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Service composition provides a flexible way to quickly enable new application functionalities in next generation networks. Composite services are turned to be more and more complex, and the performance analysis of composite service is an important issue for service providers who want to get better QoS. In this paper, we proposed a stochastic Petri net based(More)
Spectroscopic data often suffers from common problems of bands overlap and random Gaussian noise. Spectral resolution can be improved by mathematically removing the effect of the instrument response function (IRF). In this paper, a novelty model is proposed to deconvolute the measured spectrum with the sparsity regularization. The proposed model is solved(More)
Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional(More)
Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially when the problems of instrument aging has become more and more serious recently. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering similarity of the latent spectrum(More)
In this paper, we will propose a new framework which can estimate the desired signal and the instrument response function (IRF) simultaneously from the degraded spectral signal. Firstly, the spectral signal is considered as a distribution, thus, new entropy (called differential-entropy, DE) is defined to measure the distribution with a uniform distribution,(More)
Remote sensing image often suffers from the common problems of stripe noise and random noise. In this paper, we present a destriping method with unidirectional gradient L0 norm and L0 sparsity priori. The major novelty of the proposed method is that combining the unidirectional gradient L0 norm with the sparsity priori to address the destriping and(More)