IEEE Sensors Journal最新文献_第4页

PMMA Microsphere-Based Fabry-Perot Fiber-Optic Humidity Sensor

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2025.3525655

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

{"title":"PMMA Microsphere-Based Fabry-Perot Fiber-Optic Humidity Sensor","authors":"Xiangming He;Huaping sGong;Weichen Li;Ben Xu;Chunliu Zhao","doi":"10.1109/JSEN.2025.3525655","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525655","url":null,"abstract":"Polymethyl methacrylate (PMMA) microsphere-based Fabry-Perot (F-P) fiber-optic humidity sensor is proposed. A hollow-core fiber (HCF) is spliced with a single-mode fiber (SMF), and a PMMA microsphere is inserted into the HCF. Three sensors with different PMMA microsphere sizes (110, 120, and <inline-formula> <tex-math>$130~mu $ </tex-math></inline-formula>m) were fabricated by using PMMA microsphere as a moisture-sensitive material, and the moisture-sensing characteristics of these sensors with different cavity lengths (45, 60, 75, and <inline-formula> <tex-math>$90~mu $ </tex-math></inline-formula>m) were measured in the range of relative humidity (RH) from 35%RH to 95%RH. The results show that the sensor with a PMMA microsphere size of <inline-formula> <tex-math>$110~mu $ </tex-math></inline-formula>m and a cavity length of <inline-formula> <tex-math>$45~mu $ </tex-math></inline-formula>m has the best sensitivity to humidity of 0.074 nm/%RH. Its repeatability, stability, response time, and temperature effect are also investigated. This is the first fiber-optic humidity sensor with an F-P structure using PMMA microsphere as filler instead of moisture-sensitive film or gel. Compared to moisture-sensitive film or gel that are prone to aging and hardening leading to a short service life and are prone to deliquescence under high humidity, the sensor proposed in this article has the advantages of wide measurement range that can be applied to high humidity measurement, fast response time, good long-term stability, and long service life.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6396-6403"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446278","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

Miniaturized Plasmonic Sensor With Dual-Function Capability for Pressure and Flow Rate Detection at Subwavelength Levels

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2025.3525968

Rummanur Rahad;Joyonta Das Joy;Md. Shakibur Rahman;Md Jahidul Hoq Emon;Mohammad Ashraful Haque

引用次数: 0

Space Grafted Velocity 3-D Boat Detection for Unmanned Surface Vessel via mmWave Radar and Camera

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2024.3524537

Hu Xu;Ju He;Xiaomin Zhang;Yang Yu

{"title":"Space Grafted Velocity 3-D Boat Detection for Unmanned Surface Vessel via mmWave Radar and Camera","authors":"Hu Xu;Ju He;Xiaomin Zhang;Yang Yu","doi":"10.1109/JSEN.2024.3524537","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524537","url":null,"abstract":"Recently, unmanned surface vessels (USVs) have played an increasingly important role in autonomous exploration, and boat detection is an important task for USVs. While most existing boat detection methods focus on 2-D detection, 3-D detection that provides valuable spatial direction for moving target estimation has not been studied in the boat detection field. However, 3-D boat detection on water surfaces faces challenging problems, such as small sizes of detected targets and diverse moving directions. Considering that traditional LiDAR-based 3-D boat detection methods require high hardware costs, we fuse millimeter-wave (MMW) radar and high semantic camera to achieve low-cost and high-quality 3-D boat detection. We propose a novel radar-camera fusion boat 3-D detection model named RCBDet. The proposed RCBDet uses a new dual radar encoder and first introduces Doppler speed information from MMW radar into neural network to overcome sparse radar points. A new radar-camera attention module is designed to effectively combine camera features, radar spatial features, and radar velocity features, encapsulating not only shape and semantic attributes but also spatial orientation information. In our collected boat 3-D detection dataset, RCBDet achieves state-of-the-art accuracy compared with other single-modality baselines and radar-camera fusion baselines. Moreover, we conducted comprehensive ablation experiments to validate the efficacy of the designed modules. The experimental results demonstrated that the proposed radar-camera fusion model effectively fuses MMW radar features and camera features.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7642-7654"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438360","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 Dictionary-Enhanced Clustering Compressive Sensing Routing Protocol for Large-Scale WSNs

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2025.3525759

Junjie Tong;Shenwei Shou;Hui Wang

{"title":"A Dictionary-Enhanced Clustering Compressive Sensing Routing Protocol for Large-Scale WSNs","authors":"Junjie Tong;Shenwei Shou;Hui Wang","doi":"10.1109/JSEN.2025.3525759","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525759","url":null,"abstract":"Designing an efficient energy-saving routing protocol to optimize network lifespan is a pivotal challenge in large-scale wireless sensor networks (WSNs). In this article, a dictionary-enhanced clustering compressive sensing routing (DEC2R) protocol is designed to conserve energy and provide network load balancing. In DEC2R, the optimal number of clusters for each round is accurately calculated based on the analysis of the optimal cluster size. Through learning the sparse dictionary method, a low-coherence sensing matrix is constructed for data transmission and compression. On this basis, the optimal cluster heads (CHs) are selected based on a cost function (including remaining energy and distance). Non-CHs determine whether to join a cluster based on energy and distance, ultimately completing the clustering formation. In each cluster, data nodes multiply the data by measurement coefficients and transmit it to the CH via the shortest path. Between clusters, each CH forwards the data packet to the next CH along the transmission path. In the end, the sink node receives the entire compressed packets. The simulation results demonstrate the effectiveness of DEC2R. Compared with LEACH, PEGASIS, CDG, and EIREC protocols, dictionary-enhanced clustering compressive sensing routing (DEC2R) significantly extends the lifetime of the network and improves energy efficiency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7445-7456"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446347","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 Novel Compound Fault Diagnosis Method for Rotating Machinery Based on Dynamic Adaptive MWPE and Dual-Graph Regularization Strategy

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2024.3523323

Wei Zhang;Jialong He;Guofa Li;Jingfeng Wei

{"title":"A Novel Compound Fault Diagnosis Method for Rotating Machinery Based on Dynamic Adaptive MWPE and Dual-Graph Regularization Strategy","authors":"Wei Zhang;Jialong He;Guofa Li;Jingfeng Wei","doi":"10.1109/JSEN.2024.3523323","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3523323","url":null,"abstract":"Detection of compound fault in rotating machinery under complex operation environment is a challenge in fault diagnosis. Machine learning occupies an important position in the field of fault diagnosis due to its broad applicability and high efficiency, while feature extraction and feature selection are key aspects in the machine learning process. As a result, this article aims to enhance the performance of compound fault diagnosis methods by improving these two aspects. First, to address the nonlinearity and nonstationarity of vibration signals under variable operation conditions, this article proposes a dynamic adaptive multiscale weighted permutation entropy (DAMWPE) method. In addition, this article decomposes the vibration signals with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and extracts the sensitive intrinsic modal functions’ (IMFs’) DAMWPE (SI-DAMWPE) as the initial feature vector, which more accurately reveals the intrinsic time-scale characteristics of the vibration signals. Second, to address the problem that most feature selection methods ignore the correlation between faults, this article proposes a novel multilabel feature selection method called dual-graph regularization considering feature redundancy feature selection (DRFRFS). The method employs the feature and label graph regularization strategy to comprehensively capture the relationship between fault labels and features. Finally, the top-ranked features from the DRFRFS method are selected and fed into a multilabel k-nearest neighbor (MLKNN) classifier to complete the diagnosis task. By comparing six multilabel classification evaluation metrics for two rotating machinery cases, the results show that the proposed method possesses high accuracy and stability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6850-6868"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446220","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

Gradient-Based Optimization of Coherent Distributed Arrays

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2024.3524327

Michael V. Lipski;Sastry Kompella;Ram M. Narayanan

{"title":"Gradient-Based Optimization of Coherent Distributed Arrays","authors":"Michael V. Lipski;Sastry Kompella;Ram M. Narayanan","doi":"10.1109/JSEN.2024.3524327","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3524327","url":null,"abstract":"In a coherent communication system consisting of an open-loop distributed transmit array sending messages to a distributed receive array, the combined transmit-receive gain is characterized by the coherent communication gain (CCG). We consider the problem of optimizing CCG using the positions of the individual transmitter and receiver nodes as well as the beam angle of the transmit array as degrees of freedom. We focus on the use of gradient descent to find locally optimal configurations for node positions, which is motivated by two observations: first, the NP-hardness of the problem precludes an exhaustive search for the globally optimal configuration of node positions; and second, the positions of the network nodes are likely not arbitrary. That is, the initial, nonoptimized node placement is intentional and is determined by higher-layer network objectives. The hypothesis is that the CCG of a communication network can be improved in a deterministic fashion using the steepest descent algorithm to make relatively small adjustments to node positions. We develop the closed-form expressions for the rate of change of CCG with respect to node positions and transmit array beam angle. Next, we use the expressions to implement a spherical quadratic steepest descent (SQSD) algorithm and use simulations to test SQSD alongside pattern search and particle swarm optimization to determine theoretical gain improvements achieved by the algorithms, as well as the expected average node displacement.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7417-7430"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446287","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

Efficient and Highly Integrated Millimeter-Wave Receiver for Simultaneous Wireless Information and Power Transfer in Sensor Networks

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2024.3502205

Zhen Yue;Yong Liu;Shi-Wei Dong;Ya Zhou Dong;Si-Hui Wu;Xin Xu;Xian Qi Lin

{"title":"Efficient and Highly Integrated Millimeter-Wave Receiver for Simultaneous Wireless Information and Power Transfer in Sensor Networks","authors":"Zhen Yue;Yong Liu;Shi-Wei Dong;Ya Zhou Dong;Si-Hui Wu;Xin Xu;Xian Qi Lin","doi":"10.1109/JSEN.2024.3502205","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3502205","url":null,"abstract":"This article presents a high-efficiency and integrated millimeter-wave (mm-Wave) rectenna, which aims to provide stable wireless power supply for small wireless sensor networks in the Internet of Things (IoT) environments. The antenna adopts slot-coupled feeding, with the feed line and the radiating patch located on opposite sides of the ground plane, and it can effectively reduce the interference between the antenna and circuitry and increase the effective radiating area. Two Schottky diodes are symmetric parallel mounted in rectifier, which can reduce the power losses caused by the junction resistance and improve the power conversion efficiency (PCE). The measurement results show that the rectenna has achieved a maximum gain of 19.2 dBi and PCE of 66.1% with 188-<inline-formula> <tex-math>$Omega $ </tex-math></inline-formula> load at 21-dBm input power. Additionally, by using the tiled stacking technique, a dc-dc power management module, a Bluetooth transmitting module and temperature, humidity, and light sensors were integrated with the rectenna to construct a highly integrated mm-Wave power receiver with the dimensions of only <inline-formula> <tex-math>$32times 32times 10$ </tex-math></inline-formula> mm3. This receiver can stably receive RF power from external wireless power source and drive the sensors and information transmitter module to return data in real time, achieving simultaneous wireless power and information transfer (SWIPT), which demonstrate significant application potential in IoT scenarios.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6534-6540"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446346","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

IDMF-VINS: Improving Visual-Inertial SLAM for Complex Dynamic Environments With Motion Consistency and Feature Filtering

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2024.3525063

Xuanzhi Peng;Pengfei Tong;Xuerong Yang;Chen Wang;An-Min Zou

{"title":"IDMF-VINS: Improving Visual-Inertial SLAM for Complex Dynamic Environments With Motion Consistency and Feature Filtering","authors":"Xuanzhi Peng;Pengfei Tong;Xuerong Yang;Chen Wang;An-Min Zou","doi":"10.1109/JSEN.2024.3525063","DOIUrl":"https://doi.org/10.1109/JSEN.2024.3525063","url":null,"abstract":"The detection of dynamic feature points presents a substantial challenge to dynamic scene analysis for simultaneous localization and mapping (SLAM). Conventional methods based on semantic segmentation, which are capable of producing complete object outlines, are expensive and not compatible with applications that run in real time. This study proposes a novel method combining YOLOv5 object detection information with motion consistency results to accurately differentiate between dynamic feature points and the corresponding states of predefined objects. To roughly distinguish background and dynamic objects within the object detection bounding boxes, a deep clustering approach is employed. The cluster centers have been optimized through iterative computation. In addition, a depth-based anomaly outlier filtering algorithm is employed to exclude stationary points in extremely close proximity to dynamic objects, thereby enhancing the capacity to distinguish between dynamic objects. The proposed method effectively minimizes the distortion resulting from dynamic feature points throughout pose estimation, which enhances the overall performance of the system while preserving a comparable quantity of feature points.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6995-7005"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430467","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

FIAFusion: A Feedback-Based Illumination-Adaptive Infrared and Visible Image Fusion Method

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-10 DOI: 10.1109/JSEN.2025.3525700

Shiquan Ding;Jun Huang;Zhanchuan Cai;Yong Ma;Kangle Wu;Fan Fan

{"title":"FIAFusion: A Feedback-Based Illumination-Adaptive Infrared and Visible Image Fusion Method","authors":"Shiquan Ding;Jun Huang;Zhanchuan Cai;Yong Ma;Kangle Wu;Fan Fan","doi":"10.1109/JSEN.2025.3525700","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525700","url":null,"abstract":"Infrared (IR) and visible (VI) image fusion enables to combine the strengths of both original images adequately, retaining essential target information and abundant detailed textures. Existing fusion methods mainly cater to well-illuminated scenes. Although some researchers have explored complex scenes, there are still some unresolved issues, such as suboptimal lighting levels and loss of local details. To overcome these issues, we introduce a novel method named FIAFusion. FIAFusion is structured into three primary components: initially, the illumination-adaptive network (IAN) adjusts the illumination of the original VI image adaptively. Subsequently, the fusion network (FUN) efficiently merges the complementary information from the original IR image and the illumination-adapted VI image into a fused image of high visual quality. To achieve an ideal illumination level in the fused image, the feedback network (FEN) is designed to feedback on the illumination information of the fused image to both IAN and FUN, guiding the illumination correction to facilitate mutual promotion between illumination adaptation and fusion process effectively. Extensive comparative and supplementary experiments conducted on LLVIP and MSRS datasets indicate that our method surpasses state-of-the-art (SOTA) IR and VI image fusion methods. Moreover, our method demonstrates significant performance improvements in pedestrian detection tasks.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7667-7680"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438325","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

Motor Failure Prediction Using Hybrid Entropy and Combined Forecasting Model

IF 4.3 2区综合性期刊

IEEE Sensors Journal Pub Date : 2025-01-09 DOI: 10.1109/JSEN.2025.3525541

Jiangtian Yang;Xiaoqian Duo;Mingguang Liu

{"title":"Motor Failure Prediction Using Hybrid Entropy and Combined Forecasting Model","authors":"Jiangtian Yang;Xiaoqian Duo;Mingguang Liu","doi":"10.1109/JSEN.2025.3525541","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525541","url":null,"abstract":"The fault prognosis of the motor plays a key role in reducing unplanned maintenance and improving machine reliability and safety. The main problem of industrial applications lies in usually only a small amount of operation data of motors is available. Establishing an effective forecasting model is a challenging task. A novel prognostics approach based on the hybrid entropy of motor current signal and a combined forecasting model is proposed. First, the wavelet packet energy entropy and Renyi spectrum entropy are extracted from the online motor current signal and then are integrated into a unified one. Since the hybrid entropy describes the change in current signals from the views of concentration degree of time-frequency-domain energy and the uniformity degree of spectrum distribution systematically, it represents motor working conditions accurately. Next, a hybrid approach based on wavelet transform, autoregressive integrated moving average (ARIMA), and improved GM(1, 1) model is employed. The time series of entropy values was decomposed into different trend items by wavelet transform, and the growth trend and random trend are described by the background value optimization GM(1, 1) model and ARIMA model, respectively. Finally, the prediction output was obtained by wavelet reconstruction. Industrial experiment results demonstrate the effectiveness of the proposed approach for motor fault prediction based on small amounts of data.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"7006-7014"},"PeriodicalIF":4.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430492","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