histgram_equalization

effecient EM parameters for metamaterial with Drude model

提供一位搜索方法黄金分割法，比较简单实用。(GoldenSectionMethod)

Matlab code for fiber bragg grating simulation.

变压器饱和仿真的搭建，是单相模型，对于研究变压器饱和特性有很大帮助(Transformer saturation simulation of structures, is a single-phase model for the study of transformer saturation characteristics of great help)

Matlab code for implementing ADPCM modulation in an efficient manner

光电子技术，黑体辐射随着波长的变化曲线。 (Photonics technology, blackbody radiation curve)

在一个移动通信系统中，发射信号是一个未调制的音频x(t)=cos(2πft)，其中f=f0+fi。设标称载波为f0=1GHz，并设fi =512Hz。假定移动用户在一个繁忙的高速公路上距离基站l公里处的地方，从开始从每小时60公里的速度开始，其速度（在0.01秒的时间间隔内测量）以1/2的概率随机的增加或减少1/10公里每小时。试仿真移动用户在这2s的时间间隔内所接收到的信号(及其频谱)。接收信号可以表示为： y(t)=a(t)Cos[2π(fo+f1)(t-△t)] 式中 △(t)=d(t)/c 且 a(t)=k/d2(t) 在上面的表达式中d(t)是在t时刻基站与移动用户的距离，c是光速，△(t)是传播延迟，而a(t)是信号在t时刻的衰减，并假设在这2s内a(t)是一个常数。利用复低通等效模型来进行仿真。(In a mobile communication system, the transmitted signal is an unmodulated audio x (t) = cos (2πft), where f = f0+ fi. Let nominal carrier of f0 = 1GHz, and set fi = 512Hz. Assume that mobile users on a busy motorway station l kilometers place, starting the beginning of 60 kilometers per hour, the speed (measured in 0.01 second interval) to increase the probability of a random 1/2 or reducing 1/10 km/h. Simulation test signal which the mobile subscriber in the 2s time intervals of the received (and its spectrum). The received signal can be expressed as: y (t) = a (t) Cos [2π (fo+ f1) (t- △ t)] Where △ (t) = d (t)/c And a (t) = k/d2 (t) In the above expression, d (t) at time t is a base station and a mobile user s distance, c is the speed of light, △ (t) is the propagation delay, and a (t) is the attenuation of the signal at time t, and assuming that 2s within a (t) is a constant. Using complex low-pass equivalent model for emulation.)

说明：  k-means算法实现以及Iris数据集(Implementation of K-means algorithm and Iris data set)

matlab web server 开发(matlab web server development)

说明：  一种核密度估计，或者称作带宽选择的方法，可以估计二维尺度参数，至于多维以上的估计方法尚在开发，多维情况下个人经验好的方法是多次实验取较好值(kernel density estimation, bandwidth selection, two-dimensional scale parameter can be estimated ,for the multi-dimensional approaches are still under development, multi-dimensional cases, personal experience is to do the experiment many times)