IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Multimodal Semi-Supervised Domain Adaptation Using Cross-Modal Learning and Joint Distribution Alignment for Cross-Subject Emotion Recognition","authors":"Magdiel Jiménez-Guarneros;Gibran Fuentes-Pineda;Jonas Grande-Barreto","doi":"10.1109/TIM.2025.3551924","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551924","url":null,"abstract":"Multimodal physiological data from electroencephalogram (EEG) and eye movement (EM) signals have been shown to be useful in effectively recognizing human emotional states. Unfortunately, individual differences reduce the applicability of existing multimodal classifiers to new users, as low performance is usually observed. Indeed, existing works mainly focus on multimodal domain adaptation from a labeled source domain and unlabeled target domain to address the mentioned problem, transferring knowledge from known subjects to new one. However, a limited set of labeled target data has not been effectively exploited to enhance the knowledge transfer between subjects. In this article, we propose a multimodal semi-supervised domain adaptation (SSDA) method, called cross-modal learning and joint distribution alignment (CMJDA), to address the limitations of existing works, following three strategies: 1) discriminative features are exploited per modality through independent neural networks; 2) correlated features and consistent predictions are produced between modalities; and 3) marginal and conditional distributions are encouraged to be similar between the labeled source data, limited labeled target data, and abundant unlabeled target data. We conducted comparison experiments on two public benchmarks for emotion recognition, SEED-IV and SEED-V, using leave-one-out cross-validation (LOOCV). Our proposal achieves an average accuracy of 92.50%–96.13% across the three available sessions on SEED-IV and SEED-V, only including three labeled target samples per class from the first recorded trial.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740388","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}

{"title":"Pedestrian Trajectory Projection Based on Adaptive Interpolation Factor Linear Interpolation Quaternion Attitude Estimation Method","authors":"Ling-Feng Shi;Yi-Fan Dai;Hao Yin;Yifan Shi","doi":"10.1109/TIM.2025.3551847","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551847","url":null,"abstract":"In recent years, with the continuous development of the Internet of Things (IoT) technology, smart devices, such as smart homes and smart phones, have been widely used, so the demand for providing specific location services has gradually increased, while the traditional positioning service technology based on satellite information is difficult to provide reliable accuracy in indoor environments due to various constraints; meanwhile, because of the specific conditions of use, positioning methods requiring the presetting of auxiliary equipment will be ineffective. In order to solve these problems, autonomous indoor positioning technology using only a single sensor has an irreplaceable role. This article takes a low-cost, high-precision indoor positioning technique based on step heading style using only a single magnetic angular rate and gravity (MARG) sensor and proposes an interpolation factor-adaptive quaternionic attitude solving algorithm based on linear interpolation (LERP). The method uses a motion state metric matrix for describing the intensity of the current motion state and calculates the interpolation factor adaptively using the motion state metric matrix. Based on the characteristic that different motion states have different optimal interpolation factors, the method adopts the adaptive updating calculation method to automatically update the interpolation factors, which gets rid of the problems that may arise from the pregiven interpolation factors and extends the scope of the method, and the corresponding heading angle calculation can be carried out for all the motion states, and finally, by combining the proposed heading angle estimation method with the high-precision step segmentation method and step length estimation method, a step-length heading-based positioning method is proposed, which achieves an average positioning accuracy of 0.3825 m. Moreover, only one MARG sensor fixed at the waist is used, and the cost of the device is very low at only U.S. 14.9, which satisfies the requirements of high accuracy and low cost at the same time.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726548","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}

{"title":"Fast Atmospheric Aerosol Size and Shape Imaging Instrument: Design, Calibration, and Intelligent Interaction","authors":"Li Dong;Yong Han;Maohai Hu;Yurong Zhang;Qicheng Zhou","doi":"10.1109/TIM.2025.3551849","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551849","url":null,"abstract":"Atmospheric aerosol particles have a significant impact on radiation, climate, and human health, with their size and shape being fundamental physical parameters for atmospheric change research. Due to the widespread effects and applications of aerosol particles, the direct measurement of aerosol size and shape has become crucial. Nevertheless, several challenges persist in aerosol measurement instruments, including limited resolution, complex operation, poor synchronization, and inaccurate inversion methods. Therefore, we developed a new scientific instrument and corresponding image intelligent interaction system, whose name is the fast atmospheric aerosol size and shape imaging instrument (FASI). The instrument is designed for transmission imaging that contains a light source, imaging chamber, microscope objective, tube lens, extension tube, camera, etc. Before the operation, the FASI calibrates background field, pixel size, characteristic gray value (CGV), and depth of field (DOF) based on image processing. During intelligent interaction, the FASI extracts aerosol particles by image denoising and edge detection, and then uses our proposed defocus and duplicate particle detection algorithms for secondary screening of aerosols. Aerosol size and shape parameters are measured in parallel by the central processing unit (CPU) and the graphics processing unit (GPU) using heterogeneous computation. Polystyrene latex (PSL) calculations and quantitative experiments indicate that FASI can accurately detect 0.5–<inline-formula> <tex-math>$20~mu $ </tex-math></inline-formula>m aerosol particles. In particular, the FASI measures aerosol particles supplied by an aerosol generator, dryer, and neutralizer, demonstrating that the aerosol size distribution range of oil solutions (0.5–<inline-formula> <tex-math>$3.5~mu $ </tex-math></inline-formula>m) is narrower than that of aqueous solutions (0.5–<inline-formula> <tex-math>$7.5~mu $ </tex-math></inline-formula>m). For all samples, 92.12% of aerosols have an aspect ratio (AR) exceeding 1, and the shape of these nonspherical aerosols varies greatly from each other. The evaluations of computational efficiency indicate that the FASI is capable of near-real-time operation at 20–35 frames per second (FPS). This instrument has the advantages of noncontact, stable, fast, accurate, and simultaneous automatic measurement of aerosol particle size and shape, which can help solve some key scientific problems related to climate and environmental effects.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-17"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726560","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}

{"title":"Multi-Scale Graph Channel Attention Detectors for Sonar Images in Smart Ocean","authors":"Sensen Li;Yu Zhang;Zhengda Ma;Jie Ding;Binbin Zou","doi":"10.1109/TIM.2025.3551424","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551424","url":null,"abstract":"Object detection in sonar images is challenging since the performance based on generic object detectors is generally not well on sonar datasets and the prior for the scale distribution of the objects is usually ignored. To overcome these issues, in this article, multi-scale graph channel attention (MGCA) detectors are proposed, in which multi-scale box copy-paste is designed for data augmentation and graph transformer channel attention (GTCA) based on ConvNeXt is introduced to learn powerful visual representations for sonar images. GTCA contains three basic modules: graph structure transformation module for feature maps, graph convolution module for aggregating and updating information, and feedforward network module for feature transformation. With multi-scale box copy-paste to balance scale distribution and channel attention network in a graph form to recalibrate feature maps, MGCA detectors can effectively detect multi-scale objects in sonar images through transfer learning. The MGCA detector with Cascade R-CNN (CR) detection head achieves detection accuracies of 95.6 mAP and 72.7 mmAP on the sonar common target detection (SCTD) dataset, outperforming the previous best detector with an increase of 2.8 mAP and 11.8 mmAP. Extensive experimental results and ablation studies on two sonar datasets demonstrate the superiority of the proposed method compared with state-of-the-art methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726438","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}

{"title":"Improved Butterfly Optimization Algorithm for Parameter Identification of Various Photovoltaic Models Including Power Station","authors":"Kai He;Yong Zhang;Henry Leung","doi":"10.1109/TIM.2025.3551857","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551857","url":null,"abstract":"Parameter identification of photovoltaic models (PIPM) is essential for controlling a photovoltaic (PV) system. However, due to its complexity, most existing methods still suffer from problems such as low accuracy, sensitivity to initial values, and local optima. For this, an improved butterfly optimization algorithm (DLBOA) with dimension differential learning is proposed. First, a new adaptive fragrance is introduced to optimize the instability caused by target differences and improve convergence performance. Second, the proposed dimension differential learning strategy improves butterflies’ position by utilizing the excellent dimension information within the population, thereby reinforcing interindividual learning and enhancing population balancing and diversity, ultimately escaping from local optima. Then, after evaluating based on CEC2022, DLBOA identified the parameters for eight models across five materials and outperformed nine state-of-the-art algorithms in terms of accuracy, robustness, and promoting percentage. DLBOA is further compared with nine existing PIPM methods including five numerical methods. Finally, applying DLBOA to the YL PV station in China Guizhou Power Grid under a dynamic climate, multiple metrics confirm DLBOA’s outstanding accuracy, with the reconstructed I-V and P-V curves closely matching synthesized curves. The statistical analysis results demonstrate that the proposed method effectively enhances the robustness of parameter identification while also strengthening the ability to escape local optima, demonstrating the potential to improve PIPM accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-23"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735292","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}

{"title":"Swift and Accurate Point Cloud Registration Using SGUformer","authors":"Jiaxiang Luo;Duoqin Dong;Haiming Liu","doi":"10.1109/TIM.2025.3551795","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551795","url":null,"abstract":"Point cloud registration aims to find a rigid transformation that aligns two point clouds. Applications such as augmented reality (AR) and robot navigation often require real-time performance for point cloud registration algorithms. In this article, we propose SGUformer, a novel point cloud registration method that achieves fast alignment by redesigning the feature extraction pipeline and employing a lightweight global feature extraction framework. The gating mechanism is utilized, and local coordinates are embedded to enhance the representation of point-level features. To facilitate the extraction of global features, a Transformer with 3-D rotary position embedding (RoPE) is implemented, circumventing the need to compute relative position information, thereby improving computational efficiency. Furthermore, a part attention mechanism is designed to tackle outlier pollution issues. In the final registration stage, the registration results obtained from each patch pair, weighted by their respective confidence scores, are combined to vote and acquire a more robust final result. In the conducted experiment, the superior quality of features derived from the novel structure’s feature extractor enabled our method to attain a better feature matching recall (FMR) in comparison to existing leading methodologies. Moreover, the implementation of the proposed registration method resulted in the highest recorded registration success rate, exceeding the second-best method by 0.8%. In addition, our approach demonstrated remarkable efficiency, being 26% faster than the alternative methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740389","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}

{"title":"Underwater Image Enhancement via Adaptive Bi-Level Color-Based Adjustment","authors":"Yun Liang;Lianghui Li;Zihan Zhou;Lieyu Tian;Xinjie Xiao;Huan Zhang","doi":"10.1109/TIM.2025.3551931","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551931","url":null,"abstract":"Underwater images often exhibit severe color distortions and reduced contrast due to light absorption and scattering, presenting substantial challenges for image enhancement techniques. To address these challenges, this article presents BCTA-Net, an adaptive bi-level color-based network specifically engineered to enhance the quality of underwater images by addressing distortions in dynamic and complex environments. The network integrates content-aware global and local restoration strategies. On a local scale, a color-aware attention mechanism is proposed which employs color histograms to adaptively correct nonuniform color distortions and enhance local color fidelity. In addition, a triple attention (TA) module restores spatially varying local details in a content-aware manner, improving clarity and texture precision of enhancement. These elements are combined into a dual-branch architecture aimed at reducing local contrast, color fidelity, and detail precision issues. On a global scale, contrastive learning focused on background lightness corrects color distortions due to uneven illumination. The integration of these components results in a lightweight, dynamic global-local model with robust generalization capabilities across various underwater scenarios, as demonstrated by comprehensive experiments that show significant performance improvements over existing methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726390","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}

{"title":"Phase Noise Reduction in Optoelectronic Oscillator With Quadratic Fiber Bragg Grating Dispersion Engineering","authors":"B Renuka;Mandeep Singh","doi":"10.1109/TIM.2025.3551794","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551794","url":null,"abstract":"This article presents the design, implementation, and performance analysis of an optoelectronic oscillator (OEO) incorporating a quadratic fiber Bragg grating (Q-FBG). Integrating Q-FBG in OEO architecture introduces enhanced filtering capabilities and precise frequency control, which are critical for applications requiring high stability and low phase noise. It provides a tailored reflection spectrum, enabling improved mode selection and reduced spurious tones. Experiments confirm oscillator’s superior performance metrics, including phase noise reduction and frequency stability. Theoretical modeling and simulation corroborate the experimental results, confirming the Q-FBG’s effectiveness in optimizing OEO performance. An error vector magnitude (EVM) of 2.5% is obtained for the generated signal, indicating high quality and improved modulation accuracy of the microwave signal. The uncertainty of measurement, particularly the standard deviation in EVM values, is analyzed to assess system reliability. The potential applications of the proposed OEO include telecommunications, radar systems, and precision measurement instruments. The study underscores the significant advantages of incorporating Q-FBG in OEOs and paves the way for further advancements in microwave photonics technology.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740279","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}

{"title":"SCADA Data-Driven Spatio-Temporal Graph Convolutional Neural Network for Wind Turbine Fault Diagnosis","authors":"Jiachen Ma;Yang Fu;Tianle Cheng;Deqiang He;Hongrui Cao;Bin Yu","doi":"10.1109/TIM.2025.3551875","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551875","url":null,"abstract":"Supervisory control and data acquisition (SCADA) systems collect vast amounts of multi-sensor monitoring data, which is widely used in the intelligent fault diagnosis of wind turbines with the fast development of deep learning technologies. However, the complex structure of wind turbines and their time-varying operating conditions result in intricate spatio-temporal correlations within SCADA data, presenting significant challenges for feature extraction and accurate fault diagnosis. Current spatio-temporal fusion methods often treat SCADA data as Euclidean data, limiting their ability to capture the complex spatio-temporal coupling characteristics, which leads to reduced diagnostic accuracy. To solve abovementioned problems, a novel deep learning-based spatio-temporal graph convolutional neural network (STGCN) is developed for intelligent fault diagnosis of wind turbines in this article. First, an adjacency matrix is constructed based on the Gaussian kernel function to graphically represent the SCADA data, so as to improve the representation capacities for spatial characteristics. Then, the spatial and temporal fault features are extracted using the graph convolutional network (GCN) and the 1-D convolutional network (1D-CNN), respectively. Finally, a spatio-temporal feature fusion module is developed as the sandwich structure to construct the proposed STGCN. The feasibility and effectiveness of the proposed method are verified by two cases of blade icing detection and main bearing wear diagnosis. The results show that the proposed method is able to accurately describe the spatio-temporal correlation of SCADA data, and improve the diagnostic accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740291","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}

{"title":"MFICNet: A Multimodality Fusion Network With Information Compensation for Accurate Indoor Visual Localization","authors":"Zhendong Fan;Kun Dai;Jilong Guo;Zhiqiang Jiang;Hongbo Gao;Tao Xie;Ruifeng Li;Ke Wang","doi":"10.1109/TIM.2025.3551843","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551843","url":null,"abstract":"As a crucial technology in numerous visual applications, visual localization has been extensively studied, with an effective solution known as scene coordinate regression (SCoRe). Generally, SCoRe methods generate scene coordinates using convolutional neural networks (CNNs) and then determine the camera pose with a PnP algorithm. While these methods demonstrate impressive localization accuracy, they primarily rely on a single modality, e.g., RGB camera, which leads to texture dependency and structural ambiguity problems. Specifically, perceptual confusion caused by similar image textures in real indoor scenes causes a severe decline in localization accuracy, as the performance of the networks heavily depends on the semantic information of objects. In addition, current methods struggle to robustly recover the structural details of objects because RGB images lack 3-D geometric structural information. We think that these two issues stem from the inherent limitations of single modality. There is potential for complementarity between semantic and structural information. Toward this end, we propose MFICNet, a novel visual localization network that simultaneously utilizes RGB and depth images to achieve accurate visual localization. This pioneering architecture establishes a new paradigm for multimodality-based visual localization. Technically, MFICNet employs a heterogeneous backbone to extract features from RGB images and depth images separately. The structural feature obtained from depth images enhances the identifiability of similar image patches and imposes structural constraints for scene coordinates. After that, an information compensation module is introduced to evaluate the contributions of semantic and structural features and perform deep fusion to generate discriminative features. Extensive experiments are conducted on the 7-Scenes dataset and our newly released indoor dataset STIVL, which specializes in similar textures. The results show that MFICNet significantly outperforms state-of-the-art (SOTA) methods. The source code and STIVL dataset are available at <uri>https://github.com/fazhdo/MFICNet</uri>.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726365","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}