IEEE Sensors Journal最新文献

2024 Reviewers List

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-12-17 DOI: 10.1109/JSEN.2024.3500252

引用次数: 0

IEEE Sensors Council

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-12-17 DOI: 10.1109/JSEN.2024.3509300

引用次数: 0

IEEE Sensors Council

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-27 DOI: 10.1109/JSEN.2024.3498799

引用次数: 0

A Clustered Routing Algorithm Based on Forwarding Mechanism Optimization 基于转发机制优化的聚类路由算法

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-13 DOI: 10.1109/JSEN.2024.3467055

Qian Sun;Jialong Pang;Xiaoyi Wang;Zhiyao Zhao;Jing Li

{"title":"A Clustered Routing Algorithm Based on Forwarding Mechanism Optimization","authors":"Qian Sun;Jialong Pang;Xiaoyi Wang;Zhiyao Zhao;Jing Li","doi":"10.1109/JSEN.2024.3467055","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3467055","url":null,"abstract":"Given the intrinsic low energy and high consumption characteristics of sensor nodes, it is imperative to explore strategies for achieving energy-efficient routing within wireless sensor networks (WSNs). A significant body of existing research on clustered routing algorithms for WSNs has concentrated on employing heuristic optimization algorithms to facilitate the selection of routing paths. However, once the number of sensor nodes or the deployment environment changes, the algorithm’s performance can fluctuate significantly, potentially requiring redesign and retuning. In this article, we propose the clustered routing algorithm based on forwarding mechanism optimization (CRFMO), which defines separate routing rules for intracluster and intercluster communication, providing suitable communication paths for nodes. The algorithm eschews the complex procedure of parameter tuning during the routing path selection process and contributes to expediting WSN deployment and balancing node load pressure, ultimately extending the network’s operational lifespan. Simulation outcomes reveal that, in comparison to LEACH-IACA and IMP-LEACH, the CRFMO algorithm markedly enhances energy distribution balance, equalizes the burden among nodes, sustains high network coverage over an extended period, which enhances the quality of network monitoring, and significantly extends the lifetime of the network.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"38071-38081"},"PeriodicalIF":4.3,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645531","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 电气和电子工程师学会传感器理事会

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-13 DOI: 10.1109/JSEN.2024.3490979

引用次数: 0

Zero Velocity Detection for Pedestrian Inertial Navigation Based on Spatiotemporal Feature Fusion

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-11 DOI: 10.1109/JSEN.2024.3491161

Xinye Wang;Kaiqiang Feng;Jie Li;Xiaoting Guo;Huiyan Han;Shengjie Cao

{"title":"Zero Velocity Detection for Pedestrian Inertial Navigation Based on Spatiotemporal Feature Fusion","authors":"Xinye Wang;Kaiqiang Feng;Jie Li;Xiaoting Guo;Huiyan Han;Shengjie Cao","doi":"10.1109/JSEN.2024.3491161","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3491161","url":null,"abstract":"Zero velocity detection is a critical component in zero velocity update (ZUPT)-aided foot-mounted pedestrian navigation systems. Robust and accurate zero velocity detection significantly enhances the precision of pedestrian trajectory estimation. Existing zero velocity detectors based on fixed threshold and gait cycle segmentation techniques struggle to adapt to the complexity and variability of human motion. To address this issue, we propose an adaptive zero velocity detector based on deep learning. The raw inertial data possess spatial features with significant differences and temporal features that conform to certain patterns. This detector utilizes a contrastive learning (CL) network and a long short-term memory (LSTM) neural network (NN) to extract the spatial and temporal features of the inertial data, respectively. Experimental results demonstrate that the detector can achieve adaptive zero velocity detection and improve trajectory estimation accuracy regardless of individual differences or motion types. In two indoor experiments, the 2-D position error is 0.410 m for a mixed walking and running path, and the 3-D position error is 0.546 m for a mixed walking, running, and up/down stairs path.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"41932-41940"},"PeriodicalIF":4.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844407","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

Highly Selective and Extensive Range Room Temperature Hydrogen Gas Sensor Based on Pd-Mg Alloy Thin Films

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-11 DOI: 10.1109/JSEN.2024.3491793

Durvesh Gautam;Yogendra K. Gautam;Ashwani Kumar;Sagar Vikal;Amit Sanger;Anil K. Malik;Beer Pal Singh

{"title":"Highly Selective and Extensive Range Room Temperature Hydrogen Gas Sensor Based on Pd-Mg Alloy Thin Films","authors":"Durvesh Gautam;Yogendra K. Gautam;Ashwani Kumar;Sagar Vikal;Amit Sanger;Anil K. Malik;Beer Pal Singh","doi":"10.1109/JSEN.2024.3491793","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3491793","url":null,"abstract":"In this study, MgPd alloy thin films were deposited on a glass substrate using dc/RF magnetron sputtering technique with varying sputtering power (30–60 W) of Pd target to achieve different compositions of alloy. X-ray diffractometry (XRD) and X-ray photoelectron spectroscopy (XPS) analyses provide the crystalline structure and elemental composition of the MgPd alloy thin films, respectively. The surface morphology study through field emission scanning electron microscopy (FE-SEM) reveals nanoflakes. Gas sensing study reveals that the sensitivity of the Mg65Pd35 alloy thin film sensor is 3.41% for 500 ppm and 13% for 1 bar of hydrogen (H2) at room temperature (RT). The response/recovery times of the sensor are 5 s/3 min and 85 s/6 min for 1 bar H2 gas and 500 ppm H2, respectively. A remarkable selectivity toward the H2 gas is observed in comparison to other gases such as CO, NO2, and NH3. Long-term stability was observed even with increased Pd composition over multiple hydrogenation/dehydrogenation cycles. These findings suggest that Pd-capped MgPd alloy thin films are promising for the applications of highly durable, selective, cost-effective, and room-temperature hydrogen gas sensors.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40423-40430"},"PeriodicalIF":4.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844275","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

Sensor-Based Gymnastics Action Recognition Using Time-Series Images and a Lightweight Feature Fusion Network

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-11 DOI: 10.1109/JSEN.2024.3492004

Wanyue Wang;Chao Lian;Yuliang Zhao;Zhikun Zhan

{"title":"Sensor-Based Gymnastics Action Recognition Using Time-Series Images and a Lightweight Feature Fusion Network","authors":"Wanyue Wang;Chao Lian;Yuliang Zhao;Zhikun Zhan","doi":"10.1109/JSEN.2024.3492004","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3492004","url":null,"abstract":"With the development of micro-electromechanical systems (MEMSs) and artificial intelligence technology, the application of wearable devices in human motion capture and recognition has gradually become a research hotspot. However, existing action recognition methods based on wearable sensors still face issues such as limited feature extraction capability and insufficient information utilization, leaving significant room for improvement in recognition accuracy. To address these challenges, this article proposes a motion recognition method based on time-series images and a lightweight feature fusion network. First, two time-series-to-image conversion methods, raw sequence image (RI) and raw sequence change image (RCI), are proposed, which fully leverage the advantages of convolutional neural networks (CNNs) in image processing. Second, a dual-channel feature fusion network is designed, enhancing the ability to extract features of gymnastic movements through the selection of backbone networks and the design of feature fusion modules. Finally, the effectiveness of the proposed method in gymnastics action recognition is validated. The experimental results show that the proposed method achieves an accuracy of 99.35%, which is at least 4.77% higher than existing machine learning methods and at least 2.03% higher than advanced deep learning methods. This demonstrates a significant improvement in recognition accuracy, proving the effectiveness and superiority of the proposed method in human action recognition. This method is expected to be extended to more application scenarios and provide technical support for the development of wearable devices.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"42573-42583"},"PeriodicalIF":4.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844325","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

Optical Fiber Humidity Sensor With Enhanced Sensitivity by Double-Sided Contact Film

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-11 DOI: 10.1109/JSEN.2024.3491817

Weichen Li;Huaping Gong;Xiangming He;Ben Xu;Chunliu Zhao

{"title":"Optical Fiber Humidity Sensor With Enhanced Sensitivity by Double-Sided Contact Film","authors":"Weichen Li;Huaping Gong;Xiangming He;Ben Xu;Chunliu Zhao","doi":"10.1109/JSEN.2024.3491817","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3491817","url":null,"abstract":"A highly sensitive humidity sensor based on open cavity fiber Fabry-Perot interferometer (FPI) with double-sided contact film is proposed. The sensor is fabricated by inserting a section of single-mode fiber (SMF) into a hollow-core fiber (HCF), and coating with polyvinyl alcohol (PVA) on the end of HCF. The difference in diameter between the two fibers is used to form an air channel so that both the left and right sides of the moisture-sensitive membrane can interact with moisture, which enhances the sensitivity. The humidity characteristics of the sensors with different cavity lengths are investigated experimentally. The results show that in the relative humidity range of 35%–85%RH, the sensor with a cavity length of \u0000<inline-formula> <tex-math>$100~mu $ </tex-math></inline-formula>\u0000m has a high sensitivity of 2.103 nm/%RH. By comparing the humidity performances of the open cavity with the closed cavity, it is verified that the sensitivity of the open cavity sensor is specifically increased to 1.56 times that of the closed cavity. The open cavity sensor exhibits high sensitivity, simple structure, and rapid response time.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 24","pages":"40988-40993"},"PeriodicalIF":4.3,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844251","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

Arc-Discharge Realized Optical Microfiber Couplers With Ultralow Temperature Coefficient for Sensing Applications

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2024-11-11 DOI: 10.1109/JSEN.2024.3492068

Mohsen Soltani-Tehrani;Hamidreza Karimi-Alavijeh;Hasan Zamani

引用次数: 0