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Search Results: 1 - 10 of 208267 matches for " L. Velimirovic "
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Simple Solution for Designing the Piecewise Linear Scalar Companding Quantizer for Gaussian Source
J. Nikolic,Z. Peric,L. Velimirovic
Radioengineering , 2013,
Abstract: To overcome the difficulties in determining an inverse compressor function for a Gaussian source, which appear in designing the nonlinear optimal companding quantizers and also in the nonlinear optimal companding quantization procedure, in this paper a piecewise linear compressor function based on the first derivate approximation of the optimal compressor function is proposed. We show that the approximations used in determining the piecewise linear compressor function contribute to the simple solution for designing the novel piecewise linear scalar companding quantizer (PLSCQ) for a Gaussian source of unit variance. For the given number of segments, we perform optimization procedure in order to obtain optimal value of the support region threshold which maximizes the signal to quantization noise ratio (SQNR) of the proposed PLSCQ. We study how the SQNR of the considered PLSCQ depends on the number of segments and we show that for the given number of quantization levels, SQNR of the PLSCQ approaches the one of the nonlinear optimal companding quantizer with the increase of the number of segments. The presented features of the proposed PLSCQ indicate that the obtained model should be of high practical significance for quantization of signals having Gaussian probability density function.
DPCM with Forward Gain-Adaptive Quantizer and Simple Switched Predictor for High Quality Speech Signals
DESPOTOVIC, V. M.,PERIC, Z. H.,VELIMIROVIC, L.,DELIC, V. D.
Advances in Electrical and Computer Engineering , 2010, DOI: 10.4316/aece.2010.04015
Abstract: In this article DPCM (Differential Pulse Code Modulation) speech coding scheme with a simple switched first order predictor is presented. Adaptation of the quantizer to the signal variance is performed for each particular frame. Each frame is classified as high or low correlated, based on the value of the correlation coefficient, then the selection of the appropriate predictor coefficient and bitrate is performed. Low correlated frames are encoded with a higher bitrate, while high correlated frames are encoded with a lower bitrate without the objectionable loss in quality. Theoretical model and experimental results are provided for the proposed algorithm.
Simple Solution for Designing the Piecewise Linear Scalar Companding Quantizer for Gaussian Source
Jelena Nikolic,Zoran Peric,Lazar Velimirovic
Mathematics , 2012,
Abstract: To overcome the difficulties in determining an inverse compressor function for a Gaussian source, which appear in designing the nonlinear optimal companding quantizers and also in the nonlinear optimal companding quantization procedure, in this paper a piecewise linear compressor function based on the first derivate approximation of the optimal compressor function is proposed. We show that the approximations used in determining the piecewise linear compressor function contribute to the simple solution for designing the novel piecewise linear scalar companding quantizer (PLSCQ) for a Gaussian source of unit variance. For the given number of segments, we perform optimization procedure in order to obtain optimal value of the support region threshold which maximizes the signal to quantization noise ratio (SQNR) of the proposed PLSCQ. We study how the SQNR of the considered PLSCQ depends on the number of segments and we show that for the given number of quantization levels, SQNR of the PLSCQ approaches the one of the nonlinear optimal companding quantizer with the increase of the number of segments. The presented features of the proposed PLSCQ indicate that the obtained model should be of high practical significance for quantization of signals having Gaussian probability density function.
Determining the best fitting distribution of annual precipitation data in Serbia using L-moments method
Milan Gocic,Lazar Velimirovic,Miomir Stankovic
Statistics , 2014,
Abstract: The monthly precipitation data from 29 meteorological stations for the period 1946–2012 from Serbia were used. To describe the behaviour of precipitation data at a specific location, it is necessary to identify the distribution that best fits the data. For this purpose, three distributions i.e. generalized extreme value (GEV), generalized Pareto (GPD) and generalized logistic (GLO) distribution were fitted using the method of L-moment. The goodness-of-fit for the selected three distributions was confirmed using L-moment ratio diagram and three tests namely relative root mean square error, relative mean absolute error and probability plot correlation coefficient. From the result of this analysis, the GEV distribution was selected as the best fitting distribution of annual precipitation data in Serbia. The 2, 5, 10, 20, 50, 100 and 1000-years return levels are provided.
Design of companding quantizer for Laplacian source using the approximation of probability density function
Lazar Velimirovic,Zoran Peric,Miomir Stankovic,Nikola Simic
Mathematics , 2012,
Abstract: In this paper both piecewise linear and piecewise uniform approximation of probability density function are performed. For the probability density function approximated in these ways, a compressor function is formed. On the basis of compressor function formed in this way, piecewise linear and piecewise uniform companding quantizer are designed. Design of these companding quantizer models is performed for the Laplacian source at the entrance of the quantizer. The performance estimate of the proposed companding quantizer models is done by determining the values of signal to quantization noise ratio (SQNR) and approximation error for the both of proposed models and also by their mutual comparison.
Asymmetrical two-level scalar quantizer with extended Huffman coding for compression of Laplacian source
Zoran Peric,Jelena Nikolic,Lazar Velimirovic,Miomir Stankovic,Danijela Aleksic
Mathematics , 2012,
Abstract: This paper proposes a novel model of the two-level scalar quantizer with extended Huffman coding. It is designed for the average bit rate to approach the source entropy as close as possible provided that the signal to quantization noise ratio (SQNR) value does not decrease more than 1 dB from the optimal SQNR value. Assuming the asymmetry of representation levels for the symmetric Laplacian probability density function, the unequal probabilities of representation levels are obtained, i.e. the proper basis for further implementation of lossless compression techniques is provided. In this paper, we are concerned with extended Huffman coding technique that provides the shortest length of codewords for blocks of two or more symbols. For the proposed quantizer with extended Huffman coding the convergence of the average bit rate to the source entropy is examined in the case of two to five symbol blocks. It is shown that the higher SQNR is achieved by the proposed asymmetrical quantizer with extended Huffman coding when compared with the symmetrical quantizers with extended Huffman coding having equal average bit rates.
Numerical determination of the optimal value of quantizer's segment threshold using quadratic spline functions
Lazar Velimirovic,Zoran Peric,Miomir Stankovic,Jelena Nikolic
Mathematics , 2013,
Abstract: In this paper, an approximation of the optimal compressor function using the quadratic spline functions has been presented. The coefficients of the quadratic spline functions are determined by minimizing the mean-square error (MSE). Based on the obtained approximative quadratic spline functions, the design for companding quantizer for Gaussian source is done. The support region of proposed companding quantizer is divided on segments of unequal size, where the optimal value of segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). It is shown that by the companding quantizer proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.
Design of Compandor Based on Approximate the First-Degree Spline Function
Lazar Velimirovic,Zoran Peric,Miomir Stankovic,Jelena Nikolic
Mathematics , 2013,
Abstract: In this paper, the approximation of the optimal compressor function using spline function of the first-degree is done. For the companding quantizer designed on the basis of the approximative spline function of the first-degree, the support region is numerically optimized to provide the minimum of the total distortion for the last segment. It is shown that the companding quantizer with the optimized support region threshold provides the signal to quantization noise ratio that is very close to the one of the optimal companding quantizer having an equal number of levels.
Scalar Compandor Design Based on Optimal Compressor Function Approximating by Spline Functions
Zoran H. Peric,Lazar Velimirovic,Miomir Stankovic,Aleksandra Jovanovic,Dragan Antic
Computer Science , 2013,
Abstract: In this paper the approximation of the optimal compressor function using the first-degree spline functions and quadratic spline functions is done. Coefficients on which we form approximative spline functions are determined by solving equation systems that are formed from treshold conditions. For Gaussian source at the input of the quantizer, using the obtained approximate spline functions a companding quantizer designing is done. On the basis of the comparison with the SQNR of the optimal compandor it can be noticed that the proposed companding quantizer based on approximate spline functions achieved SQNR arbitrary close to that of the optimal compandor.
Computing Reachable Sets as Capture-Viability Kernels in Reverse Time  [PDF]
No?l Bonneuil
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.311219
Abstract: The set SF(x0;T) of states y reachable from a given state x0 at time T under a set-valued dynamic x’(t)∈F(x (t)) and under constraints x(t)∈K where K is a closed set, is also the capture-viability kernel of x0 at T in reverse time of the target {x0} while remaining in K. In dimension up to three, Saint-Pierre’s viability algorithm is well-adapted; for higher dimensions, Bonneuil’s viability algorithm is better suited. It is used on a large-dimensional example.
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