pipeFlow

卷积编码MATLAB仿真,Code Rate=1/2(Convolutional coding MATLAB simulation, Code Rate = 1/2)

这是Matlab模糊逻辑工具箱函数，希望对大家有用。(This is the Matlab Fuzzy Logic Toolbox function, in the hope that useful to everyone.)

说明：  和数字信号处理有关的MATLAB程序，应该会大家有用的(And digital signal processing the MATLAB program, we should be useful)

可以计算双包层光纤耦合系数，采用耦合模方程计算(Can be calculated double cladding fiber coupling coefficient)

MATLAB CODE FOR POWER FLOW 2

如何在matlab中输出图形的文章 (How to output graphics in matlab article)

强主动声纳信号干扰被动声探测的仿真分析.被动声探测是探测和跟踪水中噪声目标的重要手段,但强主动声纳信号的出现将对被动声探测产生很强的干扰作用,影响被动声纳对噪声目标的检测或跟踪。本文通过计算机仿真,分析被动声纳的积分时间、强主动声纳信号的强度、脉冲宽度和发射周期四个因素对强主动声纳信号干扰被动声探测程度的影响。(Simulation analysis of passive acoustic detection of a strong active sonar signal interference passive acoustic detection is an important means to detect and track water noise goals, but the emergence of strong active sonar signal will be passive acoustic detection generate strong interference effect, the impact of passive acoustic satisfied noise target detection or tracking. This paper through computer simulation, and analysis of the four factors of the integration time of the passive sonar, a strong active sonar signal intensity, pulse width, and launch cycle impact of passive acoustic detection of a strong active sonar signal interference.)

Runge-Kutta programme solving ODE. Not using solver but equations ste-by-step. Rayleigh-Plesset.

脑电特征提取的的matlab算法EEG feature extraction(EEG feature extraction algorithm matlab EEG feature extraction)

说明：  按FPE定阶的 源程序：fpe.cpp M序列：M序列.txt 白噪声：Gauss.txt 程序中先用依模型阶次递推算法估计模型的参数，再用fpe方法判断模型的阶次。 程序运行结果如下： n: 1 判断阶次FPE的值: 0.0096406 -0.481665 1.07868 n: 2 判断阶次FPE的值: 0.00875755 -0.446739 0.00498181 1.07791 0.0527289 n: 3 判断阶次FPE的值: 0.0087098 -0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982 n: 4 判断阶次FPE的值: 0.000396884 -0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236 n: 5 判断阶次FPE的值: 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771 n: 6 判断阶次FPE的值: 3.23349e-007 -1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688 从以上结果可以看出，当n=5时，fpe值最小，所以这时的模型阶次和参数估计值为最优结果： 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)