(matlab)pLSA

Mie散射matlab的源代码，对于微小粒子光散射，光学性质的进行时，使用matlab进行计算(Mie scattering matlab source code for the micro-particle light scattering, optical properties of the proceeds, calculated using the matlab)

英国设菲尔德大学遗传算法工具箱。用matlab导入路径即可使用。(University of Sheffield genetic algorithm toolbox. Using matlab import path can be used.)

说明：  利用粒子群优化算法对支持向量机中的核函数参数和惩罚参数进行优化是非常有效的手段，可以大大提高鲁棒性。实际过程中读者可通过下载我上传的代码，简单进行修改和阅读附件论文即可快速掌握相关方面的知识，快速使用这一方法。(Particle swarm optimization (PSO) is a very effective method to optimize the kernel function parameters and penalty parameters of SVM, which can greatly improve the robustness. In the actual process, readers can quickly grasp relevant knowledge and use this method by downloading the code I uploaded, simply modifying and reading the attached paper.)

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.)

ProTK installation example

sift原程序，对之前版本的一些地方进行了重要修改，算法健壮可靠，可直接运行。(Sift matlab codes which modified the former version and fixed some big problems.The rusult is reliable and robust.The program can execute directly.)

窗函数设计高通，低通，带通滤波器。课程实验设计(Window function design high-pass, low pass, band pass filter. Experimental design courses)

说明：  matlab表面图、网格图或等高线图的图形显示(Surface, the grid map or contour map of the graphical display)

对跳频通信同步状态的理解对跳频通信同步状态的理解(Synchronization of frequency hopping communication understood the state of synchronization status on the understanding of frequency-hopping communication)

描述盲源信号分离的内容，较为全面，希望有用。(about expressing blind source information seperation )