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008 150903s2013 xxk| o |||| 0|eng d
020 _a9781447149415
_99781447149415
024 7 _a10.1007/9781447149415
_2doi
035 _avtls000339965
039 9 _a201509030841
_bVLOAD
_c201404300407
_dVLOAD
_y201402061013
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aTA1637-1638
100 1 _aShukla, K. K.
_eautor
_9315712
245 1 0 _aEfficient Algorithms for Discrete Wavelet Transform :
_bWith Applications to Denoising and Fuzzy Inference Systems /
_cby K. K. Shukla, Arvind K. Tiwari.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aIx, 91 páginas 46 ilustraciones, 31 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringerBriefs in Computer Science,
_x2191-5768
500 _aSpringer eBooks
505 0 _aIntroduction -- Filter Banks and DWT -- Finite Precision Error Modeling and Analysis -- PVM Implementation of DWT-Based Image Denoising -- DWT-Based Power Quality Classification -- Conclusions and Future Directions.
520 _aTransforms are an important part of an engineer’s toolkit for solving signal processing and polynomial computation problems. In contrast to the Fourier transform-based approaches where a fixed window is used uniformly for a range of frequencies, the wavelet transform uses short windows at high frequencies and long windows at low frequencies. This way, the characteristics of non-stationary disturbances can be more closely monitored. In other words, both time and frequency information can be obtained by wavelet transform. Instead of transforming a pure time description into a pure frequency description, the wavelet transform finds a good promise in a time-frequency description. Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in digital signal processing (speech and image processing), communication, computer science and mathematics. Wavelet transforms are known to have excellent energy compaction characteristics and are able to provide perfect reconstruction. Therefore, they are ideal for signal/image processing. The shifting (or translation) and scaling (or dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. The nature of wavelet computation forces us to carefully examine the implementation methodologies. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients. Hence, the overall performance depends significantly on the precision of the intermediate DWT coefficients. This work presents new implementation techniques of DWT, that are efficient in terms of computation requirement, storage requirement, and with better signal-to-noise ratio in the reconstructed signal.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aTiwari, Arvind K.
_eautor
_9315713
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781447149408
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4941-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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