A method of blind source separation based on order statistics is introduced.

介绍一种基于二阶统计量的盲源分离方法.

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A new image recognition method based on high - order statistics is proposed.

提出了一种基于图像高阶统计量的识别算法.

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Order statistics are widely used in reliability evaluation and life prediction.

顺序统计量在可靠性评估和寿命预测中有广泛的应用.

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Problem Set 3 : Asymptotics of percentile order statistics, non - reversing random walk, self - trapping random walk, self - avoiding random walk.

问题 3: 有序统计百分数的近似 、 可逆随机漫步 、 已诱捕随机漫步 、 已避免随机漫步.

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It is shown that the higher order statistics approaches restrain effectively the effect of coloured noise.

表明高阶统计量方法可以有效地抑制有色噪声的影响.

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High - order statistics applies to all problem that need consider non - gauss, nonnon - linear and circular calm.

高 阶统计 被广泛应用于所有需要考虑 非 高斯 、 非 最小相位、有色噪声、非线性或循环平稳性的各类问题中.

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Because fourth - order statistics can restain the gaussian noise, the method has high stability in noise conditions.

由于四阶累量对高斯噪声的抑制性, 该方法还具有很强的抗噪性.

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Problem Set 3 : Asymptotics percentile order statistics, non - reversing random walk, self - trapping random walk, self - avoiding random walk.

问题 3: 有序统计百分数的近似 、 非 可逆随机漫步 、 自已诱捕随机漫步 、 自已避免随机漫步.

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But Second Order Statistics can be used to separate the - stationary signals and colored signals.

但二阶统计量可以实现 非 平稳信号和有色信号的盲分离.

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