vt8500_update_software_tool_user_guide

说明：  在OFDM系统中，这个是发端的编码程序，比较详细的说明(OFDM systems, this is the originator of the coding process)

程序实现了生成决策树并使用决策树对离散特征向量进行分类。在判断是否要继续生成叶节点时采用entropy来作为impurity的measurement.(Program and use the decision tree decision tree to generate discrete feature vectors for classification. In determining whether to continue generating leaf nodes using entropy as the impurity of the measurement.)

programme matlab codeur rs

自编的matlab gui小程序。功能包括：打开文件夹、在列表框中显示打开文件夹下的所有图像文件、点击列表框中图像则显示图像，并给出图像宽度高度信息在文本框中(Directed the matlab gui applet. Features include: Open the folder to display all the image files open folder in the list box, click on the image list box displays the image, the image width and height information given in the text box)

在MATLAB仿真环境下，该程序是对整个OFDM系统的模拟仿真实现。(In the MATLAB simulation environment, the program is the OFDM system simulation experiment.)

interior permanent magnet synchronous machine speed control using pi controller

The Kalman filter is a mathematical power tool that is playing an increasingly important role in computer graphics as we include sensing of the real world in our systems. The good news is you don’t have to be a mathematical genius to understand and effectively use Kalman filters. This tutorial is designed to provide developers of graphical systems with a basic understanding of this important mathematical tool.

An example that illustrates the importance of shift-invariance is image denoising by thresholding where the lack of shift-invariance causes pseudo-Gibbs phenomena around singularities.In addition to shift-invariance, it has been recognized that an efficient image representation has to account for the geometrical structure pervasive in natural scenes. In this direction, several representation schemes have recently been proposed

说明：  卡尔曼滤波相关代码，内含线性卡尔曼，扩展卡尔曼，无迹卡尔曼，交互多模型卡尔曼的代码(Kalman filter related code, including linear Kalman, extended Kalman, unscented Kalman, interactive multi model Kalman code)

在matlab中读取.dat中的数据，并求取均值和方差。(In matlab to read. Dat the data and obtain the mean and variance.)