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Tracking Error Suppression of Maglev Planar Actuator With FPGA-Based MPC

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhou, Shun Shi, Yuan Zou, Menglong Xu, Fengqiu Xu, Xianze Wuhan Univ Elect Informat Sch Wuhan 430070 Peoples R China

The precision positioning equipment is essential in many manufacture areas. This article develops a novel noncontact six-degree-of-freedom maglev planar actuator (MLPA) for applications in precision manufacturing area. To achieve the tracking and positioning of MLPA, a model predictive control (MPC) algorithm is introduced. However, the high frequency feedback control of maglev system up to kilohertz level poses the challenges in the online optimization of MPC. We argue that the field-programmable gate array (FPGA), a high throughput and low latency processor, accelerates the computation of MPC, and the high-level synthesis (HLS) tool simplifies the implementation of the optimization algorithm, which is the most complex module in MPC. In this work, the dual-gradient projection method is introduced to realize the online optimization with its characteristics of parallelism. Benefiting from the parallel architecture of FPGA, the dual-gradient projection method successfully runs in the millisecond level with the acceleration performance highlighted by comparing with active set and interior point methods (IPMs). Furthermore, the FPGA-based MPC is deployed for MLPA to inhibit the trajectory tracking error and improve the step response speed. The synthesis of the proposed FPGA-based MPC scheme indicates the prediction horizon of MPC is able to theoretically reach about 20, and the experiments undertaken on the real-time system shows that tracking performance of MLPA is improved with the prediction horizon expanding.

关键词： Photonic crystals Magnetic materials Optimization Field programmable gate arrays Actuators Real-time systems Magnetic levitation Computational efficiency Trajectory tracking signal processing algorithms Field-programmable gate array (FPGA) maglev planar actuator (MLPA) precision positioning real-time system

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A Novel Localized Coherence Rule Delay-and-Multiply Algorithm Toward Long-Range Ultrasound Imaging With Narrow Field-of-View pMUTs

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IEEE SENSORS JOURNAL 2025年第10期25卷 16830-16845页

作者： Giubilei, Gaia Sarafianou, Mantalena Choong, David Sze Wai Goh, Duan Jian Koh, Yul ASTAR Inst Microelect IME Singapore 138634 Singapore Politecn Torino Dept Phys Engn Micro & Nanotechnol Integrated Syst I-10126 Turin Italy ASTAR IME Singapore 138634 Singapore

This work proposes an innovative signal postprocessing algorithm for long-range ultrasound imaging by using a narrow field-of-view (narrow-FoV) lead zirconate titanate (PZT) piezoelectric micromachined ultrasonic transducer (pMUT) array. Two ${3} \times {3}$ mm2 dies of a 1-D array pMUT, whose resonant frequency is 6.5 MHz, consisting of 18 channels, were employed for this demonstration. The entire system has 18 channels by electrically combining two columns of pMUT membranes as one to increase the transmission power with the cost of narrowing FoV. In this work, experimental testing using single and multiple 5-mm diameter spherical reflectors at various arrangements was performed to validate the 2-D imaging capabilities of the developed pMUT array with the proposed image reconstruction algorithm achieving a 1.5-mm lateral resolution.

关键词： Imaging Crosstalk Continuous wavelet transforms Image reconstruction signal processing algorithms Ultrasonic imaging Lead zirconate titanate Array signal processing Distortion measurement signal to noise ratio Piezoelectric micromachined ultrasonic transducer (pMUT) receive (RX) beamforming ultrasonic imaging

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Phase Unwrapping Algorithm for Distributed Acoustic Sensing Based on Unlimited Sampling and Phase Diversity

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IEEE SENSORS JOURNAL 2025年第10期25卷 17102-17109页

作者： Dureck, Eduardo H. Gomes, Danilo F. Weber, Guilherme H. Brusamarello, Beatriz Passarin, Thiago A. R. Martelli, Cicero Pipa, Daniel R. da Silva, Jean Carlos Cardozo Fed Univ Technol Grad Program Elect & Comp Engn BR-80230901 Curitiba Parana Brazil

Distributed acoustic sensing (DAS) detects strain on optical fibers over long distances with a typical spatial resolution of a couple of meters and an acquisition rate of up to 100 kHz. DAS is utilized to diagnose events in applications such as pipeline monitoring, seismic detection, structural diagnostics, and sea current observation, offering high precision for complex environments. Given the requirements of these applications, the fidelity of the data from DAS is crucial to obtain the greatest accuracy of the monitored event. However, this equipment is subject to signal distortions due to the effects of noise and the sensor's dynamic range. To mitigate these effects, this article introduces a new data interpretation methodology based on the unlimited sampling algorithm, which enhances the dynamic range of the sensor during the signal unwrapping process and incorporates phase diversity for noise fading reduction. This work introduces a dataset using the new algorithm compared with the state of the art. The setup follows SEAFOM guidelines using a fiber stretcher to generate signals on the fiber. An improvement of 18.44% in detecting the signal's fundamental frequency was achieved, and a hyperparameter mapping was conducted to enable future application in systems, where the current standard DAS algorithm fails to resolve signals accurately.

关键词： Optical fiber sensors Optical fibers Sensors Noise Distortion Heuristic algorithms Spatial resolution Strain signal processing algorithms Optical distortion Distributed acoustic sensing (DAS) phase demodulation phase diversity phase unwrapping unlimited sampling

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DOA Estimation by Jointly Exploiting L1-SVD and Enhanced Spatial Smoothing in Coherent Environment

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Jingchao Li, Muheng Bai, Longxin Qiao, Liyan Harbin Inst Technol HIT Dept Automat Test & Control Harbin 150080 Peoples R China

As a sparse-based direction of arrival (DOA) estimation algorithm, the L1-singular value decomposition (SVD) algorithm is widely used to measure the orientation of targets. In real measurements, the coherent environment that often arises due to multipath propagation leads to the deterioration of the noise immunity and estimation accuracy of the L1-SVD algorithm. Although the decoherence of L1-SVD can be enhanced by introducing spatial smoothing after SVD, which is called SS-L1-SVD, the algorithm does not fully utilize the available information in the observed data. In this article, we propose a new method called L1-enhanced spatial smoothing decomposition (ESSD). ESSD combines spatial smoothing with matrix decomposition by utilizing the relationship among the covariance matrix and the left singular matrix and the singular value matrix. ESSD not only improves the decoherence ability of the algorithm but also makes full use of the information in the observed data and reduces the computational complexity, which makes the algorithm more practical than the traditional algorithms in real measurements. In order to further verify the performance of the new algorithm, we not only performed simulation experiments but also designed a physical experimental platform that can be used for DOA estimation and constructed a real coherent environment caused by multipath propagation and performed physical experiments. The results of simulation and physical experiments show that the L1-ESSD algorithm reduces the error by about 1 degrees and the computation time by about 8 s compared with the conventional L1-SVD algorithm.

关键词： Estimation Direction-of-arrival estimation signal processing algorithms Smoothing methods Noise Antenna arrays Accuracy Vectors Classification algorithms Array signal processing Coherent environment direction of arrival (DOA) enhanced spatial smoothing decomposition (ESSD) L1-singular value decomposition (SVD) multipath effect

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Feature-PLPD: Feature-Point and Line Points Detection for Real-Time Embedded Visual Odometry-Based Systems

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IEEE signal processing LETTERS 2025年 32卷 1890-1894页

作者： Mamri, Ayoub El Hadri, Abdelhafid Benallegue, Abdelaziz Univ Paris Saclay UVSQ LISV F-78140 Velizy Villacoublay France

Detection of interest features is a fundamental pre-processing task for visual odometry and visual simultaneous localization and mapping. The combination of line segments and corners is a trend for those applications, and it is challenging to meet real-time constraints. To pursue this goal, this letter presents a novel Points and Line Points feature Detection (PLPD) algorithm designed to be highly efficient and accurate for real-time VO-based systems. The proposed algorithm adopts a novel Multi-level Edge Detector (MED) based on gradient intensity and structure tensor that is not confined solely to extracting corners, but rather, it exploits feature-line points properties to extract long-line segments directly without merging collinear fragment segments. The main focus of this work is to fulfill the accuracy, real-time, and embeddability constraints of our proposed method by adopting the GPU-aware software design for a suitable implementation in GPU-based embedded heterogeneous architecture. Based on this, we exhibit extensive benchmarking with other state-of-the-art algorithms for feature-line segment extraction and corner detection, showing the efficiency of the proposed algorithm and ensuring a real-time performance up to 33 FPS using our dataset.

关键词： Feature extraction Real-time systems Image edge detection Graphics processing units Computer architecture Detectors Tensors signal processing algorithms Kernel Accuracy Line segment detection corner detection GPU-aware software design GPU-based embedded heterogeneous architecture

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Moving Average-Based Variable Projection for Separable Nonlinear Problems

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IEEE signal processing LETTERS 2025年 32卷 1900-1904页

作者： Xue, Peng Gan, Min Yuan, Fang Chen, Guang-Yong Chen, C. L. Philip Fuzhou Univ Coll Comp Sci & Big Data Fuzhou 350116 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

The identification of separable nonlinear models, prevalent in tasks such as signal analysis, image processing, time series analysis, and machine learning, presents a non-convex optimization challenge that necessitates the development of efficient identification algorithms. The Variable Projection (VP) algorithm has been proven to be quite effective for addressing these problems;however, traditional VP relying on the Hessian matrix and its inverse are highly time-consuming and unsuitable for complex, large-scale applications. This letter introduces a novel approach that employs the exponential moving average of gradient and gradient estimation bias to indirectly estimate the curvature of the objective landscape, proposing a Moving Average-based Variable Projection method (MAVP). The proposed algorithm utilizes only gradient information and can properly tackle the coupling relationships between different parameters during the optimization process, thereby achieving faster convergence. Numerical results on nonlinear time series analysis and image reconstruction demonstrate that the MAVP algorithm exhibits significant efficiency and effectiveness.

关键词： Optimization signal processing algorithms Couplings Approximation algorithms Jacobian matrices Convergence Estimation Vectors Machine learning algorithms Linear programming Separable nonlinear optimization problem variable projection system identification

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Adaptive Attention Based on Mixture Distribution for Zero-Shot Non-Line-of-Sight Imaging

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IEEE signal processing LETTERS 2025年 32卷 1690-1694页

作者： Zhang, Qinghua Liu, Jun Duan, Yuping Beijing Normal Univ Sch Math Sci Lab Math & Complex Syst Minist Educ China Beijing 100875 Peoples R China

Non-line-of-sight (NLOS) imaging is an ill-posed problem to reconstruct hidden 3D scenes by leveraging photon time-of-flight information from diffusely reflected light. In the existing regularization models, the spatial residuals were handled by a single distribution, failing to account for the distinct characteristics of background and target objects. In this paper, we propose a novel NLOS reconstruction method that models the non-Gaussian residuals with a mixture distribution. Through a dual method, we derive an adaptive weighted residual model, where the weights generated in the dual space act as a zero-shot attention mechanism to control the contributions of different regions. The corresponding optimization problem can be effectively solved using the alternating minimization algorithm. Numerical experiments on both synthetic and real-world datasets demonstrate that our method surpasses the related existing approaches, achieving state-of-the-art performance.

关键词： Image reconstruction Imaging Gold Three-dimensional displays Attention mechanisms Vectors Probability density function Training signal processing algorithms Lighting Non-line-of-sight imaging mixture distribution duality theory attention mechanism

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A New CFAR Detector Based on the EM Algorithm

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IEEE signal processing LETTERS 2025年 32卷 2035-2039页

作者： Orlando, Danilo Ricci, Giuseppe Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy Univ Salento Dipartimento Ingn Innovaz I-73100 Lecce Italy

This paper proposes a simple Constant False Alarm Rate (CFAR) approach relying on the expectation-maximization algorithm to deal with clutter edges. The possible signal backscattered from a target is modeled as an incoherent pulse train and the newly-introduced CFAR technique is assessed in conjunction with an energy detector. Natural competitors are the well-known Cell-Averaging (CA), Greatest Of (GO), and Smallest Of (SO) CFAR techniques. The performance analysis shows that the proposed approach outperforms the state-of-the-art competitors in terms of false alarms control while maintaining satisfying detection performance.

关键词： Detectors Clutter Vectors signal processing algorithms Program processors Probability density function Image edge detection Covariance matrices Radar detection Noise Cell-averaging constant false alarm rate (CA CFAR) clutter edges detection expectation-maximization algorithm greatest of (GO) CFAR radar smallest of (SO) CFAR

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An Adaptive Target Detection Architecture for Mismatched signals

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IEEE signal processing LETTERS 2025年 32卷 1860-1864页

作者： Jin, Yuxi Yin, Chaoran Wang, Tianqi Hao, Chengpeng Orlando, Danilo Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

This letter addresses the problem of adaptive target detection in the presence of possible mismatched sidelobe interfering signals assumed orthogonal to the nominal target signature in the whitened space. To this end, we devise a joint Maximum Likelihood (ML)-Bayesian based detector that simultaneously improves the target detection performance and the rejection capability of the mismatched signals. Specifically, we first inject an orthogonal interfering signal into the null hypothesis of the traditional binary hypothesis test and, then, solve it by means of the latent variable model and the Expectation Maximization algorithm. Finally, we maximize the posterior probability of the hypotheses for decision. In addition, we prove the Constant False-Alarm Rate property of the proposed detection architecture. The illustrative examples conducted on synthetic data corroborate the enhanced detection performance and rejection capability with respect to the state-of-the-art.

关键词： Detectors Vectors Object detection Computer architecture Bayes methods Probability density function signal to noise ratio signal processing algorithms Radar detection Radar A posteriori probability Bayesian framework expectation maximization orthogonal rejection parameter estimation radar target detection

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Power Minimization-Based Secure Beamforming for MISO VLC Having Both Perfect and Imperfect CSI

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IEEE signal processing LETTERS 2025年 32卷 1830-1834页

作者： Yu, Yin-Chang Zeng, Zhi-Zhao Lu, Ding-Shan Su, Dao-Peng Wang, Jin-Yuan Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210003 Peoples R China

The majority of research on visible light communications (VLC) neglects the non-line-of-sight (NLoS) channel gains, which cannot be ignored for physical-layer security analysis. Moreover, the commonly used semidefinite relaxation technique for power minimization problems may not always yield an optimal solution due to the removal of the rank-one constraint. In this letter, we investigate a power minimization-based secure beamforming problem for a multiple-input single-output VLC system having both LoS and NLoS components. Assuming perfect channel state information (CSI), we formulate an optimal secure beamforming problem with a worst-case secrecy rate constraint and a per-lamp power constraint. Instead of removing the rank-one constraint, we incorporate such a constraint into the objective function and treat it as a penalty term. Then, we propose a semidefinite programming and penalty function (SDPPF) algorithm to solve the non-convex problem. To enhance practicality, we also consider the case of imperfect CSI and explore a robust beamforming scheme. Numerical results demonstrate that the proposed SDPPF algorithm can achieve a power gain of at least 3 dB compared to existing algorithms. Moreover, the necessity of considering the NLoS channel gains is also verified.

关键词： Array signal processing Visible light communication Security Vectors Resource management Nonlinear optics Light emitting diodes signal processing algorithms Reflection NOMA Non-line-of-sight optimal power allocation secure beamforming visible light communication

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