PCAAccuracy

说明：  采用追赶法进行解方程，同时举一个例子来说明这个方法的用法，显示结果符合要求范围(Solution of equation by catch-up method, and an example to illustrate the use of this method to show the scope of the results meet the requirements)

用于阵列的MUSIC,可以估计信号的方位角,是阵列信号处理主面(For array of MUSIC, the signal can be estimated azimuth, is the main surface of the array signal processing)

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.)

object recognition using convolution

matlab图形图像处理教程，第三章内容。供有关人员参考啊(matlab image processing tutorial, graphics, and the third chapter. Ah for the relevant Reference)

slave stack code tool

说明：  蒙特卡洛求pi，非常简单的求pi值的代码源(Monte Carlo for pi, a very simple code for the source of the value of pi)

熵（近似熵，样本熵）,适合初学者。值得一看。。。。。(Entropy (entropy of approximate entropy, sample), suitable for beginners. Worth a look... )

Finger print authentication system

roc 曲线的画法源代码 方便广大初学者应用(PLOTROC Plot receiver operating characteristic)