VolterraRls

彩色活动轮廓模型,活动轮廓模型,图像分割，活动轮廓模型(Color active contour model, active contour model, image segmentation, active contour model)

经典匹配追踪程序。附带经典lena图像、并对其进行处理。(Classic match-tracing program. With classic lena image and its processing.)

Matlab，线性方程组求解典型算法，含共轭速降法（大规模矩阵），一种正则化方法（处理病态矩阵），含相应算例与user guide(Matlab, solving linear equations typical algorithms the conjugated downhill Act (mass matrix), a regularization method (treatment of morbid matrix) containing the corresponding numerical examples and user guide)

THE ablity of the dfig lvrt

此文件为matlab程序仿真 所用工具为PNN(PNN simulation)

design for buck converter

说明：  通过em算法实现对数据的高斯混合模型的分类(Em algorithm through implementation of data Gaussian mixture model classification)

均衡效果好，这是基于最大似然估计的均衡器(With the development of high-speed optical fiber communication systems, polarization mode dispersion (PMD) has become a vital limiting factor. However, the traditional compensation methods which in optical domain have the disadvantages of complexity and cost. In recent years, the compensation methods which in electronic domain are given more attention because of their outstanding price advantage and their strong adaptability. The maximum-likehihood sequence estimation (MLSE) equalizer based on the Viterbi algorithm was designed in this paper.The MLSE equalizer can effectively compensate the intersymbol interference caused by polarization mode dispersion (PMD). First, the background and the spectrum of the text were introduced also the principles of PMD and the FFE/DFE equalizer were analysed. Second, the principles of Viterbi and the structure of MLSE were researched also the MLSE equalizer was simulated with the Matlab program in the optical fiber communication systems. T)

给出了谱估计中参数估计的线谱的估计模型的relax方法的程序代码(PSD line )

奇异值分解用于英文文本检索，画出频率矩阵第一奇异值向量和第二奇异值向量的分布图( a fast retrieval algorithm for English texts is proposed. First of all, the English texts are mapped into frequency matrixes of order 26 and the dimensions of texts representation space are reduced through Singular Value Decomposition. 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. )