Cuthill-Mckee-bandwidth-Reduction

he form of the Burgers equation considered here is: du du d^2 u -- + u * -- = nu * ----- dt dx dx^2 for -1.0 < x < +1.0, and 0.0 < t. Initial conditions are u(x,0) = - sin(pi*x). Boundary conditions are u(-1,t) = u(+1,t) = 0. The viscosity parameter nu is taken to be 0.01 / pi, although this is not essential. The authors note an integral representation for the solution u(x,t), and present a better version of the formula that is amenable to approximation using Hermite quadrature. This program library does little more than evaluate the exact solution at a user-specified set of points, using the quadrature rule. Internally, the order of this quadrature rule is set to 8, but the user can easily modify this value if greater accuracy is desired.

program for lms.it is the lms algorithm for bpsk modulated sound wave

EM code for neural net(matlab code for neural net parameter estimation )

各种升降压斩波电路的simulink 仿真(Buck Chopper variety of simulink simulation)

阵列信号处理方面的LCMV优化设计阵列处理信号的算法(Array signal processing to optimize the design of LCMV signal array processing algorithms)

AOFDM系统matlab仿真程序，包括映射、fft/ifft，加去循环前缀，信道估计等关键模块(AOFDM system Matlab simulation program, including mapping, fft/ifft. to increase cyclic prefix, channel estimation and other key modules)

genetic fuzzy (ga_fuzzy) pid gain optimal

一个利用元胞自动机仿真matlab程序，有详细动画效果！很好玩哦！很棒哦！(Using cellular automata model to simulate the traffic flow situation, to achieve overtaking, and finally generate the empty spots in Figure)

一种流形学习算法（很好用），借鉴了主成分分析算法（PCA），课程设计时编写的matlab程序代码。( A fluid manifold learning algorithm (good use), It draws on principal component analysis algorithm (PCA), Course designed to prepare the matlab program code.)

ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model see help icp for more information (ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model- matrix with model points, data- matrix with data points, OUTPUT: R- rotation matrix and T- translation vector accordingly so newdata = R*data+ T . newdata are transformed data points to fit model see help icp for more information )