We present a novel object localization approach based on the Global Structure Constraint model (GSC) and Optimal Algorithm. In GSC, Objects are described as constellations of points satisfied with their specific globa...
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Aiming at the dynamic policy trigger problem in policy based network management, this thesis first introduces the uncertain information generated by onepoint coverage random sets theory and also the unified framework ...
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Full-text indices are data structures that can be used to find any substring of a given string. Many full-text indices require space larger than the original string. In this paper, we introduce the canonical Huffman c...
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Full-text indices are data structures that can be used to find any substring of a given string. Many full-text indices require space larger than the original string. In this paper, we introduce the canonical Huffman code to the wavelet tree of a string T[1. . .n]. Compared with Huffman code based wavelet tree, the memory space used to represent the shape of wavelet tree is not needed. In case of large alphabet, this part of memory is not negligible. The operations of wavelet tree are also simpler and more efficient due to the canonical Huffman code. Based on the resulting structure, the multi-key rank and select functions can be performed using at most nH0 + jRj(lglgn + lgn lgjRj)+O(nH0) bits and in O(H0) time for average cases, where H0 is the zeroth order empirical entropy of T. In the end, we present an efficient construction algorithm for this index, which is on-line and linear.
In this paper, we mainly discuss the relationship between the extended Pawlak flow graph (EFG) with granular computing (GrC), and develop a both simple and concrete model for EFG using GrC. The distinct advantage is t...
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IEEE802.16, as a wireless MAN broadband access standard, defines a flexible QoS mechanism in MAC layer for wild variety classes of services. However, it dose not define specific admission control strategy and scheduli...
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Model-based diagnosis of discrete event systems is more and more active in artificial intelligence. In this paper, diagnosability analysis of discrete event systems is concerned, which is a very important step before ...
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Model-based diagnosis of discrete event systems is more and more active in artificial intelligence. In this paper, diagnosability analysis of discrete event systems is concerned, which is a very important step before on line diagnosing discrete event systems in general. Firstly, an extended hierarchical framework for definitions of diagnosability of discrete event systems is given, according to their inner restriction. Next, some formal comparisons among them are presented, thanks to which, we can further understand the relations between related definitions. Finally, some future work about diagnosability of discrete event systems is discussed as well.
WiMax (World Interoperability for Microwave Access) technology is the hotspot of wireless access technologies and attracts much attention. WiMax technology provides a wireless access method for users and do not constr...
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WiMax (World Interoperability for Microwave Access) technology is the hotspot of wireless access technologies and attracts much attention. WiMax technology provides a wireless access method for users and do not constrained by physical position and cable restriction. A communication platform was designed under the protocol WiMax. The platform contains client (Vehicles) and server (Base stations). The platform contains 5 function modules including stimulating moving, position information transfer, sound communication, file transfer and routing selection. Building a mature communication platform is the most important precondition to improve the transfer efficiency in intelligent transport system. Intelligent transport system with the characteristic of low cost, less budget and high speed of transferring information is urgently needed by every city.
Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs from data is helpful to understand the casual relation between variables. But Learning BNs is ...
Bayesian Networks is a popular tool for representing uncertainty knowledge in artificial intelligence fields. Learning BNs from data is helpful to understand the casual relation between variables. But Learning BNs is a NP hard problem. This paper presents an immune genetic algorithm for learning Markov equivalence classes, which combining dependency analysis and search-scoring approach together. Experiments show that the immune operators can constrain the search space and improve the computational performance.
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