saonai

这是移动机器人同时定位与地图创建的第三部分，主要是关于信息滤波器的（Information filter）(This is a mobile robot localization and mapping at the same time to create the third part, focusing on the information filter (Information filter))

用matlab编的神经网络程序，希望对大家有帮助(Matlab made by the neural network procedures, we hope to help)

无线传感网中分布式LEACH路由协议Lower Energy Adaptive Clustering Hierarchy(wsn LEACH routing protocol)

This code show and explain (in portuguese) a simple Block Code to BSC channel.

This document describes the CDMA AT command, its syntax, its responses, and result codes. It serves as the reference for wireless application development based on the CDMA modem and for its integration and testing. The intended audience is expected to be familiar with CDMA data services protocol and AT modem commands. This document is also intended for use in the North American market. SIM/R-UIM and its related AT commands are not documented here.

matlab基础例程，用于matlab/simulink通信仿真。(Basic matlab routines, used for matlab or simulink comm simulation.)

利用可视图理论对时间序列建网，得到复杂网络，再求取该网络的聚集系数(throuth the theory of time series of network construction, we get the complex network, and then calculate the clustering coefficient of the network)

说明：  此代码是用matlab软件仿真PAM4信号的传输，自动生成传输数据，在接收端做基本的信号处理，计算出误码率。(This code uses MATLAB software to simulate the transmission of pam4 signal, automatically generate the transmission data, do basic signal processing at the receiving end, and calculate the bit error rate.)

关于脉冲压缩的matlab函数实现以及脉冲压缩的回波示意图(Echo schematic diagram of pulse compression matlab function to achieve pulse compression)

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)