• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2766-2770
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• 2009

Recently, the Green IT is noticed because of the effects of greenhouse gas emissions, a drain on natural resources and pollution. In this paper, Visible Light Communication (VLC) systems with Turbo Coded scheme using LED is proposed and simulated in an optical wireless channel. As a forward error correction scheme to reduce information losses, turbo coding was employed. To decode the codewords, The Map (Maximum a Posteriori) algorism and SOVA (Soft Output Viterbi Algorithm) is used. The above mentioned schemes are described and simulation results are analyzed. As using turbo codes scheme, BER performance of proposed VLC systems is improved about 5 [dB].

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.5C
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• pp.411-421
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• 2002

In this paper, we present a new survivor path memory management method and a dedicated hardware architecture for the design of high-speed Viterbi decoders in modern digital communication systems. In the proposed method, a novel use of k-starting node number deciding circuits enables to acheive the immediate traceback of the merged survivor path from which we can decode output bits, and results in smaller survivor path memory size and processing delay time than the previously known methods. Also, in the proposed method, the survivor path memory can be constructed with ease using a simple standard dual-ported memory since one read-pointer and one write-pointer, that are updated at the same rate, are required for managing the survivor path: the previously known algorithms require either complex k-ported memory structure or k-times faster read capability than write. With a moderate hardware cost for immediate traceback capability the proposed method is superior to the previously known methods for high-speed Viterbi decoding.

• Journal of information and communication convergence engineering
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• v.1 no.4
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• pp.177-182
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• 2003

Conventional multilayer feedforward artificial neural networks are very effective in dealing with spatial problems. To deal with problems with time dependency, some kinds of memory have to be built in the processing algorithm. In this paper we show how the newly proposed Serial Input Neuron (SIN) convolutional decoders can be derived. As an example, we derive the SIN decoder for rate code with constraint length 3. The SIN is tested in Gaussian channel and the results are compared to the results of the optimal Viterbi decoder. A SIN approach to decode convolutional codes is presented. No supervision is required. The decoder lends itself to pleasing implementations in hardware and processing codes with high speed in a time. However, the speed of the current circuits may set limits to the codes used. With increasing speeds of the circuits in the future, the proposed technique may become a tempting choice for decoding convolutional coding with long constraint lengths.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.89.1-89
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• 2002

Conventional multilayer feedforward artificial neural networks are very effective in dealing with spatial problems. To deal with problems with time dependency, some kinds of memory have to be built in the processing algorithm. In this paper we show how the newly proposed Serial Input Neuron (SIN) convolutional decoders can be derived. As an example, we derive the SIN decoder for \ulcornerrate code with constraint length 3. The SIN is tested in Gaussian channel and the results are compared to the results of the optimal Viterbi decoder. A SIN approach to decode convolutional codes is presented. No supervision is required. The decoder lends itself to pleasing implementations in hardware and processing...