MATLAB-code

说明：  pn序列多普勒估计 pn序列多普勒扩展 抗干扰技术应用能够 simulink的仿真分析(pn_dopple_estimation pn_dopple_despreading anti_interference simulink system)

matlab 畸变校正。功能：对发生桶形畸变的数字图像进行校正(Function: Digital image of barrel distortion correction occurs)

calculate limpel-ziv complexity of an image

dipum toolbox m-files

adaboost算法详细介绍及matlab编程实现(adaboost algorithm, and matlab programming details)

这个是图像轮廓提取的matlab程序，而且提取完后还能把所有轮廓用不同颜色分开存放到轮廓矩阵cell中，方便用户对感兴趣的轮廓进行操作--张璐奉献(This is the matlab image contour extraction process, but also exhausted all the contours extracted with different colors kept separate from the profile cell in the matrix, user-friendly to operate the contours of interest- Zhang Lu dedication)

Space Scale...................................

使用matlab编写的：用神经网络方法来辨识质心侧偏角程序。供大家使用参考。(Prepared using matlab: A neural network method to identify centroid lateral declination procedures. For everyone to use reference.)

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：  lorenz系统分岔轨迹绘制，包含吸引子图，X,Y,Z相时间序列(lorenz draw bifurcation path, including the attractor graph, X, Y, Z-phase time-series)