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Creators/Authors contains: "Ivaniš. P."

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  1. ; ; (, Telfor Journal)
    In this paper, we analyze applicability of singleand two-hidden-layer feed-forward artificial neural networks, SLFNs and TLFNs, respectively, in decoding linear block codes. Based on the provable capability of SLFNs and TLFNs to approximate discrete functions, we discuss sizes of the network capable to perform maximum likelihood decoding. Furthermore, we propose a decoding scheme, which use artificial neural networks (ANNs) to lower the error-floors of low-density parity-check (LDPC) codes. By learning a small number of error patterns, uncorrectable with typical decoders of LDPC codes, ANN can lower the error-floor by an order of magnitude, with only marginal average complexity incense. 
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    Accepted Manuscript Full Text Available
  2. ; ; (, Conference on Electronics, Telecommunication, Computing, Automation and Nuclear Engineering)
    In this paper we propose the approaches that combine two types of iterative decoding algorithms that are usually used for decoding of low density parity check codes (LDPC). One strategy is based on a low-complexity bit-flipping algorithm, and the proposed modification enable significant performance improvement, with no significant increase of the average computing complexity. The other strategy is based on belief propagation decoder, and the resulting decoder has improved error correction capabilities for the codes with short codeword length. 
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    Accepted Manuscript Full Text Available
  3. ; ; (, Proc. of LXVI Conference on Electronics, Telecommunication, Computing, Automation and Nuclear Engineering – ETRAN 2022)
    Accepted Manuscript Full Text Available