Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606059
Bed Prakash Das;Kaushik Das Sharma;Amitava Chatterjee;Jitendra Nath Bera
Estimating unknown inputs in indoor heating, ventilation, and air conditioning (HVac) systems, particularly under the influence of diverse environmental constraints and time-varying relative humidity, presents a significant challenge. A viable solution is to use a weighted least-squares (WLS) approach for estimating unknown inputs, which uses an unbiased minimum variance (UMV) estimator in conjunction with an unscented Kalman filter (UKF)-based nonlinear filtering technique. This allows for the simultaneous estimation of the system’s state and the unknown inputs. To accurately represent the real-life nonlinear thermal profile influenced by these uncertain inputs, it is essential to adopt an RC network-based mathematical modeling approach that captures the system’s dynamic behavior over time. The integration of the UMV-based optimal estimator with the UKF culminates in the proposed UKF with UMV for unknown inputs (UKF-UMV-UI) estimation algorithm. Extensive experimentation with the proposed UKF-UMV-UI algorithm has been conducted in a laboratory-scale realistic environment, dealing with uncertain and challenging unknown inputs. The results of the investigation indicate that the proposed method outperforms the UKF with unknown input (UKF-UI) by 41.64% and 35.85% in cumulative mean squared error (CuMSE) for two distinct measurement conditions, respectively.
{"title":"Time-Varying Unknown Input Constrained UKF With Unbiased Minimum Variance Estimator for Nonlinear Dynamic Indoor Thermal Profile Estimation","authors":"Bed Prakash Das;Kaushik Das Sharma;Amitava Chatterjee;Jitendra Nath Bera","doi":"10.1109/TIM.2025.3606059","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606059","url":null,"abstract":"Estimating unknown inputs in indoor heating, ventilation, and air conditioning (HVac) systems, particularly under the influence of diverse environmental constraints and time-varying relative humidity, presents a significant challenge. A viable solution is to use a weighted least-squares (WLS) approach for estimating unknown inputs, which uses an unbiased minimum variance (UMV) estimator in conjunction with an unscented Kalman filter (UKF)-based nonlinear filtering technique. This allows for the simultaneous estimation of the system’s state and the unknown inputs. To accurately represent the real-life nonlinear thermal profile influenced by these uncertain inputs, it is essential to adopt an RC network-based mathematical modeling approach that captures the system’s dynamic behavior over time. The integration of the UMV-based optimal estimator with the UKF culminates in the proposed UKF with UMV for unknown inputs (UKF-UMV-UI) estimation algorithm. Extensive experimentation with the proposed UKF-UMV-UI algorithm has been conducted in a laboratory-scale realistic environment, dealing with uncertain and challenging unknown inputs. The results of the investigation indicate that the proposed method outperforms the UKF with unknown input (UKF-UI) by 41.64% and 35.85% in cumulative mean squared error (CuMSE) for two distinct measurement conditions, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606043
Yiding Wang;Shengxin Lin;Chongyu Jin;Donghua Pan;Yitao Chen;Yuxiao Zhang;Liyi Li
Optically pumped magnetometers (OPMs) have emerged as a promising magnetic sensor for magnetoencephalography and magnetocardiography (MEG and MCG), owing to their low cost, high spatiotemporal resolution, and excellent magnetic-field sensitivity. Self-shielded coils—which generate a highly uniform internal field while rapidly decaying external fields—serve critical roles in OPM development: as in-magnetic shielding room (MSR) standard magnetic sources, they enable distortion-free uniform field for OPM calibration; as in-probe modulation magnetic sources, they provide stable, low-crosstalk modulation field. The external field decay and internal field uniformity of these coils are key performance metrics. To overcome the limitations inherent in conventional cylindrical self-shielded coil topologies, this article proposes a dual-layer spherical self-shielded coil structure and optimizes its geometry with respect to the field of the target region. Theoretical analysis shows that compared to common cylindrical designs, the proposed spherical structure reduces the minimum crosstalk-free distance by 50% when used as a modulation source, and expands the uniform field region by a factor of 1.9 when used as a standard source within MSR. Experimental validation corroborates these predictions, proving the efficacy of the spherical coil topology and optimization methodology in advancing OPM performance and suppressing crosstalk.
{"title":"Dual-Layer Spherical Coil: A Novel Design Method for Self-Shielded Uniform Field Coil","authors":"Yiding Wang;Shengxin Lin;Chongyu Jin;Donghua Pan;Yitao Chen;Yuxiao Zhang;Liyi Li","doi":"10.1109/TIM.2025.3606043","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606043","url":null,"abstract":"Optically pumped magnetometers (OPMs) have emerged as a promising magnetic sensor for magnetoencephalography and magnetocardiography (MEG and MCG), owing to their low cost, high spatiotemporal resolution, and excellent magnetic-field sensitivity. Self-shielded coils—which generate a highly uniform internal field while rapidly decaying external fields—serve critical roles in OPM development: as in-magnetic shielding room (MSR) standard magnetic sources, they enable distortion-free uniform field for OPM calibration; as in-probe modulation magnetic sources, they provide stable, low-crosstalk modulation field. The external field decay and internal field uniformity of these coils are key performance metrics. To overcome the limitations inherent in conventional cylindrical self-shielded coil topologies, this article proposes a dual-layer spherical self-shielded coil structure and optimizes its geometry with respect to the field of the target region. Theoretical analysis shows that compared to common cylindrical designs, the proposed spherical structure reduces the minimum crosstalk-free distance by 50% when used as a modulation source, and expands the uniform field region by a factor of 1.9 when used as a standard source within MSR. Experimental validation corroborates these predictions, proving the efficacy of the spherical coil topology and optimization methodology in advancing OPM performance and suppressing crosstalk.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606061
Sukjae Yoon;Kyoduk Ku;Hoyoung Yoo
This article introduces an innovative interpolation-based radar simulation system (IRSS) designed to simulate Doppler frequencies across multiple frequencies with minimal hardware complexity. Traditional radar simulation systems, such as Analog Radar System Simulators (ARSSs) and Digital Radar System Simulators (DRSSs), face challenges when supporting multifrequency simulations due to the need for parallel processing of individual Doppler frequencies. The proposed IRSS exploits linear interpolation and the superposition property, enabling a single interpolation process to handle multiple frequency components efficiently. The IRSS structure was implemented using a field programmable gate array (FPGA)-based universal software radio peripheral (USRP), and its performance was evaluated through experimental testing. The results demonstrated that the IRSS accurately generated Doppler frequencies for both single-frequency and multifrequency signals, maintaining consistency with theoretical predictions. The system effectively simulated Doppler shifts for various target speeds while preserving hardware simplicity, unlike traditional simulators that require increased resources proportional to the number of frequencies. This research highlights the advantages of using linear interpolation to reduce hardware complexity and improve scalability in radar simulators. Consequently, the proposed IRSS provides a cost-effective and efficient solution for modern radar systems that demand multifrequency capabilities, making it well-suited for applications in complex environments such as autonomous vehicles, military operations, and aviation.
{"title":"Efficient Doppler Frequency Simulator for Multifrequency","authors":"Sukjae Yoon;Kyoduk Ku;Hoyoung Yoo","doi":"10.1109/TIM.2025.3606061","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606061","url":null,"abstract":"This article introduces an innovative interpolation-based radar simulation system (IRSS) designed to simulate Doppler frequencies across multiple frequencies with minimal hardware complexity. Traditional radar simulation systems, such as Analog Radar System Simulators (ARSSs) and Digital Radar System Simulators (DRSSs), face challenges when supporting multifrequency simulations due to the need for parallel processing of individual Doppler frequencies. The proposed IRSS exploits linear interpolation and the superposition property, enabling a single interpolation process to handle multiple frequency components efficiently. The IRSS structure was implemented using a field programmable gate array (FPGA)-based universal software radio peripheral (USRP), and its performance was evaluated through experimental testing. The results demonstrated that the IRSS accurately generated Doppler frequencies for both single-frequency and multifrequency signals, maintaining consistency with theoretical predictions. The system effectively simulated Doppler shifts for various target speeds while preserving hardware simplicity, unlike traditional simulators that require increased resources proportional to the number of frequencies. This research highlights the advantages of using linear interpolation to reduce hardware complexity and improve scalability in radar simulators. Consequently, the proposed IRSS provides a cost-effective and efficient solution for modern radar systems that demand multifrequency capabilities, making it well-suited for applications in complex environments such as autonomous vehicles, military operations, and aviation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606028
Bo Lu;Xiangxing Zheng;Zhenjie Zhu;Yuhao Guo;Ziyi Wang;Bruce X. B. Yu;Mingchuan Zhou;Peng Qi;Huicong Liu;Yunhui Liu;Lining Sun
Efficient laparoscopic scene segmentation holds significant potential for surgical assistive intelligence and image-guided task autonomy in robotic surgery. However, the abdominal cavity with intricate tissues and surgical tools under varying conditions challenges the balance between segmentation accuracy and efficiency. To resolve this problem, we propose a pixel-level discriminative knowledge distillation network (PLDKD-Net), a novel pixel-level student–teacher knowledge distillation (KD) framework, in which the student model selectively distills the teacher’s profound knowledge while exploring rich visual features with a graph-based fusion mechanism for efficient segmentation. Specifically, we first introduce our confidence-based KD (Confi-KD) scheme, in which a pixel-level confidence generator (PCG) is proposed to assess the teacher’s performance by discriminatively evaluating its probability map and the raw image, generating a confidence map that can facilitate a selective KD for the student model. To balance the model’s accuracy and efficiency, we devise a novel heterogeneous student architecture with a bi-stream visual parsing pipeline to capture multiscale and interspatial visual features. These features are then fused using a relational graph convolutional network (RGCN), which can adaptively tune the fusion degrees of multilatent knowledge, ensuring visual parsing completeness while avoiding computational redundancy. We extensively validate PLDKD-Net on two public laparoscopic benchmarks, Endovis18 and CholecSeg8K, and in-house surgical videos. Benefiting from our schemes, the experimental outcomes demonstrate superior quantitative and qualitative performance compared to state-of-the-art (SOTA) methods. With the selective KD mechanism, our model yields competitive or even higher performance than the cumbersome teacher model while exhibiting quasi-real-time efficiency, which demonstrates its greater potential for intelligent robotic surgical scene understanding.
{"title":"PLDKD-Net: Pixel-Level Discriminative Knowledge Distillation for Surgical Scene Segmentation With Graph-Based Visual Parsing","authors":"Bo Lu;Xiangxing Zheng;Zhenjie Zhu;Yuhao Guo;Ziyi Wang;Bruce X. B. Yu;Mingchuan Zhou;Peng Qi;Huicong Liu;Yunhui Liu;Lining Sun","doi":"10.1109/TIM.2025.3606028","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606028","url":null,"abstract":"Efficient laparoscopic scene segmentation holds significant potential for surgical assistive intelligence and image-guided task autonomy in robotic surgery. However, the abdominal cavity with intricate tissues and surgical tools under varying conditions challenges the balance between segmentation accuracy and efficiency. To resolve this problem, we propose a pixel-level discriminative knowledge distillation network (PLDKD-Net), a novel pixel-level student–teacher knowledge distillation (KD) framework, in which the student model selectively distills the teacher’s profound knowledge while exploring rich visual features with a graph-based fusion mechanism for efficient segmentation. Specifically, we first introduce our confidence-based KD (Confi-KD) scheme, in which a pixel-level confidence generator (PCG) is proposed to assess the teacher’s performance by discriminatively evaluating its probability map and the raw image, generating a confidence map that can facilitate a selective KD for the student model. To balance the model’s accuracy and efficiency, we devise a novel heterogeneous student architecture with a bi-stream visual parsing pipeline to capture multiscale and interspatial visual features. These features are then fused using a relational graph convolutional network (RGCN), which can adaptively tune the fusion degrees of multilatent knowledge, ensuring visual parsing completeness while avoiding computational redundancy. We extensively validate PLDKD-Net on two public laparoscopic benchmarks, Endovis18 and CholecSeg8K, and in-house surgical videos. Benefiting from our schemes, the experimental outcomes demonstrate superior quantitative and qualitative performance compared to state-of-the-art (SOTA) methods. With the selective KD mechanism, our model yields competitive or even higher performance than the cumbersome teacher model while exhibiting quasi-real-time efficiency, which demonstrates its greater potential for intelligent robotic surgical scene understanding.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606025
Fuqian Li;Qican Zhang;Yajun Wang
In industrial 3-D metrology, the low signal-to-noise ratio (SNR) issue is commonly encountered, due to inappropriate illumination intensity, limited imaging dynamic range, or complex scene material, etc. Compared with nonlearning-based methods, deep-learning-based methods excel in efficiency and fidelity for the low SNR issue. However, most of them are data-driven, thus have limited generalization ability. Besides, they require advanced computing hardware for network training, greatly increasing the metrology cost. To tackle these problems, a physics-informed zero-shot learning (PZL) method with an ultralightweight neural network (UNN) is proposed for low-SNR scene measurement. There are two major contributions in our method. First, by blending physics priors for phase retrieval and fringe noise, a generalized PZL framework with a noisy-sinusoidal-component-to-noisy-sinusoidal-component (NS2NS) mapping is established. The low SNR issue of various challenging scenes including the low-illumination, high-dynamic-range, strong-ambient-light, and large-depth-range scenes is unified in a single enhancement framework. Moreover, no training dataset is required other than the degraded fringe itself, and the generalization ability for fringe enhancement is significantly improved. Second, based on the PZL framework, a symmetrized optimization strategy along with the UNN is proposed. Valid 3-D reconstruction of fine surface details can be achieved on computing-resource-constrained platforms, even on a CPU. Experiments verify the superiority of our method in efficiency, fidelity, generalization ability, and computing hardware cost. And to our knowledge, it is the first time such a simultaneous achievement has been accomplished.
{"title":"Optical 3-D Measurement for Low-SNR Scenes via Physics-Informed Zero-Shot Learning","authors":"Fuqian Li;Qican Zhang;Yajun Wang","doi":"10.1109/TIM.2025.3606025","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606025","url":null,"abstract":"In industrial 3-D metrology, the low signal-to-noise ratio (SNR) issue is commonly encountered, due to inappropriate illumination intensity, limited imaging dynamic range, or complex scene material, etc. Compared with nonlearning-based methods, deep-learning-based methods excel in efficiency and fidelity for the low SNR issue. However, most of them are data-driven, thus have limited generalization ability. Besides, they require advanced computing hardware for network training, greatly increasing the metrology cost. To tackle these problems, a physics-informed zero-shot learning (PZL) method with an ultralightweight neural network (UNN) is proposed for low-SNR scene measurement. There are two major contributions in our method. First, by blending physics priors for phase retrieval and fringe noise, a generalized PZL framework with a noisy-sinusoidal-component-to-noisy-sinusoidal-component (NS2NS) mapping is established. The low SNR issue of various challenging scenes including the low-illumination, high-dynamic-range, strong-ambient-light, and large-depth-range scenes is unified in a single enhancement framework. Moreover, no training dataset is required other than the degraded fringe itself, and the generalization ability for fringe enhancement is significantly improved. Second, based on the PZL framework, a symmetrized optimization strategy along with the UNN is proposed. Valid 3-D reconstruction of fine surface details can be achieved on computing-resource-constrained platforms, even on a CPU. Experiments verify the superiority of our method in efficiency, fidelity, generalization ability, and computing hardware cost. And to our knowledge, it is the first time such a simultaneous achievement has been accomplished.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606055
Yuntong Liu;Xiaoyue Meng;Feng Chen;Yang Wang;Yu Tao;Chaofeng Ye
The swift advancement of e-commerce has led to an increased transit of magnetic items via air freight, which may jeopardize airplane safety. It is essential to detect and assess the magnetic anomalies for maintaining flight safety. However, the industry still lacks online detection equipment for magnetic anomaly measurement. This article presents an automated magnetic anomaly detection system that employs array tunneling magnetoresistance (TMR) sensors and a deep learning calculation algorithm. The system has four sensor arrays that are located on the four sides of a cargo conveyor belt to continuously monitor the magnetic field. The magnetic abnormalities are detected and quantified as the cargo passes through the sensor arrays. A deep learning algorithm is developed to ascertain the position and magnetic moment of magnetic sources, enabling a quantitative evaluation of the risk associated with magnetic abnormalities. A prototype system including 64 sensor modules has been developed and tested on an airport cargo conveyor belt to evaluate the practicality of the technology. Experimental validation on airport cargo belts shows that, for single-source cases, the system attains a position RMSE of 3.22 cm and a dipole-angle RMSE of 1.07°. In double-source scenarios, the corresponding errors are 13.18 cm and 25.07°, confirming reliable performance across both simple and complex magnetic configurations. This automated technology significantly improves the efficiency and reliability of magnetic anomaly detection in air transportation operations compared to the traditional method of using a handheld magnetometer.
{"title":"Magnetic Anomaly Evaluation for Air Cargo Employing Array TMR Sensors and Deep Learning Algorithm","authors":"Yuntong Liu;Xiaoyue Meng;Feng Chen;Yang Wang;Yu Tao;Chaofeng Ye","doi":"10.1109/TIM.2025.3606055","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606055","url":null,"abstract":"The swift advancement of e-commerce has led to an increased transit of magnetic items via air freight, which may jeopardize airplane safety. It is essential to detect and assess the magnetic anomalies for maintaining flight safety. However, the industry still lacks online detection equipment for magnetic anomaly measurement. This article presents an automated magnetic anomaly detection system that employs array tunneling magnetoresistance (TMR) sensors and a deep learning calculation algorithm. The system has four sensor arrays that are located on the four sides of a cargo conveyor belt to continuously monitor the magnetic field. The magnetic abnormalities are detected and quantified as the cargo passes through the sensor arrays. A deep learning algorithm is developed to ascertain the position and magnetic moment of magnetic sources, enabling a quantitative evaluation of the risk associated with magnetic abnormalities. A prototype system including 64 sensor modules has been developed and tested on an airport cargo conveyor belt to evaluate the practicality of the technology. Experimental validation on airport cargo belts shows that, for single-source cases, the system attains a position RMSE of 3.22 cm and a dipole-angle RMSE of 1.07°. In double-source scenarios, the corresponding errors are 13.18 cm and 25.07°, confirming reliable performance across both simple and complex magnetic configurations. This automated technology significantly improves the efficiency and reliability of magnetic anomaly detection in air transportation operations compared to the traditional method of using a handheld magnetometer.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1109/TIM.2025.3606048
Wei Li;Guohui Tian;Xuyang Shao
Indoor environments, as typical unstructured settings, present significant challenges for the localization of mobile robots. In such environments, robots are prone to getting lost or mismatched to incorrect locations. Existing solutions heavily rely on 2-D light detection and ranging (LiDAR), which can only scan the horizontal plane of the environment, thus failing to fully observe spatial objects, resulting in insufficient available features for the localization of robots. In response to this challenge, this article introduces a system that measures 3-D structural features for 2-D localization. First, a vision sensor is employed to capture the 3-D structural features of the scene. A hierarchical strategy is then introduced to extract key structural features, mapping the 3-D features into 2-D hierarchical submaps. A map selection algorithm is further proposed to filter the localization map. Next, we propose a method to convert point cloud data into 2-D pseudo-laser representations, allowing for parallel matching between the hierarchical submaps and the pseudo-laser data to obtain multiple localization results. Building on this, we investigate an observation residual evaluation method to assess the performance of multiple localization results, enabling fused localization. Both simulation and real-world experiments demonstrate that the introduced approach significantly improves the accuracy and robustness of localization for mobile robots.
{"title":"A 2-D Indoor Localization System Using 3-D Structural Features for Mobile Robots","authors":"Wei Li;Guohui Tian;Xuyang Shao","doi":"10.1109/TIM.2025.3606048","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606048","url":null,"abstract":"Indoor environments, as typical unstructured settings, present significant challenges for the localization of mobile robots. In such environments, robots are prone to getting lost or mismatched to incorrect locations. Existing solutions heavily rely on 2-D light detection and ranging (LiDAR), which can only scan the horizontal plane of the environment, thus failing to fully observe spatial objects, resulting in insufficient available features for the localization of robots. In response to this challenge, this article introduces a system that measures 3-D structural features for 2-D localization. First, a vision sensor is employed to capture the 3-D structural features of the scene. A hierarchical strategy is then introduced to extract key structural features, mapping the 3-D features into 2-D hierarchical submaps. A map selection algorithm is further proposed to filter the localization map. Next, we propose a method to convert point cloud data into 2-D pseudo-laser representations, allowing for parallel matching between the hierarchical submaps and the pseudo-laser data to obtain multiple localization results. Building on this, we investigate an observation residual evaluation method to assess the performance of multiple localization results, enabling fused localization. Both simulation and real-world experiments demonstrate that the introduced approach significantly improves the accuracy and robustness of localization for mobile robots.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027908","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 : 2025-09-04DOI: 10.1109/TIM.2025.3606042
Lin Zou;Mingming Dong;Yun Kong;Wei Li;Weiwei Lv
Recent advancements in affordable single-channel electroencephalogram (EEG) devices have garnered considerable attention due to their ability to reduce hardware complexity. However, effectively suppressing eyeblink artifacts in single-channel EEG signals remains a substantial challenge for biomedical applications. This article proposes a nonconvex sparse regularization methodology (NSRM), which explores the generalized minimax-concave (GMC) penalty for eyeblink artifact suppression from single-channel EEG signals. The contaminated EEG signals can be initially modeled within the sparse representation framework as a combination of target and noise components. The proposed methodology preserves the convexity of the sparsity-regularized least square objective function, allowing the global minimum to be reached through convex optimization. Specifically, a forwardbackward splitting (FBS) algorithm is developed to resolve the nonconvex sparse regularization problem of eyeblink artifact suppression. In addition, we introduce an adaptive selection strategy for the regularization parameter. The advantage over conventional methods is that NSRM can better preserve useful information from EEG signals while suppressing eyeblink artifacts. To validate the efficacy of NSRM, a semisimulated EEG dataset and two real experiment datasets have been analyzed. Results demonstrate that our NSRM methodology eliminates eyeblink artifacts effectively and accurately from single-channel EEG signals, outperforming the $L1$ norm-based sparse regularization method, as evidenced by quantitative metrics. Finally, comparison results with the advanced K-means singular value decomposition (K-SVD) have also confirmed the superiority of our proposed NSRM for eyeblink artifact suppression in the context of the sparse representation paradigm.
{"title":"Nonconvex Sparse Regularization Method for Eyeblink Artifact Suppression From Single-Channel EEG Signals","authors":"Lin Zou;Mingming Dong;Yun Kong;Wei Li;Weiwei Lv","doi":"10.1109/TIM.2025.3606042","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606042","url":null,"abstract":"Recent advancements in affordable single-channel electroencephalogram (EEG) devices have garnered considerable attention due to their ability to reduce hardware complexity. However, effectively suppressing eyeblink artifacts in single-channel EEG signals remains a substantial challenge for biomedical applications. This article proposes a nonconvex sparse regularization methodology (NSRM), which explores the generalized minimax-concave (GMC) penalty for eyeblink artifact suppression from single-channel EEG signals. The contaminated EEG signals can be initially modeled within the sparse representation framework as a combination of target and noise components. The proposed methodology preserves the convexity of the sparsity-regularized least square objective function, allowing the global minimum to be reached through convex optimization. Specifically, a forwardbackward splitting (FBS) algorithm is developed to resolve the nonconvex sparse regularization problem of eyeblink artifact suppression. In addition, we introduce an adaptive selection strategy for the regularization parameter. The advantage over conventional methods is that NSRM can better preserve useful information from EEG signals while suppressing eyeblink artifacts. To validate the efficacy of NSRM, a semisimulated EEG dataset and two real experiment datasets have been analyzed. Results demonstrate that our NSRM methodology eliminates eyeblink artifacts effectively and accurately from single-channel EEG signals, outperforming the <inline-formula> <tex-math>$L1$ </tex-math></inline-formula> norm-based sparse regularization method, as evidenced by quantitative metrics. Finally, comparison results with the advanced K-means singular value decomposition (K-SVD) have also confirmed the superiority of our proposed NSRM for eyeblink artifact suppression in the context of the sparse representation paradigm.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145057478","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 : 2025-09-04DOI: 10.1109/TIM.2025.3606067
Gunhwi Moon;Seongwook Lee;Jeong-Hoon Park;Young-Jun Yoon;Seong-Cheol Kim
In this article, we present a novel radar system for estimating ego-motion from the ground-scattered signals and synthetic aperture radar (SAR) imaging based on the estimated ego-motion. Accurate ego-motion estimation is essential to obtain high-resolution SAR images, because the ego-motion determines spatial data sampling interval for SAR image generation. Our proposed method enables accurate ego-motion estimation by using the ground-scattered signals with a single-input–single-output antenna system. We evaluate ego-motion estimation accuracy by comparing the generated SAR images of point targets. The SAR images generated using the proposed ego-motion estimation achieve an improved resolution of 0.284 m, compared with the 0.308-m resolution obtained with Global Navigation Satellite Systems (GNSS) sensor-based ego-motion estimation. We confirm that the proposed method can generate enhanced SAR images using only radar sensors without requiring additional sensors.
{"title":"Enhanced SAR Image Generation Using Ego-Motion Estimation Based on Ground Scatterers for Automotive Radar Systems","authors":"Gunhwi Moon;Seongwook Lee;Jeong-Hoon Park;Young-Jun Yoon;Seong-Cheol Kim","doi":"10.1109/TIM.2025.3606067","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606067","url":null,"abstract":"In this article, we present a novel radar system for estimating ego-motion from the ground-scattered signals and synthetic aperture radar (SAR) imaging based on the estimated ego-motion. Accurate ego-motion estimation is essential to obtain high-resolution SAR images, because the ego-motion determines spatial data sampling interval for SAR image generation. Our proposed method enables accurate ego-motion estimation by using the ground-scattered signals with a single-input–single-output antenna system. We evaluate ego-motion estimation accuracy by comparing the generated SAR images of point targets. The SAR images generated using the proposed ego-motion estimation achieve an improved resolution of 0.284 m, compared with the 0.308-m resolution obtained with Global Navigation Satellite Systems (GNSS) sensor-based ego-motion estimation. We confirm that the proposed method can generate enhanced SAR images using only radar sensors without requiring additional sensors.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110292","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}
Metallic cylinders are extensively used across a range of industries. The inspection of their properties through eddy current testing (ECT) is crucial to ensure the desired performance of the piece in practical applications. This article proposes for the first time an analytical model for the mutual inductance variation of a coil pair encircling an eccentric metallic cylinder, applicable to 3-D asymmetric cases where vibration and wobble exist. The analytical solution is further simplified for faster calculation while maintaining high consistency with the complete model. Moreover, an inverse approach is proposed to simultaneously measure rod conductivity and its eccentricity from the center based on the simplified analytical model, exploiting the crossing frequency between the real and imaginary parts of the inductance spectra. A modified Newton–Raphson method is employed to reduce the estimation error further. Experiments are carried out using a multifrequency eddy current sensor to test different metallic specimens, the results of which validated the effectiveness of the analytical solution. Finally, the proposed inverse approach achieves high-accuracy estimations for both conductivity and eccentricity.
{"title":"Simultaneous Estimation of Conductivity and Radial Eccentricity of Metallic Cylinders Using Eddy Current Testing","authors":"Xun Zou;Saibo She;Xinnan Zheng;Kuohai Yu;Jialong Shen;Anthony Peyton;Wuliang Yin","doi":"10.1109/TIM.2025.3606056","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606056","url":null,"abstract":"Metallic cylinders are extensively used across a range of industries. The inspection of their properties through eddy current testing (ECT) is crucial to ensure the desired performance of the piece in practical applications. This article proposes for the first time an analytical model for the mutual inductance variation of a coil pair encircling an eccentric metallic cylinder, applicable to 3-D asymmetric cases where vibration and wobble exist. The analytical solution is further simplified for faster calculation while maintaining high consistency with the complete model. Moreover, an inverse approach is proposed to simultaneously measure rod conductivity and its eccentricity from the center based on the simplified analytical model, exploiting the crossing frequency between the real and imaginary parts of the inductance spectra. A modified Newton–Raphson method is employed to reduce the estimation error further. Experiments are carried out using a multifrequency eddy current sensor to test different metallic specimens, the results of which validated the effectiveness of the analytical solution. Finally, the proposed inverse approach achieves high-accuracy estimations for both conductivity and eccentricity.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078624","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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