Conventional phased-array ultrasound can detect defects in nuts; however, accurately reconstructing their irregular shapes and precise spatial structures remains challenging. To faithfully recover the structural distribution of nut cross sections, a dedicated ultrasound tomographic imaging system and a corresponding reconstruction method were developed to generate spatially resolved images of nuts with irregular surfaces. The imaging process includes three steps. First, as the nut rotates on the experimental platform, the ultrasonic array elements transmit and receive signals to form a signal matrix. Second, the collected sparse data are interpolated using the proposed adaptive interpolation algorithm and then reconstructed into an image through filtered back-projection. Finally, the reconstructed image is processed with a diffusion modelbased super resolution (SR) algorithm to produce a high-resolution, large-scale tomographic image. Employing a 5 MHz, 64-element linear array with water as the coupling medium for signal acquisition, the proposed imaging algorithm achieves optimal structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) values of 0.961 and 29.264 after adaptive interpolation under noise-free conditions. Following SR processing, it attains superior no-reference quality scores with natural image quality evaluator (NIQE), CLIP-based image quality assessment (CLIPIQA), and blind/reference-less image spatial quality evaluator (BRISQUE) scores of 2.4933, 0.6655, and 34.5602, outperforming conventional SR methods across these metrics. These results demonstrate superior performance in image quality. Physical experiments further indicate that the system can produce high-precision tomographic images of nuts with minimal signal sampling, transmission, and storage, highlighting its practical application potential.
{"title":"Ultrasonic Tomography System for Nut Defect Detection Using Linear Arrays","authors":"Shiyuan He;Jianhong Yang;Chuanjiang Hu;Xuejin Zhou;Huaiying Fang","doi":"10.1109/TIM.2025.3638929","DOIUrl":"https://doi.org/10.1109/TIM.2025.3638929","url":null,"abstract":"Conventional phased-array ultrasound can detect defects in nuts; however, accurately reconstructing their irregular shapes and precise spatial structures remains challenging. To faithfully recover the structural distribution of nut cross sections, a dedicated ultrasound tomographic imaging system and a corresponding reconstruction method were developed to generate spatially resolved images of nuts with irregular surfaces. The imaging process includes three steps. First, as the nut rotates on the experimental platform, the ultrasonic array elements transmit and receive signals to form a signal matrix. Second, the collected sparse data are interpolated using the proposed adaptive interpolation algorithm and then reconstructed into an image through filtered back-projection. Finally, the reconstructed image is processed with a diffusion modelbased super resolution (SR) algorithm to produce a high-resolution, large-scale tomographic image. Employing a 5 MHz, 64-element linear array with water as the coupling medium for signal acquisition, the proposed imaging algorithm achieves optimal structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) values of 0.961 and 29.264 after adaptive interpolation under noise-free conditions. Following SR processing, it attains superior no-reference quality scores with natural image quality evaluator (NIQE), CLIP-based image quality assessment (CLIPIQA), and blind/reference-less image spatial quality evaluator (BRISQUE) scores of 2.4933, 0.6655, and 34.5602, outperforming conventional SR methods across these metrics. These results demonstrate superior performance in image quality. Physical experiments further indicate that the system can produce high-precision tomographic images of nuts with minimal signal sampling, transmission, and storage, highlighting its practical application potential.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879956","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 : 2025-11-27DOI: 10.1109/TIM.2025.3637988
Jiayi Zhou;Xiaoli Wang;Weihua Gui;Chunhua Yang;Stephen George Pooley
This study presents a novel soft sensor modeling algorithm for industrial processes, known as the hierarchical attention-based quadruple S (HAQS) model, specifically designed to uncover nonlinear dynamic features within semi-supervised process data. It integrates spatial and process temporal attention with an LSTM layer during encoding, enabling the learning of spatio-process-temporal features. The model utilizes an unsupervised decoder to reconstruct the input data sequence, facilitating the understanding of the intrinsic features of the input data. During the supervised decoding phase, the predicted value of the key variable is fed into the subsequent LSTM cell. This enables the model to learn effectively from a limited amount of key variable data. The HAQS model displayed superior performance in prediction accuracy and stability, outperforming other models like the semi-supervised dynamic feature extracting (SSDFE) network in a practical case study involving a mineral processing grinding-classification circuit. The HAQS model has demonstrated substantial promise for real-world application. Its ability to extract features from complex industrial datasets, along with its semi-supervised learning capabilities, makes it a powerful tool for the optimization of industrial processes.
{"title":"Hierarchical Attention-Based Semi-Supervised Sequence-to-Sequence Soft Sensor Model for Complex Industrial Processes","authors":"Jiayi Zhou;Xiaoli Wang;Weihua Gui;Chunhua Yang;Stephen George Pooley","doi":"10.1109/TIM.2025.3637988","DOIUrl":"https://doi.org/10.1109/TIM.2025.3637988","url":null,"abstract":"This study presents a novel soft sensor modeling algorithm for industrial processes, known as the hierarchical attention-based quadruple S (HAQS) model, specifically designed to uncover nonlinear dynamic features within semi-supervised process data. It integrates spatial and process temporal attention with an LSTM layer during encoding, enabling the learning of spatio-process-temporal features. The model utilizes an unsupervised decoder to reconstruct the input data sequence, facilitating the understanding of the intrinsic features of the input data. During the supervised decoding phase, the predicted value of the key variable is fed into the subsequent LSTM cell. This enables the model to learn effectively from a limited amount of key variable data. The HAQS model displayed superior performance in prediction accuracy and stability, outperforming other models like the semi-supervised dynamic feature extracting (SSDFE) network in a practical case study involving a mineral processing grinding-classification circuit. The HAQS model has demonstrated substantial promise for real-world application. Its ability to extract features from complex industrial datasets, along with its semi-supervised learning capabilities, makes it a powerful tool for the optimization of industrial processes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886535","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 : 2025-11-21DOI: 10.1109/TIM.2025.3635810
Zhifeng Zhang;Tian Zhou;Weidong Du;Qijia Guo
Errors in acoustic arrays can degrade detection performance by compromising the accuracy of direction-of-arrival (DOA) estimation and reducing processing gain. Most conventional array calibration methods are based on far-field conditions, which are challenging to implement in confined spaces. In order to address this problem, we propose a self-calibration method for compensating gain-phase and position errors in linear acoustic arrays under near-field conditions. In this method, the DOA of the signal is used to estimate the array element positions, which are then fit to the actual array, while the relative spatial positions of the source and the array are employed to refine the DOA estimation. This alternating iterative procedure enables the accurate estimation of both the DOA and array element errors. Simulation results confirm the effectiveness of the proposed method. Tank test results demonstrate that the accuracy of DOA estimation after array calibration is improved by an average of 0.2°, and the peak sidelobe ratio (PSLR) of the beam pattern is reduced by an average of 2.03 dB.
{"title":"A Near-Field Gain-Phase and Position Errors Calibration Method for Acoustic Arrays","authors":"Zhifeng Zhang;Tian Zhou;Weidong Du;Qijia Guo","doi":"10.1109/TIM.2025.3635810","DOIUrl":"https://doi.org/10.1109/TIM.2025.3635810","url":null,"abstract":"Errors in acoustic arrays can degrade detection performance by compromising the accuracy of direction-of-arrival (DOA) estimation and reducing processing gain. Most conventional array calibration methods are based on far-field conditions, which are challenging to implement in confined spaces. In order to address this problem, we propose a self-calibration method for compensating gain-phase and position errors in linear acoustic arrays under near-field conditions. In this method, the DOA of the signal is used to estimate the array element positions, which are then fit to the actual array, while the relative spatial positions of the source and the array are employed to refine the DOA estimation. This alternating iterative procedure enables the accurate estimation of both the DOA and array element errors. Simulation results confirm the effectiveness of the proposed method. Tank test results demonstrate that the accuracy of DOA estimation after array calibration is improved by an average of 0.2°, and the peak sidelobe ratio (PSLR) of the beam pattern is reduced by an average of 2.03 dB.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886566","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 : 2025-11-11DOI: 10.1109/TIM.2025.3627380
Andrzej Dukata;Waldemar Susek;Mirosław Czyżewski
The traditional Nicolson–Ross–Weir (NRW) method of extracting permittivity and permeability was used in the above work. Some of the tested materials were anisotropic and nonmagnetic, while others exhibited both electric and magnetic anisotropies. As is known, the NRW method developed for isotropic materials fails in the latter case. Appropriate formulas for determining permittivity and permeability tensors of anisotropic materials, which should be used in the commented article, are presented and briefly explained.
{"title":"Comments on “Measurement of Anisotropic Material by Using Orthomode Transducer for High Efficiency”","authors":"Andrzej Dukata;Waldemar Susek;Mirosław Czyżewski","doi":"10.1109/TIM.2025.3627380","DOIUrl":"https://doi.org/10.1109/TIM.2025.3627380","url":null,"abstract":"The traditional Nicolson–Ross–Weir (NRW) method of extracting permittivity and permeability was used in the above work. Some of the tested materials were anisotropic and nonmagnetic, while others exhibited both electric and magnetic anisotropies. As is known, the NRW method developed for isotropic materials fails in the latter case. Appropriate formulas for determining permittivity and permeability tensors of anisotropic materials, which should be used in the commented article, are presented and briefly explained.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-3"},"PeriodicalIF":5.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510075","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}
Quantifying Young’s modulus and Poisson’s ratio of underfill (UF) is critical in clarifying material failure mechanisms for better optimizing their performance in reducing thermal stresses on solder joints in flip-chip devices. Conventional mechanical methods are generally developed for the pure UF block and cannot directly measure thin UF layers sandwiched in heterogeneous multilayered flip-chip devices during service. Nondestructive ultrasonic methods are more promising but are challenged by complex wave propagation induced by multilayered, anisotropic acoustic properties. This work presents a noncontact laser ultrasonic (LU) method for in situ and simultaneous measurement of elastic constants and thickness of a thin UF layer in a silicon–UF–silicon sandwich structure. Longitudinal and transverse waves in divergent propagation directions are acquired by using a line-scan point laser transmitter and a fixed-point receiver on opposite surfaces. Young’s modulus, Poisson’s ratio, and thickness of the sandwiched UF layer are inversely determined by iteratively matching experimental LU travel times with theoretical predictions considering interface reflection, refraction, and mode conversion effects, with relative errors to ultrasonic measured reference values <3.722%. The in situ LU method is advantageous in continuously monitoring mechanical property evolution during various cyclic tests, advancing reliability assessment and fabrication optimization of UF materials in real-life electronic packaging applications.
{"title":"In Situ Measurement of Elastic Constants and Thickness of Silicon–Underfill–Silicon Sandwiched Electronic Package Using Noncontact Laser Ultrasound Array","authors":"Huanqing Cao;Zhijun Yao;Qimin Zhu;Ruoyu Zhang;Gaolong Lv;Pengli Zhu;Jian Yang;Xinyu Wu;Shifeng Guo","doi":"10.1109/TIM.2025.3627352","DOIUrl":"https://doi.org/10.1109/TIM.2025.3627352","url":null,"abstract":"Quantifying Young’s modulus and Poisson’s ratio of underfill (UF) is critical in clarifying material failure mechanisms for better optimizing their performance in reducing thermal stresses on solder joints in flip-chip devices. Conventional mechanical methods are generally developed for the pure UF block and cannot directly measure thin UF layers sandwiched in heterogeneous multilayered flip-chip devices during service. Nondestructive ultrasonic methods are more promising but are challenged by complex wave propagation induced by multilayered, anisotropic acoustic properties. This work presents a noncontact laser ultrasonic (LU) method for in situ and simultaneous measurement of elastic constants and thickness of a thin UF layer in a silicon–UF–silicon sandwich structure. Longitudinal and transverse waves in divergent propagation directions are acquired by using a line-scan point laser transmitter and a fixed-point receiver on opposite surfaces. Young’s modulus, Poisson’s ratio, and thickness of the sandwiched UF layer are inversely determined by iteratively matching experimental LU travel times with theoretical predictions considering interface reflection, refraction, and mode conversion effects, with relative errors to ultrasonic measured reference values <3.722%. The in situ LU method is advantageous in continuously monitoring mechanical property evolution during various cyclic tests, advancing reliability assessment and fabrication optimization of UF materials in real-life electronic packaging applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886567","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 : 2025-10-24DOI: 10.1109/TIM.2025.3625329
František Martínek;Vilém Neděla;Vladimír Tichý;Adam Antálek
A new tool for the simulation of electron-gas-sample interaction phenomena under real experimental conditions of elevated gas pressure in electron microscopes is introduced. It allows in-silico testing and optimization of detection systems of any given geometry, taking spatially variable electric field and gas flow into account. Its possibilities are demonstrated through a detailed analysis of a widely used environmental secondary detector variant (ESD-V) geometry, leading to a proposal and experimental proof of a significant increase in its secondary electron (SE) collection efficiency. In addition, suppression of the detected BSE signal, i.e., material contrast and edge effect, is demonstrated on the image of an embolized human benign tumor. The validity of the model is verified by comparison with experimental data of overall signal development in varying pressure.
{"title":"A New Tool for Numerical Analysis of Signal Creation Processes in ESEM/A-ESEM","authors":"František Martínek;Vilém Neděla;Vladimír Tichý;Adam Antálek","doi":"10.1109/TIM.2025.3625329","DOIUrl":"https://doi.org/10.1109/TIM.2025.3625329","url":null,"abstract":"A new tool for the simulation of electron-gas-sample interaction phenomena under real experimental conditions of elevated gas pressure in electron microscopes is introduced. It allows in-silico testing and optimization of detection systems of any given geometry, taking spatially variable electric field and gas flow into account. Its possibilities are demonstrated through a detailed analysis of a widely used environmental secondary detector variant (ESD-V) geometry, leading to a proposal and experimental proof of a significant increase in its secondary electron (SE) collection efficiency. In addition, suppression of the detected BSE signal, i.e., material contrast and edge effect, is demonstrated on the image of an embolized human benign tumor. The validity of the model is verified by comparison with experimental data of overall signal development in varying pressure.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455788","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 : 2025-10-09DOI: 10.1109/TIM.2025.3619275
Dongjian Wang;Xiufen Ye;Hong Liu;Hanjie Huang;Xianye Ben
In seabed topography reconstruction, side-scan sonar (SSS) provides wide-area seafloor imaging as the detection equipment moves, but it cannot capture the region directly beneath the equipment, leaving gaps in coverage. On the other hand, forward-looking sonar (FLS), typically mounted at the front of the equipment, offers real-time imaging of the seafloor ahead, though its detection range is limited and it cannot cover the entire area. Therefore, combining FLS with SSS, along with navigation data, enables comprehensive seafloor mapping by filling the gaps in SSS coverage and improving the accuracy of seabed topography reconstruction. Existing methods insert FLS images into the SSS coverage gaps by combining navigation data, but FLS images suffer from low resolution, blurred target details, and visible seams between the filled regions and the surrounding SSS images, leading to a lack of overall image coherence. This article proposes a multisensor data fusion method that integrates data from SSS, FLS, altimeter, GPS, and inertial navigation systems (INSs). The method employs FLS image enhancement, intensity matching, and a weight-adjusted fusion strategy to improve the clarity of FLS images and significantly enhance the overall quality of the fused imagery. Experimental results show that our method greatly improves the visual coherence of the fused FLS and SSS images, achieves smooth edge transitions, eliminates visible seams, and enhances the precision of seabed topography reconstruction.
{"title":"A Multisensor Data Fusion Method for Seabed Topography Reconstruction Based on Image Enhancement and Intensity Matching","authors":"Dongjian Wang;Xiufen Ye;Hong Liu;Hanjie Huang;Xianye Ben","doi":"10.1109/TIM.2025.3619275","DOIUrl":"https://doi.org/10.1109/TIM.2025.3619275","url":null,"abstract":"In seabed topography reconstruction, side-scan sonar (SSS) provides wide-area seafloor imaging as the detection equipment moves, but it cannot capture the region directly beneath the equipment, leaving gaps in coverage. On the other hand, forward-looking sonar (FLS), typically mounted at the front of the equipment, offers real-time imaging of the seafloor ahead, though its detection range is limited and it cannot cover the entire area. Therefore, combining FLS with SSS, along with navigation data, enables comprehensive seafloor mapping by filling the gaps in SSS coverage and improving the accuracy of seabed topography reconstruction. Existing methods insert FLS images into the SSS coverage gaps by combining navigation data, but FLS images suffer from low resolution, blurred target details, and visible seams between the filled regions and the surrounding SSS images, leading to a lack of overall image coherence. This article proposes a multisensor data fusion method that integrates data from SSS, FLS, altimeter, GPS, and inertial navigation systems (INSs). The method employs FLS image enhancement, intensity matching, and a weight-adjusted fusion strategy to improve the clarity of FLS images and significantly enhance the overall quality of the fused imagery. Experimental results show that our method greatly improves the visual coherence of the fused FLS and SSS images, achieves smooth edge transitions, eliminates visible seams, and enhances the precision of seabed topography reconstruction.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351899","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 : 2025-10-08DOI: 10.1109/TIM.2025.3619247
Shuai Cao;Shengwei Chen;Yaping Liu;Ruizhi Chen
Traditional time difference of arrival (TDoA)-based localization methods cannot autonomously detect or discard large anomalies, leading to significant errors with unbalanced measurements. This study introduces a TDoA-based clustering combined weighted (CCW) method that leverages measurement consistency to identify anomalies in redundant measurements, using only those with normal errors to enhance localization accuracy in unbalanced scenarios. In 2-D localization, at least three base stations are needed to determine the position. With three stations, the CCW method simplifies the solution by transforming the coordinate system and resolving the co-linear problem. When more than three stations are present, the method generates multiple three-base station combinations (TBSCs), solving each to obtain minimal measurement solutions. These solutions are clustered to find subsets that meet the clustering criteria. If all minimal measurement solutions diverge or fail to converge, the CCW method performs a systematic, exhaustive search to identify and exclude unreliable base stations until a valid aggregated class is found or only three stations remain. The final position estimate is the weighted average of the elements in this class, with weights based on the TBSC’s Cramér–Rao lower bound (CRLB). Simulation results show that under unbalanced noise conditions, especially with six and eight base stations, a threshold effect occurs. When the ratio of the abnormal error level to the normal error level exceeds a certain level, the CCW method outperforms other techniques by detecting and removing abnormal measurements, thus improving localization accuracy by focusing on primarily accurate measurements. In real acoustic indoor localization experiments, the CCW method significantly outperformed other methods. In static tests, it achieved sub-0.36 m accuracy for 90% of estimated positions, and in dynamic tests, it closely matched real trajectories with an average anomaly rate of only 1.1%.
{"title":"Clustering Combined Weighted TDoA Localization for Outlier Suppression","authors":"Shuai Cao;Shengwei Chen;Yaping Liu;Ruizhi Chen","doi":"10.1109/TIM.2025.3619247","DOIUrl":"https://doi.org/10.1109/TIM.2025.3619247","url":null,"abstract":"Traditional time difference of arrival (TDoA)-based localization methods cannot autonomously detect or discard large anomalies, leading to significant errors with unbalanced measurements. This study introduces a TDoA-based clustering combined weighted (CCW) method that leverages measurement consistency to identify anomalies in redundant measurements, using only those with normal errors to enhance localization accuracy in unbalanced scenarios. In 2-D localization, at least three base stations are needed to determine the position. With three stations, the CCW method simplifies the solution by transforming the coordinate system and resolving the co-linear problem. When more than three stations are present, the method generates multiple three-base station combinations (TBSCs), solving each to obtain minimal measurement solutions. These solutions are clustered to find subsets that meet the clustering criteria. If all minimal measurement solutions diverge or fail to converge, the CCW method performs a systematic, exhaustive search to identify and exclude unreliable base stations until a valid aggregated class is found or only three stations remain. The final position estimate is the weighted average of the elements in this class, with weights based on the TBSC’s Cramér–Rao lower bound (CRLB). Simulation results show that under unbalanced noise conditions, especially with six and eight base stations, a threshold effect occurs. When the ratio of the abnormal error level to the normal error level exceeds a certain level, the CCW method outperforms other techniques by detecting and removing abnormal measurements, thus improving localization accuracy by focusing on primarily accurate measurements. In real acoustic indoor localization experiments, the CCW method significantly outperformed other methods. In static tests, it achieved sub-0.36 m accuracy for 90% of estimated positions, and in dynamic tests, it closely matched real trajectories with an average anomaly rate of only 1.1%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455782","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 : 2025-09-29DOI: 10.1109/TIM.2025.3614817
Wei Tian;De Zhang;Shuhao Zhang;Haoran Ji;Lei Wang;Ying Pang;Fei Lan;Jinghua Li
In order to achieve efficient power transmission between the transducer and the power amplifier, and to solve the problem of acoustic wave distortion in the transmission, this article proposes a tunable matrix capacitive impedance matching network (TMCIMN) for the electroacoustic transduction system (ETS). First, the circuit model of the giant magnetostrictive transducer (GMT) was analyzed, and a method for fitting its load impedance was proposed. Subsequently, the TMCIMN topology, working principle, and matching range were introduced. This network implements controlled switching of matrix capacitors across operational frequency bands, enabling wideband efficient power transmission. To solve the amplitude–frequency response distortion in transducer sound waves, a predistortion method based on a finite impulse response (FIR) filter frequency compensation was proposed. This method estimates the system impulse response using an equalization algorithm. The equalizer preprocesses the reference signal to compensate for missing frequency components and suppress excessive ones, thereby improving the sound output linearity. The simulation and experimental results demonstrated that, compared to traditional static impedance matching networks, the proposed method combines dynamic bandwidth matching switching with acoustic distortion compensation capability, achieving over eightfold bandwidth expansion.
{"title":"A Tunable Matrix Capacitive Impedance Matching Network for Electroacoustic Transduction Systems","authors":"Wei Tian;De Zhang;Shuhao Zhang;Haoran Ji;Lei Wang;Ying Pang;Fei Lan;Jinghua Li","doi":"10.1109/TIM.2025.3614817","DOIUrl":"https://doi.org/10.1109/TIM.2025.3614817","url":null,"abstract":"In order to achieve efficient power transmission between the transducer and the power amplifier, and to solve the problem of acoustic wave distortion in the transmission, this article proposes a tunable matrix capacitive impedance matching network (TMCIMN) for the electroacoustic transduction system (ETS). First, the circuit model of the giant magnetostrictive transducer (GMT) was analyzed, and a method for fitting its load impedance was proposed. Subsequently, the TMCIMN topology, working principle, and matching range were introduced. This network implements controlled switching of matrix capacitors across operational frequency bands, enabling wideband efficient power transmission. To solve the amplitude–frequency response distortion in transducer sound waves, a predistortion method based on a finite impulse response (FIR) filter frequency compensation was proposed. This method estimates the system impulse response using an equalization algorithm. The equalizer preprocesses the reference signal to compensate for missing frequency components and suppress excessive ones, thereby improving the sound output linearity. The simulation and experimental results demonstrated that, compared to traditional static impedance matching networks, the proposed method combines dynamic bandwidth matching switching with acoustic distortion compensation capability, achieving over eightfold bandwidth expansion.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455785","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 : 2025-09-16DOI: 10.1109/TIM.2025.3604934
Min-Che Tsai;Chao-Chung Peng
The Mecanum wheel car (MWC) is increasingly becoming the mainstream automated guided vehicle (AGV) in factory automation, replacing traditional transport vehicles due to its flexibility and maneuverability. With its widespread applications, there is a corresponding high demand for system inspection and maintenance policies. However, the estimation of kernel parameters without the system disassembly is less investigated. To solve this problem, this article starts from a framework of nonholonomic constraints and uses the Lagrange equations to derive a complete dynamic model of the MWC. Next, a measurement equation using the signal filtering method (FM) is derived. However, the design of the filtering factors is the key issue of the tradeoff between estimation precision and noise suppression. To effectively solve this design problem, particle swarm optimization (PSO) is used to optimize the filtering factor. The proposed method not only avoids interference from noisy acceleration measurements of the MWC but also significantly improves parameter estimation accuracy. The feasibility of the proposed method was validated through both numerical simulations and experiments. The experimental results demonstrate that the parameter estimation method proposed in this article can accurately estimate the internal parameters of the system, enabling precise prediction of the MWC’s motion behavior.
{"title":"Model-Based Particle Swarm Optimization Filtering Algorithm for Mecanum Wheel Car Parameter Identification With Measurement Noise","authors":"Min-Che Tsai;Chao-Chung Peng","doi":"10.1109/TIM.2025.3604934","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604934","url":null,"abstract":"The Mecanum wheel car (MWC) is increasingly becoming the mainstream automated guided vehicle (AGV) in factory automation, replacing traditional transport vehicles due to its flexibility and maneuverability. With its widespread applications, there is a corresponding high demand for system inspection and maintenance policies. However, the estimation of kernel parameters without the system disassembly is less investigated. To solve this problem, this article starts from a framework of nonholonomic constraints and uses the Lagrange equations to derive a complete dynamic model of the MWC. Next, a measurement equation using the signal filtering method (FM) is derived. However, the design of the filtering factors is the key issue of the tradeoff between estimation precision and noise suppression. To effectively solve this design problem, particle swarm optimization (PSO) is used to optimize the filtering factor. The proposed method not only avoids interference from noisy acceleration measurements of the MWC but also significantly improves parameter estimation accuracy. The feasibility of the proposed method was validated through both numerical simulations and experiments. The experimental results demonstrate that the parameter estimation method proposed in this article can accurately estimate the internal parameters of the system, enabling precise prediction of the MWC’s motion behavior.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100358","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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