• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.14-15
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• 2008

In this paper, a new iterative decoding of LDPC codes is proposed. The decoding is based on the posteriori probability of each belief propagation (BP) decoding and an additional postprocessing, that is, erasure decoding of LDPC codes. It turned out that the new method consistently improves the decoding performance on various classes of LDPC codes. For example it removes the error floor of Margulis codes effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2355-2364
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• 1998

In conventional fractal decoding procedure, the reconstructed image is obtained by a rpredefined number of iterations starting with an arbitrary initial image. Its convergence speed depends on the selection of the initial image. It should be solved to get high speed convergence. In this paper, we theoretically reveal that conventional method is approximately decomposed into the decoding of the DC and AC components. Based on this fact, we proposed a novel fast fractal decoding algorithm made up of two steps. The averaged-image considered as an optimal initial image is estimated in the first step. In the second step, the reconstructe dimag eis genrated from the output image obtained in the first step. From the simulations, it is shown that the output image of the first step approximately converges to the averaged-image with only 15% calculations for one iteration of conventional method. And the proposed method is faster than various decoding mehtods and evenly equal to conventioanl decoding with the averaged-image. In addition, the proposed method can be applied to the compressed data resulted from the various encoding methods because it does not impose any constraints in the encoding procedure to get high decoding speed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.2
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• pp.325-333
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• 1997

In fractral decoding procedure, the reconstructed image is obtained by iteratively applying the contractive transform to an arbitrary initial image. But this method is not suitable for the fast decoding because convergence speed depends on the selection of initial image. Therefore, the initial image to achieve fast decoding should be selected. In this paper, we propose an initial image estimation that can be applied to various decoding methods. The initial image similar to the original image is estimated by using only the compressed data so that the proposed method does not affect the compression ratio. From the simulation, the PSNR of the proposed initial image is 6dB higher han that of ones iterated output image of conventional decoding with Babaraimage. Computations in addition and multiplication are reduced about 96%. On the other hands, if we apply the proposed initial image to other decoding algorithms, the faster convergence speed is expected.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1143-1149
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• 2006

In this paper, the modified iterative decoding algorithm[8] by partitioning check nodes is applied to low-density parity-check(LDPC) codes in IEEE 802.16e standards, which gives us the improvement for convergence speed of decoding. Also, the new method of check node partitioning which is suitable for decoding of the LDPC codes in IEEE 802.16e system is proposed. The improvement of convergence speed in decoding reduces the number of iterations and thus the computational complexity of the decoder. The decoding method by partitioning check nodes can be applied to the LDPC codes whose decoder cannot be implemented in the fully parallel processing as an efficient sequential processing method. The modified iterative decoding method of LDPC codes using the proposed check node partitioning method can be used to implement the practical decoder in the wireless communication systems.

• ETRI Journal
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• v.31 no.5
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• pp.510-517
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• 2009

A high throughput parallel decoding method is developed for context-based adaptive variable length codes. In this paper, several new design ideas are devised and implemented for scalable parallel processing, a reduction in area, and a reduction in power requirements. First, simplified logical operations instead of memory lookups are used for parallel processing. Second, the codes are grouped based on their lengths for efficient logical operation. Third, up to M bits of the input stream can be analyzed simultaneously. For comparison, we designed a logical-operation-based parallel decoder for M=8 and a conventional parallel decoder. High-speed parallel decoding becomes possible with our method. In addition, for similar decoding rates (1.57 codes/cycle for M=8), our new approach uses 46% less chip area than the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.753-757
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• 2003

In this paper, We propose an efficient iteration decoding control method with variable iteration decoding for iteration codes decoding. As the number of iterations increases, the bit error rate and frame error rate of the decoder decrease and the incremental improvement gradually diminishes. However, as the iteration decoding number is increase, it require much delay and amount of processing for decoding. Thus we propose variable iteration control method to adapt variation of channel using Frame Error-Check indicator. Therefore, the CRC method requires the fewest iterations and less computation than the CE method and the SCR methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3749-3768
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• 2018

As one of the most potential types of low-density parity-check (LDPC) codes, CPM-QC-LDPC code has considerable advantages but there still exist some limitations in practical application, for example, the existing decoding algorithm has a low convergence rate and a high decoding complexity. According to the structural property of this code, we propose a new method based on a CPM-RID decoding algorithm that decodes block-by-block with weights, which are obtained by neural network training. From the simulation results, we can conclude that our proposed method not only improves the bit error rate and frame error rate performance but also increases the convergence rate, when compared with the original CPM-RID decoding algorithm and scaled MSA algorithm.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.342-351
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• 2017

The neural decoding is a procedure that uses spike trains fired by neurons to estimate features of original stimulus. This is a fundamental step for understanding how neurons talk each other and, ultimately, how brains manage information. In this paper, the strategies of neural decoding are classified into three methodologies: rate decoding, temporal decoding, and population decoding, which are explained. Rate decoding is the firstly used and simplest decoding method in which the stimulus is reconstructed from the numbers of the spike at given time (e. g. spike rates). Since spike number is a discrete number, the spike rate itself is often not continuous and quantized, therefore if the stimulus is not static and simple, rate decoding may not provide good estimation for stimulus. Temporal decoding is the decoding method in which stimulus is reconstructed from the timing information when the spike fires. It can be useful even for rapidly changing stimulus, and our sensory system is believed to have temporal rather than rate decoding strategy. Since the use of large numbers of neurons is one of the operating principles of most nervous systems, population decoding has advantages such as reduction of uncertainty due to neuronal variability and the ability to represent a stimulus attributes simultaneously. Here, in this paper, three different decoding methods are introduced, how the information theory can be used in the neural decoding area is also given, and at the last machinelearning based algorithms for neural decoding are introduced.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.410-421
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• 2016

In this paper, we propose a framework that attempts to incorporate landmarks into a segment-based Mandarin speech recognition system. In this method, landmarks provide boundary information and phonetic class information, and the information is used to direct the decoding process. To prove the validity of this method, two kinds of landmarks that can be reliably detected are used to direct the decoding process of a segment model (SM) based Mandarin LVCSR (large vocabulary continuous speech recognition) system. The results of our experiment show that about 30% decoding time can be saved without an obvious decrease in recognition accuracy. Thus, the potential of our method is demonstrated.

• Korean Journal of Mathematics
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• v.10 no.1
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• pp.89-95
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• 2002

We present a simple but new way of decoding the binary Golay code.