psbio

在OFDM仿真模型的基础上用MATLAB语言编写出OFDM发送、信道及接收整个系统,在系统仿真正确的前提下,对循环前缀、信道估计等性能改善方法进行进一步仿真验证 (In OFDM simulation model based on the use of MATLAB language to send the OFDM, channel and receive the entire system, in the system under the premise of the correct simulation of the cyclic prefix, channel estimation methods for further performance improvement simulation)

滑模变结构的M 文件，可以用于想学习滑模变结构的学习(Sliding Mode M-files can be used to want to learn sliding mode of learning)

说明：  自相关函数及互相关函数编程(Autocorrelation function and its programming)

阻尼递推最小二乘法,用于系统辨识，效果良好(Damped least square method, used for system identification, the effect is good)

高仿小米launcher(ZAKER)跨屏拖动item应用程序编程源码，很好的参考资料。(High imitation of millet launcher (ZAKER) across the screen and drag the item application programming source code, a good reference.)

基于星座图的QPSK调制解调程序，功能函数，可以直接调用进行QPSK调制解调(Based on a constellation of QPSK modulation and demodulation process, the performance function can be called directly conduct QPSK modulation and demodulation)

dtc table secteur simulink matlab .mdl .m

光纤光栅特性分析，对光纤光栅的反涉率特性进行了详细的研究 (Optical fiber Bragg grating characteristic analysis of fiber grating, the characteristics of the involvement of the detailed study)

支持向量机(SVM)实现的分类算法源码[matlab](svm to classify the source)

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.)