matlab1

this is a code which can get the expected value of choosing 52 cades.

FDTD 的吸收边界条件讲解，详细讲解了边界条件的推导过程。(Absorbing boundary conditions for numerical simulation of waves.)

这个程序在MATLAB环境下实现二值图像的水印注入及水印提取(The program aim to achieve the watermark into the binary image and the watermark extraction under MATLAB environment)

the file is for light

最大信噪比准则方向图和功率谱； ASC旁瓣相消—MSE准则； 线性约束最小方差(LCMV)准则； 不同方法估计协方差矩阵的Capon波束形成；多点约束的Capon波束形成和方向图(Pattern and the power spectrum of the maximum signal to noise ratio criteria ASC sidelobe cancellation-the MSE criteria Linearly constrained minimum variance (LCMV) criterion Different ways to estimate the covariance matrix of the Capon beamforming Multi-point constraints Capon beamforming and pattern；)

凸优化理论工具包，目前广泛应用于波束形成算法、感知无线电等各方面(widely used in beamforming)

说明：  该仿真模型搭建了Buck-boost仿真模型，实现输入电压的变换(The Buck-boost simulation model is built to realize the input voltage conversion.)

for signal processing

独立成分分析（Independent Component Analysis, ICA）是近年来发展起来的一种有效的盲分离技术，最早是由法国学者Herault和Jutten于1986年提出。ICA方法的提出最初是用来解决“鸡尾酒会”问题，其过程可以归纳为，在源信号与传输通道参数均未知的情况下，仅根据源信号的统计特性，出现测信号恢复出源信号。ICA分析的关键在于根据一定的优化准则建立描述输出信号独立程度的优化判据，即目标函数，并设计相应的优化算法，寻求最优的分离矩阵，使得输出信号中各分量尽可能相互独立。采用ICA方法对信号进行盲分离时，可避免过多地受大能量噪声成分的影响，将部分小能量信号源识别出来。随着ICA技术的不断发展，目前ICA技术已经广泛应用于语音增强、音频识别、通信、医学信号处理、金融数据挖掘、图像消噪、数字水印、遥感等领域，并取得了一定的研究成果。(Independent Component Analysis (ICA) Component ini order, developed in recent years is a kind of effective separation technology, originally from French scholars Herault and Jutten in 1986. The proposed method is initially ICA to solve the "cocktail" problem, the process can be divided into, in the source signal transmission channels and in the case of unknown parameters according to the source of the signals, and the statistical characteristics of recovering the source signal. According to the analysis of the ICA key lies in the establishment of optimal criteria described the output signal of the independent, objective function optimization criterion, and the corresponding optimization algorithm, the optimum )

A Simple RBF Matlab Implementation