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An Unbiased Symmetric Matrix Estimator for Topology Inference Under Partial Observability

IEEE signal processing LETTERS 2022年 29卷 1257-1261页

作者： Chen, Yupeng Wang, Zhiguo Shen, Xiaojing Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China

Network topology inference is a fundamental problem in many applications of network science, such as locating the source of fake news and brain connectivity network detection. Many real-world situations suffer from a critical problem in which only a limited number of observed nodes are available. In this work, the problem of network topology inference under the framework of partial observability is considered. Based on the vector autoregression model, we propose a novel unbiased estimator for symmetric network topology with Gaussian noise and the Laplacian combination rule. Theoretically, we prove that this estimator converges in probability to the network combination matrix. Furthermore, by utilizing the Gaussian mixture model algorithm, an effective algorithm called the network inference Gauss algorithm is developed to infer the network structure. Finally, compared with state-of-the-art methods, numerical experiments demonstrate that better performance is obtained in the case of small sample sizes when using the proposed algorithm.

关键词： Network topology Symmetric matrices Inference algorithms Clustering algorithms Observability signal processing algorithms Laplace equations Topology inference symmetric matrix unbiased estimator partial observation

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Semi-Supervised Text Detection With Accurate Pseudo-Labels

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IEEE signal processing LETTERS 2022年 29卷 1272-1276页

作者： Zhou, Yu Xie, Hongtao Fang, Shancheng Zhang, Yongdong Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China

Recent scene text detection methods have made great progress. However, existing methods rely heavily on extensive labeled data, which is very time-consuming and expensive. In this letter, we propose a novel semi-supervised text detection method to alleviate the dependence of text detectors on labeled data by generating accurate pseudo-labels and performing effective data augmentations. Specifically, a dual-threshold pseudo-label generation algorithm is designed to divide the prediction results into background regions, text regions, and uncertain regions. The definition of uncertain regions obviously improves the accuracy of pseudo-labels. To obtain accurate pseudo-labels for text at various scales, we first design a scale-aware loss function to adaptively adjust the loss weight of different scale texts. Then, a multi-scale feature extraction module is proposed to extract multi-scale text features and adaptively weight these features according to the scale of the text. Moreover, effective data augmentations are explored to use unlabeled data to improve the robustness of the model to various texts. Experiments show that our method achieves state-of-the-art performance on several datasets(e.g., outperforms existing methods by 2.0% on TD500).

关键词： Feature extraction Data models Training signal processing algorithms Prediction algorithms Adaptation models Detection algorithms Deep learning loss function multi-scale text semi-supervised text detection

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An Efficient MMW 3-D Imaging Algorithm for Near-Field MIMO-SAR With Nonuniform Transmitting Array

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2022年第10期21卷 2035-2039页

作者： Chen, Xu Wang, Hongqiang Yang, Qi Zeng, Yang Deng, Bin Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

In this letter, an efficient millimeter-wave (MMW) frequency-domain imaging algorithm is presented for 3-D object reconstruction of the near-field multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. For each single-input-multiple-output (SIMO) case of the MIMO-SAR setup, the precise spectral echo expression is derived by spherical-wave decomposition and fast Fourier transform (FFT) operations. To obtain the linear phase in range direction, the multiorder approximation theory is employed to expand the coupling phases, and thus the target subimage can be reconstructed by utilizing phase compensation, central wavenumber approximation, and inverse FFT. Finally, the fully focused 3-D image for MIMO setup will be realized through accumulating all the SIMO subimages. Compared with the existing representative algorithms, our method avoids complicated interpolation and achieves high-quality fast imaging with only matrix multiplication and a small amount of iteration operations. The experimental results validate the effectiveness of the proposed algorithm.

关键词： Imaging Three-dimensional displays Transmitters MIMO communication Approximation algorithms signal processing algorithms Receivers Frequency-domain algorithm multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) millimeter wave (MMW) 3-D imaging multiorder approximation

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A Pipelined Algorithm and Area-Efficient Architecture for Serial Real-Valued FFT

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第11期69卷 4533-4537页

作者： Fang, Hongji Zhang, Bo Yu, Feng Zhao, Bei Ma, Zhenguo Zhejiang Univ Key Lab Biomed Engn Minist Educ Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Coll Comp Sci & Technol Hangzhou 310005 Peoples R China

In this brief, we present a novel pipelined architecture for real-valued fast Fourier transform (RFFT), which is dedicated to processing serial input data. An optimized algorithm is proposed for stage division in RFFT to achieve an area-efficient RFFT computing structure with full hardware utilization. A single path butterfly and a real rotator are merged into one processing element (PE) in each stage, except the last stage, to reduce hardware resource utilization. In addition, a novel shift-adder is designed for N-point RFFT with W-bit signals, and a new data management method based on the PEs is proposed, which saves resources with a more regular flow graph.

关键词： Computer architecture Hardware Adders signal processing algorithms Three-dimensional displays Registers Pipelines Fast fourier transform (FFT) real-valued signals pipelined architecture serial commutator

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Data-Reuse Recursive Least-Squares algorithms

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IEEE signal processing LETTERS 2022年 29卷 752-756页

作者： Paleologu, Constantin Benesty, Jacob Ciochina, Silviu Univ Politehn Bucuresti Bucharest 060042 Romania Univ Quebec INRS EMT Montreal PQ H5A 1K6 Canada

There are different strategies to improve the overall performance of the recursive least-squares (RLS) adaptive filter. In this letter, we focus on the data-reuse approach, aiming to improve the convergence rate/tracking of the algorithm by reusing the same set of data (i.e., the input and reference signals) several times. First, we present a computationally efficient data-reuse RLS algorithm, which is the result of a low complexity implementation of the data-reuse process. Moreover, we extend the idea to the fast RLS algorithm. Simulations performed in the context of echo cancellation support the performance gain.

关键词： signal processing algorithms Convergence Covariance matrices Computational complexity Indexes Kalman filters Jacobian matrices Adaptive filters echo cancellation data-reuse recursive least-squares (RLS) algorithm fast RLS (FRLS) algorithm convergence tracking

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A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control Based on Deep Learning

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IEEE signal processing LETTERS 2022年 29卷 1102-1106页

作者： Luo, Zhengding Shi, Dongyuan Gan, Woon-Seng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. Nonetheless, its slow convergence has a detrimental effect on dynamic noise attenuation. Therefore, this paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level than the SFANC method. A lightweight one-dimensional convolutional neural network (1D CNN) is designed to automatically select the most suitable pre-trained control filter for each frame of the primary noise. Meanwhile, the FxNLMS algorithm continues to update the coefficients of the chosen pre-trained control filter at the sampling rate. Owing to the effective combination of the two algorithms, experimental results show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.

关键词： Filtering algorithms signal processing algorithms Band-pass filters Noise reduction Classification algorithms Training Process control Active noise control selective fixed-filter ANC deep learning hearables

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Induction Machine Sensorless Control Based on Saliency Extraction That Uses One Single SVPWM Active State

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第2期58卷 2097-2106页

作者： Montero, Eduardo Rodriguez Vogelsberger, Markus Baumgartner, Felix Wolbank, Thomas Vienna Univ Technol Inst Energy Syst & Elect Drives A-1040 Vienna Austria ALSTOM Transport Austria GmbH Components Div DRIVES Rolling Stock Platform A-1220 Vienna Austria

Some induction motors present saliencies that periodically modify the air-gap flux following a sinusoidal shape. Consequently, these modulations are reflected in the transient leakage inductance. Therefore, transient leakage inductance can be expressed as a constant term or offset, which comes from the symmetrical machine, plus all motor saliency sinusoids, modulated by the mentioned saliencies. Since saliencies are linked to the rotor position or flux, calculation of transient leakage inductance enables encoderless vector control. A way to calculate transient leakage inductance is to excite the machine with voltage steps and estimate the resulting phase current slopes since they are inversely proportional to phase transient inductance. For creating a vector, only the three current slopes resulting from a single active state are sufficient. The vector sum of the three current slopes leads to the so-called saliency-offset vector, which is a vector composed of an offset, which is created due to the contribution of the transient inductance offset, plus several saliency phasors, superposed to the offset. To access saliencies, it is critical to accurately identify the offset of the saliency-offset vector, especially in induction motors since offset is much bigger than saliencies. This article presents an advanced signal processing scheme that identifies the offset in the saliency-offset vector, assuming offset amplitude differsdepending on the voltage step applied. Using a feedforward scheme, the offset is eliminated, leading to a saliency vector where rotor slotting can be extracted from. A novel excitation is proposed to reduce additional current distortions coming from the one-active excitation.

关键词： Inductance Transient analysis Switches Space vector pulse width modulation Inverters Rotors Induction motors Harmonic analysis induction motors sensorless control signal processing algorithms space vector pulsewidth modulation (SVPWM)

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Unmatched Preconditioning of the Proximal Gradient Algorithm

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IEEE signal processing LETTERS 2022年 29卷 1122-1126页

作者： Savanier, Marion Chouzenoux, Emilie Pesquet, Jean-Christophe Riddell, Cyril Univ Paris Saclay INRIA Cent Supelec CVN Gif Sur Yvette France GE Healthcare F-78530 Buc France

This work addresses the resolution of penalized least-squares problems using the proximal gradient algorithm (PGA). PGA can be accelerated by preconditioning strategies. However, typical effective choices of preconditioners may correspond to intricate matrices that are not easily inverted, leading to increased complexity in the computation of the proximity step. To relax these requirements, we propose an unmatched preconditioning approach where two metrics are used in the gradient step and the proximity step. We provide convergence conditions for this new iterative scheme and characterize its limit point. Simulations for tomographic image reconstruction from undersampled measurements show the benefits of our approach for various simple choices of metrics.

关键词： Electronics packaging Convergence Measurement Linear programming Standards signal processing algorithms Image reconstruction Computed tomography convergence analysis image reconstruction matrix approximation proximal methods

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Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval

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IEEE signal processing LETTERS 2022年 29卷 1619-1623页

作者： Vial, Pierre-Hugo Magron, Paul Oberlin, Thomas Fevotte, Cedric Univ Toulouse CNRS IRIT F-31042 Toulouse France Univ Lorraine CNRS INRIA LORIA F-54000 Nancy France Univ Toulouse ISAE SUPAERO F-31042 Toulouse France

This letter considers the phase retrieval (PR) problem, which aims to reconstruct a signal from phaseless measurements such as magnitude or power spectrograms. PR is generally handled as a minimization problem involving a quadratic loss. Recent works have considered alternative discrepancy measures, such as the Bregman divergences, but it is still challenging to tailor the optimal loss for a given setting. In this letter we propose a novel strategy to automatically learn the optimal metric for PR. We unfold a recently introduced ADMM algorithm into a neural network, and we emphasize that the information about the loss used to formulate the PR problem is conveyed by the proximity operator involved in the ADMM updates. Therefore, we replace this proximity operator with trainable activation functions: learning these in a supervised setting is then equivalent to learning an optimal metric for PR. Experiments conducted with speech signals show that our approach outperforms the baseline ADMM, using a light and interpretable neural architecture.

关键词： signal processing algorithms Optimization Convex functions Training Phase measurement Loss measurement Computer architecture ADMM audio deep unfolding phase retrieval proximity operator learning

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Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2022年 3卷 169-195页

作者： Brambilla, Mattia Gaglione, Domenico Soldi, Giovanni Mendrzik, Rico Ferri, Gabriele LePage, Kevin D. Nicoli, Monica Willett, Peter Braca, Paolo Win, Moe Z. Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB I-20133 Milan Italy Politecn Milan Dipartimento Ingn Gest DIG I-20156 Milan Italy NATO STO Ctr Maritime Res & Expt CMRE I-19126 La Spezia Italy Ibeo Automot Syst GmbH D-22143 Hamburg Germany Univ Connecticut Storrs CT 06269 USA Massachusetts Inst Technol MIT Lab Informat & Decis Syst LIDS Cambridge MA 02139 USA

This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions. Agents have to both localize themselves in the sensor network and, at the same time, perform multitarget tracking in the presence of clutter and miss detection. These two problems are jointly resolved using a holistic and centralized approach where graph theory is used to describe the statistical relationships among agent states, target states, and observations. A scalable message passing scheme, based on the sum-product algorithm, enables to efficiently approximate the marginal posterior distributions of both agent and target states. The proposed method is general enough to accommodate a full multistatic network configuration, with multiple transmitters and receivers. Numerical simulations show superior performance of the proposed joint approach with respect to the case in which cooperative self-localization and multitarget tracking are performed separately, as the former manages to extract valuable information from targets. Lastly, data acquired in 2018 by the NATO Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE) through a network of autonomous underwater vehicles demonstrates the effectiveness of the approach in a practical application.

关键词： Target tracking Measurement uncertainty signal processing algorithms Surveillance Electronic mail Uncertainty Sum product algorithm Belief propagation factor graph maritime surveillance message passing probabilistic data association

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