q1

信号系统实验软件，matlab编写，实现时域和频域，离散和连续的各种处理(Signal system experimental software, matlab writing, to achieve time-domain and frequency domain, a variety of discrete and continuous processing )

Matlab code for bacterial foraging

5扩32 扩频方法仿真 进行SNR估计 产生pn码 基带仿真(5 extended to 32 spread-spectrum method is estimated SNR simulation)

用c#编写了一套FIR窗口滤波器，其中数据段的提取来自于matlab的混合编程(Written in c# a FIR filter window, including data extracted from the mixture of matlab programming)

说明：  sift特征匹配代码，并带有相关说明文档(SIFT feature matching code with relevant documentation)

MATLAB 6_5 辅助神经网络分析与设计 飞思科技 电子工业出版社(MATLAB 6_5-assisted neural network analysis and design of fly Synopsys Publishing House of Electronics Industry)

不仅仅是闲了二进制二进制随机序列的频率调制，而且实现了其相应的解调过程，FSK完整程序(Not just free of the binary random sequence of binary frequency modulation, and the realization of the corresponding demodulation process, FSK complete procedures)

本程序计算局部窗口的累积直方图，可用于驱动水平集和纹理分割( in this test program, we calculate the cumulative histogram in a local window centered at each pixel,this local cumulative histogram can be used to drive the level set for image and texture segmentation. Author: Associate Prof. Yuanquan Wang, Affiliation: Tianjin Key Lab of Intelligent Computing and Novel Software Technology, School of Computer Science, Tianjin University of Technology, Tianjin 300191, China 01/20/2008 Reference: 1. Tony Chan, Selim Esedoglu, and Kangyu Ni, Histogram Based Segmentation Using Wasserstein Distances, SSVM 2007, LNCS 4485, pp. 697–708, 2007. 2. Kangyu Ni, Xavier Bresson, Tony Chan, Selim Esedog, Local Histogram based Segmentation using the Wasserstein Distance, at: www.math.lsa.umich.edu/~esedoglu/Papers_Preprints/chan_esedoglu_ni.pdf or at :ftp://ftp.math.ucla.edu/pub/camreport/cam08-47.pdf )

2nd one, this is the next file for code

Description All the filters have needed neighbor data the current pixel in the image,here this function(res_window=cover_window(how many neighbor value for row ,how many neighbor value for column ,input image) produce the result( res_window(row,col,total size)) input inp_img=2d data(row * col) w=cover size w=[3 3] = 3x3 cover pixel generation output o_img=result 3d o_img(w(1),w(2),r*col) if inp_img=[1 2 3 4 5 6 ] w=[3 3] Soloution it will construct new image new_img=[0 0 0 0 0 0 1 2 3 0 0 4 5 6 0 0 0 0 0 0] result o_img(:,:,1)=[0 0 0 0 1 2 0 4 5] o_img(:,:,2)=[0 0 0 1 2 3 4 5 6] finally o_img(1:3,1:3,6) example a=imread( pears.png ) b=rgb2gray(a) c=cover_window( b,[3 3]) 3x3 window construction