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LWLFkuiba
- 一个一维溃坝模拟动画，使用复合有限差分法，效果很好。刚学刚做，希望对大家有点用处。内含源程序。-a one-dimensional dam break simulation animation, the use of composite finite difference method with good results. Just do just learning, and I hope to you a bit useless. Intron source.
JPDAF
- 进行蒙特卡罗仿真，采用Jpadf对两个有杂波的目标进行跟踪。仿真结果表明，跟踪效果很好，基本与真实真是轨迹重叠-Monte-Carlo simulation carried out using two Jpadf clutter on the target track. The simulation results show that good tracking results, the basic trajectory is overlapping with the real
qiuxinhaopiyudebijiao
- 求人体信号的一个周期序列的CZT和FFT计算结果比较。是很好的程序，可以直观的看到效果。-Order to signal a cycle of the human sequence comparison CZT and FFT calculations. Is a very good program, you can see the visual effect. The body s signals.
ARIMA
- ARIMA模型在数学建模中的使用。具有很好的预测效果。可以一试。-ARIMA model is the use of mathematical modeling. Has a good prediction. Can try.
2
- 很好的C语言应用程序，实现了PID算法功能，效果较好。-Good C language applications, PID algorithm better.
brinv
- brinv实现了数学上的计算功能，求某些数的协方差，实现很好的效果。-brinv achieve a mathematical calculation functions, seeking certain number of covariance, to achieve very good results.
Trigonometric-fast-algorithm
- 三角函数快速算法,在keil中实际测试，效果很好。-Trigonometric fast algorithm
foubeng_v81
- 有信道编码，调制，信道估计等，包括最后计算压缩图像的峰值信噪比和压缩效果的源码,ML法能够很好的估计信号的信噪比。- Channel coding, modulation, channel estimation, Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, ML estimation method can be a
faimie_v56
- ML法能够很好的估计信号的信噪比,本程序的性能已经超过其他算法，包括最后计算压缩图像的峰值信噪比和压缩效果的源码。- ML estimation method can be a good signal to noise ratio, This program has exceeded the performance of other algorithms, Including the final calculation of the compressed image peak signal to
Occam1DCSEM_V3.16
- 一维海洋电磁和大地电磁反演程序。Occam反演程序。最新版本，效果很好。-One-dimensional marine electromagnetic and magnetotelluric inversion program. Occam inversion program. The latest version, works well.
lie_v25
- 包括最后计算压缩图像的峰值信噪比和压缩效果的源码,是学习PCA特征提取的很好的学习资料，已经调试成功.内含m文件，可直接运行。- Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Is a good learning materials to learn PCA feature extraction, Has been