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ITD
ITD法模态识别，基于自由振动曲线进行的模态识别(modal analysis)
- 2010-07-23 08:50:28下载
- 积分：1
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pca
pca主成分分析，matlab程序，用于图像特征提取，降维等
有中文注释(Principal component analysis)
- 2010-03-11 11:23:02下载
- 积分：1
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N-sparse
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.
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- 2013-10-21 20:43:18下载
- 积分：1
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var
基于matlab的var模型应用 youyong(matlab var)
- 2012-04-21 20:51:18下载
- 积分：1
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FDTD-1D-in-Nonmagnetic-Plasma
This FDTD 1D simulation based on Dennis Sullivan book s. Wave propagation in vacuum background and penetrates Nonmagnetic Plasma.
- 2012-11-13 15:49:13下载
- 积分：1
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lhsdesign
说明： 生成n维变量的拉丁超立方样本点，直接导出样本点。(Generate the sample points of n-dimensional input vectors.)
- 2021-02-25 15:19:38下载
- 积分：1
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bom
bom provide the orginal details before calculation
- 2010-01-11 16:47:13下载
- 积分：1
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4141
This is a matlab code and is a beneficial source code about base mapping
- 2014-12-28 09:56:44下载
- 积分：1
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Codes used to beamform
Codes used to beamform with a horizontal line array
- 2020-11-23 22:49:33下载
- 积分：1
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PDC
Apply Ben-Israel and Iyigun s Probabilistic D-Clustering (PDC) algorithm for clustering.
This implementation is based on:
Adi Ben-Israel and Cem Iyigun, "Probabilistic D-Clustering", Journal of Classification 25:5-26 (2008)
- 2010-03-04 15:57:29下载
- 积分：1
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