IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Nanonewton-Level Thrust Measurement Based on Stiffness Adjustment Through Total Variation Denoising Under Heavy Loads","authors":"Jiabin Wang;Jianfei Long;Jiawen Xu;Mingshan Wu;Linxiao Cong;Luxiang Xu;Yelong Zheng;Bin Wang;Ning Guo","doi":"10.1109/TIM.2025.3573377","DOIUrl":"https://doi.org/10.1109/TIM.2025.3573377","url":null,"abstract":"Sub-micronewton thrusters on space satellites are widely used in spaceborne gravitational wave detection, making accurate measurement of microthrust-generated thrust essential. Moreover, micropropulsion systems often exhibit substantial mass, posing challenges for thrust measurement. This article prompted the development of a sub-microthrust measurement device based on the inverted pendulum principle, which withstands 8-kg loads and boasts a large thrust-to-weight ratio exceeding 10⁹. The experimental results demonstrate that adjusting the stiffness of the inverted pendulum can effectively enhance the resolution of the device. Piecewise constant signal (PCS) thrust force was introduced, and the nonlinear total variation denoising (TVD) algorithm method was adopted for signal denoising. It is validated that thrust measurement in the range of 0–<inline-formula> <tex-math>$261~mu $ </tex-math></inline-formula>N is achieved with a minimum resolution of 24nN. The device design and stiffness adjustment method proposed in this article, as well as the data denoising method utilized, greatly enhance the thrust resolution measurement. This approach enables the development of an accurate microthrust model for spaceborne gravitational wave detection missions while simultaneously offering a novel solution for sub-micronewton-level thrust measurement under heavy-load conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272721","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":"Sparsity-Driven Gesture Recognition Using Lightweight TCNFormer Networks in Millimeter-Wave Radar","authors":"Hao Wu;Biao Jin;Zhenkai Zhang;Zhuxian Lian;Baoxiong Xu;Jin Liang;Xiangqun Zhang;Genyuan Du","doi":"10.1109/TIM.2025.3576011","DOIUrl":"https://doi.org/10.1109/TIM.2025.3576011","url":null,"abstract":"Gesture recognition with millimeter-wave radar has broad application prospects in human-computer interaction. However, the traditional recognition methods generate overly redundant features and construct large-scale networks, rendering them unsuitable for embedded devices with limited memory. To address this challenge, we propose a sparse-driven dynamic gesture recognition network in millimeter-wave radar, named time convolutional network and transFormer (TCNFormer). First, we employ a 2-D fast Fourier transform (2D-FFT) to obtain the range-Doppler maps (RDMs). These maps are then processed through incoherent integration of multiple frames to produce Doppler-time maps (DTMs). We subsequently use the orthogonal matching pursuit (OMP) algorithm to achieve a sparse representation of the Doppler-time trajectories and integrate the RDM to extract the range features of gestures, obtaining the multidimensional sparse sequences encompassing the range-Doppler-time feature. We then design a TCNFormer network tailored to these multidimensional sparse sequences. This network leverages a shallow TCN to learn local features, a Transformer network to capture global features and an adaptive weighting method to fuse these local and global features effectively. Experimental results demonstrate that our network fully exploits the sparse multidimensional sequences, achieving a recognition accuracy of 99.17% on a self-built dataset. The parameter size of the network is only 0.13 M, significantly outperforming the existing state-of-the-art models in relevant metrics, thereby proving its suitability for embedded applications in human-computer interaction.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272723","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":"Dynamic Observer-Based Multi-AUV Enclosing Control for Target Without Velocity Measurement","authors":"Zheping Yan;Yuyang Yu;Wen Xing;Yan Shi","doi":"10.1109/TIM.2025.3575973","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575973","url":null,"abstract":"This article focuses on the problem of enclosing control for moving targets using multiple underactuated autonomous underwater vehicles (AUVs). For the target with unknown velocity measurement, a dynamic observer is designed to provide estimated positions and velocities of the target to AUVs and possess the capability to adapt to dynamic changes in interaction topology and interruptions in link connectivity. Leveraging a self-organized scheme, a kinematic controller based on the dynamic observer is developed, enabling the AUVs to rearrange their positions in the formation, thereby increasing the flexibility of their collaboration. To compensate for the impacts of external interference, an extended state observer (ESO) is employed to estimate the aggregated disturbances, which encompass uncertain dynamics and perturbations. Then, an ESO-based dynamic controller is derived using the backstepping method to ensure that the actual velocities of AUVs align with the commanded velocities generated by the kinematic controller. Sufficient conditions for stability and numerical examples are finally provided to demonstrate the effectiveness and advantages of the theoretical results.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272724","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":"An Active Contour Model Based on Fuzzy Superpixel Centers and Nonlinear Diffusion Filter for Instance Segmentation","authors":"Yiyang Chen;Fuzheng Zhang;Guina Wang;Guirong Weng;Daniele Fontanelli","doi":"10.1109/TIM.2025.3573369","DOIUrl":"https://doi.org/10.1109/TIM.2025.3573369","url":null,"abstract":"The significant weaknesses of the active contour model (ACM) are the manual setting of contour and the inability to process images with complex information, which limits its efficiency and application scope. In this article, an ACM, called FSC&NDF, is combined with fuzzy superpixel centers (FSCs) and nonlinear diffusion filter (NDF) to solve the above two problems simultaneously. YOLOv9 is adopted to locate the superpixels of interest; the joint boundaries of these superpixels are set as the initial contour, which is close to the morphological features of the target. Improved fuzzy superpixel clustering is applied to extract image features and yield superpixel centers, and the clusters are integrated into the main body of the energy function, NDF module further enhances boundary positioning and suppresses noise. In addition, the proposed connection mechanism makes it possible to convert object detection to instance segmentation. Experimental results show that FSC&NDF overcomes the limitations of previous ACMs in all aspects and its FPS, AP, AP50, and <inline-formula> <tex-math>$mathrm {AP}_{M}$ </tex-math></inline-formula> are higher than mainstream deep learning algorithms. The platform experiment based on the telecentric lens further proves the practicality of FSC&NDF.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255592","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":"Patch-Based Fourier Attention-Enhanced Contrastive Learning Networks for Robust Drift Diagnosis in Long-Sequence Bearing Data","authors":"Guan Yuan;Ziqing Zhu;Qiang Niu;Gang Shen;Zhencai Zhu;Yan Zhou;Qingguo Wang","doi":"10.1109/TIM.2025.3575982","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575982","url":null,"abstract":"In industrial applications, the nonstationary nature of long time-series data from bearing operations poses a significant challenge due to data drift, influenced by varying operating conditions and environments. To tackle this issue, we propose a novel fault diagnosis model leveraging contrastive learning. This approach utilizes domain-differentiated contrastive feature learning to construct positive and negative sample pairs, fully capturing the commonalities within the same fault type and the differences between different fault types, thereby enhancing the model’s robustness against interference. Moreover, the model employs a patch-based Transformer to capture dependencies in local subsequences, reducing computational complexity while maintaining the ability to abstract comprehensive signal representations. Additionally, the integration of multihead Fourier attention allows simultaneous analysis of time-domain and frequency-domain characteristics, enriching the feature extraction process. Our method is validated through comparative, parameter analysis, and ablation studies on datasets, demonstrating its effectiveness and potential for improving fault diagnosis accuracy in bearing systems, thereby reducing downtime and enhancing operational safety.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288684","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":"Hybrid Ultrasonic Tomography With Multilevel Fusion in Particulate Flow: Visualizations on Inhomogeneous Solid (Metal)–Liquid Biphasic Medium","authors":"Jianfei Gu;Song Hu;Lianfu Han","doi":"10.1109/TIM.2025.3575991","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575991","url":null,"abstract":"Both dynamic and static visualizations of biphasic fluid have been the focus of attention that received wide notices. This work focused on carrying out research on solid-liquid biphasic visualization, which was common in slurry transportation or hydrocyclone. Multilevel fusion algorithms named single-path data fusion (SPDF), full-path data fusion (FPDF), and a hybrid algorithm of FPDF reconstruction with boundary enhancement (BE) method were proposed, which resulted in developing hybrid-ultrasonic process tomography (H-UPT). To verify the effects of tomographic fusion, different cases of monodisperse and polydisperse medium with spherical, ellipsoidal, and cubical distributions were carried out. The designed array mounted on polymethyl methacrylate pipe and the dynamic experiments of solid-liquid medium were executed. The quantitative analysis through the postprocessing of experimental results had illustrated good consistence with the real dynamic level by a deviation no more than 6.04%. In overall considering of the accuracy (such as imaging spatial error and correlation coefficient) and average time-consuming of the FPDF algorithm, the H-UPT illustrated an effective application and technological potential for completing high-precision imaging in particulate flow of varying shapes, dimensions, and quantities.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289255","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":"EvoNAS4Battery: An Evolutionary NAS Approach for Predicting the Remaining Useful Life of Lithium-Ion Batteries","authors":"Xueqian Chen;Zhaoyong Mao;Zhiwei Chen;Junge Shen","doi":"10.1109/TIM.2025.3573358","DOIUrl":"https://doi.org/10.1109/TIM.2025.3573358","url":null,"abstract":"Long-term use of lithium batteries inevitably leads to performance decay due to complex internal reactions and external interference, which can impact impacting battery lifespan and potentially causing equipment failure. Therefore, accurately predicting the remaining useful life (RUL) of batteries is crucial for predictive maintenance. While existing prediction methods based on deep learning have shown excellent performance, manually designing neural network structures remains a time-consuming and challenging task. To address this issue, we propose a neural architecture search (NAS)-based framework for battery RUL prediction. We introduce a novel network model based on the Transformer architecture to handle battery capacity regeneration interference and enhance time series information extraction. To efficiently find the optimal Transformer architecture, we use a NAS method assisted by a surrogate model as a predictor. Compared with the current state of research, extensive experimental results validate that our proposed method achieves the best overall performance.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289253","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":"Progressive Inertial Poser: Progressive Real-Time Kinematic Chain Estimation for 3-D Full-Body Pose From Three IMU Sensors","authors":"Zunjie Zhu;Yan Zhao;Yihan Hu;Guoxiang Wang;Hai Qiu;Bolun Zheng;Chenggang Yan;Feng Xu","doi":"10.1109/TIM.2025.3570339","DOIUrl":"https://doi.org/10.1109/TIM.2025.3570339","url":null,"abstract":"The motion capture system that supports full-body virtual representation is of key significance for virtual reality. Compared with vision-based systems, full-body pose estimation from sparse tracking signals is not limited by environmental conditions or recording range. However, previous works either face the challenge of wearing additional sensors on the pelvis and lower body or rely on external visual sensors to obtain global positions of key joints. To improve the practicality of the technology for virtual reality applications, we estimate full-body poses using only inertial data obtained from three inertial measurement unit (IMU) sensors worn on the head and wrists, thereby reducing the complexity of the hardware system. In this work, we propose a method called progressive inertial poser (ProgIP) for human pose estimation, which combines neural network estimation with a human dynamics model, considers the hierarchical structure of the kinematic chain, and employs a multistage progressive network estimation with increased depth to reconstruct full-body motion in real time. The encoder combines Transformer encoder and bidirectional LSTM (TE-biLSTM) to flexibly capture the temporal dependencies of the inertial sequence, while the decoder based on multilayer perceptrons (MLPs) transforms high-dimensional features and accurately projects them onto skinned multiperson linear (SMPL) model parameters. Quantitative and qualitative experimental results on multiple public datasets show that our method outperforms state-of-the-art methods with the same inputs and is comparable to recent works using six IMU sensors.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255744","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":"Self-Supervised Structured Illumination Microscopy Image Denoising Based on Multiresolution Analysis Resampling","authors":"Hong Yang;Xianqiang Yang","doi":"10.1109/TIM.2025.3575962","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575962","url":null,"abstract":"In live-cell imaging under low-light conditions, acquiring images with low signal-to-noise ratios (SNRs) presents a major challenge for structured illumination microscopy (SIM), limiting its effectiveness in super-resolution (SR) imaging and subcellular process investigation. The difficulty of obtaining high-SNR images, coupled with the scarcity of sample data in low-photon environments, impedes the application of deep learning-based approaches for SIM image denoising and reconstruction. To address these limitations, we propose MRS-SIM, a self-supervised denoising method based on multiresolution analysis (MRA) resampling, which enables high-fidelity, artifact-free reconstruction without requiring high-SNR References. We systematically evaluate MRS-SIM through both simulated experiments across varying noise levels and real-world microscopy data at different photon counts. Compared with HiFi-SIM, a typical SIM reconstruction baseline, MRS-SIM achieves up to 5.22 dB peak SNR (PSNR), and 0.32 structural similarity index (SSIM) improvement under extremely low SNR (−12 dB), while maintaining lower variability across trials. Furthermore, ablation studies confirm the critical role of MRA resampling in enhancing denoising accuracy and validating the contributions of key components. With its ability to overcome noise-related challenges and facilitate high-quality image reconstruction in low-light conditions, MRS-SIM offers a robust and efficient solution for low-photon live-cell imaging, particularly in applications requiring the preservation of fluorescence integrity for subcellular dynamics studies.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288677","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":"iFBI: Lightweight Breed and Individual Recognition for Cats and Dogs","authors":"Yuanze Zhang;Yimeng Zhang;Kexu Li;Jinpeng Luo;Gang Liu;Rong Pan","doi":"10.1109/TIM.2025.3576017","DOIUrl":"https://doi.org/10.1109/TIM.2025.3576017","url":null,"abstract":"As the pet industry develops, fine-grained breed recognition and individual recognition have emerged as essential components in biometric measurement systems for intelligent pet monitoring, aiming to identify the specific breed of a pet in an image and to recognize the same individual across multiple images. These capabilities lay the foundation for downstream tasks such as posture estimation and emotion analysis, supporting a wide range of real-world applications. Despite the substantial advancements achieved in existing research, two critical issues remain to be solved: the diversity of object poses affects representation in complex scenarios, and the conflict between model complexity and performance hinders application in resource-constrained conditions. To address the above issues, we propose integrated face and body information (iFBI) for a lightweight breed and individual recognition scheme that integrates multiple pose information by a lightweight model. Specifically, a face alignment (FA) module and a body posture-guided (BPG) module are proposed to separate face and body information from the input images, fully capturing the posture details while suppressing background areas. To further maximize the discrimination between individual samples, we design a multilevel representation recognition (MRR) module that dynamically integrates complementary semantic features of face and body, consequently generating more discriminative features. In addition, a dynamic convolutional model compression (DCMC) method is implemented with an improved dual-branch backbone that significantly reduces computational costs while enhancing model performance. Extensive experiments on two self-built datasets—pet with fine-grained breed (Pet-FB) dataset and pet with diverse posture (Pet-DP) dataset—and four public datasets indicate that iFBI yields superior performance in both fine-grained breed recognition and individual recognition tasks. The source code and self-built datasets—Pet-FB and Pet-DP—are available at our GitHub repository.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291711","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}