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OFDM_simple_matlab
一个非常好的例子关于OFDM的，非常简单，但是有效(A very good example on OFDM, very simple, but effective)
- 2010-12-16 10:57:58下载
- 积分：1
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5
说明： matlab算法，grubb，dixon准则等实现剔除异常实验数据(matlab learning)
- 2010-05-13 14:35:17下载
- 积分：1
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fisher
本例是Fisher分类，以一个小例子解释了Fisher判别的本质(The case is Fisher classification, with a small example to explain the nature of the Fisher discriminant)
- 2014-09-07 14:52:45下载
- 积分：1
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usage-of-slider-in-GUI
Matlab GUI中滑动条的应用(源代码)
usage of slider in GUI/Matlab(usage of slider in GUI/Matlab)
- 2011-12-03 11:41:35下载
- 积分：1
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seriesbase
series normal condition 23
- 2012-01-24 22:12:55下载
- 积分：1
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Eng_Pouya
Reduced model of ieee 39 bus test system in MATLAB
- 2014-12-15 17:28:13下载
- 积分：1
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OFDMblindidentification
基于子空间的
OFDM盲识别和检测，使用MATLAB实现(blind OFDM identificaiton)
- 2010-10-04 21:09:14下载
- 积分：1
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chirp
对雷达系统中的常用LFM线性调频（chirp）信号及其匹配滤波器的设计进行MATLAB仿真(right of the radar system used LFM LFM (chirp) signals and matched filter into the design OK MATLAB)
- 2006-10-30 10:45:29下载
- 积分：1
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Desktop
特徵粹取（feature extraction）是特徵選取（feature selection）的延伸，簡單地說，我們希望將資料群由高維度的空間中投影到低維度的空間，因此，我們必須找出一組基底向量（base）來進行線性座標轉換，使得轉換後的座標，能夠符合某一些特性。
我們可以將特徵粹取分成「包含類別資訊」和「不包含類別資訊」兩大類。包含類別資訊指的是我們已經知道哪些資料分別歸屬於哪一類；而不包含類別資訊的特徵粹取則適用於我們不知道手上的資料點分別該歸屬於哪一類，甚至連該劃分成幾類都不知道的情況。對於這兩大類資料，可以分述如下：
對於「不包含類別資訊」的資料，我們通常使用「主要分量分析」（principal component analysis），簡稱 PCA。 (Cuiqu features (feature extraction) is feature selection (feature selection) extension, simply put, we want to base the projection data the high-dimensional space to low-dimensional space, so we have to find a set of basis vectors ( base) to the linear coordinate conversion, so that after the coordinate conversion, can meet a number of features. We can feature Cuiqu into " category contains information" and " does not contain a category of information," two categories. Contains categories of information refers to what we already know what kind of information are attributed to Cuiqu features include categories of information are not applicable to information we do not know the points are in the hands of the home in which category, and even that is divided into several class do not know the situation. For these two categories of information, can be divided as follows: For information " does not include the categories of information" , we usually use the " )
- 2014-12-18 12:19:30下载
- 积分：1
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entropy-
一阶熵、二阶熵和差分熵c++程序 求熵用的
(A first order, second order entropy and entropy difference entropy ofc++ program)
- 2013-11-26 18:34:19下载
- 积分：1
