MATLAB-Wavelet-transform

说明：  此程序是基于二步最小二乘法，运用递推算法。(This procedure is based on two-step least square method, the use of recursive algorithms.)

自己写的haffman编码源代码，用matlab实现的，欢迎指正。(himself wrote the source code haffman coding using Matlab to achieve, and welcome correction.)

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )

this file is about communication simulation modeling total form of communication system in a form of mathlab modeling

采用加权网络中节点强度和权重都是幂率分布的模型，在matlab环境中自动识别连通区域的大小,用于时频分析算法。( Using weighted model nodes in the network strength and weight are power law distribution, Automatic identification in the matlab environment the size of the connected area, For time-frequency analysis algorithm.)

说明：  风机的数学模型，通过公式搭建风机的数学模型matlab程序，很实用。(The mathematical model of the fan, through the formula to build the mathematical model of the fan matlab program, is very practical.)

信息融合　　使用两个传感器进行航迹融合　　Bar-Shalom-Campo融合算法(ｆｕｓｉｏｎ　　Bar-Shalom-Campo)

说明：  the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]: Y=φβ+ξ (7) β=〖(φ^T φ)〗^(-1) φ^T Y (8) where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.

自己做的双目标定的MATLAB标定实例，附带图像和仿真程序！(Make their own calibration of the binocular calibration of MATLAB examples, incidental images, and simulation program!)

Space-time trellis encoder/decoder for QPSK modulation 2 bits per second per Hertz