IEEE Sensors Journal最新文献_第9页

An Electronic Nose Combined With DFCC-Net for Origin Identification of Mung Beans 结合dfc - net的绿豆原产地鉴定电子鼻

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3534229

Meng Yang;Ruotong Zhu;Wenyong Jin;Yongsheng Wang

{"title":"An Electronic Nose Combined With DFCC-Net for Origin Identification of Mung Beans","authors":"Meng Yang;Ruotong Zhu;Wenyong Jin;Yongsheng Wang","doi":"10.1109/JSEN.2025.3534229","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3534229","url":null,"abstract":"Because of varying ecological factors such as climate, soil, temperature, and precipitation, the quality of mung beans from different origins exhibits significant differences. A fast and effective method for identifying the origin of mung beans is essential for protecting origin-specific products and safeguarding consumer rights. In this work, an electronic nose (e-nose) combined with a deep learning algorithm is proposed to identify the gas information of mung beans from different origins. First, gas information of mung beans in six renowned origins of China is detected using an e-nose system. Next, based on the time-series characteristics and cross-sensitivity in gas information, a deep feature computing module (DFCM) is proposed to adaptively compute the deep gas features along both the time and sensor directions. Finally, a deep feature computing and classification network (DFCC-Net) is designed to identify the gas information of mung beans at different origins. Through visual analysis of the gas information, ablation studies, and comparison with state-of-the-art gas classification methods, DFCC-Net demonstrates superior performance, achieving an accuracy of 97.93%, a precision of 98.09%, and a recall of 98.09%. Meanwhile, the gradient-weighted class activation mapping (Grad-CAM) visualization method is employed to highlight key gas features, further validating the effectiveness of feature computation and classification by DFCC-Net. In conclusion, the integration of the e-nose system with DFCC-Net offers an effective approach for accurately identifying the origin of mung beans and protecting origin-specific products.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 8","pages":"14173-14182"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839815","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

See-Through Walls With Wi-Fi: Joint Utilization of RSSI and CSI for Enhancing Building Layout Reconstruction 带Wi-Fi的透视墙：RSSI和CSI联合应用，增强建筑布局改造

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555686

Jiahui Chen;Shisheng Guo;Fangrui Yu;Zihan Xu;Xue Huang;Nian Li;Guolong Cui

{"title":"See-Through Walls With Wi-Fi: Joint Utilization of RSSI and CSI for Enhancing Building Layout Reconstruction","authors":"Jiahui Chen;Shisheng Guo;Fangrui Yu;Zihan Xu;Xue Huang;Nian Li;Guolong Cui","doi":"10.1109/JSEN.2025.3555686","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555686","url":null,"abstract":"In building layout reconstruction (BLR), electromagnetic (EM) wave propagation in indoor environments involves not only direct paths (DPs) but also multiple non-DPs due to reflections, diffractions, and scattering. Traditionally, received signal strength indicator (RSSI)-based methods have been employed for BLR. However, RSSI is highly susceptible to multipath interference, leading to degraded reconstruction accuracy. To address this limitation, this article leverages the fine-grained characterization capabilities of channel state information (CSI) to mitigate the weaknesses of RSSI in multipath environments. Specifically, we propose a novel method that integrates CSI phase fluctuations with RSSI measurements to achieve high-quality BLR for various building structures. This approach enables the independent reconstruction of different building features, which are subsequently fused to enhance the overall BLR image. Experimental results demonstrate that, compared to conventional RSSI-based BLR methods, the proposed approach significantly reduces multipath interference, resulting in improved reconstruction accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17717-17726"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073312","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 Optical Fiber Sensor Based on FeOOH Nanorod Arrays for Humidity and Temperature Measurement 基于FeOOH纳米棒阵列的光纤温湿度传感器

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555588

Pengju Cao;Tingting Wu;Jian Wen;Haidong Tan;Su Sheng;Chao Jiang

引用次数: 0

MyoStep: Feature-Based GNN Model for Estimating Knee Joint Angles by Fusing Signals From sEMG and IMU MyoStep：融合表面肌电信号和IMU信号估计膝关节角度的基于特征的GNN模型

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555668

Bian Wu;Wei Chen;Dewei Liu;Jihua Lu;Lihui Feng

{"title":"MyoStep: Feature-Based GNN Model for Estimating Knee Joint Angles by Fusing Signals From sEMG and IMU","authors":"Bian Wu;Wei Chen;Dewei Liu;Jihua Lu;Lihui Feng","doi":"10.1109/JSEN.2025.3555668","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555668","url":null,"abstract":"Smooth and continuous control over exoskeletons remains challenging for practical applications. Applying surface electromyography (sEMG) and inertial measurement unit (IMU) to predict knee joint angles faces several issues, including complex deployment, precise locating muscles, and equipment interfering with movement. Furthermore, present estimation methods seldom consider the topology of the device. We propose a MyoStep method that applies a feature-based graph neural network (GNN) model to estimate knee joint angles by combining signals from sEMG electrodes and IMU. First, the self-developed leg band collects signals and its sensors are mapped as the graph nodes. Features are extracted and then weighted by the neighborhood component feature selection algorithm, and the top five weighted features are exploited as graph properties. Furthermore, the topological links between the sEMG electrodes and IMU are associated with the edges of the graph, and the mean correlation coefficients between neighboring nodes are computed as the edge attributes. Finally, the graph features are obtained by the ReadOut function of the model and then fed into the fully connected layers to estimate knee joint angles. The sensor deployment of MyoStep is simpler and causes less interference. In addition, the estimated knee joint angles’ root-mean-square error (RMSE), coefficient of determination (<inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula>), and Pearson correlation coefficient (CC) are 2.82°, 0.993, and 0.997, respectively. Compared to the models of convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and GNN-baseline, the <inline-formula> <tex-math>${R}^{{2}}$ </tex-math></inline-formula> and CC of MyoStep increase by 18% and 7%, respectively, and the RMSE decreases by 78%. Therefore, the MyoStep method has considerable applicability in simplified deployment and precise control of exoskeleton robots.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17750-17760"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072998","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 Bi-Polymer Graphite Electrode Decorated With NiCo₂O₄ Nano-Cubes for Rapid Detection of Theaflavin in Black Tea 四硫酸镍修饰双聚合物石墨电极用于红茶中茶黄素的快速检测

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555887

Madhurima Moulick;Shreya Nag;Debangana Das;Ajanto Kumar Hazarika;Santanu Sabhapondit;Runu Banerjee Roy

{"title":"A Molecular Imprinted Bi-Polymer Graphite Electrode Decorated With NiCo₂O₄ Nano-Cubes for Rapid Detection of Theaflavin in Black Tea","authors":"Madhurima Moulick;Shreya Nag;Debangana Das;Ajanto Kumar Hazarika;Santanu Sabhapondit;Runu Banerjee Roy","doi":"10.1109/JSEN.2025.3555887","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555887","url":null,"abstract":"Theaflavin (TF) is a major constituent in tea and is responsible for quality profiling in terms of taste attributes. This research aims to fabricate a molecular imprinted electrode modified with nanoparticles of nickel cobalt oxide for the detection of TF in tea. The proposed electrode is prepared from a co-polymer of acrylic acid (AA) and methacrylic acid (MAA) and imprinted with TF template. The nanoparticles of nickel cobalt oxide are prepared in the laboratory and further characterized by Fourier transform infrared (FTIR) and scanning electron microscope (SEM). Cyclic and differential pulse voltammetry (DPV) is performed to study the electrochemical behavior of the electrode. The electrode shows a good detection limit of 13.89 <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>M and has a wide linear operating range of 80–1000 <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>M. The electrode is repeatable and shows satisfactory reproducibility. The proposed electrode is also employed to determine the total TF in different black tea samples by correlating with standard high-performance liquid chromatography (HPLC) values using the partial least square regression (PLSR) model and a prediction accuracy of 92.6% is obtained.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"16621-16627"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073162","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 FE-S-BiLSTM and Heatmap-Based State-of-Health Estimation Method for Lithium-Ion Batteries 基于FE-S-BiLSTM和热图的锂离子电池健康状态估计方法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555876

Kui Shao;Chao Zhai;Chaolong Zhang;Yigang He;Bolun Du;Ji Wu

{"title":"An FE-S-BiLSTM and Heatmap-Based State-of-Health Estimation Method for Lithium-Ion Batteries","authors":"Kui Shao;Chao Zhai;Chaolong Zhang;Yigang He;Bolun Du;Ji Wu","doi":"10.1109/JSEN.2025.3555876","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555876","url":null,"abstract":"Accurate battery state-of-health (SOH) estimation can improve battery reliability and ensure its safe and efficient operation. Therefore, this study proposes a novel method for battery SOH estimation. First, a thermocouple temperature sensor monitors the battery’s operating temperature and provides feedback to the thermostat for precise temperature control during experiments. The battery’s charging voltage and current are measured using voltage transmitters and Hall current sensors, respectively, and the two are fused to obtain the battery’s charging power. Next, 1-D charging power data are converted into 2-D heatmaps using image encoding techniques. The heatmap corresponding to the first cycle is selected as the reference image, and the difference between the heatmaps of subsequent cycles and the reference image is quantified using structural similarity (SSIM). The final results serve as an indicator for battery health. In addition, this study proposes a novel battery SOH estimation model, the feature enhancement-simplification-bidirectional long short-term memory (FE-S-BiLSTM). The feature enhancement layer in the FE-S-BiLSTM model enriches global static features through enhancement learning. Based on the model’s bidirectional long short-term memory (BiLSTM) layer and simplification layer, dynamic features in the time-space domain are double captured. Finally, this study utilizes six batteries and designed a variety of experiments to validate the effectiveness of the proposed method. The experimental design comprises three tasks: SOH estimation based on full-charging data, SOH estimation based on random SOC interval charge data, and cross-battery SOH estimation. The experimental results demonstrate that the proposed SOH estimation method for batteries exhibits significant potential for practical applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17727-17738"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073313","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

Lithium Niobate Microphone With High SNR Potential 高信噪比电位铌酸锂传声器

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555885

Xiaoyu Niu;Vakhtang Chulukhadze;Zihuan Liu;Ehsan Vatankhah;Yinan Wang;Yuqi Meng;Lezli Matto;Mark S. Goorsky;Ruochen Lu;Neal A. Hall

{"title":"Lithium Niobate Microphone With High SNR Potential","authors":"Xiaoyu Niu;Vakhtang Chulukhadze;Zihuan Liu;Ehsan Vatankhah;Yinan Wang;Yuqi Meng;Lezli Matto;Mark S. Goorsky;Ruochen Lu;Neal A. Hall","doi":"10.1109/JSEN.2025.3555885","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555885","url":null,"abstract":"A bimorph lithium niobate (LiNbO3, LN) transducer has been proposed as a microphone. Surface electrodes sense the lateral in-plane electric field in thin LN films resulting from out-of-plane deformation due to acoustic pressure. The bimorph is implemented using LN films with opposing polarization, achieved with a wafer bonding approach. The article summarizes our work on an LN microphone using an LN diaphragm with <inline-formula> <tex-math>$300~mu $ </tex-math></inline-formula>m thickness and a quarter-inch diameter. Laser Doppler vibrometer (LDV) measurements are performed using piezoelectric excitation of the diaphragm to characterize mode shapes of the diaphragm. Pitch-catch acoustic measurements are performed in the air using tone burst waveforms. We characterized acoustic sensitivity and noise floor. Lumped element and finite element analysis (FEA) are used to predict the acoustical performance. This is the first work demonstrating LN material in microphone applications. We ultimately envision using a bimorph LN film with <inline-formula> <tex-math>$2~mu $ </tex-math></inline-formula>m thickness in total on the silicon substrate, where an approximately <inline-formula> <tex-math>$1times 1$ </tex-math></inline-formula> mm diaphragm is formed via a backside through wafer etch. A rigorous lumped element model is used to simulate the LN microelectromechanical systems (MEMS) microphone with a 73.58-dB signal-to-noise ratio (SNR). Improving MEMS microphone SNR beyond the current state-of-the-art is challenging. LN microphones may be a viable path.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18115-18122"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072814","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

Optimized Encapsulation Design for Reducing Low-Frequency Loss in MEMS Ciliary Hydrophones 降低MEMS纤毛水听器低频损耗的优化封装设计

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555804

Zimeng Guo;Guojun Zhang;Ruimin Zhang;Yuhao Huang;Jiangjiang Wang;Wenqing Zhang;Wendong Zhang

引用次数: 0

An Analytical Model of Dynamic Charge Transfer Process for CMOS Image Sensors CMOS图像传感器动态电荷传递过程的解析模型

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555631

Jing Gao;Chen Chen;Jinghua Ao;Tao Luo;Hong Yin

引用次数: 0

Laser Scribed CNTs/PEEK/TPU Composites Film for High-Performance Stretchable Electrodes 用于高性能可拉伸电极的激光刻划CNTs/PEEK/TPU复合材料薄膜

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-04-03 DOI: 10.1109/JSEN.2025.3555629

Caiyun Jiang;Lei Tang;Jian Hou;Guqiao Ding;Bin Sheng

{"title":"Laser Scribed CNTs/PEEK/TPU Composites Film for High-Performance Stretchable Electrodes","authors":"Caiyun Jiang;Lei Tang;Jian Hou;Guqiao Ding;Bin Sheng","doi":"10.1109/JSEN.2025.3555629","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3555629","url":null,"abstract":"In this article, an elastic composite material comprising carbon nanotubes (CNTs), polyether-ether-ketone (PEEK), and thermoplastic polyurethane (TPU) is presented, in which direct laser-induced graphitization was used to fabricate high-performance stretchable electrodes. The introduction of CNTs can enhance the conductivity and mechanical strength of the composites, thus improving the overall performance of the sensors. Flexible strain sensors with porous structures were prepared by appropriate encapsulation methods, which exhibited superior gauge factors (GFs) of 72, 367, and 972 in the 0%–30%, 30%–44%, and 44%–53% strain regions, respectively. Furthermore, it features a rapid response time (100 ms), excellent stability and durability, and is capable of detecting both large-scale human movement and monitoring of small physical signals. In addition, patterned laser-induced graphene interdigital electrodes (LIG-IDEs) were fabricated on composites using laser light. Highly sensitive humidity sensors were obtained using graphene oxide as the humidity-sensing material for respiratory monitoring and noncontact sensing. The thermoplastic properties of TPU allow the straight fiber to be thermoformed into highly stretchable helical electrode, which was of a helix index of 5 and with a high-quality factor (<inline-formula> <tex-math>${Q} =130$ </tex-math></inline-formula>) at 1250% strain. In conclusion, the preparation of LIG on stretchable composite films by direct laser scribing shows a promising approach for large-scale fabrication of wearable devices.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"16666-16674"},"PeriodicalIF":4.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073279","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