000 02814cam a22003134a 4500
001 16105956
003 OSt
005 20250321092916.0
008 100224s2010 nyua b 001 0 eng
010 _a 2010007111
020 _a9780521190497 (hardback)
040 _aDLC
_cIISERB
_dDLC
042 _apcc
050 0 0 _aTK5102.9
_b.B5434 2010
082 0 0 _a621.3822 B57F
_223
100 1 _aBlahut, Richard E.
_931261
245 1 0 _aFast algorithms for signal processing /
_cRichard E. Blahut.
260 _aNew York :
_bCambridge University Press,
_c2010.
300 _axiii, 453 p. :
_bill. ;
_c26 cm.
504 _aIncludes bibliographical references and index.
505 8 _aMachine generated contents note: 1. Introduction; 2. Introduction to abstract algebra; 3. Fast algorithms for the discrete Fourier transform; 4. Fast algorithms based on doubling strategies; 5. Fast algorithms for short convolutions; 6. Architecture of filters and transforms; 7. Fast algorithms for solving Toeplitz systems; 8. Fast algorithms for trellis search; 9. Numbers and fields; 10. Computation in finite fields and rings; 11. Fast algorithms and multidimensional convolutions; 12. Fast algorithms and multidimensional transforms; Appendices: A. A collection of cyclic convolution algorithms; B. A collection of Winograd small FFT algorithms.
520 _a"Efficient signal processing algorithms are important for embedded and power-limited applications since, by reducing the number of computations, power consumption can be reduced significantly. Similarly, efficient algorithms are also critical to very large scale applications such as video processing and four-dimensional medical imaging. This self-contained guide, the only one of its kind, enables engineers to find the optimum fast algorithm for a specific application. It presents a broad range of computationally-efficient algorithms, describes their structure and implementation, and compares their relative strengths for given problems. All the necessary background mathematics is included and theorems are rigorously proved, so all the information needed to learn and apply the techniques is provided in one convenient guide. With this practical reference, researchers and practitioners in electrical engineering, applied mathematics, and computer science can reduce power dissipation for low-end applications of signal processing, and extend the reach of high-end applications"--
650 0 _aSignal processing
_xDigital techniques.
_931262
650 0 _aAlgorithms.
_931263
856 4 2 _3Cover image
_uhttp://assets.cambridge.org/97805211/90497/cover/9780521190497.jpg
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