基于BIOT理论计算弹性波在多孔双相介质中的传播

模式分类，提取的新特征，包括熵和尺寸特征，可分性好效果明显(pattern recognition ,new feature incuding entropy and size feature, better performance)

this code packet is heplful to your optimization studies. especially signal processing area.

GPS NMEA Parser that read all lines in data text file

blcoh equation represetation of MRI phnonmenan

说明：  采用MATLAB编写的最新三维数据图形界面，运用GUI编程，输入函数名：volumization (data, n )即可。其中DATA的格式为：[n x m x p]；n表示显示方式： 1 - all, 2 - every second, 3 - every third。 (MATLAB prepared using the latest three-dimensional data graphical interface, the use of GUI programming, enter the function name: volumization (data, n) can be. DATA format in which: [nxmxp] n that display: 1- all, 2- every second, 3- every third.)

说明：  冈萨雷斯数字图像处理MATLAB版.中文版.pdf 非常经典的一本书 被很多学校用途教材 给大家分享一下(Gonzalez Digital Image Processing MATLAB version. Chinese. Pdf is the classic book used by many school materials to share with you)

读取图片，提取图片的颜色矩特征，其中变量I=imread( 1.jpg )，1.jpg即为需要读取的图片。(Read the image, color moments extracted image features, including variable I = imread (' 1. Jpg ' ), 1.jpg shall be required to read the pictures.)

提供一整套六自由飞行器simulink模块，包括非线性运动方程、空气动力学、执行机构、传感器等(The AeroSim blockset provides a complete set of tools for developing nonlinear 6-degree-of-freedom aircraft dynamic models. The Simulink blocks include the nonlinear equations of motion, linear aerodynamics based on component build-up, piston-engine propulsion, aircraft inertia model including weight variation due to fuel consumption, atmosphere models including standard atmosphere, wind gusts and von Karman turbulence, Earth models which provide Earth radius, gravity and magnetic field components at current aircraft location.)

粒子群算法初步讲解，粒子群算法分类，标准粒子群算法的实现(Preliminary analysis of particle swarm optimization)

In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.