Pub Date : 2025-09-10DOI: 10.1109/TIM.2025.3608335
Qingying He;Xiao Li;Chengming Tian;Fangyu Shen;Yuanyuan Liu;Hao Sun
High-precision pose estimation using fiducial markers has many applications in medical device tracking, virtual reality alignment, navigation, and more. However, the accuracy of pose estimation and detection capabilities are often constrained by the shape and scale of the fiducial marker plane. In this article, we propose a triangular planar fiducial marker affixed to a positive icosahedron for pose estimation. This design expands the angular observation range, increases the marker scale, and consequently enhances estimation accuracy and recognition distance. The 2-D coordinates of the feature points from the markers are detected and extracted from the environment. Subsequently, the 3-D coordinates of these feature points are obtained using the triangulation method. This process results in the formation of 2-D–3-D point pairs. High-quality interior points are then filtered using the random sample consensus (RANSAC) method. The initial position is determined through the efficient perspective-n-point (EPnP) method, followed by the application of Levenberg–Marquardt (LM) optimization. We evaluated the performance of IcoTag3D through both simulations and physical experiments. The results from the simulation experiments indicate that IcoTag3D exhibits significantly lower maximum rotation angle error, reprojection error, and translation error at the submillimeter level. In addition, it demonstrates an improved recognition distance compared with the method of attaching ArUco markers to icosahedra. Physical experiments have further confirmed the feasibility of IcoTag3D.
{"title":"IcoTag3D: Enhanced 6-DoF Pose Estimation for Robotic Arms Using TriangleTag Markers on an Icosahedron","authors":"Qingying He;Xiao Li;Chengming Tian;Fangyu Shen;Yuanyuan Liu;Hao Sun","doi":"10.1109/TIM.2025.3608335","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608335","url":null,"abstract":"High-precision pose estimation using fiducial markers has many applications in medical device tracking, virtual reality alignment, navigation, and more. However, the accuracy of pose estimation and detection capabilities are often constrained by the shape and scale of the fiducial marker plane. In this article, we propose a triangular planar fiducial marker affixed to a positive icosahedron for pose estimation. This design expands the angular observation range, increases the marker scale, and consequently enhances estimation accuracy and recognition distance. The 2-D coordinates of the feature points from the markers are detected and extracted from the environment. Subsequently, the 3-D coordinates of these feature points are obtained using the triangulation method. This process results in the formation of 2-D–3-D point pairs. High-quality interior points are then filtered using the random sample consensus (RANSAC) method. The initial position is determined through the efficient perspective-n-point (EPnP) method, followed by the application of Levenberg–Marquardt (LM) optimization. We evaluated the performance of IcoTag3D through both simulations and physical experiments. The results from the simulation experiments indicate that IcoTag3D exhibits significantly lower maximum rotation angle error, reprojection error, and translation error at the submillimeter level. In addition, it demonstrates an improved recognition distance compared with the method of attaching ArUco markers to icosahedra. Physical experiments have further confirmed the feasibility of IcoTag3D.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090079","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}
Deep learning techniques have made impressive progress in the field of remote sensing change detection (RSCD) in recent years. However, existing RSCD methods still exhibit limitations in bi-temporal feature fusion, making it difficult to adequately mine critical change information. Moreover, they often overlook the semantic inconsistency between features at different levels during feature aggregation, which limits the accurate reconstruction of the internal structure of change objects. To address the above issues, this article proposes a multiscale spatial frequency fusion and prior change guidance network, called MPNet, aiming to enhance the complete reconstruction of change objects. The proposed MPNet has two advantages. First, a multiscale spatial frequency fusion (MSFF) module is proposed to capture the bi-temporal features in the frequency domain and different scale spatial domains, and perform dynamic adaptive fusion through the attention mechanism, thereby realizing the adequate mining of global and local change information. Second, a prior change guidance (PCG) module is designed to generate a prior change mapping by fusing high-level semantic information with low-level texture details. This prior mapping guides multilevel feature learning, effectively correcting semantic discrepancies across different feature layers and enabling the extraction of more discriminative change feature representations. Experimental results on the LEVIR-CD, WHU-CD, and SYSU-CD datasets demonstrate that the proposed MPNet significantly outperforms other state-of-the-art (SOTA) methods in the complete detection of the internal structure of change objects. The code is available at https://github.com/NjustHGWei/MPNet.
{"title":"Multiscale Spatial Frequency Fusion and Prior Change Guidance Network for Remote Sensing Change Detection","authors":"Hongguang Wei;Yuan Liu;Yueran Ma;Dongdong Pang;Yuanxin Ye;Xiubao Sui;Qian Chen","doi":"10.1109/TIM.2025.3608333","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608333","url":null,"abstract":"Deep learning techniques have made impressive progress in the field of remote sensing change detection (RSCD) in recent years. However, existing RSCD methods still exhibit limitations in bi-temporal feature fusion, making it difficult to adequately mine critical change information. Moreover, they often overlook the semantic inconsistency between features at different levels during feature aggregation, which limits the accurate reconstruction of the internal structure of change objects. To address the above issues, this article proposes a multiscale spatial frequency fusion and prior change guidance network, called MPNet, aiming to enhance the complete reconstruction of change objects. The proposed MPNet has two advantages. First, a multiscale spatial frequency fusion (MSFF) module is proposed to capture the bi-temporal features in the frequency domain and different scale spatial domains, and perform dynamic adaptive fusion through the attention mechanism, thereby realizing the adequate mining of global and local change information. Second, a prior change guidance (PCG) module is designed to generate a prior change mapping by fusing high-level semantic information with low-level texture details. This prior mapping guides multilevel feature learning, effectively correcting semantic discrepancies across different feature layers and enabling the extraction of more discriminative change feature representations. Experimental results on the LEVIR-CD, WHU-CD, and SYSU-CD datasets demonstrate that the proposed MPNet significantly outperforms other state-of-the-art (SOTA) methods in the complete detection of the internal structure of change objects. The code is available at <uri>https://github.com/NjustHGWei/MPNet</uri>.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090085","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}
Pub Date : 2025-09-10DOI: 10.1109/TIM.2025.3608349
Zichen Wang;Tao Zhang;Yunjie Yang;Qi Wang
Complex-valued electrical impedance tomography (Cv-EIT) has insights that visualize the electrical properties (conductivities and permittivity) of various healthy and injured organizations/tissues, which is a promising technique in industrial and medical imaging. Nevertheless, most of the current research has mainly focused on the conductivity parameter, ignoring the influence of the impact on the permittivity. To address the above challenges, a novel learning-based Cv-EIT image reconstruction method is proposed, referred to as DeepcomplexEIT, which could reconstruct the distributions of conductivity and permittivity simultaneously with the multiphysics information interactions. The DeepcomplexEIT is designed to obtain high-quality complex-valued admittivity distributions by leveraging the advantages of both convolutional neural networks and Transformers. In detail: 1) the U-shaped architecture is modified using depth-separable convolution and pooling in the complex-valued domain; 2) the 2-D filter with learnable cutoff frequency is proposed for featuring the multifrequency information in the spatial and spectral domains; and 3) a novel complex-valued Vision Transformer (Cv-ViT) and cross-domain attention are designed for featuring the local–global multiscale information with the multiphysics interactions and complementation. Our extended experiments demonstrate that DeepcomplexEIT outperforms state-of-the-art (SOTA) complex-valued models in terms of the complicated shape features and multiphase distributions with respect to the admittivity. The performances are evaluated using the tank phantoms with a 16-electrode EIT system and about 67-dB signal-to-noise ratio (SNR), where the average quantitative metrics (conductivity/permittivity) are root-mean-square error (RMSE) of 1.982/0.946 and structural similarity index metric (SSIM) of 0.992/0.994 with multiphase inclusions, as well as RMSE of 2.593/2.506 and SSIM of 0.989/0.992 with lung-shaped inclusions, respectively. Overall, the DeepComplexEIT is expected to further promote the multiparameter visualization in practical applications.
{"title":"DeepcomplexEIT: Exploring the Image Reconstruction of Complex-Valued EIT","authors":"Zichen Wang;Tao Zhang;Yunjie Yang;Qi Wang","doi":"10.1109/TIM.2025.3608349","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608349","url":null,"abstract":"Complex-valued electrical impedance tomography (Cv-EIT) has insights that visualize the electrical properties (conductivities and permittivity) of various healthy and injured organizations/tissues, which is a promising technique in industrial and medical imaging. Nevertheless, most of the current research has mainly focused on the conductivity parameter, ignoring the influence of the impact on the permittivity. To address the above challenges, a novel learning-based Cv-EIT image reconstruction method is proposed, referred to as DeepcomplexEIT, which could reconstruct the distributions of conductivity and permittivity simultaneously with the multiphysics information interactions. The DeepcomplexEIT is designed to obtain high-quality complex-valued admittivity distributions by leveraging the advantages of both convolutional neural networks and Transformers. In detail: 1) the U-shaped architecture is modified using depth-separable convolution and pooling in the complex-valued domain; 2) the 2-D filter with learnable cutoff frequency is proposed for featuring the multifrequency information in the spatial and spectral domains; and 3) a novel complex-valued Vision Transformer (Cv-ViT) and cross-domain attention are designed for featuring the local–global multiscale information with the multiphysics interactions and complementation. Our extended experiments demonstrate that DeepcomplexEIT outperforms state-of-the-art (SOTA) complex-valued models in terms of the complicated shape features and multiphase distributions with respect to the admittivity. The performances are evaluated using the tank phantoms with a 16-electrode EIT system and about 67-dB signal-to-noise ratio (SNR), where the average quantitative metrics (conductivity/permittivity) are root-mean-square error (RMSE) of 1.982/0.946 and structural similarity index metric (SSIM) of 0.992/0.994 with multiphase inclusions, as well as RMSE of 2.593/2.506 and SSIM of 0.989/0.992 with lung-shaped inclusions, respectively. Overall, the DeepComplexEIT is expected to further promote the multiparameter visualization in practical applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-17"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090167","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}
Pub Date : 2025-09-10DOI: 10.1109/TIM.2025.3608327
Gaoyi Zhu;Yong Zhou;Jie Wang;Mei Wang;Lanxin Jiang;Yiwei Wang
Fully supervised image segmentation can effectively extract power line (PL) and transmission tower (TT) from aerial images. However, its performance is constrained by the lack of sufficiently detailed and high-confidence annotations. Furthermore, PL is the hard sample due to its slender shape and low proportion of feature information. To address the aforementioned challenges, this work innovatively introduces unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) into the PL and TT segmentation task, and designs a new framework named UDASSL-Seg. Specifically, UDA is employed for pretraining, enabling the segmentation network to learn generic features and knowledge of hard sample. Subsequently, SSL is employed for fine-tuning, enabling the segmentation network to acquire generalization capabilities on the target dataset. Additionally, in order to further augment the segmentation network’s performance, the new designed dynamic co-perturbation consistency (DCPC) was proposed to extend the perturbation space by combining multiple image-level and dynamic feature-level perturbations. Extensive experiments were conducted on both self-built and public datasets. The results demonstrate the superiority of the proposed UDASSL-Seg over several state-of-the-art semi-supervised segmentation methods.
{"title":"Combining Unsupervised Domain Adaptation and Semi-Supervised Learning for Power Line and Transmission Tower Segmentation","authors":"Gaoyi Zhu;Yong Zhou;Jie Wang;Mei Wang;Lanxin Jiang;Yiwei Wang","doi":"10.1109/TIM.2025.3608327","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608327","url":null,"abstract":"Fully supervised image segmentation can effectively extract power line (PL) and transmission tower (TT) from aerial images. However, its performance is constrained by the lack of sufficiently detailed and high-confidence annotations. Furthermore, PL is the hard sample due to its slender shape and low proportion of feature information. To address the aforementioned challenges, this work innovatively introduces unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) into the PL and TT segmentation task, and designs a new framework named UDASSL-Seg. Specifically, UDA is employed for pretraining, enabling the segmentation network to learn generic features and knowledge of hard sample. Subsequently, SSL is employed for fine-tuning, enabling the segmentation network to acquire generalization capabilities on the target dataset. Additionally, in order to further augment the segmentation network’s performance, the new designed dynamic co-perturbation consistency (DCPC) was proposed to extend the perturbation space by combining multiple image-level and dynamic feature-level perturbations. Extensive experiments were conducted on both self-built and public datasets. The results demonstrate the superiority of the proposed UDASSL-Seg over several state-of-the-art semi-supervised segmentation methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-17"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110269","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}
Pub Date : 2025-09-10DOI: 10.1109/TIM.2025.3608351
Yanhong Guo;Zenghua Liu;Mengqi Su;Jinjie Cheng;Kunsong Zheng;Yang Zheng;Xin Zhao;Cunfu He
HBVD-EMAT is an electromagnetic acoustic transducer (EMAT) composed of a Halbach magnet and a variable distance meander-line coil. By introducing a linear frequency-modulated (LFM) signal into the coil, wideband pulse compression surface waves can be generated. This article proposes an optimization method for HBVD-EMAT based on the fusion of orthogonal experiment and a multilayer perceptron (MLP) to enhance its performance in both the time and frequency domains. First, the finite-element simulation method is used to perform a four-factor, five-level orthogonal experiment on the size of the Halbach magnet. Then, the time- and frequency-domain response variables of the signal from the simulation results are extracted to analyze the orthogonal experimental results. The EMAT performance evaluation index is constructed based on this analysis. Finally, the MLP model is established with the performance evaluation index as the objective function. The orthogonal experimental results are used as training data to predict the optimal EMAT factor-level combination corresponding to the maximum objective function. The EMAT detection experimental results show that, compared with the nonoptimized HBVD-EMAT, the increase of incident surface wave generated by the optimized HBVD-EMAT in four response variables is 98%, 26%, 95%, and 10%, respectively. EMAT performance evaluation index is increased from 0.13 to 0.86. After optimization, the signal-to-noise ratio (SNR) of EMAT’s crack defect reflection signal and transmission signal is increased by 11.4 and 12.5 dB, respectively.
{"title":"Optimization of HBVD-EMAT Based on Orthogonal Experimental and Multilayer Perceptron Fusion Method","authors":"Yanhong Guo;Zenghua Liu;Mengqi Su;Jinjie Cheng;Kunsong Zheng;Yang Zheng;Xin Zhao;Cunfu He","doi":"10.1109/TIM.2025.3608351","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608351","url":null,"abstract":"HBVD-EMAT is an electromagnetic acoustic transducer (EMAT) composed of a Halbach magnet and a variable distance meander-line coil. By introducing a linear frequency-modulated (LFM) signal into the coil, wideband pulse compression surface waves can be generated. This article proposes an optimization method for HBVD-EMAT based on the fusion of orthogonal experiment and a multilayer perceptron (MLP) to enhance its performance in both the time and frequency domains. First, the finite-element simulation method is used to perform a four-factor, five-level orthogonal experiment on the size of the Halbach magnet. Then, the time- and frequency-domain response variables of the signal from the simulation results are extracted to analyze the orthogonal experimental results. The EMAT performance evaluation index is constructed based on this analysis. Finally, the MLP model is established with the performance evaluation index as the objective function. The orthogonal experimental results are used as training data to predict the optimal EMAT factor-level combination corresponding to the maximum objective function. The EMAT detection experimental results show that, compared with the nonoptimized HBVD-EMAT, the increase of incident surface wave generated by the optimized HBVD-EMAT in four response variables is 98%, 26%, 95%, and 10%, respectively. EMAT performance evaluation index is increased from 0.13 to 0.86. After optimization, the signal-to-noise ratio (SNR) of EMAT’s crack defect reflection signal and transmission signal is increased by 11.4 and 12.5 dB, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073277","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}
Gyroscopes play a pivotal role in applications ranging from navigation and robotics to aerospace and consumer electronics, where denoising is often critical to improve overall system performance. Traditional Kalman-based filters are often regarded as the gold standard for inertial sensor denoising, yet they require assumptions on the system’s dynamics that may not always hold, particularly in the presence of abrupt or unpredictable maneuvers. Several alternative approaches avoid such assumptions, but typically exhibit inferior performance compared to Kalman filters (KFs). Here we report on a novel wavelet-based denoising algorithm that operates in real time without relying on prior knowledge of the sensor’s dynamic conditions. Our technique adaptively calibrates the threshold by modeling noise with a generalized Gaussian distribution (GGD) and adjusts it according to the ongoing signal variance. This strategy offers two core advantages: it preserves relevant signal discontinuities and handles broad noise distributions effectively, including non-Gaussian noise. We validate the algorithm on two distinct gyroscope platforms: a state-of-the-art fiber optic gyroscope, characterized by low noise and non-Gaussian behavior, and a commercial MEMS gyroscope with primarily Gaussian noise. Standard test signals—such as blocks, step, heavisine, and Doppler—reveal that our approach surpasses the KF by up to 1 dB and outperforms alternative wavelet-based techniques by at least 4 dB in signal-to-noise ratio (SNR) enhancement. Furthermore, the algorithm exhibits minimal overshoot at signal discontinuities, ensuring accurate angular rate reconstruction. These results establish our method as a high-performance and robust solution for gyroscope denoising especially in high-end inertial sensing. The algorithm operates without any prior knowledge of the host platform’s motion model; it relies only on weak, sensor-level statistical assumptions that are satisfied by practically all gyroscopes.
{"title":"Gyroscope Real-Time Denoising by an Adaptive Threshold Wavelet Algorithm: Achieving Over 12 dB SNR Improvement","authors":"Teresa Natale;Pedro Bossi Núñez;Ludovico Dindelli;Francesco Dell’Olio","doi":"10.1109/TIM.2025.3608316","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608316","url":null,"abstract":"Gyroscopes play a pivotal role in applications ranging from navigation and robotics to aerospace and consumer electronics, where denoising is often critical to improve overall system performance. Traditional Kalman-based filters are often regarded as the gold standard for inertial sensor denoising, yet they require assumptions on the system’s dynamics that may not always hold, particularly in the presence of abrupt or unpredictable maneuvers. Several alternative approaches avoid such assumptions, but typically exhibit inferior performance compared to Kalman filters (KFs). Here we report on a novel wavelet-based denoising algorithm that operates in real time without relying on prior knowledge of the sensor’s dynamic conditions. Our technique adaptively calibrates the threshold by modeling noise with a generalized Gaussian distribution (GGD) and adjusts it according to the ongoing signal variance. This strategy offers two core advantages: it preserves relevant signal discontinuities and handles broad noise distributions effectively, including non-Gaussian noise. We validate the algorithm on two distinct gyroscope platforms: a state-of-the-art fiber optic gyroscope, characterized by low noise and non-Gaussian behavior, and a commercial MEMS gyroscope with primarily Gaussian noise. Standard test signals—such as blocks, step, heavisine, and Doppler—reveal that our approach surpasses the KF by up to 1 dB and outperforms alternative wavelet-based techniques by at least 4 dB in signal-to-noise ratio (SNR) enhancement. Furthermore, the algorithm exhibits minimal overshoot at signal discontinuities, ensuring accurate angular rate reconstruction. These results establish our method as a high-performance and robust solution for gyroscope denoising especially in high-end inertial sensing. The algorithm operates without any prior knowledge of the host platform’s motion model; it relies only on weak, sensor-level statistical assumptions that are satisfied by practically all gyroscopes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090166","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}
Correct cable connection is critical for safe and reliable operation of the low-voltage switchgear but currently relies on time-consuming and labor-intensive manual inspection. To improve inspection accuracy and efficiency, a novel multiscale edge-enhanced deep learning (MEDL) framework is designed to visually inspect cable connections in a dense cable scenario. Specifically, the MEDL detects the keypoints at the cable–terminal junctions through an encoder–decoder architecture with an edge enhancement (EE) module and a multiscale feature extraction (MSFE) module, followed by a matching stage. The EE module is designed to highlight the edges of the cables, which can, to some extent, suppress environmental interferences. The MSFE module is designed to extract multiscale features at the cable–terminal junctions while guiding the MEDL model to focus on the target regions. In the matching stage, the HDBSCAN is combined with a shared nearest neighbor (SNN) distance metric to cluster candidate keypoints for keypoint matching. The experimental results on cable connection images acquired in real-world scenarios demonstrate the superiority of the MEDL to some existing deep learning methods, achieving a matching accuracy (MA) of 0.9463 at an acceptable inspection speed.
{"title":"Multiscale Edge-Enhanced Deep Learning for Cable Connection Visual Inspection of Low-Voltage Switchgear","authors":"Yigeng Wang;Feng Zou;Lexuan Lai;Nian Cai;Wenzhao Liang","doi":"10.1109/TIM.2025.3608344","DOIUrl":"https://doi.org/10.1109/TIM.2025.3608344","url":null,"abstract":"Correct cable connection is critical for safe and reliable operation of the low-voltage switchgear but currently relies on time-consuming and labor-intensive manual inspection. To improve inspection accuracy and efficiency, a novel multiscale edge-enhanced deep learning (MEDL) framework is designed to visually inspect cable connections in a dense cable scenario. Specifically, the MEDL detects the keypoints at the cable–terminal junctions through an encoder–decoder architecture with an edge enhancement (EE) module and a multiscale feature extraction (MSFE) module, followed by a matching stage. The EE module is designed to highlight the edges of the cables, which can, to some extent, suppress environmental interferences. The MSFE module is designed to extract multiscale features at the cable–terminal junctions while guiding the MEDL model to focus on the target regions. In the matching stage, the HDBSCAN is combined with a shared nearest neighbor (SNN) distance metric to cluster candidate keypoints for keypoint matching. The experimental results on cable connection images acquired in real-world scenarios demonstrate the superiority of the MEDL to some existing deep learning methods, achieving a matching accuracy (MA) of 0.9463 at an acceptable inspection speed.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090045","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}
A highly compact and superior sensitivity dual-channel Michelson interferometer for seawater temperature and salinity sensing is demonstrated, which is successively constructed by splicing single-mode fiber (SMF)–multimode fiber (MMF)–twin-core fiber (TCF) in sequence. Employing femtosecond laser microprocessing technology, two D-type microcavities are precisely fabricated on the dual fiber cores of the TCF for the purpose of temperature and salinity sensing. Furthermore, an enhanced high-reflectivity mirror is employed to improve the spectral contrast. The results obtained from the experiment illustrate that the proposed sensor demonstrates a remarkably stable temperature performance of −3.26 nm/°C in $10~^{circ }$ C–$25~^{circ }$ C. Besides, in the salinity interval of 5‰–40‰, a superior salinity sensitivity of −2.95 nm/‰ (equivalent to $-15~265$ nm/RIU) is proved. Moreover, the proposed sensor exhibits remarkable stability and high repeatability, thus proffering an innovative perspective for the surveillance of the marine environment.
{"title":"Highly Sensitive Dual-Channel Michelson Interferometer for Seawater Temperature and Salinity Sensing","authors":"Yuxi Ma;Bing Han;Qian Cheng;Yiming Tao;Yihan Qiu;Luyao Wang;Ting Feng;Yong Zhao","doi":"10.1109/TIM.2025.3604931","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604931","url":null,"abstract":"A highly compact and superior sensitivity dual-channel Michelson interferometer for seawater temperature and salinity sensing is demonstrated, which is successively constructed by splicing single-mode fiber (SMF)–multimode fiber (MMF)–twin-core fiber (TCF) in sequence. Employing femtosecond laser microprocessing technology, two D-type microcavities are precisely fabricated on the dual fiber cores of the TCF for the purpose of temperature and salinity sensing. Furthermore, an enhanced high-reflectivity mirror is employed to improve the spectral contrast. The results obtained from the experiment illustrate that the proposed sensor demonstrates a remarkably stable temperature performance of −3.26 nm/°C in <inline-formula> <tex-math>$10~^{circ }$ </tex-math></inline-formula>C–<inline-formula> <tex-math>$25~^{circ }$ </tex-math></inline-formula>C. Besides, in the salinity interval of 5‰–40‰, a superior salinity sensitivity of −2.95 nm/‰ (equivalent to <inline-formula> <tex-math>$-15~265$ </tex-math></inline-formula> nm/RIU) is proved. Moreover, the proposed sensor exhibits remarkable stability and high repeatability, thus proffering an innovative perspective for the surveillance of the marine environment.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036720","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}
Pub Date : 2025-09-09DOI: 10.1109/TIM.2025.3604936
Yan He;Jialiang Chen;Qinghua Yu;Chuang Zhang;Ben Ge
The phase factor in optical interferometric imaging serves as a direct metric of the target’s phase across various spatial frequencies, making accurate acquisition of the phase factor crucial for reconstructing spatial target images. Current phase factor measurement methods rely on precise zero optical path difference (OPD) positions or require phase reference sources, imposing stringent conditions on precise OPD control or limiting application scenarios, which hinder the utilization of interferometric imaging. To tackle this challenge, we analyze the spatiotemporal coherence characteristics of time-delayed interference signals in interferometric imaging contexts and derive the modulation relationship between the interferential phase factor and the extrema of time-delayed interference. By decoupling these two aspects, we propose a phase factor inversion method for interferometric imaging based on the analysis of time-delayed extrema sequences, which do not rely on precise zero OPD positions. This method only requires the acquisition of interference fringe extrema sequences to invert the phase factor, significantly reducing the complexity of measuring the phase factor in interferometric imaging. Experimental results indicate that the phase inversion accuracy offered by this method surpasses $0.1pi $ , satisfying the requirements for image reconstruction in interferometric imaging. This method introduces a novel phase measurement (PM) approach for applications of interferometric imaging.
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Transient signal characteristics contain crucial information about the operating status of equipment, and their precise enhancement is crucial for monitoring complex conditions such as bearing faults and radar interference. The core challenge lies in extracting the dynamic evolution patterns of weak transient components from high noise and nonstationary observations. To address the limitations of traditional methods, which are constrained by fixed state assumptions, struggle to analyze multiscale transient mechanisms, and are prone to amplitude distortion in nonstationary signals, this article proposes an adaptive enhancement framework for transient signals based on hidden state dynamic inference. Utilizing the hierarchical Dirichlet process (DP) hidden semi-Markov model (HDP-HSMM), our method automatically identifies hidden state types and duration distributions through nonparametric Bayesian inference, overcoming traditional methods’ reliance on predefined state counts. We also introduce a dynamic allocation strategy for group sparse regularization parameters that enhances multiple transient components based on signal structure priors. A nonconvex group sparse dictionary residual regularization algorithm is designed to ensure optimization convergence while avoiding L1 norm underestimation of signal amplitudes. Experimental validation using bearing fault impact signals and data from a dual-channel MIMO RF transceiver shows that our method outperforms traditional convex optimization and nonconvex regularization techniques in transient signal enhancement, demonstrating robustness and applicability in complex operating conditions.
{"title":"Nonparametric Bayesian Learning Driven Dynamic Group Sparse Regularization for Transient Signal Enhancement","authors":"Yuhang Liang;Zhen Liu;Xiaoting Tang;Yuhua Cheng;Hang Geng","doi":"10.1109/TIM.2025.3606034","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606034","url":null,"abstract":"Transient signal characteristics contain crucial information about the operating status of equipment, and their precise enhancement is crucial for monitoring complex conditions such as bearing faults and radar interference. The core challenge lies in extracting the dynamic evolution patterns of weak transient components from high noise and nonstationary observations. To address the limitations of traditional methods, which are constrained by fixed state assumptions, struggle to analyze multiscale transient mechanisms, and are prone to amplitude distortion in nonstationary signals, this article proposes an adaptive enhancement framework for transient signals based on hidden state dynamic inference. Utilizing the hierarchical Dirichlet process (DP) hidden semi-Markov model (HDP-HSMM), our method automatically identifies hidden state types and duration distributions through nonparametric Bayesian inference, overcoming traditional methods’ reliance on predefined state counts. We also introduce a dynamic allocation strategy for group sparse regularization parameters that enhances multiple transient components based on signal structure priors. A nonconvex group sparse dictionary residual regularization algorithm is designed to ensure optimization convergence while avoiding L1 norm underestimation of signal amplitudes. Experimental validation using bearing fault impact signals and data from a dual-channel MIMO RF transceiver shows that our method outperforms traditional convex optimization and nonconvex regularization techniques in transient signal enhancement, demonstrating robustness and applicability in complex operating conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090082","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}
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