Shortest path routing algorithm for hierarchical interconnection network-on-chip

Interconnection networks play a significant role in efficient on-chip communication for multicore systems. This paper introduces a new interconnection topology called the Hierarchical Cross Connected Recursive network (HCCR) and a shortest path routing algorithm for the HCCR. Proposed topology offers a high degree of regularity, scalability, and symmetry with a reduced number of links and node degree. A unique address encoding scheme is proposed for hierarchical graphical representation of HCCR networks, and based on this scheme a shortest path routing algorithm is devised. The algorithm requires 5(k-1) time where k=logn4-2 and k>0, in worst case to determine the next node along the shortest path

A Dependency-Based Neural Network for Relation Classification

Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency information. We first propose a new structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop dependency-based neural networks (DepNN): a recursive neural network designed to model the subtrees, and a convolutional neural network to capture the most important features on the shortest path. Experiments on the SemEval-2010 dataset show that our proposed method achieves state-of-art results.Comment: This preprint is the full version of a short paper accepted in the annual meeting of the Association for Computational Linguistics (ACL) 2015 (Beijing, China

Path planning algorithm for a car like robot based on Coronoi Diagram Method

The purpose of this study is to develop an efficient offline path planning algorithm that is capable of finding optimal collision-free paths from a starting point to a goal point. The algorithm is based on Voronoi diagram method for the environment representation combined with Dijkstra’s algorithm to find the shortest path. Since Voronoi diagram path exhibits sharp corners and redundant turns, path tracking was applied considering the robot’s kinematic constraints. The results has shown that the Voronoi diagram path planning method recorded fast computational time as it provides simpler, faster and efficient path finding. The final path, after considering robot’s kinematic constraints, provides shorter path length and smoother compared to the original one. The final path can be tuned to the desired path by tuning the parameter setting; velocity, v and minimum turning radius, Rmin. In comparison with the Cell Decomposition method, it shows that Voronoi diagram has a faster computation time. This leads to the reduced cost in terms of time. The findings of this research have shown that Voronoi Diagram and Dijkstra’s Algorithm are a good combination in the path planning problem in terms of finding a safe and shortest path

On the Efficiency of Data Representation on the Modeling and Characterization of Complex Networks

Specific choices about how to represent complex networks can have a substantial effect on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically as matrices or dynamically as spase structures. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance