The sparse Fourier transform : theory and practice [electronic resource] / Haitham Hassanieh.
Material type:
TextSeries: ACM books ; #19.Publication details: [New York] : Association for Computing Machinery ; [San Rafael, California] : Morgan & Claypool, c2018Description: 1 online resources (xvii, 260 pages) : illustrations, chartsISBN: 9781947487062 (epub); 9781947487055 (pdf)Subject(s): Fourier transformations | Sparse matrices | Fourier transformations | Sparse matricesDDC classification: 515/.723 LOC classification: QC20.7.F67 | H37 2018ebOnline resources: Available in ACM Digital Library. Requires Log In to view full text. Summary: "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 sample all the data points, have become necessary. This book addresses the above problem by developing the Sparse Fourier Transofrm algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits. This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award" --
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APU Library Online Database | E-Book | QC20.7.F67 H37 2018eb (Browse shelf (Opens below)) | Available |
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| QA76.9.U83 O95 2017eb The handbook of multimodal-multisensor interfaces. Volume 1, Foundations, user modeling, and common modality combinations | QA76.9.U83 O95 2019eb The handbook of multimodal-multisensor interfaces. Volume 2, Signal processing, architectures, and detection of emotion and cognition | QA267.7 .R83 2019eb Hardness of approximation between P and NP | QC20.7.F67 H37 2018eb The sparse Fourier transform : theory and practice | QP551.5 .S75 2017eb Computational prediction of protein complexes from protein interaction networks | TK7867.2 .G65 2014eb Embracing interference in wireless systems |
Includes bibliographical references (pages 249-260).
"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 sample all the data points, have become necessary. This book addresses the above problem by developing the Sparse Fourier Transofrm algorithms and building practical systems that use these algorithms to solve key problems in six different applications: wireless networks; mobile systems; computer graphics; medical imaging; biochemistry; and digital circuits. This is a revised version of the thesis that won the 2016 ACM Doctoral Dissertation Award" --
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