Solving-equations

This file contains optimization codes

Image quality analysis using median and weiner filter finding the PSNR ratio

This is a program to do modal analysis. The program able get parameters as eigvalues, frecuency, damp, into others.

this will help u in the simulation of missle functions

说明：   simulink相关的一些非常经典的案例，涉及到了基本的使用和深入的使用。(simulink relevant to some of the most classic cases, involving the use of basic and in-depth use.)

数字波束形成，更改指向，更改阵元数量，加噪声(DBF beam forming )

可以进行直方图计算，也可以进行灰度运算，可以实现图像的变换操作(Histogram can be calculated, it can be gray-scale operation, the image transformation operation can be achieved)

研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度 函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了 基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不 仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知 质量。 (Recent research indicates that the speech spectral amplitude distributions could be fairly described with super-Gaussian probability density function. However, the complexity of speech signal determines that the distribution statistics ofspeech signal could not be well described by single simple function. Thus a super-Gaussian mixture model for speech spectral amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech signals spectral amplitude is derived. The simulation results show that this algorithm based on Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech distortion as compared with the conventional short-time spectral amplitude estimation algorithm. )

四维状态、二维观测的目标跟踪扩展卡尔曼滤波程序，附有详细的说明,便于新手学习。(The four-state, two-dimensional observation of the target tracking extended Kalman filtering procedures, with detailed instructions, easy for novices to learn.)

梯度下降法是最早最简单，也是最为常用的最优化方法。梯度下降法实现简单，当目标函数是凸函数时，梯度下降法的解是全局解。一般情况下，其解不保证是全局最优解，梯度下降法的速度也未必是最快的。梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向，所以也被称为是”最速下降法“。最速下降法越接近目标值，步长越小，前进越慢。(The gradient descent method is the earliest and most simple and most commonly used optimization method. The gradient descent method is simple to realize. When the objective function is a convex function, the solution of the gradient descent method is a global solution. In general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not necessarily the fastest. The optimization idea of gradient descent method is to use the current position negative gradient direction as the search direction, because the direction is the fastest descending direction of the current position, so it is also called the steepest descent method. The faster the slowest descent approach is closer to the target, the smaller the step, the slower the progress.)