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TSIPN Articles

<p>TSIPN Articles</p>

Performance Analysis of Smart Grid Wide Area Network With RIS Assisted Three Hop System

In this paper, we investigate the performance of a wide area network (WAN) with three hops over a mixed radio frequency (RF), reconfigurable intelligent surface (RIS) assisted RF and Free space optics (FSO) channel. Here RIS and decode-and-forward (DF) relays are used to improve the coverage and system performance. For general applicability, the RF and FSO links are modelled with Saleh-Valenzuela (S-V) and Gamma-Gamma distribution, respectively.

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Joint Graph Learning and Blind Separation of Smooth Graph Signals Using Minimization of Mutual Information and Laplacian Quadratic Forms

The smoothness of graph signals has found desirable real applications for processing irregular (graph-based) signals. When the latent sources of the mixtures provided to us as observations are smooth graph signals, it is more efficient to use graph signal smoothness terms along with the classic independence criteria in Blind Source Separation (BSS) approaches. In the case of underlying graphs being known, Graph Signal Processing (GSP) provides valuable tools; however, in many real applications, these graphs can not be well-defined a priori and need to be learned from data. 

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Graph Wedgelets: Adaptive Data Compression on Graphs Based on Binary Wedge Partitioning Trees and Geometric Wavelets

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings.

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Cooperative Learning of Multi-Agent Systems Via Reinforcement Learning

In many specific scenarios, accurateand practical cooperative learning is a commonly encountered challenge in multi-agent systems. Thus, the current investigation focuses on cooperative learning algorithms for multi-agent systems and underpins an alternate data-based neural network reinforcement learning framework. To achieve the data-based learning optimization, the proposed cooperative learning framework, which comprises two layers, introduces a virtual learning objective.

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Decentralized Federated Learning: Balancing Communication and Computing Costs

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus.

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Online Change Point Detection for Weighted and Directed Random Dot Product Graphs

Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to endow sequential change-point detection (CPD) techniques with a graph representation learning substrate based on the versatile Random Dot Product Graph (RDPG) model. We consider efficient, online updates of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal RDPG.

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Joint State and Fault Estimation of Complex Networks Under Measurement Saturations and Stochastic Nonlinearities

In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously.

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Improved Interval Estimation Method for Cyber-Physical Systems Under Stealthy Deception Attacksterpretable Deep Multi-Modal Hashing for Large-Scale Multimedia Retrieval

This paper investigates the problem of interval estimation for cyber-physical systems subject to stealthy deception attacks. The cyber-physical system is supposed to be compromised by malicious attackers and on the basis of that, a stealthy attack strategy is formulated. Moreover, the stealthiness of the attack strategy against χ2 -detector is analyzed. To accomplish interval estimation, the interval observer is designed by the monotone system method. Then, a novel method which combines reachable set analysis with H technique is proposed. 

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