OFDM

说明：  包含matlab各种命令函数的简介及查询，包含电子书籍《matlab命令大全》。(Matlab command function includes a variety of briefings and inquiries, including e-books &quot matlab command Tai Quan.&quot )

文本信号读取、单点干扰信号的陷波滤波、低通滤波、GUI界面设计以及程序的清晰解释(Text-read, single notch filter interference signals, low pass filtering, GUI interface design as well as clear explanations of procedures)

波达方向（DOA） 估计的基本问题就是确定同时处在空间某一区 域多个感兴趣的信号的空间位置(即多个信号到达阵列参考阵元的方 向角)。最早的也是最经典的超分辨率(DOA)估计算法是著名的多信号 分类（Multiple Signal Classification） 法,简称 MUSIC 算法,它是 一类经典的基于特征结构分析的空间谱估计方法。该算法利用了信号 子空间和噪声子空间的正交性,构造空间谱函数,通过谱峰搜索,检测 信号的 DOA。(The basic problem of DOA estimation is to determine the simultaneous location in a certain region of space. Spatial position of multiple interested signals in the domain (i.e. the direction of multiple signals reaching the array reference elements) Directional angle). The earliest and most classical super-resolution (DOA) estimation algorithm is the well-known multi-signal.Multiple Signal Classification, referred to as MUSIC algorithm, is A classical spatial spectrum estimation method based on eigenstructure analysis. The algorithm utilizes signals. The orthogonality of subspace and noise subspace, the construction of spatial spectral function, through peak search, detection DOA of signal.)

《Extremum seeking control and applications》 第三章实例代码(Extremum seeking control and applications code of the third chapter )

自己编写的关于连通域编辑的MATLAB程序。(I have written on the connected domain editing MATLAB.)

受控对象变化后模型参考自适应仿真模型程序(Changes in the controlled object simulation model)

该程序提供了基于支持向量的判别分析的matlab实现，可应用于模式分类(Discriminant Analysis via Support Vectors)

说明：  找寻局部极大值的程序，采用反复差分方法，简单方便(Find the local maximum of the procedure for the repeated difference method, simple)

Alamouti方案的仿真，双发双收，双发单收，单发多收的性能比较，信道为准静态平坦水落信道，信道噪声为高斯白噪声。(Alamouti scheme simulation, twin double income, double single income, single overcharged performance comparison, static channel subject to flat Mizuochi channel, channel noise is Gaussian white noise.)

在matlab中实现哈夫曼压缩和解压缩。(Huffman compression and decompression in matlab.)