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1. Synchronous distributed load balancing on dynamic networks

Author

Bahi, Jacques, Couturier, Raphaël, and Vernier, Flavien

Subjects
*ALGORITHMS, *HYPOTHESIS, *GEOMETRY, *STOCHASTIC convergence
Abstract

Abstract: In this paper, three distributed load-balancing algorithms for dynamic networks are investigated. Dynamic networks are networks in which the topology may change dynamically. The definition of a dynamic network is introduced and its graph model is presented. The main result of this study consists in proving the convergence toward the uniform load distribution of the diffusion algorithm on an arbitrary dynamic network despite communication link failures. We also give two adaptations of this algorithm (the GAE and the relaxed diffusion). Note that the hypotheses of our result are realistic and that for example the network does not have to be maintained connected. To study the behavior of these algorithms, we compare the load evolution by several simulations. [Copyright &y& Elsevier]

Published
2005
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2. Marginal multi-object Bayesian filter with multiple hypotheses.

Author

Liu, Zong-xiang, Chen, Wei, Chen, Qi-yue, and Li, Liang-qun

Subjects
*KALMAN filtering, *PROBABILITY density function, *ASSIGNMENT problems (Programming), *HYPOTHESIS, *ALGORITHMS, *MATHEMATICAL models, *LINEAR systems
Abstract

This paper proposes a marginal multi-object Bayesian filter with multiple hypotheses to track multiple objects in the presence of object appearing and object disappearing, missed detection and clutter. This filter delivers the probability of existence and probability density function of each object. A mathematical model for searching K-best hypotheses is set up by the maximization of the generalized joint likelihood ratios of hypotheses, which results in a 2-dimensional assignment problem. The K-best hypotheses can be acquired by using the Murty algorithm to solve the 2-dimensional assignment problem. According to the K-best hypotheses, the existence probabilities and probability density functions of objects are formed. Furthermore, an implementation of this filter for a linear Gaussian system is developed and is extended to nonlinear observations. Experimental result demonstrates that the proposed filter outperforms other available filters at various numbers of clutter and different detecting probabilities. [ABSTRACT FROM AUTHOR]

Published
2021
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