• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.69-80
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• 2021

Neural machine translation using deep neural network has emerged as a mainstream research, and an abundance of investment and studies on model structure and parallel language pair have been actively undertaken for the best performance. However, most recent neural machine translation studies pass along decoding strategy to future work, and have insufficient a variety of experiments and specific analysis on it for generating language to maximize quality in the decoding process. In machine translation, decoding strategies optimize navigation paths in the process of generating translation sentences and performance improvement is possible without model modifications or data expansion. This paper compares and analyzes the significant effects of the decoding strategy from classical greedy decoding to the latest Dynamic Beam Allocation (DBA) in neural machine translation using a sequence to sequence model.

• 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.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.28 no.2
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• pp.271-279
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• 2007

We suggest a novel method for the evaluation of cochlear implant (CI) speech processing strategy based on neural spike train decoding. From formant trajectories of input speech and auditory nerve responses responding to the electrical pulse trains generated from a specific CI speech processing strategy, optimal linear decoding filter was obtained, and used to estimate formant trajectory of incoming speech. Performance of a specific strategy is evaluated by comparing true and estimated formant trajectories. We compared a newly-developed strategy rooted from a closer mimicking of auditory periphery using nonlinear time-varying filter, with a conventional linear-filter-based strategy. It was shown that the formant trajectories could be estimated more exactly in the case of the nonlinear time-varying strategy. The superiority was more prominent when background noise level is high, and the spectral characteristic of the background noise was close to that of speech signals. This confirms the superiority observed from other evaluation methods, such as acoustic simulation and spectral analysis.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.3
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• pp.221-227
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• 2009

For successful restoration of visual function by a visual neural prosthesis such as retinal implant, electrical stimulation should evoke neural responses so that the informat.ion on visual input is properly represented. A stimulation strategy, which means a method for generating stimulation waveforms based on visual input, should be developed for this purpose. We proposed to use the decoding of visual input from retinal ganglion cell (RGC) responses for the evaluation of stimulus encoding strategy. This is based on the assumption that reliable encoding of visual information in RGC responses is required to enable successful visual perception. The main purpose of this study was to determine the influence of inter-dependence among stimulated RGCs activities on decoding accuracy. Light intensity variations were decoded from multiunit RGC spike trains using an optimal linear filter. More accurate decoding was possible when different types of RGCs were used together as input. Decoding accuracy was enhanced with independently firing RGCs compared to synchronously firing RGCs. This implies that stimulation of independently-firing RGCs and RGCs of different types may be beneficial for visual function restoration by retinal prosthesis.

• Journal of IKEEE
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• v.24 no.4
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• pp.1039-1049
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• 2020

Neural decoding is important to recognize the user's intention for controlling a neuro-prosthetic hand. This paper proposes a real-time decoding method for multi-channel peripheral neural activity. Peripheral nerve signals were measured from the median and radial nerves, and motion artifacts were removed based on locally fitted polynomials. Action potentials were then classified using a k-means algorithm. The firing rate of action potentials was extracted as a feature vector and its dimensionality was reduced by a self-organizing feature map. Finally, a multi-layer perceptron was used to classify hand motions. In monkey experiments, all processes were completed within a real-time constrain, and the hand motions were recognized with a high success rate.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.4
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• pp.27-36
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• 1996

It is well known that the viterbi algorithm proposed as a mthod of decoding convolutional codes is in fact maximum likelihood (ML) and therefore optimal. But, because hardware complexity grows exponentially with the constraint length, there will be severe constraints on the implementation of the viterbi decoders. In this paper, the three-layered backpropagation neural networks are proposed as an alternative in order to get sufficiently useful performance and deal successfully with the problems of the viterbi decoder. This paper shows that the neural convolutional decoder (NCD) can make a decision in the point of ML in decoding and describes simulation results. The cause of the difference between stochastic results and simulation results is discussed, and then thefuture prospect of the NCD is described on the basis of the characteristic of the transfer function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.185-190
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• 2005

In IS-95 and IMT-2000 systems using variable code rates and constraint lengths, this paper limits code rate 1/2 and constraint length 3 and realizes forward real-time decoder using Sliding-Window with decoding length 6 and PVSL(Prototype Vector Selecting Logic), LVQ(Learning Vector Quantization) in Neural Network. In comparison condition to theoretically constrained AWGN channel environment at $S/(N_{0}/2)=1$ I verified the superiority of forward real-time decoder through hard-decision and soft-decision comparison between Viterbi decoder and forward real-time decoder such as BER and Secure Communication and H/W Structure.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.59-62
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• 2006

전산 신경과학은 신경 시스템을 생물물리학, 신경회로, 그리고 시스템 레벨 등 여러 가지 관점에서 크기와 구조를 모델링하여 신경 신호의 전달과 전달되는 정보의 내용을 이해하고자 하는 분야이다. 전산 신경과학은 기존의 생물학적인 신경과학 연구에 대한 보완적인 연구방법으로 이론적이고 계산적인 방법을 사용한다. 본 논문에서는 신경 세포에서 반응 인코딩에 해당되는 신호 발생율(firing rate)과 스파이크 통계(spike statistics)를 설명하고, 신경 세포에서 반응 디코딩에 해당되는 스파이크-트레인 디코딩(spike-train decoding)에 대하여 설명한다.