103244863BPSK121

Hopfield BAM 双向异联想的简单实现，内含说明pdf文件(bam on Hopfield Network)

一维高斯函数拟合，分析其方差和期望、残差平方和、决定系数等(Guass fitting)

蒙的卡罗法的源代码，用于随机过程的编码，是我们老师编的(By Saul the source code card method for random process coding, is made of our teacher)

MATLAB开发的精美立体时钟3,界面美观漂亮.(MATLAB development of three-dimensional clock 3 beautiful, beautiful interface beautiful.)

心电信号处理及其应用，用MATLAB图像显示(ECG Signal Processing)

稀疏分量分解。 function [y A]=sparseBSS1(X,L,langda,G,h,delta)(function [y A]=sparseBSS1(X,L,langda,G,h,delta) ---------------------------------------------------------------- 2009-04-15 YangZhicong X: observed signal,each row correspond to a sensor observations L: the length of FFT (or the length of Hanning window) langda: adjust the desired angular width G: discretize the potential field by taking a sample of G points h: threshold y: estimated sourses A: estimated mix matrix )

核磁共振方法是一项尖端技术，在物理学和医学等领域有着较普遍的应用。 其无损性和高分辨率的特点是这项技术最大的优势。而利用核磁共振方法来找水 是其在地学中应用的一个新领域，为地球物理方法提供了新的思路。(NMR is a cutting-edge technology, medicine and other fields in physics and has more general application. The high-resolution non-destructive and are characterized by the biggest advantage of this technology. The use of NMR to come to learn that water is applied in a new area for geophysical methods to provide a new way of thinking.)

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.

The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants. The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France). The version 2.0 (February 2005) handles sparse matrices and contains an example

This tool is a simplification of the ballpivoting algorithm. It imagines a bella eating a delaunay traignulation in order to extract a manifold surface. It requires a single parameter: radius of the fretting ball. Ball Fretting Given a uniform sampled filled point cloud returns a tight triangulation. Input: ■tetr: a set of tetraedrons, nx4 array. If the cloud is not tesselated yet you need to call a delaunay triangulator prior calling this function. ■p : nx3 array, 3D set of points. ■r : the only parameter of the algorithm, the radius of the fretting ball Output: ■t : triangles ids, nx3 array ■tnorm: normals of triangles with outwards orientation