hilbert2

说明：  　本文对变步长自适应滤波算法进行了讨论,建立了步长因子μ与误差信号e(n) 之间另一种新的非线性函数关系. 该函数比已有的sigmoid 函数简单,且在误差e(n)接近零处具有缓慢变化的特性,克服了Sigmoid 函数在 自适应稳态阶段步长调整过程中的不足. 由此函数本文得出了另一种新的变步长自适应滤波算法,并且分析了参数α,β的取值原则及对算法收敛性能的影响. 该算法有较好的收敛性能且计算量少. 计算机仿真结果与理论分析相一致,证实了该算法的收敛性能优于已有的算法.(In this paper, variable step adaptive filter algorithm to discuss the establishment of a step factor μ and the error signal e (n) between another new nonlinear function. The function has a sigmoid function than the simple and the error e (n) close to zero Department has a slow change in the characteristics of a Sigmoid function to overcome the steady-state phase in the adaptive step length adjustment during the process. this function, this paper gives another new variable step size adaptive filtering algorithms, and analysis of the parameters α, β the value principles and the convergence performance. The algorithm has better convergence performance easy to calculate. Simulation results and theoretical analysis, confirmed the convergence of the algorithm better performance than existing algorithms.)

自己编写的程序，产生Sierpinski衬垫图案,简易实现。附带在网上下载的两个Sierpinski程序供大家一起学习。(their preparation procedures have Sierpinski liner pictorial, simple to achieve. Fringe in the downloaded two Sierpinski procedures for all learning together.)

书名为:<MATLAB实用教程> 主编:苏金明,阮沈勇. (Title: <MATLAB实用教程> editor: Su Jin-ming, RUAN Shen-yong.)

说明：  K分布仿真的一部分，一部分是斑纹分量 还有是结构分量 两者点乘得到K分布序列 (K distribution part of the simulation, in part, there is a structural component markings component of both the dot distribution sequence obtained K)

最优化计算一书中整数规划源代码，已测试验证，可直接运行！(Optimization Calculation of integer programming source code of a book, testing and certification, can be directly run!)

64-ary QAM (64QAM) BER simulation

二阶广义积分器加上反馈的模型，单一的二阶广义积分器会让信号一直放大，加上反馈可以让信号稳定( U4E8C u9636 u5E7F u4E49 u79EF u7EF u5206 u5206 u5668 u52A0 u4E0A u53CD u9988 u7684 u6A21 u578B uFF0C u5355 u4E00 U4F1A u8BA9 u4FE1 u53F7 u53F7 u4E00 u76F4 u653E u5927 uFF0C u52A0 u4E0A u53CD u9988 u53EF u4EE5 u8BA9 u4FE1 u53F7 u7A33 u5B9A)

boa-constructor 基于wxPython and Python 的GUI构建器(boa-constructor in wxPython and Python-based GUI Builder)

说明：   有一个长度为整数L(1<=L<=10000)的马路，可以想象成数轴上长度为L的一个线段，起点是坐标原点，在每个整数坐标点有一棵树，即在0,1,2，...，L共L+1个位置上有L+1棵树。 现在要移走一些树，移走的树的区间用一对数字表示，如 100 200表示移走从100到200之间（包括端点）所有的树。 可能有M(1<=M<=100)个区间，区间之间可能有重叠。现在要求移走所有区间的树之后剩下的树的个数。 输入： 两个整数L(1<=L<=10000)和M(1<=M<=100)。 接下来有M组整数，每组有一对数字。 输出： 可能有多组输入数据，对于每组输入数据，输出一个数，表示移走所有区间的树之后剩下的树的个数。(Has a length of integer L (1 &lt = L &lt = 10000) of the road, one can imagine a number line segment of length L, the starting point is the coordinate origin, each integer coordinate points in a tree, i.e. 0,1 , 2, ..., L total has L+1 L+1 positions on the tree. Now to remove some trees, tree removed by a range of digitally represented, such as 100 200 represents removed (inclusive) between 100 and 200 all the trees. There may be M (1 &lt = M &lt = 100) intervals, there may be overlap between the intervals. Now, after the number of trees require the removal of all remaining sections of the tree. Input: two integer L (1 &lt = L &lt = 10000) and M (1 &lt = M &lt = 100). Then there is an integer M groups, each with a pair of numbers. Output: there may be multiple sets of input data, for each set of input data, the output of a number indicating the number of the removal of all remaining sections of the tree after tree.)

Lorenz系统的李雅谱诺夫指数，指数谱，大家多支持下哦(very good dajia juedebucuojiu zhichi xia )