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jade算法
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  jade algorithm
     The Independent components can be achieved by ICA processing to watermarking information, then, using iterative mixing embeds watermarking and extracting the watermarking by blind JADE algorithm.
     其思想是:首先对水印图像经过ICA处理得出若干个独立分量水印,利用迭代混合的方法嵌入水印,再利用盲源JADE算法提取水印,从而达到用较小的载体带宽嵌入水印信息的目的。
短句来源
     Iterative mixing embeds watermarking are used and extracting the watermarking by blind JADE algorithm.
     利用混合的方法嵌入水印,再利用盲源JADE算法提取水印。
短句来源
     Learning algorithm of matrix decomposing is the key technology for the Blind Source Separation,matrix joint-diagnalizing pre-whitening JADE algorithm is a learning algorithm based on 4th-order cumulation.
     分离(或解混合)矩阵的学习算法是盲信号分离的关键技术,矩阵联合对角化的预白化JADE算法是一种基于四阶累计量的学习算法。
短句来源
     The independent components could be achieved by ICA processing to watermarking information, and then, watermarking was embedded by using iterative mixing and the watermarking was extracted by blind JADE algorithm. The extraction of watermarking information could be implemented with less carrier.
     其思想是:首先对水印图像经过ICA处理得出若干个独立分量水印,利用迭代混合的方法嵌入水印,再利用盲源JADE算法提取水印,达到用较少的载体代价就可以嵌入水印信息的目的。
短句来源
  “jade算法”译为未确定词的双语例句
     (2) For joint DOA and delay estimation (JADE), because of vectorization (column stacking), Khatri-Rao product and Kronecker product, the dimension of the data matrix is increased, which influences real-time processing seriously.
     (2)在JADE算法中,由于向量化函数(按列堆栈)、Khatri-Rao积和Kronecker积的引入大大地增加了数据矩阵的维数,严重地影响算法的实时性。
短句来源
     By comparing the efficiencies of five ICA algorithms Infomax, Extended Infomax, Jade extracting blink artifacts and power noise in the EEG signals, The Infomax and Jade have better convergence. Though blink slow waves can be extracted by Infomax algorithm, power noise is unlikely to be removed by it.
     通过比较Infomax、Extended- Infomax、Jade独立分量分析 (Independent Com ponent Analysis,ICA)算法用于诱发电位分离眼动伪差与工频干扰的结果 ,确证 Infom ax和 Jade算法有较好的收敛性。
短句来源
     Applying Extended Infomax and Jade algorithms, blink artifacts and power noise contained in the 16 channel EEG signals of visually evoked potential (VEP) were removed successfully. It is the fundament of estimation and analysis of VEP.
     Infom ax算法可以分离出眼动慢波 ,但难以消除工频干扰 ,Ex-tended Infom ax ICA算法和 Jade算法可以从 16导联视觉诱发电位 EEG信号中分离出眼动伪差和工频干扰并将其消除 ,为进一步提取诱发电位信号建立了基础。
短句来源
     In ICA, the JADE based on jointly approximate diagonalisation of eigen-matrices is a kind of robust and steady algorithm, which is specially appropriate to feature extraction of multivariate data.
     其中,基于特征矩阵联合近似对角化的JADE算法是一种鲁棒且数值稳定的代数ICA方法,特别适合用于多变量特征抽取。
短句来源
     By comparing the efficiencies of five ICA algorithms-Infomax, Extended-Info-max. Jade extracting blink artifacts and power noise in the EEC signals, The Infomax and Jade have better convergence. Though blink slow waves can be extracted by Infomax algorithm, power noise is unlikely to be removed by it.
     通过比较Infomax、Extended-Infomax、Jade独立分量分析(Independent Component Analysis,ICA)算法用于诱发电位分离眼动伪差与工频干扰的结果,确证Infomax和Jade算法有较好的收敛性。
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  相似匹配句对
     The algorithm is an extension of the two-valued cover-most algorithm proposed by M. C.
     该算法是M. C.
短句来源
     AN ALGORITHM OF PRIME NUMBERS
     素数的算法
短句来源
     Shortage of JADE Blind Source Separation Algorithm
     JADE盲信号分离算法失效问题研究
短句来源
     Application of JADE to Separation of Statistically Correlated Sources
     应用JADE盲分离算法分离统计相关源
短句来源
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  jade algorithm
The Jade algorithm, which is frequently used ine+e--annihilation, is found to perform less well compared to other algorithms.
      
A neural implementation of the JADE algorithm using higher-order neurons.
      
Estimated independent components of the heart beat of a pregnant woman, using the public domain JADE algorithm.
      
Firstly, fixed-order perturbative corrections are quite sizeable for the Jade algorithm.
      
This suggests that the robust SOC blind multi-user detector is able to correct the estimation errors caused by the JADE algorithm.
      
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Blink artifacts and power noise are constantly found in EEG signals whose estimation and analysis can be strongly influenced by them. By comparing the efficiencies of five ICA algorithms Infomax, Extended Infomax, Jade extracting blink artifacts and power noise in the EEG signals, The Infomax and Jade have better convergence. Though blink slow waves can be extracted by Infomax algorithm, power noise is unlikely to be removed by it. Applying Extended Infomax and Jade algorithms, blink artifacts and power noise...

Blink artifacts and power noise are constantly found in EEG signals whose estimation and analysis can be strongly influenced by them. By comparing the efficiencies of five ICA algorithms Infomax, Extended Infomax, Jade extracting blink artifacts and power noise in the EEG signals, The Infomax and Jade have better convergence. Though blink slow waves can be extracted by Infomax algorithm, power noise is unlikely to be removed by it. Applying Extended Infomax and Jade algorithms, blink artifacts and power noise contained in the 16 channel EEG signals of visually evoked potential (VEP) were removed successfully. It is the fundament of estimation and analysis of VEP. ICA has a possible important value in the biomedical signal processing, especially in the clinic medicine engineering and is worthy of being completely studied.

眼动伪差和工频干扰是脑电图 (electroencephalogram ,EEG)中常见噪声 ,严重影响 EEG信号提取和分析 .通过比较Infomax、Extended- Infomax、Jade独立分量分析 (Independent Com ponent Analysis,ICA)算法用于诱发电位分离眼动伪差与工频干扰的结果 ,确证 Infom ax和 Jade算法有较好的收敛性。 Infom ax算法可以分离出眼动慢波 ,但难以消除工频干扰 ,Ex-tended Infom ax ICA算法和 Jade算法可以从 16导联视觉诱发电位 EEG信号中分离出眼动伪差和工频干扰并将其消除 ,为进一步提取诱发电位信号建立了基础。 ICA在生物医学信号处理中尤其在临床医学工程中潜在着重要应用价值 ,值得深入研究

Artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for patterns clustering and recognition. And, independent component anslysis (ICA) is a powerful tool for analyzing nongaussian data. In ICA, the JADE based on jointly approximate diagonalisation of eigen-matrices is a kind of robust and steady algorithm, which is specially appropriate to feature extraction of multivariate data. In this paper, the JADE is firstly proposed for...

Artificial neural network (ANN), especially the self-organizing map (SOM) based on unsupervised learning is a kind of excellent method for patterns clustering and recognition. And, independent component anslysis (ICA) is a powerful tool for analyzing nongaussian data. In ICA, the JADE based on jointly approximate diagonalisation of eigen-matrices is a kind of robust and steady algorithm, which is specially appropriate to feature extraction of multivariate data. In this paper, the JADE is firstly proposed for feature extraction of different mechanical patterns (including normal and gear pitting), followed by certain a typical ANN (for example MLP, RBFN or SOM) which implements the final classification. By means of ICA and the further feature extraction strategys based on residual mutual information (RMI), higher than second order features embedded in multi-channel vibration measurements can be captured effectively. Thus, mechanical fault patterns can be recognized correctly. The results from contrast experiments showed that the compound ICA-SOM classifier can be constructed in simpler way, and classify various fault patterns at considerable accuracy, both of which imply its great potential in health condition monitoring of machines.

神经网络、特别是基于无导师学习的自组织映射(SOM)网络是一种优良的模式聚类与识别方法,而独立分量分析(ICA)则是一个强有力的非高斯数据分析工具。其中,基于特征矩阵联合近似对角化的JADE算法是一种鲁棒且数值稳定的代数ICA方法,特别适合用于多变量特征抽取。本文首先利用JADE进行不同机械状态模式(包括正常和齿轮点蚀故障状态)的特征提取,随后以此训练某一典型神经网络(如多层感知器、径向基网络或自组织映射网络),以实现模式的最终分类。借助ICA及基于残余互信息(RMI)的二次特征抽取策略,隐藏于多通道振动观测中的高阶特征得以有效提取,从而实现机械状态模式的准确识别。对照分类实验结果表明,基于ICA-SOM分类方法不仅具有较好的故障模式分类能力,且实现简单,在机器健康状况监测中有较大的应用潜力。

This work proposed a method for estimating the parameters of multiple received frequency-hopping (FH) signals without any a priori knowledge. First the unknown signals were separated by joint approximate diagonalisation of eigen-matrices (JADE) algorithm. Then the method used the smoothed pseudo Wigner-Ville distribution (SPWVD) of each separated FH signal to estimate its transmission parameters such as hop duration, time-offset and hopping frequency pattern. By using multiple overlapped SPWVD windows, the effect...

This work proposed a method for estimating the parameters of multiple received frequency-hopping (FH) signals without any a priori knowledge. First the unknown signals were separated by joint approximate diagonalisation of eigen-matrices (JADE) algorithm. Then the method used the smoothed pseudo Wigner-Ville distribution (SPWVD) of each separated FH signal to estimate its transmission parameters such as hop duration, time-offset and hopping frequency pattern. By using multiple overlapped SPWVD windows, the effect of cross-term interference in the time-frequency analysis was minimized. Simulation results showed that the new estimation algorithm greatly improved the accuracy and reliability of parameter estimation.

针对接收到的多个未知任何先验参数的跳频信号,提出一种先分离各个信号再对其分别进行时频分析来估计跳频参数的方法.首先采用特征矩阵联合近似最优化(JADE)算法分离跳频信号,再利用多窗口重叠的平滑伪Wigner Ville分布(SPWVD)来估计出跳频信号的跳周期(hop duration)、定时偏差(time offset)和跳频频率(跳频图案)等参数.通过将多个时频分析的窗口重叠来克服时频分析中交叉项的影响.仿真结果表明,该估计算法显著提高了估计的准确度和可靠性.

 
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