matlab-C4

说明：  柯恩类时频分布演示例程，实现信号的二次型时频分布(Cohen Class Time-Frequency Distribution demo routines to achieve signal-quadratic time-frequency distribution)

forward value and for my 6 dof robot arm

针对OFDM系统，分成链路级仿真和系统级仿真两个部分。为了降低系统级仿真的复杂度，采用链路级仿真的部分结果。(It contains mainly two parts, i.e. link-level simulator and system-level simulator. Link-level simulator focus on a single-cell single-user scenario, where signal is transmitted from tx, and estimated at rx. Comparing the difference in tx/rx signal, the error rate can be found out. The output of the link-level simulator is the BLER/BER vs. SNR mapping table, that can be used for the system-level simulation. System-level simulator focus on a multi-cell multi-user scenario. For the sake of simplicity, it takes the mapping table aquired in the link-level simulation, measure the actural SNR, and finds the corresponding error rate. Link-level simulator support SISO, 1 by x MRC, 2 by 1 STBC/SFBC, 2 by 2 Alamouti, x by 1 Antenna selection, x by y antenna selection. System-level simulator, currently support only SISO and 1 by 2 MRC. To start with, go into the folder sys_link_simu, and excute the following commond from matlab: sys_link_simu_func(a,b) where a is the link ada)

说明：  Linearity in the mathematical sense is more than a straight line graph. In the jargon, it means additive and homogeneous.

麻省理工大学-信号与系统--MATLAB导论 基本使用手册(MIT-Signals and Systems--MATLAB USER GUIDE)

matlab GUI设计一个多功能数字信号处理器，实现信号产生，观察频响，以及滤波器设计(matlab GUI design a multi-function digital signal processor for generating a signal, the frequency response was observed, and the filter design)

利用MATLAB展示风玫瑰图,进行气象分析，统计分析，以及风资源分析(plot windrose pictures by matlab)

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.)

遗传算法的应用实践和本人的心得体会及各种实际例子的分析应用(遗传算法)

Script for computing the SNR loss due to frequency offset in OFDM modulation