bentley

说明：  DE算法,matlab的实现，可以直接进行优化，很好用的(DE Algrothm)

一个提取遥感影像光谱特征的程序，易于理解，分类精度高(An extraction of remote sensing image spectral characteristics of the program, easy to understand, high classification accuracy)

Inverse Filters on matlab

quantized high pass windowed sync filter for digital systems / plot/develop input output filter response

说明：  绘制两自由度非线性动力学系统刀具和工件时域图和相图(Drawing time domain and phase diagram of two degree of freedom dynamic system)

捷联惯导系统的陀螺及加速度计数据发生器。(SINS gyro and accelerometer data generator)

Image color model conversion from any image format to RGB, and vice versa.

说明：  通信系统中的数字信号处理仿真程序， 内容很多，从source forge上下来的， 我用来研究信道仿真的(Communication System digital signal processing simulation program, many things, from the source to come down to the forge, I used to study channel simulation)

说明：  四维超混沌系统，平衡点 图像，李雅普诺夫指数，完全同步(Four-dimensional hyperchaotic system, balance the image, Lyapunov exponent, fully synchronized)

特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。 我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下： 對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )