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qamandqpm
在加性高斯白噪声信道里,BPSK调制信号与MPSK调制信号的比较(in the awgn channel , bpsk system vs mpsk)
- 2010-10-10 21:57:15下载
- 积分:1
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chap4
It is Radar System design and signal prcessing code based on standard algorithm .this is vey important for biggener those who are working on Radar signal processing area.This code contain various chapter. this is basically chapter No4
- 2009-11-02 14:26:03下载
- 积分:1
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weibgf.m
matlab code diagnostic by weibull function
- 2011-04-20 16:44:09下载
- 积分:1
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mm1Sp.m
MM1 code ..please check this out. This is MM1 tandem code that I used recently. May need to modify a bit to your situation. Ok have fun and enjoy!!!!
- 2013-03-25 07:12:33下载
- 积分:1
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haimmse
基于matlab的haimmse算法的应用程序(Based the matlab' s haimmse algorithm application procedures)
- 2012-09-24 16:31:22下载
- 积分:1
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MATLAB-PPT
MATLAB系统与语言简介
矩阵运算与数组运算
(The MATLAB Language Introduction matrix computing array operations)
- 2012-11-30 22:58:45下载
- 积分:1
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Rotorsystemcriticalspeedbeforethethird-ordermethod
汽轮机转子系统前三阶临界转速的传递矩阵法(Turbine rotor system critical speed before the third-order transfer matrix method)
- 2009-05-02 16:14:26下载
- 积分:1
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16-QAM-P-fadingPAWGN
16-QAM + fading + AWGN
Rayleigh Fading Ampalitude without Phase Fading
- 2011-08-04 23:07:37下载
- 积分:1
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CLBP
使用改进的LBP算法CLBP实现图像纹理特征的提取,并使用卡方统计方法计算类间距离并实现图像分布。本人已经实验,对15类病毒图像进行分类,不调任何参数的情况下实现67 以上的准确率。(
Using an improved algorithm CLBP LBP texture feature extraction of image, and use the chi-square statistic calculated inter-class distance and achieve image distribution. I have experimented on 15 viroid image classification, and more than 67 accuracy rate without adjusting any parameters.)
- 2016-05-26 21:00:57下载
- 积分:1
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SCSToolboxV2
将压缩感知用于谱估计中,根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse
and compressible signals based on randomized dimensionality reduction. To recover a signal from its
compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or
frame that agrees with the measurements. A great many applications feature smooth or modulated signals
that are frequency sparse and can be modeled as a superposition of a small number of sinusoids.
Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the
sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery
performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery
algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a
signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)
- 2012-06-29 10:10:42下载
- 积分:1