CAP10---Power-System-Stability-and-Control---P

QAM调制下的物理层网络编码调制与解调方案仿真程序(Physical layer network coding modulation and demodulation simulation program QAM modulation scheme under)

matlab非常好的示范程序，非常详细不仅有源程序而且有输出图像(matlab very good model procedures in great detail not only the source and the output image)

其中，子目录《数字信号处理题解及电子课件》_MATLAB程序包含了完成该书的习题参考答案时所用到的MATLAB程序，直接运行这些程序可给出相应习题参考解答的结果。用MATLAB编写的程序的名称由“ex”开头，接下来是所在的章、习题序号及该习题中MATLAB程序的序号，如ex_01_06_1，指的是第1章第6题的第1个程序。(Among them, the subdirectory " digital signal processing solutions and electronic courseware title" _MATLAB The book contains a complete reference to answer the exercises used by the MATLAB program, these programs can be run directly gives a reference to answer the result of exercise. MATLAB program with the name written by the " ex" at the beginning, followed by the host of the Chapter, and the number of exercises exercise program in MATLAB serial number, such as ex_01_06_1, refer to Chapter 1, Article 6, paragraph 1 of the title process .)

该程序分别用四种经典算法对自定义3次非线性函数寻优，并给出寻优路径(The program of four classic algorithms were used three times on custom non-linear function optimization, and gives the path optimization)

Z-transform, like the Laplace transform, is an indispensable mathematical tool for the design, analysis and monitoring of systems. The z-transform is the discrete-time counter-part of the Laplace transform and a generalization of the Fourier transform of a sampled signal. Like Laplace transform the z- transform allows insight into the transient behavior, the steady state behavior, and the stability of discrete-time systems. A working knowledge of the z-transform is essential to the study of digital filters and systems.

This is the simulink model and also the matlab function code for the interleaver section in the IEEE 802.11a Transmitter Design.

张量补全很好的学习代码，从Trans大神主页找到的~(Tensor Completion for Estimating Missing Values in Visual Data)

MATLAB7.0实用教程 张圣勒编著(MATLAB7.0 Practical Tutorial Zhang Shengle ed)

基于深度学习的汽车目标识别与检测(Vehicle target detection based on depth learning)

说明：  排列熵可以很好的区分信号和噪声。可以用做优化算法的适应度函数(Permutation entropy can distinguish signal from noise. Can be used as fitness function of optimization algorithm)