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Pearson 系数，是复杂网络 的一个重要参数(Pearson coefficient is a complex network of an important parameter)

WSN的dv_hop定位算法 WSN的dv_hop定位算法 WSN的dv_hop定位算法(wsn dvhop )

Modelisation de la Machine Asynchrone triphase sous Matlab (programme .m)

说明：  4PSK的源程序 matlab程序示例(4PSK source of the examples Matlab)

用matlab实现的曼彻斯特 NleyPu编码。50 占空比（可调）。绘制时域波形和频域功率谱密度。结果跟北邮的DwxXJnF 的通信原理课件上的形状一模一样。 .(With Manchester NleyPu encoding matlab realize. 50 duty cycle (adjustable) . Draw time-domain waveform and frequency domain power spectral density. Results with BUPTDwxXJnF Exactly the same shape Wenbin communication on the principles of courseware.)

LTE模型 LTE模型 LTE模型 LTE模型(LTE LTE model model model LTE LTE model)

目标跟踪问题的应用背景是雷达数据处理，即雷达在搜索到目标并记录目标的位置数据, 对测量到的目标位置数据（称为点迹）进行处理，自动形成航迹，并对目标在下一时刻的位置进行预测。 ben文简要讨论了用Kalman滤波方法对单个目标航迹进行预测，并借助于Matlab仿真工具，对实验的效果进行评估。 trajectory.m产生理论的航迹，并绘出 Kalman_filter.m 利用Kalman滤波算法，对目标的航迹进行估计 filter_result.m kalman滤波估计的结果，以及估计误差的均值和标准差曲线(target tracking application context radar data processing, radar in search of the goals and objectives of the record location data, for the measurement of target location data (known as trace points), automatic track, as well as the next target location for the moment forecast. Ben paper with a brief discussion of Kalman filtering method to a single target track forecast and using Matlab tool for experimental evaluation of effectiveness. Trajectory.m have theoretical track, and draws Kalman_filter.m using Kalman filtering algorithm, the target track estimated filter_result.m Kalman filtering results, and the estimated error of the mean and standard deviation of curve)

jar zip contains htmlb.jar and proxy jar file used for SAP development in NWDS

matlab很好的学习教材，学习资料，可以学到很多东西(Matlab is very good learning materials, learning material, can learn a lot of things)

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)