voicebox

主分量分析的MATLAB程序，对于初学者比较有用，可以好好看一下(MATLAB program, the principal component analysis is useful for beginners, you can take a look at)

说明：  熟悉MATLAB信息统计分析的各种命令；会用MATLAB的信息统计分析的命令进行各种信息统计分析。(Familiar with a variety of statistical analysis of MATLAB command information will use the information MATLAB commands statistical analysis statistical analysis of all kinds of information.)

k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

局部加权线性回归，解决非线性回归问题，可以在Matlab直接测试。(This code is the Locally Weighted Linear Regression,which is mainly used to cope with non-linear regression.)

用MATLAB中Simlink工具箱对一类随机中立型时滞系统进行图像仿真.(Simlink toolbox with MATLAB, a class of stochastic neutral delay systems, image simulation.)

说明：  用小波多尺度分析进行基于医学图像形状特征的检索.(wavelet multiscale analysis based medical image retrieval features of the shape.)

均匀平面阵方向图，m*n元，波束指向phi=0°，theta=90°(Uniform plane array pattern, m* n element, the beam points to phi = 0 °, theta = 90 °)

感知器算法的基本思想是，对初始的或迭代中的增广权矢量 ，用已知的训练模式检验它的合理性，当不合理时，对其进行校正，校正方法实际上是最优化技术中的梯度下降法，上传的是用matlab解决感知器问题。(The Perceptron Algorithm The basic idea augmented weight vector of the initial iteration, the known training mode to test it reasonable, unreasonable, its correction, the correction method is actually the most optimization techniques the gradient descent method, upload matlab to solve perception problems.)

超宽带定位系统，较好的一本书，涉及很多方面的内容(Ultra-wideband positioning system, a good book, involving many aspects)

1.将BMP图像内容读入内存数组。 2. 调整图像的灰度，对图像进行灰度变换（反变换）。 3. 对图像进行较正,分别取值为0.4,0.6,0.8.(1. The BMP image content is read into memory array. 2 Adjust the gray image, the image gray-scale transformation (inverse transform). 3 more positive image, respectively, the value of 0.4,0.6,0.8.)