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mutilvariableDMC
多变量的动态矩阵预测控制,很实用，欢迎下载(Multivariable dynamic matrix predictive control, very practical, welcome to download)
- 2009-11-16 09:57:45下载
- 积分：1
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gp11
Openness is the property of distributed systems such that each subsystem is continually open to interaction with other systems (see references).
- 2010-01-07 19:04:36下载
- 积分：1
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NBCOFdEst
窄带相关脉冲估计多普勒频移(包括解模糊算法)(Narrowband related pulse Doppler shift estimation (including solution fuzzy algorithm))
- 2013-11-23 22:49:27下载
- 积分：1
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ookd
ookd是相位调制的一种代码程序，可以看到调制效果。(ookd)
- 2009-03-11 12:56:27下载
- 积分：1
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ofdmsnr
ofdm snr is designed for signals
- 2013-07-30 21:45:15下载
- 积分：1
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cg2
CG MATLAB实现算法，算法很简单，只有几行(CG)
- 2010-12-02 21:24:02下载
- 积分：1
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UG586-7SeriesDMIUserGuide
UG586 - Zynq-7000 All Programmable SoC and 7 Series Devices Memory Interface Solutions v2.3 User Guide ( ver2.3, 18511 KB )(UG586- Zynq-7000 All Programmable SoC and 7 Series Devices Memory Interface Solutions v2.3 User Guide ( ver2.3, 18511 KB ))
- 2015-02-05 20:02:21下载
- 积分：1
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HMF_!
Hidden markov random filed
- 2014-01-03 13:02:19下载
- 积分：1
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DTW
DTW（Dynamic Time Warping，动态时间归整）算法，该算法基于动态规划（DP）的思想，解决了发音长短不一的模板匹配问题，是语音识别中出现较早、较为经典的一种算法。用于孤立词识别，DTW算法与HMM算法在训练阶段需要提供大量的语音数据，通过反复计算才能得到模型参数，而DTW算法的训练中几乎不需要额外的计算。所以在孤立词语音识别中，DTW算法仍然得到广泛的应用。 (DTW (Dynamic Time Warping, dynamic time warping) algorithm based on dynamic programming (DP) ideas, sounds of varying lengths to solve the template matching problem, is speech recognition appeared earlier, more classic kind of algorithm. For isolated word recognition, DTW algorithm and HMM algorithm in the training phase need to provide a large number of voice data, obtained by repeated calculations to model parameters, while the DTW algorithm is almost no additional training calculations. Therefore, in isolated word speech recognition, DTW algorithm is still widely used.)
- 2010-09-27 16:54:22下载
- 积分：1
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classical_music_2
MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)
- 2013-09-15 20:26:53下载
- 积分：1
