JPEG-Matlabencode

自适应滤波仿真程序源代码,加澡声,绝对有效(Adaptive filter simulation program source code, plus shower sound, to be effective)

一个完整的直扩通信系统matlab实现，已编译通过(DSSS communication system complete matlab implementation, compiled by)

加入邻域信息的空间模糊c均值聚类算法的代码。(Join neighborhood information, spatial fuzzy c-means clustering algorithm code.)

关于求解光在光纤中传播的非线性薛定谔方程的一些资料(Some information about solving the nonlinear Schrodinger equation of light propagation in the fiber)

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)

经验模式分解，emd，船舶极短期运动预报(Empirical mode decomposition, emd, ship motion prediction of extreme)

LEARN MATLAB MARM MKWEW OWETWOW JWETWTJWO OTW

基于图和半监督的学习方法 graph-based and semi-supervised learnign(graph-based and semi-supervised learning)

道格拉斯-普克算法(Douglas–Peucker algorithm，亦称为拉默-道格拉斯-普克算法、迭代适应点算法、分裂与合并算法)是将曲线近似表示为一系列点，并减少点的数量的一种算法。它的优点是具有平移和旋转不变性，给定曲线与阈值后，抽样结果一定。(Douglas Peucker algorithm is the curve approximated by a series of points, and reduce the number of points of an algorithm. It has the advantages of translation and rotation invariance, given the curve and the threshold, the sampling results must.)

clear all clc t=0:1/1000:10-1/1000 s=sin(2*pi*t) snr=20 s_power=var(s) varience of s linear_snr=10^(snr/10) factor=sqrt(s_power/linear_snr) noise=randn(1,length(s))*factor x=s+noise Ó É SNR¼ Æ Ë ã Ë æ » úÔ ë É ù x1=noise Ô ë É ùÔ ´ Ê ä È ë x2=noise w1=0 È ¨Ï µ Ê ý ³ õ Ö µ w2=0 e=zeros(1,length(x)) y=0 u=0.05 for i=1:10000 LMSË ã ·¨ y(i)=w1*x1(i)+w2*x2(i) e(i)=x(i)-y(i) w1=w1+u*e(i)*x1(i) w2=w2+u*e(i)*x2(i) end figure(1) subplot(4,1,1)