Targoviste, Romania
Targoviste, Romania

The Valahia University of Târgovişte is a university in Targoviste, Dambovita, Romania. Wikipedia.


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Enescu D.,University Valahia of Targoviste | Virjoghe E.O.,University Valahia of Targoviste
Renewable and Sustainable Energy Reviews | Year: 2014

This paper deals with a review of the main research aspects concerning the formulation of the parameters indicating the characteristics and performance of thermoelectric cooling devices, with particular reference to a number of recent publications. The specific aspects addressed include some practical considerations referring to the thermoelectric figure of merit, the characterization of the cooling capacity, and the assessment of the coefficient of performance (COP). The contribution of this paper starts by categorizing the topics addressed by recent review papers, showing that these reviews generally had a wide focus and provided little specific details on thermoelectric cooling parameters and performance. Then, the dimensionless thermoelectric figure of merit is addressed by focusing on its conventional and modified definitions and indicating the values obtained for different thermoelectric cooling materials. Furthermore, the expressions of the cooling capacity for single-stage and multi-stage thermoelectric coolers are reviewed. Concerning the COP, its dedicated expressions are constructed starting from the classical formulation and introducing additional factors or modifications in order to take into account the Thomson effect, the dependence on temperature of the thermoelectric materials, and the effects of the electrical contact resistance, thermal resistance, thermoelement length and current. Finally, on the basis of the indications taken from the literature, further considerations are included on the COP values found in thermoelectric cooling applications, as well as on how to obtain COP improvements. © 2014 Elsevier Ltd.


Coltuc D.,University Valahia of Targoviste
IEEE Transactions on Information Forensics and Security | Year: 2011

This paper aims at reducing the embedding distortion of prediction error expansion reversible watermarking. Instead of embedding the entire expanded difference into the current pixel, the difference is split between the current pixel and its prediction context. The modification of the context generates an increase of the following prediction errors. Global optimization is obtained by tuning the amount of data embedded into context pixels. Prediction error expansion reversible watermarking schemes based on median edge detector (MED), gradient-adjusted predictor (GAP), and a simplified GAP version, SGAP, are investigated. Improvements are obtained for all the predictors. Notably good results are obtained for SGAP-based schemes. The improved SGAP appears to outperform GAP-based reversible watermarking. © 2011 IEEE.


Mortici C.,University Valahia of Targoviste
Computers and Mathematics with Applications | Year: 2011

The aim of this paper is to improve the Ramanujan formula for approximation the gamma function. A fast asymptotic series is constructed. © 2011 Elsevier Ltd. All rights reserved.


Mortici C.,University Valahia of Targoviste
Computers and Mathematics with Applications | Year: 2011

The aim of this paper is to refine Gurland's formula for approximating pi. We prove the complete monotonicity of a class of functions related to Gurland's formula, and then we state some sharp inequalities. © 2011 Elsevier Ltd. All rights reserved.


Mortici C.,University Valahia of Targoviste
Computers and Mathematics with Applications | Year: 2010

We propose new sequences containing a modified logarithmic term which converge to the Euler-Mascheroni constant faster than sequences known from the literature. © 2010 Elsevier Ltd. All rights reserved.


Mortici C.,University Valahia of Targoviste
Mathematical and Computer Modelling | Year: 2010

In this paper we solve an open problem posed by Muqattash and Yahdi [I. Muqattash, M. Yahdi, Infinite family of approximations of the digamma function, Math. Comput. Modelling 43 (2006) 1329-1336], also rediscussed by Qi and Guo [F. Qi, B.-N. Guo, Sharp inequalities for the psi function and harmonic numbers, arXiv:0902.2524v1 [math.CA]]. © 2010 Elsevier Ltd. All rights reserved.


Mortici C.,University Valahia of Targoviste
Computers and Mathematics with Applications | Year: 2010

We introduce Stirling's formula in a more general class of approximation formulas to extend the integral representation of Liu [Z. Liu, A new version of the Stirling formula, Tamsui Oxf. J. Math. Sci. 23 (4) (2007) 389-392]. Finally, an accurate approximation for the factorial function is established. © 2009 Elsevier Ltd. All rights reserved.


Mortici C.,University Valahia of Targoviste
Mathematical and Computer Modelling | Year: 2010

The aim of this paper is to establish new, increasingly accurate formulas for evaluating the ratio of gamma functions and the Wallis ratio. © 2010 Elsevier Ltd. All rights reserved.


Dragoi I.-C.,University Valahia of Targoviste | Coltuc D.,University Valahia of Targoviste
IEEE Transactions on Image Processing | Year: 2014

This paper investigates the use of local prediction in difference expansion reversible watermarking. For each pixel, a least square predictor is computed on a square block centered on the pixel and the corresponding prediction error is expanded. The same predictor is recovered at detection without any additional information. The proposed local prediction is general and it applies regardless of the predictor order or the prediction context. For the particular cases of least square predictors with the same context as the median edge detector, gradient-adjusted predictor or the simple rhombus neighborhood, the local prediction-based reversible watermarking clearly outperforms the state-of-the-art schemes based on the classical counterparts. Experimental results are provided. © 1992-2012 IEEE.


Coltuc D.,University Valahia of Targoviste
IEEE Transactions on Image Processing | Year: 2012

This paper proposes a low-distortion transform for prediction-error expansion reversible watermarking. The transform is derived by taking a simple linear predictor and by embedding the expanded prediction error not only into the current pixel but also into its prediction context. The embedding ensures the minimization of the square error introduced by the watermarking. The proposed transform introduces less distortion than the classical prediction-error expansion for complex predictors such as the median edge detector or the gradient-adjusted predictor. Reversible watermarking algorithms based on the proposed transform are analyzed. Experimental results are provided. © 2011 IEEE.

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