• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.05a
• /
• pp.651-654
• /
• 2008

In finite field operations based on $GF(2^m)$, additions and subtractions are easily implemented. On the other hand, multiplications and divisions require mathematical elaboration of complex equations. There are two dominant way of approaching the solutions of finite filed operations, normal basis approach and polynomial basis approach, each of which has both benefits and weakness respectively. In this study, we adopted the mathematically feasible polynomial basis approach and suggest the optimization techniques of finite field operations based of mathematical principles.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.1
• /
• pp.135-142
• /
• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.5
• /
• pp.11-22
• /
• 2010

Recently, Fan and Dai introduced a Shifted Polynomial Basis and construct a non-pipeline bit-parallel multiplier for $F_{2^n}$. As the name implies, the SPB is obtained by multiplying the polynomial basis 1, ${\alpha}$, ${\cdots}$, ${\alpha}^{n-1}$ by ${\alpha}^{-\upsilon}$. Therefore, it is easy to transform the elements PB and SPB representations. After, based on the Modified Shifted Polynomial Basis(MSPB), SPB bit-parallel Mastrovito type I and type II multipliers for all irreducible trinomials are presented. In this paper, we present a bit-parallel architecture to multiply in SPB. This multiplier have a space complexity efficient than all previously presented architecture when n ${\neq}$ 2k. The proposed multiplier has more efficient space complexity than the best-result when 1 ${\leq}$ k ${\leq}$ (n+1)/3. Also, when (n+2)/3 ${\leq}$ k < n/2 the proposed multiplier has more efficient space complexity than the best-result except for some cases.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.6
• /
• pp.117-123
• /
• 2014

This paper proposes the constructing method of effective multiplier using basis transformation. Th proposed multiplier is composed of the standard-dual basis transformation circuit module to change one input into dual basis the operation module to generate from bm to bm+k by the m degree irreducible polynomial, and the polynomial multiplicative module to consist of $m^2$ AND and m(m-1) EX-OR gates. Also, the dual-standard basis transformation circuit module to change the output part to be shown as a dual basis into standard basis is composed. The operation modules to need in each operational part are defined.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.4
• /
• pp.862-870
• /
• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.19 no.2
• /
• pp.49-61
• /
• 2009

Finite Field multiplication operation is one of the most important operations in the finite field arithmetic. Recently, Fan and Dai introduced a Shifted Polynomial Basis(SPB) and construct a non-pipeline bit-parallel multiplier for $F_{2^n}$. In this paper, we propose a new bit-parallel shifted polynomial basis type I and type II multipliers for $F_{2^n}$ defined by an irreducible trinomial $x^{n}+x^{k}+1$. The proposed type I multiplier has more efficient the space and time complexity than the previous ones. And, proposed type II multiplier have a smaller space complexity than all previously SPB multiplier(include our type I multiplier). However, the time complexity of proposed type II is increased by 1 XOR time-delay in the worst case.

• Korean Journal of Computational Design and Engineering
• /
• v.5 no.3
• /
• pp.276-284
• /
• 2000

Usual practice of the transformation of a B-spline curve into a set of piecewise polynomial curves in a power form is done by either a knot refinement followed by basis conversions or applying a Taylor expansion on the B-spline curve for each knot span. Presented in this paper is a new algorithm, called a direct expansion algorithm, for the problem. The algorithm first locates the coefficients of all the linear terms that make up the basis functions in a knot span, and then the algorithm directly obtains the power form representation of basis functions by expanding the summation of products of appropriate linear terms. Then, a polynomial segment of a knot span can be easily obtained by the summation of products of the basis functions within the knot span with corresponding control points. Repeating this operation for each knot span, all of the polynomials of the B-spline curve can be transformed into a power form. The algorithm has been applied to both static and dynamic curves. It turns out that the proposed algorithm outperforms the existing algorithms for the conversion for both types of curves. Especially, the proposed algorithm shows significantly fast performance for the dynamic curves.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.261-263
• /
• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 각 노드의 후반부 파라미터들은 최소자승법을 이용하여 최적화 하였다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.9 no.4
• /
• pp.3-10
• /
• 1999

More concerns are concentrated in finite fields arithmetic as finite fields being applied for Elliptic curve cryptosystem coding theory and etc. Finite fields arithmetic is affected in represen -tation of those. Optimal normal basis is effective in hardware implementation and polynomial field which is effective in the basis conversion with optimal normal basis and show that the arithmetic of finite field with the basis is effective in software implementation.

• Bulletin of the Korean Mathematical Society
• /
• v.40 no.3
• /
• pp.425-435
• /
• 2003

In this paper, a new kind of matrices, i.e., $level-{\kappa}$ II-circulant matrices is considered. Algorithms for computing minimal polynomial of this kind of matrices are presented by means of the algorithm for the Grobner basis of the ideal in the polynomial ring. Two algorithms for finding the inverses of such matrices are also presented based on the Buchberger's algorithm.