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Fast Equiconcept Search on Graph Formal Context by Attribute Topology

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 1469-1479页

作者： Zhang, Tao Li, Shaoze Xue, Zaifa Lu, Xin Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066000 Peoples R China Key Lab Informat Transmiss & Signal Proc Hebei Pro Qinhuangdao 066000 Peoples R China

The equiconcept, as an emerging topic in formal concept analysis, provides new ideas for analyzing the behavior in social computing. However, it takes too much time for calculation because of the high complexity. In this article, a fast equiconcept search method on graph formal context by attribute topology is proposed to improve computational efficiency. First, attribute topology is optimally represented as a graph context attribute topology (GcAT) model on the graph formal context. Then, it is proved that there is a one-to-one match between the maximal complete polygon in GcAT and the equiconcept in the graph formal context. Finally, combined with the existing attribute topology formal concept searching algorithm, the fast equiconcept search is completed. The experimental results show that by the proposed method not only all equiconcepts are searched, but also the time consumption is significantly reduced compared with the other methods over seven datasets. In addition, with the size of the dataset increasing, the computational efficiency of GcAT model improves more obviously, which indicates its superiority. All of that demonstrate the proposed method has greatly improved the efficiency of equiconcept search through GcAT on graph formal context.

关键词： Topology Lattices Social networking (online) Computational modeling Formal concept analysis Visualization signal processing algorithms Equiconcept formal concept analysis fast equiconcept search graph formal context graph context attribute topology

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Online Learning of Expanding Graphs

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 247-255页

作者： Rey, Samuel Das, Bishwadeep Isufi, Elvin King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain Delft Univ Technol Intelligent Syst Dept NL-2628 CD Delft Netherlands

This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.

关键词： Topology Network topology Heuristic algorithms signal processing algorithms signal processing Covariance matrices Mathematical models Inference algorithms Computational complexity Vectors Topology inference dynamic graph learning online graph learning expanding graphs graph signal processing

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Efficient Coherent Integration Detection for Space-Moving Target With Complex Azimuth Doppler Ambiguity

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IEEE SENSORS JOURNAL 2024年第24期24卷 41174-41192页

作者： Zhang, Xin Wan, Jun Liu, Ruixin Guan, Weisen Zhang, Yangzhou Chen, Zhanye Huang, Yan Li, Qiang Wen, Cai Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Space Informat Network & Intelli Chongqing 400044 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311200 Zhejiang Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Southeast Univ Inst Electromagnet Space Nanjing 210096 Peoples R China Suzhou Lab Suzhou 215164 Peoples R China Southeast Univ Purple Mt Lab Nanjing 211100 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

The space-moving target (SMT) poses a great challenge for coherent integration detection because of the across range cell (ARC) and Doppler frequency broadening (DFB) induced by the relative motions between SMT and radar platform. Moreover, the intricate azimuth Doppler ambiguity complicates the coherent integration of SMT. To tackle these challenges, a proficient method for the coherent integration of SMT detection under complex azimuth Doppler ambiguity is introduced. First, a modified second-order keystone transform (MSoKT) operation based on the new deramp function with regulatory factor is presented to address the quadratic ARC (QARC) and Doppler spectrum ambiguity. Second, the linear ARC (LARC) is eliminated by the proposed novel processing procedure based on the improved range frequency reversal transform (IRFRT) and modified 2-D scaled Fourier transform (2DSCFT) without searching of Doppler ambiguity number. Third, the DFB is removed by the 1-D scaled Fourier transform (1DSCFT) with minimal computational overhead. After that, the well-focused result of SMT is obtained. The proposed method demonstrates computational efficiency, as its implementation involves only the fast Fourier transform (FFT) and complex multiplication operations, while also eliminating the need to search for Doppler ambiguity numbers. The presented method exhibits robustness against complex azimuth Doppler ambiguity and can effectively address the ambiguous speed sidelobe (ASSL). The multitarget scenario is further investigated, and a strategy for identifying spurious peaks caused by cross terms is proposed. Real data results are provided to validate the method's performance in scenarios involving both single and multiple targets.

关键词： Doppler effect Azimuth Fourier transforms Sensors Computational efficiency Spaceborne radar Fast Fourier transforms signal to noise ratio signal processing algorithms Radar detection Coherent integration complex azimuth Doppler ambiguity modified scaled Fourier transform modified second-order keystone transform (MSoKT) operation space-moving target (SMT) detection

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An Efficient Analytic Solution for Joint Blind Source Separation

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 2436-2449页

作者： Gabrielson, Ben Baker Siddique Akhonda, Mohammad Abu Lehmann, Isabell Adali, Tulay Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Paderborn Univ Dept Elect Engn & IT Signal & Syst Theory Grp D-33098 Paderborn Germany

Joint blind source separation (JBSS) is a powerful methodology for analyzing multiple related datasets, able to jointly extract sources that describe statistical dependencies across the datasets. However, JBSS can be computationally prohibitive with high-dimensional data, thus there exists a key need for more efficient JBSS algorithms. JBSS algorithms typically rely on numerical solutions, which may be expensive due to their iterative nature. In contrast, analytic solutions follow consistent procedures that are often less expensive. In this paper, we introduce an efficient analytic solution for JBSS. Denoting a set of sources dependent across the datasets as a "source component vector" (SCV), our solution minimizes correlation among separate SCVs by minimizing distance of the SCV cross-covariance's eigenvector matrix from a block diagonal matrix. Under the orthogonality constraint, this leads to a system of linear equations wherein each subproblem has an analytic solution. We derive identifiability conditions of our solution's estimator, and demonstrate estimation performance and time efficiency in comparison with other JBSS algorithms that exploit source correlation across datasets. Results demonstrate that our solution achieves the lowest asymptotic computational complexity among JBSS algorithms, and is capable of superior estimation performance compared with algorithms of similar complexity.

关键词： Vectors Correlation signal processing algorithms Covariance matrices Blind source separation Indexes Mathematical models Joint blind source separation independent vector analysis multiset canonical correlation analysis

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Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear Distributed Active Noise Control

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF signal processing 2025年 6卷 1-15页

作者： Kranthi, Rajapantula Vasundhara Kar, Asutosh Christensen, Mads Grasboll Natl Inst Technol Dept Elect & Commun Engn Warangal 506004 India Dr BR Ambedkar Natl Inst Technol Jalandhar Dept Elect & Commun Engn Jalandhar 144008 India Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark

Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.

关键词： Noise Splines (mathematics) Microphones Convergence Adaptive systems Adaptation models Prevention and mitigation Loudspeakers Adaptive filters signal processing algorithms Charbonnier function nonlinear distributed ANC quasi hyperbolic momentum spline

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Snake Optimizer Improved Variational Mode Decomposition for Short-Term Prediction of Vehicle Charging Loads

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2025年 12卷 76-87页

作者： Guan, Quanxue Liu, Qinhe Tao, Shaocong Xu, Yunjian Zhou, Di Chen, Haoyong Tan, Xiaojun Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Shenzhen Acad Metrol & Qual Inspect Shenzhen 100062 Peoples R China South China Univ Technol Sch Elect Power Guangzhou 510640 Peoples R China

The rapid proliferation of electric vehicles (EVs) significantly impacts the power grid, necessitating effective forecasting of charging loads. For ultra short-term load prediction, this paper proposes a Snake Optimization (SO)-Variational Mode Decomposition (VMD)-Long Short-Term Memory (LSTM) algorithm trained by only the historical charging data. Before the prediction starts, the VMD method is utilized to minimize the data complexity, yielding several multiple Intrinsic Mode Functions (IMFs) that correspond to the charging load features at different time scales. The VMD parameters are automatically adjusted using the SO method, instead of the trial-and-error method, to trade off the prediction accuracy against computational overhead. Once the parameters of the VMD are determined, the same number of LSTM networks are employed to forecast the corresponding charging loads from these IMFs, with one LSTM for each IMF. Due to the VMD, IMFs with spanned center frequencies containing few irregularities make the prediction simple. These LSTM outcomes are then summed to obtain the overall load prediction. Experiments are carried out to show that the proposed parallel structure of multiple LSTM networks can achieve high prediction accuracy without requiring complex model structures. Our proposed algorithm outperforms the traditional prediction methods including Gate Recurrent Unit, Extreme Learning Machine, LSTM, and their combination with VMD, significantly reducing the Root Mean Square Error and the Mean Absolute Error by 30.1% and 32.9% in comparison with the optimal VMD-LSTM approach, and by 59.3% and 62.6% with respect to the baseline LSTM method.

关键词： Long short term memory Load modeling Predictive models Prediction algorithms Feature extraction Accuracy Optimization Data models signal processing algorithms Convolutional neural networks Electric vehicle charging station load forecasting long short-term memory snake optimization variational mode decomposition

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Fast H.266/VVC Intra Coding by Early Skipping Joint Coding of Chroma Residuals

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IEEE signal processing LETTERS 2024年 31卷 2465-2469页

作者： Wang, Yifan Feng, Shixuan Zhang, Wei Li, Kai Yang, Fuzheng Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Pengcheng Lab Tao Technol Dept Shenzhen 518055 Peoples R China Alibaba Grp Dept Tao Technol Hangzhou 311121 Peoples R China

Versatile Video Coding (H.266/VVC) significantly enhances compression performance but also increases encoding complexity. Numerous fast intra coding algorithms have been proposed to balance the performance gains with the increased complexity introduced by various advanced tools. However, few fast algorithms are specific to Joint Coding of Chroma Residuals (JCCR), a newly introduced technique that requires additional rate-distortion optimization (RDO) processes. To address this gap, this letter introduces a fast intra coding algorithm that reduces complexity by early skipping unnecessary JCCR. Specifically, we propose skipping JCCR when the correlation between chroma components is low, and we construct a JCCR normalized reconstruction distortion to measure this correlation. The skip conditions are determined by statistical analysis, intelligently reducing the number of RDO processes for chroma components. Experimental results show that, compared to the Fraunhofer Versatile Video Encoder (VVenC), our algorithm achieves encoding time savings of 1.68% and 2.82% under Random Access (RA) and All Intra (AI) settings, respectively, with a performance loss of 0.03% and 0.09% only, demonstrating the effectiveness of our fast algorithm.

关键词： Encoding Distortion Complexity theory Correlation signal processing algorithms Accuracy Transforms Joint coding of chroma residuals fast intra coding versatile video coding

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Class-Agnostic Repetitive Action Counting Using Wearable Devices

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第6期47卷 4984-4995页

作者： Nguyen, Duc Duy Nguyen, Lam Thanh Huang, Yifeng Pham, Cuong Hoai, Minh Univ Adelaide Australian Inst Machine Learning Adelaide SA 5005 Australia VinAI Hanoi 10000 Vietnam SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA Posts & Telecommun Insitute Technol Hanoi 10000 Vietnam

We present Class-agnostic Repetitive action Counting (CaRaCount), a novel approach to count repetitive human actions in the wild using wearable devices time series data. CaRaCount is the first few-shot class-agnostic method, being able to count repetitions of any action class with only a short exemplar data sequence containing a few examples from the action class of interest. To develop and evaluate this method, we collect a large-scale time series dataset of repetitive human actions in various context, containing smartwatch data from 10 subjects performing 50 different activities. Experiments on this dataset and three other activity counting datasets namely Crossfit, Recofit, and MM-Fit show that CaRaCount can count repetitive actions with low error, and it outperforms other baselines and state-of-the-art action counting methods. Finally, with a user experience study, we evaluate the usability of our real-time implementation. Our results highlight the efficiency and effectiveness of our approach when deployed outside the laboratory environments.

关键词： Feature extraction Time series analysis Vectors Training Data mining Wearable Health Monitoring Systems signal processing algorithms Noise Few shot learning Performance evaluation Class-agnostic few-shot learning action counting multivariate time series smartwatch wearable

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Stochastic IHT With Stochastic Polyak Step-Size for Sparse signal Recovery

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IEEE signal processing LETTERS 2024年 31卷 2035-2039页

作者： Li, Changhao Ma, Zhixin Sun, Dazhi Zhang, Guoming Wen, Jinming Jinan Univ Dept Informat Sci & Technol Guangzhou 510632 Peoples R China Jinan Univ Human Resources Off Guangzhou 510632 Peoples R China Jilin Inst Chem Technol Coll Resources & Environm Engn Jilin 132022 Peoples R China Jinan Univ Shenzhen Eye Hosp Shenzhen 518040 Peoples R China

Sparse signal recovery arises from many applications. However, deterministic algorithms often require significant time, especially for large-scale systems. Hence, stochastic algorithms like the Stochastic Iterative Hard Thresholding Algorithm (StoIHT) were proposed to address large-scale problems. In this letter, we propose using the stochastic Polyak step size method to design step sizes and provide theoretical convergence analysis. Experimental results suggest that our algorithm demonstrates comparable performance to other stochastic algorithms in sparse signal recovery and image reconstruction with faster convergence.

关键词： signal processing algorithms Convergence Vectors Iterative algorithms Stochastic processes Sun Linear programming Polyak step size sparse signal recovery stochastic algorithms

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Robust Sequential Phase Estimation Using Multi-Temporal SAR Image Series

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IEEE signal processing LETTERS 2025年 32卷 811-815页

作者： El Hajjar, Dana Ginolhac, Guillaume Yan, Yajing El Korso, Mohammed Nabil Univ Savoie Mont Blanc LISTIC F-74000 Annecy France Univ Paris Saclay Cent Supelec L2S F-91190 Gif Sur Yvette France

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) exploits Synthetic Aperture Radar images time series (SAR-TS) for surface deformation monitoring via phase difference (with respect to a reference image) estimation. Most of the actual state-of-the-art MT-InSAR rely on temporal covariance matrix of the SAR-TS, assuming Gaussian distribution. However, these approaches become computationally expensive when the time series lengthens and new images are added to the data vector. This paper proposes a novel approach to sequentially integrate each newly acquired image using Phase Linking (PL) and Maximum Likelihood Estimation (MLE). The methodology divides the data into blocks, using previous images and estimations as a prior to sequentially estimate the phase of the new image. Actually, this framework allows to consider non Gaussian distributions, such as a mixture of scaled Gaussian distribution, which is particularly important to consider when dealing with urban areas.

关键词： Covariance matrices Coherence Synthetic aperture radar signal processing algorithms Radar polarimetry Maximum likelihood estimation Vectors Time series analysis Radar imaging Gaussian distribution Multi-temporal interferometric synthetic aperture radar maximum likelihood estimation covariance matrix estimation mixture of scaled Gaussian distribution

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