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Multivariate Dynamic Mode Decomposition and Its Application to Bearing Fault Diagnosis

IEEE SENSORS JOURNAL 2023年第7期23卷 7514-7524页

作者： Zhang, Qixiang Yuan, Rui Lv, Yong Li, Zhaolun Wu, Hongan Wuhan Univ Sci & Technol WUST Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol WUST Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

In practical engineering applications, the multivariate signal contains more fault feature information than the single-channel signal. How to realize synchronous extraction of fault features from the multivariate signal is of great significance in fault diagnosis of rotary machinery. Dynamic mode decomposition (DMD) has attracted much attention due to its excellent dynamic feature extraction ability. However, DMD lacks mode aliasing property when dealing with the multivariate signal, which may lead to the loss of critical fault feature information. Cater to this problem, this article proposed a multivariate DMD (MDMD) algorithm that is the multivariate extension of DMD. First, snapshot tensors are defined to convert multivariate signals to tensor format. Then, the MDMD algorithm is proposed by introducing tensor operations into the original DMD algorithm, where a tensor low tubal rank component extraction framework is constructed to enable simultaneous extraction of bearing fault features from the multivariate signal, to enhance the robustness and effectiveness of the algorithm. Finally, both numerical simulations and experiments verify that the proposed MDMD has higher fault diagnosis accuracy than other multivariate signal-processing methods.

关键词： Feature extraction Tensors Heuristic algorithms Fault diagnosis signal processing algorithms Matrix decomposition Sensors Bearing fault diagnosis multivariate dynamic mode decomposition (MDMD) multivariate signal processing transient feature extraction

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Model-Driven Based Deep Unfolding Equalizer for Underwater Acoustic OFDM Communications

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第5期72卷 6056-6067页

作者： Zhao, Hao Yang, Cui Xu, Yalu Ji, Fei Wen, Miaowen Chen, Yankun South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Peoples R China Guangdong Prov key Lab short rangewireless detect Guangzhou 510640 Peoples R China Minist Nat Resources Guangdong Prov key Lab short rangewireless detect Key Lab Marine Environm Survey Technol & Applicat Guangzhou 510640 Peoples R China

It is challenging to design an equalizer for the complex time-frequency doubly-selective channel. In this paper, we employ the deep unfolding approach to establish an equalizer for the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, namely UDNet. Each layer of UDNet is designed according to the classical minimum mean square error (MMSE) equalizer. Moreover, we consider the QPSK equalization as a four-classification task and adopt minimum Kullback-Leibler (KL) to achieve a smaller symbol error rate (SER) with the one-hot coding instead of the MMSE criterion. In addition, we introduce a sliding structure based on the banded approximation of the channel matrix to reduce the network size and aid UDNet to perform well for different-length signals without changing the network structure. Furthermore, we apply the measured at-sea doubly-selective UWA channel and offshore background noise to evaluate the proposed equalizer. Experimental results show that the proposed UDNet performs better with low computational complexity. Concretely, the SER of UDNet is nearly an order of magnitude lower than that of MMSE.

关键词： Equalizers OFDM Symbols signal processing algorithms Receivers Heating systems Underwater acoustics Unfolding-based equalizer deep learning doubly-selective underwater acoustic communication

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A Fast 2-D Phase Unwrapping Algorithm Based on Convolutional Neural Network

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 7518-7528页

作者： Li, Han Zhong, Heping Tian, Zhen Zhang, Peng Tang, Jinsong Naval Univ Engn Naval Inst Underwater Acoust Technol Wuhan 430033 Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410073 Peoples R China

Two-dimensional phase unwrapping (2-D PU) is the process of converting the measured phase into the real phase in interferometric signal processing. Reliable unwrapping results are critical for digital elevation model generation using interferometric synthetic aperture radar (InSAR) and interferometric synthetic aperture sonar (InSAS). The majority of previous research has concentrated on accuracy, whereas the computational efficiency must be taken into account for the interferometric measurement system that requires real-time processing. This article proposes a low-time-consuming algorithm that can accomplish high-precision 2-D PU for this application scenario. The neural network and a new path-based 2-D PU algorithm make up this algorithm. First, the incorrect region in the gradient field is predicted and corrected using the neural network. The output channelwise variance is then calculated and used to generate the quality maps. Finally, to achieve phase reconstruction, the path-based algorithm performs path planning and flooding integral according to quality maps and compensated gradient. This article also provides a recommended data structure implementation to ensure the algorithm's high efficiency. Experimental results using InSAR and InSAS data show that the proposed algorithm is highly efficient and accurate.

关键词： Prediction algorithms Neural networks Convolutional neural networks signal processing algorithms Real-time systems Synthetic aperture radar Floods Convolutional neural network digital elevation model (DEM) interferometric synthetic aperture radar (InSAR) interferometric synthetic aperture sonar (InSAS) phase unwrapping real-time processing

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A signal Dependent Analysis of Orthogonal Least Squares Based on the Restricted Isometry Property

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1574-1585页

作者： Bosse, Jonathan Univ Paris Saclay DEMR ONERA F-91123 Palaiseau France

The Orthogonal Least Squares (OLS) is a widespread greedy algorithm for sparse signal approximation or reconstruction. We here address the problem of sufficient condition for exact signal support recovery via OLS. Based on the generalized Wielandt inequality, new bounds on the OLS selection rule are provided that enable a simple approach of the Restricted Isometry Property based analysis of OLS. Since these new bounds naturally take the signal magnitude distribution into account, they allow a derivation of a new relaxed exact support recovery condition in the noise-free case. Moreover, we study their influence on the design of such new conditions in bounded noise and Gaussian noise cases.

关键词： Matching pursuit algorithms signal processing algorithms Greedy algorithms Dictionaries Upper bound Gaussian noise Eigenvalues and eigenfunctions Compressed sensing greedy algorithms orthogonal least squares (OLS) Restricted Isometry Property (RIP) sparse reconstruction

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Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications

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IEEE signal processing MAGAZINE 2023年第1期40卷 85-97页

作者： Kamilov, Ulugbek S. Bouman, Charles A. Buzzard, Gregery T. Wohlberg, Brendt Washington Univ Computat Imaging Grp St Louis MO USA Purdue Univ Elect & Comp Engn & Biomed Engn W Lafayette IN USA Purdue Univ Math Fac W Lafayette IN 47907 USA Los Alamos Natl Lab Theoret Div Los Alamos NM USA

Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving computational imaging problems through the integration of physical models and learned models. PnP leverages high-fidelity physical sensor models and powerful machine learning methods for prior modeling of data to provide state-of-the-art reconstruction algorithms. PnP algorithms alternate between minimizing a data fidelity term to promote data consistency and imposing a learned regularizer in the form of an image denoiser. Recent highly successful applications of PnP algorithms include biomicroscopy, computerized tomography (CT), magnetic resonance imaging (MRI), and joint ptychotomography. This article presents a unified and principled review of PnP by tracing its roots, describing its major variations, summarizing main results, and discussing applications in computational imaging. We also point the way toward further developments by discussing recent results on equilibrium equations that formulate the problem associated with PnP algorithms.

关键词： Training data Machine learning algorithms Computational modeling Magnetic resonance imaging Computed tomography Imaging signal processing algorithms

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Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver With Frequency-Dependent I/Q Imbalance

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第4期22卷 2364-2378页

作者： Huang, Xiaojing Le, Anh Tuyen Guo, Y. Jay Univ Technol Sydney Global Big Data Technol Ctr Ultimo NSW 2007 Australia

An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop's convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.

关键词： Interference cancellation Transceivers signal processing algorithms Wireless communication Tracking loops RF signals Mathematical models In-band full duplex (IBFD) self-interference cancellation (SIC) analog least mean square (ALMS) loop joint analog and digital SIC

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State-of-the-Art Analysis of Deep Learning-Based Monaural Speech Source Separation Techniques

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IEEE ACCESS 2023年 11卷 4242-4269页

作者： Soni, Swati Yadav, Ram Narayan Gupta, Lalita Maulana Azad Natl Inst Technol Dept Elect & Commun Engn Bhopal 462003 Madhya Pradesh India

The monaural speech source separation problem is an important application in the signal pro-cessing field. But recent interaction of deep learning algorithms with signal processing achieves remarkable performance improvement for speech source separation problems. This paper explores the numerous state-of-the-art deep learning-based monaural speech source separation algorithms in the time-frequency (T-F), time, and hybrid domains. The motivation, algorithm, and framework of different deep learning models for monaural speech source separation are analyzed. The benchmarked algorithms in the T-F domain can be categorized as deep neural networks (DNN), clustering, permutation, multi-task learning, computational auditory sense analysis (CASA), and phase reconstruction-based techniques, whereas the state-of-the-art time-domain approaches can be categorized as CNN, RNN, multi-scale fusion (MSF), and transformer-based techniques. The end-to-end post filter (E2EPF) is a hybrid algorithm combining T-F and time-domain works to achieve enhanced results. Time-domain models have shown improvement in separation performance compared to the T-F and hybrid domain models with small model sizes. Methods in T-F, time, and hybrid domains are compared using SDR, SI - SDR, SI - SNR, PESQ, and STOI as quality assessment metrics on some benchmark datasets.

关键词： Source separation Hidden Markov models Time-domain analysis Speech enhancement Deep learning signal processing algorithms Computational modeling Deep-clustering deep learning monaural speech source separation permutation invariant training time domain speaker separation

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Bayesian Deep Learning for Image Reconstruction: From structured sparsity to uncertainty estimation

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IEEE signal processing MAGAZINE 2023年第1期40卷 73-84页

作者： Dong, Weisheng Wu, Jinjian Li, Leida Shi, Guangming Li, Xin Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Nanyang Technol Univ Singapore Singapore Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Rapid Rich Object Search Lab Singapore Singapore Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Univ Sci & Technol China Elect Engn & informat Sci Hefei Peoples R China West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Conventional wisdom in model-based computational imaging incorporates physics-based imaging models, noise characteristics, and image priors into a unified Bayesian framework. Rapid advances in deep learning have inspired a new generation of data-driven computational imaging systems with performances even better than those of their model-based counterparts. However, the design of learning-based algorithms for computational imaging often lacks transparency, making it difficult to optimize the entire imaging system in a complete manner.

关键词： Deep learning Uncertainty Image coding Computational modeling Imaging signal processing algorithms Network architecture

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Altimetric Parameter Estimation in Long Coherent processing by Airborne Delay/Doppler Altimetry Based on Bayesian Learning

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 8135-8148页

作者： Fang, Cheng Sun, Ming Huang, Bo Guan, Fangheng Zhou, Honghao Liao, Xianhua Yang, Lei Civil Aviat Univ China Tianjin Key Lab Adv Signal Proc Tianjin 300300 Peoples R China China Acad Engn Phys Inst Elect Engn Mianyang 621900 Peoples R China

The along-track resolution of conventional airborne synthetic aperture radar altimetry (SARAL), where the coherent processing interval (CPI) is the burst length, is not a full resolution due to beam limited. For a fully focused radar altimeter image, a novel airborne SARAL processing algorithm based on a long CPI is proposed in this article. The long CPI is capable of achieving higher azimuthal resolution than that in the conventional altimeter with limited number of pulses. To conquer the problem of atmosphere turbulence and in accordance of motion deviation of the airborne platform, a subaperture phase gradient autofocusing framework is introduced to alleviate nonsystematic phase errors (NsPE). In this framework, NsPE is estimated and compensated in along-track so that a fully focused delayed Doppler map (DDM) can be guaranteed. Finally, the height parameter estimation is performed to the 1-D altimeter echoes after multilooking processing of DDM to improve the estimation accuracy. Given that the conventional retracking algorithm is sensitive to noise, which may degrade the estimation accuracy, a flexible Bayesian method is designed in a hierarchical manner for the SARAL retracking. The SARAL raw data are utilized in the experiment. The results of image entropy and RMSE demonstrate the effectiveness and superiority of the proposed algorithm both qualitatively and quantitatively.

关键词： Delays Electronics packaging Doppler effect Altimetry signal processing algorithms Bayes methods Synthetic aperture radar Coherent processing interval (CPI) hierarchical Bayesian learning phase gradient autofocusing (PGA) synthetic aperture radar altimeter (SARAL)

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Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization With Variance Reduction and Nonasymptotic Convergence Rates

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Hu, Jinhui Chen, Guo Li, Huaqing Guo, Xiaoyu Ran, Liang Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Hunan Peoples R China City Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

Decentralized stochastic gradient algorithms efficiently solve large-scale finite-sum optimization problems when all agents in the network are reliable. However, most of these algorithms are not resilient to adverse conditions, such as malfunctioning agents, software bugs, and cyber attacks. This article aims to handle a class of general composite optimization problems over multiagent systems (MASs) in the presence of an unknown number of Byzantine agents. Building on a resilient aggregation mechanism and the proximal-gradient mapping method, a Byzantine-resilient decentralized stochastic proximal-gradient algorithmic framework is proposed, dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only local computations and communications. To asymptotically reduce the noise variance arising from local gradient estimation and accelerate the convergence, we incorporate two localized variance-reduced (VR) techniques (SAGA and LSVRG) into Prox-DBRO-VR to design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. By analyzing the contraction relationships among the gradient-learning error, resilient consensus condition, and convergence error in a unified theoretical framework, it is proved that both Prox-DBRO-SAGA and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size, converge linearly (resp., sublinearly) inside an error ball around the optimal solution to the original problem under standard assumptions. A tradeoff between convergence accuracy and Byzantine resilience in both linear and sublinear cases is also characterized. In numerical experiments, the effectiveness and practicability of the proposed algorithms are manifested via resolving a decentralized sparse machine learning problem under various Byzantine attacks.

关键词： Optimization Convergence Reliability Resilience signal processing algorithms Machine learning algorithms Filtration Vectors Software algorithms Security Byzantine-resilient algorithms composite optimization decentralized stochastic optimization system security variance reduction

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