• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3690-3713
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• 2019

According to the main group key management schemes logical key hierarchy (LKH), exclusion basis systems (EBS) and other group key schemes are limited in network structure, collusion attack, high energy consumption, and the single point of failure, this paper presents a group key management scheme for wireless sensor networks based on Lagrange interpolation polynomial characteristic (AGKMS). That Chinese remainder theorem is turned into a Lagrange interpolation polynomial based on the function property of Chinese remainder theorem firstly. And then the base station (BS) generates a Lagrange interpolation polynomial function f(x) and turns it to be a mix-function f(x)' based on the key information m(i) of node i. In the end, node i can obtain the group key K by receiving the message f(m(i))' from the cluster head node j. The analysis results of safety performance show that AGKMS has good network security, key independence, anti-capture, low storage cost, low computation cost, and good scalability.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.6
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• pp.235-240
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• 2002

This paper presents a new method for estimating the block pulse series coefficients by using the Lagrange's second order interpolation polynomial. Block pulse functions have been used in a variety of fields such as the analysis and controller design of the systems. When the block pulse functions are used, it is necessary to find the more exact value of the block pulse series coefficients. But these coefficients have been estimated by the mean of the adjacent discrete values, and the result is not sufficient when the values are changing extremely. In this paper, the method for improving the accuracy of the block pulse series coefficients by using the Lagrange's second order interpolation polynomial is presented.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.286-293
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• 2002

This paper presents a new method for finding the Block Pulse series coefficients and deriving the Block Pulse integration operational matrices which are necessary for the control fields using the Block Pulse functions. In order to apply the Block Pulse function technique to the problems of continuous-time dynamic systems more efficiently, it is necessary to find the more exact value of the Block Pulse series coefficients and drives the related integration operational matrices by using the Lagrange second order interpolation polynomial.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.6
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• pp.351-358
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• 2003

This paper presents a new method for finding the Block Pulse series coefficients, deriving the Block Pulse integration operational matrices and generalizing the integration operational matrices which are necessary for the control fields using the Block Pulse functions. In order to apply the Block Pulse function technique to the problems of state estimation or parameter identification more efficiently, it is necessary to find the more exact value of the Block Pulse series coefficients and integral operational matrices. This paper presents the method for improving the accuracy of the Block Pulse series coefficients and derives the related integration operational matrices and generalized integration operational matrix by using the Lagrange second order interpolation polynomial.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.78-83
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• 2008

Interpolation filters are widely used in symbol timing recovery systems to interpolate new sample values at arbitrary points between the existing discrete-time samples. Polynomial interpolation is interpolated by coefficient made inputted information. This paper presents an efficient way to implement polynomial base interpolation filters using support filter changing input. By an example, it is shown that the proposed structure out performs the conventional interpolation structure with less hardware cost.

• Journal of the Korean Institute of Telematics and Electronics
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• v.15 no.5
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• pp.29-33
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• 1978

In this paper, the properties of Galois fields defined over any finite field are analysed to derive Galois switching functions and the arithmetic operation methods over any finite field are showed. The polynomial expansions over finite fields by Lagrange's interpolation method are derived and proved. The results are applied to multivalued single variable logic networks.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.161-163
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• 2005

In this paper, we propose an efficient motion vector recovery algorithm for the new coding standard H.264, which makes use of the Lagrange interpolation formula. In H.264/AVC, a 16$\times$16 macroblock can be divided into different block shapes for motion estimation, and each block has its own motion vector. In the natural video the motion vector is likely to move in the same direction, hence the neighboring motion vectors are correlative. Because the motion vector in H.264 covers smaller area than previous coding standards, the correlation between neighboring motion vectors increases. We can use the Lagrange interpolation formula to constitute a polynomial that describes the motion tendency of motion vectors, and use this polynomial to recover the lost motion vector. The simulation result shows that our algorithm can efficiently improve the visual quality of the corrupted video.

• Proceedings of the KIEE Conference
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• 2003.07e
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• pp.55-57
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• 2003

This paper presents a new method for finding the Block Pulse series coefficients and deriving the Block Pulse integration operational matrices which are necessary for the control fields using the Block Pulse functions. In this paper, the accuracy of the Block Pulse series coefficients derived by using the Lagrange second order interpolation polynomial is approved by the mathematical method.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.571-576
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• 2004

In this paper, by using Lagrange polynomial interpolation with a trick such that for $f(x)^{3}$ we shall use $f(x_i)^{3}I_i(x)^{3}$ instead of $I(x)^{3}$ where $I{x}{\;}={\;}\sum_{i}^{f}(x_i)I_i(x)$. We show the convergence and stability and calculate errors. These errors are approximately less than $C(\frac{1}{N})^{N-1} hN(N-1)(\frac{N}{2})^{N-1} /(\frac{N}{2})!$ where N is a polynomial degree.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.37-41
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• 2021

This paper presents a study to implement a touch screen capable of real-time response processing in a low-end embedded system. This was done by introducing an algorithm using an interpolation method to represent real-time response characteristics when a touch input is performed. In this experiment, we applied a linear interpolation algorithm that estimates random data by deriving a first-order polynomial from 2-point data. We also applied a Lagrange interpolation algorithm that estimates random data by deriving a quadratic polynomial from 3-point data. As a result of the experiment, it was found that the Lagrange interpolation method was more complicated than the linear interpolation method, and the processing speed was slow, so the text was not smooth. When using the linear interpolation method, it was confirmed that the speed displayed on a screen is 2.4 times faster than when using the Lagrange interpolation method. For real-time response characteristics, it was confirmed that smaller size of the executable file of the algorithm is more advantageous than the superiority of the algorithm itself. In conclusion, in order to secure real-time response characteristics in a low-end embedded system, it was confirmed that a relatively simple linear interpolation algorithm performs touch operations with better real-time response characteristics than the Lagrange interpolation method.