MatlabODEs

一个关于线程池的小demo，windows环境下，可以直接运行。( A little on the thread pool demo, windows environment, can be directly run.)

MATLAB:A Tool for Algorithms Development and System Analysis

说明：  最优化理论 认知无线电 功率控制方面的资料，写得很详细。( Probability-based optimization of inter-sensing duration and power control in cognitive radio )

simulink for Low-density parity-check code

说明：  基于地统计半变异函数的单Y拓阶程序,由MATLAB编写,调用定阶来确定拓阶数,调用拓阶来完成Y的拓阶,输出为M*N矩阵(Single Y statistical semi variation function extension order based procedures, prepared by MATLAB, call order to determine the extension order number, call the extension order to complete the Y extension of the order, the output for the M*N matrix)

This function runs some steps of a particle filter and return some statistics. by J.L. Blanco - University of Malaga, Spain

这个函数提供了一种基音频率检测算法，具体的算法内容详见MATLAB文件的说明，有详细的注释，工程中已经应用过，有较好的应用价值(This function provides a pitch detection algorithm, the specific content of the algorithm see MATLAB documentation, detailed annotations, engineering has been applied, has good application value)

此文件包含了matlab中几种常见的算法，便于初学者参考学习。(Detailed introduces the mathematical modeling of several common mathematical model and modeling method. Convenient first contacts the mathematical model of the people learn. )

The result of the program is uncompensated and compensated plot of the control system.

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)