Chapter 12 Conclusion
作者: Haitham Hassanieh
作者单位: University of Illinois at Urbana-Champaign
英文丛书称: ACM Books
出版社: ACMMC,   2018
ISBN: 978-1-94748-707-9
来源数据库: Association for Computing Machinery
DOI: 10.1145/3166186.3166199
原始语种摘要: The Fourier transform is one of the most fundamental tools for computing the frequency representation of signals. It plays a central role in signal processing, communications, audio and video compression, medical imaging, genomics, astronomy, as well as many other areas. Because of its widespread use, fast algorithms for computing the Fourier transform can benefit a large number of applications. The fastest algorithm for computing the Fourier transform is the Fast Fourier Transform (FFT) which runs in near-linear time making it an indispensable tool for many applications. However, today, the runtime of the FFT algorithm is no longer fast enough especially for big data problems where each dataset can be few terabytes. Hence, faster algorithms that run in sublinear time, i.e., do not even...
全文获取路径: ACM 
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关键词翻译
关键词翻译
  • applications 应用程序
  • complexity 错综性
  • runtime 运行时刻
  • imaging 图像形成
  • enabling 启动
  • front 工祖
  • hardware 硬件
  • computational 计算的
  • viewpoint 对景点
  • compression 压缩