chap15

测试文本，选用著名的gettysburg这篇英文文本做为测试文本(Second, it fuses the features of the first singular value component and the second one, and then gets the complex feature vectors which reflect not only the statistic frequency but also the sequential structure of letters. In the end, the cosine similarity of texts is used to measure the similarity between the query and documents.)

important document written in matlab useful for deploying solution

信号与系统实验：gibs现象、卷积、信号的运算、网络函数！(Signal and System Experiment: gibs phenomenon, convolution, signal operations, network function!)

ML法能够很好的估计信号的信噪比,DC-DC部分采用定功率单环控制，代码里有很完整的注释和解释。( ML estimation method can be a good signal to noise ratio, DC-DC power single-part set-loop control, Code, there are very complete notes and explanation.)

it describes about the Bwt transform for image

一维光子晶体带隙分析计算的matlab仿真(Matlab simulation of one-dimensional photonic band gap calculated)

协同过滤算法 基于用户和基于项目（在readme中有详细解释） 里面已dataset数据集(Collaborative filtering)

Finally, probability analysis, based on the chosen database, is performed and judgment whether the person is recognized is pronounced. Two different modeling techniques can be suggested for purposes of person identification or verification. The approach based on Hidden Markov Speaker Models (HMMs) with the mixtures of Gaussian distribution is text-dependent one. For this technology a speaker is described by a set of HMMs, constructing the set of speech groups spoken by that person. The speech groups may be phonemes or words [7]. The text-independent technology is rested upon Gaussian Mixture Speaker Models. For this technology each speaker is defined by only one model describing all utterances of that person. Identification in the range of both approaches is executed by evaluation of maximum a posteriori probability [7].

its a nice toolbox for compressive sensing

volley源码。找的源码 ，最近要读源码(volley source. Find the source code to read recently)