real-Time, and Lightweight YOLOv10-Based Algorithm for Deep-Sea Fish Detection","authors":"Zhenyu Hu;Qi Chen","doi":"10.1109/JSEN.2025.3574723","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574723","url":null,"abstract":"The use of autonomous underwater vehicles (AUVs) is an efficient mean to detect deep-sea fishes due to their great ability to complete undersea mission autonomously. However, research on deep-sea fishes suffers from dynamic and small objects in degraded environments, making it difficult to recognize deep sea. In this article, an accurate, real-time, and lightweight deep-sea fish detection algorithm is proposed based on YOLOv10, named MOA-YOLO. First, an underwater image enhancement algorithm based on adaptive standardization and normalization networks is adopted to correct the color and contrast of deep-sea images. Then, the multihead latent attention (MLA) mechanism is added to the front of each detection head of MOA-YOLO to extract more object features, enhancing the discrimination between objects and surroundings. Next, an optimized normalized Wasserstein distance (ONWD) loss function is introduced to replace the complete intersection over union (CIOU) loss function, improving the ability to detect small objects. Finally, CBS and C2f modules in the backbone and neck are replaced with alterable kernel convolution (AKConv) and AKC2f modules, respectively, balancing the detection speed and accuracy. The comparative experiments are operated on both the desktop computer and Nvidia Jetson Xavier NX. Compared with mainstream algorithms, the precision, recall, mean average precision (<inline-formula> <tex-math>${text {mAP}}_{{50}}$ </tex-math></inline-formula>), <inline-formula> <tex-math>${text {mAP}}_{text {50:95}}$ </tex-math></inline-formula>, and frames per second (FPS) of MOA-YOLO are improved by at least 3.1%, 13.7%, 8.0%, 6.9%, and 8.8%, respectively. The experimental results prove that MOA-YOLO achieves excellent performance on deep-sea fish detection with the AUV. The code is available at <uri>https://github.com/hu167/code</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"23933-23947"},"PeriodicalIF":4.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557777","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}

引用次数: 0

An Adaptive Step Detection Method for Smartphones Based on Time-Dependent Decay Mechanism 基于时变衰减机制的智能手机自适应阶跃检测方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-04 DOI: 10.1109/JSEN.2025.3574693

Litao Han;Qirun Sun;Zhenyong Wang;Teng Ma

{"title":"An Adaptive Step Detection Method for Smartphones Based on Time-Dependent Decay Mechanism","authors":"Litao Han;Qirun Sun;Zhenyong Wang;Teng Ma","doi":"10.1109/JSEN.2025.3574693","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574693","url":null,"abstract":"As an indoor positioning method, pedestrian dead reckoning (PDR) is crucial for positioning and navigation in environments where satellite signals are blocked, such as shopping malls, hospitals, and tunnels. The performance of PDR is mainly influenced by step detection, step length estimation, and heading angle calculation. The accuracy and real-time performance of step detection play a crucial role in achieving high-precision indoor positioning. Most of the current step counting methods for smartphones, however, suffer from time delays. Meanwhile, the location of smartphones and pedestrian movement patterns have a significant impact on step counting accuracy. We, therefore, propose an adaptive step detection method based on a time-dependent decay mechanism to overcome the time delays and the influences of smartphone locations and pedestrian movement patterns. The proposed method first preprocesses the acceleration data of a smartphone and identifies its stationary state using a decision tree. Second, the first two peaks are identified based on the number and magnitude of acceleration increases. Third, the adaptive peak threshold and time difference threshold at the current time are calculated in real time based on the time-dependent decay mechanism to determine whether to count a step. Finally, the count of steps is corrected according to the pedestrian’s end state to achieve more accurate step counting. Experimental results demonstrate that the proposed method is less affected by smartphone locations and pedestrian movements, achieving a step counting accuracy of 97.4% under complex motion conditions. Furthermore, the method exhibits good real-time performance, meeting the low-latency requirements of indoor positioning based on smartphones.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25363-25372"},"PeriodicalIF":4.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550552","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}

引用次数: 0

Report on the 3rd Thematic School on Dielectrics 2024 第三届电介质专题学校报告2024

IF 2.6 4区工程技术

IEEE Electrical Insulation Magazine Pub Date : 2025-06-03 DOI: 10.1109/MEI.2025.11023184

Sombel Diaham;Gilbert Teyssèdre;Petru Notingher

引用次数: 0

EEGCMCNet: A Hybrid Network for Sleep-Stage Classification EEGCMCNet：睡眠阶段分类的混合网络

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-03 DOI: 10.1109/JSEN.2025.3574141

Leyuan Huang;Weijie Zhang;Chang Li;Wei Zhao;Hu Peng;Xun Chen

{"title":"EEGCMCNet: A Hybrid Network for Sleep-Stage Classification","authors":"Leyuan Huang;Weijie Zhang;Chang Li;Wei Zhao;Hu Peng;Xun Chen","doi":"10.1109/JSEN.2025.3574141","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574141","url":null,"abstract":"In this article, we introduce a hybrid neural network architecture designed for the analysis and detection of electroencephalogram (EEG) signals, named EEG CNN Mamba capsule network (EEGCMCNet), which significantly optimizes the determination of sleep stages. The EEGCMCNet integrates a multiscale convolutional neural network (MSCNN), the selective state-space series model (Mamba), the squeeze-and-excitation (SE) block, and capsule networks. In the EEGCMCNet, the MSCNN initially extracts low- and high-frequency features from the EEG signals, and then the features are fed into the Mamba module for dynamic time-series analysis and feature integration. This is followed by the SE block that further optimizes channel weights, highlights important features, and suppresses secondary information. Finally, the capsule network performs a detailed analysis of spatial relationships and executes precise classification. The strength of our model lies in providing a new solution to the neglected aspects in this field, that is, to accommodate local feature extraction, global context feature extraction for long time-series data, and also focus on the spatial relationships between features of EEG signals. Experiments demonstrate that the EEGCMCNet excels on three datasets, with multiple metrics for sleep-stage classification surpassing existing advanced methods. This provides a new approach for deep learning analysis of EEG signals.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"25324-25337"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550639","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}

引用次数: 0

New Self-Compensated Optoelectronic Circuit for Highly Reliable Read-Out of FBG Sensor Arrays 用于光纤光栅传感器阵列高可靠读出的新型自补偿光电电路

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-06-03 DOI: 10.1109/JSEN.2025.3574375

Vincenzo Romano Marrazzo;Francesco Fienga;Michele Riccio;Andrea Irace;Giovanni Breglio

{"title":"New Self-Compensated Optoelectronic Circuit for Highly Reliable Read-Out of FBG Sensor Arrays","authors":"Vincenzo Romano Marrazzo;Francesco Fienga;Michele Riccio;Andrea Irace;Giovanni Breglio","doi":"10.1109/JSEN.2025.3574375","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3574375","url":null,"abstract":"This article reports the description and functional tests of a novel wavelength-encoded fiber-optic sensors (FOSs) read-out circuit. The proposed design can transduce the optical signal reflected by the FOS under test into a voltage or current output, suitable for the industrial environment. It is based on two fully analog boards, designed to provide the ratio between the FOS-related analog signal and a reference signal thereby enhancing its ability to reject source fluctuations and possible losses in the optical transmission channel. After a theoretical analysis, the circuit was validated both in static and dynamic conditions using a fiber Bragg grating (FBG) sensor, which was subjected to temperature variations for static testing and high-speed strain variations for dynamic testing. The circuit was designed to display different output values based on its working state (e.g., normal operation or a broken sensor), enhancing fault analysis and paving the way for the adoption of FOS technology in environments where digital processing is prohibited. Finally, compared with the state-of-the-art interrogator, the proposed fully analog self-compensated circuit is more robust, rejecting the unwanted signal on the output line, as demonstrated experimentally.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 13","pages":"24106-24114"},"PeriodicalIF":4.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557833","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}

引用次数: 0

News from Japan 来自日本的新闻

IF 2.6 4区工程技术

IEEE Electrical Insulation Magazine Pub Date : 2025-06-03 DOI: 10.1109/MEI.2025.11023209

Yoshimichi Ohki

引用次数: 0

Ni-SiO2 Cell-Assisted Thermally Stable Broadband Metamaterial Emitter Ni-SiO2电池辅助热稳定宽带超材料发射极

IF 2.1 4区工程技术

IEEE Transactions on Nanotechnology Pub Date : 2025-06-03 DOI: 10.1109/TNANO.2025.3576246

Muhammad Saqlain;Muhammad Abuzar Baqir;Pankaj Kumar Choudhury

引用次数: 0