• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.3
• /
• pp.91-102
• /
• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.281-282
• /
• 2007

We address a new representation of DCT/DFT/Wavelet matrices via one hybrid architecture. Based on an element inverse matrix factorization algorithm, we show that the OCT, OFT and Wavelet which based on Haar matrix have the similarrecursive computational pattern, all of them can be decomposed to one orthogonal character matrix and a special sparse matrix. The special sparse matrix belongs to Jacket matrix, whose inverse can be from element-wise inverse or block-wise inverse. Based on this trait, we can develop a hybrid architecture.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.30A no.5
• /
• pp.52-62
• /
• 1993

This paper describes the implementation of a layout generation system for the gate matrix style to implement multi-level logic. To achieve improved layouts from general net lists, the proposed system performs flexible net binding for series nets. Also the system reassings gates by the heuristic information that shorter net lengths are better for the track minimization. By track minimizing with subdividing layout column information, the system decreases the number of necessary layout tracks. Experimental results show that the system generates more area-reduced (approximately 7.46%) layouts than those by previous gate matrix generation systems using net list inputs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.6
• /
• pp.2634-2648
• /
• 2020

In this paper, an adaptive sparse singular value decomposition (ASSVD) algorithm is proposed to estimate the signal matrix when only one data matrix is observed and there is high dimensional white noise, in which we assume that the signal matrix is low-rank and has sparse singular vectors, i.e. it is a simultaneously low-rank and sparse matrix. It is a structured matrix since the non-zero entries are confined on some small blocks. The proposed algorithm estimates the singular values and vectors separable by exploring the structure of singular vectors, in which the recent developments in Random Matrix Theory known as anisotropic Marchenko-Pastur law are used. And then we prove that when the signal is strong in the sense that the signal to noise ratio is above some threshold, our estimator is consistent and outperforms over many state-of-the-art algorithms. Moreover, our estimator is adaptive to the data set and does not require the variance of the noise to be known or estimated. Numerical simulations indicate that ASSVD still works well when the signal matrix is not very sparse.

• Proceedings of the Korea Contents Association Conference
• /
• 2007.11a
• /
• pp.501-505
• /
• 2007

Recently KISTI has developed the KnowlegeMatrix which is a stand-alone type bibliographic data analysis software. In this paper, we try to benchmark test on the performance level of KnowledgeMatrix with well-known S/Ws such as VantagePoint and BibTechMon. We compare distinctive functions and usability of each S/Ws on comparative categories including Data, Matrix, Analysis, Visualization and Preprocessing. Test results show that all S/Ws have differentiated specific feature, but there is some performance gaps. KnowledgeMatrix overally shows better performance than others.

• Journal of electromagnetic engineering and science
• /
• v.6 no.4
• /
• pp.244-252
• /
• 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.

• Journal of Korea Spatial Information System Society
• /
• v.11 no.1
• /
• pp.157-168
• /
• 2009

Geospatial information, which deeply has an effect on our life, have been evolved as intelligent geospatial information in Ubiquitous era. Also, Various services are introduced using the intelligent geospatial information. However, there is no classification system, for understanding the intelligent geospatial information services, considering any developers and users. It needs to be classification system to classify these services. In this paper, we introduced a concept of intelligent geospatial information and developed a service classification matrix regarding to the features of the services. This service classification matrix has three scales; service domain, service intelligent level, and geo-location accuracy. The propose of this matrix can be utilized in two aspects. First, the matrix can improve the reality that doesn't reflect actual demands for the services. Second, the matrix can present the goal of the new services or the development direction. The matrix can be utilized to the geospatial industry as creating the new blue ocean services. However, the service classification matrix needs to modify and complement to have no anything wrong when the various services are applied to the matrix. In the long run, the matrix has to be utilized as a material to make out a service roadmap or TRM(Technical Reference Model).

• Journal of applied mathematics & informatics
• /
• v.17 no.1_2_3
• /
• pp.585-590
• /
• 2005

An efficient procedure for finding the inverse of a nonsingular matrix is developed by solving directly for the UL decomposition of the inverse based on the entries of the original nonsingular matrix.

• Journal of Broadcast Engineering
• /
• v.16 no.5
• /
• pp.782-792
• /
• 2011

In this paper, we address a new fast DCT-II/DFT/HWT hybrid transform architecture for digital video and fusion mobile handsets based on Jacket-like sparse matrix decomposition. This fast hybrid architecture is consist of source coding standard as MPEG-4, JPEG 2000 and digital filtering discrete Fourier transform, and has two operations: one is block-wise inverse Jacket matrix (BIJM) for DCT-II, and the other is element-wise inverse Jacket matrix (EIJM) for DFT/HWT. They have similar recursive computational fashion, which mean all of them can be decomposed to Kronecker products of an identity Hadamard matrix and a successively lower order sparse matrix. Based on this trait, we can develop a single chip of fast hybrid algorithm architecture for intelligent mobile handsets.

• Current Optics and Photonics
• /
• v.5 no.3
• /
• pp.322-328
• /
• 2021

In this paper, a pretreatment method for a matrix in convex optimization is proposed to optimize the spectral reconstruction process of a disordered dispersion spectrometer. Unlike the reconstruction process of traditional spectrometers using Fourier transforms, the reconstruction process of disordered dispersion spectrometers involves solving a large-scale matrix equation. However, since the matrices in the matrix equation are obtained through measurement, they contain uncertainties due to out of band signals, background noise, rounding errors, temperature variations and so on. It is difficult to solve such a matrix equation by using ordinary nonstationary iterative methods, owing to instability problems. Although the smoothing Tikhonov regularization approach has the ability to approximatively solve the matrix equation and reconstruct most simple spectral shapes, it still suffers the limitations of reconstructing complex and irregular spectral shapes that are commonly used to distinguish different elements of detected targets with mixed substances by characteristic spectral peaks. Therefore, we propose a special pretreatment method for a matrix in convex optimization, which has been proved to be useful for reducing the condition number of matrices in the equation. In comparison with the reconstructed spectra gotten by the previous ordinary iterative method, the spectra obtained by the pretreatment method show obvious accuracy.