SVMLS_v0

用于关联成像（鬼成像）的一维仿真模拟matlab程序源码。(One-dimensional simulation matlab program source code for the associated imaging (ghost imaging).)

leach protocol for wsn

初学雷达信号仿真的同学们可以好好看看。应该说是最基础 里面有MATLAB程序。写的比较好，对雷达发射信号有个很好的理解，以及和脉冲压缩的思想。同时也附有matlab程序。 内容提要： 雷达工作原理 线性调频（LFM）信号 LFM脉冲的匹配滤波 matlab仿真结果(Radar signal simulation of beginner students to have a look. Should say is the foundation which are MATLAB program. Write better, on the radar transmit signal to have a good understanding, and pulse compression of ideas. At the same time with MATLAB procedures.Abstract:The radar working principleLinear frequency modulated ( LFM ) signalLFM pulse matching filterMatlab simulation results)

to calculate the similarity between many functions

PD雷达LFM波形相干积累，距离单元未发生走动，matlab源代码(PD Radar LFM waveform coherent accumulation from the unit did not occur to walk, Matlab source code)

lssvm相关教程，工具箱的使用指南包括中文版，对于初学者lssvm的使用说明(lssvm tutorials, tool use guide includes Chinese version, for starters lssvm instructions)

基于non-local means的maltab算法(nlm' s maltab algorithm)

这是一本关于地球物理反演方法的经典著作《Parameter Estimation and Inverse Problems》，学地球物理的可以看看。(this book is for geophysics inverse problem base study. worth reading if you are a student of geophysics.)

Ensemble修改程序，包括源程序和调用程序及实例。(Ensemble modify the program, including the source and call procedures and examples.)

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. (The newest work tools of svm,it will be very convenient to have it.)