Training-based-channel-estimation

this file is about filter design

matlab编程中所需要的优化工具箱，可对波形进行优化等(matlab programming required in the optimization toolbox, optimization of the waveform can be)

MATLAB GUI操作相关指南，有详细的例程，方便初学者学习及探索。对于新手而言，有较高的使用价值。提供了较为简洁的开发平台手册。通俗易懂，方便使用。(MATLAB GUI related to the operation guide, with detailed routines, easy for beginners to learn and explore. For starters, there is a higher use value.)

光学传播，格林函数，matlab模拟小程序(optical transmission, the Green's function, small Matlab simulation procedures)

optxf filter in the matlab

说明：  Levenberg-Marqardt最优迭代算法(Levenberg-Marqardt optimal iterative algorithm)

说明：  将pca和elman神经网络结合起来，找到了主要的变量(The PCA and the Elman neural network to combine to find the main variables)

本文对变步长(VSS) 自适应滤波算法进行了分析,针对输入信号高度相关时算法收敛速度下降导致性能下降 的问题,提出了一种解相关变步长归一化LMS 自适应算法,引入相关性原理,使得算法保持良好的收敛性能。计算机仿真 结果与理论分析相一致(In this paper , we discuss and analyze the variable step2size adaptive algorithm(VSSLMS) ,in which the descent of it s convergence speed leads to the reduction of performance when input signals are heavily correlated . As a result , we propose an uncorrelated variable step2size normalized LMS adaptive algorithm. Due to the principle of correlation ,the algo2 rithm can achieve good performance. The simulations agree to the theoretical analysis.)

光学双稳特性曲线 调制作用:It=Ii*T(phi) 反馈作用:phi=phi_0+K*It 得透射率T(phi)与相移phi的反馈关系是 T(phi)=[phi-phi_0]/[K*Ii] 式中phi_0为初始相移 对于多干涉(F-P干涉)有: T(phi)=1/[1+F*(sin(phi/2))^2](Optical bistable characteristic curve modulation role: It = Ii* T (phi) feedback: phi = phi_0+ K* It may transmittance T (phi) and phase-shifting relationship between phi feedback T (phi) = [phi-phi_0 ]/[K* Ii] where phi_0 for the initial phase shift for the multi-interference (FP interference) are: T (phi) = 1/[1+ F* (sin (phi/2)) ^ 2])

Total Variation Denoising(TV denoising (scalar fidelity term) [ROF92] Reduces the total-variation of the image. Filters out noise while preserving edges. Textures and fine-scale details are also removed. In this demo the assumption is that a white Gaussian noise is added with a-priori known (or estimated) noise power (variance). The fidelity term to the input image is calculated automatically so that the power of the noise is reduced. )