k60单片机采集程序例程

c#考试系统 源码下载

河流污物浓度求解程序 用vc++实现 数值计算示例-river dirt concentration vc solving procedures used to achieve numerical examples

stm32的can总线应用例程，程序已通过调试(STM32 can bus application routines, programs through debugging)

cuda5 and matlab code for Gaussian filtering and parallel image processing code, we hope to help

神经网络PID控制算法的实现，相比于PID，神经网络PID具有更好的控制效果。(Neural network PID control algorithm, compared to PID, PID neural network has better control effect.)

这是一个练习使用位写入/写出的程序，利用bitio.c和bitio.h工具整合，可以向文本文件E:\bitio1.txt中读取和写入数个位，使用者可以通过调试功能进一步了解位写入写出的作用。进而给今后做数据压缩打下基础。(This is a practice using bit write/write program, using bitio.c and bitio.h tool integration, you can read and write several bits to the text file E: \ bitio1.txt, the user can pass Debugging function to further understand the role of bit write write. And then lay the foundation for future data compression.)

通过使用matlab进行模拟仿真，得到比较可靠的模拟数据与实验进行对比，方便日后类似的数据处理(Obtain reliable simulation data and experimental comparison, to facilitate future similar data processing by using matlab simulation)

说明：  ICM20948实现的9轴DMP姿态解算,强于MPU6050的DMP6(The 9-axis DMP attitude resolution implemented by ICM20948 is better than that of MPU6050.)

nRF24Z1的AVR程序，要了半天才要到的，吐血传上来，希望对大家有用！！做2.4G无线语音的可以参考！！(nRF24Z1 of AVR procedures, only half a day to go to, and vomiting blood transmission up, we want to be useful! ! 2.4G wireless voice can make reference! !)

灰色系统理论建模源代码 function GM1_1(X0) %format long  [m,n]=size(X0);   X1=cumsum(X0);   %累加  X2=[]; for i=1:n-1      X2(i,:)=X1(i)+X1(i+1); end  B=-0.5.*X2  t=ones(n-1,1);  B=[B,t]  ;      % 求B矩阵 YN=X0(2:end)  ;  P_t=YN./X1(1:(length(X0)-1)) %对原始数据序列X0进行准光滑性检验，                             %序列x0的光滑比P(t)=X0(t)/X1(t-1) A=inv(B."*B)*B."*YN."  a=A(1)  u=A(2)  c=u/a  ; b=X0(1)-c    X=[num2str(b),"exp","(",num2str(-a),"k",")",num2str(c)];  strcat("X(k+1)=",X)  %syms k;   for t=1:length(X0)      k(1,t)=t-1;  end  k  Y_k_1=b*exp(-a*k)+c; for j=1:length(k)-1     Y(1,j)=Y_k_1(j+1)-Y_k_1(j); end  XY=[Y_k_1(1),Y]    %预测值 CA=abs(XY-X0)     %残差数列  Theta=CA       %残差检验 绝对误差序列  XD_Theta= CA ./ X0   %残差检验 相对误差序列 AV=mean(CA); &