Linear-System-Root-locus

基站容量分析，matlab,通过香农公式求，信噪比求解等(The base station capacity analysis, matlab, by Shannon formula to find, SNR solving, etc.)

用matlab语言实现的图像匹配，效果还不错！(Language implementation using matlab image matching results were pretty good!)

SAPF model matlab simulink model

迂回位相编码全息再现，是基于MATLAB的全息再现术。(Detour phase encoding holographic reproduction is based on the MATLAB representation of the holographic technique.)

digital signal prosecing code

龙格库塔的matlab和c++的源代码程序，可解四阶常微分方程(Runge-Kutta matlab and c++ source code program, fourth-order ordinary differential equations solvable)

hybrid of ICA and K-mean to improve K-mean

图像2倍上采样和下采样，快速且准确，成像效果较好。(Photo 2 times upsampling and downsampling, fast and accurate.)

用七元线形天线阵ULA，实现传统波束形成法、Bartlett波束形成法、Capon波束形成法(failed to translate)

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.