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本文提供了在不同信噪比条件下衰落信道上多分辨率MQAM同相比特的BER表达式(This article provides different SNR with fading channels compared to multi-resolution characteristics of MQAM BER expression)

这个代码是matlab仿真的在没有协作用户的情况下，进行的误码率性能仿真。(Matlab simulation of this code is the collaboration of users in the absence of circumstances, to carry out simulation of BER performance.)

This program in Matlab code, calculates resonance frequencies of a 1D cavity by using FDTD method

function dg=gplus(x,y,z,x1,x2,y1,y2,z1,z2,p) G=6.67e-11 t222=myfun(x,y,z,x2,y2,z2) t122=myfun(x,y,z,x1,y2,z2) t212=myfun(x,y,z,x2,y1,z2) t221=myfun(x,y,z,x2,y2,z1) t211=myfun(x,y,z,x2,y1,z1) t121=myfun(x,y,z,x1,y2,z1) t112=myfun(x,y,z,x1,y1,z2) t111=myfun(x,y,z,x1,y1,z1) dg=-G*p*1e6*(t222-t122-t212-t221+t211+t121+t112-t111) 转化为g.u. function y=myfun(x,y,z,xi,eta,zeta) R=sqrt((xi-x).^2+(eta-y).^2+(zeta-z).^2) temp1=(xi-x).*log((eta-y)+R) temp2=(eta-y).*log((xi-x)+R) temp3=(zeta-z).*atan((zeta-z).*R/((xi-x).*(eta-y))) 书中所给公式 temp3=-(zeta-z).*atan((xi-x).*(eta-y)./(zeta-z)./R) 上式变换 y=temp1+temp2+temp3 (function dg = gplus (x, y, z, x1, x2, y1, y2, z1, z2, p) G = 6.67e-11 t222 = myfun (x, y, z, x2, y2, z2) t122 = myfun (x, y, z, x1, y2, z2) t212 = myfun (x, y, z, x2, y1, z2) t221 = myfun (x, y, z, x2, y2, z1) t211 = myfun (x, y, z, x2, y1, z1) t121 = myfun (x, y, z, x1, y2, z1) t112 = myfun (x, y, z, x1, y1, z2) t111 = myfun (x, y, z, x1, y1, z1) dg =-G* p* 1e6* (t222-t122-t212-t221+t211+t121+t112-t111) converted the Gu function y = myfun, (x y, z, xi, eta, zeta) R = sqrt ((xi-x). ^ 2+ (eta-y). ^ 2+ (zeta-z). ^ 2) temp1 = (xi-x).* log ((eta-y)+R) temp2 = (eta-y).* log ((xi-x)+R) temp3 = (zeta-z).* atan ((zeta-z).* R/( (xi-x).* (eta-y))) book given formula Temp3 =- (Zeta-z)* ATAN ((Xi-X)* (ETA-y)/(Zeta- Z)/R) the above formula transform Y = temp1+temp2+temp3 )

UWB信号的产生，一定数量的比特经过重复编码，加入TH码，并进行PPM调制，经过成型滤波，输出uwb信号波形(Generating, a certain number of bits UWB signals through repetition coding, TH code, and PPM modulation, after shaping filter, the output signal waveform uwb)

a radix-2,3 and 5 FFT, hopefully this is new and helpful :)

用BP网络完成函数的逼近 P网络通常有一个或多个隐层，隐层中的神经元均采用sigmoid型变换函数，输出层的神经元采用纯线性变换函数。本例应用一个两层BP网络来完成函数逼近的任务，其中隐层的神经元个数是5。(network to be completed by BP function approximation P networks usually have one or more hidden layer, a hidden layer neurons are used sigmoid-type transformation function, the output layer neurons using pure linear transformation function. The application of a two-tier cases BP network function approximation to complete the task, hidden layer neuron number is 5.)

说明：  和声搜索算法，进行参数改进，并且对程序的模块做细致的说明(Harmony search algorithm, carry on the improvement of parameters, and the program module for detailed instructions )

This is the paper face recognition in matlab.

Power system load flow analysis using NR Power Flow method with implementation of crout algorithm .