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Matlab-English
包括了MATLAB相关的工具箱的详细介绍，是学习matlab的很好的参考资料。(Including the MATLAB toolbox related details is a good learning matlab reference.)
- 2009-06-16 21:55:12下载
- 积分：1
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16-QAM-fadingchannels
series of codes for simulation of 16-QAM BER over fading and AWGN channels
- 2010-02-07 22:26:55下载
- 积分：1
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EX3
仿真V-BALST结构ZF检测算法性能，调制方式为QPSK(Simulation V-BALST structure of ZF detection algorithm performance, the modulation method is QPSK)
- 2012-05-18 14:04:08下载
- 积分：1
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MatlabToolboxiRobotCreate
matlab robotics toolkit
- 2013-03-30 06:24:48下载
- 积分：1
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802.11a_ofdm_code
现在我终于找到了一份,它以FUNCTION的形式,再现了OFDM映射,IDFT/DFT,同步(时间/频率同步)和信道估计,以及频率偏移的估计等一系列代码.希望大家能从中得到你需要的东西.
(Now I finally found a copy of it to the form of FUNCTION, reproduces OFDM mapping, IDFT/DFT, synchronized (time/frequency synchronization), and channel estimation, as well as the frequency deviation of the estimated range of the code. I hope that everyone will be you need.)
- 2009-03-20 09:23:40下载
- 积分：1
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frankotchellappa
An implementation of Frankot and Chellappa a algorithm for constructing an integrable surface from gradient information.
matlab file
- 2010-05-07 12:38:49下载
- 积分：1
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gauss
运用C语言对高斯信道进行仿真，简洁快速的实现(The use of C language Gaussian channel simulation, simple rapid implementation)
- 2013-09-02 10:37:03下载
- 积分：1
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isar_proccessing
this file contains some m-files about calculating the fractional fourier transform and isar processig
- 2010-10-24 18:04:21下载
- 积分：1
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AFSA
artificial fishswarm algorithm
- 2010-11-26 20:19:20下载
- 积分：1
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bootgmregress
自举是一种由重采样估计，独立和（蒙特卡洛重采样）等概率设置一个单一的数据统计变化的一个途径。允许的措施估计那里的潜在分布是未知的或者样本量很小。他们的结果与这些分析方法的统计特性相一致。
在这里，我们使用非参数逼近。非参数引导更简单。它不使用该模型的结构，建造人工数据。矢量[易西]是重采样，而不是直接与replecement。这些参数是从这些对构建。
二，回归模型时，应使用在回归方程中的两个变量是随机的，会有错误的，即不是由研究者控制。模式，我用普通最小二乘回归低估了变量之间的错误时，他们都含有线性关系的斜率。据索卡尔和罗尔夫（1995），模型二回归的主题是一对哪些研究和争论仍在继续，最终建议是很难做出。
BOOTGMREGRESS模型II是一个引导程序。这需要s引导和规范样品前坡计算变量。这两个变量的每个转化为具有零均值和标准差的一个。由此产生的斜率是线性回归系数的Y在X里克创造了这个词（1973）几何平均数并给出了一个模型II回归广泛审查。它也被称为引导标准的主要轴线(The bootstrap is a way of estimating the variability of a statistic from a single data set by resampling it independently and with equal probabilities (Monte Carlo resampling). Allows the estimation of measures where the underlying distribution is unknown or where sample sizes are small. Their results are consistent with the statistical properties of those analytical methods.
Here, we use the Non-parametric Bootstrap. Non-parametric bootstrap is simpler. It does not use the structure of the model to construct artificial data. The vector [yi, xi] is instead directly resampled with replecement. The parameters are constructed from these pairs.
Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), t)
- 2011-05-21 13:19:38下载
- 积分：1
