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Maximum Likelihood Line Spectral Estimation in the signal Domain: A Rank-Constrained Structured Matrix Recovery Approach

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4156-4169页

作者： Wu, Xunmeng Yang, Zai Stoica, Petre Xu, Zongben Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Uppsala Univ Dept Informat Technol S-75105 Uppsala Sweden

Maximum likelihood estimation (MLE) provides a well-known benchmark for line spectral estimation and has been extensively studied in the parameter domain using a variety of optimization algorithms. To overcome the sensitivity of these algorithms to parameter initialization, in this paper we study the MLE in the signal domain. We formulate the MLE as an equivalent rank-constrained structured matrix recovery problem that admits a unique matrix solution containing the signal, from which the parameters of interest are uniquely retrieved. The alternating direction method of multipliers (ADMM) is used to solve the rank-constrained problem and it is shown to have a good convergence behavior. The proposed approach is generalized to the case of missing data and arbitrary-dimensional line spectral estimation. Extensive numerical results are provided that corroborate our analysis and confirm that the proposed approach globally solves the MLE problem and outperforms state-of-the-art algorithms.

关键词： Maximum likelihood estimation signal processing algorithms Convex functions Noise measurement Frequency estimation Convergence Optimization methods Line spectral estimation maximum likelihood estimation Hankel-Toeplitz optimization model rank-constrained structured matrix recovery ADMM nonconvex optimization

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Graph Moving Object Segmentation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第5期44卷 2485-2503页

作者： Giraldozuluaga, Jhony H. Javed, Sajid Bouwmans, Thierry La Rochelle Univ Lab MIA Math Image & Applicat F-17000 La Rochelle France Khalifa Univ Ctr Autonomous Robot Syst Abu Dhabi 127788 U Arab Emirates

Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of unseen videos;and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has attracted significant attention in many computer vision applications since they provide tools to exploit the geometrical structure of data. In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals. Second, theoretical developments are introduced, showing one bound for the sample complexity in semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm is also adapted for Video Object Segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several state-of-the-art methods in challenging conditions.

关键词： Videos Task analysis signal processing algorithms Object segmentation Semisupervised learning Deep learning Complexity theory Moving object segmentation graph signal processing semi-supervised learning unseen videos video object segmentation

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Interpolated DFT Algorithm for Frequency Estimation by Using Maximum Sidelobe Decay Windows

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IEEE ACCESS 2022年 10卷 95748-95762页

作者： Wu, Huihao Song, Huanhuan Bai, Yuchan Fan, Lei Jin, Jiyu Xing, Jun Dalian Polytech Univ Sch Informat Sci & Engn Dalian 116034 Peoples R China

A sinusoidal frequency estimator based on interpolated Discrete Fourier Transform (DFT) algorithm by using Maximum Sidelobe Decay (MSD) windows is proposed in this paper. Firstly, the received sinusoid is weighted by an appropriate MSD window. Then DFT is carried out on the weighted sinusoid and the coarse estimation is acquired by finding the position of the maximum DFT sample. Different from all the existing algorithms, the presented estimator adopts the maximum DFT sample and two Discrete Time Fourier Transform (DTFT) spectral lines which are on the same side of the maximum DFT sample in the fine estimation step. MSE formulas of the presented estimator in additive white noise are derived. Simulation results indicate that the presented estimator outperforms the competing estimators.

关键词： Discrete Fourier transforms Frequency estimation Estimation signal processing algorithms Time-domain analysis White noise Prediction algorithms DFT DTFT frequency estimation MSD window

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Massive Access in Media Modulation Based Massive Machine-Type Communications

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第1期21卷 339-356页

作者： Qiao, Li Zhang, Jun Gao, Zhen Ng, Derrick Wing Kwan Di Renzo, Marco Alouini, Mohamed-Slim Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci ARIMS Beijing 100081 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2025 Australia Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91192 Gif Sur Yvette France King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive multi-input multi-output base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit a doubly structured sparsity in both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed DS-AMP algorithm over state-of-the-art algorithms in the uncoded case. Also, our results confirm that the proposed SIC-based IDS-AMP algorithm can enhance the data decoding performance in the coded case and verify the validity of the proposed data-aided CSI update strategy.

关键词： Modulation Media signal processing algorithms Matching pursuit algorithms Detectors Uplink Wireless communication Massive access media modulation massive multi-input multi-output massive machine-type communications

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Wire Rope Damage Detection signal processing Using K-Singular Value Decomposition and Optimized Double-Tree Complex Wavelet Transform

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Wang, Hongyao Li, Qinyi Han, Shuang Li, Pengbo Tian, Jie Zhang, Shijing Nanchang Hangkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China

Steel wire ropes are used for transporting personnel and materials in coal mines. Its quality directly affects the safety of mining operations, and hence, it is important to accurately identify the defects. Since electromagnetic detection methods are affected by large amounts of noise, more precise strategies are required. This article proposes a noise reduction method based on K-singular value decomposition (K-SVD) optimized double-tree complex wavelet transform (DTCWT). First, the discrete coefficients of the evaluation index of different decomposition levels in DTCWT are calculated to obtain the optimal decomposition levels. Afterward, K-SVD is used to update the sparse dictionary. The decomposed high-frequency modes were optimized for noise reduction. Finally, DTCWT was used to reconstruct the damage detection signal from the wire rope, after noise reduction. The experimental results show that the signal-to-noise ratio obtained using the proposed method is 30% higher than the other algorithms. The smoothing coefficient obtained is lower than 0.01, whereas the correlation coefficient is found to be more than 0.95, which indicates that the proposed algorithm can effectively reduce the noise in the detection signal and significantly retain its original characteristics, thereby improving the safety of the wire rope.

关键词： Wavelet transforms Noise reduction Transforms Wires Sparse matrices Dictionaries signal processing algorithms Double-tree complex wavelet transform (DTCWT) K-singular value decomposition (K-SVD) magnetic leakage detection method sparse noise reduction wire rope electromagnetic nondestructive testing

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The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, algorithms and Experiments

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 616-630页

作者： Florescu, Dorian Krahmer, Felix Bhandari, Ayush Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Tech Univ Munich Dept Math D-85748 Garching Germany

The Unlimited Sensing Framework (USF) was recently introduced to overcome the sensor saturation bottleneck in conventional digital acquisition systems. At its core, the USF converts a continuous-time high-dynamic-range (HDR) signal into folded, low-dynamic-range, modulo samples and allows the recovery of the HDR signal via algorithmic unfolding. In hardware, however, implementing ideal modulo folding requires careful calibration, analog design and high precision. At the interface of theory and practice, this paper explores a computational sampling strategy that relaxes strict hardware requirements via a novel, mathematically guaranteed reconstruction. We start with a generalized model for USF with two new parameters modeling hysteresis and folding transients in addition to the modulo threshold. Hysteresis accounts for mismatches between the reset threshold and the amplitude displacement at the folding time and the folding transient is a continuous transition period in the implementation of a reset. Both these effects are motivated by our hardware experiments and also occur in previous, domain-specific applications. We show that hysteresis is beneficial for USF and leverage it to derive the first recovery guarantees in the context of our generalized USF model for a certain sampling rate regime. Additionally, we show how the sampling rate requirement can be greatly reduced via a direct generalization of the proposed recovery. Our theoretical work is corroborated by hardware experiments with a hysteresis enabled, modulo ADC testbed comprising off-the-shelf electronic components. Thus, by capitalizing on a collaboration between hardware and algorithms, our paper enables an end-to-end pipeline for HDR sampling allowing more flexible hardware implementations.

关键词： Hysteresis Hardware Transient analysis Sensors signal processing algorithms Mathematical models Data models Analog-to-digital conversion (ADC) modulo sampling HDR sensing Shannon sampling theory thresholding

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Light-Field Microscopy for the Optical Imaging of Neuronal Activity: When model-based methods meet data-driven approaches

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IEEE signal processing MAGAZINE 2022年第2期39卷 58-72页

作者： Song, Pingfan Verinaz-Jadan, Herman Howe, Carmel L. Foust, Amanda J. Dragotti, Pier Luigi Imperial Coll London London England Univ Cambridge Dept Engn Cambridge CB2 1PZ England Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Imperial Coll London Dept Bioengn London SW7 2AZ England Imperial Coll London Ctr Neurotechnol London SW7 2AZ England

Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieving this goal is to be able to observe the dynamics of large populations of neurons over a large area of the brain. Light-field microscopy (LFM), which uses a type of scanless microscope, is a particularly attractive candidate for high-speed 3D imaging. It captures volumetric information in a single snapshot, allowing volumetric imaging at video frame rates. Specific features of imaging neuronal activity using LFM call for the development of novel machine learning approaches that fully exploit the priors embedded in physics and optics models.

关键词： Three-dimensional displays Microscopy Computational modeling Neurons Sociology signal processing algorithms Optical imaging

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Unsupervised and Supervised Learning Combined Power Load Curve Classification Based on Sequential Trajectory Feature Extraction Algorithm

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IEEE ACCESS 2022年 10卷 90312-90320页

作者： Zhang, Jing Zhang, Yikun Xu, Xiao Fu, Xiaofan He, Yu Guizhou Univ Sch Elect Engn Guiyang 550025 Guizhou Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China China Elect Power Res Inst Power Automat Dept Beijing 100192 Peoples R China

Advancements in smart grid technology and the extensive applications of electric power big data have made in-depth exploration of the behavioral characteristics of power consumers highly necessary for further development of the electricity market. This paper proposes an effective and interpretable load curve classification method that based on sequential trajectory feature learning and a random forest algorithm. Firstly, the unsupervised K-medoid clustering algorithm is used to obtain and filter precise category labels. Next, the fused lasso generalized eigenvector (FLAG) technique is used to search for interpretable sub-sequences from the labeled data in order to properly account for the sequential trajectory feature of load curves and increase the speed of the computation process. Following that, shapelet transformation is used to extract the sequential trajectory features from original data. Finally, in order to inherit the interpretability of shapelet, the random forest is trained on the sequential trajectory features. The simulated examples based on the real load curves of the specific city in China were investigated in order to assess the performance of the proposed load curve categorization approach. The results of the simulation demonstrate that the proposed approach has considerable advantages in terms of effectiveness, accuracy, and interpretability of load classification.

关键词： Classification algorithms Clustering algorithms Random forests Load modeling Feature extraction Data mining signal processing algorithms Unsupervised learning Supervised learning Sequential analysis Load curve classification K-mediods shapelet random forest sequential trajectory features unsupervised learning supervised learning

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Communication-Censored Distributed Stochastic Gradient Descent

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第11期33卷 6831-6843页

作者： Li, Weiyu Wu, Zhaoxian Chen, Tianyi Li, Liping Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Sci & Technol China Sch Gifted Young Hefei 230026 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510300 Peoples R China Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China

This article develops a communication-efficient algorithm to solve the stochastic optimization problem defined over a distributed network, aiming at reducing the burdensome communication in applications, such as distributed machine learning. Different from the existing works based on quantization and sparsification, we introduce a communication-censoring technique to reduce the transmissions of variables, which leads to our communication-censored distributed stochastic gradient descent (CSGD) algorithm. Specifically, in CSGD, the latest minibatch stochastic gradient at a worker will be transmitted to the server if and only if it is sufficiently informative. When the latest gradient is not available, the stale one will be reused at the server. To implement this communication-censoring strategy, the batch size is increasing in order to alleviate the effect of stochastic gradient noise. Theoretically, CSGD enjoys the same order of convergence rate as that of SGD but effectively reduces communication. Numerical experiments demonstrate the sizable communication saving of CSGD.

关键词： Servers Convergence Optimization Stochastic processes Machine learning algorithms Sun signal processing algorithms Communication censoring communication efficiency distributed optimization stochastic gradient descent (SGD)

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Novel Fast and Automatic Condition Monitoring Strategy Based on Small Amount of Labeled Data

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第2期52卷 1266-1275页

作者： Attoui, Issam Res Ctr Ind Technol CRTI Res Unit Adv Mat Cheraga 16014 Algeria

signal-based automatic condition monitoring techniques are one of the effective ways to ensure the operational safety of modern industrial systems. Currently, the main challenge is to increase their effectiveness under noisy environment and with limited labeled data. In this article, a novel fast and automatic signal-based fault diagnosis procedure that does not require any kinds of machine learning algorithms is proposed. This procedure is based on the measurement of the similarity in the frequency domain between the collected data and a small amount of labeled reference signals (LRSs). The LRSs are obtained under various known operation conditions using fast Fourier transform (FFT) algorithm. The proposed approach is suitable for a real-time implementation and capable of successfully overcoming the challenge of condition monitoring of rotating machines under noisy environment and with limited labeled data. The merits, fastness, and also robustness against noise of the proposed technique are demonstrated experimentally through different practical applications, as well as compared to state-of-the-art procedures on a database of vibration signals measured under a variety of machine health and working conditions.

关键词： Vibrations Condition monitoring Fault diagnosis Frequency-domain analysis signal processing algorithms Machinery Automatic fault diagnosis frequency analysis multiple bearing defects rotating machinery fault diagnosis signal processing

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