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62nd IEEE Vehicular Technology Conference

作者： Huang, HS Wu, J Florida Atlantic Univ Dept Comp Sci & Engn Boca Raton FL 33431 USA

ISBN: (纸本)0780391527

A wireless sensor network consists of nodes that can communicate with each other via wireless links. One way to support efficient communication between sensors is to organize the network into several groups, called clusters, with each cluster electing one node as the head of cluster. The paper describes a constant time clustering algorithm that can be applied on wireless sensor networks. This approach is an extension to the Younis and Fahmy method [1]. The simulation results show that the extension can generate a small number of cluster heads in relatively few rounds, especially in sparse networks.

关键词： wireless sensor networks cluster head probabilistic algorithms

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Interval Probabilities of State Transitions in probabilistic Automata

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11th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Piech, Henryk Siedlecka-Lamch, Olga Czestochowa Tech Univ Czestochowa Poland

ISBN: (纸本)9783642293498

Working principle of the probabilistic automaton is based on the state transition probability [4], [6]. Construction of the automaton depends on its purpose. We assume that all transitions probabilities are constant[3]. In situations where low levels of information (entropy) suggest assurance solutions, we use interval probabilities (remembering that the sum of the probabilities of transition to the given state must remain equal to 1). This variant generates additional problems, which are: how to prevent the redundancy of the probabilities sum;the physical interpretation of this situation;undervaluing of the probabilities sum;determine the criteria of the structure and parameters optimization (minimization) relative to the semantic resources of formal languages described by the automaton;change in the level of information entropy.

关键词： probabilistic automata probabilistic algorithms interval analysis

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probabilistic Robust Control System Design by Stochastic Optimization

Probabilistic Robust Control System Design by Stochastic Opt...

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作者： Li, Xiang PennState University Libraries

学位级别：Doctor of Philosophy

This dissertation concentrates on recent results on probabilistic robust controller design. In contrast to approaches taken in classical robustness theory, probabilistic robust controller design allows for a small risk of performance violation. This results, in many cases, in a significant reduction of the computational complexity of the controller design cycle and/or a significant reduction of the controller complexity even for a low level of risk of performance violation. In contrast to several of the probabilistic approaches in the control literature, we explore the problems' structure, i.e., convexity, to design more efficient algorithms. For a class of design problems which are convex in controller parameters, we introduce stochastic optimization methods to solve them. For a large class of non-convex problems, we provide a new approach which is shown to converge to the desired robust controller. This is accomplished by choosing an appropriate set of intermediate optimization variables at each iteration. Most of the results provided address the problem of designing robust output feedback controllers, where one directly determines the transfer function of the controller. Preliminary results are also presented on the design of robust static linear state feedback controllers.

关键词： probabilistic algorithms stochastic optimization robust control

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probabilistic matching of planar regions

Probabilistic matching of planar regions

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作者： Alt, Helmut Scharf, Ludmila Schymura, Daria Institut für Informatik Freie Universität Berlin Berlin Germany

We analyze a probabilistic algorithm for matching shapes modeled by planar regions under translations and rigid motions (rotation and translation). Given shapes A and B, the algorithm computes a transformation t such that with high probability the area of overlap of t(A) and B is close to maximal. In the case of polygons, we give a time bound that does not depend significantly on the number of vertices, but on perimeter and area of the shapes and, in the case of rigid motions, also on the diameter. © 2009 Elsevier B.V. All rights reserved.

关键词： Approximation algorithms Computational geometry probabilistic algorithms Shape matching

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Continuous versus discrete model in autodiagnosis systems for wireless networks

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2008年第6期7卷 673-681页

作者： Barco, Raquel Lazaro, Pedro Diez, Luis Wille, Volker Univ Malaga ETSI Telecomunicac Dept Commun Engn E-29071 Malaga Spain Nokia Siemens Networks Consulting & Syst Integrat Huntingdon PE29 6YJ Cambs England

In the near future, several radio access technologies will coexist in Beyond 3G mobile networks ( B3G), and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e., those that self-configure, self-protect, self-heal, and self-optimize) are the solution to tackle the high complexity inherent to these networks. In this context, this paper proposes a system for autodiagnosis in the Radio Access Network ( RAN) of wireless systems. The malfunction of the RAN may be due not only to a hardware fault but also ( and more difficult to identify) to a bad configuration. The proposed system is based on the analysis of Key Performance Indicators ( KPIs) in order to isolate the cause of the network malfunction. In this paper, two alternative probabilistic systems are compared, which differ on how KPIs are modeled ( continuous or discrete variables). Experimental results are examined in order to support the theoretical concepts, based on data from a live network. The drawbacks and benefits of both systems are studied, and some conclusions on the scenarios under which each model should be used are presented.

关键词： network operations wireless communication probabilistic algorithms decision support mobile communication systems automation

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Finding points on curves over finite fields

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SIAM JOURNAL ON COMPUTING 2003年第6期32卷 1436-1448页

作者： Von zur Gathen, J Shparlinski, I Sinclair, A Univ Paderborn Fak Elektrotech Informat & Math D-33095 Paderborn Germany Macquarie Univ Dept Comp Sydney NSW 2109 Australia ICSI Berkeley CA USA Univ Calif Berkeley Div Comp Sci Berkeley CA 94720 USA

We solve two computational problems concerning plane algebraic curves over finite fields: generating a uniformly random point, and finding all points deterministically in amortized polynomial time (over a prime field,... 详细信息

关键词： finite fields algebraic curves algebraic geometry computer algebra probabilistic algorithms random sampling

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CLUSTERED MATRIX APPROXIMATION

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SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS 2016年第4期37卷 1531-1555页

作者： Savas, Berkant Dhillon, Inderjit S. Linkoping Univ Dept Sci & Technol S-60174 Norrkoping Sweden Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

In this paper we develop a novel clustered matrix approximation framework, first showing the motivation behind our research. The proposed methods are particularly well suited for problems with large scale sparse matrices that represent graphs and/or bipartite graphs from information science applications. Our framework and resulting approximations have a number of benefits: (1) the approximations preserve important structure that is present in the original matrix;(2) the approximations contain both global-scale and local-scale information;(3) the procedure is efficient both in computational speed and memory usage;and (4) the resulting approximations are considerably more accurate with less memory usage than truncated SVD approximations, which are optimal with respect to rank. The framework is also quite flexible as it may be modified in various ways to fit the needs of a particular application. In the paper we also derive a probabilistic approach that uses randomness to compute a clustered matrix approximation within the developed framework. We further prove deterministic and probabilistic bounds of the resulting approximation error. Finally, in a series of experiments we evaluate, analyze, and discuss various aspects of the proposed framework. In particular, all the benefits we claim for the clustered matrix approximation are clearly illustrated using real-world and large scale data.

关键词： matrix approximation dimensionality reduction low rank matrix approximation probabilistic algorithms graph mining social network analysis clustering coclustering

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OPTIMAL FILE SHARING IN DISTRIBUTED NETWORKS

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SIAM JOURNAL ON COMPUTING 1995年第1期24卷 158-183页

作者： NAOR, M ROTH, RM TECHNION ISRAEL INST TECHNOL DEPT COMP SCIIL-32000 HAIFAISRAEL

The following file distribution problem is considered: Given a network of processors represented by an undirected graph G=(V, E) and a file size k, an arbitrary file w of k bits is to be distributed among all nodes of G. To this end, each node is assigned a memory device such that by accessing the memory of its own and of its adjacent nodes, the node can reconstruct the contents of w. The objective is to minimize the total size of memory in the network. This paper presents a file distribution scheme which realizes this objective for k much greater than log Delta(G), where Delta(G) stands for the maximum degree in G: For this range of k, the total memory size required by the suggested scheme approaches an integer programming lower bound on that size. The scheme is also constructive in the sense that given G and k, the memory size at each node in G, as well as the mapping of any file tv into the node memory devices, can be computed in time complexity which is polynomial in k and \V\. Furthermore, each node can reconstruct the contents of such a file w in O(k(2)) bit operations. Finally it is shown that the requirement of k being much larger than log Delta(G) is necessary in order to have total memory size close to the integer programming lower bound.

关键词： DERANDOMIZATION DISTRIBUTED NETWORKS FILE ASSIGNMENT INTEGER PROGRAMMING LINEAR CODES LINEAR PROGRAMMING probabilistic algorithms RESOURCE SHARING SET COVER

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AN OPTIMAL ALGORITHM FOR GENERATING MINIMAL PERFECT HASH FUNCTIONS

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INFORMATION PROCESSING LETTERS 1992年第5期43卷 257-264页

作者： CZECH, ZJ HAVAS, G MAJEWSKI, BS UNIV QUEENSLAND KEY CTR SOFTWARE TECHNOLDEPT COMP SCIST LUCIAQLD 4072AUSTRALIA SILESIA TECH UNIV DEPT COMP SCIPL-44100 GLIWICEPOLAND

A new algorithm for generating order preserving minimal perfect hash functions is presented. The algorithm is probabilistic, involving generation of random graphs. It uses expected linear time and requires a linear number of words to represent the hash function, and thus is optimal up to constant factors. It runs very fast in practice.

关键词： DATA STRUCTURES probabilistic algorithms ANALYSIS OF algorithms HASHING RANDOM GRAPHS

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Detecting objects of variable shape structure with hidden state shape models

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2008年第3期30卷 477-492页

作者： Wang, Jingbin Athitsos, Vassilis Sclaroff, Stan Betke, Margrit Google Inc Mountain View CA 94043 USA Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA Boston Univ Dept Comp Sci Boston MA 02215 USA

This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale, and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude and produces highly accurate results in experiments on nonrigid hand motion tracking.

关键词： object detection shape modeling probabilistic algorithms dynamic programming

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