IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Accurate High-Speed Equivalent-Time Sampling Receiver: Architecture and Performance Metrics","authors":"Aaron D. Pitcher;Charl W. Baard;Mihail S. Georgiev;Natalia K. Nikolova","doi":"10.1109/TIM.2025.3604952","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604952","url":null,"abstract":"An equivalent-time (ET) sampling ultra-wideband (UWB) dual-channel receiver is proposed, which is controlled by a field-programmable gate array (FPGA). It has a programmable repetition period and ET sampling rate [up to 20 gigasamples per second (GSa/s)]. The architecture employs a programmable delay chip (PDC) to achieve ultra-high speed of over 8900 traces/s for a typical <inline-formula> <tex-math>$1~mu $ </tex-math></inline-formula>s repetition period. Compared with previously reported high-speed (PDC-based) receivers, it offers superior time-sampling accuracy. The design incorporates a custom dual-channel radio frequency (RF) front end with a low-jitter clock source, critical in achieving time-sampling stability. Importantly, a simple yet effective method is proposed to correct the systematic timebase distortions due to the PDC, whose delay inaccuracies are the main signal-degradation factor in ET receivers realizing picosecond sampling intervals. The realized low-cost system operates as a high-speed oscilloscope with a 10-dB receiver bandwidth of 6 GHz and with accuracy comparable to that of bench-top high-speed oscilloscopes. Performance metrics and measurement procedures are proposed to evaluate and compare time-sampling receivers. These are applied to the proposed receiver, including tests as part of a compact pulsed radar. Its performance is compared with two high-speed bench-top oscilloscopes as well as previously reported ET receiver prototypes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11147170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036781","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":"High-Efficiency Calibration Methodology of Interchannel Mismatches in TIADCs Based on Digital Orthogonal Local Oscillation Signal","authors":"Xin Li;Mengdi Miao;Ying Pan;Jiaqi Chen;Chenghu Dai;Yu Liu;Chunyu Peng;Xiulong Wu;Zhiting Lin","doi":"10.1109/TIM.2025.3604984","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604984","url":null,"abstract":"The article proposes a novel foreground integrated calibration method based on the digital orthogonal local oscillation signal to address interchannel mismatches in time-interleaved analog-to-digital converters (TIADCs). In contrast to existing approaches, this methodology eliminates the requirement for analog auxiliary circuit configuration while obviating the necessity of employing extensive matrix operations and inverse Fourier transform computations. The employment of the phase difference elimination technique, in conjunction with the approximate calculation of the inverse tangent function, has been demonstrated to yield enhanced calibration performance and optimized design resource utilization. The register transfer level (RTL) simulation results demonstrate that for a 12-bit four-channel TIADC model, the amplitude of the mismatch-induced spurs is suppressed below −82 dB following calibration, and signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) reach 72.6- and 83.7-dB frequency of sampling (FS), respectively. The experimental results of the field-programmable gate array (FPGA)-based measurement demonstrate that, under the sinusoidal input frequencies of 498 and 1448 MHz, the SNDR increased by 27.4 and 24.5 dB, respectively, both prior to and following calibration. Furthermore, the SFDR increased by 35.7 and 30.7 dB, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036814","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":"Torque Measurement of Heavy Truck Gearbox Input Shaft Using Wireless Telemetry With a Resistance Sensing Model","authors":"Yingming Ren;Shanheng Yan;Jianfeng Li;Lingshuo Meng;Zongyang Zhang;Houhua Sun;Xin Xie;Wanyu Sun","doi":"10.1109/TIM.2025.3604976","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604976","url":null,"abstract":"The oil-seal structure positioned around the gearbox input shaft limits the installation of signal transmission devices for torque measurement. To overcome this limitation, this study proposes a novel torque measurement method and system for determining gearbox input shaft torque using a redesigned oil-seal structure. This system employs two double-shear strain gauges to detect the input shaft deformation resulting from applied torque and transmits the signal based on wireless telemetry. A finite element simulation model was developed to evaluate the mechanical integrity of the redesigned oil-seal structure. The torque measurement accuracy was assessed using a static statistical model. Furthermore, the reliability of the input shaft torque measurement was validated based on the transmission ratio by comparing the torque values of the gearbox input shaft and transmission shaft, yielding an average error of 2.96%. This study offers a technical solution for torque measurement in rotating shafts within constrained spaces where traditional methods are not feasible.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036943","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":"Gaussian Mixture Filter-Incorporated Self-Attention Residual Neural Network for UAV Joint Error Identification and Target Localization","authors":"Xiuli Xin;Xinran Chen;Hongyu Zhou;Xiaoxue Feng;Weixing Li;Zhenxu Li;Feng Pan","doi":"10.1109/TIM.2025.3604958","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604958","url":null,"abstract":"Target localization is a key technology for unmanned aerial vehicle (UAV) applications in various fields, such as target tracking and task planning. However, the accuracy of UAV localization is significantly affected by systematic and random errors in attitude data, and the nonlinearity of the measurement model, together with the unknown distribution of measurement noise. To achieve robust and precise localization in long-distance oblique scenarios based on dynamic platforms, this article proposes a Gaussian Mixture Filter-incorporated self-attention (SA) Residual Neural Network (GMAR) algorithm for target localization. Firstly, an end-to-end SA residual neural network (SA-ResNN) model is built to accurately model both systematic and random errors in attitude angle. The SA mechanism is innovatively introduced to enhance the global feature representation capability of the residual module. Then, the Gaussian mixture (GM) filter utilizes a GM model to model the prior and posterior probability density functions, which can effectively capture the uncertainty in the state probability density function under nonlinear measurement models and enhance the robustness of the localization system. Finally, simulations and flight experiments demonstrate that the proposed GMAR algorithm can significantly improve the localization accuracy and robustness of ground targets in long-distance oblique scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050799","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":"YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multibranch Feature Interaction","authors":"Ziyu Lin;Yunfan Wu;Yuhang Ma;Junzhou Chen;Ronghui Zhang;Jiaming Wu;Guodong Yin;Liang Lin","doi":"10.1109/TIM.2025.3604925","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604925","url":null,"abstract":"Traffic sign detection is essential for autonomous driving and advanced driver assistance systems (ADASs). However, existing methods struggle to address the challenges of poor image quality and insufficient information under low-light conditions, leading to a decline in detection accuracy and affecting driving safety. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. YOLO-LLTS introduces three main contributions: the high-resolution feature map for small object detection (HRFM-SOD) module retains more information about distant or tiny traffic signs compared to traditional methods; the multibranch feature interaction attention (MFIA) module interacts features with different receptive fields to improve information utilization; the prior-guided feature enhancement (PGFE) module enhances detection accuracy by improving brightness, edges, contrast, and supplementing detailed information. Additionally, we construct a new dataset, Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios. Experiments show that YOLO-LLTS achieves state-of-the-art performance, outperforming previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, 7.5% mAP50 and 9.8% mAP50:95 on GTSDB-night, and superior results on CCTSDB2021. Deployment on edge devices confirms its real-time applicability and effectiveness. The code and the dataset are available at <uri>https://github.com/linzy88/YOLO-LLTS</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-18"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051010","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":"Machine Learning-Based High-Voltage Circuit Breaker Defect Classification Utilizing Savitzky–Golay Filter","authors":"Sajjad Asefi;Soheil Asefi;Hossein Afshari;Jako Kilter;Ebrahim Shayesteh;Patrik Hilber;Tommie Lindquist","doi":"10.1109/TIM.2025.3604980","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604980","url":null,"abstract":"High-voltage circuit breakers (HVCBs) are critical components in power systems to maintain reliable operation. Accurate condition monitoring of HVCBs is vital to reduce maintenance costs and consequently to enhance the grid reliability. However, achieving this with low-cost measurement devices, which often provide noisy signals, poses a significant challenge. In this article, a novel defect classification framework for HVCBs is proposed that uses the Savitzky–Golay filter to preprocess the most common condition monitoring signal, which is the trip/close coil current. This filter is well-known for denoising while preserving critical signal features. Following signal preprocessing, a robust defect detection and classification methodology is introduced, combining time-series similarity assessment techniques, such as Euclidean distance and dynamic time warping (DTW), with machine learning (ML) algorithms. Moreover, an experimental setup is designed to emulate the behavior of an HVCB’s coil mechanism. To further enhance the model transparency, Shapley additive explanations (SHAP) analysis is applied, providing interpretability into feature contributions toward model decisions. The obtained results validate the effectiveness of the proposed hybrid approach, demonstrating its potential to provide a cost-effective, accurate, and reliable solution for HVCB condition monitoring.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021364","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 Prototype Learning Framework Based on Continual Learning for Motor Incremental Fault Diagnosis Under Few-Shot Conditions","authors":"Heng Shan;Xiaofei Zhang;Weizhi Liang;Zeping Wu;Haidong Shao;Guojun Qin","doi":"10.1109/TIM.2025.3604938","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604938","url":null,"abstract":"In the actual industrial scenario, motor fault classes gradually increase, and the fault data need to be acquired incrementally during the service. Continual learning (CL) has been introduced to the field of fault diagnosis (FD), aiming at constructing an FD model with continuous evolution capability. However, the current research has the following limitations: 1) assigning parameters for each task independently leads to a linear increase in model complexity and 2) it relies heavily on the quality and completeness of historical data and the research on incremental FD under few-shot conditions is not sufficient. To address the above limitations, this article proposes a prototype learning framework for motor incremental FD (PLFIFD) under few-shot conditions. First, a prototype classifier based on distance metric is proposed to avoid uncontrollable changes due to the update of parameters in the output layer. The prototype set is dynamically expanded instead of assigning network parameters individually. Second, a multiobjective loss function is designed to jointly optimize the classification boundary and prototype space distribution to enhance intraclass compactness and interclass separability and to improve the generalization ability under few-shot conditions and stability in dynamic scenarios. Finally, the effectiveness of PLFIFD is verified on induction motor (IM) and permanent magnet synchronous motor (PMSM).","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011341","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":"LungScope: An Intelligent Embedded System With a Lightweight Model for Real-Time Lung Sound Analysis","authors":"Fan Wang;Xiaochen Yuan;Guoheng Huang;Chan-Tong Lam;Sio-Kei Im","doi":"10.1109/TIM.2025.3604924","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604924","url":null,"abstract":"Lung auscultation is crucial for early respiratory disease diagnosis. However, limited resources hinder accurate and timely assessment in many regions. In this article, we present LungScope, an intelligent embedded system designed for real-time lung sound classification. We first introduce LungLite, a lightweight classification model optimized based on our previous work, targeting deployment in resource-constrained environments. The architecture adopts redesigned LungLite blocks to reduce computational complexity while maintaining accuracy. In addition, it integrates advanced attention modules, such as SimAM and CBAM, to further enhance classification accuracy. LungLite was evaluated on the SPRSound dataset, achieving SC scores of 0.7008 for the three-class classification task and 0.5657 for the five-class classification task, with only 2.984M parameters and 0.494G FLOPs. LungLite is further integrated into LungScope by deploying it on a Raspberry Pi 4 Model B (Pi4B) with a custom-designed expansion circuit board. This integration enables optimized control, real-time lung sound acquisition, classification, and result display. The proposed portable embedded system provides an effective solution for real-time lung sound classification, supporting the basic service of healthcare in resource-limited urban settings.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078677","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":"Antinoise Bearing Fault Diagnosis Using Time-Reassigned Multisynchrosqueezing Transform and Complex Sparse Learning Dictionary","authors":"Wu Deng;Hongbin Li;Huimin Zhao","doi":"10.1109/TIM.2025.3604987","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604987","url":null,"abstract":"Bearings are core components of rotating machinery, and their operating status monitoring and fault diagnosis are crucial for equipment health management. Accurately identifying fault impulse signatures in bearing signals under complex operating conditions is a key challenge in bearing fault diagnosis. Therefore, this article proposes a bearing fault time–frequency diagnosis method [time-frequency complex sparse coding K-SVD (TFCSCK)] based on the time-reassigned multisynchrosqueezing transform (TMSST) and the improved k-singular value decomposition (KSVD) algorithm [complex KSVD (CKSVD)]. First, TMSST is used to obtain a high-resolution time–frequency representation (TFR) to enhance the accuracy of impulse signature localization. Second, to address the problem that the traditional KSVD algorithm only works in the real domain and ignores time–frequency phase information, a CKSVD algorithm is proposed. This algorithm utilizes complex sparse coding and dictionary updating to preserve the time–frequency phase characteristics, improving the robustness of feature extraction under complex interference. Third, a transient component time–frequency mask decomposition (TFTCD) algorithm is proposed. This algorithm preserves the time-domain waveform details of the fault impulse through mask-weighted separation and inverse transform reconstruction. Finally, the effectiveness of the proposed method is verified using numerical simulations and real fault signals. The experimental results show that TFCSCK improves the accuracy of inner race fault frequency extraction by 2.53% compared to TMSST and KSVD on the inner race data of the TYS1-8 platform. Based on measured data from aircraft engine bearings, even when both TMSST and KSVD fail, TFCSCK still extracts high-speed rotational frequency and inner race fault frequency.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027977","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":"KANet: Memory-Managed Recurrent Kolmogorov–Arnold Network for Indoor Inertial Navigation","authors":"Qiaolin Pu;Yunhai Li;Mu Zhou","doi":"10.1109/TIM.2025.3604962","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604962","url":null,"abstract":"Although inertial measurement units (IMUs) have emerged as a promising solution for indoor positioning, due to their low cost, energy efficiency, and infrastructure-independent nature, the accumulation of measurement errors remains a critical challenge that hinders their widespread application. In recent years, with the advancement of deep learning, data-driven methods have been proven to effectively solve this issue through trained neural networks. However, current mainstream data-driven models predominantly adopt simple multilayer perceptron (MLP) architectures. These architectures exhibit inherent limitations in scalability and interpretability when processing IMU data, resulting in inefficient parameter utilization and suboptimal positioning accuracy in inertial navigation applications. Hence, this article proposes a novel data-driven model named KANet, which consists of two key components: recurrent Kolmogorov–Arnold networks (RKANs) and long short-term memory (LSTM)-inspired memory management units. RKAN fundamentally integrates the powerful function approximation capability of Kolmogorov–Arnold networks (KANs) with the sequential modeling strength of recurrent neural networks (RNNs), while the LSTM-inspired memory mechanisms enhance temporal dependency modeling in long sequences. By leveraging learnable activation functions for nonlinear pattern representation with effective memory retention characteristics, this architecture enhances the model’s capacity to process complex sequential IMU data. This innovative design overcomes the limitations of traditional models in processing complex sequence patterns, demonstrating competitive accuracy and improved parameter efficiency in multistep time-series prediction. Experimental results demonstrate that the proposed framework achieves superior performance compared with traditional PDR models and state-of-the-art neural networks, exhibiting higher parameter efficiency across diverse datasets while maintaining high positioning accuracy. In addition, comprehensive benchmarking against tactical-grade IMU systems reveals that KANet-assisted consumer-grade MEMS sensors can achieve positioning accuracy that, in certain aspects, approaches or even matches the performance levels of higher precision IMU systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998069","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}