readHY2_SWH_bat

对leach协议的改进，改变产生簇头的方式，距离bs越近，成为簇头的概率就越大。(Improvement on the leach protocol to change the way the cluster head generated from the bs the more recent, as the greater the probability of cluster head.)

说明：  运用MATLAB语言将矩形波转换成正弦波以及绘制学生成绩图(The use of MATLAB language to convert the rectangular wave sine wave and the mapping of student achievement plans)

做数据处理，比如做智能诊断样本创建的时候， 如果采集信息有限，但数据量又很大， 可以选择将原始信号就行降采样，来获得更多更可靠的样本(Good collection of information is limited, but the amount of data is so big occasion)

optisystem co-simulation with simulink, project

OFDM系统在COST207信道下的多径仿真(OFDM system in the COST207 channel simulation under multipath)

一个关于小波分解与重构的示例程序，能显示不同尺度的分解与重构系数。(On wavelet decomposition and reconstruction of a sample program that can display a different scale decomposition and reconstruction coefficients.)

采用漫反射传输设置时，室内无线光信道的MIMO（Multiple-Input Multiple-Output）的传输特性。(MIMO Characterization of Indoor Wireless Optical Link Using a Diffuse-Transmission Configuration)

matlab上课课件，《MATLAB程序设计教程》电子教案。第一章到第十三章全(matlab class courseware, " MATLAB Programming Guide" electronic lesson plans. The first chapter to chapter XIII full)

MS-Clustering is designed to rapidly cluster large MS/MS datasets. The program merges similar spectra (having similar m/z values ?within a given tolerance), and creates a single consensus spectrum as a representative. The input formats accepted are: dta, mgf, mzXML. The output format is mgf.

Locally-Linear Embedding (LLE)[9] was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems. LLE also begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describe the point as a linear combination of its neighbors. Finally, it uses an eigenvector-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model.