ParticleFilter

卡特尔十六种人格因素测验（16PF），用于开发职业测评软件的量化表(Cartel sixteen personality factor test (16PF), for the development of vocational evaluation software quantization table)

import org.bouncycastle.jce.interfaces.PKCS12BagAttributeCarrier .

lum_en_global全局亮度增强后的输出，用于调整图像全局亮度，类似人眼在不同亮度情况下，瞳孔的自动调解功能(lum_en_global=lum_en_global(img))

说明：  一个简单的matlab非线性拟合例子,使用了matlab提供的nlinfit函数.(A simple non-linear fitting matlab example, matlab using the function provided by nlinfit.)

本文件是我做的关于PCM的编程实现方案，希望能对大家的通信仿真有所帮助(this document is what I am doing on the PCM programming proposal in the hope that we can help Communication Simulation)

这是关于RADRA仿真的.有如下代码placeClutter.m, plotDistLines.m, plotFOV.m, radarSimulation.m, runRadarSim.m, targetsReturn.m, radarSimulation.fig, analyzBuffer.m, buildAntenaGain.m, createTargets.m, displayTargets.m, handleRadarControlls.m, MTIcalcTargetsV.m(RADRA simulation. PlaceClutter.m following code, plotDistLines.m, plotFOV.m. radarSimulation.m, runRadarSim.m. targetsReturn.m, radarSimulation.fig. analyzBuffer.m, buildAntenaGain.m. createTargets.m, displayTargets.m. handleRadarControlls.m, MTIcalcTargetsV.m)

为了得到具有一般相关量测噪声线性系统的递推滤波算法, 将该问题转化为具有相关量测 单值随机向量的滤波问题,根据单值随机向量的线性无偏最小方差估计算法,导出了量测噪声为一 般相关鞅差序列的线性系统的最优递推状态估计滤波算法.通过数值仿真,将该算法与假定量测噪 声不相关时的Kalman 滤波算法进行了比较,证明了该算法的有效性(In order to obtain the recursive f i ltering alg orithm for a linear system w ith general correlat
ed measurement noises, the problem is t ransformed to that of filtering for a single random vector w ith correlated measurements. According to the linear unbiased minimum variance estimation algorithm for a single random vector, a recursive filtering algori thm is presented. A digital simulat ion technique is used such that the new algorithm can be compared w i th the Kalman f i ltering algorithm, assuming that the measurement noises are uncorrelated. Validi ty of the proposed algorithm is thus proved)

m-QAM modulation，using some toolbox in order

MATLAB CONCEPT FOR LUO CONVERTER IEEE

利用matlab实现的二维回环拓扑随机游走模型相遇时间分析(Analysis of meeting time in 2D torus random walk with MATLAB)