• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.180-187
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• 2002

The decoding method using covering polynomials is an extended form of error-trapping decoding, and is a simple and effective means to implement decoders for cyclic codes. Covering polynomials can be used for soft-decision decoding as well as for decoding beyond the bounded distance of the code. The implementation complexity is proportional to the number of covering polynomials employed. In this paper, the soft-decision decoding procedure using covering polynomials is described, and the procedure is applied to the [23,12] Golay code. A new set of covering polynomials is derived for the procedure, which is presented as a generalized closed-form solution. The set can be efficiently utilized for decoding a class of cyclic codes including the Golay code. Computer simulation of the described procedure is performed to show the trade-offs between the decoder performance and complexity. It is demonstrated that soft-decision decoding of the Golay code using the derived set of covering polynomials has less than 0.2dB deviation from the optimal performance of maximum-likelihood decoding, with a reduced complexity when compared to the Chase Algorithm 2 combined with hard-decision decoding that has nearly identical performance.

• Kyungpook Mathematical Journal
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• v.43 no.4
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• pp.489-494
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• 2003

In this paper, we first establish an interesting theorem involving the $\bar{H}$-function introduced by Inayat-Hussain ([7], [8]). The convergence and existence condition, basic properties of this function were given by Buschman and Srivastava ([2]). Next, we obtain certain new integrals and an expansion formula by the application of our theorem. On account of the most general nature of the functions involved herein, our main findings are capable of yielding a large number of new, interesting and useful integrals, expansion formulae involving simple special functions and polynomials as their special cases. A known special case of our main theorem in also given ([11]).

• Bulletin of the Korean Mathematical Society
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• v.52 no.2
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• pp.467-481
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• 2015

Choi and Park introduced an invariant of a finite simple graph, called signed a-number, arising from computing certain topological invariants of some specific kinds of real toric manifolds. They also found the signed a-numbers of path graphs, cycle graphs, complete graphs, and star graphs. We introduce a signed a-polynomial which is a generalization of the signed a-number and gives a-, b-, and c-numbers. The signed a-polynomial of a graph G is related to the $Poincar\acute{e}$ polynomial $P_{M(G)}(z)$, which is the generating function for the Betti numbers of the real toric manifold M(G). We give the generating functions for the signed a-polynomials of not only path graphs, cycle graphs, complete graphs, and star graphs, but also complete bipartite graphs and complete multipartite graphs. As a consequence, we find the Euler characteristic number and the Betti numbers of the real toric manifold M(G) for complete multipartite graphs G.

• Computers and Concrete
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• v.5 no.5
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• pp.449-459
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• 2008

This paper focuses on the application of two dimensional orthogonal polynomials in the regression analysis for the relationship of product parameters viz. compressive strength, bulk density and water absorption of fly ash cement bricks with other process parameters such as percentages of fly ash, sand and cement. The method has been validated by linear and non-linear two parameter regression models. The use of two dimensional orthogonal system makes the analysis computationally efficient, simple and straight forward. Corresponding co-efficient of determination and F-test are also reported to show the efficacy and reliability of the relationships. By applying the evolved relationships, the product parameters of fly ash cement bricks may be approximated for the use in construction sectors.

• East Asian mathematical journal
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• v.34 no.3
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• pp.295-303
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• 2018

Gautam et al. [9] introduced the multivariable A-function, which is very general, reduces to yield a number of special functions, in particular, the multivariable H-function. Here, first, we aim to establish two very general integral formulas involving product of the general class of Srivastava multivariable polynomials and the multivariable A-function. Then, using those integrals, we find a solution of partial differential equations of heat conduction at zero temperature with radiation at the ends in medium without source of thermal energy. The results presented here, being very general, are also pointed out to yield a number of relatively simple results, one of which is demonstrated to be connected with a known solution of the above-mentioned equation.

• Journal of applied mathematics & informatics
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• v.1 no.1
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• pp.21-30
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• 1994

We give a simple unified proof of various characterizations of discrete classical orthogonal polynomials.

• Honam Mathematical Journal
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• v.20 no.1
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• pp.45-55
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• 1998

• Communications of the Korean Mathematical Society
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• v.19 no.2
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• pp.205-210
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• 2004

According to Diriclet's Theorem, there are infinitely many primes of the form An+ 1 for any fixed positive integer A. For this, there already exists a classical simple proof using cyclotomic polynomials. In this paper, we develop another elementary proof of this statement.

• Journal of Industrial Technology
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• v.11
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• pp.27-31
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• 1991

Consider a problem to keep half of the poles unchanged when some of the coefficients of stable characteristic polynomials are perturbed. A procedure was proposed for the problem. However, the pole assignment procedure has not been addressed. A simple algorithm for the procedure is proposed in this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.534-540
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• 2014

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.