Quantum evolutionary algorithm (QEA) and particle swarm optimization (PSO) are two different types of intelligent optimization algorithm. Many efforts on these two algorithms have progressed actively in recent years. ...
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Quantum evolutionary algorithm (QEA) and particle swarm optimization (PSO) are two different types of intelligent optimization algorithm. Many efforts on these two algorithms have progressed actively in recent years. In this paper, six typical and complex benchmark testing functions are applied to verify their abilities for dealing with numerical optimization problems. The results show that QEA has better global search capability, which is manifested very outstandingly for the multi peak function optimization, but its convergence speed is slower. Contrarily, PSO has faster convergence speed, and it can solve certain optimization problem rapidly, especially for single peak functions.
Motion blurs are pervasive in real captured video data, especially for hand-held cameras and smartphone cameras because of their low frame rate and material quality. This paper presents a novel Kernel-based motion-Blu...
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Deep Web information is heterogeneous and dynamic. How to make use of this information effectively is a challenging task. In order to obtain the newest content in the Deep Web, a data generative model of Deep Web has ...
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Deep Web is autonomous, independently updating, and its data are always in a state of frequent update. However, the user always hopes to obtain the newest content in the current Web database. Different from previous r...
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Deep Web is autonomous, independently updating, and its data are always in a state of frequent update. However, the user always hopes to obtain the newest content in the current Web database. Different from previous research, this paper wants to emphasize the importance of updating frequency in the study of Deep Web information acquisition. And, an approach on incremental information acquisition based on logical reinforcement learning has been proposed. Then, we find in our research that under the same condition of constraint resources, the novel approach can improve the freshness of data, discovery efficiency of new data and the service quality of Deep Web information integration.
With the rapid development and the richer content of the Internet, the copyright protection of the works should be further developed. The watermarking has become a possible solution. In this paper, firstly, a novel hy...
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With the rapid development and the richer content of the Internet, the copyright protection of the works should be further developed. The watermarking has become a possible solution. In this paper, firstly, a novel hybrid multiplicative rule is presented and utilized to embed the watermark into DWT coefficients controlled by the secret key. Secondly, the optimum and locally optimum hybrid multiplicative multi-watermarking decoders are proposed, respectively, which are based on the minimum Bayesian risk criterion. The DWT coefficients are modeled as the generalized Gaussian distribution. Nextly, the performance of the optimum hybrid watermarking decoder, i.e. the average bit error rate is theoretically analyzed. Then the security of the hybrid multiplicative watermarking scheme is compared with the existing schemes. Finally, experimental results prove the theoretical analysis valid.
Time-domain and frequency-domain features were extracted. The research of process and architecture of an audio classification system based on SVM was done, and the SVM audio classifier was designed. The results of exp...
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Time-domain and frequency-domain features were extracted. The research of process and architecture of an audio classification system based on SVM was done, and the SVM audio classifier was designed. The results of experiments show that the audio classification system designed in the paper can classify audio signal effectively, and the average identification accuracy is about 90%.
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