LEADER 03782nam 2200505Ia 450 001 9910437576903321 005 20200520144314.0 010 $a1-4471-4941-6 024 7 $a10.1007/978-1-4471-4941-5 035 $a(OCoLC)826292328 035 $a(MiFhGG)GVRL6WPT 035 $a(CKB)2670000000341762 035 $a(MiAaPQ)EBC1156142 035 $a(EXLCZ)992670000000341762 100 $a20100125d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aEfficient algorithms for discrete wavelet transform $ewith applications to denoising and fuzzy inference systems /$fK. K. Shukla, Arvind K. Tiwari 205 $a1st ed. 2013. 210 $aLondon $cSpringer London $cImprint: Springer$d2013 215 $a1 online resource (ix, 91 pages) $cillustrations (some color) 225 0 $aSpringerBriefs in computer science 300 $a"ISSN: 2191-5768." 311 $a1-4471-4940-8 320 $aIncludes bibliographical references. 327 $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. 330 $aTransforms are an important part of an engineer?s toolkit for solving signal processing and polynomial computation problems. In contrast to the Fourier transform-based approaches where a fixed window is used uniformly for a range of frequencies, the wavelet transform uses short windows at high frequencies and long windows at low frequencies. This way, the characteristics of non-stationary disturbances can be more closely monitored. In other words, both time and frequency information can be obtained by wavelet transform. Instead of transforming a pure time description into a pure frequency description, the wavelet transform finds a good promise in a time-frequency description. Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in digital signal processing (speech and image processing), communication, computer science and mathematics. Wavelet transforms are known to have excellent energy compaction characteristics and are able to provide perfect reconstruction. Therefore, they are ideal for signal/image processing. The shifting (or translation) and scaling (or dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. The nature of wavelet computation forces us to carefully examine the implementation methodologies. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients. Hence, the overall performance depends significantly on the precision of the intermediate DWT coefficients. This work presents new implementation techniques of DWT, that are efficient in terms of computation requirement, storage requirement, and with better signal-to-noise ratio in the reconstructed signal. 410 0$aSpringerBriefs in computer science. 606 $aDigital images$xMathematics 606 $aWavelets (Mathematics) 615 0$aDigital images$xMathematics. 615 0$aWavelets (Mathematics) 676 $a515.723 700 $aShukla$b K. K$01060929 701 $aTiwari$b Arvind K$01763336 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437576903321 996 $aEfficient algorithms for discrete wavelet transform$94203721 997 $aUNINA