Name
Abstract | ||
|---|---|---|
Adapted wavelet analysis in the sense of wavelet packet algorithms is a highly relevant procedure in different types of applications, like, e.g. data compression, feature extraction, classification problems, data analysis, numerical mathematics, etc. Given a large or high-dimensional data set the computational demand is too high for interactive or “nearly-interactive” processing. Therefore, parallel processing is one of the possibilities to accelerate the processing speed. In this case, special attention has to be paid towards handling of the large amount of data in addition to the proper organization of the computations. We investigate different data decomposition approaches, border handling techniques and programming paradigms. The memory consuming decomposition into a given arbitrary basis after adaptive basis choice is resolved by a localized decomposition strategy. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0167-739X(00)00079-0 | Future Generation Comp. Syst. |
Keywords | Field | DocType |
wavelet packets,parallel adaptive wavelet analysis,mimd algorithms,best basis algorithm,parallel processing,wavelet analysis,data compression,data analysis,feature extraction,programming paradigm,high dimensional data | Programming paradigm,Computer science,Algorithm,Feature extraction,Theoretical computer science,Second-generation wavelet transform,Cascade algorithm,Data compression,Stationary wavelet transform,Wavelet packet decomposition,Distributed computing,Wavelet | Journal |
Volume | Issue | ISSN |
18 | 1 | Future Generation Computer Systems |
Citations | PageRank | References |
3 | 0.42 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rade Kutil | 1 | 61 | 8.80 |
| Andreas Uhl | 2 | 1958 | 223.07 |