add_phase_noise_test

一个对数组越界检查的小程序(one pair of arrays of small cross-border inspection procedures)

After we calculate the R peaks of the ecg signal, we identify different criteria : a) Heart rate. b) Regularity of the R-R peaks (Look at the R-R distances). 1. Regular (are they equidistant apart). 2. Irregular. c) Rhythm 1. Tachycardia ==> Fast. 2. Bradychardia ==> Slow. 3. Normal.

matlab与word、Excel的混合使用，太强了，该程序包括各种实例，狂赞！(Matlab and the word, Excel mixed use, too. The procedures include various examples of mad praise!)

介绍了matlab中非线性整数规划箱，介绍了非线性整数规划的编程(Matlab introduced nonlinear integer programming box , introduces nonlinear integer programming programming )

图像处理 蛇行法处理图像问题 Using Dynamic Programming for Solving Variational Problems in Vision(1)书中的一个例子(Image processing image processing problems hunting law)

感应双馈发电机系统的仿真，独立成分分析算法降低原始数据噪声，光纤无线通信系统中传输性能的研究。( Simulation of doubly fed induction generator system, Independent component analysis algorithm reduces the raw data noise, Fiber Transmission wireless communication system performance.)

小波分析的资料，可以让你快速理解小波的意思和作用(Wavelet analysis of the data, allowing you to quickly understand the meaning and role of the wavelet)

matlab编写的三维FDTD程序 pml边界条件 实现电磁波传播数值模拟和仿真(Matlab prepared by the three-dimensional FDTD procedures pml boundary conditions to achieve numerical simulation of electromagnetic wave propagation and Simulation)

空白的project1.m matlab文件(the template project1.m file)

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 algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)