mcode

说明：  图像的伪彩色增强，把灰度图像转换为彩色图像(pseudo-color enhancement of image)

信号处理中的基于burg算法的功率谱估计的matlab仿真程序(matlab program of burg power spectrum estimation algorithm )

近似熵是由pincus在1991年提出的一种度量序列的复杂性和统计量化的规则(Approximate entropy pincus proposed in 1991 a measure of the complexity of the sequence and statistical quantization rules)

多维标度变换是应用广泛的无线传感器网络定位算法(Multi-Dimensional Scaling)

一个KNN的matlab程序，供感兴趣的人士学习(A KNN matlab program for those interested in learning)

图像配准技术及其MATLAB编程实现源码及自己测试M文件(Image Registration and MATLAB programming source code and test their own M-file)

matlab实现傅里叶变换源代码 快速傅里叶变换(Fourier transform matlab source code to achieve fast Fourier transform)

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)

说明：  使用遗传算法优化36单元圆形天线阵列,有需要的可以分享一下(optimise the 36 elements of antena arrays)

刘金琨老师书上的滑模matlab模型程序，很具有代表性的例子，适合各阶段的滑模学习。(Liu Jinkun teacher book sliding mode matlab model program, it is a typical example.)