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A Novel Bilayer MXene/GO Pressure Sensor Array for Multimode and High-Precision Frequency Regulation 用于多模高精度频率调节的新型双层MXene/GO压力传感器阵列

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-08 DOI: 10.1109/JSEN.2025.3584335

Yu Gu;Yixin Cao;Debo Wang

{"title":"A Novel Bilayer MXene/GO Pressure Sensor Array for Multimode and High-Precision Frequency Regulation","authors":"Yu Gu;Yixin Cao;Debo Wang","doi":"10.1109/JSEN.2025.3584335","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3584335","url":null,"abstract":"With the rapid development of communication technology, the modulation of signal frequency has become urgent. In order to solve the issues of single mode and low precision, a novel <inline-formula> <tex-math>$3times 3$ </tex-math></inline-formula> bilayer MXene/GO pressure sensor array is designed and then integrated into the relaxation oscillator circuit for the first time. In this work, each sensing unit is fabricated by the cost-effective coating technique (MXene dispersion and GO aqueous solution mix at a volume ratio of 1:6). By recognizing the external pressure distribution (“Y,” “X,” “J,” “H,” and “O”), the sensor array exhibits different rates of resistance variation, thereby achieving multimode frequency regulation. At the same time, due to the extraordinary synergistic effect between MXene and graphene, the sensing units demonstrate ultrahigh sensitivity (14.21 <inline-formula> <tex-math>${mathrm {kPa}}^{-{1}}text {)}$ </tex-math></inline-formula> in the low-pressure range (0–1.64 kPa). In addition, characteristics, including short response time (5.9 ms), recovery time (7.6 ms), and low hysteresis (3.9%), ensure high-precision frequency regulation. This MXene/GO pressure sensor array can satisfy diverse modulation demands, showing a wide range of applicability in circuits with adjustable frequency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28184-28191"},"PeriodicalIF":4.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758383","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}

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Spatial Resolution Enhancement of Computational Brillouin Optical Time- Domain Reflectometry Using Differential Pulsewidth Pair Technique 差分脉宽对技术提高计算布里渊光时域反射计的空间分辨率

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-08 DOI: 10.1109/JSEN.2025.3584360

Dayong Shu;Tuo Lv;Zhi-Han Cao;Da-Peng Zhou;Wei Peng

引用次数: 0

Recent Advances in SLAM for Degraded Environments: A Review 退化环境SLAM研究进展综述

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-07 DOI: 10.1109/JSEN.2025.3584218

Wenda Wang;Qiuzhao Zhang;Yongfeng Hu;Michal Gallay;Wen Zheng;Jianye Guo

{"title":"Recent Advances in SLAM for Degraded Environments: A Review","authors":"Wenda Wang;Qiuzhao Zhang;Yongfeng Hu;Michal Gallay;Wen Zheng;Jianye Guo","doi":"10.1109/JSEN.2025.3584218","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3584218","url":null,"abstract":"Simultaneous localization and mapping (SLAM) is a technology that relies on self-carried sensors, such as light detection and ranging (LiDAR), inertial measurement units (IMUs), and cameras, to perform autonomous navigation positioning and mapping in unknown environments, characterized by noncontact operation, global coverage, and high precision. SLAM in optimal conditions is already a well-established field. However, there is still a significant need to develop SLAM techniques that can effectively operate in degraded scenarios, such as during sensor failures or perception degradation. This review article focuses on the recent advancement of SLAM, particularly in degraded environments, including Global Navigation Satellite System (GNSS)-denied, visually degraded, and feature-correspondence degradation settings. This article first introduces the development of SLAM. Second, it details the progress of SLAM in single degraded environments, such as GNSS-denied, visually degraded, and feature-correspondence degradation settings. Following this, the progress of SLAM in complex degraded environments such as mines, tunnels, and indoor-degraded environments is introduced. Finally, this article summarizes the advancements of SLAM technology in complex degraded environments and discusses potential future development trends. This detailed review article is of great significance for autonomous exploration and mapping in degraded environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"27898-27921"},"PeriodicalIF":4.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758361","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}

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Guest Editorial Special Issue on Machine Learning for Radio Frequency Sensing 《射频传感中的机器学习》特刊

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-04 DOI: 10.1109/JSEN.2025.3573462

Avik Santra;Ashish Pandharipande;Pu Perry Wang;George Shaker;Bhavani Shankar Mysore;Guido Dolmans;Yan Chen;Negin Shariati Moghadam

引用次数: 0

Robust Heart Rate Monitoring From Wrist Pulse Signals Corrupted by Motion Artifacts Using Dual Microphones and Adaptive Filtering 基于双麦克风和自适应滤波的运动伪影干扰腕部脉搏信号鲁棒心率监测

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-03 DOI: 10.1109/JSEN.2025.3583789

Zhengdi Sun;Anle Mu;Ye Qian;Zhongnan Lv;Yvpeng Wang

{"title":"Robust Heart Rate Monitoring From Wrist Pulse Signals Corrupted by Motion Artifacts Using Dual Microphones and Adaptive Filtering","authors":"Zhengdi Sun;Anle Mu;Ye Qian;Zhongnan Lv;Yvpeng Wang","doi":"10.1109/JSEN.2025.3583789","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583789","url":null,"abstract":"This study addresses the degradation of radial arterial acoustic pulse signals during hand motion by proposing a novel heart rate (HR) detection algorithm, recursive minimum error entropy-variational mode extraction (RMEE-VME). The algorithm synchronously collects wrist acoustic signals using dual microphones, with one channel detecting the pulse waves and the other capturing motion noise. To suppress motion artifacts (MAs), an adaptive filtering algorithm based on the MEE criterion is employed. In addition, variational mode extraction and spectral peak tracking techniques are integrated to mitigate random interference and enhance HR estimation accuracy. Experimental results from 12 volunteers across five motion modes demonstrate the superior performance of RMEE-VME. The algorithm achieves an average absolute error, mean absolute error (MAE) of 2.16 beats per minute (bpm), outperforming recursive least square (RLS) filtering (3.51 bpm), and unfiltered signals (16.13 bpm). Each in high-interference scenarios, such as free hand movement, RMEE-VME maintains robustness, achieving an MAE of 2.60 bpm. Furthermore, it ensures stable real-time tracking with minimal HR fluctuations, achieving reliable performance despite the significant overlap between motion and HR frequencies. By integrating a dual-microphone acquisition system with spectral tracking, RMEE-VME enables high precision and continuous monitoring, demonstrating strong resistance to motion interference and high accuracy for next-generation wearable devices.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29336-29346"},"PeriodicalIF":4.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751041","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

Identifying the Respiratory Sound Based on Single-Channel Separation and Hyperdimensional Computing 基于单通道分离和超维计算的呼吸声识别

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-03 DOI: 10.1109/JSEN.2025.3557909

Jie Zheng;Yixuan Wang;Jinglong Niu;Yan Shi;Fei Xie

引用次数: 0

Frequency-Domain Feature Interaction Combined With Multiscale Attention for Remote Sensing Change Detection 结合多尺度关注的频域特征交互遥感变化检测

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-03 DOI: 10.1109/JSEN.2025.3583301

Zhongxiang Xie;Shuangxi Miao;Zhewei Zhang;Xuecao Li;Jianxi Huang

{"title":"Frequency-Domain Feature Interaction Combined With Multiscale Attention for Remote Sensing Change Detection","authors":"Zhongxiang Xie;Shuangxi Miao;Zhewei Zhang;Xuecao Li;Jianxi Huang","doi":"10.1109/JSEN.2025.3583301","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583301","url":null,"abstract":"Change detection (CD) in remote sensing images has seen significant advancement due to the powerful discriminative capabilities of deep convolutional networks. However, the domain gap and pseudo-changes between the bi-temporal images, caused by variations in imaging conditions such as illumination, shadow, and background, remain a challenge. Furthermore, multiscale variations in complex scenes complicate the accurate identification of change regions and their boundary delineation. To address these issues, this article introduces the frequency-domain feature interaction and multiscale attention mechanism network (FIMANet). Specifically, to mitigate the impact of pseudo-change interference, the FIMANet reduces the domain gap and facilitates information coupling within intralevel representations through frequency-domain feature interaction (FDFI). To prevent information loss and noise introduction, a multiple kernel inception (MKI) module is devised to capture multiscale features and perform progressive fusion. Finally, to enhance the extraction of changes in scale-sensitive regions, the FIMANet constructs a cross-scale feature aggregator (CSFA) module, composed of attention at various scales and a transformer, to capture fine-grained details and global dependencies. Comparative experiments with nine methods on three commonly used datasets validate the effectiveness of FIMANet, achieving the highest <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-score of 73.98% on the CLCD dataset, 90.55% on the WHU-CD, and 91.01% on the LEVIR-CD. The code is available at <uri>https://github.com/zxXie-Air/FIMANet</uri>","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29284-29295"},"PeriodicalIF":4.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751114","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

A Lightweight Subtraction-Convolution Network via Adaptive Sparse Feature Extraction for Interpretable Intelligent Edge Diagnosis 基于自适应稀疏特征提取的轻量级减-卷积网络可解释智能边缘诊断

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-03 DOI: 10.1109/JSEN.2025.3583820

Qihang Wu;Zhiming Wang;Yuanyuan Xu;Wenbin Huang;Xiaoxi Ding

{"title":"A Lightweight Subtraction-Convolution Network via Adaptive Sparse Feature Extraction for Interpretable Intelligent Edge Diagnosis","authors":"Qihang Wu;Zhiming Wang;Yuanyuan Xu;Wenbin Huang;Xiaoxi Ding","doi":"10.1109/JSEN.2025.3583820","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583820","url":null,"abstract":"From the perspective of signal processing collaborated with deep learning (DL), the interpretability of the features separated from the DL model is an important factor affecting reliability and accuracy. Considering the challenges of large amount of data transmission and large-size model deployment in real-time fault diagnosis, this study proposed a lightweight subtraction-convolution network (SCN) for industrial intelligent edge fault diagnosis. An array of randomly initialized sparse kernels (SKs) is designed to interpretably achieve adaptive sparse spectrum feature separation with the <inline-formula> <tex-math>${L} ^{{1}}$ </tex-math></inline-formula> regularization constraint introduced. Additionally, the depthwise separable convolution (DSC) is subsequently employed as a substitute for the conventional convolution operation to diminish computational burden and design a more lightweight model named SCN-L. Self-made extensive experiments indicated that the proposed SCN and SCN-L have shown great lightweight performance, high accuracy effect, and interpretability. A public dataset is used to illustrate the generalizability of the proposed model. Furthermore, an intelligent edge diagnosis node (EDN) hardware with SCN-L is designed to implement efficient industrial intelligent edge diagnosis. The experimental results show that the proposed model performs efficiently in edge diagnosis, indicating great potential for industrial application.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"29325-29335"},"PeriodicalIF":4.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751025","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

A Molecular Imprinted Quartz Crystal Microbalance Sensor for Reliable Detection of Alpha-Terpineol in Various Pine Essential Oils 分子印迹石英晶体微平衡传感器可靠检测各种松木精油中α -松油醇

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-03 DOI: 10.1109/JSEN.2025.3583871

Deepam Gangopadhyay;Sumit Kundu;Mahuya Bhattacharyya Banerjee;Shreya Nag;Panchanan Pramanik;Runu Banerjee Roy

{"title":"A Molecular Imprinted Quartz Crystal Microbalance Sensor for Reliable Detection of Alpha-Terpineol in Various Pine Essential Oils","authors":"Deepam Gangopadhyay;Sumit Kundu;Mahuya Bhattacharyya Banerjee;Shreya Nag;Panchanan Pramanik;Runu Banerjee Roy","doi":"10.1109/JSEN.2025.3583871","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3583871","url":null,"abstract":"<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>-terpineol (A-Te), a bioactive monoterpenoid, is widely used in cosmetics and aroma therapy for its appeasing fragrance and flavor. Recent studies have acknowledged its immense potential in biological applications and naturopathy. This study aims to develop a low-cost detection mechanism of A-Te using a sensitive quartz crystal microbalance (QCM) sensor, employing a highly selective molecularly imprinted polymer (MIP) of methyl methacrylate (MMA) and acrylic acid (AA). Frequency deviation of the sensor has been utilized for A-Te detection in four commercial-grade pine essential oils (PEOs). This has yielded a remarkable sensitivity of 0.149 Hz/ppm with a wide linear range of 5–800 ppm. Reliability of the sensor has been assessed in terms of a reproducibility and repeatability study, showcasing promising values of 90.70% and 92.32%, respectively. The limit of detection (LOD) has been achieved at 1.33 ppm. Polymer characterization and surface morphology of the sensor have been analyzed through Fourier transform infrared spectroscopy (FTIR) and scanning electron microscope (SEM), respectively. Furthermore, responses obtained from PEO samples were correlated with the conventional gas chromatographic method using principal component regression (PCR) and random forest regression (RFR) models. Notably, a high prediction accuracy (96.38%) has been achieved from PCR.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"27966-27973"},"PeriodicalIF":4.3,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758291","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

IEEE Sensors Council IEEE传感器委员会

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-07-03 DOI: 10.1109/JSEN.2025.3580308

引用次数: 0