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Your search keyword '"Guo, Dequan"' showing total 2 results
Start Over Author "Guo, Dequan" Topic membrane computing
2 results on '"Guo, Dequan"'

1. A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

Author

Yuan, Jianying, Guo, Dequan, Zhang, Gexiang, Paul, Prithwineel, Zhu, Ming, and Yang, Qiang

Subjects
*ALGORITHMS, *COMPUTER vision, *IMAGING systems, *DIGITAL image processing, *COMPUTER simulation
Abstract

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O (1) can be achieved regardless of image resolution theoretically. [ABSTRACT FROM AUTHOR]

Published
2019
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2. A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems.

Author

Dong, Jianping, Zhang, Gexiang, Luo, Biao, Yang, Qiang, Guo, Dequan, Rong, Haina, Zhu, Ming, and Zhou, Kang

Subjects
*COMBINATORIAL optimization, *KNAPSACK problems, *EVOLUTIONARY algorithms, *SWARM intelligence, *MATHEMATICAL optimization, *QUANTUM information science
Abstract

• Proposes a distributed adaptive optimization spiking neural P system with a distributed population structure and a new adaptive learning rate considering population diversity. • Extensive experiments on knapsack problems show that DAOSNPS gains much better and more stable solutions than OSNPS, AOSNPS and other two optimization algorithms. An optimization spiking neural P system (OSNPS) aims to obtain the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators of evolutionary algorithms or swarm intelligence algorithms. To develop the promising and significant research direction, this paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) with a distributed population structure and a new adaptive learning rate considering population diversity. Extensive experiments on knapsack problems show that DAOSNPS gains much better solutions than OSNPS, adaptive optimization spiking neural P system, genetic quantum algorithm and novel quantum evolutionary algorithm. Population diversity and convergence analysis indicate that DAOSNPS achieves a better balance between exploration and exploitation than OSNPS and AOSNPS. [ABSTRACT FROM AUTHOR]

Published
2022
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