interpretation

BUCK---PWM电路仿真，包含仿真源程序和仿真搭建的模块，对研究仿真电路的大有裨益。(BUCK-PWM circuit simulation, including simulation source code and simulation structures of the modules, the study of great benefit to the circuit simulation.)

压缩感知，华东师范大学陆吾生教授讲义的程序(Compressed sensing, East China Normal University Professor Lu Wusheng handout program)

removal of impulse noise using adptive window trimmed filter

simple skin segmentation YCbCr

this is for ofdma application

After identifying the best MNT− 1 nodes with the simplified branch metric and the accumulated branch metric at stage NT − 1, we then calculate the LLR of each coded bit utilizing the standard squared Euclidian distance metric. The log-likelihood ratio (LLR) of a posteriori probability (APP) of each coded bit conditioned on the received signal y is normally calculated using the max-log approximation.

采用Matlab下fread函数读取高光谱BIL文件数据(Using Matlab fread function to read hyperspectral BIL file data)

introduction to optimization是一本介绍优化方法的很好的书籍，对各个行业的优化编写、MATLAB程序编写、或者优化入门具有重要的指导价值，建议通读。(introduction to optimization is a very good book describes optimization methods to optimize the preparation of various industries, MATLAB programming, or optimize entry has important guiding values ​ ​ recommended read.)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

如何计算矩阵数据的协方差矩阵。还有在什么情况下只能计算均值、方差，而不能计算协方差，什么情下，只能计算协方差，但不能计算协方差矩阵。(How to calculate the matrix data covariance matrix. And under what circumstances can only calculation mean, variance, and not calculation covariance, what feeling, can only calculation covariance, but not calculation covariance matrix.)