• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.10
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• pp.585-592
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• 2014

It is well known that serial belief propagation (BP) decoding for low-density parity-check (LDPC) codes achieves faster convergence without any increase of decoding complexity per iteration and bit error rate (BER) performance loss than standard parallel BP (PBP) decoding. Serial BP (SBP) decoding, such as horizontal SBP (H-SBP) decoding or vertical SBP (V-SBP) decoding, updates check nodes or variable nodes faster than standard PBP decoding within a single iteration. In this paper, we propose combined horizontal-vertical SBP (CHV-SBP) decoding. By the same reasoning, CHV-SBP decoding updates check nodes or variable nodes faster than SBP decoding within a serialized step in an iteration. CHV-SBP decoding achieves faster convergence than H-SBP or V-SBP decoding. We compare these decoding schemes in details. We also show in simulations that the convergence rate, in iterations, for CHV-SBP decoding is about $\frac{1}{6}$ of that for standard PBP decoding, while the convergence rate for SBP decoding is about $\frac{1}{2}$ of that for standard PBP decoding. In simulations, we use recently proposed generalized LDPC (GLDPC) codes with binary cyclic codes (BCC).

• English Language & Literature Teaching
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• v.11 no.2
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• pp.35-50
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• 2005

Thirty six EFL freshman students at the College of Languages and Translation, King Saud University, Riyadh, Saudi Arabia were given a dictation, a listening comprehension test and a decoding test. The purpose of the study was to find out whether EFL freshmen students' spelling ability correlates with their listening comprehension and decoding skills. Data analysis showed that the typical EFL freshman student misspelled 41.5% of the words on the dictation, gave 49.5% correct responses on the listening comprehension test, and 52% correct responses on the decoding test. The median and mean scores showed that the subjects' spelling, listening and decoding achievement is low, which implied that the subjects were having spelling, listening comprehension and decoding difficulties. The students' spelling errors and correct listening comprehension and decoding responses revealed strong correlations between spelling ability, listening comprehension and decoding skills. This means that good spelling ability in EFL is related to good listening comprehension and good decoding skills. The better the listening comprehension and decoding abilities, the fewer the spelling errors. When listening comprehension and decoding skills are poor, spelling ability is also poor. Recommendations for spelling, listening and decoding instruction are given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1296-1309
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• 2023

In this paper, based on the soft cancellation (SCAN) bit-flipping (SCAN-BF) algorithm, a generalized SCAN bit-flipping (GSCAN-BF-Ω) decoding algorithm is carried out, where Ω represents the number of bits flipped or corrected at the same time. GSCAN-BF-Ω algorithm corrects the prior information of the code bits and flips the prior information of the unreliable information bits simultaneously to improve the block error rate (BLER) performance. Then, a joint threshold scheme for the GSCAN-BF-2 decoding algorithm is proposed to reduce the average decoding complexity by considering both the bit channel quality and the reliability of the coded bits. Simulation results show that the GSCAN-BF-Ω decoding algorithm reduces the average decoding latency while getting performance gains compared to the common multiple SCAN bit-flipping decoding algorithm. And the GSCAN-BF-2 decoding algorithm with the joint threshold reduces the average decoding latency further by approximately 50% with only a slight performance loss compared to the GSCAN-BF-2 decoding algorithm.

• ETRI Journal
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• v.28 no.3
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• pp.301-310
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• 2006

In mobile communications, a class of variable-complexity algorithms for convolutional decoding known as sequential decoding algorithms is of interest since they have a computational time that could vary with changing channel conditions. The Fano algorithm is one well-known version of a sequential decoding algorithm. Since the decoding time of a Fano decoder follows the Pareto distribution, which is a heavy-tailed distribution parameterized by the channel signal-to-noise ratio (SNR), buffers are required to absorb the variable decoding delays of Fano decoders. Furthermore, since the decoding time drawn by a certain Pareto distribution can become unbounded, a maximum limit is often employed by a practical decoder to limit the worst-case decoding time. In this paper, we investigate the relations between buffer occupancy, decoding time, and channel conditions in a system where the Fano decoder is not allowed to run with unbounded decoding time. A timeout limit is thus imposed so that the decoding will be terminated if the decoding time reaches the limit. We use discrete-time semi-Markov models to describe such a Fano decoding system with timeout limits. Our queuing analysis provides expressions characterizing the average buffer occupancy as a function of channel conditions and timeout limits. Both numerical and simulation results are provided to validate the analytical results.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.133-136
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• 2000

A RVLC(Reversible Variable Length Code) with an efficient table memory is proposed in this paper. In the conventional decoding methods, the weight of symbols and code values are used for the decoding table. These methods can be applied for Huffman decoding. In VLC decoding, many studies have been done for memory efficiency and decoding speed. We propose an improved table construction method for general VLC and RVLC decoding, which uses the transition number of bits within a symbol with an enhanced weight decomposition. In this method, tile table for RVLC decoding can be implemented with a smaller memory

• 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.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1984.04a
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• pp.22-24
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• 1984

In this paper, the (15,7) cylic codesused EG(2,2) were decoded by one step majority logic decoding. This decoding algorithm is based on the properties of finite geometries and can be simply implemented for moderate length n. especially one step majority logic decoding is attractive because the complexity and the cost of the majority logic decoder increase very rapidly with L, the number of decoding steps. The theorectical and experimental results show that the majority logic decoding presented in this paper is a relatively effective decoding scheme.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2479-2496
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• 2013

Increasing demand for Full High-Definition (FHD) video and Ultra High-Definition (UHD) video services has led to active research on high speed video processing. Widespread deployment of multi-core systems has accelerated studies on high resolution video processing based on parallelization of multimedia software. Even if parallelization of a specific decoding step may improve decoding performance partially, such partial parallelization may not result in sufficient performance improvement. Particularly, entropy decoding has often been considered separately from other decoding steps since the entropy decoding step could not be parallelized easily. In this paper, we propose a parallelization technique called Integrated Multi-Threaded Parallelization (IMTP) which takes parallelization of the entropy decoding step, with other decoding steps, into consideration in an integrated fashion. We used the Simultaneous Multi-Threading (SMT) technique with appropriate thread scheduling techniques to achieve the best performance for the entire decoding step. The speedup of the proposed IMTP method is up to 3.35 times faster with respect to the entire decoding time over a conventional decoding technique for H.264/AVC videos.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.3
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• pp.9-14
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• 2009

Recently, a rate-2, $2{\times}2$ space-time code with simple ML decoding has been designed. Though the simple ML decoding algorithm does reduce the ML decoding complexity, there is still need for improvement. In this paper, we propose an efficient decoding algorithm for the rate-2, $2{\times}2$ space-time code using interference cancellation techniques with performance virtually identical to that of ML decoding. Also, the decoding complexity of the proposed algorithm is significantly reduced compared to the conventional simple ML decoding, especially for large modulation orders.