We classify a type of language called a reflectable language. We then develop a generic algorithm that can be used to list all strings of length n for any reflectable language in Gray code order. The algorithm general...
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We classify a type of language called a reflectable language. We then develop a generic algorithm that can be used to list all strings of length n for any reflectable language in Gray code order. The algorithm generalizes Gray code algorithms developed independently for k-ary strings, restricted growth strings, and k-ary trees. as each of these objects can be represented by a reflectable language. Finally, we apply the algorithm to open meandric systems which can also be represented by a reflectable language. (C) 2008 Elsevier B.V. All rights reserved.
Quasi-symmetric designs are the special type of block designs with two block intersection sizes. In very recent years 2011 and 2012, investigations on quasi-symmetric 2-(v, k, lambda) designs with the difference of bl...
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Quasi-symmetric designs are the special type of block designs with two block intersection sizes. In very recent years 2011 and 2012, investigations on quasi-symmetric 2-(v, k, lambda) designs with the difference of block intersection sizes 2 and 3, have been studied by Pawale (2011) [10] and Mavron et al. (2012) [7] respectively. In this paper, we consider the quasi-symmetric 2-design for the block intersection sizes x and y = x + 3 and establish an upper bound for the replication number r which is stronger than that of Mavron et al. (2012) [7] keeping the smallest value of lambda as just 6. (C) 2013 Elsevier B.V. All rights reserved.
In a directed graph with edge weights, the mean weight of a directed cycle is the weight of its edges divided by their number. The minimum cycle mean of the graph is the minimum mean weight of a cycle. Karp gave a cha...
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In a directed graph with edge weights, the mean weight of a directed cycle is the weight of its edges divided by their number. The minimum cycle mean of the graph is the minimum mean weight of a cycle. Karp gave a characterization of minimum cycle mean and an O(nm) algorithm to compute it, where n is the number of vertices and m is the number of edges. However, an algorithm he suggested for identifying a cycle with this mean weight is not correct. We propose an alternative. (C) 2017 Elsevier B.V. All rights reserved.
In this paper we propose a randomized algorithm which can solve the satisfiability problem with the probability of failure not exceeding epsilon in polynomial average time.
In this paper we propose a randomized algorithm which can solve the satisfiability problem with the probability of failure not exceeding epsilon in polynomial average time.
We consider protocols for maximum finding on a multiple access network such as an Ethernet. In the maximum finding problem there is a set of nodes C on the network, and each node in C has an associated real value. We ...
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We consider protocols for maximum finding on a multiple access network such as an Ethernet. In the maximum finding problem there is a set of nodes C on the network, and each node in C has an associated real value. We want to quickly identify the node of maximum value in C. Maximum finding is a fundamental problem with applications to load-balancing, prioritized transmission protocols, distributed and parallel sorting algorithms, and many other problems. We show that if there are c nodes in the set C the maximum can be identified in expected O(log c) transmissions. We also prove a matching lower bound which shows that this bound is optimal.
Face recognition with occlusion is one of the main problems countered in face recognition in practical application. The occlusion in the image will decline the performance of global-based methods, so most of existing ...
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Face recognition with occlusion is one of the main problems countered in face recognition in practical application. The occlusion in the image will decline the performance of global-based methods, so most of existing methods for this problem are block-based. Our method also divides image into modules. Considering that different modules have different discriminative information, we identify a new criterion to compute modular weight. The modular weight can not only depress the effect of low discriminant module but also can detect the occlusion module to some extent. The weighting function is based on the modular Fisher rate and the modular residual. The successful application of sparse representation-based classification (SRC) in image recognition inspires us to use SRC on the weighted dictionary and test image to perform the final identification. Experiments on the AR and extended Yale B database verify the effectiveness and robustness of the method. (C) 2015 Elsevier B.V. All rights reserved.
In a graph G, a spanning tree T is said to be a tree t-spanner of the graph G if the distance between any two vertices in T is at most t times their distance in G. The tree t-spanner has many applications in networks ...
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In a graph G, a spanning tree T is said to be a tree t-spanner of the graph G if the distance between any two vertices in T is at most t times their distance in G. The tree t-spanner has many applications in networks and distributed environments. In this paper, we present an algorithm to find a tree 4-spanner on trapezoid graphs in O(n) time, where n is the number of vertices.
In this paper, we introduce an O(n) time algorithm to solve the maximum weight independent set problem on an interval graph with n vertices given its interval representation with sorted endpoints list. Based on this l...
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In this paper, we introduce an O(n) time algorithm to solve the maximum weight independent set problem on an interval graph with n vertices given its interval representation with sorted endpoints list. Based on this linear algorithm, we design an O(n2) time algorithm using O(n2) space to solve the maximum weight 2-independent set problem on an interval graph with n vertices. With a slight extension and modification of our algorithm, the maximum weight k-independent set problem on an interval graph with n vertices can be solved in O(n(k)) time using O(n(k)) space.
We cast two algorithms to generate the inorder and preorder of the labels of a binary tree in the context of reversible computing nearly three decades after they were first examined in the light of program inversion. ...
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We cast two algorithms to generate the inorder and preorder of the labels of a binary tree in the context of reversible computing nearly three decades after they were first examined in the light of program inversion. The reversible traversals directly define the inverse algorithms that reconstruct the binary tree and have the same linear time and space requirements as the traversals. A reversible while-language is extended with reversible recursion. (C) 2019 Elsevier B.V. All rights reserved.
This paper addresses a group-based collective keyword (GBCK) query problem in road networks. We model the road network as an undirected graph, where each node locating in a two-dimensional space represents a road inte...
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This paper addresses a group-based collective keyword (GBCK) query problem in road networks. We model the road network as an undirected graph, where each node locating in a two-dimensional space represents a road intersection or a Points of Interest (POI), and each edge with weight represents a road segment. The GBCK query aims to find a region containing a set of POIs that covers all the query keywords and these POIs are close to the group of users and are close to each other. We show the problem of answering the GBCK query is NP-hard. To solve this problem, we develop a series of query processing algorithms. We first propose an efficient algorithm, which gives a 5-factor approximation to find a feasible region. The cost of this region is further used to limit the search space in the other algorithms. We then propose an exact algorithm, and an approximate algorithm with a 15/7-factor approximation. Extensive performance studies using real datasets confirm the efficiency and accuracy of the proposed algorithms. (C) 2016 Elsevier B.V. All rights reserved.
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