SCDL

这主要是一些课程文档是关于MATLAB神经网络应用设计的，有图像处理相关的，还有编程指导(the book of MATlab which is related with image recognition and MATLAB programing)

I,Q通道的 噪声过程是高斯的的,而且是互不相关的,他的功率谱也是白的. 因为I和Q通道的噪声过程是高斯的,而且是互不相关的 我们可以得到他们是相互独立(Papoulis,1984). 最后,由于I和Q通道是独立的零均值高斯过程,因此可以推导出:复信号:I+jQ,幅度是瑞丽分布的,幅度的平放是指数分布的,相位角arctan(Q/I)是在(0,2π]上均匀分布(I Q Gaussian noise)

EMD/HHT是一种处理非线性非稳态信号的经典算法，自98年提出以来已经获得广泛的使用，作者huang曾经因次获得NASA的最高奖。(EMD/HHT is a deal with the nonlinear non-stationary signals classical algorithm, since 98 has been raised since the widespread use huang had dimensionless author was NASA's highest award.)

MATLAB程序，用以实现2PSK数字信号的产生(MATLAB program to achieve generation of 2PSK digital signal)

line codes: Nrz-L of an 8 bits secuence

文利用带噪语音经特征基函数矩阵转换后所具有的稀疏特性，用最大似然估计方法对转换后得到的稀疏 分量进行非线性压缩去噪，然后再经过反变换和重构恢复出原始语音信号的估计。特征基函数矩阵反映了语音数据本 身的统计特性，因此具有很好的合理性和可取性。仿真结果表明利用稀疏编码方法能极大程度地抑制背景噪卢，与小波消噪法相比优势明显。(a speech enhancement algorithm based Compressed Sensing.)

说明：  粒子群优化算法(Particle Swarm optimization,PSO)又翻译为粒子群算法、微粒群算法、或微粒群优化算法。是通过模拟鸟群觅食行为而发展起来的一种基于群体协作的随机搜索算法。通常认为它是群集智能 (Swarm intelligence, SI) 的一种。它可以被纳入多主体优化系统(Multiagent Optimization System, MAOS)。粒子群优化算法是由Eberhart博士和kennedy博士发明。(The probabilistic neural network has a simple structure and is easy to design Particle Swarm Optimization (PSO) is translated as Particle Swarm Optimization, Particle Swarm Optimization, or Particle Swarm Optimization. It is a random search algorithm based on group collaboration developed by simulating the foraging behavior of birds. It is generally considered to be a type of swarm intelligence (SI). It can be incorporated into Multiagent Optimization System (MAOS). The particle swarm optimization algorithm was invented by Dr. Eberhart and Dr. Kennedy.)

解非线性方程 能过解复根 实根 但需要逼近(help you save equation conclude different answer)

Audio processing using matlab

盲源分离（BSS: Blind Source Separation），又称为盲信号分离，是指在信号的理论模型和源信号无法精确获知的情况下，如何从混迭信号（观测信号）中分离出各源信号的过程。盲源分离和盲辨识是盲信号处理的两大类型。盲源分离的目的是求得源信号的最佳估计，盲辨识的目的是求得传输通道混合矩阵。(Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process. This problem is in general highly underdetermined, but useful solutions can be derived under a surprising variety of conditions. Much of the early literature in this field focuses on the separation of temporal signals such as audio. However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever.)