IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Dim and Small Target Detection Based on Spatio-Temporal Jitter Estimation","authors":"Xiangsuo Fan;Tingting Li;Qing-Nan Huang;Wenlin Qin;Lei Min;Yuan Gao","doi":"10.1109/TIM.2025.3540120","DOIUrl":"https://doi.org/10.1109/TIM.2025.3540120","url":null,"abstract":"To mitigate the impact of the halo effect on target detection in backgrounds of strong light, we propose a dim and small target detection algorithm based on spatio-temporal jitter estimation. First, a multidirectional spiral vignetting correction model algorithm, which introduces temporal and spatial information to represent the gradient distribution disparities between the target and its surrounding blocks, thus estimating the optimal background and negating the influence of light halo while realizing the background suppression. Subsequently, this article introduces a multiframe energy accumulation algorithm based on jitter estimation, this algorithm combines data from the 2-D Gaussian surface fitting and the optical flow method (OFM). Based on the jitter estimated, interframe registration is conducted, efficiently facilitating multiframe energy accumulation and enabling the detection of the target signal. Verification through experiments in varying scenes indicates that the detection performance of the algorithm proposed herein is significantly enhanced and capably detecting targets.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526622","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":"Soft Fault Location and Imaging Using Residual Voltage Inversion in Cable Networks","authors":"Chuanxu Chen;Quansheng Guan;Quanxue Guan;Xin Jin;Zhan Shi","doi":"10.1109/TIM.2025.3542111","DOIUrl":"https://doi.org/10.1109/TIM.2025.3542111","url":null,"abstract":"Locations and parameters monitoring for soft faults in a cable network is of importance to prevent hard faults at an early stage and maintain the stability of the power system. The existing fault detection methods often identify faults using the fixed parameters in a reference cable model to locate the faults. However, the changes of the reference model by faults bring model mismatch, for example, the signal propagation speed is different in the line-like soft faults. The model mismatch will lead to inaccurate fault location and not to mention parameter imaging for faults. To this end, this article proposes a residual voltage inversion (RVI) method to learn the model of the cable network with unknown faults. RVI uses the residual voltages, that is, the difference between the scattering voltages measured at the ports and those generated by the current model, as the gradient to update the multiple parameter distributions of the cable network iteratively. The learned model can then be used to calculate the precise location, imaging of the length, capacitance, and resistance for line-like soft faults. The simulation results show that RVI locates the range of line-like soft faults with an accuracy over 90%, and achieves insulation layer and core conductor imaging with accuracies larger than 95% and 80%, respectively. In addition, the experimental tests are carried out to verify the feasibility and performance of RVI.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489236","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":"Adaptive Variational Mode Extraction-Based Fault Diagnosis Method for Wind Turbine Bearings Under Strong Nonstationary Regimes","authors":"Zonglin Li;Mingxing Jia;Yutong Lv;Dapeng Niu","doi":"10.1109/TIM.2025.3542149","DOIUrl":"https://doi.org/10.1109/TIM.2025.3542149","url":null,"abstract":"The nonstationary operation of wind turbines leads to evolving fault characteristics and spectral leakage in collected vibration signals. These complex operating conditions often mask weaker faults beneath more severe ones, causing existing methods to frequently overlook less prominent fault features. This article introduces a novel adaptive variational mode extraction (AVME) method to tackle this challenge. The key innovation of AVME is its data-driven approach to frequency band segmentation, which significantly reduces interference from both adjacent high-energy signals and nonperiodic signals. It utilizes K-means to segment the autopower spectrum into distinct bands and iteratively adjusts the variational mode extraction (VME) parameters to match the characteristics of each band. Furthermore, this article presents an AVME-based tacholess order tracking (TOT) technique for analyzing fault characteristic signals under nonstationary conditions. Validation of real-world wind turbine data confirms that the AVME method effectively handles complex operating conditions and accurately extracts fault characteristics from raw signals, demonstrating superior efficacy in fault feature extraction compared with spectrum kurtosis (SK), bandwidth-aware adaptive chirp mode decomposition (BA-ACMD), and variational mode decomposition (VMD).","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564015","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":"Guest Editorial Special section on IEEE MeMeA 2023","authors":"Emiliano Schena;Lorenzo Scalise","doi":"10.1109/TIM.2025.3537644","DOIUrl":"https://doi.org/10.1109/TIM.2025.3537644","url":null,"abstract":"","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-2"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422794","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":"Electronic Circuit for the Signal Conditioning of PSD Optical Sensors","authors":"José Luis Lázaro-Galilea;Álvaro De-La-Llana-Calvo;Carlos Andrés Luna-Vázquez;Alfredo Gardel-Vicente;Ignacio Bravo-Muñoz;Carlos Cruz-De-La-Torre","doi":"10.1109/TIM.2025.3542147","DOIUrl":"https://doi.org/10.1109/TIM.2025.3542147","url":null,"abstract":"This article presents the methodological process to be followed in the design of signal conditioning circuits for optical sensors (position sensitive detector (PSD) or photodiodes), which generally supply currents of nanoamperes that must be suitably managed before the analog-to-digital conversion process. The first step is to define the requirements for the signal supplied by the sensors and the signal to be sent to the analog-to-digital converter (ADC), taking into account the operating conditions and the configuration of the sensors. The various aspects to be taken into account in the design stages are presented below. In the first stage of the conditioning circuit, the output of the sensors is adapted with a high-gain transimpedance stage and low-pass filtering. In the next stages, the gain is increased to the values required to make maximum use of the dynamic range of the ADC (total gain close to 20M). Two alternatives are studied, one of first order and the other of second order. In both cases, high-pass filtering is performed to eliminate low-frequency optical signal noise, the noise bandwidth is reduced to obtain a flat response in the passband, and the output offset is limited. In the third stage, a gain adjustment is introduced and the appropriate bias is added to the signal to be delivered to the ADC. The design methodology is analyzed using SPICE to verify that the necessary requirements are met. The circuits are then implemented to verify that they behave as required.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489235","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":"MFECNet: Multifeature Fusion Extra Convolutional Network Based on FMCW Radar for Human Activity Recognition","authors":"Xinrui Yuan;Jing Li;Qiannan Chen;Guoping Zou","doi":"10.1109/TIM.2025.3541753","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541753","url":null,"abstract":"The advent of the Internet of Things (IoT) has opened up a plethora of possibilities for human activity recognition (HAR) in a multitude of domains, including smart homes and health monitoring. However, conventional techniques, such as video and optical sensors, are constrained by shortcomings pertaining to privacy protection and environmental adaptation. Frequency modulated continuous wave (FMCW) radar has emerged as a prominent area of research due to its robust anti-interference capabilities and penetration, particularly its exceptional privacy protection. Nevertheless, there is a paucity of research in the field of computationally constrained mobile devices. Furthermore, the majority of the existing studies are limited by the use of a single input feature and similar activities that are susceptible to confusion. Consequently, the practical applications of the model must strike a balance between lightweight and accuracy. In this article, a self-built FMCW radar human activity dataset comprising seven classes of activities is built, and we have conducted a targeted study on one of the hazardous activities, falling. To address the existing problem, a threshold-convolutional denoising (TCD) algorithm for the generation of feature maps, with the objective of reducing the computational cost of the system, a multifeature fusion extra convolutional neural network (MFECNet) for activity recognition is proposed. In contrast to preceding the models of HAR systems based on FMCW radar, MFECNet combines the extra convolutional attention module and the universal inverted bottleneck (UIB) structure to develop a lightweight model. Introducing range-time maps based on the Doppler-time maps, enables the realization of multifeature input and recognizes the fused features, thereby enhancing the accuracy of the recognition of confusable activities. The resulting overall accuracy is 99.67%, in which the rate of missed and false alarms of falls was reduced to 0%. Meanwhile, the MFECNet validates the generalization of the model in the Glasgow dataset with a validation set accuracy of 99.62%, which is an improvement of 1.62%–3.62% compared to the model using the same dataset in references. The results show that the model proposed in this article achieves lightweight while improving the accuracy, which is more suitable for practical application scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527570","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":"Circuits for the Measurement of Remote Resistive Sensors: A Review","authors":"Ferran Reverter;Anoop Chandrika Sreekantan;Boby George","doi":"10.1109/TIM.2025.3542136","DOIUrl":"https://doi.org/10.1109/TIM.2025.3542136","url":null,"abstract":"In this article, electronic read-out circuits recently proposed for the measurement of remote resistive sensors are presented and critically reviewed. This is carried out for remote resistive sensors with a two-, three-, and four-wire interconnection and also for resistive sensor bridges with a five- and six-wire topology. For each of the previous scenarios, a discussion is provided about conventional and novel read-out circuits, as well as the corresponding limitations. In addition, for each of the scenarios, the novel read-out circuits lately proposed in the literature are compared with each other in terms of different performance parameters. For the sensors and circuits community, this review has a twofold benefit. On the one hand, this is a summary that identifies the pros and cons of the different circuits so as to correctly select the most appropriate alternative for a given application. On the other hand, current gaps and future opportunities are identified, thus opening future lines of research.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521529","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":"Regression Coefficients Suppress Early Signal Noise in Helicopter Transient Electromagnetic System","authors":"Yue Zhang;Yanzhang Wang;Quan Xu;Qizhou Gong;Zhaowen Liu;Shilong Wang","doi":"10.1109/TIM.2025.3541792","DOIUrl":"https://doi.org/10.1109/TIM.2025.3541792","url":null,"abstract":"Due to the increasing demand for mineral resources, mineral exploration is advancing toward difficult terrain areas. When a helicopter transient electromagnetic (HTEM) system is operating in complex terrain areas, severe-induced voltage noise occurs in the early signals, which affects shallow detection. Through analysis, we found that this noise essentially comes from the slight variation in the relative position between the receiving coil and the transmitting coil, resulting in a change in mutual inductance between the two. This change causes the primary transient electromagnetic (TEM) field to change constantly and affects observations of the secondary TEM field. Frequency amplitude analysis of actual early signals shows that the noise frequency can reach 0.06 Hz, which is lower than the 25-Hz excitation frequency. Therefore, based on the assumption that the mutual inductance remains constant within each half-cycle of excitation, this article proposes a method to suppress early signal noise by calculating the regression coefficient vector of high-altitude data. This method only uses induced voltage data to suppress noise and has a short processing time. Actual exploration line results verify that the peak value of the earliest gate signal decreases from 1856.9 to 155.12 nT/s, with an early observation time of <inline-formula> <tex-math>$104.67 ; mu $ </tex-math></inline-formula>s.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489211","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":"Channel Emulator-Enabled Over-the-Air Testing Platform for Integrated Sensing and Communication Systems: Framework and Experimental Validation","authors":"Zhiqin Wang;Chunhui Li;Hao Sun;Shangbing Qiao;Wei Fan","doi":"10.1109/TIM.2025.3542113","DOIUrl":"https://doi.org/10.1109/TIM.2025.3542113","url":null,"abstract":"Integrated sensing and communication (ISAC) is considered as a promising technology for the next-generation wireless communications. As the ISAC-powered base station (BS) integrates both communication and sensing capabilities, the testing system must possess the ability to test both aspects simultaneously. In this work, a novel channel emulator (CE)-enabled over-the-air (OTA) testing platform for ISAC systems is proposed. The main idea is to extend the basic channel emulation capabilities of the CE, which has been employed mainly for communication testing, to generate deceptive echoes for sensing testing, thereby achieving a comprehensive evaluation of the joint communication and sensing functionalities of ISAC BSs. The proposed testing platform is cost-efficient, as it extends the functionality of the existing CE without introducing new and expensive instruments. The platform is also capable of emulating diverse realistic propagation scenarios for ISAC testing. Since the CE is not specifically designed for sensing test, we conduct experimental investigations to explore the capabilities and limitations of the CE in delay and Doppler emulations. Finally, a commercial vehicle-mounted millimeter-wave (mm-Wave) radar is tested using the proposed platform, and the results indicate that the CE possesses potential to support ISAC BS testing.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527527","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":"Fault Diagnosis of Power Transformer Using Data Enhancement–Input Feature Preference Learning Framework","authors":"Jianwei Shao;Junhua Wang;Xiao Pan;Ruilin Wang;Shuxun Liu;Ziqing Wang;Yanpu Zhao","doi":"10.1109/TIM.2025.3542112","DOIUrl":"https://doi.org/10.1109/TIM.2025.3542112","url":null,"abstract":"This proposes a learning framework for transformer fault diagnosis using a convolutional neural network (CNN), with data enhancement and input feature preference. The conditional variational autoencoder (CVAE) model is used to expand each category of samples. The binary encoded genetic algorithm (BEGA) is utilized to optimize the input features, while the diagnostic effectiveness of combinations containing different input features is evaluated using CNN fault diagnosis algorithms. The diagnosis results are comprehensively evaluated using a confusion matrix and multiple indicators. The test results indicate that the proposed data enhancement approach effectively addresses misclassification for minority samples, enhancing fault diagnosis accuracy. The accuracy and recall rate of diagnosing small sample fault types improved by 11.9% and 23.6%, respectively, compared to the fault diagnosis results before sample enhancement. In addition, the learning framework achieved higher diagnostic accuracy with nine preferred input features. Comparing the gas concentration features and initial gas features, the diagnostic accuracy of the preferred input features increased by 4.90% and 3.29%, respectively. This learning framework is applicable to sample imbalances and is somewhat extensible.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564032","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}