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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2018 - 3

A Computationally Efficient Pipelined Architecture for 1D/2D Lifting Based Forward and Inverse Discrete Wavelet Transform for CDF 5/3 Filter

CEKLI, S. See more information about CEKLI, S. on SCOPUS See more information about CEKLI, S. on IEEExplore See more information about CEKLI, S. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,641 KB) | Citation | Downloads: 161 | Views: 339

Author keywords
digital systems, discrete wavelet transforms, multiprocessing systems, pipeline processing, programmable logic arrays

References keywords
wavelet(33), transform(20), lifting(16), architecture(14), discrete(13), systems(12), circuits(10), signal(9), processing(9), image(9)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 17 - 26
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02003
Web of Science Accession Number: 000434245000003
SCOPUS ID: 85047844169

Abstract
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In this study, a simple lifting based pipeline DWT (Discrete Wavelet Transform) architecture is proposed for the operation of the CDF 5/3 (Cohen-Daubechies-Feauveau 5/3) filter. This scalable architecture is faster and capable of fulfilling the transformation utilizing the parallel processing operation units. The symmetric boundary extension method is used at the signal boundaries to obtain the best result in the case of 1D/2D. The proposed architecture utilizes the hardware resources in a quite efficient way by means of the pipeline technique. The architectural design is constituted by using RTL (Register Transfer Level) design process and coded by the Verilog HDL. The proposed architecture is tested for several 1D/2D inputs to examine its operation. The related architecture is synthesized for the FPGA board to check the results. The reverse operation is fulfilled by using the same structure only by changing the shift amounts of the shifting units. The DWT coefficients are calculated on this architecture for the 1D/2D situation. The hardware resources are used effectively by utilizing the constituted architecture in folded structure in the 2D case. Satisfying results have been obtained when the different numbers of parallel processing units are utilized.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 3,027 TCR
SCOPUS® Citations for all references: 19,302 TCR

Web of Science® Average Citations per reference: 74 ACR
SCOPUS® Average Citations per reference: 471 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-10-22 00:46 in 286 seconds.




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