M_sequence

XOR using Neural network

主要用途是对抗多径衰落。由于多径衰落的存在，使得同一个帧由于路径不同，到达用户端的时延有先有后。在相关的频域上的会造成子载波之间的相互干扰，影响 性能。另外，由于有了循环前缀，使得IFFT FFT操作把原来的线性卷积变成循环卷积，大大简化了相应的信号处理复杂度(Graduate study do not to upload copyrighted controversial content and Trojan code and agent software to access foreign sites)

基于 sml 算法的类模型，以图像为训练样本(Class model based on SML algorithm)

说明：  线性调频信号在雷达上的实现，文中给出了设计过程，具有很好的参考价值(LFM radar signals the realization of paper gives the design process, with a very good reference value)

Abstract：Noise frequency modulation(FM)jamming。which belongs to blanket jamming。is already become the main form ofnoise jamming at present。because the wideband was gained by it．Tne spectnlnl ofnoise FM jamming is analyzed by time domain autocorrelation method in this paper．It’S jamm g peculiarity and几out— putting signal’S jamming peculiarity ale explained．At last，these time series models ofnoise FM jalllIIling sig— nal and几outputting signal ale built．(Abstract: Noise frequency modulation (FM) jamming. which belongs to blanket jamming. is already becomethe main form ofnoise jamming at present. because the wideband was gained by it. Tne spectnlnl ofnoise FMjamming is analyzed by time domain autocorrelation method in this paper. It S jamm g peculiarity and several out-putting signal S jamming peculiarity ale explained. At last, these time series models ofnoise FM jalllIIling sig-nal and a few outputting signal ale built.)

有关无刷电机的仿真程序，适合研究电机的人 (something about brushless dc motor)

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )

simultion of wind power generation in matlab

粒子群算法详细的程序，埃及人wael korani写的采用粒子群算法（pso）代码调用simulink模型优化pid参数，与大家共享(PID and State Feedback Control of DC Motors)

说明：  基于超低功耗微型心电听诊仪的设计.pdf(2006MAY19 ME DSP TS1.pdf)