emd

：介绍了扩展卡尔曼滤波算法和无迹变换(unscented transformation，UT)算法，并对扩展卡尔曼滤波算法(EKF)和无 迹卡尔曼滤波算法(UKF)进行比较，阐明了UKF优于EKF。在此基础上，提出了一种基于Unscented变换(uT)的高斯和滤 波算法，该算法首先通过合并准则得到适当个数的混合高斯模型，逼近系统中非高斯噪声的概率密度(: Introduction of the extended Kalman filter algorithm and unscented transform (unscented transformation, UT) algorithm, the extended Kalman filter algorithm (EKF) and unscented Kalman filter (UKF) for comparison to clarify the UKF is superior to EKF. On this basis, we propose an approach based on Unscented Transform (uT) and the Gaussian filtering algorithm, which first of all, by merging the appropriate number of criteria to be a mixture of Gaussian model, which was close to the system of the Central African Gaussian noise probability density)

文件中包含几个用matlab编写的图像去噪程序，如邻域平均滤波‘维纳滤波法，巴特沃斯滤波法，并给出了计算PSNR值的公式。( delete the noise in the image)

说明：  主要介绍了在Matlab中怎样仿真FIR滤波器组的设计(Describes how the simulation in Matlab, the design of FIR filter banks)

判定滤波器系统稳定性；以及对于单位阶跃序列的全响应输出波形(Determine the filter system stability And the sequence of unit step response output waveform)

深度学习工具箱，很多深度学习的源码，可以用于后级开发(deep learning toolbox)

最小二乘支持向量机工具包，内嵌在Matlab环境中运行(LS—SVM tools )

自己关于TVL1方法的改进 用来去除人脸上面的光照得到人脸的纹理图像，对于光照下人脸识别有相当大的作用。也可以用来定位(Their own methods to improve on TVL1 people face to face to remove the light to be Face of texture images, for face recognition under illumination a significant role. Can also be used to position)

shell. Le presse papier (copier-coller) se trouve dans le menu edit . Enfin, pour accéder au logiciel Matlab, vous pouvez utiliser le menu démarrer ou taper simplement la commande matlab dans un shell

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

wind turbine simulation