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A Novel Tracker of Adaptive Directional Ridge Separation and Prediction for Detecting Whistles

IEEE JOURNAL OF OCEANIC ENGINEERING 2025年第1期50卷 13-24页

作者： Miao, Yongchun Li, Jianghui Li, Yingsong Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China MNR Key Lab Southeast Coast Marine Informat Intelligen Zhangzhou 363000 Peoples R China Xiamen Univ Coll Ocean & Earth Sci State Key Lab Marine Environm Sci Xiamen 361102 Peoples R China

Whistle detection of marine mammal signals with close and overlapping components of varying amplitudes is a key task for overlapping source separation. In this article, we propose a novel tracker, called adaptive directional ridge separation and prediction, for detecting whistles, which are typically analyzed using a time-frequency (TF) representation. Inspired by TF reassignment, a new reassignment scheme based on time-scale changes is developed to acquire instantaneous TF points with high energy concentration. To address the mutual interference among various types of components, a tone-pulse separation model is introduced for the aliased TF components, utilizing these instantaneous TF points and instantaneous rotating operators. An adaptive directional ridge predictor is established for application in automatic overlapping whistle detection, ensuring unbroken detection even when a whistle becomes nearly indistinguishable in the TF representation. Experimental results, obtained using both a simulated signal and recorded calls of marine mammals, demonstrate the superiority of the proposed method compared to other state-of-the-art methods. This method is capable of performing whistle detection and separating overlapping sources even in the presence of splash noises, which may cause partial distortion or disconnection of components from the TF representation.

关键词： Chirp Time-frequency analysis signal processing algorithms Noise Jacobian matrices Complexity theory Whales Taylor series Source separation Predictive models Adaptive directional ridge prediction (ADRP) instantaneous rotating operator (IRO) time-frequency (TF) representation tone-pulse separation (TPS) whistle detection

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Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing With a Dynamic Grid

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 781-794页

作者： Liu, An Zhou, Yufan Xu, Wenkang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

We investigate the problem of recovering a structured sparse signal from a linear observation model with an uncertain dynamic grid in the sensing matrix. The state-of-the-art expectation maximization based compressed sensing (EM-CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have a relatively slow convergence speed due to the double-loop iterations between the E-step and M-step. Moreover, each inner iteration in the E-step involves a high-dimensional matrix inverse in general, which is unacceptable for problems with large signal dimensions or real-time calculation requirements. Although there are some attempts to avoid the high-dimensional matrix inverse by majorization minimization, the convergence speed and accuracy are often sacrificed. To better address this problem, we propose an alternating estimation framework based on a novel subspace constrained VBI (SC-VBI) method, in which the high-dimensional matrix inverse is replaced by a low-dimensional subspace constrained matrix inverse (with the dimension equal to the sparsity level). We further prove the convergence of the SC-VBI to a stationary solution of the Kullback-Leibler divergence minimization problem. Simulations demonstrate that the proposed SC-VBI algorithm can achieve a much better tradeoff between complexity per iteration, convergence speed, and performance compared to the state-of-the-art algorithms.

关键词： Estimation Sparse matrices Sensors Bayes methods Heuristic algorithms Convergence signal processing algorithms Minimization Matrix decomposition Compressed sensing Subspace constrained variational Bayesian inference (SC-VBI) structured sparse signal recovery dynamic grid

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Tensorized Graph Learning for Spectral Ensemble Clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2662-2674页

作者： Cao, Zhe Lu, Yihang Yuan, Jinghui Xin, Haonan Wang, Rong Nie, Feiping Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China

Ensemble clustering based on co-association matrices integrates multiple connective matrices from base clusterings to achieve superior results. However, these methods primarily focus on inter-sample relationships, neglecting variations across different base clusterings, potentially introducing noise. Additionally, they overlook interactions between samples and base clusterings, which are crucial for extracting common information and avoiding post-processing steps that may cause information loss and instability in clustering results. To address these issues, we propose the Tensorized Graph Learning for Spectral Ensemble Clustering (TGLSEC) model. TGLSEC stacks all connective matrices into a third-order tensor, employs Fast Fourier Transform (FFT) for encoding, and elucidates inter-relations in the frequency domain. By minimizing the tensor Schatten p-norm, TGLSEC extracts common information in the low-rank space, eliminating noise and improving the quality of the common shared graph. Incorporating Laplacian rank constraints, TGLSEC learns a common shared graph with c-connected components, directly representing the clustering structure and avoiding post-processing steps, leading to more stable clustering results. To enhance computational efficiency for large-scale datasets, TGLSEC has been expanded into a bipartite-graph-based model, TGLSEC-BG, reducing complexity and computational time. Extensive experiments on real-world datasets demonstrate that TGLSEC and TGLSEC-BG exhibit superior clustering performance and robustness to noise.

关键词： Tensors Noise Fast Fourier transforms Data mining Clustering algorithms Vectors Computational modeling signal processing algorithms Laplace equations Frequency-domain analysis Ensemble clustering Schatten p-norm tensorized graph learning bipartite graph

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Byzantine-Robust and Communication-Efficient Personalized Federated Learning

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 26-39页

作者： Zhang, Jiaojiao He, Xuechao Huang, Yue Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China KTH Royal Inst Technol Div Decis & Control Syst S-11428 Stockholm Sweden

This paper explores constrained non-convex personalized federated learning (PFL), in which a group of workers train local models and a global model, under the coordination of a server. To address the challenges of efficient information exchange and robustness against the so-called Byzantine workers, we propose a projected stochastic gradient descent algorithm for PFL that simultaneously ensures Byzantine-robustness and communication efficiency. We implement personalized learning at the workers aided by the global model, and employ a Huber function-based robust aggregation with an adaptive threshold-selecting strategy at the server to reduce the effects of Byzantine attacks. To improve communication efficiency, we incorporate random communication that allows multiple local updates per communication round. We establish the convergence of our algorithm, showing the effects of Byzantine attacks, random communication, and stochastic gradients on the learning error. Numerical experiments demonstrate the superiority of our algorithm in neural network training compared to existing ones.

关键词： Servers Stochastic processes signal processing algorithms Data models Computational modeling Vectors Federated learning Convergence Adaptation models Robustness Personalized federated learning communication efficiency Byzantine-robustness constrained non-convex optimization

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Convergence and Privacy of Decentralized Nonconvex Optimization With Gradient Clipping and Communication Compression

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2025年第1期19卷 273-282页

作者： Li, Boyue Chi, Yuejie Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communication compression. We propose PORTER, which considers two variants of gradient clipping added before or after taking a mini-batch of stochastic gradients, where the former variant PORTER-DP allows local differential privacy analysis with additional Gaussian perturbation, and the latter variant PORTER-GC helps to stabilize training. We develop a novel analysis framework that establishes their convergence guarantees without assuming the stringent bounded gradient assumption. To the best of our knowledge, our work provides the first convergence analysis for decentralized nonconvex optimization with gradient clipping and communication compression, highlighting the trade-offs between convergence rate, compression ratio, network connectivity, and privacy.

关键词： signal processing algorithms Convergence Optimization Stochastic processes Privacy Differential privacy Perturbation methods Linear programming Vectors Training Communication compression convergence rate gradient clipping local differential privacy

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A Time-Domain Frequency Analyzer Based on Goertzel Algorithm

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第6期33卷 1516-1529页

作者： Zareie, Mahsa El-Sankary, Kamal Colombo, Dalton Martini El-Masry, Ezz Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3H 4R2 Canada Univ Fed Minas Gerais Elect Engn Dept BR-31270901 Belo Horizonte Brazil

This article presents a novel time-domain implementation of the second-order Goertzel frequency analyzer, which can be extended for use in infinite impulse response (IIR)/finite impulse response (FIR) filters. A set of time-domain arithmetic circuits, including a one-step time register (TR), time amplifier (TA), time adder, and unit delay operator (z(-1)), are introduced to overcome the limitations of conventional time-domain filters. The working principles and nonidealities of each block are analyzed and compared with the existing methods. The proposed filter is implemented in a 180-nm CMOS process with a 0.9-V supply voltage. The designed frequency analyzer is tunable to extract the amplitude and phase angle of signals up to 400 Hz. Simulation results, targeting a 280-Hz signal at a 19.88-kHz sampling frequency, demonstrate that the filter can detect the amplitude and phase of a voltage signal in the time domain with an error below 5%. The filter achieves a resolution of 76.7 dBV/s, consumes less than 24 mu W of power, and the estimated silicon area is almost 0.828 mm(2).

关键词： Time-domain analysis Filters Delays Circuits Inverters Time-frequency analysis signal processing algorithms Voltage IIR filters Discrete Fourier transforms Goertzel algorithm infinite impulse response (IIR) filter time amplifier (TA) time domain time register (TR) voltage-to-time converter (VTC)

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Semi-Tensor Product Compressed Sensing With Its Applications: A Review

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IEEE SENSORS JOURNAL 2025年第3期25卷 4096-4114页

作者： Zhou, Rongpei Li, Rongfa Wu, Yaqian Chen, Jie Hong, Jin Yu, Lisu Liu, Qiegen Zhang, Yudong Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Natl Univ Def Technol Coll Elect Engn Hefei 230027 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England

Recently, as an emerging signal processing technology, the semi-tensor product compressed sensing (STP-CS) has attracted widespread attention in the fields of image processing, communications, and bioinformatics. This article reviews the theoretical foundations, algorithmic designs, and practical applications of STP-CS. It begins by revisiting the basic concepts of compressed sensing (CS) and the definition of the semi-tensor product (STP), followed by a detailed discussion on the theoretical model of STP-CS, optimization of the measurement matrix, and reconstruction algorithms. Furthermore, the article explores the practical applications of STP-CS in areas such as sensor nodes, visual security, image encryption, and spectrum sensing, analyzing its performance advantages and potential challenges in these fields. A comprehensive analysis indicates that STP-CS offers significant benefits in saving storage space, reducing computational complexity, and enhancing data security, making it a promising technology in the field of signal processing.

关键词： Sensors Sparse matrices Image reconstruction Size measurement signal processing Compressed sensing Optimization Data processing signal processing algorithms Reviews Compressed sensing (CS) image encryption semi-tensor product (STP) signal processing

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An NN Framework for Target Number Detection in FMCW Radar Systems via Hankelization: A Low-Dimensional Data Representation Perspective

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IEEE ACCESS 2025年 13卷 58823-58834页

作者： Kim, Jung-Hwan Kim, Gyung-Eun Lee, Woong-Hee Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea

In this paper, we introduce a novel neural network (NN)-based algorithm that significantly improves the target number detection in frequency modulated continuous wave (FMCW) radar systems. By integrating the mathematical processes of Hankelization and singular value extraction, we can perform input data manipulation for effective target number detection, resulting in constructing an efficient NN framework. This is based on the following mathematical properties: 1) A sequence obtained by uniform sampling of the superposition of K radio waves can be represented as a superposition of K geometric sequences;2) A Hankelized matrix formed by the superposition of K geometric sequences exhibits low-rank characteristics;and 3) In an FMCW radar system with K targets, if the received signal, which is represented as a matrix, is ideal, the vectors obtained by extracting this matrix in row, column, diagonal, and anti-diagonal patterns can all be modeled as a superposition of K geometric sequences. The proposed NN framework showcases remarkable improvements in accuracy and efficiency for target number detection, leveraging a small sized dataset and a compact NN design to achieve unprecedented performance levels. Numerical results validate the superiority of our method across various scenarios, establishing a new benchmark for low-dimensional data representation in radar systems.

关键词： Radar Estimation Accuracy Heuristic algorithms Radar detection Radar antennas Artificial neural networks Vectors signal processing algorithms Data visualization Hankelization singular value extraction neural networks radar signal processing

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A Linear Mapping Dispersion Compensation Method for Debonding Detection of Honeycomb Sandwich Structure Based on Air-Coupled Ultrasound

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhang, Hui Zong, Zhaoyu Rui, Xiaobo Liu, Si Tianjin Univ State Key Lab Precis Measurement Technol & Instrum Tianjin 300072 Peoples R China

During the operational lifespan of the honeycomb sandwich structure (HSS), it is necessary to monitor the structural integrity to ensure its safety. Air-coupled ultrasonic guided wave technology is an efficient and convenient noncontact detection method. Due to the large acoustic impedance difference between the air and the structure and the dispersion characteristics of guided wave in the HSS, the air-coupled signals lead to significant energy loss and are easily stacked, posing challenges for the detection of debonding defects. To tackle this challenge, this study investigates the dispersion characteristics and propagation properties of guided waves in the HSS, and the effect of debonding defects on guided wave signals is researched by finite element models. The proposed linear mapping dispersion compensation algorithm refactors the linearization dispersion relationship in the frequency domain of air-coupled guided wave signals. It effectively reconstructs the stacked signal and separates the direct wave packet. Damage probability imaging is realized by using the amplitude of the direct wave to construct the damage index (DI). The imaging evaluation indexes of intersection over union (IoU) and recall rate for debonding defects of two sizes are compared, which demonstrates an improvement in defect detection accuracy. The proposed method has strong potential for real-time monitoring applications.

关键词： Dispersion Atmospheric modeling Transducers Skin Sensors Acoustics signal processing algorithms Sandwich structures Propagation Mathematical models Air-coupled ultrasound dispersion compensation guided wave honeycomb sandwich structure (HSS) lamb wave

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State-of-the-Art Review: Electronic Warfare Against Radar Systems

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IEEE ACCESS 2025年 13卷 57530-57567页

作者： Reddy, Reeshen Sinha, Saurabh Univ Johannesburg Fac Engn & Built Environm ZA-2094 Johannesburg South Africa Council Sci & Ind Res CSIR Dept Radar & Elect Warfare ZA-0184 Pretoria South Africa Univ Canterbury Fac Engn Christchurch 8041 New Zealand

The electromagnetic spectrum (EMS) is emerging as a sixth battlespace domain in military applications and is essential for wireless communications and remote sensing. Due to the demand for EMS, it is increasingly contested, congested, constrained, and shared. Evolving threats, doctrine, and tactics drive significant advancements in the battlespace. Electronic Warfare (EW) or Electromagnetic Warfare products need to keep pace by leveraging advances in technology driven by Industry 4.0, artificial intelligence (AI), direct radio frequency (RF) sampling, RF System on Chip (RFSoC), and ultrawideband antenna arrays. These trends drive a need for technology-intensive innovation in EW products underpinned by a coherent business-product-technology strategy. This article reviews advancements in Electronic Warfare against radar systems using a novel approach that integrates quantitative bibliometric and patentometric analyses with a qualitative assessment of EW architectures, Electronic Attack (EA) algorithms, and Electronic Support (ES) algorithms. This article marks the first instance of such a methodology being applied to systematically assess the landscape of research publications and technological innovations in the field of EW against radar systems. A taxonomy of EW System Types by military domain against radar and their role in the modern battlefield is presented together with their typical role and real-world examples. The quantitative and qualitative insights are synthesized to outline a notional next-generation EW architecture and a technology roadmap for its realization that researchers can use to guide the development of innovative EW technologies and methodologies and by EW practitioners to inform system design, operational deployment, and capability enhancements.

关键词： Radar Reviews Bibliometrics Technological innovation Market research signal processing algorithms Databases Artificial intelligence Metadata Soft sensors Digital radio frequency memory electromagnetic warfare electronic attack electronic counter measure electronic support electronic warfare Industry 4.0 radar radar countermeasures radar direction finding radar jamming strategy technology road mapping

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