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detenciontemprana
algoritmo de detencion temprana
- 2011-07-21 23:45:35下载
- 积分:1
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kzs
using fft function and other function that woring with signals, and picture in matlab
- 2009-11-01 23:21:20下载
- 积分:1
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PolarCodeEncoderandDecoder
polar code 编译码 与RM码的比较 (polar code
rm code)
- 2010-12-14 21:25:51下载
- 积分:1
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find-peaks
emprical mode decomposition( emd, find peaks)
- 2015-02-03 00:30:14下载
- 积分:1
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ranqilunji
燃气轮机matlab模型,相信会对研究微网的同学有帮助。(matlab generator)
- 2021-04-17 12:18:52下载
- 积分:1
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siso20M
A SISO 20M OFDM system based on IEEE802.11n standard, using Matlab.
- 2008-01-19 20:35:59下载
- 积分:1
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GWR_r
统计分析R语言的GWR代码,具体参考R语言“SPGWR”程序包,R语言3.1.2以上版本才有此包。(Statistical analysis R code for GWR)
- 2020-12-26 13:39:03下载
- 积分:1
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Desktop
基于matlab软件实现等距离离散化为粗糙集约简前准备(matlab Equidistance discretization)
- 2013-12-24 17:30:20下载
- 积分:1
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adboost-demo
adboost算法的一个例子。在Kearns和Valiant在1989年大作中指出了这种算法的可行性。而后,Freund在
1990年以及他和Schapire在 1994-1996年提出了boosting整个算法思路,似乎这种算法走到
了实际应用的开端。然而直到AdaBoost被viola在其人脸识别系统中运用(2001Viola和
Jones),这种方法才彻底开始暴火.(An example adboost algorithm. Kearns and Valiant pointed at the feasibility of this method in 1989 masterpiece. Then, Freund and Schapire he made in 1990 and in 1994-1996 the idea of boosting the entire algorithm, this algorithm seems to come to the beginning of the practical application. However, until the use of AdaBoost is viola (2001Viola and Jones) in its face recognition systems, this approach was completely start a fire storm.)
- 2013-12-28 22:44:27下载
- 积分:1
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VistaRestoreTools1.0
denoise
In BayesShrink[5] we determine the threshold for
each subband assuming a Generalized Gaussian
Distribution(GGD) . The GGD is given by
GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6)
¡ 1 < x < 1 ¯ > 0, where
® (¾ X ¯ ) = ¾ ¡ 1
X [ ¡ (3=¯ )
¡ (1=¯ ) ]1=2
and
C(¾ X ¯ ) = ¯ ¢ ® (¾ X ¯ )
2¡ ( 1
¯ )
and ¡ (t) =
R1
0 e¡ uut¡ 1du.
The parameter ¾ X is the standard deviation and ¯
is the shape parameter It has been observed[5] that
with a shape parameter ¯ ranging from 0.5 to 1, we
can describe the the distribution of coefficients in a
subband for a large set of natural images.Assuming
such a distribution for the wavelet coefficients, we empirically
estimate ¯ and ¾ X for each subband and try
to find the threshold T which minimizes the Bayesian
Risk, i.e, the expected value of the mean square error.
- 2012-06-08 01:38:48下载
- 积分:1