fir-filter

换能器导纳图绘制函数，需要自备换能器导纳数据，代入即可(Transducer admittance mapping function, you need to bring their own transducer admittance data can be substituted into)

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.)

小波神经网络的分类识别，学习率因子，网络动量项因子，网络多分辨级数，网络平移参数 都可自己选。本例输入节点5，输出5。可识别五种信号。(wavelet neural network classification, the learning rate factor, network momentum contribution factor, network multiresolution series. Translation network parameters can be of his own choosing. The five cases input nodes, output 5. Five identifiable signal.)

雷达目标检测仿真 正弦信号做自适应对消(Radar target detection sinusoidal signal simulation do adaptive cancellation)

说明：  改进后的Pitch Synchronous Overlap Add方法，用于在不引起失真的情况下，改变语音的基频和时长(Improved Pitch Synchronous Overlap Add method, used in the case does not cause distortion, change of voice fundamental frequency and length of)

分布GUI. With this GUI you can find the best distribution that fits your data.(With this GUI you can find the best distribution that fits your data.)

An extended Kalman filter (EKF) is employed to estimate the motor state variables by only using measurements of the stator fine voltages and currents. When applying the EKF, it was necessary to solve some specific problems related to the voltage and current waveforms of the BLDCM

混频系统分析，包括零极点图、单位冲击响应和频率响应(Mixing system analysis, including pole-zero map, unit impulse response and frequency response)

this is computer communication network book

动态K-S检验边缘分布的copula 分布估计算法( Marginal distribution in copula estimation of distribution algorithm based dynamic K-S test)