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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
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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
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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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  4/2020 - 2

Gaussian Source Coding using a Simple Switched Quantization Algorithm and Variable Length Codewords

PERIC, Z. See more information about PERIC, Z. on SCOPUS See more information about PERIC, Z. on IEEExplore See more information about PERIC, Z. on Web of Science, PETKOVIC, G. See more information about  PETKOVIC, G. on SCOPUS See more information about  PETKOVIC, G. on SCOPUS See more information about PETKOVIC, G. on Web of Science, DENIC, B. See more information about  DENIC, B. on SCOPUS See more information about  DENIC, B. on SCOPUS See more information about DENIC, B. on Web of Science, STANIMIROVIC, A. See more information about  STANIMIROVIC, A. on SCOPUS See more information about  STANIMIROVIC, A. on SCOPUS See more information about STANIMIROVIC, A. on Web of Science, DESPOTOVIC, V. See more information about  DESPOTOVIC, V. on SCOPUS See more information about  DESPOTOVIC, V. on SCOPUS See more information about DESPOTOVIC, V. on Web of Science, STOIMENOV, L. See more information about STOIMENOV, L. on SCOPUS See more information about STOIMENOV, L. on SCOPUS See more information about STOIMENOV, L. on Web of Science
 
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Download PDF pdficon (1,293 KB) | Citation | Downloads: 771 | Views: 1,981

Author keywords
Gaussian distribution, quantization, source coding, signal processing algorithms, signal to noise ratio

References keywords
source(8), coding(8), speech(7), signal(7), gaussian(7), quantization(6), scalar(5), quantizers(5), logarithmic(5), optimal(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-11-30
Volume 20, Issue 4, Year 2020, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.04002
Web of Science Accession Number: 000594393400002
SCOPUS ID: 85098140347

Abstract
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This paper introduces an algorithm based on switched scalar quantization utilizing a novel -law quantization model (optimized in terms of minimal distortion) and variable length codewords, for high-quality encoding of the signals modeled by Gaussian distribution. The implemented -law quantizer represents an improvement of the standard -law quantizer in terms of bit rate, at the same time providing the equal signal quality. The main concept of the algorithm is to divide the range of the input signal variances into a certain number of sub-ranges, and to design the optimal quantizer for each sub-range. The signal is processed frame-by-frame, and for each frame the best performing quantizer is chosen, where the estimated frame variance is used as the switching criterion. Theoretical results indicate that the proposed algorithm achieves performance comparable to the standard -law quantizer, enabling the compression of about 0.5 bit/sample. The simulation results are provided to confirm the correctness of the proposed model.


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Cited-By CrossRef

[1] Robust 2-bit Quantization of Weights in Neural Network Modeled by Laplacian Distribution, PERIC, Z., DENIC, B., DINCIC, M., NIKOLIC, J., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 21, 2021.
Digital Object Identifier: 10.4316/AECE.2021.03001
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