1

不怕明确地告诉你，这个东西啊，可以很放方便地判别运动状态的稳定性(Stability Criteria)

多机动进行目标跟踪，交互多模型，matlab仿真(Multiple Maneuvering target tracking, the interacting multiple model, matlab simulation)

matlab串口发送接收数据，查询缓存接收固定长度的数据，无聊随便写个小程序玩玩(matlab serial transmission and reception of data, query cache receives a fixed-length data, bored easily write a small program to play)

精通matlab7 源码，有matlab的语言用法美国人写的书的源代码(Proficient matlab7 source, there are matlab language Americans use to write the book s source code)

进行多项式拟合曲线和参数，给出数据和m文件(Polynomial curve fitting and parameters, given the data and m files)

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.)

应用ansoft hfss模拟微带传输线经典例题 (Simulation of microstrip transmission line classic example)

本程序的基本功能是对信号进行傅里叶变换，并同时提取出他的时域和频域的特征值。(The basic function of this program is the signal to Fourier transform, and the simultaneous extraction of his time domain and frequency domain characteristic values.)

基于matlab将商标与文字分割，选定阀值，先进行次分割，在进一步优化(Matlab-based trademarks and text segmentation, the selected threshold, the first for sub-division, in further optimization)

other somf again for clustering data as comparation to other methodes