super-resolution

subtractive clustering

calculation of load flow in power system using matlab power engineering field

自适应遗传算法源码，matlab环境，很好的，适合初学者学习(adaptive genetic algorithm source code, Matlab environment, and good, for beginners learning)

偏微分方程的MATLAB解法,主要介绍非线性方程组求解方法,给出不同方法的数值解法。(MATLAB Solution of partial differential equations, describes the nonlinear equations solving methods, given the different methods of numerical solution solution.)

微分方程组数值解方法，IDW距离反比加权方法，数据模型归一化，模态振动，isodata 迭代自组织的数据分析，研究生时的现代信号处理的作业，是路径规划的实用方法。(Numerical solution of differential equations method, IDW inverse distance weighting method, Normalized data model, modal vibration, Isodata iterative self-organizing data analysis, Modern signal processing jobs when the graduate, Is a practical method of path planning.)

geologie fousil par ikar

这个是我自己写的关于qpsk的MIMO信道的vblast的vblast算法,站长帮帮忙,我现在很需要一篇<信道估计算法>(基于瑞利衰落信道的LS算法的Matlab程序),后天就要交了,救人一命胜造7级浮屠啊!(this is what I wrote on the qpsk MIMO channel operator vblast the vblast France, the head of help out, I am now a need)

永磁同步电机的矢量控制系统仿真，采用id=0的控制策略。在此基础上加入位置环，实现永磁同步电机的伺服系统(Permanent magnet synchronous motor vector control system simulation, using the id = 0 control strategy. On this basis, adding the position loop, to achieve permanent magnet synchronous motor servo system)

盲信号分离是当前信号处理研究的热点课题之一，在无线数据通信、医学、语音以及地震信号处理等领域有着广阔的应用前景。基于负熵最大的FastICA算法用于实现盲信号分离。该方法的基本思路是以非高斯信号为研究对象，在独立性假设的前提下，对多路观测信号进行盲源分离。在满足一定的条件下，能够从多路观测信号中，较好地分离出隐含的独立源信号。(Blind signal separation is the study of signal processing, one of the hot topics in wireless data communications, medical, speech and seismic signal processing and other fields have broad application prospects. Negentropy based on the largest FastICA Algorithm for Blind Signal Separation realize. The basic idea is based on non-Gaussian signal as the research object, in the independence assumptions on the multi-channel observation signal blind source separation. In meeting certain conditions, be able to multi-channel observation signal, the better to isolate the independent source signal implied.)

RPCA (Robust Principal Component Analysis)是目前用于矩阵填充、图像去噪的最有效的优化方法。该代码是求解RPCA的一种数值算法——Exact ALM（Exact Augmented Lagrange Multiplier）(The most basic form of the exact ALM function is [A, E] = exact_alm_rpca(D, λ), and that of the inexact ALM function is [A, E] = inexact_alm_rpca(D, λ), where D is a real matrix and λ is a positive real number. We solve the RPCA problem using the method of augmented Lagrange multipliers. The method converges Q-linearly to the optimal solution. The exact ALM algorithm is simple to implement, each iteration involves computing a partial SVD of a matrix the size of D, and converges to the true solution in a small number of iterations. The algorithm can be further speeded up by using a fast continuation technique, thereby yielding the inexact ALM algorithm.)