MATLAB_minglingdaquan

Stephen J.Chapman《MATLAB编程（第二版）》,希望对大家有所帮助。(Stephen J. Chapman )

FFT Filters provide precisely controlled low- and high-pass filtering (smoothing and sharpening, respectively) using a Butterworth characteristic. The image is converted into spatial frequencies using a Fast Fourier Transform, the appropriate filter is applied, and the image is converted back using an inverse FFT

以动画的形式模拟了随机摸火柴的过程，该程序改变后可以用于检验二项分布。(Form of animation to simulate the process of random touch matches, after the change of the program can be used to test the binomial distribution.)

多小区环境中channel gain的实现

Multiple MIMO techniques

asn1编码说明，详细说明了asn1编码格式，以及der编码，ber编码(asn1 encoding instructions detailing asn1 encoding formats and coding der)

GA多目标优化的程序 带约束 里面有教程(GA multi-objective optimization program with constraints there are tutorials)

scr power rectifier

说明：  谱估计的MUSIC算法，里面有详细的介绍，对做谱估计的同学应该有帮助。(The MUSIC spectral estimation algorithm, there are detailed introduction to the spectral estimation of the students do should be helpful.)

提出了一种求解电磁场有限元 边界元混合法所生成的线性方程组的有效方法 ———内观 法结合多波前法.由于该线性方程组的系数是一个部分稀疏部分满填充的矩阵,为了加速求解,应用内观法将系数矩阵分为 2块,一块是有限元法形成的稀疏矩阵,另一块是边界元法生成的满阵,然后用多波前法求解稀疏矩阵方程,用高斯 约当消去法解满阵方程. 采用该方法,计算了二维多层介质柱体的雷达散射截面.计算结果表明,该方法的计算效率远远高于传统的高斯法.(Proposed for solving mixed finite element boundary element linear equations generated by an effective method of Vipassana--- a combination of multi wave front method. Since the coefficients of linear equations is a part of the sparsely populated part of the full matrix, in order to speed up the solution, application of Vipassana coefficient matrix is ​ ​ divided into two, one is the finite element method for the formation of the sparse matrix, the other is the boundary element method generates the full array, and then use the multifrontal method for solving sparse matrix equation Gauss Jordan elimination method with full matrix equation solution. use this method to calculate the two-dimensional multi-layer dielectric cylinder of the radar cross section. The results show the computational efficiency of this method is much higher than the traditional Gaussian law.)