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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 105 days


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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

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-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

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.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  4/2011 - 6

Design of Piecewise Uniform Scalar Quantizer with Geometric Progression of Segment Width

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, NIKOLIC, J. See more information about NIKOLIC, J. on SCOPUS See more information about NIKOLIC, J. on SCOPUS See more information about NIKOLIC, J. on Web of Science
 
Click to see author's profile on 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 (456 KB) | Citation | Downloads: 833 | Views: 2,292

Author keywords
digital signal processing, quantization, signal to quantization noise ratio

References keywords
speech(5), theory(4), quantizers(4), adaptive(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 37 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04006
Web of Science Accession Number: 000297764500006
SCOPUS ID: 84856600025

Abstract
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Full text preview
In this paper, the design procedure of the piecewise uniform scalar quantizer having segments with widths that form a geometric progression is presented. The proposed quantizer design optimization for the Laplacian source of the unit variance is performed by optimizing the distortion in respect to the support region threshold and the scaling factor which defines the geometric progression of the consecutive segment widths. For the proposed quantizer model and its forward adaptive version, the signal to quantization noise ratio robustness analysis in a wide variance range is also presented in the paper. Regarding the performance comparision of the proposed quantizer, the uniform quantizer and the G.711 quantizer, the conclusion of the proposed quantizer significance is distinctly shown in the paper.


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

[1] N. Jayant, P. Noll, "Digital Coding of Waveforms: Principles and Applications to Speech and Video", Prentice Hall, 1st edition, March 1984.

[2] L. Hanzo, C. Somerville, J. Woodard, "Voice and Audio Compression for Wireless Communications", John Wiley & Sons - IEEE Press, 2nd edition, October 8, 2007.

[3] L. Rabiner, R. Schafer, "Introduction to Digital Speech Processing", Now, November 30, 2007.

[4] ITU-T, Recommendation G.711, "Pulse Code Modulation (PCM) of Voice Frequencies", International Telecommunication Union, Geneva, Switzerland, 1988.

[5] D. Kazakos, K. Makki, "Quantization Approach utilizing Piecewise Linear Companders", Proceedings of the IEEE Southeastcon 2008, Huntsville, 3-6 April, 2008, pp. 165-169.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[6] D. Kazakos, K. Makki, "Robust Companders", Proceedings of the 6th WSEAS International Conference on Telecommunications and Informatics, Dallas, Texas, 2007, pp. 32-35.

[7] J. Nikolic, Z. Peric, D. Antic, A. Jovanovic, D. Denic, "Low Complex Forward Adaptive Loss Compression Algorithm and its Application in Speech Coding", Journal of Electrical Engineering, vol. 62, no. 1, pp. 19-24, 2011.
[CrossRef]


[8] F. Kuhlmann, J. Bucklew, "Picewise Uniform Vector Quantizers", IEEE Transactions on Information Theory, vol. 34, no. 5, pp. 1259-1263, 1988.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 18]


[9] S. Na, D. Neuhoff, "On the Support of MSE-Optimal, Fixed-Rate, Scalar Quantizers", IEEE Transactions on Information Theory, vol. 47, no. 7, pp. 2972-2982, November, 2001.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 39]


[10] S. Na, "Asymptotic Formulas for Mismatched Fixed-Rate Minimum MSE Laplacian Quantizers", IEEE Signal Processing Letters, vol. 15, pp. 13-16, 2008.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 22]


[11] D. Goodman, A. Gersho, "Theory of an Adaptive Quantizer", IEEE Transactions on Communications, vol. 22, no. 8, pp. 1037-1045, August, 1974.
[CrossRef] [SCOPUS Times Cited 91]


[12] V. Despotovic, Z. Peric, L. Velimirovic, V. Delic, "DPCM with Forward Gain-Adaptive Quantizer and Simple Switched Predictor for High Quality Speech Signals", Advances in Electrical and Computer Engineering, vol. 10, no. 4, 2010.

[13] J. Nikolic, Z. Peric, "Lloyd-Max's Algorithm Implementation in Speech Coding Algorithm Based on Forward Adaptive Technique", Informatica, vol. 19, no. 2, pp. 255-270, 2008.

[14] S. Na, "On the Support of Fixed-Rate Minimum Mean-Squared Error Scalar Quantizers for a Laplacian Source", IEEE Transactions on Information Theory, vol. 50, no. 5, pp. 937-944, May, 2004.
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 25]


[15] O. Hersent, J. Petit, D. Gurle, "Beyond VoIP Protocols: Understanding Voice Technology and Networking Techniques for IP Telephony", John Wiley & Sons, 1st edition, March 11, 2005.

[16] ITU-T Recommendation G.712, "Transmission Performance Characteristics of Pulse Code Modulation channels", International Telecommunication Union, Geneva, Switzerland, 2001.

References Weight

Web of Science® Citations for all references: 88 TCR
SCOPUS® Citations for all references: 198 TCR

Web of Science® Average Citations per reference: 6 ACR
SCOPUS® Average Citations per reference: 12 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 2017-10-22 17:24 in 51 seconds.




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


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