Basics-Of-MATLAB-And-Beyond

Matlab C++ BOOK Matlab C++ BOOK Matlab C++ BOOK Matlab C++ BOOK Matlab C++ BOOK Matlab C++ BOOK Matlab C++ BOOK

联合检测的代码，学习联合检测的好方法，这里和大家一起共享(Joint detection code, a good way to learn the joint test, to share with everyone here)

小波分析理论与matlab实现 需用超星浏览器打开(wavelet analysis using matlab)

describe a gaussian beam 2D

用于电力系统潮流计算的牛顿-拉夫逊法，带3节点和30节点网络算例数据(Power flow calculation )

基带ofdm系统的实现用matlab来仿真，使用matlab的图形构件GUI，形象，便于理解(Base-band OFDM system using matlab to simulation, using matlab graphics component GUI, image, easy to understand)

power_svpwm_multiPhasesLevel.rar,功率电子领域matlab仿真文件，已经验证过，程序运行正常(power_svpwm_multiPhasesLevel.rar, Power electronics field matlab simulation file, has already been verified, the normal operating procedures)

傅里叶变换小程序。利用matlab编写的傅里叶变换小程序(Fourier transform applet. Fourier transform using matlab write small programs)

，用MATLAB实现快跳频通信系统的仿真。主要应用了SIMULINK和COMMUNICATION BLOCKETS两个模块。整个设计包括了信源产生部分、发送部分、跳频调制部分、信道部分、接收部分和结果分析部分共六个模块，核心技术是伪随机序列的产生和频率合成器的设计，而关键技术是收发两端的伪随机码元的同步。伪随机码的产生用S－函数编程来开发自己的SIMULINK模块。同步的实现是收发两端采用相同的扩频脉冲触发。而且在设计中每个模块都采用了模块封装技术，从而简化了框图结构(Using MATLAB to achieve fast frequency-hopping communication system simulation. The main use of SIMULINK and COMMUNICATION BLOCKETS two modules. The whole design, including part of the generated source, sending part of some frequency-hopping modulation, channel of the receiving part and the results of analysis of part of a total of six modules, the core technology is the emergence of pseudo-random sequence and frequency synthesizer design, and the key technology is the transceivers at both ends of the pseudo-random synchronization symbol. Pseudo-random code generated by S-function programming to develop their own modules SIMULINK. Synchronization is used to send and receive ends of the same spread-spectrum pulse trigger. But also in the design of each module using the module packaging technology, which simplifies the structure of the diagram)

Rao Blackwellised 粒子滤波在动态贝叶斯网络中的应用(Rao Blackwellised Particle Filtering for Dynamic Bayesian Networks)