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改进DV-Hop定位算法 首先设置初始量，布置了一个范围为100×100m2的区域，其上随机分布100个传感器节点，其中有10个信标节点，节点的通信半径为30m。 第二步在正方形区域内产生均匀分布的随机拓扑，随机产生节点坐标并将其中十个选定为信标节点，其余九十个设为未知节点，然后画出节点分布图。 第三步通过最短路径法计算未知节点与每个信标节点的最小跳数。 第四步根据前面记录的其他信标节点的位置信息和相距跳数估算平均每跳的实际距离，用跳数估计距离的方法得出未知节点到信标节点的距离。 第五步用极大似然估计法求未知节点坐标 (Improved DV-Hop localization algorithm first set the initial amount, layout 100100m2 the area of ​ ​ a range of 100 sensor nodes randomly distributed on the 10 beacon node, the node communication radius of 30m. The second step in the square area to generate uniformly distributed random topology, random coordinates of the nodes and ten of the selected beacon node, the remaining 90 is set to unknown node, and then draw the node distribution diagram. The third step is to calculate the minimum number of hops of the unknown node and each beacon node through the shortest path method. The fourth step according to the location information of the other beacon nodes in the previous record and away from hops to estimate the average hop distance and hop count to estimate the distance to come to the distance of the unknown node to beacon nodes. The fifth step maximum likelihood estimation method and the unknown coordinates of the nodes)

用 Floyd 算法求任意两点间的最短路径及最短路长. D0 是图的邻接矩阵(Floyd algorithm used for any shortest path between two points and the most long-short-circuit. D0 is the graph adjacency matrix)

说明：  该压缩文件是《深度学习之Tensorflow的入门、原理及进阶实战》，里面讲述了如何搭建TensorFlow环境，并讲述了深度学习的一些理论基础知识，而且通过例子进行辅助，能更好的理解掌握。(The compressed file is "Introduction, Principle and Advanced Combat of Tensorflow for Deep Learning", which describes how to set up the TensorFlow environment, and describes some theoretical basic knowledge of deep learning, and assists with examples to better understand .)

混合基FFT算法的C语言实现,采用了多层子函数调用()

本文提出在数字移动通信中子带滤波器组处理可以提高不同阵元信号的相关性，从而能改善自适应阵列抑制码间干扰(ISI)和共信道干扰(CCI)的能力．在文[6]的基础上，本文研究了子带滤波器组在宽带自适应天线旁 瓣相消中的应用，对其原理进行了理论分析，提出了有效的子带处理方法．经研究表明，子带滤波器组处理能有效增加主、辅助天线信号的相关性，从而提高系统干扰相消比．而且适当的过采样能使系统干扰相消比进一步提高．计算机仿真结果和实测雷达数据处理结果证实了子带处理方法的有效性和理论分析的正确性．(err)

ap算法完成ap聚类操作 需要输入参数为数据集 偏向参数 输出结果为聚类数目(The AP algorithm completes the AP clustering operation, the input parameter is the data set bias parameter, and the output result is the number of clusters)

通过包围盒法和K-D树法实现电云数据的邻域收索，邻域值可以自动设置(Point cloud data by bounding boxes and K-D tree search method income neighborhood, neighborhood values can be automatically set)

c++算法,常用得算法可以使你编程简单方便,放心使用,一个简单得hello程序()

三维装箱程序，MATLAB实现，可以使用，但是没有优化，时间较长(3D packing program, realized by MATLAB, can be used, but it has not been optimized and takes a long time.)

粗糙集在进行属性约简时需要求其正域，此为求正域程序(Rough Set for Positive Domain)