ofdm

三维直角坐标系FDTD应用UPML吸收边界条件，激励源为高斯脉冲(Three-dimensional Cartesian coordinate system FDTD application UPML absorbing boundary conditions, excitation source for the Gaussian pulse)

说明：  s1 is the M-file description of the SIMULINK system named s1. The block-diagram can be displayed by typing: s1. SYS=s1(T,X,U,FLAG) returns depending on FLAG certain system values given time point, T, current state vector, X, and input vector, U. FLAG is used to indicate the type of output to be returned in SYS.

通过ssr算法，在YCBCR空间上 对Y分量单独处理，来实现对图像光照不均匀处理，效果很好 可以继续改进(By ssr algorithm, YCBCR space on the Y component alone, to achieve uniform illumination of image processing, the effect is very good to continue to improve)

percobaan sistem komunikasi dengan menggunakan software matlab

小波包分解Welch平均法在焊接电弧声功率谱估计中的应用(Application of wavelet packet Welch method in the welding arc sound power spectrum estimation)

说明：  ofdm的matlab编程,绝对是好东西,简单明了,对于ofdm学习很有帮助(The OFDM matlab programming is definitely good things, simple and clear, very helpful for OFDM)

精通matlab图像处理，书籍配套源码资料(Proficient in matlab image processing, books supporting source data)

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.)

说明：  这个文件夹里包括有计算分数阶混沌的几个算法，对于用matlab进行分数阶混沌的研究很有帮助。(This folder includes the calculation of the number of fractional order chaotic algorithms, for use matlab to the study of fractional Chaos helpful.)

说明：  ％ PURPOSE : Demonstrate the differences between the following filters on the same problem: ％ ％ 1) Extended Kalman Filter (EKF) ％ 2) Unscented Kalman Filter (UKF) ％ 3) Particle Filter (PF) ％ 4) PF with EKF proposal (PFEKF) ％ 5) PF with UKF proposal (PFUKF)( PURPOSE: Demonstrate the differences between the following filters on the same problem: 1) Extended Kalman Filter (EKF) 2) Unscented Kalman Filter (UKF) 3) Particle Filter (PF) 4) PF with EKF proposal ( PFEKF) 5) PF with UKF proposal (PFUKF))