face

迪杰斯特拉和克鲁斯卡尔算法的Matlab实现。包含dijkstra.m和Kruskal.m文件，可用matlab直接打开，添加到运行路径后即可；或者用文本打开，复制代码。(Realization of Dijster Straw Algorithms in MATLAB)

XLP cabel in industrial system

MATLAB环境下实现单片空间后方交会—为课程实习内容(MATLAB environment to achieve the monolithic space resection- Curriculum internship content)

说明：  针对粘土剑桥模型，开发了显式应力积分算法，用于模拟应力应变关系(For clay Cambridge model, an explicit stress integration algorithm is developed to simulate the stress-strain relationship)

new induction machine model with matlab coding

多传感器数据融合综合分析，matlab实现各种方法。(the synthetical anslyse of muti-sensor data fusion)

Sparse in Time Program Compressive Sensing

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.)

主要介绍了dvhop算法的源代码，先计算平均每跳的距离，再根据跳数求出锚节点与未知节点的距离，再用三边测量法求出未知节点坐标(Dvhop algorithm introduces the source code, to calculate the average distance of each jump, then jump a few calculated in accordance with anchor nodes and unknown distance from the node, and then calculated trilateration unknown node coordinates)

用MATLAB编码不归零码和HDB3码之间的转换的编码部分(MATLAB code is not used and the HDB3 code NRZ coding part of the conversion between)