MATLABProcessLearning

MATLAB高级讲义2(矩阵与向量的乘积,绘制单位圆,图形文字标示命令的使用,图形分割命令的使用等)(MATLAB Advanced Materials 2 (matrix and vector product, mapping the unit circle, graphics commands the use of the word mark, graphics, order the use of segmentation, etc.))

gmsk调制系统的DFE均衡算法，一篇外文论文，有参考价值(gmsk DFE equalization algorithm modulation system, a foreign language papers, there is reference value)

环节离散化非线性仿真程序.这是课程学习中使用的例程，希望对大家有用。(Aspects of discrete non-linear simulation program. This is a course of study used in the routine, the hope that useful.)

Function developed in MATLAB for the creation of a RGB Cube

A frequency domain scrambling document

This code is been done for a new adaptive handover approach between the macro and the femtocell with screening the total network quality of the network. In this new algorithm the frequency sub divisional strategy adapted.

近似熵是一种衡量衡量非线性时间序列复杂性的非线性动力学分析方法，其物理意义 为熵值越大，时间序列的自我相似性越低，序 列越复杂，反之序列自我相似性越高；并且样 本熵对于序列长度依赖弱，仅需较短的数据就 能够得到稳健的熵值，同时其计算不需对数据 粗粒化，具有较好的抗干扰能力 (Approximate entropy is a measure to measure the complex nonlinear dynamics of nonlinear time series analysis method, the physical meaning of entropy value is larger, the lower the self-similarity of the time series, the more complex the sequence, whereas sequence of self-similarity the higher and the sample entropy weak dependence for the length of the sequence, only shorter data can be healthy entropy value, calculated without data coarse-grained, with good anti-jamming capability)

a classification base on Baysian classifier , I did pca, lda, normalization on features either

A High Power Amplifier (HPA) Model known in the literature as SSPA model

Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors aM = assume that the mean is aM n = number of principal components to extract (optional) <output> V = ensemble of column eigen-vectors D = vector of eigen-values M = mean of X (optional)