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03837nam a22004695i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20140310151112.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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130125s2013 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781447149415 |
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978-1-4471-4941-5 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA1637-1638 |
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| Classification number |
TA1637-1638 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.6 |
| Edition number |
23 |
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|---|
| Classification number |
006.37 |
| Edition number |
23 |
| 264 #1 - |
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| -- |
London : |
| -- |
Springer London : |
| -- |
Imprint: Springer, |
| -- |
2013. |
| 912 ## - |
|---|
| -- |
ZDB-2-SCS |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Shukla, K. K. |
| Relator term |
author. |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Efficient Algorithms for Discrete Wavelet Transform |
| Medium |
[electronic resource] : |
| Remainder of title |
With Applications to Denoising and Fuzzy Inference Systems / |
| Statement of responsibility, etc |
by K. K. Shukla, Arvind K. Tiwari. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
IX, 91 p. 46 illus., 31 illus. in color. |
| Other physical details |
online resource. |
| 440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
SpringerBriefs in Computer Science, |
| International Standard Serial Number |
2191-5768 |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Introduction -- 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 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Transforms 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer science. |
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| Topical term or geographic name as entry element |
Computer software. |
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| Topical term or geographic name as entry element |
Computer vision. |
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| Topical term or geographic name as entry element |
Computer Science. |
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| Topical term or geographic name as entry element |
Image Processing and Computer Vision. |
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| Topical term or geographic name as entry element |
Signal, Image and Speech Processing. |
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| Topical term or geographic name as entry element |
Algorithm Analysis and Problem Complexity. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tiwari, Arvind K. |
| Relator term |
author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Display text |
Printed edition: |
| International Standard Book Number |
9781447149408 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
http://dx.doi.org/10.1007/978-1-4471-4941-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
E-Book |
