rjMCMC

RSA Encryption and Decryption using Matlab by Thunyawat Rajatasereekul and Voranon Kiettrsalpipop ECE575, Winter 2002, Oregon State University Prof, Cetin Keya Koc The program set contains thirteen files listed below. errormeg.fig errormsg.m helpmsg.fig helpmsg.m inputmsg.fig inputmsg.m mesgcut.m pro2.fig pro2.m rsacore.m readme.txt screenshot.gif In order to run the program please call pro2.m under Matlab 6.0 Environment. System Requirement Matlab version 6.0 and if necessary, Maple version 6.0 on any platform PentiumII 300 MHz or equivalent.(RSA Encryption and Decryption using Matlabby Thunyawat Rajatasereekul and Voranon KiettrsalpipopECE575, Winter 2002, Oregon State UniversityProf, Cetin Keya KocThe program set contains thirteen files listed below.errormeg.figerrormsg.mhelpmsg.fighelpmsg.minputmsg.figinputmsg.mmesgcut.mpro2.figpro2.mrsacore. mreadme.txtscreenshot.gifIn order to run the program please call pro2.m under Matlab 6.0 Environment.System RequirementMatlab version 6.0 and if necessary, Maple version 6.0 on any platformPentiumII 300 MHz or equivalent.)

mablab6.0时尚创作百例(mablab6.0 creative fashion 100 cases)

不知道上次是不是传上去了，再传一个吧，还是数字水印的提取程序(Do not know the last time was not passed up, re-transmission of a bar, or a digital watermark extraction procedure)

M-QAM constellation mapping

说明：  本书着重介绍MATLAB程序设计编程，更有利于学习MATLAB的基础编程(This book focuses on MATLAB programming programming, more conducive to learning the basis of MATLAB Programming)

说明：  《精通matlab7》书中的绘图程序，二维、三维、特殊图形的绘制(&quot Proficient matlab7&quot book drawing program, two-dimensional, three-dimensional, special graphics rendering)

厦门大学数值分析课件。基于matlab实现。(Courseware for Numerical Analysis of Xiamen University. Matlab-based implementation.)

说明：  基于泊松方程的抠图技术，此程序为演示程序，可供研究参考(Poisson equation-based matting techniques, this procedure for the demonstration program for the study of reference)

robocup 2d 仿真足球base代码(the robocup 2d simulation Soccer base code)

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.