communication

this code implement a new face recognition method

These are Gath Geva and Gustafon Kassel source codes in matlab as implementation of clustering algorithm in Fuzzy approach.

说明：  泊松均匀分布的WSNs的网络拓扑Matlab程序(Poisson network structure for WSN)

该为matlab详尽学些资料第八章“数据分析”(Learn some of the detailed information for the matlab Chapter 8, " Data Analysis" )

这个程序是关于示波器70ly53力矩电机的控制方法。(Oscilloscope 70ly53 torque motor control. )

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

解power flow CODE(學電力系統必備的東西)(內含詳細註解)(Solution of power flow CODE (learn something essential power systems) (includes detailed annotations))

This code deals with data at any z-level specified by zlevel1 and uses the output generated from make_zeta_u.c, make_zeta_v.c and make_zeta_T.c file i.e. it uses the files ufile??zlevel.dat, vfile??zlevel.dat and Tfile??zlevel.dat. The output includes qiver or velocity vector plots in that particular z-level. Dependencies include quivers.m file

Bayesian macroeconometrics

说明：  波达方向（DOA） 估计的基本问题就是确定同时处在空间某一区 域多个感兴趣的信号的空间位置(即多个信号到达阵列参考阵元的方 向角)。最早的也是最经典的超分辨率(DOA)估计算法是著名的多信号 分类（Multiple Signal Classification） 法,简称 MUSIC 算法,它是 一类经典的基于特征结构分析的空间谱估计方法。该算法利用了信号 子空间和噪声子空间的正交性,构造空间谱函数,通过谱峰搜索,检测 信号的 DOA。(The basic problem of DOA estimation is to determine the simultaneous location in a certain region of space. Spatial position of multiple interested signals in the domain (i.e. the direction of multiple signals reaching the array reference elements) Directional angle). The earliest and most classical super-resolution (DOA) estimation algorithm is the well-known multi-signal.Multiple Signal Classification, referred to as MUSIC algorithm, is A classical spatial spectrum estimation method based on eigenstructure analysis. The algorithm utilizes signals. The orthogonality of subspace and noise subspace, the construction of spatial spectral function, through peak search, detection DOA of signal.)