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滑动窗的英文

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"滑动窗"怎么读用"滑动窗"造句

英文翻译手机手机版

  • gliding window
  • sliding window

例句与用法

  • Threshold and sliding window decide the sensitivity and stability
    阈值和滑动窗的大小决定了算法的灵敏度和稳健性。
  • Information systems - data compression method - adaptative coding with sliding window for information interchange
    信息系统.数据压缩法.信息交换用带滑动窗的自适应编码
  • Secondly , the methods to realize the shaping - filter are introduced , which include the fir algorithm , the fft - ifft algorithm and the symbol sliding - window algorithm . the genetic algorithm ( ga ) is used to optimize the filter coefficients
    接着介绍了成形滤波器的几种实现方法,包括fir算法、 fft - ifft算法和码元滑动窗算法,并将遗传算法用于确定成形滤波器的系数。
  • When it comes to feature - based image fusion , we make use of mean - shift to extract appropriate features according to the characteristics of radarsat and landsat images , then apply the bayes theory to feature level fusion classification
    在特征级的图像融合中,我们针对radarsat和landsat图像的特点,采用滑动窗方法提取适当的特征,然后用bayes理论来进行特征层的融合分类。
  • Finally , the small computing load and error , and the better estimation effect can be obtained when sampling frequency is more than 4hz and sliding window is between 10 and 20 and polynomial exponent equals 3 or 4 by the computer - based simulation
    最后通过计算机仿真说明在预测海上舰船运动时要求采样频率大于4hz ,滑动窗大小取10 20 ,多项式为3或4阶时计算量和误差较小,预测效果较好。
  • Sencondly , passive location system nonuniformly - sampled data synchronization algorithm is designed based on the particularity of the passive location nonuniformly - sampled data , which including adaptive system period algorithm with sliding window and fast interpolation algorithm
    Cep其次,针对无源定位系统非均匀采样数据的特殊问题提出了无源定位系统非均匀数据同步化算法,即滑动窗自适应系统周期计算和快速内插算法。
  • A modified synchronization approach by using sliding windows is proposed . the pre - processing for obtaining principal eigenvectors is a smoothing procedure for the dsss signal . the computer simulations show that the approach is more convenience in low snr comparing with other code synchronization methods
    3 、提出了一种改进滑动窗的盲同步方法,该方法利用求主特征向量的过程,对扩频信号进行平滑降噪预处理,实现了较低信噪比下的码同步。
  • Secondly , spfm ( sliding polynomial fitting method ) was discussed and the arithmetic has been compared with pfm ( polynomial fitting method ) . meanwhile , the model was corrected , and the parameters of arithmetic - - system sampling frequency , the length of sliding window and the exponent of fitting polynomial - - have been optimized
    并在此基础上讨论了滑动窗多项式拟合预测算法( spfm ) ,对该算法与普通的多项式外推预测进行了比较,对模型进行了适应性修正,并对算法的参数:系统采样频率、滑动窗大小、拟合多项式指数进行了优化。
用"滑动窗"造句  
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