Two methods to determining the time complexity of algorithm

The problem of the great group of a figure is the famous NP-difficult problem. There exists an algorithm of solving the great group of figure or only applying to some of the special figure .There need time price is index level, and is low efficiency. It puts forward a kind of solving the minimax group partition algorithm with the most magnanimous nodes for elicitation information. This algorithm can be applied to any simple figure, and the maximum time complexity of algorithm is O(sn3).

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Research of Flood Prediction Based on Subjective/Objective Evidences Fusion Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1272 ◽

2012 ◽

Vol 532-533 ◽

pp. 1272-1276

Author(s):

Wei Wu ◽

Yan Ming Chen

Keyword(s):

Bp Neural Network ◽

Time Complexity ◽

High Complexity ◽

Fusion Model ◽

Accuracy Rate ◽

Flood Prediction ◽

Complexity Of Algorithms ◽

Feature Level Fusion ◽

Complexity Of Algorithm ◽

Level Fusion

This paper presents a model by combining BP neural network and DS evidential reasoning, which not only achieves the feature level fusion of all subjective and objective evidences in various domains and layers, but also makes distinct models complement each other. By the experiment, this method improves classification precision by 7.9 percent and reduces the time complexity of algorithm. The model solves the problems such as high complexity of algorithms and low accuracy rate of classifications lie in the flood prediction using single models.

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Indoor Location Based on an LANDMARC Improved Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.4105 ◽

2014 ◽

Vol 687-691 ◽

pp. 4105-4109 ◽

Cited By ~ 2

Author(s):

Yong Jun Zhang ◽

She Nan Li

Keyword(s):

Time Complexity ◽

Indoor Positioning ◽

High Time ◽

Positioning Accuracy ◽

Indoor Location ◽

Simulation Results ◽

Complexity Of Algorithm ◽

Positioning Algorithm ◽

Improved Algorithm

A novel indoor positioning algorithm named BPNN-LANDMARC is proposed in this paper to increase the positioning accuracy and reduce the high time complexity of classical LANDMARC algorithm. Simulation results prove that the proposed BPNN-LANDMARC algorithm can improve the average positioning accuracy by 24.35%. In addition, the improved algorithm reduces the time complexity of algorithm obviously.

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Recognition of Birds in Blurred and Illumination Images by Local Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0128 ◽

2017 ◽

Vol 7 (7) ◽

pp. 243

Author(s):

Suresha .M ◽

. Sandeep

Keyword(s):

Computer Vision ◽

Time Complexity ◽

Feature Detection ◽

Feature Matching ◽

Fast Algorithms ◽

Local Features ◽

Experimental Demonstration ◽

Harris Corner ◽

Blurred Image ◽

Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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