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Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser Network

Autor: Du, Mingjun, Zheng, Sihui, Zhang, Xiao-Ping, Dong, Yuhan

In data driven deep learning, distributed sensing and joint computing bring heavy load for computing and communication. To face the challenge, over-the-air computation (OAC) has been proposed for multi-sensor data aggregation, which enables the serve

Externí odkaz: http://arxiv.org/abs/2409.00738

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Characterization of Collective Behaviors for Directed Signed Networks

Autor: Du, Wen, Meng, Deyuan, Du, Mingjun

This paper targets at exploring how to characterize collective behaviors of directed signed networks. The right eigenvector of the Laplacian matrix associated with zero eigenvalue is further investigated and its mathematical expression is proposed. I

Externí odkaz: http://arxiv.org/abs/2105.02402

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Distributed Averaging Problems over Directed Signed Networks

Autor: Du, Mingjun, Meng, Deyuan, Wu, Zheng-Guang

This paper aims at addressing distributed averaging problems for signed networks in the presence of general directed topologies that are represented by signed digraphs. A new class of improved Laplacian potential functions is proposed by presenting t

Externí odkaz: http://arxiv.org/abs/1908.06250

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Convergence Behavior Analysis of Directed Signed Networks Subject to Nonidentical Topologies

Autor: Meng, Deyuan, Du, Mingjun, Liang, Jianqiang

This paper addresses the behavior analysis problems for directed signed networks that involve cooperative-antagonistic interactions among agents. Of particular interest is to explore the convergence behaviors of directed signed networks with agents o

Externí odkaz: http://arxiv.org/abs/1908.04467

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