chap2

copy from fs while checksumming, otherwise like csum_partial.

这是802.11n 工作组对测试案例定义(This is 802.11n usage models document used by IEEE 802.11n Task group)

数字图像处理和机器识别C++和matlab识别书籍第二部分，讲解详尽，适合学习(Digital image processing and machine identification C++ and matlab identification books first part, explain the detailed, suitable for learning)

Local directional pattern has implemented to extract texture multi orientation

该程序是自己编写的，是基于matlab的多径信道的建立。该多径信道是基于电力线信道特性而构建的，其中的衰落系数是通过阅读大量文献而得到的。该程序能够顺利运行，并画出了信道的各个参数图。(The program is written by myself,which is to build a multi-path channel matlab based. The multi-path channel is based on the characteristics of the power line channel constructed, which is fading coefficients obtained by reading a lot of literature. The program can run smoothly, and draw a diagram of each parameter channel.)

本资料详细介绍了MATLAB在自控的应用，希望会对大家有用！(This information is described in detail the application of MATLAB in control, I hope will be useful for everyone!)

流行学习英文文献，主要是局部线性嵌入方法的改进(Pop learning English literature, mainly locally linear embedding Improvement)

matlab扩展编程——matlab扩展编程完整版，pdf格式，包含语音部分的两章的matlab例程(Matlab programming expansion-- Matlab version of the expansion program integrity, pdf format, part of the speech contains two chapters of Matlab routines)

This is modeling of DCmotor and current control with PI controller and speed control with PI controller.

在这项工作中所使用的方法是基于一个由大卫阿诺德教程。 http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf 这将运行该Logistic.m带来了图形用户界面。 1。放弃在列的格式文本文件中的x值 2。放弃在山口格式文本文件中的y值 3。的阴谋初始 按钮将绘制的分布 4。 查找适合 按钮，会找到最适合 5。 重置 将删除的情节（虽然我想打扫所有的领域 -没有时间） 5。地下K，糖尿病在下列公式中的值 Ÿ 等于k/（1+进出口（- G *的（十型录））） 6。上证所给出了squred误差之间的拟合函数与实际数据的总和 7。消委会提供的相关合作关系的实际数据拟合功能和效率(The method used in this work is based on a tutorial by David Arnold. http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf RUN The Logistic.m this will bring up the GUI. 1. Give the x values on a text file in column format 2. Give the y values on a text file in col format 3. Plot Initial Button will plot the distribution 4. Find Fit button will find the best fit 5. Reset will remove the plot (Although I wanted to clean all the fields- did not have time) 5. K, G, Dm are the values in the following equation y = K./(1+exp(-G*(x-Dm))) 6. SSE gives the sum of squred error between the fitted function and the actual data 7. CC give the correlation co-efficient between the fitted function and actual data)