• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.180-187
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• 2002

The decoding method using covering polynomials is an extended form of error-trapping decoding, and is a simple and effective means to implement decoders for cyclic codes. Covering polynomials can be used for soft-decision decoding as well as for decoding beyond the bounded distance of the code. The implementation complexity is proportional to the number of covering polynomials employed. In this paper, the soft-decision decoding procedure using covering polynomials is described, and the procedure is applied to the [23,12] Golay code. A new set of covering polynomials is derived for the procedure, which is presented as a generalized closed-form solution. The set can be efficiently utilized for decoding a class of cyclic codes including the Golay code. Computer simulation of the described procedure is performed to show the trade-offs between the decoder performance and complexity. It is demonstrated that soft-decision decoding of the Golay code using the derived set of covering polynomials has less than 0.2dB deviation from the optimal performance of maximum-likelihood decoding, with a reduced complexity when compared to the Chase Algorithm 2 combined with hard-decision decoding that has nearly identical performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.461-465
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• 2002

When general software system wants to need continuous Dept of Software Engineering by discovery of specification imperative sentence in some program, programmer analyzes program through practice orbit chase of program about value of input data. This paper that see therefore applies object intention program Slicing to existent program Slicing method, and express practice orbit of program in object intention subordination graph and show process that become Slicing. Proposed algorithm defenses crab extension is possible and can be used advantageously in case program is developed gradually as well as.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.