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Median-Based Resilient Multi-Object Fusion With Application to LMB Densities IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-04-17 Yao Zhou, Giorgio Battistelli, Luigi Chisci, Lin Gao, Gaiyou Li, Ping Wei
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Causal Inference From Slowly Varying Nonstationary Processes IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-04-12 Kang Du, Yu Xiang
Causal inference from observational data following the restricted structural causal models (SCMs) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or non-linearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time series remains a challenging task. In this
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Multi-Agent Bipartite Flocking Control over Cooperation-Competition Networks with Asynchronous Communications IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-04-03 Zhuangzhuang Ma, Lei Shi, Kai Chen, Jinliang Shao, Yuhua Cheng
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Distributed Event-Triggered Fault-Tolerant Consensus Control of Multi-Agent Systems Under DoS Attacks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-04-03 Chun Liu, Bin Jiang, Yang Li, Ron J. Patton
This study investigates the distributed fault-tolerant consensus issue of multi-agent systems subject to complicated abrupt and incipient time-varying actuator faults in physical hierarchy and aperiodic denial-of-service (DoS) attacks in networked hierarchy. Decentralized estimators are devised to estimate consecutive system states and actuator faults. A unified framework with an absolute local output-based
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Composite Output Consensus Control for General Linear Multi-Agent Systems with Heterogeneous Mismatched Disturbances IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-27 Pan Yu, Yifan Ding, Kang-Zhi Liu, Xiaoli Li
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Bipartite Graph Approximation by Eigenvalue Optimization IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-25 Aimin Jiang, Xintong Shi, Yibin Tang, Yanping Zhu, Hon Keung Kwan
Graphs are a powerful tool for representing entities and their relationships. Current advances in graph signal processing have made it possible to analyze graph-based data more effectively. Recent research show that, to ensure critical sampling, manyfilterbank design algorithms are only applicable to bipartite graphs. However, general graph signals may not exist on a bipartite graph structure. To overcome
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Scalable Distributed Optimization of Multi-Dimensional Functions Despite Byzantine Adversaries IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-22 Kananart Kuwaranancharoen, Lei Xin, Shreyas Sundaram
The problem of distributed optimization requires a group of networked agents to compute a parameter that minimizes the average of their local cost functions. While there are a variety of distributed optimization algorithms that can solve this problem, they are typically vulnerable to “Byzantine” agents that do not follow the algorithm. Recent attempts to address this issue focus on single dimensional
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Partial Diffusion With Quantization Over Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-21 Xiaoxian Lao, Chunguang Li
Distributed estimation over networks has drawn much attention in recent years. In the problem of distributed estimation, a set of nodes is requested to estimate some parameter of interest from noisy measurements. The nodes interact with each other to carry out the task jointly. Many algorithms have been proposed for solving the distributed estimation problem, among which the diffusion strategy is well-accepted
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Graph Receptive Transformer Encoder for Text Classification IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-21 Arda Can Aras, Tuna Alikaşifoğlu, Aykut Koç
By employing attention mechanisms, transformers have made great improvements in nearly all NLP tasks, including text classification. However, the context of the transformer's attention mechanism is limited to single sequences, and their fine-tuning stage can utilize only inductive learning. Focusing on broader contexts by representing texts as graphs, previous works have generalized transformer models
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Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-19 Guangrui Yang, Qing Zhang, Lihua Yang
Signal representations on graphs are at the heart of most graph signal processing techniques, allowing for targeted signal models for tasks such as denoising, compression, sampling, reconstruction and detection. This paper studies the piecewise-constant representation of bandlimited graph signals, thereby establishing the relationship between the bandlimited graph signal and the piecewise-constant
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Consensus Analysis for Cooperative-Competitive Multiagent Systems Under False Data Injection Attacks via Dynamic Event-Triggered Observers IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-19 Sangli Shi, Zhengxin Wang, Min Xiao, Guo-Ping Jiang, Jinde Cao
Distributed secure control is investigated for cooperative-competitive multiagent systems suffered from false data injection attacks (FDIAs) via event-triggered observers. Attack signals are injected into controller-to-actuator channels. A static event-triggered control is first presented, then an auxiliary-variable-based dynamic event-triggered control is further put forward. The dynamic event-triggered
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Sensor-Fault Detection, Isolation and Accommodation for Natural-Gas Pipelines Under Transient Flow IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-13 Khadija Shaheen, Apoorva Chawla, Ferdinand Evert Uilhoorn, Pierluigi Salvo Rossi
The monitoring of natural gas pipelines is highly dependent on the information provided by different types of sensors. However, sensors are prone to faults, which results in performance degradation and serious hazards such as leaks or explosions. To prevent catastrophic failures and ensure the safe and efficient operation of the pipelines, it is crucial to timely diagnose sensor faults in natural gas
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Distributed Optimisation With Linear Equality and Inequality Constraints Using PDMM IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Richard Heusdens, Guoqiang Zhang
In this article, we consider the problem of distributed optimisation of a separable convex cost function over a graph, where every edge and node in the graph could carry both linear equality and/or inequality constraints. We show how to modify the primal-dual method of multipliers (PDMM), originally designed for linear equality constraints, such that it can handle inequality constraints as well. The
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Graph Signal Reconstruction Under Heterogeneous Noise via Adaptive Uncertainty-Aware Sampling and Soft Classification IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Alessio Fascista, Angelo Coluccia, Chiara Ravazzi
Reconstructing bandlimited graph signals from a subset of noisy measurements is a fundamental challenge within the realm of signal processing. Historically, this problem has been approached assuming uniform noise variance across the network. Nevertheless, practical scenarios often present heterogeneous noise landscapes, greatly complicating the signal reconstruction process. This study tackles reconstruction
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Probability-Guaranteed Distributed Estimation for Two-Dimensional Systems Under Stochastic Access Protocol IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Meiyu Li, Jinling Liang
This paper studies the probability-guaranteed distributed estimation problem for a kind of two-dimensional shift-varying sensor networks under the stochastic access protocol (SAP). The considered system is affected by unknown-but-bounded perturbations and sector bounded nonlinearity. The communication architecture of a multi-node network is expressed by a digraph. Due to the limited communication channel
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Finite-Time Asymmetric Bipartite Consensus for Multi-Agent Systems Using Data-Driven Iterative Learning Control IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Jiaqi Liang, Xuhui Bu, Zhongsheng Hou
A general finite-time bipartite consensus problem is studied for multi-agent systems with completely unknown nonlinearities. An asymmetric bipartite consensus task is defined by introducing a proportional-related coefficient and a relationship-related index, which arranges that the agents reach an agreement with proportional modulus and opposite signs. With the cooperative-antagonistic interactions
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Event-Triggered Distributed Estimation With Inter-Event Information Retrieval IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Xiaoxian Lao, Chunguang Li
Distributed estimation has attracted great attention in the last few decades. In the problem of distributed estimation, a set of nodes estimate some parameter from noisy measurements. To leverage joint effort, the nodes communicate with each other in the estimation process. The communications consume bandwidth and energy resources, and these resources are often limited in real-world applications. To
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Fully Distributed Consensus Control for a Class of Disturbed Linear Multi-Agent Systems Over Event-Triggered Communication IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Jia Deng, Fuyong Wang, Zhongxin Liu, Zengqiang Chen
This article is concerned with the fully distributed consensus control problem of a class of disturbed general linear multi-agent systems under event-triggered communication. Different from existing works, the disturbances considered in this article are more practical and complex. Each agent is subject to disturbances generated by exosystems and each exosystem is considered to exist with possible modelling
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Unifying Epidemic Models With Mixtures IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-03-11 Arnab Sarker, Ali Jadbabaie, Devavrat Shah
The COVID-19 pandemic has emphasized the need for a robust understanding of epidemic models. Current models of epidemics are classified as either mechanistic or non-mechanistic: mechanistic models make explicit assumptions on the dynamics of disease, whereas non-mechanistic models make assumptions on the form of observed time series. Here, we introduce a simple mixture-based model which bridges the
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Distributed Generalized Nash Equilibria Computation of Noncooperative Games Via Novel Primal-Dual Splitting Algorithms IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-02-09 Liang Ran, Huaqing Li, Lifeng Zheng, Jun Li, Zhe Li, Jinhui Hu
This article investigates the generalized Nash equilibria (GNE) seeking problem for noncooperative games, where all players dedicate to selfishly minimizing their own cost functions subject to local constraints and coupled constraints. To tackle the considered problem, we initially form an explicit local equilibrium condition for its variational formulation. By employing proximal splitting operators
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Strong Convergence of a Random Actions Model in Opinion Dynamics IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-02-01 Olle Abrahamsson, Danyo Danev, Erik G. Larsson
We study an opinion dynamics model in which each agent takes a random Bernoulli distributed action whose probability is updated at each discrete time step, and we prove that this model converges almost surely to consensus. We also provide a detailed critique of a claimed proof of this result in the literature. We generalize the result by proving that the assumption of irreducibility in the original
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Online Signed Sampling of Bandlimited Graph Signals IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-22 Wenwei Liu, Hui Feng, Feng Ji, Bo Hu
The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as “like” or “dislike” for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal can be measured. In this paper, we are interested
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Protocol-Based Distributed Security Fusion Estimation for Time-Varying Uncertain Systems Over Sensor Networks: Tackling DoS Attacks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-22 Lijuan Zha, Yaping Guo, Jinliang Liu, Xiangpeng Xie, Engang Tian
This article studies the distributed fusion estimation (DFE) issue for networked multi-sensor systems (NMSSs) with stochastic uncertainties, bandwidth-constrained network and energy-constrained denial-of-service (DoS) attacks. The stochastic uncertainties reflected in both the state and measurement models are characterized by multiplicative noises. For reducing the communication burden, local estimation
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Lyapunov-Optimized and Energy-Constrained Stable Online Computation Offloading in Wireless Microtremor Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-19 Ruyun Tian, Hongyan Xing, Yihan Cao, Huaizhou Zhang
The microtremor survey method (MSM) holds great potential for obtaining subsurface shear wave velocity structures in exploration geophysics. However, the lack of an instant imaging mechanism with local fast computation and processing has become a significant bottleneck hindering the development of MSM. In instant imaging tasks, the computational resources of ordinary nodes employed for imaging are
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Hybrid-Triggered Output Feedback Containment Control for Multi-Agent Systems With Missing Measurements IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-18 Arumugam Parivallal, Sangwoon Yun, Yoon Mo Jung
In this paper, we investigate the output feedback containment control problem for multi-agent systems with missing measurements. The primary objective is to design a hybrid-triggered controller that not only reduces the unwanted data transmission but also ensures the required control performance. The proposed hybrid-triggered controller is developed by combining the time-triggered and event-triggered
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IEEE Signal Processing Society Information IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-10
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A Graph-Assisted Framework for Multiple Graph Learning IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-10 Xiang Zhang, Qiao Wang
In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting the underlying topological relationships between them. The difficulty lies in how to design a regularizer that accurately describes the intricate topological relationships, especially without prior knowledge. This problem becomes more challenging for the scenarios where data for different graphs are stored
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A Multi-View Rumor Detection Framework Using Dynamic Propagation Structure, Interaction Network, and Content IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-10 Marzieh Rahimi, Mehdy Roayaei
Social networks (SN) have been one of the most important media for information diffusion in recent years. However, sometimes SN are used to spread rumors, which results in many social issues. Many researches have been done to detect rumors automatically. Previous works mostly exploit a single modality, especially the textual content, thus ignoring other modality such as the propagation structure and
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Channel Pricing and Sensor Scheduling for Distributed Estimation Based on a Stackelberg Game Framework IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-10 Rui Tang, Wen Yang, Zhihai Rong, Chao Yang, Yang Tang
Since communication quality between sensors can directly affect distributed estimation, we consider the communication channel pricing and sensor scheduling problem for distributed estimation over a wireless sensor network with limited resources. Each sensor's choice of channels depends on its estimation performance and the channel communication cost which sets by a communication network server. Thus
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Detection and Recovery of Hidden Submatrices IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-10 Marom Dadon, Wasim Huleihel, Tamir Bendory
In this paper, we study the problems of detection and recovery of hidden submatrices with elevated means inside a large Gaussian random matrix. We consider two different structures for the planted submatrices. In the first model, the planted matrices are disjoint, and their row and column indices can be arbitrary. Inspired by scientific applications, the second model restricts the row and column indices
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Distributed Model-Free Adaptive Predictive Control for MIMO Multi-Agent Systems With Deception Attack IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2024-01-01 Zhenzhen Pan, Ronghu Chi, Zhongsheng Hou
This work explores the challenging problems of nonlinear dynamics, nonaffine structures, heterogeneous properties, and deception attack together and proposes a novel distributed model-free adaptive predictive control (DMFAPC) for multiple-input-multiple-output (MIMO) multi-agent systems (MASs). A dynamic linearization method is introduced to address the nonlinear heterogeneous dynamics which is transformed
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A Localized Primal-Dual Method for Centralized/Decentralized Federated Learning Robust to Data Heterogeneity IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-12-25 Iifan Tyou, Tomoya Murata, Takumi Fukami, Yuki Takezawa, Kenta Niwa
Generalized Edge-Consensus Learning (G-ECL) is a primal-dual method to solve loss-sum minimization problems. We propose Local Generalized Edge-Consensus Learning (Local G-ECL) as an extension of previous G-ECL, aiming to be a decentralized/centralized FL algorithm robust to heterogeneous data sets with a large number of local updates. Our contributions are as follows: (C1) success in theoretical gradient
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Learning Hypergraphs Tensor Representations From Data via t-HGSP IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-12-20 Karelia Pena-Pena, Lucas Taipe, Fuli Wang, Daniel L. Lau, Gonzalo R. Arce
Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing. However, a meaningful hypergraph may only be available
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Gradient-Based Spectral Embeddings of Random Dot Product Graphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-12-15 Marcelo Fiori, Bernardo Marenco, Federico Larroca, Paola Bermolen, Gonzalo Mateos
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions. Accordingly, the embedding task of estimating these vectors from an observed graph is typically posed as a low-rank
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Exponential Synchronization of Reaction-Diffusion Systems on Networks via Asynchronous Intermittent Control IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-12-01 Jian Liu, Yan Yang, Yongbao Wu, Seaar Al-Dabooni, Lei Xue, Donald C. Wunsch
In this article, the exponential synchronization (ES) of the reaction-diffusion systems on networks is studied under an asynchronous aperiodic intermittent control strategy. Different from the preceding studies, the control strategy of each node is different, which is more general and challenging. Meanwhile, to address the asynchrony problem of the asynchronous intermittent control, a new asynchronous
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A Closed-Form Solution for Graph Signal Separation Based on Smoothness IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-11-28 Mohammad-Hassan Ahmad Yarandi, Massoud Babaie-Zadeh
Using smoothness criteria to separate smooth graph signals from their summation is an approach that has recently been proposed (Mohammadi et al., 2023) and shown to have a unique solution up to the uncertainty of the average values of source signals. In this correspondence, closed-form solutions of both exact and approximate decompositions of that approach are presented. This closed-form solution in
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Encoding-Decoding-Based Distributed Fusion Filtering for Multi-Rate Nonlinear Systems With Sensor Resolutions IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-11-23 Jun Hu, Shuting Fan, Cai Chen, Hongjian Liu, Xiaojian Yi
The paper investigates the distributed fusion filtering problem for time-varying multi-rate nonlinear systems (TVMRNSs) with sensor resolutions based on the encoding-decoding scheme (EDS) over sensor networks, where the iterative method is applied to the transformation of TVMRNSs. In order to enhance signal interference-resistant capability and improve transmission efficiency, the EDS based on dynamic
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Distributed Estimation by Partial Sensor Measurements Through Transmission Scheduling for Stochastic Systems IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-11-06 Yun Chen, Yuhang Jin, Jianjun Bai, Mengze Zhu
This paper is concerned with the partial-sensor-measurements-based (PSMB) distributed estimation problem for a class of stochastic systems (SSs) with randomly occurring nonlinearities, persistent bounded noises and quantization effects. The observations of partial sensor nodes are available to be transmitted to the estimators. In order to enhance the utilization efficiency of limited resources, the
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Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-30 Matin Macktoobian, Zhan Shu, Qing Zhao
Faults occurring in ad-hoc robot networks may fatally perturb their topologies leading to disconnection of subsets of those networks. Optimal topology synthesis is generally resource-intensive and time-consuming to be done in real time for large ad-hoc robot networks. One should only perform topology re-computations if the probability of topology recoverability after the occurrence of any fault surpasses
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Practical Fixed-Time Consensus Tracking for Second-Order Multi-Agent Systems With Mismatched Disturbances IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-27 Jiayi Gong, Fuyong Wang, Zhongxin Liu, Zengqiang Chen
This article focuses on the practical fixed-time consensus tracking problem of second-order multi-agent systems (MASs) with mismatched disturbances and matched disturbances under directed graph. Actually, the leader can be a virtual signal or an actual agent. Considering these two situations, followers can track the leader in a finite time with uniform bound. By using the adding-a-power-integrator
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Leader-Following Containment Control of Hybrid Fractional-Order Networked Agents With Nonuniform Time Delays IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-23 Weihao Li, Lei Shi, Mengji Shi, Jiangfeng Yue, Boxian Lin, Kaiyu Qin
Time delays, such as transmission delays or measurement delays, are common phenomena in practical networked control systems. These delays directly threaten the effective completion of cooperative tasks. In this study, the leader-following containment control problem of hybrid fractional-order networked agents with nonuniform time delays is addressed. The position and velocity loops of each double-integrator
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Personalized Graph Federated Learning With Differential Privacy IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-23 Francois Gauthier, Vinay Chakravarthi Gogineni, Stefan Werner, Yih-Fang Huang, Anthony Kuh
This paper presents a personalized graph federated learning (PGFL) framework in which distributedly connected servers and their respective edge devices collaboratively learn device or cluster-specific models while maintaining the privacy of every individual device. The proposed approach exploits similarities among different models to provide a more relevant experience for each device, even in situations
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Noise Resilient Distributed Average Consensus Over Directed Graphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-16 Vivek Khatana, Murti V. Salapaka
Motivated by the needs of resiliency, scalability, and plug-and-play operation, distributed decision making is becoming increasingly prevalent. The problem of achieving consensus in a multi-agent system is at the core of distributed decision making. In this article, we study the problem of achieving average consensus over a directed multi-agent network when the communication links are corrupted with
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Time-Aware Distributed Sequential Detection of Gas Dispersion via Wireless Sensor Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-13 Gianluca Tabella, Domenico Ciuonzo, Yasin Yilmaz, Xiaodong Wang, Pierluigi Salvo Rossi
This work addresses the problem of detecting gas dispersions through concentration sensors with wireless transmission capabilities organized as a distributed Wireless Sensor Network (WSN). The concentration sensors in the WSN perform local sequential detection (SD) and transmit their individual decisions to the Fusion Center (FC) according to a transmission rule designed to meet the low-energy requirements
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Online Joint Topology Identification and Signal Estimation From Streams With Missing Data IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-13 Bakht Zaman, Luis Miguel Lopez-Ramos, Baltasar Beferull-Lozano
Identifying the topology underlying a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model-based topologies capture dependencies among time series and are often inferred from observed spatio-temporal data. When data are affected by noise and/or missing samples, topology identification and signal recovery (reconstruction) tasks
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Communication-Efficient and Privacy-Aware Distributed Learning IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-10-11 Vinay Chakravarthi Gogineni, Ashkan Moradi, Naveen K. D. Venkategowda, Stefan Werner
Communication efficiency and privacy are two key concerns in modern distributed computing systems. Towards this goal, this article proposes partial sharing private distributed learning (PPDL) algorithms that offer communication efficiency while preserving privacy, thus making them suitable for applications with limited resources in adversarial environments. First, we propose a noise injection-based
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Distributed Saddle Point Problems for Strongly Concave-Convex Functions IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-09-28 Muhammad I. Qureshi, Usman A. Khan
In this article, we propose GT-GDA , a distributed optimization method to solve saddle point problems of the form: ${\min _{\mathbf {x}} \max _{\mathbf {y}} \lbrace F(\mathbf x,\mathbf y) :=G(\mathbf x) + \langle \mathbf y, \overline{P} \mathbf x \rangle - H(\mathbf y) \rbrace }$ , where the functions $G(\cdot)$ , $H(\cdot)$ , and the coupling matrix $\overline{P}$ are distributed over a strongly connected
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Joint Multi-Ground-User Edge Caching Resource Allocation for Cache-Enabled High-Low-Altitude-Platforms Integrated Network IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-09-14 Yongyi Yuan, Enchang Sun, Hanxing Qu
This article examines the cache-enabled high-low-altitude-platforms integrated network (CHLIN), which consists of multiple high-altitude platforms (HAPs) and cacheable low-altitude platforms (LAPs). CHLIN aims to leverage the edge caching, the flexibility of LAPs and the broad coverage and stability of HAPs to realize multi-ground-user content transmission. Considering the low endurance, dynamics,
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Frequency-Domain Diffusion Bias-Compensated Adaptation With Periodic Communication IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-09-12 Yishu Peng, Sheng Zhang, Hongyang Chen, Zhengchun Zhou, Xiaohu Tang
When the input signal of each node is interfered by noise, the distributed frequency-domain adaptive algorithm yields biased estimation. To eliminate the noise-induced bias with reduced communication load, this article proposes the frequency-domain diffusion bias-compensated adaptive filtering with periodic communication. By minimizing the bias-eliminating cost function, the frequency-domain diffusion
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Temporal Multiple Rotation Averaging on a Distributed Dynamic Network IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-09-11 Aidan Blair, Amirali Khodadadian Gostar, Ruwan Tennakoon, Alireza Bab-Hadiashar, Reza Hoseinnezhad
This article proposes a solution for multiple rotation averaging on time-series data such as video. In applications using video data such as target tracking, in addition to the data found in individual frames, temporal information across multiple frames such as target trajectories can be used to more accurately estimate target states. Existing techniques for robust rotation averaging, including traditional
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Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-09-11 Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney
We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant
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Optimization Based Sensor Placement for Multi-Target Localization With Coupling Sensor Clusters IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-28 Linlong Wu, Nitesh Sahu, Sheng Xu, Prabhu Babu, Domenico Ciuonzo
Since the Cramér-Rao lower bounds (CRLB) of target localization depends on the sensor geometry explicitly, sensor placement becomes a crucial issue in many target or source localization applications. In the context of simultaneous time-of-arrival (TOA) based multi-target localization, we consider the sensor placement for multiple sensor clusters in the presence of shared sensors. To minimize the mean
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Communication Compression for Decentralized Learning With Operator Splitting Methods IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-25 Yuki Takezawa, Kenta Niwa, Makoto Yamada
In decentralized learning, operator splitting methods using a primal-dual formulation (e.g., Edge-Consensus Learning (ECL)) have been shown to be robust to heterogeneous data and have attracted significant attention in recent years. However, in the ECL, a node needs to exchange dual variables with its neighbors. These exchanges incur significant communication costs. For the Gossip-based algorithms
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A Nonconvex Low Rank and Sparse Constrained Multiview Subspace Clustering via $l_{\frac{1}{2}}$-Induced Tensor Nuclear Norm IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-17 Jobin Francis, Baburaj Madathil, Sudhish N. George, Sony George
In the realm of clustering of multi-view data, many of the clustering methods, generate view-specific representations for individual views and conjoin them for final grouping. However, in most of the cases,such methods fail to effectively discover the underlying complementary information and higher order correlations present in a multi-view data. Unlike many of the existing works, this paper proposes
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Asymptotically Efficient Moving Target Localization in Distributed Radar Networks IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-17 Mohammad Reza Jabbari, Mohammad Reza Taban, Saeed Gazor, Mehrdad Kaimasi
In this article, we investigate the joint estimation of the position and velocity of a moving target in distributed networks of moving radars using Time Of Arrival (TOA) and Doppler Shift (DS) measurements. In contrast to most of the existing/recent methods, we avoid the use of Nuisance Variables (NVs) by employing algebraic manipulations. We reformulate a new set of equations that are linear with
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Game-Theoretic Distributed Empirical Risk Minimization With Strategic Network Design IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-17 Shutian Liu, Tao Li, Quanyan Zhu
This article considers a game-theoretic framework for distributed empirical risk minimization (ERM) problems over networks where the information acquisition at a node is modeled as a rational choice of a player. In the proposed game, players decide both the learning parameters and the network structure. The Nash equilibrium (NE) characterizes the tradeoff between the local performance and the global
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A Linearly Convergent Optimization Framework for Learning Graphs From Smooth Signals IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-10 Xiaolu Wang, Chaorui Yao, Anthony Man-Cho So
Learning graph structures from a collection of smooth graph signals is a fundamental problem in data analysis and has attracted much interest in recent years. Although various optimization formulations of the problem have been proposed in the literature, existing methods for solving them either are not practically efficient or lack strong convergence guarantees. In this article, we consider a unified
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Multivariate Time Series Forecasting With GARCH Models on Graphs IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-10 Junping Hong, Yi Yan, Ercan Engin Kuruoglu, Wai Kin Chan
Data that house topological information is manifested as relationships between multiple variables via a graph formulation. Various methods have been developed for analyzing time series on the nodes of graphs but research works on graph signals with volatility are limited. In this article, we propose a graph framework of multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH)
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Information Fusion via Importance Sampling IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-07 Augustin A. Saucan, Víctor Elvira, Pramod K. Varshney, Moe Z. Win
Information fusion is a procedure that merges information locally contained at the nodes of a network. Of high interest in the field of distributed estimation is the fusion of local probability distributions via a weighted geometrical average criterion. In numerous practical settings, the local distributions are only known through particle approximations, i.e., sets of samples with associated weights
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Decentralized Eigendecomposition for Online Learning Over Graphs With Applications IEEE Trans. Signal Inf. Process. Over Netw. (IF 3.2) Pub Date : 2023-08-07 Yufan Fan, Minh Trinh-Hoang, Cemil Emre Ardic, Marius Pesavento
In this article, the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis, is studied, and a decentralized online learning algorithm is proposed. Instead of collecting all information in a fusion center, the proposed algorithm involves only local interactions among adjacent agents. It benefits from the representation