Pub Date : 2025-09-08DOI: 10.1109/TIM.2025.3604929
Lina Qiu;Weisen Feng;Liangquan Zhong;Xianyue Song;Zuorui Ying;Jiahui Pan
Hybrid brain–computer interfaces (BCIs) integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) hold great potential, but effectively fusing their complementary information remains challenging. In this work, we propose a novel end-to-end EEG-fNIRS fusion network, EFMLNet. EFMLNet comprises two personalized feature extractors and a cross-modal mutual information learning module, designed to fully exploit the spatial and temporal characteristics of each modality. This architecture enables efficient extraction and fusion of complementary information from EEG and fNIRS signals. We evaluate EFMLNet through extensive cross-subject experiments on two public BCI datasets, motor imagery (MI) and mental arithmetic (MA), and show that its classification accuracy reaches 76.8% and 76.5%, respectively, surpassing existing fusion methods. These results demonstrate the effectiveness of EFMLNet in improving hybrid BCI performance.
{"title":"Mutual Information Learning-Based End-to-End Fusion Network for Hybrid EEG-fNIRS Brain–Computer Interface","authors":"Lina Qiu;Weisen Feng;Liangquan Zhong;Xianyue Song;Zuorui Ying;Jiahui Pan","doi":"10.1109/TIM.2025.3604929","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604929","url":null,"abstract":"Hybrid brain–computer interfaces (BCIs) integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) hold great potential, but effectively fusing their complementary information remains challenging. In this work, we propose a novel end-to-end EEG-fNIRS fusion network, EFMLNet. EFMLNet comprises two personalized feature extractors and a cross-modal mutual information learning module, designed to fully exploit the spatial and temporal characteristics of each modality. This architecture enables efficient extraction and fusion of complementary information from EEG and fNIRS signals. We evaluate EFMLNet through extensive cross-subject experiments on two public BCI datasets, motor imagery (MI) and mental arithmetic (MA), and show that its classification accuracy reaches 76.8% and 76.5%, respectively, surpassing existing fusion methods. These results demonstrate the effectiveness of EFMLNet in improving hybrid BCI performance.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The mouse retina serves as a critical model for studying human eye diseases. Optical coherence tomography (OCT) has rapidly advanced as a technique for retinal imaging, with OCT angiography (OCTA) and optoretiongraphy (ORG) emerging as significant functional extensions. High-speed, multifunctional imaging systems markedly enhance the efficiency of experiments by enabling fast and comprehensive data collection from the living mouse retina. However, integrating both high-speed operations and multiple functionalities poses challenges in data acquisition, real-time processing, postprocessing, and system complexity. To address these challenges, we developed a high-speed imaging system leveraging a high-speed swept laser source and a high-speed digitizer for data acquisition. The data acquisition software, developed with C++ and Compute Unified Device Architecture (CUDA), is optimized for rapid and efficient data capture and processing. We reduced system complexity by integrating OCT, OCTA, and ORG protocols and reprogramming postprocessing software. Our system, operating at a 400 kHz A-scan rate, supports both structural and functional imaging with a 5.0 $mu $ m axial resolution and consistent sensitivity of 53 dB across a 2 mm depth. Utilizing the temporal speckle averaging (TSA) technique, we achieved high contrast-to-noise ratio (CNR) images, allowing us to delineate retinal structures and blood vessels. For ORG analysis, we developed intensity-based and phase-based methods to evaluate the retina’s light-evoked responses. The intensity-based approach effectively detects photoreceptor elongation and scattering changes, while the phase-based method provides a highly sensitive detection with a temporal resolution of up to 1 ms, revealing subtle changes in the length of the outer segment (OS). Overall, this system, to our knowledge, offers the most comprehensive and high-speed imaging capabilities available, delivering detailed structural and functional insight into the living mouse retina.
{"title":"Development of a High-Speed Swept-Source OCT/OCTA/ORG System for Structural and Functional Imaging of the Living Mouse Retina","authors":"Yuxiang Zhou;Mingliang Zhou;Bo Wang;Xiaoting Yin;Jing Bai;Shuai Wang;Kai Neuhaus;Bernhard Baumann;Yifan Jian;Pengfei Zhang","doi":"10.1109/TIM.2025.3606015","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606015","url":null,"abstract":"The mouse retina serves as a critical model for studying human eye diseases. Optical coherence tomography (OCT) has rapidly advanced as a technique for retinal imaging, with OCT angiography (OCTA) and optoretiongraphy (ORG) emerging as significant functional extensions. High-speed, multifunctional imaging systems markedly enhance the efficiency of experiments by enabling fast and comprehensive data collection from the living mouse retina. However, integrating both high-speed operations and multiple functionalities poses challenges in data acquisition, real-time processing, postprocessing, and system complexity. To address these challenges, we developed a high-speed imaging system leveraging a high-speed swept laser source and a high-speed digitizer for data acquisition. The data acquisition software, developed with C++ and Compute Unified Device Architecture (CUDA), is optimized for rapid and efficient data capture and processing. We reduced system complexity by integrating OCT, OCTA, and ORG protocols and reprogramming postprocessing software. Our system, operating at a 400 kHz A-scan rate, supports both structural and functional imaging with a 5.0 <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m axial resolution and consistent sensitivity of 53 dB across a 2 mm depth. Utilizing the temporal speckle averaging (TSA) technique, we achieved high contrast-to-noise ratio (CNR) images, allowing us to delineate retinal structures and blood vessels. For ORG analysis, we developed intensity-based and phase-based methods to evaluate the retina’s light-evoked responses. The intensity-based approach effectively detects photoreceptor elongation and scattering changes, while the phase-based method provides a highly sensitive detection with a temporal resolution of up to 1 ms, revealing subtle changes in the length of the outer segment (OS). Overall, this system, to our knowledge, offers the most comprehensive and high-speed imaging capabilities available, delivering detailed structural and functional insight into the living mouse retina.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-05DOI: 10.1109/TIM.2025.3602566
Shuang Qiu;Guangzhe Zhao;Xueping Wang;Feihu Yan;Benwang Lin
While current unsupervised multiclass anomaly detection methods aim to build unified models for industrial applications, they face a critical dilemma between generalization capability and localization precision. Existing approaches using fixed encoders risk anomalous feature contamination during reconstruction, whereas adaptive encoders sacrifice cross-category generalization through single-class overfitting. To address this fundamental contradiction, we present manifold-constrained dynamic decoupling (MCDD) learning for unsupervised multiclass anomaly detection, which achieves dual constraints on normal feature manifolds through refinement of multiscale features from frozen encoders and robust reconstruction with learnable decoders. Specifically, we first propose the cross-hierarchy attentive bottleneck (CHAB) module, employing channel–spatial dual-domain attention gating to filter shallow texture features and deep structural features, constructing hybrid-scale normal base features. Furthermore, the noise-augmented feature expansion (NAFE) module locates critical encoder regions through attention mechanisms and injects learnable Gaussian noise during decoder upsampling, forcing reconstruction to focus on essential normal attributes. In addition, we construct the hybrid perception reasoning decoder (HPR-Decoder), integrating Visual Mamba’s long-range dependency modeling with graph attention convolution’s local correlation reasoning to achieve fine-grained generation of pixelwise anomaly maps. Experiments on MVTec AD and VisA datasets demonstrate that our method maintains superior multiclass detection performance with a single model while keeping model parameters within a reasonable range.
{"title":"Manifold-Constrained Dynamic Decoupling Learning for Unsupervised Multiclass Anomaly Detection","authors":"Shuang Qiu;Guangzhe Zhao;Xueping Wang;Feihu Yan;Benwang Lin","doi":"10.1109/TIM.2025.3602566","DOIUrl":"https://doi.org/10.1109/TIM.2025.3602566","url":null,"abstract":"While current unsupervised multiclass anomaly detection methods aim to build unified models for industrial applications, they face a critical dilemma between generalization capability and localization precision. Existing approaches using fixed encoders risk anomalous feature contamination during reconstruction, whereas adaptive encoders sacrifice cross-category generalization through single-class overfitting. To address this fundamental contradiction, we present manifold-constrained dynamic decoupling (MCDD) learning for unsupervised multiclass anomaly detection, which achieves dual constraints on normal feature manifolds through refinement of multiscale features from frozen encoders and robust reconstruction with learnable decoders. Specifically, we first propose the cross-hierarchy attentive bottleneck (CHAB) module, employing channel–spatial dual-domain attention gating to filter shallow texture features and deep structural features, constructing hybrid-scale normal base features. Furthermore, the noise-augmented feature expansion (NAFE) module locates critical encoder regions through attention mechanisms and injects learnable Gaussian noise during decoder upsampling, forcing reconstruction to focus on essential normal attributes. In addition, we construct the hybrid perception reasoning decoder (HPR-Decoder), integrating Visual Mamba’s long-range dependency modeling with graph attention convolution’s local correlation reasoning to achieve fine-grained generation of pixelwise anomaly maps. Experiments on MVTec AD and VisA datasets demonstrate that our method maintains superior multiclass detection performance with a single model while keeping model parameters within a reasonable range.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neural networks are widely applied in fault diagnosis of rotating machinery due to their powerful feature extraction and classification capabilities. However, their inherent black-box nature and reliance on predefined signal processing methods limit interpretability and adaptability in complex industrial scenarios. Knowledge distillation (KD) offers an effective approach to transfer knowledge from complex models to lightweight models while preserving the original performance of the model, but KD highly requires pretrained complex models. This article proposed a self-guided learning model (SGLM) that integrates adaptive feature extraction with knowledge transfer mechanisms, achieving both high diagnostic accuracy and physical interpretability. Specifically, the proposed SGLM employs learnable wavelet kernel functions to dynamically decompose raw vibration signals into multilevel subbands, adaptively capturing critical features for fault diagnosis. Further, the proposed SGLM eliminates dependence on external complex models by partitioning the network into hierarchical subsections, where knowledge from deeper layers can guide shallow layers. Experimental results on two datasets demonstrate the superior performance of SGLM, achieving 99.50% accuracy on the bearing dataset and 99.67% accuracy on the planetary gearbox dataset. The interpretability of SGLM is proven through three interpretability mechanisms. Meanwhile, SGLM’s effectiveness and practicality are validated via ablation, cross-validation, and efficiency analysis.
{"title":"An Interpretable Self-Guided Learning Model With Knowledge Distillation for Intelligent Fault Diagnosis of Rotating Machinery","authors":"Sha Wei;Yifeng Zhu;Qingbo He;Dong Wang;Shulin Liu;Zhike Peng","doi":"10.1109/TIM.2025.3606060","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606060","url":null,"abstract":"Neural networks are widely applied in fault diagnosis of rotating machinery due to their powerful feature extraction and classification capabilities. However, their inherent black-box nature and reliance on predefined signal processing methods limit interpretability and adaptability in complex industrial scenarios. Knowledge distillation (KD) offers an effective approach to transfer knowledge from complex models to lightweight models while preserving the original performance of the model, but KD highly requires pretrained complex models. This article proposed a self-guided learning model (SGLM) that integrates adaptive feature extraction with knowledge transfer mechanisms, achieving both high diagnostic accuracy and physical interpretability. Specifically, the proposed SGLM employs learnable wavelet kernel functions to dynamically decompose raw vibration signals into multilevel subbands, adaptively capturing critical features for fault diagnosis. Further, the proposed SGLM eliminates dependence on external complex models by partitioning the network into hierarchical subsections, where knowledge from deeper layers can guide shallow layers. Experimental results on two datasets demonstrate the superior performance of SGLM, achieving 99.50% accuracy on the bearing dataset and 99.67% accuracy on the planetary gearbox dataset. The interpretability of SGLM is proven through three interpretability mechanisms. Meanwhile, SGLM’s effectiveness and practicality are validated via ablation, cross-validation, and efficiency analysis.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The region of interest (ROI) with abundant and structured textures provides robust features in an indoor environment, which can effectively facilitate accurate simultaneous localization and mapping (SLAM). However, most existing visual SLAM systems generally treat ROI and non-ROI uniformly, resulting in ineffective employment of ROI. To meet this gap, we propose a robust saliency-driven visual SLAM system for indoor environments, coined RSD-SLAM. It can increase the focus on valuable ROI with the saliency maps obtained from a novel saliency prediction (SP) model. Specifically, we first design a saliency map construction method for visual SLAM, enabling the SP model to accurately describe ROI, which generates the first indoor SP dataset integrating geometric, semantic, depth, and low-level visual information. Second, we develop a global stability constraint module for the SP model to enable the capability of keeping temporal consistency and illumination invariance. Third, we design a saliency map-based hybrid saliency-driven mechanism to increase the focus of the system on ROI. At the front end of the system, an adaptive feature-point extraction algorithm extracts more robust feature-points from the ROI, and a saliency entropy-based keyframe selection algorithm selects keyframes with the saliency value distribution of feature points. At the back end, a dynamic weighted bundle adjustment (BA) optimization algorithm heavily weights the map points of the ROI. Last, the particular focus on ROI results in a robust and accurate location. Extensive experiments, conducted on the EuRoC and TUM RGB-D datasets as well as in simulation environments, demonstrate that the proposed RSD-SLAM significantly outperforms the state-of-the-art in robustness and accuracy.
{"title":"RSD-SLAM: A Robust Saliency-Driven Visual SLAM System in Indoor Environments","authors":"Xu Lu;Cheng Zhou;Kejie Zhong;Hanyuan Huang;Zhike Chen;Guang'an Luo;Jun Liu;Xinyu Wu","doi":"10.1109/TIM.2025.3606037","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606037","url":null,"abstract":"The region of interest (ROI) with abundant and structured textures provides robust features in an indoor environment, which can effectively facilitate accurate simultaneous localization and mapping (SLAM). However, most existing visual SLAM systems generally treat ROI and non-ROI uniformly, resulting in ineffective employment of ROI. To meet this gap, we propose a robust saliency-driven visual SLAM system for indoor environments, coined RSD-SLAM. It can increase the focus on valuable ROI with the saliency maps obtained from a novel saliency prediction (SP) model. Specifically, we first design a saliency map construction method for visual SLAM, enabling the SP model to accurately describe ROI, which generates the first indoor SP dataset integrating geometric, semantic, depth, and low-level visual information. Second, we develop a global stability constraint module for the SP model to enable the capability of keeping temporal consistency and illumination invariance. Third, we design a saliency map-based hybrid saliency-driven mechanism to increase the focus of the system on ROI. At the front end of the system, an adaptive feature-point extraction algorithm extracts more robust feature-points from the ROI, and a saliency entropy-based keyframe selection algorithm selects keyframes with the saliency value distribution of feature points. At the back end, a dynamic weighted bundle adjustment (BA) optimization algorithm heavily weights the map points of the ROI. Last, the particular focus on ROI results in a robust and accurate location. Extensive experiments, conducted on the EuRoC and TUM RGB-D datasets as well as in simulation environments, demonstrate that the proposed RSD-SLAM significantly outperforms the state-of-the-art in robustness and accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-20"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606041
Yinghua Han;Yuan Li;Zilong Wang;Qiang Zhao
Nonintrusive load monitoring (NILM) enables the acquisition of appliance switch states and power consumption information, providing valuable References for energy conservation and emission reduction, making it an important tool for promoting appliance energy efficiency. However, existing NILM methods face significant issues in terms of result interpretability and label dependence. To address these challenges, this article proposes a semi-supervised learning method based on multiscale shapelet contrastive learning. By introducing shapelets, the model captures the current waveform differences generated by different appliances under the same voltage, thereby solving the interpretability problem. Furthermore, some appliances exhibit multiple waveforms due to variations in operating states and supplier differences. Single-scale shapelets are difficult to capture the diverse current information of these appliances. Therefore, this article proposes multiscale shapelets to enhance the discriminative features of different currents for the load and improve the consistency information between different scales, thereby enabling more effective learning of representative load shapelets. To reduce the reliance on a large amount of labeled data, this article adopts contrastive learning, which enhances sample views and performs contrastive optimization to maximize similarity within the same load and minimize similarity between different loads, guiding the model to learn more representative shapelets. Finally, a small amount of labeled data is used to guide the classifier to complete the load recognition task. The experimental results demonstrate that the proposed method not only effectively combines multiscale features to improve load recognition performance but also exhibits good interpretability.
{"title":"Multiscale Shapelet Contrastive Learning for Nonintrusive Load Monitoring","authors":"Yinghua Han;Yuan Li;Zilong Wang;Qiang Zhao","doi":"10.1109/TIM.2025.3606041","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606041","url":null,"abstract":"Nonintrusive load monitoring (NILM) enables the acquisition of appliance switch states and power consumption information, providing valuable References for energy conservation and emission reduction, making it an important tool for promoting appliance energy efficiency. However, existing NILM methods face significant issues in terms of result interpretability and label dependence. To address these challenges, this article proposes a semi-supervised learning method based on multiscale shapelet contrastive learning. By introducing shapelets, the model captures the current waveform differences generated by different appliances under the same voltage, thereby solving the interpretability problem. Furthermore, some appliances exhibit multiple waveforms due to variations in operating states and supplier differences. Single-scale shapelets are difficult to capture the diverse current information of these appliances. Therefore, this article proposes multiscale shapelets to enhance the discriminative features of different currents for the load and improve the consistency information between different scales, thereby enabling more effective learning of representative load shapelets. To reduce the reliance on a large amount of labeled data, this article adopts contrastive learning, which enhances sample views and performs contrastive optimization to maximize similarity within the same load and minimize similarity between different loads, guiding the model to learn more representative shapelets. Finally, a small amount of labeled data is used to guide the classifier to complete the load recognition task. The experimental results demonstrate that the proposed method not only effectively combines multiscale features to improve load recognition performance but also exhibits good interpretability.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606065
Siwoong Park;Chan Il Yeo;Young Soon Heo;Hyoung-Jun Park
Free-space optical communication (FSOC) provides secure, high-speed connectivity essential for modern networks, but is highly susceptible to severe weather-induced attenuation. This study evaluates a full-duplex mobile FSOC system under controlled heavy rainfall and thick fog using the advanced facilities at the Yeoncheon SOC Demonstration Research Center. Experimental results confirm stable 2.3-Gb/s data transmission at 35-mm/h rainfall and 10-m visibility, demonstrating system resilience. Comparative analysis with existing weather attenuation models reveals their significant limitations, especially under extreme conditions, highlighting the need for model refinement. These findings offer valuable insights for advancing FSOC performance modeling and support the deployment of FSOC in next-generation communication infrastructures, including mobile platforms, smart cities, and disaster recovery networks.
{"title":"Measurement-Based Evaluation of a Mobile Free-Space Optical Communication System Under Controlled Severe Weather Conditions","authors":"Siwoong Park;Chan Il Yeo;Young Soon Heo;Hyoung-Jun Park","doi":"10.1109/TIM.2025.3606065","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606065","url":null,"abstract":"Free-space optical communication (FSOC) provides secure, high-speed connectivity essential for modern networks, but is highly susceptible to severe weather-induced attenuation. This study evaluates a full-duplex mobile FSOC system under controlled heavy rainfall and thick fog using the advanced facilities at the Yeoncheon SOC Demonstration Research Center. Experimental results confirm stable 2.3-Gb/s data transmission at 35-mm/h rainfall and 10-m visibility, demonstrating system resilience. Comparative analysis with existing weather attenuation models reveals their significant limitations, especially under extreme conditions, highlighting the need for model refinement. These findings offer valuable insights for advancing FSOC performance modeling and support the deployment of FSOC in next-generation communication infrastructures, including mobile platforms, smart cities, and disaster recovery networks.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606022
Qian Wang;Yong Ye;Zhe Ma;Juan Xia;Xiaoting Lin;Meiqi Zhang;Zikang Zheng;Jun Li
Soil moisture is one of the key factors in agricultural production. Efficient and accurate acquisition of the soil moisture content (SMC) is essential for ensuring the proper functioning of agricultural activities. However, conventional SMC detection methods fail to meet the basic requirements for moisture detection in field environments, including real-time efficiency, cost-effectiveness, and reliability. The aim of this study was to evaluate the effectiveness of a portable acoustic detection device with temperature compensation for soil moisture detection in field environments. A soil acoustic measurement and data acquisition system was developed in this study, utilizing the pulse transmission method while considering the impact of temperature on acoustic velocity measurements. A temperature gradient of $5~^{circ }$ was set within a range of $5~^{circ }$ C–$40~^{circ }$ C while maintaining a relative humidity of 50%. The relationships among the SMC, soil temperature, and acoustic velocity were experimentally analyzed, and a temperature-compensated SMC acoustic prediction model was developed via multivariable nonlinear regression. Through hardware selection, software development, and system integration, a portable acoustic soil moisture detection device with temperature compensation was successfully developed. To assess the performance of the device, tests were conducted to evaluate its acoustic velocity detection performance, waterproof capability, and effective detection range. A 25-day field experiment was carried out in an orchard, during which the soil temperature ranged from $9.0~^{circ }$ C to $24.5~^{circ }$ C, and the results indicated that the average relative error between the device’s SMC measurements and the oven-drying method was 5.64%. When the SMC exceeded 0.275 g/g, the maximum relative error was 3.91%.
土壤水分是影响农业生产的关键因素之一。有效、准确地获取土壤水分对确保农业活动的正常进行至关重要。然而，传统的SMC检测方法无法满足现场环境中水分检测的实时性、高效性、高性价比、高可靠性等基本要求。本研究的目的是评估具有温度补偿的便携式声波探测装置在田间环境中土壤湿度检测的有效性。利用脉冲传输方法，考虑温度对声速测量的影响，研制了一套土壤声测量与数据采集系统。温度梯度为$5~^{circ}$ C - $40~^{circ}$ C，同时保持相对湿度为50%。实验分析了SMC与土壤温度、声速之间的关系，并利用多变量非线性回归建立了温度补偿SMC声学预测模型。通过硬件选型、软件开发和系统集成，研制成功了具有温度补偿功能的便携式声波土壤湿度检测装置。为了评估该装置的性能，对其声速探测性能、防水能力和有效探测范围进行了测试。在土壤温度为$9.0~ $ {circ}$ C ~ $24.5~ $ {circ}$ C的果园中进行了25 d的田间试验，结果表明，该装置的SMC测量值与烘箱干燥方法的平均相对误差为5.64%。当SMC大于0.275 g/g时，最大相对误差为3.91%。
{"title":"Development of a Portable Acoustic Soil Moisture Detection Device With Temperature Compensation","authors":"Qian Wang;Yong Ye;Zhe Ma;Juan Xia;Xiaoting Lin;Meiqi Zhang;Zikang Zheng;Jun Li","doi":"10.1109/TIM.2025.3606022","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606022","url":null,"abstract":"Soil moisture is one of the key factors in agricultural production. Efficient and accurate acquisition of the soil moisture content (SMC) is essential for ensuring the proper functioning of agricultural activities. However, conventional SMC detection methods fail to meet the basic requirements for moisture detection in field environments, including real-time efficiency, cost-effectiveness, and reliability. The aim of this study was to evaluate the effectiveness of a portable acoustic detection device with temperature compensation for soil moisture detection in field environments. A soil acoustic measurement and data acquisition system was developed in this study, utilizing the pulse transmission method while considering the impact of temperature on acoustic velocity measurements. A temperature gradient of <inline-formula> <tex-math>$5~^{circ }$ </tex-math></inline-formula> was set within a range of <inline-formula> <tex-math>$5~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$40~^{circ }$ </tex-math></inline-formula>C while maintaining a relative humidity of 50%. The relationships among the SMC, soil temperature, and acoustic velocity were experimentally analyzed, and a temperature-compensated SMC acoustic prediction model was developed via multivariable nonlinear regression. Through hardware selection, software development, and system integration, a portable acoustic soil moisture detection device with temperature compensation was successfully developed. To assess the performance of the device, tests were conducted to evaluate its acoustic velocity detection performance, waterproof capability, and effective detection range. A 25-day field experiment was carried out in an orchard, during which the soil temperature ranged from <inline-formula> <tex-math>$9.0~^{circ }$ </tex-math></inline-formula>C to <inline-formula> <tex-math>$24.5~^{circ }$ </tex-math></inline-formula>C, and the results indicated that the average relative error between the device’s SMC measurements and the oven-drying method was 5.64%. When the SMC exceeded 0.275 g/g, the maximum relative error was 3.91%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606051
Yan Wu;Ting Xue;Songlin Li;Zhuping Li;Bin Wu
The precise measurement of gas–liquid two-phase flow rate is crucial for ensuring the safety and efficiency of industrial processes. However, achieving accurate measurement remains a significant challenge. A novel method for measuring flow rates of horizontal gas–liquid two-phase flow employing optical carrier-based microwave interferometry (OCMI) technology and convolutional neural network (CNN) architecture is presented in this article, marking the first application of OCMI in gas–liquid flow rate measurement. Leveraging the distributed measurement capabilities of OCMI, the method captures the distributed information of fluid behavior along the optical fiber and gathers more comprehensive data through the combination of global and distributed interference spectra. The input data are processed utilizing dimensionality reduction techniques, including Pearson correlation and principal component analysis (PCA), and small sample sizes are expanded through data augmentation to improve the accuracy and generalization ability of the model. A decomposed CNN architecture is constructed, with convolutions performed separately along the sequence and feature dimensions, effectively overcoming the limitations of traditional demodulation methods in information extraction. The experimental results demonstrate that the proposed method accurately measures gas and liquid flow rates, offering significant advantages over other variants.
{"title":"Two-Phase Flow Rate Measurement Utilizing Optical Carrier-Based Microwave Interferometry Integrated With Convolutional Neural Network","authors":"Yan Wu;Ting Xue;Songlin Li;Zhuping Li;Bin Wu","doi":"10.1109/TIM.2025.3606051","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606051","url":null,"abstract":"The precise measurement of gas–liquid two-phase flow rate is crucial for ensuring the safety and efficiency of industrial processes. However, achieving accurate measurement remains a significant challenge. A novel method for measuring flow rates of horizontal gas–liquid two-phase flow employing optical carrier-based microwave interferometry (OCMI) technology and convolutional neural network (CNN) architecture is presented in this article, marking the first application of OCMI in gas–liquid flow rate measurement. Leveraging the distributed measurement capabilities of OCMI, the method captures the distributed information of fluid behavior along the optical fiber and gathers more comprehensive data through the combination of global and distributed interference spectra. The input data are processed utilizing dimensionality reduction techniques, including Pearson correlation and principal component analysis (PCA), and small sample sizes are expanded through data augmentation to improve the accuracy and generalization ability of the model. A decomposed CNN architecture is constructed, with convolutions performed separately along the sequence and feature dimensions, effectively overcoming the limitations of traditional demodulation methods in information extraction. The experimental results demonstrate that the proposed method accurately measures gas and liquid flow rates, offering significant advantages over other variants.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606064
Xiaotao Han;Qing Wang;Bo Zhang;Jiujing Xu;Haonan Cui;Zhenyu Yang
Machine learning is frequently used to detect multipath (MP) and nonline-of-sight (NLOS) signals in indoor pseudolite systems. Signal information redundancy and how to find algorithm’s optimal hyperparameters pose significant challenges to this task. To this end, a bi-layer optimization scheme (BOS) is proposed in this article. In the first layer, a result-data-driven principal component analysis (PCA) adjustment strategy is proposed. This strategy eliminates the correlation among the feature parameters of the original pseudolite signals and constructs a feature space with optimal dimensionality. It is contributing to reducing information redundancy in signals. In the second layer, an enhanced dung beetle optimizer (DBO) is proposed. The algorithm incorporates the good point set, opposition-based learning, and CauchyGauss Mutation strategies, and has been demonstrated to achieve faster convergence and better global optimization capability. It is employed for the adaptive selection of hyperparameters. With BOS optimization, the classification accuracy of the support vector machine (SVM) algorithm improved by 6.0% and 6.1% on the two datasets, respectively, while the classification precision of line-of-sight (LOS) signals improved by an average of 12.3%. This confirms the applicability and practical value of the BOS in indoor pseudolite systems.
{"title":"A Bi-Layer Optimization Scheme for Enhanced Detection of Indoor Pseudolite Interference Signals","authors":"Xiaotao Han;Qing Wang;Bo Zhang;Jiujing Xu;Haonan Cui;Zhenyu Yang","doi":"10.1109/TIM.2025.3606064","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606064","url":null,"abstract":"Machine learning is frequently used to detect multipath (MP) and nonline-of-sight (NLOS) signals in indoor pseudolite systems. Signal information redundancy and how to find algorithm’s optimal hyperparameters pose significant challenges to this task. To this end, a bi-layer optimization scheme (BOS) is proposed in this article. In the first layer, a result-data-driven principal component analysis (PCA) adjustment strategy is proposed. This strategy eliminates the correlation among the feature parameters of the original pseudolite signals and constructs a feature space with optimal dimensionality. It is contributing to reducing information redundancy in signals. In the second layer, an enhanced dung beetle optimizer (DBO) is proposed. The algorithm incorporates the good point set, opposition-based learning, and CauchyGauss Mutation strategies, and has been demonstrated to achieve faster convergence and better global optimization capability. It is employed for the adaptive selection of hyperparameters. With BOS optimization, the classification accuracy of the support vector machine (SVM) algorithm improved by 6.0% and 6.1% on the two datasets, respectively, while the classification precision of line-of-sight (LOS) signals improved by an average of 12.3%. This confirms the applicability and practical value of the BOS in indoor pseudolite systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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