12

一个介绍matlab的课件，介绍初等模型的，相信对大家都有用处的，很好的课件。(An introduction matlab courseware to introduce elementary model, I believe we have useful, well courseware.)

说明：  matlab与信号系统电子书和相关书中代码(matlab and signal systems and related electronic code book)

MUSIC二维算法的经典谱估计程序，已运算过，下载即可使用(MUSIC algorithm is a classic two-dimensional spectral estimation procedures have been operational before, you can use to download)

这是我自己编写的压缩感知图像重构的算法，直接观测原始信号（二维），最后重建(This is your perception image compression algorithm, direct observation review of the original signal, the reconstruction of a two-dimensional ） （ )

手指静脉 特征提取 matlab的实现。。。(The finger vein special clinic extract matlab)

基于SIMULINK的OFDM的系统设计和仿真(SIMULINK of OFDM-based system design and simulation)

通信系统中的几种多接入方式性能比较的Matlab程序(Communication systems of several multi-access performance comparison of Matlab procedures)

MATLAB 关于cie的一些程序，对于初学者来说有点用(MATLAB on cie number of procedures, some use for beginners)

MATLAB LSB İ MPORTANT

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.)