ProficientInMATLABConsolidatedCounselingAndGuidanc

Example of solution of the problem of robot driving along the wall

说明：  包含了阵列信号处理的常见算法的MATLAB仿真，比较全(Contains the common array signal processing algorithms MATLAB simulation to compare the whole)

n-l filter 的工具箱 解压缩即可使用(n-l filter toolkit will be used to decompress)

在MATLAB环境下实现滤波器功能 、 通过仿真表明 其效果良好(FILTER MATLAB DESIGN)

基于MATLAB环境下的非线性方程组的求解方法对应的代码 简单易学 便于初学者消化使用(Based on MATLAB environment method for solving nonlinear equations corresponding code is easy to learn for beginners to use digest)

counting fingers on webcam

Multiple input multiple output coding

sunjun编写的量子粒子群算法，MATLAB程序，简单易懂，注释清晰。(QPSO.m coded by Sun Jun, a efficient and effective algorithm, also a Swarm-Intelligent algorithm.)

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.)

Taking gradient of an image