• 한국통신학회논문지
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• 제30권9C호
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• pp.860-870
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• 2005

본 논문에서는 karnaugh map(k-map)상의 셀을 이용하여 $2^{n}$개의 서로 다른 극수(polarity)를 갖는 GRM(Generalized Reed-Muller)상수를 생성하는 새로운 기법을 제안하였다. n개의 입력변수에 대한 일반적인 GRM 함수의 생성 방법은 단일 변수에 대한 변환 행렬을 구하고 이를 n번의 Kronecker 곱을 행한 변환 행렬을 이용하여 GRM 상수를 구하는 것이다. 이런 방법을 사용하는 경우, 변수의 숫자가 증가함에 따라 변환 행렬의 차수가 $2^{n}\times2^{n}$로 커지는 단점을 갖는다. 이에 반하여 본 논문에서는 k-map상에서 변수를 축약시킨 셀 [$f_{i}$]을 구하고 이를 단일 변수 변환 행렬과 연산하여 GBM 상수를 구하는 새로운 기법을 제안한다. 본 논문에서 제안한 새로운 방법과 타 논문과의 비교를 한 결과, 기존 방법은 가산기, 승산기, KP(Kronecker 곱 승산기)회로가 필요한데 반하여 본 논문에서는 가산기만이 필요하므로 효율적인 VLSI 설계에 유리하다

• 한국통신학회논문지
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• 제30권9A호
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• pp.788-794
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• 2005

In this paper, we focus on the potential of dual polarized antennas in mobile system. thus, this paper designs exact dual polarized channel with Spatial Channel Model (SCM) and investigates the performance for certain environment. Using proposed the channel model; we know estimates of the channel capacity as a function of cross polarization discrimination (XPD) and spatial fading correlation. It is important that the MIMO channel matrix consists of Kronecker product dividable spatial and polarized channel. Through the channel characteristics, we propose an algorithm for the adaptation of transmit antenna configuration to time varying propagation environments. The optimal active transmit antenna subset is determined with equal power allocated to the active transmit antennas, assuming no feedback information on types of the selected antennas. We first consider a heuristic decision strategy in which the optimal active transmit antenna subset and its system capacity are determined such that the transmission data rate is maximized among all possible types. This paper then proposes singular values decision procedure consisting of Kronecker product with spatial and polarize channel. This method of singular value decision, which the first channel environments is determined using singular values of spatial channel part which is made of environment parameters and distance between antennas. level of correlation. Then we will select antenna which have various polarization type. After spatial channel structure is decided, we contact polarization types which have considerable cases It is note that the proposed algorithms and analysis of dual polarized channel using SCM (Spatial Channel Model) optimize channel capacity and reduce the number of transmit antenna selection compare to heuristic method which has considerable 100 cases.

• Kyungpook Mathematical Journal
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• 제48권3호
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• pp.473-486
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• 2008

In this paper we generate a fuzzy dynamical matrix Lyapunov system and obtain a sufficient condition for the observability of this system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1618-1637
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• 2018

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

• 대한전자공학회논문지
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• 제27권12호
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• pp.1878-1888
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• 1990

In this paper, the input-output interconnection method of the multi-valued signal processing circuit using perfect Shuffle technique and Kronecker product is discussed. Using this method, the design method of circuit of the multi-valued Reed-Muller expansions(MRME) to be used the multi-valued signal processing on finite field GF(p**m) is presented. The proposed input-output interconnection method is shown that the matrix transform is efficient and that the module structure is easy. The circuit design of MRME on FG(p**m) is realized following as` 1) contructing the baisc gates on GF(3) by CMOS T gate, 2) designing the basic cells to be implemented the transform and inverse transform matrix of MRME using these basic gates, 3) interconnecting these cells by the input-output interconnecting method of the multivalued signal processing circuits. Also, the circuit design of the multi-valued signal processing function on GF(3\ulcorner similar to Winograd algorithm of 3x3 array of DFT (discrete fourier transform) is realized by interconnection of Perfect Shuffle technique and Kronecker product. The presented multi-valued signal processing circuits that are simple and regular for wire routing and posses the properties of concurrency and modularity are suitable for VLSI.

• Interaction and multiscale mechanics
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• 제2권2호
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• pp.129-151
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• 2009

This paper presents a meshfree procedure using a convex generalized meshfree (GMF) approximation for the large deformation analysis of particle-reinforced rubber compounds on microscopic level. The convex GMF approximation possesses the weak-Kronecker-delta property that guarantees the continuity of displacement across the material interface in the rubber compounds. The convex approximation also ensures the positive mass in the discrete system and is less sensitive to the meshfree nodal support size and integration order effects. In this study, the convex approximation is generated in the GMF method by choosing the positive and monotonic increasing basis function. In order to impose the periodic boundary condition in the unit cell method for the microscopic analysis, a singular kernel is introduced on the periodic boundary nodes in the construction of GMF approximation. The periodic boundary condition is solved by the transformation method in both explicit and implicit analyses. To simulate the interface de-bonding phenomena in the rubber compound, the cohesive interface element method is employed in corporation with meshfree method in this study. Several numerical examples are presented to demonstrate the effectiveness of the proposed numerical procedure in the large deformation analysis.

• 전자공학회논문지C
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• 제36C권1호
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• pp.41-48
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• 1999

GF(3)상에서 n변수에 대한 3n개의 서로 다른 극수(Polarity)를 갖는 GRM(Generalized Reed Muller)상수를 구하는 방법을 제안하였다. n개의 입력변수에 대한 일반적인 GRM함수의 생성방법은 단변수에 대한 전달행렬을 구하고 이를 n번의 kronecker 곱을 행한 변환행렬을 이용하여 GRM상수를 구한다. 본 논문에서는 결정도를 이용한 GRM상수의 생성법을 나타내었다. 단 변수에 대한 상수 값의 변화를 결정도에 적용하고 이를 확장하여 n변수에 대한 GRM상수를 구하였다.

• Journal of electromagnetic engineering and science
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• 제6권4호
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• pp.244-252
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• 2006

A block Jacket transform and. its block inverse Jacket transformn have recently been reported in the paper 'Fast block inverse Jacket transform'. But the multiplication of the block Jacket transform and the corresponding block inverse Jacket transform is not equal to the identity transform, which does not conform to the mathematical rule. In this paper, new binary block Jacket transforms and the corresponding binary block inverse Jacket transforms of orders $N=2^k,\;3^k\;and\;5^k$ for integer values k are proposed and the mathematical proofs are also presented. With the aid of the Kronecker product of the lower order Jacket matrix and the identity matrix, the fast algorithms for realizing these transforms are obtained. Due to the simple inverse, fast algorithm and prime based $P^k$ order of proposed binary block inverse Jacket transform, it can be applied in communications such as space time block code design, signal processing, LDPC coding and information theory. Application of circular permutation matrix(CPM) binary low density quasi block Jacket matrix is also introduced in this paper which is useful in coding theory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2468-2483
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• 2017

The construction of completely random sensing matrices of Compressive Sensing requires a large number of random numbers while that of deterministic sensing operators often needs complex mathematical operations. Thus both of them have difficulty in acquiring large signals efficiently. This paper focuses on the enhancement of the practicability of the structurally random matrices and proposes a semi-deterministic sensing matrix called Partial Kronecker product of Identity and Hadamard (PKIH) matrix. The proposed matrix can be viewed as a sub matrix of a well-structured, sparse, and orthogonal matrix. Only the row index is selected at random and the positions of the entries of each row are determined by a deterministic sequence. Therefore, the PKIH significantly decreases the requirement of random numbers, which has a complex generating algorithm, in matrix construction and further reduces the complexity of sampling. Besides, in order to process large signals, the corresponding fast sampling algorithm is developed, which can be easily parallelized and realized in hardware. Simulation results illustrate that the proposed sensing matrix maintains almost the same performance but with at least 50% less random numbers comparing with the popular sampling matrices. Meanwhile, it saved roughly 15%-35% processing time in comparison to that of the SRM matrices.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.807-810
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• 1998

This paper propose the method to produce GRM(Generalized Reed-Muller)expansion. The general method to obtain GRM expansion coefficient for p valued n variable is derivation of single variable transform matrix and expand it n times using Kronecker product. In this case the size of matrix increases depending on the augmentation of variables. In this paper we propose the simple algorithm to produce GRM coefficient using a single variable transform matrix.