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The sparse Fourier transform : theory and practice / Haitham Hassanieh
(ACM books. ISSN:23746777 ; #19)

データ種別	電子ブック
版	First edition.
出版者	([New York] ; [San Rafael, California] : Association for Computing Machinery : Morgan & Claypool)
出版年	2018
大きさ	1 PDF (xvii, 260 pages) : illustrations
著者標目	*Hassanieh, Haitham author

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0005037	ACM Books Collection 1				9781947487055

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一般注記	Mode of access: World Wide Web Includes bibliographical references (pages [249]-260) 1. Introduction -- 1.1 Sparse Fourier transform algorithms -- 1.2 Applications of the sparse Fourier transform -- 1.3 Book overview -- Part I. Theory of the sparse Fourier transform -- 2. preliminaries -- 2.1 Notation -- 2.2 Basics -- 3. Simple and practical algorithm -- 3.1 Introduction -- 3.2 Algorithm -- 4. Optimizing runtime complexity -- 4.1 Introduction -- 4.2 Algorithm for the exactly sparse case -- 4.3 Algorithm for the general case -- 4.4 Extension to two dimensions -- 5. Optimizing sample complexity -- 5.1 Introduction -- 5.2 Algorithm for the exactly sparse case -- 5.3 Algorithm for the general case -- 6. Numerical evaluation -- 6.1 Implementation -- 6.2 Experimental setup -- 6.3 Numerical results -- Part II. Applications of the sparse Fourier transform -- 7. GHz-wide spectrum sensing and decoding -- 7.1 Introduction -- 7.2 Related work -- 7.3 BigBand -- 7.4 Channel estimation and calibration -- 7.5 Differential sensing of non-sparse spectrum -- 7.6 A USRP-based implementation -- 7.7 BigBand's spectrum sensing results -- 7.8 BigBand's decoding results -- 7.9 D-BigBand's sensing results -- 7.10 Conclusion -- 8. Faster GPS synchronization -- 8.1 Introduction -- 8.2 GPS primer -- 8.3 QuickSync -- 8.4 Theoretical guarantees -- 8.5 Doppler shift and frequency offset -- 8.6 Testing environment -- 8.7 Results -- 8.8 Related work -- 8.9 Conclusion -- 9. Light field reconstruction using continuous Fourier sparsity -- 9.1 Introduction -- 9.2 Related work -- 9.3 Sparsity in the discrete vs. continuous Fourier domain -- 9.4 Light field notation -- 9.5 Light field reconstruction algorithm -- 9.6 Experiments -- 9.7 Results -- 9.8 Discussion -- 9.9 Conclusion -- 10. Fast in-vivo MRS acquisition with artifact suppression -- 10.1 Introduction -- 10.2 MRS-SFT -- 10.3 Methods -- 10.4 MRS results -- 10.5 Conclusion -- 11. Fast multi-dimensional NMR acquisition and processing -- 11.1 Introduction -- 11.2 Multi-dimensional sparse Fourier transform -- 11.3 Materials and methods -- 11.4 Results -- 11.5 Discussion -- 11.6 Conclusion -- 12. Conclusion -- 12.1 Future directions -- Appendix A. Proofs -- Appendix B. The optimality of the exactly k-Sparse algorithm 4.1 -- Appendix C. Lower bound of the sparse Fourier transform in the general case -- Appendix D. Efficient constructions of window functions -- Appendix E. Sample lower bound for the Bernoulli distribution -- Appendix F. Analysis of the QuickSync system -- Analysis of the baseline algorithm -- Tightness of the variance bound -- Analysis of the QuickSync algorithm -- Appendix G. A 0.75 million point sparse Fourier transform chip -- The algorithm -- The architecture -- The chip -- References -- Author biography 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 Transform 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 Also available in print Title from PDF title page (viewed on March 30, 2018) HTTP:URL=http://dx.doi.org/10.1145/3166186 Information=Abstract with links to full text
件　名	LCSH:Fourier transformations LCSH:Sparse matrices LCSH:Electronic books
分　類	LCC:QC20.7.F67 DC23:515.723
書誌ID	EB00004425
ISBN	9781947487055