matlabshiyan1

说明：  OFDM信号及其频谱 OFDM signal and its spectrum ( Guard Interval insertion )(OFDM signal and spectrum OFDM signal and its spectrum (Guard Interval insertion))

说明：  matlab混合编程系列：matlab与bcb混合编程——使用MATCOM(Matlab mixed programming Series: matlab and bcb mixed programming-using MATCOM)

讲解如何使用Matlab进行建模与仿真，并对仿真结果进行分析和可视化(Explain how to use Matlab for modeling and simulation, and simulation results for analysis and visualization)

Function developed in MATLAB for the creation of a RGB Cube

一个非线性滤波器的实现程序，能够实现其基本的功能，对于初学者很有帮助。(A nonlinear filter is implemented procedures to achieve its basic functionality, useful for beginners.)

基于模拟电荷法求解导线周围空间的空间电场(Solving the electric field around the wire based on space charge simulation method)

这是一个通信系统的仿真，是用SIMULINK的S-FUNCTION方式编写的，共有20个M文件，1个DOC文件，本系统的仿真设计到编码、调制、IFFT、上下变频、高斯信道建模、FFT、PAPR抑制、各种同步、解调和解码等模块，每个模块都用详细的代码和注释，并通过了系统性能的仿真验证了系统设计的可靠性，可以直接拿来仿真整个系统，也可来学习各个部分的设计。(This is a communication system simulation, is the S-FUNCTION SIMULINK approach to the preparation, a total of 20 M file, a DOC file, the system simulation design to coding, modulation, IFFT, and down conversion, Gaussian channel model , FFT, PAPR suppression, various synchronization, demodulation and decoding modules, each module with a detailed code and comments, and system performance through simulation and verification of the reliability of system design, simulation of the entire system can be directly used, also to learn the various parts of the design. )

MATLAB电力系统设计与分析,一本MATLAB在电力系统分析和设计方面的教材。(power system design and analysis using MATLAB)

惯导轨迹仿真，对于初学惯导的人有很大的帮助(INS trajectory simulation, inertial navigation for beginners who have a lot of help)

选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)