matlableach

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.)

matlab对图像进行运动模糊 以及用维纳滤波 逆滤波还原(matlab and the image motion blur reduction using Wiener filtering inverse filtering)

张正友摄像机标定中已知内参和外参的情形下，求解转移矩阵M的MATLAB源码(By Zhang Zhengyou camera calibration known internal control and external reference case, solving the transfer matrix M MATLAB source)

很好用的Dijkstra求最短路的MATLAB程序，带注释，都能看懂。。。。(Find a good use of the Dijkstra shortest MATLAB programs, with comments, can understand. . . .)

说明：  人工蜂群算法的matlab实现，智能启发式算法(artificial bee colony)

MNF变换，能够实现高光谱遥感图像的MNF正逆变换，进行图像降维与重建(MNF transform, to achieve high spectral remote sensing images MNF inverse transform, image dimensionality reduction and reconstruction)

LPC program example

NRAA算法，用于在下行分布式天线系统中进行能量效率和频谱效率的权衡(NRAA algorithm for spectral efficiency and the energy efficiency trade-offs in a distributed antenna system in the downlink)

读取FEKO中导出的极化数据并进行SAR成像。(SAR image)

　支持向量机方法是建立在统计学习理论的VC 维理论和结构风险最小原理基础上的，根据有限的样本信息在模型的复杂性（即对特定训练样本的学习精度，Accuracy）和学习能力（即无错误地识别任意样本的能力）之间寻求最佳折衷，以期获得最好的推广能力[14]（或称泛化能力）。 (SVM is based on statistical learning theory and the theory of VC dimension based on structural risk minimization principle, according to the limited sample of information in the model complexity (ie, training samples of a specific learning accuracy, Accuracy) and learning ability (ie, error-free samples to identify any capacity) to find the best compromise between, in order to obtain the best generalization ability [14] (or generalization).)