MTH

这个是一个函数逼近算法源代码，希望对大家有帮助，请下载哦，(This is a function approximation algorithm for the source code, I hope all of you help, please download Oh,)

盲源分离的源代码程序包，适合ICA初学者使用(Blind source separation of the source code package, suitable for beginners to use ICA)

50hzmatlab数字限波器案例程序，非常好用(50hzmatlab digital limit wave device procedures in case, a very handy)

STFT界面 ，可用于时频分析，经分析具有良好的效果(STFT interface that can be used for time-frequency analysis, the analysis has a good effect)

基于最大信噪比的盲源分离算法（附参考文章），能够分离线性混合的超高斯和亚高斯源信号, 并且可以有效地分离语音信号。((With reference to the article), can be separated and super-Gaussian linear mixed sub-Gaussian source signal to noise ratio based on the largest blind source separation algorithm, and can effectively separate the voice signal.)

algoritma geneetiksource code

matlab绘制阵列正六边形。采用坐标方法。(Draw a regular hexagon array matlab. Using coordinate methods.)

MATLAB code for the simulation of Channel DVB-S

这是一个matlab图像运算包，多幅图像的数学运算，包括图像的代数运算、图像的逻辑运算、图像的块和邻域操作、图像几何运算等(This is a matlab image computing package, multiple images of mathematics, including algebra images, images of logical operations, the image of the block and neighborhood operations such as geometric operations)

该代码用于实现粒子滤波视觉目标跟踪(PF)、卡尔曼粒子滤波视觉目标跟踪(KPF)、无迹粒子滤波视觉目标跟踪(UPF)。它们是本人这两年来编写的核心代码，用于实现鲁棒的视觉目标跟踪，其鲁棒性远远超越MeanShift(均值转移)和Camshift之类。用于实现视觉目标跟踪的KPF和UPF都是本人花费精力完成，大家在网上是找不到相关代码的。这些代码虽然只做了部分代码优化，但其优化版本已经成功应用于我们研究组研发的主动视觉目标跟踪打击平台中。现在把它们奉献给大家！(These codes are used to realize particle filter based visual object tracking (PF), kalman particle filter based visual object tracking, unscented particle filter based visual object tracking. Their robustness is far beyond the classical visual object tracking algorithms such as Mean-Shift (MeanShift) and CamShift。The codes of KPF and UPF for visual object tracking cost a great of my energy, and you can not find any relating algorithm codes on internet! Our research group have optimized these codes and applied them to develop a platform for active visual object tracking. Now, I dedicate them to you and wish you love them! )