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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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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.

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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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  1/2017 - 14

Three-Level Delta Modulation for Laplacian Source Coding

DENIC, B. See more information about DENIC, B. on SCOPUS See more information about DENIC, B. on IEEExplore See more information about DENIC, B. on Web of Science, PERIC, Z. See more information about  PERIC, Z. on SCOPUS See more information about  PERIC, Z. on SCOPUS See more information about PERIC, Z. 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
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

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Author keywords
delta modulation, Huffman coding, predictive coding, signal to noise ratio, speech coding

References keywords
speech(10), processing(7), delta(7), coding(7), signal(5), recommendation(4), quantization(4), design(4), adaptive(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 95 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01014
Web of Science Accession Number: 000396335900014
SCOPUS ID: 85014257330

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This paper proposes a novel solution for coding of time varying signals with Laplacian distribution, which is based on delta modulation and three-level quantization. It upgrades the conventional scheme by introducing the quantizer with variable length code. Forward adaptive scheme is used, where the adaptation to the signal variance is performed on frame-by-frame basis. We employ configurations with simple fixed first-order predictor and switched first-order predictor utilizing correlation. Furthermore, we propose different methods for optimizing predictor coefficients. The configurations are tested on speech signal and compared to an adaptive two-level and four-level delta modulation, showing that proposed three-level delta modulation offers performance comparable to a four-level baseline with significant savings in bit rate.

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

[1] J. Jensen, I. Batina, R. C. Hendriks, R. Heusdens, "A study of the distribution of time-domain speech samples and discrete fourier coefficients," in Proc. IEEE First BENELUX/DSP Valley Signal Processing Symposium, 2005, pp. 155-158.

[2] N. S. Jayant, P. Noll, Digital Coding of Waveforms. New Jersey, Prentice Hall, Chapter 4, pp. 115-188, Chapter 8, pp. 372-417, 1984.

[3] K. Sayood, Introduction to Data Compression. San Francisco, Elsevier Science, Chapter 9, pp. 227-270, 2005.

[4] W. C. Chu, Speech Coding Algorithms: Foundation and Evolution of Standardized Coders. John Wiley & Sons, New Jersey, Chapter 5, pp. 143-158, 2003.

[5] D. G. Zrilic, Circuits and Systems Based on Delta Modulation. Springer, Chapter 1, pp. 1-27, 2005.

[6] J. D. Gibson, "Speech compression," Information, vol. 7, no. 32, pp. 1-22, 2016.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 11]

[7] Z. Peric, B. Denic, V. Despotovic, "Delta modulation system with a limited error propagation" in Proc. XIII International Conference SAUM, Nis, Serbia, 2016.

[8] S. Tomic, Z. Peric, J. Nikolic, "Modified BTC algorithm for audio Signal Coding," Advances in Electrical and Computer Engineering, vol. 16, no. 4, pp. 31-38, 2016.
[CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]

[9] H. Zheng, Z. Lu, "Research and design of a 2-bit delta modulator encoder/decoder," in Proc. 24th Chinese Control and Decision Conf. (CCDC), Taiyuan, 2012.
[CrossRef] [SCOPUS Times Cited 2]

[10] E. A. Prosalentis, G. S. Tombras, "2-bit adaptive delta modulation system with improved performance," EURASIP Journal on Advances in Signal Processing, Article ID 16286, 2007.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]

[11] M. Lewandowski, "A short-term analysis of a digital sigma-delta modulator with a nonstationary audio signals," in Proc. International AES Convention, Warsaw, 2015.

[12] T. Ziquan, Y. Shaojun, J. Yueming, D. Naiying, "The design of a multi-bit quantization sigma-delta modulator," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 6, no. 5, pp. 265-274, 2013.

[13] C. Canudas De Wit, J. Jaglin, C. Siclet, "Energy-aware 3-level coding and control co-design for sensor network systems," in Proc. IEEE International Conference on Control Applications, Singapore, 2007, pp. 1012 - 1017.
[CrossRef] [SCOPUS Times Cited 2]

[14] M. Azarbad, A. Ebrahimzadeh, "ECG compression using the three-level quantization and wavelet transform," International Journal of Computer Applications, vol. 59, no. 1, pp. 28-38, 2012.

[15] Z. A. Sadik, J. P. O’Shea, "Realization of ternary sigma-delta modulated arithmetic processing modules," EURASIP Journal on Advances in Signal Processing, vol. 6, no. 5, pp. 665-676, 2009.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]

[16] M. Dincic, Z. Peric, "Design of quantizers with Huffman coding for Laplacian source," Elecrtonika IR Electrotechnika, vol. 106, no. 10, pp. 129-132, 2010.

[17] 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.

[18] A. Ortega, M. Vetterly, "Adaptive scalar quantization without side information," IEEE Trans. on Image Processing, vol. 6, no. 5, pp. 665-676, 1997.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 42]

[19] 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, pp. 95-98, 2010.
[CrossRef] [Full Text] [Web of Science Times Cited 9] [SCOPUS Times Cited 10]

[20] ITU-T, Recommendation P. 862: "Perceptual evaluation of speech quality (PESQ), an objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs", 2001.

[21] J. D. Gibson, Y. Y. Li, "Rate distortion performance for wideband speech," in Proc. Information Theory and Applications Workshop (ITA), San Diego, 2012, pp. 186-191.
[CrossRef] [SCOPUS Times Cited 3]

[22] ITU-T, Recommendation G.722.1: "Low-complexity coding at 24 and 32kbit/s for hands-free operation in systems with low frame loss", 2005.

[23] D. Marco, D. Neuhoff, "Low resolution scalar quantization for Gaussian and Laplacian sources with absolute and squared distortion measures," Technical Report, 2006.

[24] ITU-T Recommendation P.862.2: "Wideband extension to recommendation P.862 for the assessment of wideband telephone networks and speech codecs", 2007.

References Weight

Web of Science® Citations for all references: 57 TCR
SCOPUS® Citations for all references: 77 TCR

Web of Science® Average Citations per reference: 2 ACR
SCOPUS® Average Citations per reference: 3 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 2019-04-19 03:10 in 83 seconds.

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