deploypcode

说明：  本书基于MATLAB 5.2版，提供了使用MATLAB的实践性指导。MATLAB已成为适合多学科、多种工作平台的功能强大、界面友好、语言自然并且开放性强的大型优秀应用软件，同时也已成为国内外高等院校高等数学、数值分析、数字信号处理、自动控制理论以及工程应用等课程的基本教学工具。本书按逻辑编排，自始至终用实例描述；内容完整且每章相对独立；是一本简明的MATLAB参考书，既适用于初学者，也适用于高级MATLAB用户。(Based on MATLAB 5.2 version of the book provides a practical guide to using MATLAB. MATLAB has become suitable for multi-disciplinary, multi-working platform powerful, user-friendly, natural and open strong language of large outstanding applications, but also have become domestic institutions of higher learning higher mathematics, numerical analysis, digital signal processing, automatic application of control theory and engineering courses of basic teaching tool. Logically organized book, from beginning to end with an example description content of each chapter is relatively complete and independent MATLAB is a concise reference book, both for beginners or advanced MATLAB users.)

在MATLAB水印随基序列生成嵌如到图象中(in MATLAB with the watermark embedded sequence generated if the image)

集成了C代码和MATLAB的主成分分析代码，可以用这两种方法解决求取特征值和特征向量。(C code and MATLAB integrates principal component analysis code, you can use two methods to solve strike eigenvalues ​ ​ and eigenvectors.)

ICA(盲源分离)的一些MATLAB源代码和一些文章，有对带参考信号的 ICA算法的扩展和盲信号分离的matlab仿真(matlab simulation for blind source separation)

说明：  在信号中添加噪声，根据周期图法，估计信号功率谱(Add noise to the signal and estimate the signal power spectrum according to the periodic graph method)

带matlab界面的FIR滤波器窗化设计，应用fft实现序列通过数字滤波器。(With matlab interface window of FIR filter design, FFT is applied to implement sequence through the digital filter.)

This is attitude control system software

bullet through the wall

prim算法 最小生成树 prim matlab (prim matlab)

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.)