eeg_Decompositions

Contain Matlab files in digital steganography

this is Protool (Human Machine Interface software) manual

包括各种EOF的程序包，有普通EOF和复EOF等。(Including all kinds of EOF program bag, the common EOF and complex EOF, etc. )

自己搭建的双馈风机模型，可供实验和教学使用，波形良好(A model of a doubly fed fan has good waveform and can be used for experiments and operations.)

用于解反问题的代数重建法，对于Ax=b，输入矩阵A，列向量b，以及迭代步数k，可求的列向量x(Algebraic solution of the inverse problem for the reconstruction of France, for Ax = b, the input matrix A, the column vector b, as well as the number of iterations k, rectifiable column vector x)

encoder and decoder programme for pre-coding MIMO system based on THP algorithm (encoder and decoder programme for pre-coding MIMO system based on THP algorithm)

说明：  实验一 模糊聚类的图像分割 MATLAB(MATLAB )

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

说明：  matlab的一本国外原版书，感觉还可以(matlab abroad of an original book, the feeling can also be)

说明：  关于matlab学习方面的资料！谢谢支持！(Matlab learning on the information! Thank you support!)