IEEE Transactions on Instrumentation and Measurement最新文献

{"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}

{"title":"Automated Stopping Criterion for Sparse Near-Field Scanning With Active Machine Learning","authors":"Yuting Xie;Ling Zhang;Da Li;Er-Ping Li","doi":"10.1109/TIM.2025.3575984","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575984","url":null,"abstract":"Active learning (AL) has been demonstrated to accelerate near-field scanning (NFS) measurement by adaptively sampling the significant points to reconstruct the complete electromagnetic field. However, an automated stopping criterion for active-learning sampling is still lacking, and the decision to stop sampling is often made manually. Since the ground-truth error using sparse samples compared to the full scanning resultisunknown,we proposea novel stopping criterion based on a statistically estimated worst case predicted error. The AL-based NFS process will be terminated when the maximum predicted error at the unsampled positions falls below a user-defined threshold, which can flexibly and intuitively satisfy the accuracy requirements of users in various experimental scenarios. The proposed stopping criterion provides sufficient accuracy and appropriate sample size for sparse NFS measurement, which is validated through both simulation and measurement cases.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291712","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":"Integrating Local and Global Features via CNN and Mamba for Semi-Supervised Change Detection","authors":"Ze Zhang;Yue Zhou;Linqing Huang;Xue Jiang;Guozheng Xu;Xingzhao Liu","doi":"10.1109/TIM.2025.3573775","DOIUrl":"https://doi.org/10.1109/TIM.2025.3573775","url":null,"abstract":"Semi-supervised change detection (SSCD) has become increasingly important in remote sensing image (RSI) analysis due to the scarcity of labeled data. While state-of-the-art SSCD methods have achieved notable results through pseudo-labeling and weak-to-strong consistency regularization with various perturbations, they face several inherent limitations: 1) learners (models or architectures with the same design) often focus exclusively on either local or global features, which fails to capture the intricate bi-temporal difference feature; 2) low-quality pseudo-labels, often resulting from inadequate learner diversity or suboptimal perturbations, struggle to reflect accurate changes, exacerbating confirmation bias; and 3) a predominant emphasis on pixel-level consistency overlooks broader image context, limiting the ability to capture complex, large-scale spatiotemporal changes. To address these challenges, we propose a novel SSCD framework, cross-supervision with convolutional neural network and Mamba (CSCM), which adopts the convolutional neural network (CNN) and Mamba as two independent learners within a cross-supervision mechanism, enabling the integration of local and global feature representations. The CNN excels at capturing fine-grained local details, while Mamba efficiently models long-range dependencies with linear complexity, making it particularly well-suited for processing large-scale RSIs. To enhance the collaboration between these architectures, we introduce the cross-architecture fusion module (CAFM), which fuses difference features extracted by the CNN and Mamba, combining local sensitivity with global awareness to produce refined pseudo-labels. Additionally, we incorporate instance-level global consistency to capture broader image context, ensuring a more comprehensive understanding of spatiotemporal changes beyond pixel-level consistency. Extensive experiments on three public datasets demonstrate that our approach significantly enhances CD accuracy and training efficiency, outperforming several state-of-the-art methods.","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":"144232056","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":"A Phase Fusion and Restoration Method Based on Global Neural Calibration Model","authors":"Xi Wang;ZhenXiong Jian;Duo Li;XinQuan Zhang;LiMin Zhu;MingJun Ren","doi":"10.1109/TIM.2025.3571128","DOIUrl":"https://doi.org/10.1109/TIM.2025.3571128","url":null,"abstract":"Fringe projector profilometry provides incomplete and noisy measurement data for machined metal surfaces that are extensively utilized in various industrial applications. Previous methods have attempted to address this issue through high dynamic range imaging or by introducing reflectance information of extra facilities with multiple lights. However, these methods decrease measurement efficiency and complicate the measurement facility. Therefore, this article combines these two strategies by utilizing the reflectance information from nonfringe images to guide the fusion and restoration of incomplete phase information at different exposure times, where an advanced anisotropic reflectance model is employed to obtain the reflectance information. A global neural calibration model based on a neural radiance field (NeRF) is proposed to bridge the phase and reflectance information. This model comprises global neural phase-to-coordinate and phase-to-light models. The global neural phase-to-coordinate model uses the NeRF to establish a relationship from the pixel position and the relative phase to the relative point coordinate. Besides, the global neural phase-to-light model employs the NeRF to describe the light distribution of the projector. The global neural calibration model transforms and propagates the reflectance information to optimize the phase information, thereby enhancing the performance of fringe projection profilometry (FPP) for machined metal surfaces. Real experiments on standard ball bars demonstrate that the global neural calibration model achieves calibration accuracy of <inline-formula> <tex-math>$31.89~mu $ </tex-math></inline-formula>m. In addition, experiments on six machined metal workpieces demonstrate that the proposed method achieves the measurement accuracy of <inline-formula> <tex-math>$58.5~mu $ </tex-math></inline-formula>m compared with coordinate measuring machine (CMM) results.","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":"144232178","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":"Broadband Complex Permittivity Determination of Thin Material Using Open-Ended Coaxial Probe and an Integrated GA–HFSS Computational Method","authors":"Jill S. K. Nakatsu;Sunny Shuxuan Zhang;Magdy F. Iskander;Zhengqing Yun","doi":"10.1109/TIM.2025.3575972","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575972","url":null,"abstract":"In this article, we present an innovative approach for a simple, nondestructive, and accurate method for broadband determination of complex permittivity of thin materials including those often used in patterning advanced metamaterial absorbers. While the measurement procedure involves using a simple coaxial probe method, the determination of complex permittivity values is based on an iterative procedure involving High Frequency Structure Simulation (HFSS) and a genetic algorithm (GA)-based zero finding technique. The HFSSs include modeling the coaxial probe when in contact with the thin material under test (MUT) and when backed by standard material and ground plane and calculation of de-embedded reflection coefficient values, <inline-formula> <tex-math>$S_{11}$ </tex-math></inline-formula>, at the probe and material interface. Through the developed GA-HFSS software interface, a GA fitness function was used to determine the complex permittivity values that best fit the measured S-parameters. Measurements were made on several standard materials and obtained results were validated when compared with measurements using the shunt capacitance at the end of a coaxial line method. Results from the two measurements methods were within 1%–2% difference in complex permittivity values in the frequency band from 500 MHz to 15 GHz, the compact radar band of interest. Measurement errors due to air gaps at the probe sample interface are analyzed to help guide measurements and achieve accurate results.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281329","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":"Variability in Spread Spectrum Time-Domain Reflection and Transmission Measurements","authors":"Samuel Makin;Mouad Addad;Brent Redhead;Cynthia M. Furse","doi":"10.1109/TIM.2025.3575981","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575981","url":null,"abstract":"Spread spectrum time-domain reflectometry (SSTDR) offers a new nonintrusive measurement technique for microwave imaging. While vector network analyzers (VNAs) are conventional tools in this domain, they present significant drawbacks such as high cost and lengthy scan times. This article explores the viability of substituting SSTDR devices for the VNA by validating their accuracy in comparison to traditional VNAs in the 0.5–4-GHz band. We conducted several tests of reflection and transmission measurements of resistor–capacitor (<italic>RC</i>) loads, analysis of variations due to cable movement, calibration consistency, and temperature-induced variations. We also measured antenna <inline-formula> <tex-math>${S}_{11}$ </tex-math></inline-formula> and <inline-formula> <tex-math>${S}_{21}$ </tex-math></inline-formula> in two oil-based phantoms to simulate human breast tissue, comparing changes with the insertion of a metal tumor substitute. We found that the mean confidence intervals (CIs) of the VNA and SSTDR are better (less) than 0.03 for <inline-formula> <tex-math>$vert {S}_{11}vert $ </tex-math></inline-formula> and 0.02 for <inline-formula> <tex-math>$vert {S}_{21}vert $ </tex-math></inline-formula> from 0.5 to 4 GHz and that metal “tumors” in the oil are detectable. These results suggest that SSTDR may be a viable alternative to VNAs for applications such as microwave breast imaging, warranting further investigation in more robust setups.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144288680","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":"Print Quality Assessment of Additively Manufactured Resonant Structures","authors":"Logan M. Wilcox;Alexander Hook;Emma T. Bohannon;Cody Morrow;Doyle T. Motes;Kristen M. Donnell","doi":"10.1109/TIM.2025.3575992","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575992","url":null,"abstract":"Due to the increased popularity of additive manufacturing (AM) and availability of electrically conductive filaments, the potential for using AM to fabricate high-frequency resonant structures has been realized. As these structures require specific operating conditions to provide the desired resonant response (such as substrate/superstrate material, operating frequency, polarization, etc.), print quality validation through an assessment of the resonant response can be difficult to accomplish. To this end, active microwave thermography (AMT) is proposed as an alternative technique to assess and validate an AM resonant structure. Two AM resonant structures were manufactured using fused deposition modeling (FDM) and the resonant and AMT responses measured. The results indicate that AMT has strong potential for print quality validation of AM resonant structures without complicated reproduction of the operating conditions required to achieve the designed-for resonant response.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-4"},"PeriodicalIF":5.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289256","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":"A Miniaturized, Low-Cost Interrogator via Intrapulse Wavelength Demodulation for Operando Fiber-Optic Sensing in Batteries","authors":"Jiahua Yang;Xibin Lu;Hongting Du;Jiaqiang Huang","doi":"10.1109/TIM.2025.3575963","DOIUrl":"https://doi.org/10.1109/TIM.2025.3575963","url":null,"abstract":"Fiber-optic sensing shows great promise in battery monitoring due to its versatile sensing capability, compact size, flexibility, and electromagnetic immunity. However, the high cost of optical interrogators limits large-scale commercialization. Herein, we develop a low-cost, miniaturized interrogator utilizing off-the-shelf distributed feedback (DFB) lasers in pulse mode to induce frequency chirping. This design maintains environmental immunity while partially replacing costly spectrum analyzers. Compared to other commercial interrogators, our device excels in cost and scanning rate, achieving a MHz scanning rate with accuracy and repeatability. We demonstrate its successful application in commercial 18650 lithium-ion batteries (LiNi0.8Co0.1Mn0.1O2/C) by implanting the fiber Bragg grating (FBG) sensor, and it features time-division multiplexing (TDM) for monitoring multiple sensing points. Additionally, the techno-economic analysis corroborates the considerable added value of the novel interrogator in diverse application scenarios. Collectively, the development of new interrogation devices will not only advance smarter energy storage systems but also provide insights into next-generation optical sensing devices.","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":"144291721","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":"Ramp Rate Resilient Clock Synchronization Control in IEEE 1588 Networks Using Kalman Filtering","authors":"Balázs Renczes;Tamás Kovácsházy;Gábor Kovács","doi":"10.1109/TIM.2025.3576015","DOIUrl":"https://doi.org/10.1109/TIM.2025.3576015","url":null,"abstract":"Clock synchronization plays a crucial role in modern real-time distributed systems, guaranteeing a common, consistent view of time. While the IEEE 1588 standard provides the foundational framework for synchronization, it remains a persistently investigated research area as emerging applications introduce new challenges. This article introduces an innovative approach for achieving robust and accurate control in high-precision IEEE 1588 networks in the presence of ramp rates. The proposed solution leverages a two-state linear Kalman filter, effectively balancing rapid response to ramp rates with small steady-state errors. This is accomplished by modeling the ramp rate as a stochastic process. The efficacy of the proposed ramp rate suppression Kalman filter (RRS-KF) solution is validated through both simulations and hardware measurements. The results demonstrate that the Kalman filter structure can significantly reduce the offset error to the order of 10 ns, while other performance metrics experience only minimal degradation.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CDBGrad-BlindSR: Collaborative Dual-Branch Network via Gradient Guidance for Efficient Blind Super Resolution","authors":"Haoran Yang;Shipeng Fu;Kai Liu;Xiaomin Yang","doi":"10.1109/TIM.2025.3573339","DOIUrl":"https://doi.org/10.1109/TIM.2025.3573339","url":null,"abstract":"Existing degradation kernel-based image super-resolution (SR) algorithms have achieved favorable performance in blind SR measurement. This addresses the limitation where bicubic kernel-based SR methods suffer performance degradation when the input image’s degradation kernel deviates from the assumed kernel. However, predicting the degradation kernel demands additional computational resources beyond the SR model. Moreover, imprecise degradation kernel estimation often results in restored images with visible artifacts and distorted structural details. To mitigate the above limitations, we introduce an efficient collaborative dual-branch network via gradient guidance for blind SR measurement. Concretely, without relying on degradation kernel estimation, we utilize the gradient spatial feature transform (GSFT) layer to enable mutual collaboration between features extracted from the restored branch and gradient prior features. These gradient prior features effectively capture the structural details of the input low-resolution (LR) image. To reduce model complexity, we adopt an information distillation mechanism in both channel and spatial attention mechanisms as the feature extractor, allowing the network to focus on essential features while bypassing redundant ones. Furthermore, to thoroughly exploit degradation priors during training without kernel estimation, we introduce a calibration mechanism. In this mechanism, the studied LR image degraded from the SR image by using the degradation kernel prior, is aligned with the ground-truth LR image under the constraints of <inline-formula> <tex-math>$L1$ </tex-math></inline-formula> loss and a second-order gradient loss. Under this constraint, the SR image can be indirectly aligned with the high-resolution (HR) image. Extensive experimental results demonstrate that our network significantly economizes model parameters and computational costs by eliminating the degradation kernel estimation process. Meanwhile, it maintains competitive blind SR performance compared to other state-of-the-art (SOTA) methods.","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":"144232169","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}