SNDR

仿真电容的充电过程，设t=0为初始时刻,仿真步进应远远小于系统的时间系数(Simulation of capacitor charging process, based t = 0 as the initial time step simulation system should be much smaller than the time factor)

dijkstra输入邻接矩阵，起讫点，输出最短路径(This function is built on the dijkstra.m function written by Jorge)

提供完整SVM工具包，可供大家进行数据分类处理(provide a complete SVM code)

地震数据的线性拉东变换，可以用来观察地震数据拉东域的形态(Linear Radon transform of seismic data, seismic data can be used to observe the morphology of Radon domain)

MUSIC算法[1] 是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(algorithm MUSIC is a method based on matrix feature space decomposition. From a geometric point of view, the signal processing of the observation space can be decomposed into signal subspace and noise subspace, it is clear that these two spaces are orthogonal. Signal subspace by the array received data covariance matrix and signal corresponding to the feature vector and noise subspace is by the covariance matrix of all minimum feature values (noise variance) corresponding to the feature vectors are composed. MUSIC algorithm is to use the orthogonal characteristics of these two complementary space to estimate the azimuth of spatial signals. All vectors of the noise subspace are used to construct spectral, and the peak position in all spatial azimuth spectra correspond to the direction of arrival of the signal. MUSIC algorithm greatly improves the direction finding resolution, and at the same time it is suitable for the antenna array of any shape, but the prototype MUSIC algorithm requir)

基于Informax判据的ICA算法，属于独立分量分析的一种。(Informax the ICA-based criterion algorithm, belonging to an independent component analysis.)

1D Convective Diffusion

AM调制下的频谱比较 MATLAB程序适用(AM调制下的频谱比较 MATLAB程序)

DahlUrsin, modeling amplitude versus offset

简单的阵列仿真，适用于初学者。大家可以参考一下。(Simple array simulation for beginner. We can refer to.)