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MCMC-and-Gibbs-Sampling
Markov Chain Monte Carlo and Gibbs Sampling
- 2014-12-24 14:23:15下载
- 积分：1
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Wavelet-Methods-for-Time-Series-Analysis-in-Matla
This document describes the feature by feature the use of Wavelet Methods for Time Series Analysis.
- 2013-12-10 06:22:58下载
- 积分：1
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二维波动方程的有限差分法
二维波动方程的有限差分法，与解析解进行了误差比对(Finite difference method for two-dimensional wave equation)
- 2020-06-27 11:00:02下载
- 积分：1
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sar
说明： sar仿真，用于雷达的。程序简单，好用，适合初学者(sar simulation for radar. Procedure is simple, easy to use, suitable for beginners)
- 2011-03-24 16:49:19下载
- 积分：1
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redeyeicprusingmachine
an article about red eye detection
- 2010-05-22 15:23:37下载
- 积分：1
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xiaoboxiaozao
用自相关法求重构相空间的延迟时间的matlab程序(With the autocorrelation method for the reconstruction phase space of the delay time of the matlab program)
- 2011-08-31 17:44:31下载
- 积分：1
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lly1
针对滚动轴承故障信号具有非平稳、非高斯的特点，提出了将时域分析与小波分析相结合的方法对滚动轴承进行故障诊断。在研究不同信号分析方法理论的基础上，以滚动轴承外圈故障振动信号为例，采用多种信号处理方法进行了分析。结果表明，各种分析方法在分析轴承故障时的特点各不相同，在实际使用中，可将时域分析与小波分析综合使用，实现轴承状态的实时监测与故障的准确定位。(For rolling bearing fault signals have non-stationary, non-Gaussian, we proposed the time-domain analysis and wavelet analysis method of combining the rolling element bearing fault diagnosis. In the study of different analytical methods based on the theory of signal to the outer ring rolling bearing fault vibration signal, for example, using a variety of signal processing methods are analyzed. The results show that a variety of analytical methods in the analysis of the characteristics of bearing failure vary, in actual use, can be time-domain analysis and synthesis using wavelet analysis, to achieve accurate positioning of the bearing condition monitoring and real-time fault.)
- 2015-01-15 16:52:13下载
- 积分：1
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fangzhen3
基于训练序列的MIMO信道最小二乘估计算法蒙特卡洛仿真(Training sequence based MIMO channel estimation algorithms least squares Monte Carlo simulation)
- 2016-04-28 19:52:38下载
- 积分：1
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ff
说明： 本程序是数字图像矩阵数据的显示及其傅立叶变换的应用(digital image data matrix display and the application of the Fourier transform)
- 2006-05-25 15:14:21下载
- 积分：1
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MVAcltbern2
MVAcltbern illustrates the (univariate) Central Limit Theorem (CLT). n*1000 sets of n-dimensional Bernoulli samples are generated and used to approximate the distribution of t = sqrt(n)*(mean(x)-mu)/sigma -> N(0,1). The estimated density (blue) of t is shown together with the standard normal (red).
- 2013-04-30 22:28:08下载
- 积分：1
