SolveSpaRSA

matlab GUI tutorial. This a very helping tutorial for matlab GUI beiggners.

matalb经典算法，包括代数方程组的解法，微分方程实验，优化问题实验(matalb classical algorithms, including the solution of algebraic equations, differential equations experiment, experimental optimization)

说明：  道路检测和分割算法。（使用的是遗传算法来实现的）(a algorithm for dividing road)

这是基于matlab的关于LPCC (Linear Predictive Cepstral Coding)(线性预测倒谱系数) 的代码，对大家理解有一定帮助。(This is based on the matlab LPCC (Linear Predictive Cepstral Coding) (Linear Predictive Cepstral) code for us to understand some help.)

BPSK的软解调算法，可用于软解调和信道译码的软信息输入。(soft demodulation of BPSK)

车辆信号被动式红外信号的滤波以及能量分析(Vehicle passive infrared signal filtering and signal energy analysis)

This algorithm solves the otimization problem min_x | y - A x |_2^2 + lambda Phi(x) and has applications in compressed sensing, image restoration and reconstruction, sparse regression, and several other problems.

该段代码生成不同长度下，双绞线信道的远端衰减冲激响应系数。双绞线频域带宽最高可达106MHz，采样率设置时必须大于两倍的频域带宽。双绞线信道模型使用ITU-T-SG15 contribution 11GS3-029中的模型。(This code generates a different length, the distal twisted channel impulse response attenuation coefficient. Twisted frequency domain bandwidth of up to 106MHz, must be greater than twice the frequency-domain bandwidth sampling rate settings. UTP channel model using the ITU-T-SG15 contribution 11GS3-029 the model.)

npg algorithm npg algorithm npg algorithm (npg algorithm npg algorithm npg algorithm npg algorithm )

设r是f(x) = 0的根，选取x0作为r初始近似值，过点（x0,f(x0)）做曲线y = f(x)的切线L，L的方程为y = f(x0)+f (x0)(x-x0)，求出L与x轴交点的横坐标 x1 = x0-f(x0)/f (x0)，称x1为r的一次近似值。过点（x1,f(x1)）做曲线y = f(x)的切线，并求该切线与x轴交点的横坐标 x2 = x1-f(x1)/f (x1)，称x2为r的二次近似值。重复以上过程，得r的近似值序列，其中x(n+1)=x(n)－f(x(n))/f (x(n))，称为r的n+1次近似值(Let r is f (x) = 0 root, select the initial approximation x0 as the r, over point (x0, f (x0)) to do the curve y = f (x) the tangent L, L the equation y = f ( x0)+ f ' (x0) (x-x0), find the intersection of L and the x-axis of abscissa x1 = x0-f (x0)/f' (x0), x1 is called an approximation r. Through points (x1, f (x1)) to do the curve y = f (x) the tangent, and find the intersection of the tangent with the x-axis of abscissa x2 = x1-f (x1)/f ' (x1), x2 is called r the second approximation. Repeat the process, get an approximation of the sequence r, where x (n+1) = x (n)-f (x (n))/f ' (x (n)), as an approximation of r n+1 times)