matlab_toolbox(7)

声纹识别的特征参数提取,盲信号用于语音识别maltab程序代码,里面有相关语音文件，很不错 (Voiceprint identification of the feature extraction and blind signal for speech recognition maltab code, which has the relevant sound files, very good)

此程序为极化对消方面的仿真应用，在电磁矢量传感器方面的仿真应用。(This procedure for the simulation of the polarization cancellation in the simulation of the electromagnetic vector sensor.)

ngui的学习手册 自学 工作两不误 针对主流的的U3D有很好的学习公用( )

欠定盲源分离的的程序，对混合信号实现分离，帮助很大(Underdetermined blind source separation of program, the mixed signal separation, are of great help)

POWER FLUX IS THE EXAMPLE FROM STEVENSON THIS DOCUMENTO IS SO GOOD

denoising method. My friend give this to me. I hope it will be helpful to others.

一个基于稀疏盲源分离的程序，很不错，值得学习下(Underdetermined Blind Source Separation using Sparse Representations)

梯度下降法是最早最简单，也是最为常用的最优化方法。梯度下降法实现简单，当目标函数是凸函数时，梯度下降法的解是全局解。一般情况下，其解不保证是全局最优解，梯度下降法的速度也未必是最快的。梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向，所以也被称为是”最速下降法“。最速下降法越接近目标值，步长越小，前进越慢。(The gradient descent method is the earliest and most simple and most commonly used optimization method. The gradient descent method is simple to realize. When the objective function is a convex function, the solution of the gradient descent method is a global solution. In general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not necessarily the fastest. The optimization idea of gradient descent method is to use the current position negative gradient direction as the search direction, because the direction is the fastest descending direction of the current position, so it is also called the steepest descent method. The faster the slowest descent approach is closer to the target, the smaller the step, the slower the progress.)

说明：  这个程序可以产生一个6阶的低通巴特沃思滤波器，截止频率是45Hz，你也可以用它来对信号进行滤波，并比较滤波前后的信号功率谱。(This program can generate a six-order low-pass Butterworth filter, cut-off frequency is 45Hz, you can also use it to filter the signal and to compare before and after filtering the signal power spectrum.)

We compare the energy efficiency of some transmission schemes in wireless sensor networks. By constraining the system to end-to-end throughput requirements, we assume that the nodes in multi-hop and cooperative schemes operate at a spectral efficiency which is twice that of the single-hop transmission. By taking into account the energy consumption of the RF circuitry, we show that cooperation may be considerably more energy efficient than non-cooperative schemes even in small transmission ranges, specially if a feedback channel is available.