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Discrete orthogonal polynomials (DOPs) have gained significant research attention owing to their crucial role in digital signal processing applications such as computer vision, pattern recognition, and compression. However, the computation of DOP coefficients often incurs a substantial computational burden, exacerbating for higher-degree moments along with the resulting numerical errors. To address this challenge, this paper exploits the inherent parallelism in Charlier polynomial coefficient calculations to achieve enhanced polynomial performance. Independent calculations are distributed among threads, making efficient use of the available processing resources. Two algorithms are presented, the first algorithm evenly distributes the rows in a sequential manner (straightforward). Additionally, to achieve a more equitable distribution of coefficient calculations, this paper proposes alternative distribution approaches, aimed at balancing processing load among threads. Through extensive comparative experiments, we have confirmed that the proposed approaches achieved high performance across different degrees (1540 to 7370) and at different numbers of threads (2 to 256). The results show processing time in the multithreaded case is improved by up to 9.1 times with respect to the unthreaded case. Furthermore, by increasing the number of threads from 2 to 256, the trend indicates that the most significant improvement occurs in the range of 32 to 128 threads, confirming the robustness of the proposed algorithm. These findings signify the importance of this paper.

The discrete Charlier moment transform, while extensively utilized in image processing, is inherently lossy and non-integer reversible, making it unsuitable for lossless image applications. To address this, we propose the Integer Reversible Charlier Transform (IRCT), which operates on integer values and produces integer coefficients, enabling perfect and unique recovery of the original input data. The IRCT maintains the orthogonality and invertibility ensuring exact similarity between original and reconstructed images. We leverage the capabilities of the IRCT to develop a novel reversible data hiding (RDH) scheme. This scheme embeds additional data into images by modifying the histogram in the IRCT domain. By capitalizing on the concentrated nature of the IRCT histogram, characterized by high peaks, our method achieves a significantly high embedding capacity while preserving image quality and robustness to statistical attacks. Comparative performance evaluations underscore the effectiveness of the IRCT-based RDH scheme over existing techniques across various domains, positioning it as a promising solution for secure data transmission.

We give new explicit formulas as well as new generating functions for the associated Meixner, Charlier, and Krawtchouk polynomials. The obtained results are then used to derive new generating functions and convolution-type formulas of the corresponding classical polynomials. Some consequences of our results are also mentioned. [ABSTRACT FROM AUTHOR]

This paper discusses the applicability of data reconciliation approaches in metrology and mentions the existed shortcomings. The semi-parametric method based on Gram-Charlier series expansion is presented for overcoming the obstacles preventing the wider spread of the measured data reconciliation in metrological practice. The proposed approach allows the fast estimation of potential accuracy increase that matters for adaptive measurement systems. The corresponded expressions and tests are presented.

Bandwidth parameter estimation in univariate Kernel Density Estimation has traditionally two approaches. Rule(s)-of-Thumb (ROT) achieve ‘quick and dirty’ estimations with some specific assumption for an unknown density. More accurate solve-the-equation-plug-in (STEPI) rules have almost no direct assumption for the unknown density but demand high computation. This article derives a balancing third approach. Extending an assumption of Gaussianity for the unknown density to be estimated in \textit{normal reference} ROT (NRROT) to near Gaussianity, and then expressing the density using Gram-Charlier A (GCA) series to minimize the asymptotic mean integrated square error, it derives GCA series based Extended ROT (GCAExROT). The performance analysis using the simulated and the real datasets suggests to replace NRROT by a modified GCAExROT rule achieving a balancing performance by accuracy nearer to STEPI rules at computation nearer to NRROT, specifically at small samples.

All thermal vibrations of atoms are in fact anharmonic – this is the underlying reason for the manifestation of many physical properties of solids, primarily its thermal expansion. By now, over three decades have passed from the seminal works of Kuhs [Kuhs Statistical description of multimodal atomic probability densities. Acta Crystallogr A. 1983;39:148–158.Kuhs Lead Article Generalized Atomic Displacements in Crystallographic Structure Analysis. Acta Cryst. 1992;A48:80–98. Kuhs The Anharmonic Temperature Factor in Crystallographic Structure Analysis. Aust J Phys. 1988;41:369], and 'anharmonic' approaches are now widely used for the description of thermal displacements of the atoms. Experimental data for studies of this phenomenon can be acquired from targeted diffraction experiments, and in this mini review, we analyse the current state of this area, mostly for the inorganic structures. The modern crystallographic hardware and software permits to correct the description of anharmonicity for the objects whereof one or two decades ago this phenomenon was either neglected or beyond the power of data processing. In general, the asphericity of electron density distribution is not necessarily caused by anharmonic motions; another possible reason is the displacement of valence electrons due to the formation of chemical bonds. We analyse the hitherto reported examples of anharmonic description of thermal motions; this phenomenon is most pronounced for univalent cations (like alkalis, silver, copper and thallium) and anions (halides). We also present the new Anharmonicity program suite which permits to determine the maxima of distribution of probability density. [ABSTRACT FROM AUTHOR]

It often arises in practice that, although the first few moments of a distribution are known, the density of the distribution cannot be determined in closed form. In such cases, Gram-Charlier and Edgeworth series are commonly used to analytically approximate the unknown density in terms of the known moments. Although convenient, these series contain polynomial factors, and can hence lead to density approximations taking negative values or becoming multimodal in general. Consequently it is of interest to determine the set of moments for which the corresponding density approximations are positive definite and unimodal. In contrast to the existing literature that determines the boundaries of such sets numerically, explicit analytic expressions for the two boundaries are given in this paper for the Gram-Charlier series. Moreover, a method for projecting a given set of moments onto the boundaries of the two regions in order to minimizes the Kolmogorov-Smirnov statistic of corresponding density approximations is also provided. [ABSTRACT FROM AUTHOR]