• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Theory, IEEE Transactions on >Prediction by Samples From the Past With Error Estimates Covering Discontinuous Signals
【24h】

Prediction by Samples From the Past With Error Estimates Covering Discontinuous Signals

机译：通过过去的样本进行预测，误差估计涵盖不连续信号

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There are several reasons why the classical sampling theorem is rather impractical for real life signal processing. First, the sinc-kernel is not very suitable for fast and efficient computation; it decays much too slowly. Second, in practice only a finite number N of sampled values are available, so that the representation of a signal f by the finite sum would entail a truncation error which decreases rather slowly for N¿ ¿, due to the first drawback. Third, band-limitation is a definite restriction, due to the nonconformity of band and time-limited signals. Further, the samples needed extend from the entire past to the full future, relative to some time t = t0. This paper presents an approach to overcome these difficulties. The sinc-function is replaced by certain simple linear combinations of shifted B-splines, only a finite number of samples from the past need be available. This deterministic approach can be used to process arbitrary, not necessarily bandlimited nor differentiable signals, and even not necessarily continuous signals. Best possible error estimates in terms of an Lp-average modulus of smoothness are presented. Several typical examples exhibiting the various problems involved are worked out in detail.
机译：经典采样定理对于现实生活中的信号处理非常不切实际有几个原因。首先，sinc内核不太适合快速高效的计算。它衰减得太慢了。第二，在实践中，只有有限数量的N个采样值可用，因此，由于第一个缺点，由有限和表示的信号f会带来截断误差，该截断误差对于N¿第三，由于频带和时间限制信号的不一致性，频带限制是确定的限制。此外，相对于某个时间t = t0，所需的样本从整个过去延伸到整个未来。本文提出了一种克服这些困难的方法。 sinc函数由移位的B样条的某些简单线性组合代替，仅需要有限数量的过去采样。这种确定性方法可用于处理任意的，不一定是带宽受限或可微分的信号，甚至不一定是连续信号。提出了关于Lp平均平滑度的最佳可能误差估计。详细阐述了几个涉及各种问题的典型示例。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号