In this paper, we study two lemma methods for accelerating Loveland's model elimination calculus: One is lemma generalization and another is non-unit lemma matching. the derivation of lemmas in this paper is a dyn...
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Ant Colony Optimization (ACO) is a paradigm that em- ploys a set of cooperating agents to solve functions or obtain good so- lutions for combinatorial optimization problems. It has previously been applied to the TSP a...
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Pure functional programming languages have been proposed as a vehicle to describe, simulate and manipulate circuit specifications. We propose an extension to Haskell to solve a standard problem when manipulating data ...
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In PCS (personal communication systems) networks, location management deals withthe problem of tracking down a mobile user. It involves two kinds of activ- ities: one is location updating and the other is paging [1]....
ISBN:
(纸本)354066856X
In PCS (personal communication systems) networks, location management deals withthe problem of tracking down a mobile user. It involves two kinds of activ- ities: one is location updating and the other is paging [1]. Each cost for location updating and paging is significantly associated with each other and therefore, has a trade-off relationship to total signaling cost. Location management strategies, as implemented in current PCS networks and as presented in this literature, are based on spatially static location areas that are built by heuristics or aggregate statistics [1,2]. the static location areas are used identically for all mobile users, even though the mobility pattern and call arrival rate of each mobile user are diffierent spatially and temporally. thus, the location management applied to same location areas for all mobile users suffers from the various mobile proper- ties of users. Consequently, these strategies can not efficiently reduce the total signaling cost.
Current standards of ATM can only support pt-pt (or unicast) connections and unidirectional point-to-multipoint (pt-mpt) connection and do not provide a scalable solution for truly multipoint-to-multipoint (mpt-mpt) c...
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A system of linear constraints is represented as a set of half-planes S = {(a j X + b j Y + c j ≤ 0) j = 1...N}. therefore, in the context of linear constraints two terms “set” and &#...
ISBN:
(纸本)354066856X
A system of linear constraints is represented as a set of half-planes S = {(a j X + b j Y + c j ≤ 0) j = 1...N}. therefore, in the context of linear constraints two terms “set” and “system” can be used interchangeably whenever one of two is suitable. A set of linear constraints represents a convex polygon that is the intersection of all half-planes in the set. the convex polygon represented by S is called the feasible polygon of S. Such sets of linear constraints can be used as a new way of represent spatial data. these sets need to be manipulated efficiently and stored using minimal storage. It is natural to store only sets of linear constraints which are feasible and in irredundant format. therefore, it is very important to find out if a given system is feasible and/or bounded and to find the minimal (irredundant) set of linear constraint which have the same feasible area withthe given one. LASSEZ and MAHER (1988) have investigated algorithms to check if a system of linear constraints over multidimensional R d is feasible. LASSEZ et al (1989) have investigated algorithms to eliminate redundant constraints from a system of linear constraints over R d . their algorithms are based on the Fourier variable elimination (similar with Gaussian elimination in solving the linear system of equations) and therefore have the running time O(N 2) where N is the number of constraints, and as such it is not efficient. DYER (1984) and MEGIDDO (1983) have independent proposed linear time algorithms to solve the linear programming problem in 2- and 3-dimension cases.
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