IEEE Transactions on Instrumentation and Measurement最新文献

{"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}

{"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}

{"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}

{"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}

{"title":"Time-Varying Unknown Input Constrained UKF With Unbiased Minimum Variance Estimator for Nonlinear Dynamic Indoor Thermal Profile Estimation","authors":"Bed Prakash Das;Kaushik Das Sharma;Amitava Chatterjee;Jitendra Nath Bera","doi":"10.1109/TIM.2025.3606059","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606059","url":null,"abstract":"Estimating unknown inputs in indoor heating, ventilation, and air conditioning (HVac) systems, particularly under the influence of diverse environmental constraints and time-varying relative humidity, presents a significant challenge. A viable solution is to use a weighted least-squares (WLS) approach for estimating unknown inputs, which uses an unbiased minimum variance (UMV) estimator in conjunction with an unscented Kalman filter (UKF)-based nonlinear filtering technique. This allows for the simultaneous estimation of the system’s state and the unknown inputs. To accurately represent the real-life nonlinear thermal profile influenced by these uncertain inputs, it is essential to adopt an RC network-based mathematical modeling approach that captures the system’s dynamic behavior over time. The integration of the UMV-based optimal estimator with the UKF culminates in the proposed UKF with UMV for unknown inputs (UKF-UMV-UI) estimation algorithm. Extensive experimentation with the proposed UKF-UMV-UI algorithm has been conducted in a laboratory-scale realistic environment, dealing with uncertain and challenging unknown inputs. The results of the investigation indicate that the proposed method outperforms the UKF with unknown input (UKF-UI) by 41.64% and 35.85% in cumulative mean squared error (CuMSE) for two distinct measurement conditions, respectively.","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":"145061821","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":"Dual-Layer Spherical Coil: A Novel Design Method for Self-Shielded Uniform Field Coil","authors":"Yiding Wang;Shengxin Lin;Chongyu Jin;Donghua Pan;Yitao Chen;Yuxiao Zhang;Liyi Li","doi":"10.1109/TIM.2025.3606043","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606043","url":null,"abstract":"Optically pumped magnetometers (OPMs) have emerged as a promising magnetic sensor for magnetoencephalography and magnetocardiography (MEG and MCG), owing to their low cost, high spatiotemporal resolution, and excellent magnetic-field sensitivity. Self-shielded coils—which generate a highly uniform internal field while rapidly decaying external fields—serve critical roles in OPM development: as in-magnetic shielding room (MSR) standard magnetic sources, they enable distortion-free uniform field for OPM calibration; as in-probe modulation magnetic sources, they provide stable, low-crosstalk modulation field. The external field decay and internal field uniformity of these coils are key performance metrics. To overcome the limitations inherent in conventional cylindrical self-shielded coil topologies, this article proposes a dual-layer spherical self-shielded coil structure and optimizes its geometry with respect to the field of the target region. Theoretical analysis shows that compared to common cylindrical designs, the proposed spherical structure reduces the minimum crosstalk-free distance by 50% when used as a modulation source, and expands the uniform field region by a factor of 1.9 when used as a standard source within MSR. Experimental validation corroborates these predictions, proving the efficacy of the spherical coil topology and optimization methodology in advancing OPM performance and suppressing crosstalk.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061823","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":"Efficient Doppler Frequency Simulator for Multifrequency","authors":"Sukjae Yoon;Kyoduk Ku;Hoyoung Yoo","doi":"10.1109/TIM.2025.3606061","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606061","url":null,"abstract":"This article introduces an innovative interpolation-based radar simulation system (IRSS) designed to simulate Doppler frequencies across multiple frequencies with minimal hardware complexity. Traditional radar simulation systems, such as Analog Radar System Simulators (ARSSs) and Digital Radar System Simulators (DRSSs), face challenges when supporting multifrequency simulations due to the need for parallel processing of individual Doppler frequencies. The proposed IRSS exploits linear interpolation and the superposition property, enabling a single interpolation process to handle multiple frequency components efficiently. The IRSS structure was implemented using a field programmable gate array (FPGA)-based universal software radio peripheral (USRP), and its performance was evaluated through experimental testing. The results demonstrated that the IRSS accurately generated Doppler frequencies for both single-frequency and multifrequency signals, maintaining consistency with theoretical predictions. The system effectively simulated Doppler shifts for various target speeds while preserving hardware simplicity, unlike traditional simulators that require increased resources proportional to the number of frequencies. This research highlights the advantages of using linear interpolation to reduce hardware complexity and improve scalability in radar simulators. Consequently, the proposed IRSS provides a cost-effective and efficient solution for modern radar systems that demand multifrequency capabilities, making it well-suited for applications in complex environments such as autonomous vehicles, military operations, and aviation.","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":"145061915","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":"PLDKD-Net: Pixel-Level Discriminative Knowledge Distillation for Surgical Scene Segmentation With Graph-Based Visual Parsing","authors":"Bo Lu;Xiangxing Zheng;Zhenjie Zhu;Yuhao Guo;Ziyi Wang;Bruce X. B. Yu;Mingchuan Zhou;Peng Qi;Huicong Liu;Yunhui Liu;Lining Sun","doi":"10.1109/TIM.2025.3606028","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606028","url":null,"abstract":"Efficient laparoscopic scene segmentation holds significant potential for surgical assistive intelligence and image-guided task autonomy in robotic surgery. However, the abdominal cavity with intricate tissues and surgical tools under varying conditions challenges the balance between segmentation accuracy and efficiency. To resolve this problem, we propose a pixel-level discriminative knowledge distillation network (PLDKD-Net), a novel pixel-level student–teacher knowledge distillation (KD) framework, in which the student model selectively distills the teacher’s profound knowledge while exploring rich visual features with a graph-based fusion mechanism for efficient segmentation. Specifically, we first introduce our confidence-based KD (Confi-KD) scheme, in which a pixel-level confidence generator (PCG) is proposed to assess the teacher’s performance by discriminatively evaluating its probability map and the raw image, generating a confidence map that can facilitate a selective KD for the student model. To balance the model’s accuracy and efficiency, we devise a novel heterogeneous student architecture with a bi-stream visual parsing pipeline to capture multiscale and interspatial visual features. These features are then fused using a relational graph convolutional network (RGCN), which can adaptively tune the fusion degrees of multilatent knowledge, ensuring visual parsing completeness while avoiding computational redundancy. We extensively validate PLDKD-Net on two public laparoscopic benchmarks, Endovis18 and CholecSeg8K, and in-house surgical videos. Benefiting from our schemes, the experimental outcomes demonstrate superior quantitative and qualitative performance compared to state-of-the-art (SOTA) methods. With the selective KD mechanism, our model yields competitive or even higher performance than the cumbersome teacher model while exhibiting quasi-real-time efficiency, which demonstrates its greater potential for intelligent robotic surgical scene understanding.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078629","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":"Optical 3-D Measurement for Low-SNR Scenes via Physics-Informed Zero-Shot Learning","authors":"Fuqian Li;Qican Zhang;Yajun Wang","doi":"10.1109/TIM.2025.3606025","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606025","url":null,"abstract":"In industrial 3-D metrology, the low signal-to-noise ratio (SNR) issue is commonly encountered, due to inappropriate illumination intensity, limited imaging dynamic range, or complex scene material, etc. Compared with nonlearning-based methods, deep-learning-based methods excel in efficiency and fidelity for the low SNR issue. However, most of them are data-driven, thus have limited generalization ability. Besides, they require advanced computing hardware for network training, greatly increasing the metrology cost. To tackle these problems, a physics-informed zero-shot learning (PZL) method with an ultralightweight neural network (UNN) is proposed for low-SNR scene measurement. There are two major contributions in our method. First, by blending physics priors for phase retrieval and fringe noise, a generalized PZL framework with a noisy-sinusoidal-component-to-noisy-sinusoidal-component (NS2NS) mapping is established. The low SNR issue of various challenging scenes including the low-illumination, high-dynamic-range, strong-ambient-light, and large-depth-range scenes is unified in a single enhancement framework. Moreover, no training dataset is required other than the degraded fringe itself, and the generalization ability for fringe enhancement is significantly improved. Second, based on the PZL framework, a symmetrized optimization strategy along with the UNN is proposed. Valid 3-D reconstruction of fine surface details can be achieved on computing-resource-constrained platforms, even on a CPU. Experiments verify the superiority of our method in efficiency, fidelity, generalization ability, and computing hardware cost. And to our knowledge, it is the first time such a simultaneous achievement has been accomplished.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078691","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":"Magnetic Anomaly Evaluation for Air Cargo Employing Array TMR Sensors and Deep Learning Algorithm","authors":"Yuntong Liu;Xiaoyue Meng;Feng Chen;Yang Wang;Yu Tao;Chaofeng Ye","doi":"10.1109/TIM.2025.3606055","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606055","url":null,"abstract":"The swift advancement of e-commerce has led to an increased transit of magnetic items via air freight, which may jeopardize airplane safety. It is essential to detect and assess the magnetic anomalies for maintaining flight safety. However, the industry still lacks online detection equipment for magnetic anomaly measurement. This article presents an automated magnetic anomaly detection system that employs array tunneling magnetoresistance (TMR) sensors and a deep learning calculation algorithm. The system has four sensor arrays that are located on the four sides of a cargo conveyor belt to continuously monitor the magnetic field. The magnetic abnormalities are detected and quantified as the cargo passes through the sensor arrays. A deep learning algorithm is developed to ascertain the position and magnetic moment of magnetic sources, enabling a quantitative evaluation of the risk associated with magnetic abnormalities. A prototype system including 64 sensor modules has been developed and tested on an airport cargo conveyor belt to evaluate the practicality of the technology. Experimental validation on airport cargo belts shows that, for single-source cases, the system attains a position RMSE of 3.22 cm and a dipole-angle RMSE of 1.07°. In double-source scenarios, the corresponding errors are 13.18 cm and 25.07°, confirming reliable performance across both simple and complex magnetic configurations. This automated technology significantly improves the efficiency and reliability of magnetic anomaly detection in air transportation operations compared to the traditional method of using a handheld magnetometer.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090048","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}