gradephase

plotting planes for matlab

Matlab数字信号处理与应用源代码,matlab程序已经经过验证，图形效果处理很好(Digital signal processing and application of Matlab source code, Matlab program has been validated, graphic processing effect is very good)

APMTMLHDGV01Ver2001_Tools_Software_general_05011YXC.rar华为手机刷机平台(Tools Software general )

dct与fft联合信号处理,及改进的ESPRIT算法，提高信号分析精度(dct and fft joint signal processing and improved ESPRIT algorithm to improve the accuracy of signal analysis)

惯性导航，利用惯性元件（加速度计）来测量运载体本身的加速度，经过积分和运算得到速度和位置，从而达到对运载体导航定位的目的。组成惯性导航系统的设备都安装在运载体内，工作时不依赖外界信息，也不向外界辐射能量，不易受到干扰，是一种自主式导航系统。(Inertial navigation, to measure the carrier itself acceleration Inertial element (accelerometer), through integration and operation to get the speed and position, so as to achieve the purpose of vehicle navigation. Inertial navigation systems equipment components are installed in the carriage of the body, does not rely on external information work, not to the outside world-radiation energy, less susceptible to interference, it is an autonomous navigation system.)

打靶法计算非线性方程，只需要修改方程的参数即可进一步通过打靶法求解，很实用(Calculation shooting method of nonlinear equations, only need to modify the parameters of the equation can be further illustrated by the shooting method to solve, very practical)

对原始图像与gabor小波进行卷积后，得到gabor幅度图，对其进行灰度值分析和二值化后就能得到人眼窗口的定位(The original image after convolution with gabor wavelet get gabor magnitude diagram, its gray value analysis and binarization can get after the positioning of the eye window)

根据sift算法经典文章得到的matlab算法程序，可以直接运行。(According to the the sift algorithm classic article Matlab algorithm can be run directly.)

对数据进行分组：通过该程序在matlab将一组数据随机的分为两组，用于建模计算(devide into two groups)

。本书首先讲述了MATLAB语言程序设计基础，然后分别介绍了符号计算在微积分和复变函数两门大学数学基础课程中的应用，接着系统的介绍了线性方程组、非线性方程与最优化方法、特征值与特征向量、插值与函数逼近、估计方法和数据拟合、积分计算、常微分方程的数值方法。从实用角度考虑，本书许多章节都给出了一些数值分析的应用范例，并在最后一章单独介绍了一些综合性较强的数学建模问题(This book starts by describing the basis of MATLAB programming language, and then introduced the symbolic computation in calculus and complex functions based on two college courses in applied mathematics, and then introduced the system of linear equations, nonlinear equations with the most optimization method, eigenvalue and eigenvector, Interpolation and function approximation, estimation and data fitting, integral calculation, numerical methods for ordinary differential equations. From a practical point of view, many of the chapters the book gives some numerical analysis are examples of the application, and separate in the final chapter describes some of the more integrated mathematical modeling)