diedaifa

高阶谱估计被开发出来，一直为人们所关注，只要信号和噪声中有一种是高斯分布，则累量的运用非常方便解决问题！(Higher order spectral estimation have been developed, has been a continuing concern, as long as there is a signal and the noise is Gaussian distribution, the cumulative amount of use is very easy to solve the problem!)

3 files: imageslicer for splitting RGB image to 8 bit plans, getTopology to extract topology features features, and getShape to extract shape features . (good features for CBIR systems)

Non-Convex Optimization

3 fisrt parts are: Download the file Square.tif and answer the following questions: i. Smooth the image with a 3 􀵈 3 spatial filter. ii. Smooth the image with an implementation of Gaussian filter in frequency domain. Change the bandwidth of the filter and show how these changes affect the result.

偏最小二乘法，能很好建立物质浓度和与相应的光谱矩阵的回归方程，且能对光谱阵降维和消除噪声影响。(Partial least squares, to establish material concentration and good with the regression equation of the corresponding spectral matrix array dimensionality of the spectrum and can eliminate noise effects. )

Simulation Montecarlo Integral2

此工具箱包括了多元校正中选择变量的一系列方法，包括ipls,bipls,mwpls,sipls等(the present toolbox contains a serie of methods used to select variables,such as ipls,bipls,mwpls,sipls.)

针对SVM法线特征筛选算法仅考虑法线对特征筛选的贡献,而忽略了特征分布对特征筛选的贡献的不足,在对SVM法线算法进行分析的基础上,基于特征在正、负例中出现概率的不同提出了加权SVM法线算法,该算法考虑到了法线和特征的分布.通过试验可以看出,在使用较小的特征空间时,与SVM法线算法和信息增益算法相比,加权SVM法线算法具有更好的特征筛选性能.(Normal feature selection for SVM algorithm only considered normal for the contribution of feature selection, to the neglect of the characteristics of the distribution of feature selection have contributed to the lack of normal SVM algorithm based on the analysis, based on the characteristics of the positive and negative cases emergence of a different probability-weighted normal SVM algorithm, which takes into account the distribution and characteristics of normal. through the test can be seen in the use of smaller feature space, the normal and the SVM algorithm and information gain algorithm, normal weighted SVM algorithm has better performance of feature selection.)

用模拟退火求解非线性规划的具体例子，很有提高，希望大家能学习学习(Nonlinear programming using simulated annealing)

QAM的系统调制与解调的MATLAB程序 (QAM modulation and demodulation system)