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线性预测分析是最有效的语音分析技术之一，在语音编码、语音合成、语音识别和说话 人识别等语音处理领域中得到了广泛的应用。语音线性预测的基本思想是：一个语音信号的 抽样值可以用过去若干个取样值的线性组合来逼近。通过使实际语音抽样值与线性预测抽样 值的均方误差达到最小，可以确定唯一的一组线性预测系数。(Linear predictive analysis is one of the most effective voice analysis technology has been widely applied in the field of speech processing speech coding, speech synthesis, speech recognition and speaker recognition and the like. The basic idea of ​ ​ linear prediction of speech is: a voice signal sample values ​ ​ can be approximated by a linear combination of a plurality of sample values ​ ​ of the past. By making the actual speech sample mean square error of the value of the smallest linear prediction sample values, to determine a unique set of linear prediction coefficients.)

卡尔曼滤波程序。实现多维卡尔曼滤波卡尔曼滤波程序。实现多维卡尔曼滤波(Kalman filter procedure.维卡尔曼realize multi-filter )

an svd syste is modelled in this file.

说明：  matlab code for home work rayleigh

Heritrix官方开发文档，crawler.archive.org/articles，提供了基本的类的开发介绍。((Heritrix official development documents, crawler.archive.org/articles, provides a basic introduction class development.))

说明：  该算法采用如下共聚类结构进行多重聚类： 1.优化地将数据集划分为多个视图。在每个视图中，对象和特征都是群集的。 2.同时分析了三类特征：高斯特征、泊松特征和范畴特征。 3.自动推断视图和簇的数量。(This algorithm carries out multiple-clustering with co-clustering structures as follows: 1.Optimally divides datasets into several views. In each view, both objects and features are clustered. 2.Simultaneously analyzes three types of features: Gaussian, Poisson and categorical features. 3.Automatically infers the number of views and clusters.)

reflectivity of dbr stack at different input angles

漢民碼譯碼器，此源碼提供各種規格的漢民碼解碼，並以副程式寫成，方便使用(hamming decoder)

SIMULATING THE LONG TERM EVOLUTION PHYSICAL LAYER

一个利用matlabGUI设计的学生成绩查询的小系统，还需要进一步完善。( Type text or a website address or translate a document. Cancel Yīgè lìyòng matlabGUI shèjì de xuéshēng chéngjī cháxún de xiǎo xìtǒng, hái xūyào jìnyībù wánshàn. Example usage of "": automatically translated by GoogleChinese (Simplified)EnglishSpanish AlphaA using matlabGUI design student achievement query system, still need further improvement.)