diffcoding

：Matlab具有强大的数值计算和分析等能力，而C／C++是目前最为流行的高级程序设计语言，两者互补结合的混合编 程在科学研究和工程实践中具有非常重要的意义。因此，从Matlab调用c，C++代码及c，C++调用m文件两方面，深入地研究 了它们之间混合编程的原理和实现机制，并且给出了具体条件下的混合编程方法和步骤。(: Matlab has powerful numerical calculation and analysis capabilities, while the C/C++ is currently the most popular high-level programming language, which combines a mixture of complementary programming in scientific research and engineering practice of great significance. Therefore, from the Matlab call c, C++ code and c, C++ to call two m files, in-depth study of a hybrid between them and the realization of the principles of programming mechanisms, and under specific conditions are given a mixture of programming methods and procedures.)

ofdm.m为主程序，可以运行，将得到迭加噪声之后的误码率曲线，并与理论QPSK加高斯白噪声误码率曲线比较，两者将重合(ofdm.m-based program, you can run, will be after the noise superposition error rate curve, and with the theoretical QPSK plus white Gaussian noise bit error rate curves, which will coincide)

T/R-R高频地波雷达舰船探测范围分析 (T/RR HF radar detection range of ship)

MATLAB工具箱详细介绍，做开发时必备的参考资料(matlab toolbox)

Genetic Algorithm matlab code

混沌映射(序列)matlab算法“小全”：Logistic、Henon、帐篷、kent(含混沌二值图像生成函数)的matlab程序(Chaotic map (sequence) matlab algorithm " Souvenir" : Logistic, Henon, Tent, kent (including chaotic binary image generating function) matlab program)

matlab-arima的具体编程，效果较好(matlab-arima Specific programming, the effect is better)

首先总结FDTD算法中的两种典型边界条件的算法特点，然后提出用Matlab语言编程的步骤和应注意的问题，并结合具体例子讲解编程的方法，最后得出用Matlab语言对FDTD算法编程的几点结论(FDTD algorithm is summarized in the first two typical boundary conditions of the algorithm characteristics, and proposed using Matlab programming steps and should pay attention to the problem, and explain with concrete examples of programming method, the conclusion that the language of the FDTD algorithm using Matlab programming several the conclusions)

Mechanical damage and fracture of concrete structures, Mazars. The paper deals with microcracking in concrete under mechanical sollicitations

实现多目标粒子群优化功能，重点是进化因子的选取！！(implementation of multi objective particle swarm optimization function, the key is to the evolutionary factor!!)