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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 75 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

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  1/2015 - 3

 HIGHLY CITED PAPER 

Sparse FIR Filter Design Based on Simulated Annealing Algorithm

WU, C. See more information about WU, C. on SCOPUS See more information about WU, C. on IEEExplore See more information about WU, C. on Web of Science, XU, X. See more information about  XU, X. on SCOPUS See more information about  XU, X. on SCOPUS See more information about XU, X. on Web of Science, ZHANG, X. See more information about  ZHANG, X. on SCOPUS See more information about  ZHANG, X. on SCOPUS See more information about ZHANG, X. on Web of Science, ZHAO, L. See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on Web of Science
 
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Download PDF pdficon (806 KB) | Citation | Downloads: 1,019 | Views: 2,602

Author keywords
digital filter design, simulated annealing, sparse FIR filter

References keywords
design(12), signal(10), processing(10), filters(10), systems(8), sparse(8), response(7), filter(7), circuits(7), simulated(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 17 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01003
Web of Science Accession Number: 000352158600003
SCOPUS ID: 84924766096

Abstract
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Design of sparse finite impulse response (FIR) filter is of great importance to reduce the implementation cost. However, design of sparse FIR filter under the prescribed constraints is a highly non-convex problem. Traditional methods generally relax the non-convex design problem to a convex one, which leads the obtained solutions suboptimal. In this paper, the non-convex design problem is modeled as a combinatorial optimization problem and an algorithm based on simulated annealing (SA) is presented to solve it. At each stage of the proposed algorithm, with a fixed sparsity of the filter coefficients, SA is employed for finding the possible sparse pattern of the FIR filter that satisfies the prescribed constraints. Once the design constraints have been satisfied, the sparsity is added by one and the algorithm moves to the next stage. The algorithm successively increases the sparsity of the filter coefficients until no sparser solution could be obtained. The proposed algorithm is evaluated by two sets of examples, and better results can be achieved than other existing algorithms.


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Cited-By SCOPUS

SCOPUS® Times Cited: 6
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Cited-By CrossRef

[1] Low complexity multiobjective finite impulse response filter design using Salp swarm algorithm and its improved version, Srivastava, Savita, Dwivedi, Atul Kumar, Nagaria, Deepak, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, ISSN 0894-3370, Issue 6, Volume 34, 2021.
Digital Object Identifier: 10.1002/jnm.2914
[CrossRef]

[2] Silicon on Insulator C-VTFET Based Design of low Complexity Sparse Quadrature Mirror Filter Using Differential Search Algorithm, Singh, Hitendra, Dwivedi, Atul Kumar, Nagaria, Deepak, Silicon, ISSN 1876-990X, Issue 17, Volume 14, 2022.
Digital Object Identifier: 10.1007/s12633-022-01858-6
[CrossRef]

[3] FIR digital filter design based on improved artificial bee colony algorithm, Lian, Lian, Tian, Zhongda, Soft Computing, ISSN 1432-7643, Issue 24, Volume 26, 2022.
Digital Object Identifier: 10.1007/s00500-022-07506-w
[CrossRef]

[4] Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution, Mohaideen Abdul Kadhar, K., Rengarajan, S., Tamilselvi, S., Karuppiah, N., Balachandran, Praveen Kumar, Thamilmaran, A., Dhanamjayulu, C., Khan, Baseem, Du, Sheng, International Transactions on Electrical Energy Systems, ISSN 2050-7038, Issue , 2023.
Digital Object Identifier: 10.1155/2023/1572996
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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