• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.11-18
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• 2017

Finite field arithmetic has been extensively used in error correcting codes and cryptography. Low-complexity and high-speed designs for finite field arithmetic are needed to meet the demands of wider bandwidth, better security and higher portability for personal communication device. In particular, cryptosystems in GF($2^m$) usually require computing exponentiation, division, and multiplicative inverse, which are very costly operations. These operations can be performed by computing modular AB multiplications or modular $AB^2$ multiplications. To compute these time-consuming operations, using $AB^2$ multiplications is more efficient than AB multiplications. Thus, there are needs for an efficient $AB^2$ multiplier architecture. In this paper, we propose a low latency Montgomery $AB^2$ multiplier using redundant representation over GF($2^m$). The proposed $AB^2$ multiplier has less space and time complexities compared to related multipliers. As compared to the corresponding existing structures, the proposed $AB^2$ multiplier saves at least 18% area, 50% time, and 59% area-time (AT) complexity. Accordingly, it is well suited for VLSI implementation and can be easily applied as a basic component for computing complex operations over finite field, such as exponentiation, division, and multiplicative inverse.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.4
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• pp.35-41
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• 2000

In this paper, the multiplicative algorithm of two polynomals over finite field GF(2$^{m}$ ) is presented. The proposed algorithm permits an efficient realization of the parallel multiplication using iterative arrays. At the same time, it permits high-speed operation. This multiplier is consisted of three operation unit: multiplicative operation unit, the modular operation unit, the primitive irreducible operation unit. The multiplicative operation unit is composed of AND gate, X-OR gate and multiplexer. The modular operation unit is constructed by AND gate, X-OR gate. Also, an efficient pipeline form of the proposed multiplication scheme is introduced. All multipliers obtained have low circuit complexity permitting high-speed operation and interconnection of the cells are regular, well-suited for VLSI realization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.331-336
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• 2004

This study focuses on the hardware implementation of fast and low-system-complexity multiplier over GF(2$^{m}$ ). From the properties of an irreducible AOP of degree m. the modular reduction in GF(2$^{m}$ ) multiplicative operation can be simplified using cyclic shift operation. And then, GF(2$^{m}$ ) multiplicative operation can be established using the away structure of AND and XOR gates. The proposed multiplier is composed of m(m+1) 2-input AND gates and (m+1)$^2$ 2-input XOR gates. And the minimum critical path delay is Τ$_{A+}$〔lo $g_2$$^{m}$ 〕Τ$_{x}$ proposed multiplier obtained have low circuit complexity and delay time, and the interconnections of the circuit are regular, well-suited for VLSI realization.n.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.3
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• pp.145-151
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• 2003

This study focuses on the hardware implementation of fast and low-complexity multiplier over GF(2$^{m}$ ). Finite field multiplication can be realized in two steps: polynomial multiplication and modular reduction using the irreducible polynomial and we will treat both operation, separately. Polynomial multiplicative operation in this Paper is based on the Permestzi's algorithm, and irreducible polynomial is defined AOP. The realization of the proposed GF(2$^{m}$ ) multipleker-based multiplier scheme is compared to existing multiplier designs in terms of circuit complexity and operation delay time. Proposed multiplier obtained have low circuit complexity and delay time, and the interconnections of the circuit are regular, well-suited for VLSI realization.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.6
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• pp.313-319
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• 2019

Finite field operations have played an important role in error correcting codes and cryptosystems. Recently, the necessity of efficient computation processing is increasing for security in cyber physics systems. Therefore, efficient implementation of finite field arithmetics is more urgently needed. These operations include addition, multiplication, division and inversion. Addition is very simple and can be implemented with XOR operation. The others are somewhat more complicated than addition. Among these operations, multiplication is the most important, since time-consuming operations, such as exponentiation, division, and computing multiplicative inverse, can be performed through iterative multiplications. In this paper, we propose a multiplexer based parallel computation algorithm that performs Montgomery multiplication over finite field using redundant basis. Then we propose an efficient multiplexer based semi-systolic multiplier over finite field using redundant basis. The proposed multiplier has less area-time (AT) complexity than related multipliers. In detail, the AT complexity of the proposed multiplier is improved by approximately 19% and 65% compared to the multipliers of Kim-Han and Choi-Lee, respectively. Therefore, our multiplier is suitable for VLSI implementation and can be easily applied as the basic building block for various applications.

• The KIPS Transactions:PartA
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• v.11A no.5
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• pp.401-406
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• 2004

Among many algorithms for the integration problems in which one wants to compute the approximation to the definite integral in the average case setting, we study the average case errors of numerical integration rules using interpolation. In particular, we choose the composite Newton-Cotes quadratures and the function values at equally spaced sample points on the given interval as information. We compute the average case error of composite Newton-Cotes quadratures and show that it is minimal(modulo a multiplicative constant).

• ETRI Journal
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• v.26 no.5
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• pp.432-436
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• 2004

In this paper, we propose the modified uniformly most powerful (UMP) belief-propagation (BP)-based decoding algorithm which utilizes multiplicative and additive factors to diminish the errors introduced by the approximation of the soft values given by a previously proposed UMP BP-based algorithm. This modified UMP BP-based algorithm shows better performance than that of the normalized UMP BP-based algorithm, i.e., it has an error performance closer to BP than that of the normalized UMP BP-based algorithm on the additive white Gaussian noise channel for low density parity check codes. Also, this algorithm has the same complexity in its implementation as the normalized UMP BP-based algorithm.

• Journal of The Korean Association For Science Education
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• v.34 no.4
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• pp.335-347
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• 2014

Size/scale is a central idea in the science curriculum, providing explanations for various phenomena. However, few studies have been conducted to explore student understanding of this concept and to suggest instructional approaches in scientific contexts. In contrast, there have been more studies in mathematics, regarding the use of number lines to relate the nature of numbers to operation and representation of magnitude. In order to better understand variations in student conceptions of size/scale in scientific contexts and explain learning difficulties including alternative conceptions, this study suggests an approach that links mathematics with the analysis of student conceptions of size/scale, i.e. the analysis of mathematical structure and reasoning for a number line. In addition, data ranging from high school to college students facilitate the interpretation of conceptual complexity in terms of mathematical development of a number line. In this sense, findings from this study better explain the following by mathematical reasoning: (1) varied student conceptions, (2) key aspects of each conception, and (3) potential cognitive dimensions interpreting the size/scale concepts. Results of this study help us to understand the troublesomeness of learning size/scale and provide a direction for developing curriculum and instruction for better understanding.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1107-1119
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• 2006

Data transmission experiences multiplicative distortion in frequency nonselective fading channel. This distortion occurs in OFDM communication channel and can be compensated using an equalizer. Usually, in the case of LMS equalizer, eigenvalue distribution of training signal is enlarged. Large eigenvalue distribution causes principally the performance of a communication system to be deteriorated. This paper proposes a new algorithm that shows the same performance as the existing fast wavelet transform algorithm with less computational complexity. The proposed algorithm was applied to an adaptive equalizer of OFDM communication system. Matlab simulation results show a better performance than the existing one. The proposed algorithm was implemented in VHDL and simulated.

• Research in Mathematical Education
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• v.25 no.4
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• pp.285-309
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• 2022

A large body of previous studies investigated mathematical tasks by analyzing the design process prior to lessons or textbooks. While researchers have revealed the significant roles of mathematical tasks within written curricular, there has been a call for studies about how mathematical tasks are implemented or what is experienced and learned by students as enacted curriculum. This article proposes a mathematical task analytic framework based on a holistic definition of tasks encompassing both written tasks and the process of task enactment. We synthesized the features of the mathematical tasks and developed a task analytic framework with multiple dimensions: breadth, depth, bridging, openness, and interaction. We also applied the scoring rubric to analyze three multiplication tasks to illustrate the framework by its five dimensions. We illustrate how a series of tasks are analyzed through the framework when students are engaged in multiplicative thinking. The framework can provide important information about the qualities of planned tasks for mathematics instruction (proactive) and the qualities of implemented tasks during instruction (reactive). This framework will be beneficial for curriculum designers to design rich tasks with more careful consideration of how each feature of the tasks would be attained and for teachers to transform mathematical tasks with the provision of meaningful learning activities into implementation.