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Spectroscopic data often suffer from common problems of band overlap and noise. This paper presents a maximum a posteriori (MAP)-based algorithm for the band overlap problem. In the MAP framework, the likelihood probability density function (PDF) is constructed with Gaussian noise assumed, and the prior PDF is constructed with adaptive total variation (ATV)(More)
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)
Band overlap and random noise are a serious problem in infrared spectra, especially for aging spectrometers. In this paper, we have presented a simple method for spectrum restoration. The proposed method is based on local operations, and involves sparse decompositions of each spectrum piece under an evolving overcomplete dictionary, and a simple averaging(More)
Automatic recommendation has become a popular research field: it allows the user to discover items that match their tastes. In this paper, we proposed an expanded autoencoder recommendation framework. The stacked autoencoders model is employed to extract the feature of input then reconstitution the input to do the recommendation. Then the side information(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)
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)
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)
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)