Efficient algorithms for discrete wavelet transform : with applications to denoising and fuzzy inference systems
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The work Efficient algorithms for discrete wavelet transform : with applications to denoising and fuzzy inference systems represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
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Efficient algorithms for discrete wavelet transform : with applications to denoising and fuzzy inference systems
Resource Information
The work Efficient algorithms for discrete wavelet transform : with applications to denoising and fuzzy inference systems represents a distinct intellectual or artistic creation found in Boston University Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Efficient algorithms for discrete wavelet transform : with applications to denoising and fuzzy inference systems
 Title remainder
 with applications to denoising and fuzzy inference systems
 Statement of responsibility
 K.K. Shukla, Arvind K. Tiwari
 Subject

 Mathematics
 Transformations (Mathematics)
 Transformations (Mathematics)
 Transformations (Mathematics)
 Transformations (Mathematics)
 Wavelet Analysis
 Wavelet Analysis
 Wavelets (Mathematics)
 Wavelets (Mathematics)
 Wavelets (Mathematics)
 Wavelets (Mathematics)
 Electronic books
 Electronic resources
 MATHEMATICS  Functional Analysis
 Mathematics
 Language
 eng
 Summary
 Transforms are an important part of an engineer’s toolkit for solving signal processing and polynomial computation problems. In contrast to the Fourier transformbased 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 nonstationary 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 timefrequency description. Due to its inherent timescale 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 signaltonoise ratio in the reconstructed signal
 Cataloging source
 GW5XE
 Image bit depth
 0
 LC call number
 QA403.3
 LC item number
 .S58 2013
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 SpringerBriefs in Computer Science,
Context
Context of Efficient algorithms for discrete wavelet transform : with applications to denoising and fuzzy inference systemsWork of
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