Synthesis and characterization of radiation cross-linked PVP hydrogels and investigation of its potential as an antileishmanial drug carrier

Polymer Bulletin - The aim of this study was to elaborate a suitable hydrogel to be used as drug carrier for antileishmanial treatment. Therefore, a PVP hydrogel was synthesized using gamma...     

Experimental study of the performance of a cooling tower used in a solar distiller

In this work, we investigated experimentally the thermal performance of a forced cooling tower used in a solar desalination system based on humidification-dehumidification of air. The cooling tower is a counter flow wet one filled with film packing materials.The measured variables were obtained for wide ranges of mass flow rates of air and water as well as for several inlet water temperatures; the tower characteristic and efficiency were then evaluated and expressed in terms of water to air mass flow rate ratio.     

基于谐波的乐纹提取和音乐检索

提出了一种基于节拍内音乐谐波特性的乐纹特征提取方法,首先求得每帧音乐的谐波信息,再利用跟踪得到的节拍,计算节拍内所有帧的谐波信息的均值,构成此节拍的乐纹特征矩阵。为了提高音乐检索的效率,设计了一个二级音乐检索算法：根据节拍信息,将与查询音乐片段的每分钟节拍数相近的音乐作为候选音乐,再逐节拍计算所查询音乐的乐纹和候选音乐乐纹的相似度,选择相似度最高的音乐作为检索结果。实验结果表明,提出的乐纹特征和音乐检索算法有效地提高了检索准确率和检索效率。     

Bayesian Unsupervised Learning for Source Separation with Mixture of Gaussians Prior

This paper considers the problem of source separation in the case of noisy instantaneous mixtures. In a previous work [1], sources have been modeled by a mixture of Gaussians leading to an hierarchical Bayesian model by considering the labels of the mixture as i.i.d hidden variables. We extend this modelization to incorporate a Markovian structure for the labels. This extension is important for practical applications which are abundant: unsupervised classification and segmentation, pattern recognition and speech signal processing. In order to estimate the mixing matrix and the a priori model parameters, we consider observations as incomplete data. The missing data are sources and labels: sources are missing data for observations and labels are missing data for incomplete missing sources. This hierarchical modelization leads to specific restoration maximization type algorithms. Restoration step can be held in three different manners: (i) Complete likelihood is estimated by its conditional expectation. This leads to the EM (expectation-maximization) algorithm [2], (ii) Missing data are estimated by their maximum a posteriori. This leads to JMAP (Joint maximum a posteriori) algorithm [3], (iii) Missing data are sampled from their a posteriori distributions. This leads to the SEM (stochastic EM) algorithm [4]. A Gibbs sampling scheme is implemented to generate missing data. We have also introduced a relaxation strategy into these algorithms to reduce the computational cost which is due to the exponential influence of the number of source components and the number of the mixture Gaussian components.     

基于动态贝叶斯网络的语音识别及音素切分研究*

研究了一种基于动态贝叶斯网络(dynamic bayesian networks, DBN)的语音识别建模方法,利用GMTK(graphical model tool kits)工具构建音素级音频流DBN语音训练和识别模型,同时与传统的基于隐马尔可夫的语音识别结果进行比较,并给出词与音素的切分结果.实验表明,在各种信噪比测试条件下,基于DBN的语音识别结果与基于HMM的语音识别结果相当,并表现出一定的抗噪性,音素的切分结果也比较准确.