qianyingwanggailv

当要隐藏信息时,在MATLAB的命令窗内输入命令: hide = myhide(carry,signal,x,y)； carry 用你的原始载体文件名代替，signal 用你所要隐藏的文件名代替 carry 和 signal 都必须是 *.* 的形式（注意不能省略单引号） x,y 是选取隐藏信息的位置坐标，在（x,y）和（y,x）处 生成的伪装载体文件名为hide.bmp 注意myhide.m文件，载体文件，隐藏文件都要在当前目录(current Directory)下 当要提取信息时，在MATLAB命令窗内输入命令： behidden = myinhide(carry,x,y)； carry 用你的伪装载体文件名代替,格式为 *.* （注意不要丢掉单引号） x,y 是选取隐藏信息的位置坐标，在（x,y）和（y,x）处，这必须和嵌入时所选位置一致。 提取出的信息文件名为behidden.bmp 同样，要注意behidden.m和伪装载体文件都要在当前目录(current Directory)下(when to hide information, in order MATLAB within a window orders : hide = myhide (carry, signal, x, y); Carry with you the original document name instead vector, signal used to hide your name in the paper and instead carry signal must be in the form of*.* ( not to single quotes omitted) x, y is to select the location of hidden information coordinates (x, y) and (y, x) The formation of camouflage vector document, entitled hide.bmp attention myhide.m documents, vector documents hidden all the documents in the current directory (current Directory) to retrieve information when, in order window MATLAB import order : behidden = myinhide (carry, x, y); carry using your camouflage vector file name instead, the format for*.* (take care not to lose a single quote) x, y election Hiding information )

说明：  无线传感器网络Leach分簇算法源代码，用matalb仿真的，完全能跑出来(wireless sensor network )

搭建SISO收发系统，BPSK调制与解调，在白高斯信道下仿真系统BER性能。(Build SISO transceiver system, BPSK modulation and demodulation, the white Gaussian channel simulation system BER performance.)

The cache manager source code implemented in Windows kernel.

这个程序关于语音信号的消噪，实现了一个基于递归最小二乘法的自适应噪声抵消器(This program about the speech signal de-noising, implements a recursive least squares based adaptive noise canceller)

Analyses performance of derict sequence spread spectrum with BPSK modulation

二维TE波圆柱FDTD的MATLAB仿真(Two dimensional TE wave cylindrical matlab simulation)

这是电网、理论中实现矩阵快速变换的程序代码，附有使用说明书，技术说明(This is a grid, matrix theory, to achieve rapid transformation of the program code, with the use of brochures, technical notes)

本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。(The purpose of this paper is threefold: firstly, the local Successive Mean Quantization Transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up Sparse Network of Winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the Receiver Operation Characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors.)

说明：  实现duffing方程的绘图，为工程上观察duffing混沌现象给出一个仿真的方法 (give a way to drew the picture of duffing)