Aiming at the shortcomings of current algorithms due to the fixed steps, which is easy to fall into local optimum, with robustness and poor transparency, and cannot be balanced against various common attacks, an optimization algorithm of digital image watermarking algorithm based on Drosophila was proposed. In the support of the virtual reality technology, the original color host image was transformed from the RGB space to YCrCb space, and the pixel block of the Y component was divided into a certain size; according to the principle of forming DC coefficients in the DCT domain, the DC coefficient of each block is calculated directly in the airspace, and the amount of modification for each DC coefficient is determined based on the watermark information and the quantization step size; according to the distribution characteristics of DC coefficient, watermarks are embedded directly in the airspace; the type of digital watermarking and digital watermarking pretreatment methods were determined by using Drosophila optimization algorithm. At the same time, digital watermark embedding, extraction rules and initial steps were selected and identified. The Drosophila optimization algorithm with step size reduces the balance between global and local search ability, which makes up for the shortcomings of traditional algorithm. The experimental results showed that the proposed algorithm can effectively balance the invisibility and robustness of the watermark, and can resist all kinds of common attacks, which with a better visual extraction effect. 2010 Mathematics Subject Classification. 60J67.

A SpeechWeb is a collection of hyperlinked applications, which are accessed remotely by speech browsers running on end-user devices. Links are activated through spoken commands. Despite the fact that protocols and technologies for creating and deploying speech applications have been readily available for several years, we have not seen the development of a PublicDomain SpeechWeb. In this paper, we show how freely available software and commonly used communication protocols can be used to change this situation.

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With the introduction of manifold assumption, Laplacian Support Vector Machine(LapSVM) has advantages over the traditional SVM classifiers. However the dual solution of LapSVM is still a major barrier on the further application of LapSVM. Primal optimization is a promising solution to this problem. In this paper, we introduce a novel primal Laplacian Support Vector Machine with Precondition Conjugate Gradient method(PCG) to the problem of hyperspectral images classification which is one type of primal optimization solution. To prove the effectiveness of the proposed method, we apply it into the hyperspectral image data set Indian Pine. The experiment results show higher accuracy and better generalization ability than dual strategy.