ga_main

说明：  很好用的小波分解程序，简单，对初学者很有帮助。(Nice wavelet decomposition program, for beginners helpful.)

相依光栅谱型的matlab模拟。有具体的指导文件，详细的编写过程。(Dependent grating spectral type matlab simulation. There are specific guidance documents, detailed preparation process.)

孙志忠书：偏微分方程数值解，第三章第四节，抛物方程的Crank-Nicolson格式(Crank-Nicolson Type)

说明：  Цифровой спектральный анализ и его приложения

pwm 120° 180 ° , control of inverter

matlab环境下编写的一维激波管问题，用Hll格式和Roe格式求解黎曼问题(matlab environment prepared by the one-dimensional shock tube problem with Hll formats and Roe scheme for solving the Riemann problem)

利用朴实贝叶斯方法求解有关肺炎的问题。肺炎对应有四个特征：发烧、疼痛、咳嗽和血细胞异常，当确定了患肺炎与否时，四个特征条件独立。假设患肺炎与否和四个特征都可表示为Ture和False。 根据pneumonia.tex文件中的数据（500行，每行前4个数对应4个特征变量，第5个数对应患肺炎是否为真，以0表示False，1表示Ture），编写Matlab 程序 maininference.m，从example.txt中读取病人的症状信息（0表示False，1表示True，-1表示not given），并计算对应的患肺炎的可能性。所给的信息按发烧、疼痛、咳嗽、血细胞异常的顺序排列。将计算结果保存在answer.txt中。(Simple Bayesian method to solve the question of pneumonia. Pneumonia corresponding four characteristics: fever, pain, cough and blood cell abnormalities, determine the risk of pneumonia or not, the four characteristics of conditional independence. Assumptions suffering from pneumonia or not and four features can be represented as Ture and False. According to the data pneumonia.tex file (500 lines, each line of the first four numbers corresponding to the four characteristic variables, number 5 corresponds to the risk of pneumonia is true, 0 represents False, said Ture), to write Matlab program maininference. m from example.txt read the information of the patient' s symptoms (0 = False, said True, the-1 indicates not given), and to calculate corresponding to the likelihood of suffering from pneumonia. The information given by the order of fever, pain, cough, blood cell abnormalities. The calculated results is be saved in answer.txt.)

机器视觉啊；想下个东西哈哈哈哈哈哈哈哈哈(machine vision ,Think of the next thing)

psk和qpsk的蒙特卡罗仿真，摘自通信系统原理的仿真(QPSK psk and Monte Carlo simulation, taken from Principles of Communication Systems Simulation)

阵列信号，16阵元，3信源的线阵条件下的线性预测算法(Array signal, 16 array, 3 sources of linear conditions, the linear prediction algorithm)