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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Ramos, Guilherme Pequito, Sérgio Department of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Lisbon 1049-001 Portugal Instituto de Telecomunicações Lisbon 1049-001 Portugal Department of Electrical and Computer Engineering Instituto Superior Técnico University of Lisbon Lisbon 1049-001 Portugal Institute for Systems and Robotics Instituto Superior Técnico University of Lisbon Lisbon 1049-001 Portugal

ISBN: (纸本)9783031817236

Strongly Connected Components (SCCs) play a crucial role in understanding the structural properties of directed graphs, with a plethora of applications across various domains including engineering, computer science and social sciences. In this paper, we propose a method for computing both the source SCCs and target SCCs in a distributed fashion. Our method leverages the decentralized nature of distributed systems to efficiently identify SCCs within large-scale graphs. We illustrate the proposed methods with examples. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： distributed computation multi-agent systems source strongly connected components Strongly connected components target strongly connected components

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distributed computation Offloading in Autonomous Driving Vehicular Networks: A Stochastic Geometry Approach

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 2701-2713页

作者： Yang, Jianjie Chen, Yingyang Lin, Zhijian Tian, Daxin Chen, Pingping Fuzhou Univ Sch Adv Mfg Quanzhou 362200 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Guangdong Key Lab Data Secur & Privacy Preserving Guangzhou 510632 Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Sch Transportat Sci & Engn Beijing Key Lab Cooperat Vehicle Infrastruct Beijing 100191 Peoples R China

With the advancement of the Internet of Vehicles (IoV), delay-sensitive vehicular applications have flourished. Among them, the autonomous driving technology is a focal point. For autonomous driving vehicles, efficiently and timely processing the ever-increasing data is critical. In real traffic scenes, the task-processing efficiency is closely related to the traffic flows. However, the traffic flow modeling is always ignored or considered roughly in the most existing studies. For this issue, a traffic model based on a stochastic geometry framework is proposed to simulate a real traffic environment of autonomous driving vehicles. To reduce the cost of processing tasks, a distributed computation offloading scheme based on mobile edge computing (MEC) is proposed by soliciting nearby vehicles and roadside units (RSUs) with rich computing resources. For the average cost minimization optimization problem, we divide the NP-hard problem into several sub-problems and take advantage of the Lagrange multiplier with KKT constraints to solve by optimizing task splitting ratios. We compare the proposed traffic model with some common ones and also consider the pros and cons of different computation offloading strategies. Simulation results show that the proposed strategy outperforms other benchmarks and the proposed modeling method is rational.

关键词： Task analysis computational modeling Stochastic processes Autonomous vehicles Geometry Servers Costs Vehicular network distributed computation mobile edge computing stochastic geometry

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distributed computation of coverage in sensor networks by homological methods

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APPLICABLE ALGEBRA IN ENGINEERING COMMUNICATION AND COMPUTING 2012年第1-2期23卷 29-58页

作者： Dlotko, P. Ghrist, R. Juda, M. Mrozek, M. WSB NLU Div Computat Math Nowy Sacz Poland Jagiellonian Univ Inst Comp Sci Krakow Poland Univ Penn Dept Math Philadelphia PA 19104 USA Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA

Recent work on algebraic-topological methods for verifying coverage in planar sensor networks relied exclusively on centralized computation: a limiting constraint for large networks. This paper presents a distributed algorithm for homology computation over a sensor network, for purposes of verifying coverage. The techniques involve reduction and coreduction of simplicial complexes, and are of independent interest. Verification of the ensuing algorithms is proved, and simulations detail the improved network efficiency and performance.

关键词： Homology Sensor network Coverage distributed computation

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distributed computation of Linear Matrix Equations: An Optimization Perspective

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第5期64卷 1858-1873页

作者： Zeng, Xianlin Liang, Shu Hong, Yiguang Chen, Jie Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Chinese Acad Sci Key Lab Syst & Control Acad Math & Syst Sci Beijing 100190 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Key Lab Biomimet Robots & Syst Minist Educ Beijing 100081 Peoples R China

This paper investigates the distributed computation of the well-known linear matrix equation in the form of AXB = F, with the matrices A, B, X, and F of appropriate dimensions, over multiagent networks from an optimization perspective. In this paper, we consider the standard distributed matrix-information structures, where each agent of the considered multiagent network has access to one of the subblock matrices of A, B, and F. To be specific, we first propose different decomposition methods to reformulate the matrix equations in standard structures as distributed constrained optimization problems by introducing substitutional variables;we show that the solutions of the reformulated distributed optimization problems are equivalent to least squares solutions to original matrix equations;and we design distributed continuous-time algorithms for the constrained optimization problems, even by using augmented matrices and a derivative feedback technique. Moreover, we prove the exponential convergence of the algorithms to a least squares solution to the matrix equation for any initial condition.

关键词： Constrained convex optimization distributed computation least squares solution linear matrix equation substitutional decomposition

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distributed computation: the new wave of synthetic biology devices

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TRENDS IN BIOTECHNOLOGY 2012年第6期30卷 342-349页

作者： Macia, Javier Posas, Francesc Sole, Ricard V. Univ Pompeu Fabra ICREA Complex Syst Lab Dept Ciencies Expt & Salut Barcelona 08003 Spain CSIC UPF Inst Biol Evolut Barcelona 08003 Spain Univ Pompeu Fabra Cell Signaling Unit Dept Ciencies Expt & Salut Barcelona 08003 Spain Santa Fe Inst Santa Fe NM 87501 USA

Synthetic biology (SB) offers a unique opportunity for designing complex molecular circuits able to perform predefined functions. But the goal of achieving a flexible toolbox of reusable molecular components has been shown to be limited due to circuit unpredictability, incompatible parts or random fluctuations. Many of these problems arise from the challenges posed by engineering the molecular circuitry: multiple wires are usually difficult to implement reliably within one cell and the resulting systems cannot be reused in other modules. These problems are solved by means of a nonstandard approach to single cell devices, using cell consortia and allowing the output signal to be distributed among different cell types, which can be combined in multiple, reusable and scalable ways.

关键词： synthetic biology distributed computation molecular circuit

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distributed computation for Sparse Recovery via Continuous-Time Neurodynamic Approach

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3372-3383页

作者： Zhao, You Liao, Xiaofeng Zhou, Mingliang He, Xing Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Sparsity has been extensively employed in multimedia sensing and computing in consumer electronics, signal and image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, and machine learning. Throughout this paper, we propose a novel distributed projection neurodynamic approach for solving the Basis Pursuit (BP) with flexible partition methods in a distributed manner. The proposed neurodynamic approach requires only that the network is undirected and connected, and no node can access the entire matrix simultaneously. First, we equivalently formulate the BP into a standard distributed optimization problem with a flexible partition-by-blocks method to obtain global information, and discuss the equivalence of their optimality conditions. Then, we propose a distributed continuous-time neurodynamic approach on the basis of primal-dual dynamical systems and projection operators, and also study its global convergence property. Finally, numerical experiments on sparse signals and image recovery further verify the effectiveness and superiority of our proposed neurodynamic approach.

关键词： Neurodynamics Optimization Convergence Sparse matrices Matching pursuit algorithms Standards Image reconstruction distributed computation neurodynamic approach sparse recovery continuous-time global convergence

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distributed computation for Marginal Likelihood based Model Choice

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BAYESIAN ANALYSIS 2023年第2期18卷 607-638页

作者： Buchholz, Alexander Ahfock, Daniel Richardson, Sylvia Amazon Berlin Berlin Germany Univ Cambridge Cambridge England Univ Cambridge MRC Biostat Unit Cambridge England Univ Queensland Sch Math & Phys Brisbane Australia

We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and no data is shared between the workers. We approximate the model evidence for the full data set through Monte Carlo sampling from the posterior on every subset generating a model evidence per subset. The results are combined using a novel approach which corrects for the splitting using summary statistics of the generated samples. Our divide-and-conquer approach enables Bayesian model choice in the large data setting, exploiting all available information but limiting communication between workers. We derive theoretical error bounds that quantify the resulting trade-off between computational gain and loss in precision. The embarrassingly parallel nature yields important speed-ups when used on massive data sets as illustrated by our real world experiments. In addition, we show how the suggested approach can be extended to model choice within a reversible jump setting that explores multiple feature combinations within one run.

关键词： marginal likelihood distributed computation

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distributed computation of A Posteriori Bit Likelihood Ratios in Cell-Free Massive MIMO 29

Distributed Computation of A Posteriori Bit Likelihood Ratio...

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29th European Signal Processing Conference (EUSIPCO)

作者： Shaik, Zakir Hussain Bjornson, Emil Larsson, Erik G. Linkoping Univ Dept Elect Engn ISY Linkoping Sweden KTH Royal Inst Technol Dept Comp Sci Stockholm Sweden

ISBN: (纸本)9789082797060

This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately, when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forward the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.

关键词： Beyond 5G radio stripes cell-free Massive MIMO distributed computation LLR

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High-Performance Computing for distributed Route computation in Traffic Flow Models

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Procedia Computer Science 2025年 255卷 83-92页

作者： Paulo Silva Pavlína Smolková Sofia Michailidu Jakub Beránek Roman Macháček Kateřina Slaninová Jan Martinovič Radim Cmar IT4Innovations VSB - Technical University of Ostrava Ostrava Czech Republic Sygic a.s. Twin City C Mlynské Nivy 16 Bratislava Slovakia

In the dynamic landscape of smart cities and traffic management, it is necessary to further explore the synergistic potential of realtime traffic data and high-performance computing to optimise traffic flow through dynamic re-routing strategies. High-performance computing plays an essential role in achieving effective traffic flow optimisation. Our research builds upon existing studies highlighting the positive correlation between the integration of live traffic updates and route optimisation. The methodology involves simulations with our Ruth traffic simulator, where vehicles dynamically adjust routes based on up to date traffic information available to them at different levels. Scalability tests are conducted with varying numbers of CPUs and nodes to assess the simulator's capacity to scale. The results showcase the impact of live traffic data on both driving time and average speed, emphasising the adaptability of our approach for broader applications. In conclusion, our work not only advances the understanding of real-time traffic optimisation but also underscores the critical role of high-performance computing in achieving scalable solutions. The findings present practical implications for the implementation of dynamic re-routing strategies in transportation systems, paving the way for future research and real-world applications on smart cities.

关键词： Traffic Modelling Alternative Route computation distributed computation Scalability

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distributed computation for Sparse Optimization with Linear Equality Constraints 38

Distributed Computation for Sparse Optimization with Linear ...

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38th Chinese Control Conference (CCC)

作者： Deng, Wen Li, Weijian Hong, Yiguang Chinese Acad Sci Acad Math & Syst Sci LSC Beijing 100190 Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230026 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

ISBN: (纸本)9789881563972

In this paper, we propose two distributed algorithms for sparse optimization problems with linear equality constraints of the vector case and the matrix case. In the l(1)-norm minimization with the linear algebraic equation constraint, we take the general block partition for the information matrix. In the l(2,1)-norm minimization with the linear operator constraint, we mainly focus on the case that every node holds the local linear constraint, while the case that all nodes have a coupling constraint can be easily obtained in a similar way. The two algorithms are both based on the primal-dual subgradient method and derivative feedback techniques. Finally, we investigate the convergence properties for the proposed algorithms, and then numerical examples are given to verify the algorithms.

关键词： distributed computation sparse optimization nonsmooth analysis multi-agent network

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