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Controllability of Indu-Bala product networks

Neural Networks 2025年 190卷 107622页

作者： Zhang, Qiang Huang, Junjie Liu, Bo Su, Housheng Chen, Alatancang School of Mathematical Sciences Inner Mongolia University Hohhot010021 China School of Information Engineering Minzu University of China Beijing100081 China National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Mathematical Sciences the Ministry of Education Key Laboratory for Infinite-dimensional Hamiltonian System and Its Algorithm Application Inner Mongolia Normal University Hohhot010022 China

A composite network formed by factor networks is a new type of complex network model that can describe different types of agents and multiple complicated relationships in complex systems. "Indu-Bala product" of graphs is a new graph operation with symmetry properties and good mathematical expression, which enables one to construct many pairs of respective co-spectral graphs. This work studies the controllability of a composite network formed by factor networks via Indu-Bala product based on Laplacian dynamics. The eigenvalues and eigenvectors of Indu-Bala product networks (IBPNs) can be represented by factors’ eigenvalues and eigenvectors, which can reduce the computational complexity and analytical burden. Based on the PBH test, sufficient and necessary algebraic criterions for controllability of IBPNs are established, which reveal the controllability relationship between entire network and its factors. This work will help one to construct, understand and improve the composite complex network models, and gain insight into the synergistic mechanisms of the networks. © 2025 Elsevier Ltd

关键词： Eigenvalues and eigenfunctions

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MCS Filter: A Multi-Channel Structure-aware Speckle Filter for SAR Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Saheya, B. Cai, Rui Zhao, Hongyu Gong, Maoguo Li, Xiaofeng Inner Mongolia Normal University College of Mathematical Science Hohhot01022 China Inner Mongolia Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application Hohhot010022 China Xidian University Key Laboratory of Collaborative Intelligence Systems Ministry of Education School of Electronic Engineering Xi’an China Inner Mongolia Normal University Academy of Artificial Intelligence Hohhot China Chinese Academy of Sciences Key Laboratory of Ocean Circulation and Waves Institute of Oceanology Qingdao266071 China

Synthetic aperture radar (SAR) is widely recognized as an effective tool for investigating oceanic processes, particularly internal wave dynamics, due to its ability to capture high-resolution imaging across diverse environmental conditions. However, speckle noise in SAR imagery poses significant challenges for accurately extracting critical parameters, such as wavefront characteristics and bright-dark distances, thereby hindering its application in detailed internal wave studies. To address this, a novel speckle filtering method is proposed, specifically designed to enhance the quality of SAR images capturing the feature of internal wave. First, a structural description matrix with complex values is introduced to encode pixel value correlations and local spatial features, enabling improved representation of wave-related contextual information within the SAR data. Second, a similarity testing framework based on the Wishart distribution is developed to identify and aggregate structurally similar regions, ensuring robust noise suppression while preserving essential structural parameters. The proposed method strikes a balance between speckle noise reduction and structural preservation, making it adaptable to a variety of SAR datasets. Experimental validation on SAR imagery of oceanic internal waves demonstrates the method’s effectiveness in retaining key internal wave information, while additional tests on terrestrial targets highlight its potential for generalization across different scenarios. © 1980-2012 IEEE.

关键词： Information filters Radar polarimetry Vectors Oceans Filtering algorithms Wiener filters Speckle Synthetic aperture radar Filtering theory Correlation

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