<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Neural Network Decoder | Jorge Kamassury — Academic CV</title><link>https://kamassury.github.io/tags/neural-network-decoder/</link><atom:link href="https://kamassury.github.io/tags/neural-network-decoder/index.xml" rel="self" type="application/rss+xml"/><description>Neural Network Decoder</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>pt-br</language><lastBuildDate>Wed, 02 Nov 2022 00:00:00 +0000</lastBuildDate><image><url>https://kamassury.github.io/media/icon_hu_982c5d63a71b2961.png</url><title>Neural Network Decoder</title><link>https://kamassury.github.io/tags/neural-network-decoder/</link></image><item><title>Otimização de um decodificador neural para códigos BCH curtos sob regime de comunicação crítica</title><link>https://kamassury.github.io/publications/journal-article/bch-neural-decoder/</link><pubDate>Wed, 02 Nov 2022 00:00:00 +0000</pubDate><guid>https://kamassury.github.io/publications/journal-article/bch-neural-decoder/</guid><description>&lt;p&gt;Extensão aplicada do método
para &lt;strong&gt;códigos BCH curtos em regime de comunicação crítica&lt;/strong&gt;, empregando uma rede neural profunda de &lt;strong&gt;complexidade reduzida&lt;/strong&gt; treinada sobre módulos e síndromes dos vetores recebidos. A abordagem iterativa de decimação supera o desempenho do decodificador neural puro em canais AWGN.&lt;/p&gt;</description></item></channel></rss>