By Maoguo Gong, Pan Linqiang, Song Tao, Ke Tang, Xingyi Zhang
This ebook constitutes the complaints of the tenth foreign convention on Bio-Inspired Computing: Theories and purposes, BIC-TA 2015, held in Hefei, China, in September 2015.The sixty three revised complete papers provided have been rigorously reviewed and chosen from 182 submissions. The papers care for the next major subject matters: evolutionary computing, neural computing, DNA computing, and membrane computing.
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Genetic and Evolutionary Computing: Proceeding of the Eighth International Conference on Genetic and Evolutionary Computing, October 18–20, 2014, Nanchang, China (Advances in Intelligent Systems and Computing, Volume 329)
This quantity of Advances in clever structures and Computing comprises authorized papers awarded at ICGEC 2014, the eighth overseas convention on Genetic and Evolutionary Computing. The convention this yr used to be technically co-sponsored by means of Nanchang Institute of expertise in China, Kaohsiung college of utilized technology in Taiwan, and VSB-Technical collage of Ostrava.
This booklet constitutes the completely refereed post-conferenceproceedings of the ninth overseas convention on excessive functionality Computing for Computational technological know-how, VECPAR 2010, held in Berkeley, CA, united states, in June 2010. The 34 revised complete papers provided including 5 invited contributions have been conscientiously chosen in the course of rounds of reviewing and revision.
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Additional resources for Bio-Inspired Computing -- Theories and Applications: 10th International Conference, BIC-TA 2015 Hefei, China, September 25-28, 2015, Proceedings
Um is the reliability of the use of the catalyst, and indicates the intensity of the reliability for the associated rules related to catalyst. The value of um is between [0, 1]. Where, the catalyst in basic membrane and the membrane which don’t include basic membrane is C[c1 , c2 , . . , cj ] to act on the j objects, and the value of um is 1. The catalyst in the membrane n that in the middle level is C[cn–(n+1) , cn–(n+1)–(n+2) , . . (k−1)–k ], / 1 ≤ n ≤ k, 1 ≤ k ≤ m, U [un–(n+1) , un–(n+1)–(n+2) , .
Sci. 87(1), 67–74 (2004) 4. : Analysis of gray scale watermark in RGB host using SVD and PSO. In: IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), pp. 1–7 (2014) 5. : SVD watermarking: particle swarm optimization of scaling factors to increase the quality of watermark. In: Proceedings of Fourth International Conference on Soft Computing for Problem Solving, pp. 205–214. Springer, India (2015) 6. : An SVD-based fragile watermarking scheme with grouped blocks.
To resolve the above problems, this paper proposes a D2D resource allocation algorithm based on fuzzy cluster grouping. This algorithm groups all D2D pairs in the system by fuzzy cluster grouping, which makes each D2D pair in the same group diﬀerentiated from others. Then considering the Qos requests of cellular user, assigns optimized radio resources of cellular user to each D2D group. Finally, the simulation results show that this algorithm can quickly allocate resources to D2D pairs and maximize the system throughput.