Pub Date : 2025-01-13DOI: 10.1109/JSEN.2025.3526621
Senhui Chuai;Jieyao Deng;Haoran Li;Kai Chen;Hang Geng;Yifan Wang;Huiliang Cao
A single silicon island concave silicon microresonant pressure sensor based on frequency difference is proposed in this article. The resonator is designed for electrostatic drive and electrostatic detection, and the lateral synovial motion is taken as the working mode so that the structure has the characteristics of structural stability and small coupling, and the force mode is torque transmission. The structure was simulated by a finite element analysis, and the structure size was determined. The working range of the overall structure was $0sim 300$ kPa, and the simulation verified that the working mode order in the range did not change with the increase of pressure, ensuring the linearity of the output. The operating resonant frequencies of the resonator are 53324.15 and 54721.82 Hz. The effect of the small deflection deformation of the sensitive film and the inclination angle of the silicon island on the sensitivity of the resonator is analyzed by simulation, and the location of the silicon island is determined. The feasibility of resonator structure is confirmed by a frequency-domain response analysis, and the sensitivity of resonator can reach 50.48 Hz/kPa.
{"title":"Method for Sensitivity Improvement of MEMS Pressure Sensor: Structural Design and Optimization of Concave Resonant Pressure Sensor","authors":"Senhui Chuai;Jieyao Deng;Haoran Li;Kai Chen;Hang Geng;Yifan Wang;Huiliang Cao","doi":"10.1109/JSEN.2025.3526621","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3526621","url":null,"abstract":"A single silicon island concave silicon microresonant pressure sensor based on frequency difference is proposed in this article. The resonator is designed for electrostatic drive and electrostatic detection, and the lateral synovial motion is taken as the working mode so that the structure has the characteristics of structural stability and small coupling, and the force mode is torque transmission. The structure was simulated by a finite element analysis, and the structure size was determined. The working range of the overall structure was <inline-formula> <tex-math>$0sim 300$ </tex-math></inline-formula> kPa, and the simulation verified that the working mode order in the range did not change with the increase of pressure, ensuring the linearity of the output. The operating resonant frequencies of the resonator are 53324.15 and 54721.82 Hz. The effect of the small deflection deformation of the sensitive film and the inclination angle of the silicon island on the sensitivity of the resonator is analyzed by simulation, and the location of the silicon island is determined. The feasibility of resonator structure is confirmed by a frequency-domain response analysis, and the sensitivity of resonator can reach 50.48 Hz/kPa.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6206-6217"},"PeriodicalIF":4.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430581","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}
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 $130~mu $ 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 $90~mu $ 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 $110~mu $ m and a cavity length of $45~mu $ 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.
{"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}
This article introduces a nanoscale plasmonic sensor with dual-function capabilities for detecting both pressure and flow rate, designed using a metal-insulator–metal (MIM) bus waveguide coupled with a resonator featuring a horizontal slot and multiple stubs. The sensor demonstrates high pressure sensitivity, achieving 1100.70 nm/MPa for pressure detection, with a figure of merit (FOM) of 4.6054, and effectively measures flow rates from 58.544 to 356.43 pL/s using an optical spectrum analyzer (OSA). Finite-element method (FEM) simulations were employed to analyze the pressure-induced wavelength shifts, enhancing the sensor’s versatility in integrated sensing applications. The sensor’s compact footprint, simplicity, and ease of fabrication make it ideal for integration into laboratory-on-a-chip devices. Its dual functionality provides a novel solution for precise, real-time monitoring in biomedical and microfluidic engineering applications.
{"title":"Miniaturized Plasmonic Sensor With Dual-Function Capability for Pressure and Flow Rate Detection at Subwavelength Levels","authors":"Rummanur Rahad;Joyonta Das Joy;Md. Shakibur Rahman;Md Jahidul Hoq Emon;Mohammad Ashraful Haque","doi":"10.1109/JSEN.2025.3525968","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3525968","url":null,"abstract":"This article introduces a nanoscale plasmonic sensor with dual-function capabilities for detecting both pressure and flow rate, designed using a metal-insulator–metal (MIM) bus waveguide coupled with a resonator featuring a horizontal slot and multiple stubs. The sensor demonstrates high pressure sensitivity, achieving 1100.70 nm/MPa for pressure detection, with a figure of merit (FOM) of 4.6054, and effectively measures flow rates from 58.544 to 356.43 pL/s using an optical spectrum analyzer (OSA). Finite-element method (FEM) simulations were employed to analyze the pressure-induced wavelength shifts, enhancing the sensor’s versatility in integrated sensing applications. The sensor’s compact footprint, simplicity, and ease of fabrication make it ideal for integration into laboratory-on-a-chip devices. Its dual functionality provides a novel solution for precise, real-time monitoring in biomedical and microfluidic engineering applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 4","pages":"6176-6182"},"PeriodicalIF":4.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430358","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}
Pub Date : 2025-01-10DOI: 10.1109/JSEN.2024.3524537
Hu Xu;Ju He;Xiaomin Zhang;Yang Yu
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1109/JSEN.2025.3525759
Junjie Tong;Shenwei Shou;Hui Wang
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1109/JSEN.2024.3523323
Wei Zhang;Jialong He;Guofa Li;Jingfeng Wei
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1109/JSEN.2024.3524327
Michael V. Lipski;Sastry Kompella;Ram M. Narayanan
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1109/JSEN.2024.3502205
Zhen Yue;Yong Liu;Shi-Wei Dong;Ya Zhou Dong;Si-Hui Wu;Xin Xu;Xian Qi Lin
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-$Omega $ 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 $32times 32times 10$ 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.
{"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}
Pub Date : 2025-01-10DOI: 10.1109/JSEN.2024.3525063
Xuanzhi Peng;Pengfei Tong;Xuerong Yang;Chen Wang;An-Min Zou
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.
{"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}
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