Illegal-E-Book-Collection

使用matlab利用一定的约简方法实现形式背景的约简和概念格的还原。(Using matlab Reduction Method using certain forms of background reduction and the reduction of concept lattices.)

利用三电平中心削波自相关法实现清、浊音判定，并提取一段话音的基频参数，绘制基频曲线，并将该参数曲线合并到前面提取的短时过零率和短时能量参数曲线图中(Using a three-level center clipping from the relevant law to achieve clear, voiced determination, and extract some of the fundamental parameters of the voice, drawing the fundamental frequency curve and merge the parametric curve to the front of the extraction rate and the short-term zero short-term energy parameters graph)

录制一段噪声，利用matlab对该材料进行声音的频谱分析。(Record a noise, the use of the material to matlab sound spectrum analysis.)

说明：  主成分分析.mht：介绍主成分分析的文章 edgepick.m：本程序的目的是实现边缘检测 drawprim.m：找到最大主分量所在的位置，从而在原图象中绘出 practical.m：一个主分量的程序，边缘检测和主分量提取 prin.m：该程序是用princomp函数来提取矩阵的主成分(Principal component analysis. Mht: the article describes the principal component analysis edgepick.m: The purpose of this program is to achieve edge detection drawprim.m: to find the location of the maximum principal component, which in the original image in the draw practical.m: a principal components process, edge detection and principal component extraction prin.m: princomp function of the program is to extract the principal component matrix)

matlab by example. programming basics-elsevier (2013)

simulation for one-D FDTD(Basic standard FDTD code for learner)

《系统辨识与自适应控制MATLAB仿真》的程序代码，有很高的参考价值。(MATLAB simulink)

This is simple lmi buliding algorithm with lmi control toolbox.

为了得到具有一般相关量测噪声线性系统的递推滤波算法, 将该问题转化为具有相关量测 单值随机向量的滤波问题,根据单值随机向量的线性无偏最小方差估计算法,导出了量测噪声为一 般相关鞅差序列的线性系统的最优递推状态估计滤波算法.通过数值仿真,将该算法与假定量测噪 声不相关时的Kalman 滤波算法进行了比较,证明了该算法的有效性(In order to obtain the recursive f i ltering alg orithm for a linear system w ith general correlat
ed measurement noises, the problem is t ransformed to that of filtering for a single random vector w ith correlated measurements. According to the linear unbiased minimum variance estimation algorithm for a single random vector, a recursive filtering algori thm is presented. A digital simulat ion technique is used such that the new algorithm can be compared w i th the Kalman f i ltering algorithm, assuming that the measurement noises are uncorrelated. Validi ty of the proposed algorithm is thus proved)

visual servoing of mobile robot