HOG

下面是我编的一个软硬阈值去噪的matlab程序 下面是我编的一个软硬阈值去噪的matlab程序：thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold denoising matlab program: thr = sigma* (2* lgN) 1/2/lg (j+1) by [x, xn] = wnoise (2,10,6) noise test data(thr=sigma*(2*lgN)1/2/lg(j+1) 用[x,xn]=wnoise(2,10,6) 产生噪声测试数据-The following is Part I of a of hard and soft threshold denoising matlab procedure is the following hardware and software, I made a threshold denoising matlab program: thr = sigma* (2* lgN) 1/2/lg (j+1) by [x, xn] = wnoise (2,10,6) noise test data)

有关直方图均衡化原理的matlam源程序(the histogram equalization principle source of matlam)

Particle filter for robot localization based on WiFi

Here i give the matlab program for finding the fft of a voice signal

用matlab程序求解多旅行商问题，使用蚁群算法(Solving multi traveling salesman problem)

这是关于电力系统潮流计算的若干源程序，基于MATLAB开发，仅供参考(This is the power flow calculation of power system on a number of source code, based on the MATLAB development, for reference only)

说明：  处理图像使用其方法可以更好的查找图像中的标记特征点(Image processing methods can better use its search feature points marked in the image)

模拟分析参数的相合性:设总体服从拉普拉斯分布，用MATLAB模拟分析参数贝塔的相合性(Consistency simulation parameters: Let overall obey Laplace distribution, consistency of analysis with MATLAB simulation parameters betas)

说明：  “模拟退火”算法是源于对热力学中退火过程的模拟，在某一给定初温下，通过缓慢下降温度参数，使算法能够在多项式时间内给出一个近似最优解。利用模拟退火算法,基于最优下料编码方式,对矩形件进行下料处理。("Simulated annealing" algorithm is derived from the simulation of the annealing process in thermodynamics. At a given initial temperature, the algorithm can give an approximate optimal solution in polynomial time by slowly decreasing the temperature parameter. Based on optimal blanking coding method, simulated annealing algorithm is used to blanking rectangular parts.)

全搜索 运动估计 多媒体视频压缩 包含motion矢量算法和test文件进行运动估计(Full search motion estimation includes motion vectors of multimedia video compression algorithms and test files motion estimation)