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Phase Current reconstruction for DTP-PMSM With Single Current Sensor Using Synchronous Filtering reconstruction Method

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2025年第6期40卷 8528-8539页

作者： Zhao, Wenxiang Ma, Sen Tao, Tao Wu, Yunjiang Ling, Jincheng Wang, Huanan Jiangsu Univ Sch Elect Informat Engn Zhenjiang 212013 Peoples R China Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China

In dual three-phase permanent magnet synchronous motor (DTP-PMSM) drive, six-phase current information is critical to motor control. However, the adoption of multiple current sensors increases the size and cost of inverters. To mitigate this issue, this article proposes a method to reconstruct six-phase currents of a DTP-PMSM by using a single current sensor (SCS). First, the SCS is repositioned to a current branch. Then, sampling is performed twice in one period based on the switching state of a single bridge arm. Therefore, two orthogonal phase currents are achieved. Furthermore, the synchronous filtering reconstruction method is proposed. Through this method, the harmonic current information is extracted from two orthogonal currents and the remaining four-phase currents are reconstructed. Overall, six-phase currents can be accurately reconstructed by using an SCS, for which the hardware cost and inverter size can be reduced. The proposed method is validated by both simulation and experiments.

关键词： Vectors reconstruction algorithms Harmonic analysis Power harmonic filters Switches Pulse width modulation Current measurement Inverters Filtering Bridge circuits Dual three-phase permanent magnet synchronous motor (DTP-PMSM) phase current reconstruction single current sensor (SCS) synchronous filtering reconstruction

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A Multiscale reconstruction Framework Based on Edge-Enhanced Guidance for In-Line X-Ray Phase-Contrast CT With Limited-Angle Projections

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Li, Yimin Zhao, Yuanyuan Ma, Chenyang Li, Fangzhi Wang, Ziyao Lv, Wenjuan Ji, Dongjiang Jian, Jianbo Zhao, Xinyan Zhao, Yuqing Hu, Chunhong Tianjin Med Univ Sch Biomed Engn & Technol Tianjin 300070 Peoples R China Tianjin Univ Technol & Educ Sch Sci Tianjin 300222 Peoples R China Tianjin Med Univ Gen Hosp Tianjin 300052 Peoples R China Capital Med Univ Beijing Friendship Hosp Liver Res Ctr Beijing 100050 Peoples R China

The in-line X-ray phase-contrast computed tomography (IL-XPCCT) serves as an effective tool for studying organ function and pathologies. However, IL-XPCCT approaches often result in high radiation doses due to long scan times. To address this issue, a limited angel sampling strategy is frequently employed. However, limited-angle projection data can lead to severe divergence phenomena at sample edges or boundaries, significantly reducing image quality and affecting subsequent image analysis. In this work, we propose a novel multiscale reconstruction framework based on edge-enhanced guidance (MSRF-EEG) for IL-XPCCT limited-angle CT reconstruction. The MSRF-EEG framework consists of two primary subnetworks: an edge-enhanced subnetwork (EESN) and a multiscale reconstruction subnetwork (MSRSN). By fully considering the imaging characteristics of the IL-XPCCT, the EESN is designed to explore the edge enhancement characteristics from the IL-XPCCT images prior to phase retrieval, which is then utilized to compensate for edge distortions during the reconstruction process. The MSRSN performs image reconstruction with edge-enhanced guidance across multiple scales simultaneously. Furthermore, to better leverage the edge enhancement characteristics before phase retrieval for reconstructing the IL-XPCCT images from limited-angle projections, an edge aggregation (EAG) module and an edge attention (EAT) module are incorporated into the MSRSN. Simulations and real data experiments are conducted to evaluate the performance of our proposed MSRF-EEG framework. Compared with the existing competitive algorithms, both quantitative and qualitative results demonstrate that the proposed method can significantly enhance reconstruction quality under different sampling angles and improve detail restoration and edge preservation.

关键词： Image reconstruction Image edge detection Computed tomography Imaging X-ray imaging TV Detectors Decoding reconstruction algorithms Phase measurement Edge enhancement in-line X-ray phase-contrast computed tomography (IL-XPCCT) limited-angle reconstruction phase retrieval

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SCA-CVENet: A Spatial-Channel-Attention Collaborative Cost Volume Enhancement Network for High-Quality Depth reconstruction

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IEEE ACCESS 2025年 13卷 58264-58280页

作者： Tian, Mao Zhao, Xudong Lv, Xiong Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China

Accurate and complete depth map prediction from a set of overlapping multi-view stereo images has received extensive attention in the fields of photogrammetry and computer vision. Despite the tremendous efforts made in multi-view depth map reconstruction in recent years, the accuracy and completeness of the reconstructed depth map in matching difficulty areas needs to be further improved. In this paper, to address this issue, we propose SCA-CVENet, which is an efficient spatial-channel-attention collaborative cost volume enhancement network for high-quality depth map reconstruction. The main contributions of SCA-CVENet are the channel attention weight (CAW)-based cost volume enhancement module that can effectively filter the redundant channel information of the feature volumes to boost the feature representation of the cost volumes, and the spatial attention weight (SAW)-based cost volume enhancement module that can significantly improve the robustness of the cost volumes in textureless and repetitive texture areas, to produce more accurate and complete depth map reconstruction results. First, a weight-sharing hierarchical feature extraction subnetwork is adopted to extract robust multi-scale feature maps. Second, the SAW generation subnetwork is adopted, which incorporates the group-wise correlation (GWC)-based cost volume adaptive fusion module and 3D hourglass module to produce an accurate SAW. Finally, the coarse-to-fine cascade depth prediction subnetwork incorporates the CAW- and SAW-based cost volume enhancement modules into the cascade network architecture to predict a high-quality depth map. Three publicly available benchmark datasets-the DTU, BlendedMVS, and Tanks & Temples datasets-were utilized to validate the performance of SCA-CVENet, and the comprehensive experimental results demonstrate that SCA-CVENet obtains a superior performance on all the datasets in terms of accuracy and completeness.

关键词： Costs Image reconstruction Accuracy Three-dimensional displays reconstruction algorithms Robustness Information filters Memory management Feature extraction Collaboration Depth prediction multi-view depth map reconstruction channel attention weight (CAW) spatial attention weight (SAW) cascade network architecture

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Coprime reconstruction of Super-Nyquist Periodic Signal and Sampling Moire Effect

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Tang, Hsien-Chung Shieh, Yueh-Tsung Roy, Rohit Jeng, Shyr-Long Chieng, Wei-Hua Natl Yang Ming Chiao Tung Univ Mech Dept Hsinchu 300093 Taiwan Lunghwa Univ Sci & Technol Mech Dept Taoyuan Taiwan

Sampling moire effect is well-known in signal processing. When a continuous periodic signal x(t) is sampled using a sampling rate fs that does not respect the Nyquist condition, and the signal-frequency fx folds over and gives a special sequence in the sampled signal. The coprime reconstruction ismade possible when the periodic signal frequency fx and the sampling frequency fs satisfy fs/fx=N/M with integer M, N, where N/M is a reduced fraction. This article shows how to rearrange the N sample data into one entire period of the signal using the M periods of the signal. The reconstruction is equivalent to the decryption of the sample data using the method referred to the mathematic operator "multiplicative inverse modulo." In the implementation to verify the coprime reconstruction, we used a commodity DSP board with 4.2 MHz sampling rate to reconstruct the 13.56 MHz signal acquired from the ADC circuit. The signal reconstruction result is comparable to the sampling result for the same signal sampled at 475 MHz sampling rate. The other simulation on the 234 kHz signal under 1 MHz sampling rate yields a very detail information on the signal with a finding that the multiplicative inverse modulo may also be used as the encryption of signal for network security use.

关键词： Signal resolution Resonant frequency reconstruction algorithms Zero current switching Time-frequency analysis Linear systems Sensors Pulse width modulation Frequency modulation Costs Moir & eacute effects coprime Nyquist theorem reconstruction sampling

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Integrating Regularization and PnP Priors for SAR Image reconstruction Using Multiagent Consensus Equilibrium

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Fan, Yizhe Zhang, Bingchen Wu, Yirong Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applica Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China

The multiagent consensus equilibrium (MACE) mechanism, which generalizes the popular method plug-and-play (PnP)-alternating direction method of multipliers (ADMM) and composite regularization in computational sensing, possesses the notable capacity to incorporate multiple priors aspect of both regularization and PnP for improving image quality. In this work, a flexible synthetic aperture radar (SAR) image reconstruction method based on MACE is proposed to integrate multiple regularization and PnP priors for various features enhancement. The partial-update approach and Mann iteration methods are implemented to increase the computational efficiency of the MACE-based SAR image reconstruction algorithm. A thorough analysis of the proposed algorithm's convergence and computational complexity is provided. High-quality SAR images necessitate low ambiguity, high target-to-background ratio (TBR), and low coherent speckle. We therefore demonstratively integrate regularization and PnP priors for azimuth ambiguity suppression, sparsity inducing, multiple features enhancement, and despeckling. The proposed method's performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data.

关键词： Radar polarimetry Image reconstruction Imaging Vectors Mathematical models Convergence Computational complexity Azimuth reconstruction algorithms Radar imaging Multiagent consensus equilibrium (MACE) SAR image reconstruction sparse signal processing synthetic aperture radar (SAR)

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A Survey of Indoor 3D reconstruction Based on RGB-D Cameras

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IEEE ACCESS 2024年 12卷 112742-112766页

作者： Zhu, Jinlong Gao, Changbo Sun, Qiucheng Wang, Mingze Deng, Zhengkai Changchun Normal Univ Sch Comp Sci & Technol Changchun 130032 Peoples R China

With the advancement of consumer-grade RGB-D cameras, obtaining depth information for indoor 3D spaces has become increasingly accessible. This paper systematically reviews 3D reconstruction algorithms for indoor scenes using these cameras, serving as a reference for future research. We cover reconstruction processes and optimization algorithms for both static and dynamic scenes. Additionally, we discuss commonly used datasets, evaluation metrics, and the performance of various reconstruction algorithms. Findings indicate that the balance between reconstruction quality and speed in static scene reconstruction, as well as deformation, occlusion, and fast motion of objects in dynamic scenes are currently major concerns. Deep learning and Neural Radiance Fields (NeRF) are poised to provide new perspectives and methods to address these challenges.

关键词： Cameras Three-dimensional displays Heuristic algorithms Dynamics Solid modeling reconstruction algorithms Surface treatment Indoor environment Neural radiance field 3D reconstruction indoor scenes static scenes dynamic scenes deep learning neural radiance fields

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Iterative Residual Optimization Network for Limited-Angle Tomographic reconstruction

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 910-925页

作者： Pan, Jiayi Yu, Hengyong Gao, Zhifan Wang, Shaoyu Zhang, Heye Wu, Weiwen Sun Yat Sen Univ Sch Biomed Engn Shenzhen 518107 Guangdong Peoples R China Univ Massachusetts Lowell Dept Elect & Comp Engn Lowell MA 01854 USA PLA Strateg Support Force Informat Engn Univ Sch Informat Syst Engn Zhengzhou 450001 Peoples R China PLA Strateg Support Force Informat Engn Univ Zhengzhou 450001 Peoples R China

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems, leading to edge divergence with degraded image quality. Recently, deep learning has been introduced into image reconstruction and achieved great results. However, existing deep reconstruction methods have not fully explored data consistency, resulting in poor performance. In addition, deep reconstruction methods are still mathematically inexplicable and unstable. In this work, we propose an iterative residual optimization network (IRON) for limited-angle tomographic reconstruction. First, a new optimization objective function is established to overcome false negative and positive artifacts induced by limited-angle measurements. We integrate neural network priors as a regularizer to explore deep features within residual data. Furthermore, the block-coordinate descent is employed to achieve a novel iterative framework. Second, a convolution assisted transformer is carefully elaborated to capture both local and long-range pixel interactions simultaneously. Regarding the visual transformer, the multi-head attention is further redesigned to reduce computational costs and protect reconstructed image features. Third, based on the relative error convergence property of the convolution assisted transformer, a mathematical convergence analysis is also provided for our IRON. Both numerically simulated and clinically collected real cardiac datasets are employed to validate the effectiveness and advantages of the proposed IRON. The results show that IRON outperforms other state-of-the-art methods.

关键词： Convolution reconstruction algorithms Transformers Iron Iterative methods Image reconstruction Optimization Limited-angle CT image reconstruction model optimization transformer convergence

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Sound Field reconstruction Using Error Function Method

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IEEE SENSORS JOURNAL 2024年第24期24卷 41213-41220页

作者： Huang, Linsen Zeng, Rui Chongqing Technol & Business Univ Sch Mech Engn Chongqing Peoples R China Zonsen Ind Grp Co Ltd Chongqing 400054 Peoples R China

The accurate sound field reconstruction, especially in medium- and low-frequency ranges, remains challenging due to limitations in traditional methods relying on the l(0) norm for sparsity promotion. These approaches are vulnerable to noise interference and may underestimate high-amplitude components, affecting reconstruction stability. In this work, we propose a novel sound field reconstruction method utilizing the error function (ERF) as a surrogate for the $\ell _{reconstruction}$ norm to improve both the speed and robustness of reconstruction. Comparative analysis with traditional methods, including the Tikhonov regularization method, monotonic two-step iterative shrinkage/thresholding (MTwIST) method, and l(1) -CVX, demonstrates the superior performance of the ERF method in terms of sound pressure accuracy and reconstruction reliability. Monte Carlo simulations, receiver operating characteristic (ROC) analysis, and experimental results further validate that the ERF method achieves improved localization and sound field reconstruction in the low-frequency domain, with better flexibility concerning measurement distance and signal-to-noise ratio (SNR).

关键词： Image reconstruction Accuracy Sensors Microphone arrays Convex functions Minimization Mathematical models Computational modeling Vectors reconstruction algorithms Compressive sensing error function (ERF) & ell p error microphone array sound field reconstruction

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Clinical and Deep-Learned Evaluation of MR-Guided Self-Supervised PET reconstruction

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IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2025年第3期9卷 337-346页

作者： Hopson, Jessica B. Ellis, Sam Flaus, Anthime Mcginnity, Colm J. Neji, Radhouene Reader, Andrew J. Hammers, Alexander Kings Coll London Dept Biomed Engn London SE1 7EH England Kings Coll London Guys & St Thomas PET Ctr London SE1 7EH England Siemens Healthcare Ltd MR Res Collaborat Camberley GU15 3YL England

Reduced dose positron emission tomography (PET) lowers the radiation dose to patients and reduces costs. Lower-count data, however, degrades reconstructed image quality. Advanced reconstruction methods help mitigate image quality losses, but it is important to assess the resulting images from a clinical perspective. Two experienced clinicians assessed four PET reconstruction algorithms for [F-18]FDG brain data, compared to a clinical standard reference (maximum-likelihood expectation-maximization (MLEM)), based on seven clinical image quality metrics: global quality rating, pattern recognition, diagnostic confidence (all on a scale of 0-4), sharpness, caudate-putamen separation (CP), noise, and contrast (on a scale between 0-2). The reconstruction methods assessed were a guided and unguided version of self-supervised maximum a posteriori EM (MAPEM) (where the guidance case used the patient's MR image to control the smoothness penalty). For 3 of the 11 patient datasets reconstructed, post-smoothed versions of the MAPEM reconstruction were also considered, where the smoothing was with the point-spread-function used in the resolution modelling. Statistically significant improvements were observed in sharpness, CP, and contrast for self-supervised MR-guided MAPEM compared to MLEM. For example, MLEM scored between 1-1.1 out of 2 for sharpness, CP, and contrast, whereas self-supervised MR-guided MAPEM scored between 1.5-1.75. In addition to the clinical evaluation, pretrained convolutional neural networks (CNNs) were used to assess the image quality of a further 62 images. The CNNs demonstrated similar trends to the clinician, showing their potential as automated standalone observers. Both the clinical and CNN assessments suggest when using only 5% of the standard injected dose, self-supervised MR-guided MAPEM reconstruction matches the 100% MLEM case for overall performance. This makes the images far more clinically useful than standard MLEM.

关键词： reconstruction algorithms Standards Positron emission tomography Image quality Smoothing methods Noise Measurement Kernel Imaging Image quality assessment image reconstruction positron emission tomography (PET) imaging PET-magnetic resonance (PET-MR) imaging self-supervision self-supervision

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Multi-Channel Optimization Generative Model for Stable Ultra-Sparse-View CT reconstruction

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2024年第10期43卷 3461-3475页

作者： Wu, Weiwen Pan, Jiayi Wang, Yanyang Wang, Shaoyu Zhang, Jianjia Sun Yat sen Univ Sch Biomed Engn Shenzhen Campus Shenzhen 518063 Guangdong Peoples R China PLA Strateg Support Force Informat Engn Univ Sch Informat Syst Engn Zhengzhou 450001 Peoples R China

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of the iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from the intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

关键词： Image reconstruction Computed tomography Mathematical models Data models reconstruction algorithms Optimization Noise reduction image reconstruction sparse-view score-based generative model data consistency

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