chap2

基于matlab的小波包使用电子书籍。可以方便大家直接使用matlab工具包进行小波变换(based on the wavelet packet Matlab use of electronic books. Can facilitate direct use Matlab tool kits wavelet transform)

用于仿真MM!排队模型，计算平均等待时间和到来的人数，并对每个人的等待时间绘图(Used to simulate the MM! Queuing model to calculate the average waiting time and the number of people coming, and everyone' s waiting time in drawing)

Processing EKG via Pan-and-Tompkins algorithm

射线追踪,包含了起点和终点的射线函数，对于地震传播研究具有重要意义！(Raytracing in a v(z) world. The toolbox contains functions forshooting fans of rays and functions for tracing rays with specific start and end points. General multi-bounce rays with any number of mode conversions can be traced. Automatic routines for P-P and P-S primaries in shot-gather geometry are provided for convenience. Any recording geometry can be used but most functions vectorize (e.g. are most efficient) for shot-gather geometry. Only traveltimes and rayparameters are detemined, not amplitudes. In these routines, the velocity models should be considered as "background" models. This means that the depths at which flections and mode conversions occur are dependent of the depths given as layer boundaries in the velocity models. Velocity models are specified by a vector of velocities and a same-size vector of depths. The j th velocity is in effect from the j th to the j+1 th depth. The bottom of the model is assumed at infinite depth. Thus v=5000 and z=0 specifies a cons)

自动变速器的速比优化提高车辆的燃油经济性(Speed ratio optimization)

Bottom Up Profiling shows the entire callstack backwards.

matlab task for sensor network in urban area with bit error rate functions

用MOM计算V型天线的电流分布、输入阻抗及辐射方向图。其中L=0.3λ，L/a=2000，激励用δ-gap模型。(MOM calculated using V-shaped antenna current distribution, input impedance and radiation pattern. Where L = 0.3λ, L/a = 2000, excitation δ-gap model.)

说明：  完整的Turbo编码解码，完整的Turbo编码解码(Turbo encoding and decoding full and complete Turbo codec)

说明：  0案例分析\MATLAB神经网络30个案例分析\源程序\案例1 BP神经网络的数据分类-语音特征信号分类(0 Case Study \ MATLAB neural network analysis of 30 cases \ source \ Case 1 BP neural network data classification- voice characteristic signal classification)