2010
DOI: 10.1049/iet-com.2009.0415
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Hybrid decoding of finite geometry low-density parity-check codes

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Cited by 4 publications
(2 citation statements)
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“…Huan et al [4] combined the ensemble empirical mode decomposition (EEMD) and a least-square support vector machine (LSSVM) to predict the dissolved oxygen sequences. Li et al [5] proposed a hybrid model of multiscale features based on EEMD and used it for dissolved oxygen prediction in aquaculture. Ren et al [6] used a genetic algorithm-optimized fuzzy neural network for the hydroponic system prediction of dissolved oxygen.…”
Section: Introductionmentioning
confidence: 99%
“…Huan et al [4] combined the ensemble empirical mode decomposition (EEMD) and a least-square support vector machine (LSSVM) to predict the dissolved oxygen sequences. Li et al [5] proposed a hybrid model of multiscale features based on EEMD and used it for dissolved oxygen prediction in aquaculture. Ren et al [6] used a genetic algorithm-optimized fuzzy neural network for the hydroponic system prediction of dissolved oxygen.…”
Section: Introductionmentioning
confidence: 99%
“…Such formulations have led to a resurgence of interest in the design (Kou et al (2001); O. Vontobel et al (2005); Tang et al (2005); Vandendriesscher (2010)) and decoding (Kou et al (2001); Li et al (2010); Liu & Pados (2005); Ngatched et al (2009) ;Tang et al (2005); Zhang et al (2010)) of finite geometry codes. The formulation in this chapter is meant to serve as a guiding principle relating finite geometric properties to algebraic ones.…”
Section: Introductionmentioning
confidence: 99%