IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Highly Efficient Power-Line Energy Harvesting With Adaptive Matching Capacitance for Residential Self-Powered Sensing","authors":"Qiuyu Chen;Jiawen Xu;Weiqun Liu;Ruqiang Yan","doi":"10.1109/TIM.2025.3545213","DOIUrl":"https://doi.org/10.1109/TIM.2025.3545213","url":null,"abstract":"Sensor nodes for smart homes require sufficient energy to perform sensing, signal processing, and communication tasks. Magnetic field energy harvesting (MEH) from power lines emerges as a promising approach. In this study, we propose a novel method for harvesting magnetic power-line energy taking advantages of LC resonance. The system consists of magnetic coils and adjustable matching capacitance to create resonant units. With consideration of the nonlinearity of the MEH system, we illustrate how the system dynamics would shift under different currents in the power line. In response to these variations, a dynamic control strategy for adjusting the matching capacitance is proposed. Experimental studies reveal that the proposed MEH system achieves a maximum average output power of 0.31 mW at an optimal resistive load of 9 k<inline-formula> <tex-math>$Omega $ </tex-math></inline-formula> under 0.04 Arms current at 50 Hz (3.88 mW/cm3/Arms). In addition, a fully functional wireless sensor node can be powered. The proposed strategy enhances both output power and adaptive capability of the MEH system, ensuring reliable performance in self-powered wireless monitoring of power cable conditions for residential and industrial buildings.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583139","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":"Learning Interpretable and Transferable Representations via Wavelet-Constrained Transformer for Industrial Acoustic Diagnosis","authors":"Jiaxin Ren;Chenye Hu;Zuogang Shang;Yasong Li;Zhibin Zhao;Ruqiang Yan","doi":"10.1109/TIM.2025.3544700","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544700","url":null,"abstract":"With the rapid development of sensing and computing technology, transfer learning has become increasingly favored for mechanical fault diagnosis due to its ability to handle distribution differences across different domains. The interpretability of backbone models used in transfer learning, such as convolutional neural network (CNN), recurrent neural network (RNN), graph neural network (GNN), and transformer, is, however, limited, hindering their acceptance and adoption by industrial users. In order to address this problem, we propose an interpretable wavelet-constrained transformer for diagnostic tasks designed to extract local features and aggregate global information. Specifically, our model applies the dual-tree complex wavelet constraint to the transformer structure, ensuring approximate shift invariance. This improves diagnostic accuracy while reducing the number of parameters. Additionally, we explore the Einstein summation (ES) for matrix multiplication in frequency band blending after wavelet transforms to reduce computational complexity and accelerate convergence speed. In order to enhance the model’s transferability across different domains, we incorporate uncertainty-constrained loss on the model output using temperature scaling and uncertainty reweighting. This effectively reduces class confusion and improves accuracy in the target domain. Considering the necessity of noncontact measurement in mechanical systems for real-world applications, we use acoustics signals to verify the effectiveness of our transferable and interpretable model. The experimental results show that, compared with other commonly used models, our model significantly improves cross-domain diagnostic accuracy without affecting interpretability.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583167","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":"Sensitive Microwave Sensor for Detection and Quantification of Water in Adulterated Honey","authors":"Hafize Hasar;Ugur Cem Hasar;Yunus Kaya;Hamdullah Ozturk;Huseyin Korkmaz;Kadir Yuzgulec;Muharrem Karaaslan;Amir Ebrahimi;Vahid Nayyeri;Omar M. Ramahi","doi":"10.1109/TIM.2025.3545196","DOIUrl":"https://doi.org/10.1109/TIM.2025.3545196","url":null,"abstract":"Honey is a rich source of sugar and is one of the indispensable ingredients in infant foods. Thus, it can be subjected to adulteration due to its cost. Measurement techniques such as liquid chromatography and near-infrared spectroscopy, used for detecting any adulteration, are expensive and need to be conducted by highly trained personnel for off-line analysis. Microwave measurements, as a fast, simple, and relatively inexpensive analysis, have recently shown great potential in detecting adulteration within honey samples. Nonetheless, sensor types used in such measurements are conventional. In this study, a reflection-type sensitive microwave sensor terminated by a metal back is proposed for the first time in the literature for the detection and quantification of water percentage (<inline-formula> <tex-math>$delta $ </tex-math></inline-formula>) level (mass-to-mass basis) within water-adulterated honey samples. When compared with other resonance-based microwave cavity sensors, thanks to its eight strips located at the centers of two closed circular loops, it demonstrates superior frequency selectivity and sensitivity (<inline-formula> <tex-math>$S =5.13$ </tex-math></inline-formula>%) validated by full-wave 3-D simulations performed by the CST Microwave Studio and equivalent circuit analysis carried out by the Advanced Design System (ADS) software. For example, for ethanol, the proposed sensor gives a frequency shift of more than 1 GHz in the X band. Resonance frequency shift and variation of the reflection coefficient amplitude (<inline-formula> <tex-math>$|S_{11}|$ </tex-math></inline-formula>) are measured at X band to detect honey samples with up to 8% adulteration level. Three different honey samples (flower honey, highland honey, and thyme honey) were examined to test the performance and applicability of the proposed sensor.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583143","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":"Multichannel Goniospectrometer System for Near-Field and Far-Field Light Spatial Distribution","authors":"Lijun Bao;Bingsen Chen;Li Chen;Jintao Chen;Peng Zhuang;Guolong Chen;Zhong Chen;Lihong Zhu;Yijun Lu","doi":"10.1109/TIM.2025.3544703","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544703","url":null,"abstract":"The spatial distribution of light sources plays an important role for the secondary optical design of luminaires. Existing spatial detection methods suffer limitations such as large volume, slow speed, and low flexibility. In this work, we proposed a compact size, multichannel spatial spectral distribution detection system for light sources, compatible with both near- and far-fields. Sixteen-channel optical fibers with pulley blocks mounted on double U-shaped tracks enable multiple points highly efficient detection of spatial irradiance distribution of light sources. With spectrum-based Monte Carlo ray-tracing algorithm, the far-field spectral and spatial distribution at arbitrary distances, along with detailed photometric and colorimetric parameters, are derived for both spherical- and planar-receiving surface scenes. Compared with reference systems, series of experiments are conducted with six types of light-emitting diode (LED) samples, from LED chip to street lamp, to validate the accuracy, consistency, and flexibility of the proposed system, and satisfactory results are gained. Overall, the proposed system is characterized by compact size, high speed, flexibility, and compatibility for various light sources and scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564194","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":"3-D Reconstruction Method of Painted Glue Surface Based on Single Image","authors":"Liwei Lv;Fupei Wu","doi":"10.1109/TIM.2025.3545217","DOIUrl":"https://doi.org/10.1109/TIM.2025.3545217","url":null,"abstract":"Three-dimensional reconstruction is widely used in industry, while it lacks applications in the process of microcamera module mounting of painted glue surface. In this article, a method is presented for recovering the 3-D information of a painted glue surface using a monocular vision system, which utilizes constraints on both global and local information of the image to accurately reconstruct the surface. First, based on the imaging model of the monocular vision system, the composition characteristics of the painted glue image are analyzed, then a mapping model for surface height information is developed based on the specular reflection. Second, analyzing the physical characteristics of the painted glue based on the quadratic surface model, a position compensation, which is denoted as H, is introduced. Third, the incident light information, the color distribution information, and shape information are extracted from the acquired image, and the 3-D reconstruction model is built based on the camera imaging model. Finally, the unknown compensation H and the height of the glue surface are computed by constraining global and local information, which helps to recover the 3-D shape of the painted glue surface. A dataset including 87 overflow samples, 212 scratch samples, 196 deficiency samples, 261 breakage samples, and 176 fine samples was created to evaluate the method’s performance. Experimental results on small-scale samples showed low average errors, with chamfer distance (CD), Intersection over Union (IoU), mean absolute deviation (MAD), and root mean squared error (RMSE) values of 0.467, <inline-formula> <tex-math>$27.369~mu $ </tex-math></inline-formula>m, <inline-formula> <tex-math>$15.988~mu $ </tex-math></inline-formula>m, and <inline-formula> <tex-math>$16.866~mu $ </tex-math></inline-formula>m, respectively. Meanwhile, the average measurement time is 0.420 s with a maximum time of 0.426 s. Compared with other methods, the proposed method exhibits better accuracy and robustness, demonstrating good suitability and economy in reconstructing 3-D surface morphology.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564100","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":"Hardware-Based Real Time EEG Signal Analysis for Depression Detection Using Interconnected Graph-Based Features","authors":"Ramnivas Sharma;Hemant Kumar Meena","doi":"10.1109/TIM.2025.3544713","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544713","url":null,"abstract":"Depression is a prevalent mental health disorder that significantly impacts individuals’ well-being and cognitive function. Electroencephalography (EEG) signals provide valuable insights into brain activity and are widely utilized for detecting mental health conditions. However, many current techniques ignore the intricate functional connections inside the brain during distinct tasks by studying EEG channels separately. To solve these constraints, this study proposes a new technique based on graph signal processing (GSP). This work proposes a method for depression detection using interconnected graph-based features extracted from EEG signals. In particular, we employ graph Fourier transform (GFT) and graph Laplacian energy (GLE) as feature extraction techniques to capture the spatial and frequency domain properties of the functional connectivity patterns in the brain. These graph-based features are input into various different machine-learning models for classification. The proposed method developed in this research has been executed on a Xilinx PYNQ-Z2 board to verify hardware compatibility. The models are compared in terms of classification accuracy to identify the most effective technique for detecting depression. Our experimental results demonstrate that the XGBoost model achieves the highest classification accuracy of 98.92%, outperforming the individual models. Among the other implemented models, random forest (RF) and convolutional neural networks (CNNs) with long short-term memory (LSTM) (CNN+LSTM) models also exhibit strong performance, achieving accuracies of 98.84% and 97.79%, respectively. These findings suggest interconnected graph-based features offer a robust framework for accurate and reliable depression detection using EEG signals.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564230","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":"Highly Sensitive Trace Gas Sensing Using Curved Body Waist Resonant Photoacoustic Cell","authors":"Bingze He;Wenjun Ni;Chunyong Yang;Ruiming Wu;Sixiang Ran;Zhongke Zhao;Ping Lu;Perry Ping Shum","doi":"10.1109/TIM.2025.3544292","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544292","url":null,"abstract":"Photoacoustic spectroscopy (PAS) is a highly sensitive detection technology for trace gas with no background noise. Photoacoustic cell (PAC) is the most significant devices in PAS system to provide a platform for light and gas molecule interactions and amplify acoustic signals. In this research, a curved body waist resonant PAC (CBWR-PAC) is proposed for the first time, which dominates the detection limitations of acetylene (C2H2) gas reaching ppb levels. CBWR-PAC features with hyperbolic generatrix structure to enhance the Q factor and enables the maximum acoustic signal focusing at the waist region. The resonant frequency and the Q factor at normal temperature and pressure are experimentally determined to be ~3.08 kHz and 99.3, respectively. The feasibility of the system is demonstrated by achieving a minimum detection limit (MDL) of 5.70 ppb, corresponding to a normalized noise equivalent absorption (NNEA) of <inline-formula> <tex-math>$6.94cdot 10^{-10}$ </tex-math></inline-formula> cm<inline-formula> <tex-math>$^{-1} cdot $ </tex-math></inline-formula>W<inline-formula> <tex-math>$cdot $ </tex-math></inline-formula>Hz<inline-formula> <tex-math>$^{-1/2}$ </tex-math></inline-formula>. Furthermore, the Allan deviation analysis achieves the MDL to 0.542 ppb at 100-s averaging time, manifesting the long-term stability and ultralow detection limitations of the system. The results reveal that the hyperbolic generatrix structure of the PAC shed new light on trace gas detection. Particularly, the novel structure paves a new direction for the PAC optimization.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564229","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":"Frequency-Assisted Contrastive Learning With Cyclic Fine-Tuning for Rotating Machinery Fault Diagnosis Under Limited Labeled Data","authors":"Xinyu Li;Changming Cheng;Zhike Peng","doi":"10.1109/TIM.2025.3544389","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544389","url":null,"abstract":"The scarcity of labeled data severely limits the performance of traditional supervised learning-based intelligent methods for rotating machinery fault diagnosis. Recently, contrastive learning (CL) has been successfully applied to rotating machinery fault diagnosis due to its capability to learn data representations without the need for labeling information. Nevertheless, existing CL-based diagnostic methods primarily suffer from two drawbacks. First, most methods merely depend on time-domain views of signals while neglecting the significance of the frequency domain, leading to weakly discriminative representations. Second, the conventional fine-tuning strategy only relies on labeled data while discarding the unlabeled one, impairing model generalization. To tackle these drawbacks, this article proposes a novel diagnostic method based on frequency-assisted CL (FACL) and cyclic fine-tuning (CFT). In the pretraining stage, FACL utilizes the time-frequency relational contrastive loss to correlate the time view with the frequency view, allowing the frequency-view data to effectively assist the time-view contrastive learning, thus yielding representations with enhanced discriminability. In the fine-tuning stage, CFT enables both labeled and unlabeled data to contribute to model fine-tuning through pseudo labeling, enhancing model generalization. Additionally, a weighted pseudo-labeling (WPL) strategy is devised to alleviate the defect of noisy labels. Comparative and ablation experiments are conducted on two public and one self-organized datasets. The superiority of the proposed method in diagnosing faults for rotating machinery with limited labeled data is evidenced by a 39.77% accuracy improvement over supervised learning and a 28.61% improvement over other CL-based methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553126","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":"Sinewave Fitting Based on Quantization Codes and Threshold Levels","authors":"Balázs Renczes;Alessio De Angelis;Antonio Moschitta;Paolo Carbone","doi":"10.1109/TIM.2025.3544373","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544373","url":null,"abstract":"This article illustrates a method for measuring the parameters of a sinewave using information about both the codes and threshold levels in a quantizer. It is shown that by adding knowledge about the threshold levels, the input signal parameters can still be recovered when very coarse quantization is applied in a case when the conventional three-parameter least-squares (LS) sinefit method fails to produce precise results. After the description of the procedure, its sensitivity to the accuracy of the frequency estimate is analyzed. Furthermore, the robustness of the method against uncertainties in the knowledge of the threshold levels is assessed. The estimation procedure is validated by extensive simulations and by experimental results obtained by measurement on a 12-bit data acquisition board. Results show that the proposed solution can significantly outperform the three-parameter LS sinefit if the number of quantizer bits is small.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580895","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":"Planning CT Guided Limited-Angle CBCT to CT Synthesis via Content-Style Decoupled Learning","authors":"Shiyu Zhu;Zhan Wu;Zhizhou Zhang;Huazhong Shu;Shipeng Xie;Jean-Louis Coatrieux;Yang Chen","doi":"10.1109/TIM.2025.3544370","DOIUrl":"https://doi.org/10.1109/TIM.2025.3544370","url":null,"abstract":"Cone-beam computed tomography (CBCT) images are usually applied to clinical tasks such as image-guided radiation therapy due to the capability of providing accurate anatomical structures of patients. CBCT data obtained by full-angle scan takes a long scanning time and has a relatively high radiation dose, which may increase the health risks and discomfort of patients. Limited-angle CBCT (LA-CBCT) can effectively decrease scanning time and radiation dose by reducing the scanning angle range. On the other hand, it suffers from serious wedge artifacts, loss of image details, and low Hounsfield unit (HU) accuracy. Hence it is worthwhile to investigate the generation of high-quality CT-like images from LA-CBCT. However, due to neglect of the recovering of missing anatomical content caused by the limited-angle scan, traditional DL-based methods fail to generate high-quality synthetic CT from LA-CBCT directly. To solve this problem, we make full use of the bidirectional mapping between CBCT and CT domain and decouple LA-CBCT to CT synthesis into image style (context, HU) learning stage and image content (anatomical structure) learning stage. To accurately correct the image texture and intensity, an edge-enhanced generative adversarial network (EEGAN) is proposed to learn the bidirectional mapping relationship between CBCT and CT images. To recover the missing content caused by the limited-angle scan, a prior-guided content supplement network (PGCS-Net) is proposed to eliminate the limited-angle artifacts and supplement the missing anatomic structure. Results on real clinical chest and head datasets indicate that synthetic CT generated with our method can effectively improve image quality and registration quality of LA-CBCT, and has great potential in some image-guided radiotherapy tasks such as patient setup error obtaining.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570706","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}