We present in this paper three deterministic broadcast and a gossiping algorithm suitable for ad hoc networks where topology changes range from infrequent to very frequent. The proposed algorithms are designed to work...
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We present in this paper three deterministic broadcast and a gossiping algorithm suitable for ad hoc networks where topology changes range from infrequent to very frequent. The proposed algorithms are designed to work in networks where the mobile nodes possessing collision detection capabilities. Our first broadcast algorithm accomplishes broadcast in O(nlog n) for networks where topology changes are infrequent. We also present an O(nlog n) worst case time broadcast algorithms that is resilient to mobility. For networks where topology changes are frequent, we present a third algorithm that accomplishes broadcast in O(triangle center dot n log n+n center dot vertical bar M vertical bar) in the worst case scenario, where vertical bar M vertical bar is the length of the message to be broadcasted and triangle the maximum node degree. We then extend one of our broadcast algorithms to develop an O(Dn log n + D-2) algorithm for gossiping in the same network model.
We designed, implemented, and tested a real-time flexible controller for manipulating different types of robots and control algorithms. The robot-independent, IBM PC-based multiprocessor system contains a DSP56001 mas...
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We designed, implemented, and tested a real-time flexible controller for manipulating different types of robots and control algorithms. The robot-independent, IBM PC-based multiprocessor system contains a DSP56001 master controller, six independent HCTL-1100 joint processors for accurate robotic joint control, and an interface computer board for processor communication, The joint processors operate in four user-defined modes and can be connected directly to an incremental optical encoder, which accommodates specialized applications and eliminates extra hardware.
A self-stabilizing algorithm cannot detect by itself that stabilization has been reached. For overcoming this drawback Lin and Simon introduced the notion of an external observer, i.e., a set of processes, one being l...
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A self-stabilizing algorithm cannot detect by itself that stabilization has been reached. For overcoming this drawback Lin and Simon introduced the notion of an external observer, i.e., a set of processes, one being located at each node, whose role is to detect stabilization. We propose here a less expensive approach, where there is a single observing process located at a unique node. This process is not allowed to detect false stabilization and it must eventually detect that stabilization is reached. Moreover it must not interfere with the observed self-stabilizing algorithm. Our result is that there exists such an observer for any problem on a distinguished network having a synchronous self-stabilizing solution. Note that our proof is constructive.
The problem of estimating the state of a nonlinear stochastic plant is considered. Unlike classical approaches such as the extended Kalman filter, which are based on the linearization of the plant and the measurement ...
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The problem of estimating the state of a nonlinear stochastic plant is considered. Unlike classical approaches such as the extended Kalman filter, which are based on the linearization of the plant and the measurement model, we concentrate on the nonlinear filter equations such as the Zakai equation. The numerical approximation of the conditional probability density function (pdf) using ordinary grids suffers from the "curse of dimension" and is therefore not applicable in higher dimensions. It is demonstrated that sparse grids are an appropriate tool to represent the pdf and to solve the filtering equations numerically. The basic algorithm is presented. Using some enhancements it is shown that problems in higher dimensions can be solved with an acceptable computational effort. As an example a six-dimensional, highly nonlinear problem, which is solved in real-time using a standard PC, is investigated.
Additive models in censored regression are considered. A randomly weighted version of the backfitting algorithm that allows for the nonparametric estimation of the effects of the covariates on the response is provided...
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Additive models in censored regression are considered. A randomly weighted version of the backfitting algorithm that allows for the nonparametric estimation of the effects of the covariates on the response is provided. Given the high computational cost involved, binning techniques are used to speed up the computation in the estimation and testing process. Simulation results and the application to real data reveal that the predictor obtained with the additive model performs well, and that it is a convenient alternative to the linear predictor when some nonlinear effects are expected. (C) 2009 Elsevier B.V. All rights reserved.
Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in orde...
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Finding an optimal subset of features that maximizes classification accuracy is still an open problem. In this paper, we exploit the speed of the Harmony Search algorithm and the Optimum-Path Forest classifier in order to propose a new fast and accurate approach for feature selection. Comparisons to some other pattern recognition and feature selection techniques showed that the proposed hybrid algorithm for feature selection outperformed them. The experiments were carried out in the context of identifying non-technical losses in power distribution systems. (C) 2011 Elsevier Ltd. All rights reserved.
The article presents a comparative study of three procedures applied for solving the inverse Stefan problem. The investigated problem consists of reconstruction of the unknown boundary condition on the basis of measur...
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The article presents a comparative study of three procedures applied for solving the inverse Stefan problem. The investigated problem consists of reconstruction of the unknown boundary condition on the basis of measurement data, and the procedures of solution differ in the way of minimizing the proper functional-in each approach considered, one of three artificial intelligence algorithms (Ant Colony Optimization, Artificial Bee Colony, and Harmony Search) is used. Methods applying the respective algorithms are compared with regard to their velocity and the precision of results.
Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative k...
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Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB). Moreover, an evidential reasoning (ER) based optimal algorithm is developed to train the fault prediction model. The screw failure in computer numerical control (CNC) milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.
The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstract...
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The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.
Link adaptation techniques, where the modulation, coding rate, and/or other signal transmission parameters are dynamically adapted to the changing channel conditions, have recently emerged as powerful tools for increa...
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Link adaptation techniques, where the modulation, coding rate, and/or other signal transmission parameters are dynamically adapted to the changing channel conditions, have recently emerged as powerful tools for increasing the data rate and spectral efficiency of wireless data-centric networks. While there has been significant progress on understanding the theoretical aspects of time adaptation in LA protocols, new challenges surface when dynamic transmission techniques are employed in broadband wireless networks with multiple signaling dimensions. Those additional dimensions are mainly frequency, especially in multicarrier systems, and space in multiple-antenna systems, particularly multiarray multiple-input multiple-output communication systems. In this article we give an overview of the challenges and promises of link adaptation in future broadband wireless networks. We suggest guidelines to help in the design of robust, complexity/cost-effective algorithms for these future wireless networks.
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