feng4

说明：  利用simulink的s-function方式仿真一个单径瑞利信道，其多普勒谱为经典谱(Simulink using the s-function simulation of a single-mode Rayleigh channel diameter, the Doppler spectrum for the classical spectrum)

详解MATLAB/Simulink通信系统建模与仿真/刘学勇编著 配套的matlab程序(Detailed MATLAB/Simulink modeling and simulation of communication systems/Liu Xueyong compiled matching matlab program)

pitchn,功能：求一句话的语音基音频率包络。(pitchn matlab)

PFCM clustering algorithm

用matlab 实现 遗传算法 本文已顺利发表 有一定学术价值(genetic algorithm)

阵列信号处理的高分辨率估计，相控阵天线的方向图（切比雪夫加权），包含飞行器飞行中的姿态控制，如侧滑角，倾斜角，滚转角，俯仰角。( High-resolution array signal processing estimates, Phased array antenna pattern (Chebyshev weights), It comprises aircraft flight attitude control, such as slip angle, tilt angle, roll angle, pitch angle.)

说明：  采用MATLAB编写的最新三维数据图形界面，运用GUI编程，输入函数名：volumization (data, n )即可。其中DATA的格式为：[n x m x p]；n表示显示方式： 1 - all, 2 - every second, 3 - every third。 (MATLAB prepared using the latest three-dimensional data graphical interface, the use of GUI programming, enter the function name: volumization (data, n) can be. DATA format in which: [nxmxp] n that display: 1- all, 2- every second, 3- every third.)

采用加窗法在matlab中进行功率谱密度计算(Windows and method for power spectral density calculation in matlab)

基于k-l分解算法的正弦信号分离算法，用于实现强背景噪声下正弦信号恢复(Sine signal separation algorithm based on k-l expansion algorithm ,which is useful in sine signal getting from strong background noise)

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.