• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.200-208
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. A block compressed sensing with projected Landweber (BCS-SPL) framework is most widely known, but, it has high computational complexity at decoder side. In this paper, by introducing adaptive exit criteria instead of fixed exit criteria to SPL framework, an effective fast algorithm is designed in such a way that it can utilize efficiently the sparsity property in DCT coefficients during the iterative thresholding process. Experimental results show that the proposed algorithm results in the significant reduction of the decoding time, while providing better visual qualities than conventional algorithm.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.141-145
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• 2007

Using the New Fast Walsh Transform algorithm, we present a new algorithm which reduces the computational amount, and it can consequently calculate the real and imaginary components for current and voltage signals of power system in sampling intervals. The calculation amount is reduced to 2X(m-1) at m samples to measure full harmonics using developed algorithm. When in single harmonic measuring, it needs only 2(log2N-1)+1 additions and subtractions. We developed hardware device for testing, and tested the developed algorithm by measuring real and imaginary components.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.173-175
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• 2013

In this paper, we propose a fast and low-complexity Motion Estimation (ME) algorithm for High Efficiency Video Coding (HEVC). Motion estimation occupies 77~81% of the amount of computation in HEVC. After all, the main key of codec implementation is to find a fast and low-complexity motion estimation algorithm and architecture. The proposed algorithm uses only 1% of the amount of operations compared to full search algorithm while maintaining compression performance with slight loss of 0.6% (BDBR).

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.529-531
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• 2005

A block-based fast motion estimation algorithm is proposed in this paper to perform motion estimation based on the efficiently reduced search ranges in MPEG-4(ERS). This algorithm divides the search areas into several small search areas and the candidate small search area that has the lowest average of sum norm difference between current macroblock and candidate macroblock is chosen to perform block motion estimation using the Nobel Successive Elimination Algorithm (NSEA). Experimental results of the proposed algorithm show that the averaging PSNR improvement is better maximum 0.125 dB than other tested algorithms and bit saving effect is maximum 20kbps for some tested sequences in low-bit rate circumstance.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.295-298
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• 2002

This paper presents a fast block-matching algorithm to improve the conventional Three-Step Search (TSS) based method. The proposed Comparison Fast Block Matching Algorithm (CFBMA) begins with DAB for adaptive search range to choose searching method, and searches a part of search window that has high possibility of motion vector like other partial search algorithms. The CFBMA also considers the opposite direction to reduce local minimum, which is ignored in almost conventional based partial search algorithms. CFBMA uses the summation half-stop technique to reduce the computational load. Experimental results show that the proposed algorithm achieves the high computational complexity compression effect and very close or better image quality compared with TSS, SES, NTSS based partial search algorithms.

• Progress in Superconductivity and Cryogenics
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• v.21 no.4
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• pp.53-58
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• 2019

The critical current is one of the key parameters of high temperature superconducting (HTS) racetrack coils. Therefore, it is significant to calculate critical currents of HTS coils. This paper introduces a fast iterative algorithm for calculating the critical current of second generation (2G) HTS coils. This model does not need to solve long charging transients which greatly reduced the amount of calculation. To validate this model, the V-I curve of four 2G HTS double racetrack coils are measured. The effect of the silicon steel sheet on the critical current of the racetrack coil is also studied based on this algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2706-2713
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• 1997

The fast implementation algorithm of M-band discrete wavelet transform is propsed using the factorization of lossless matrix when the length of discrete orthogonal wavelet bases equals to 2M. In computational complexity when direct filtering method is employed, the number of multiplicationand addition is (2M$^{2}$) and (2M$^{2}$ -M), respectively. But by proposed algorithm, it can be reduced to (M$^{2}$+M) and (M$^{2}$+2M-1), respectively. and it is possible to reduce the compuatational complexity further when unitary matrix employed to design the discrete or thogonal wavelet basis has the fast algorithm.

• ETRI Journal
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• v.45 no.6
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• pp.1035-1045
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• 2023

We propose a novel graphics processing unit (GPU) algorithm that can handle a large-scale 3D fast Fourier transform (i.e., 3D-FFT) problem whose data size is larger than the GPU's memory. A 1D FFT-based 3D-FFT computational approach is used to solve the limited device memory issue. Moreover, to reduce the communication overhead between the CPU and GPU, we propose a 3D data-transposition method that converts the target 1D vector into a contiguous memory layout and improves data transfer efficiency. The transposed data are communicated between the host and device memories efficiently through the pinned buffer and multiple streams. We apply our method to various large-scale benchmarks and compare its performance with the state-of-the-art multicore CPU FFT library (i.e., fastest Fourier transform in the West [FFTW]) and a prior GPU-based 3D-FFT algorithm. Our method achieves a higher performance (up to 2.89 times) than FFTW; it yields more performance gaps as the data size increases. The performance of the prior GPU algorithm decreases considerably in massive-scale problems, whereas our method's performance is stable.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.58-65
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• 1992

In this paper new back propagation algorithm which adds neurons to hidden layer is proposed. this proposed algorithm is applied to the pattern recognition of written number coupled with back propagation algorithm through omitting redundant learning. Learning rate and recognition rate of the proposed algorithm are compared with those of the conventional back propagation algorithm and the back propagation through omitting redundant learning. The learning rate of proposed algorithm is 4 times as fast as the conventional back propagation algorithm and 2 times as fast as the back propagation through omitting redundant learning. The recognition rate is 96.2% in case of the conventional back propagation algorithm, 96.5% in case of the back propagation through omitting redundant learning and 97.4% in the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1686-1692
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• 2015

This paper proposes a modified genetic algorithm that has the same beamforming performance and a fast convergence speed using general genetic algorithm in order to form a beam for the mobile node in a mesh network. The proposed beamforming genetic algorithm selects a part of chromosome a high fitness value in mating process to obtain fast convergence speed, and rest part of chromosome with longer fitness value in order to avoid local solution. Furthermore, the reference beam pattern with Gaussian shape reduces additional convergence speed. Simulation shows that the convergence speed of proposed algorithm improves 20% compared with that of conventional beamforming genetic algorithm.