scholarly journals Algorithm for Identification of Infinite Clusters Based on Minimal Finite Automaton

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Biljana Stamatovic ◽  
Goran Kilibarda
Keyword(s):  
Computational Complexity ◽  
Finite Automaton ◽  
Data Flow ◽  
Rectangular Lattice ◽  
Infinite Cluster ◽  
Single Pass ◽  
Infinite Clusters ◽  
Percolation Problem ◽  
Efficient Data

We propose a finite automaton based algorithm for identification of infinite clusters in a 2D rectangular lattice with L=X×Y cells. The algorithm counts infinite clusters and finds one path per infinite cluster in a single pass of the finite automaton. The finite automaton is minimal according to the number of states among all the automata that perform such task. The correctness and efficiency of the algorithm are demonstrated on a planar percolation problem. The algorithm has a computational complexity of O(L) and could be appropriate for efficient data flow implementation.

scholarly journals Physical Portrayal of Computational Complexity

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Arto Annila
Keyword(s):  
Computational Complexity ◽  
Finite Automaton ◽  
Degrees Of Freedom ◽  
Irreversible Process ◽  
Driving Forces ◽  
The State ◽  
Computational Time ◽  
Natural Processes ◽  
Final Acceptance ◽  
Nondeterministic Finite Automaton

Computational complexity is examined using the principle of increasing entropy. To consider computation as a physical process from an initial instance to the final acceptance is motivated because information requires physical representations and because many natural processes complete in nondeterministic polynomial time (NP). The irreversible process with three or more degrees of freedom is found intractable when, in terms of physics, flows of energy are inseparable from their driving forces. In computational terms, when solving a problem in the class NP, decisions among alternatives will affect subsequently available sets of decisions. Thus the state space of a nondeterministic finite automaton is evolving due to the computation itself, hence it cannot be efficiently contracted using a deterministic finite automaton. Conversely when solving problems in the class P, the set of states does not depend on computational history, hence it can be efficiently contracted to the accepting state by a deterministic sequence of dissipative transformations. Thus it is concluded that the state set of class P is inherently smaller than the state set of class NP. Since the computational time needed to contract a given set is proportional to dissipation, the computational complexity class P is a proper (strict) subset of NP.

A Low Complexity Switched Method for Enhancement of Spatial Resolution of Images

2012 ◽  
Vol 433-440 ◽  
pp. 6534-6539
Author(s):  
Hari Singh Choudhary ◽  
Vineet Khanna ◽  
Prakrati Trivedi
Keyword(s):  
Computational Complexity ◽  
Spatial Resolution ◽  
Visual Quality ◽  
Low Complexity ◽  
Interpolation Algorithm ◽  
Hybrid Scheme ◽  
Low Computational Complexity ◽  
Single Pass ◽  
Novel Approach ◽  
Block Based

This paper presents a novel approach of image interpolation based on the switching of new edge directed interpolation (NEDI) and single pass interpolation algorithm ( SPIA ) and switching is based upon the % of edges present in the blocks of the image. The switching of this interpolation algorithm is block based instead of image based or pixel based. Imperially we found that NEDI methods is better applicable for smoother images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the process interpolation. So, a hybrid scheme of combining NEDI method and SPIA method is used for better prediction of HR image. The proposed algorithm produces the better results for different varieties of images in terms of both PSNR measurement and subjective visual quality with low computational complexity as compare to recently developed interpolation algorithms.

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