2016
Stinner, Markus; Olmos, Pablo M
On the Waterfall Performance of Finite-Length SC-LDPC Codes Constructed From Protographs Artículo de revista
En: IEEE Journal on Selected Areas in Communications, vol. 34, no 2, pp. 345–361, 2016, ISSN: 0733-8716.
Resumen | Enlaces | BibTeX | Etiquetas: Analytical models, capacity-achieving codes, Complexity theory, Couplings, Decoding, Encoding, finite-length analysis, Iterative decoding, Low-density parity-check (LDPC) codes, spatially coupled LDPC codes constructed from prot
@article{Stinner2016,
title = {On the Waterfall Performance of Finite-Length SC-LDPC Codes Constructed From Protographs},
author = {Markus Stinner and Pablo M Olmos},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7339427},
doi = {10.1109/JSAC.2015.2504279},
issn = {0733-8716},
year = {2016},
date = {2016-02-01},
journal = {IEEE Journal on Selected Areas in Communications},
volume = {34},
number = {2},
pages = {345--361},
abstract = {An analysis of spatially coupled low-density parity-check (SC-LDPC) codes constructed from protographs is proposed. Given the protograph used to generate the SC-LDPC code ensemble, a set of scaling parameters to characterize the average finite-length performance in the waterfall region is computed. The error performance of structured SC-LDPC code ensembles is shown to follow a scaling law similar to that of unstructured randomly constructed SC-LDPC codes. Under a finite-length perspective, some of the most relevant SC-LDPC protograph structures proposed to date are compared. The analysis reveals significant differences in their finite-length scaling behavior, which is corroborated by simulation. Spatially coupled repeat-accumulate codes present excellent finite-length performance, as they outperform in the waterfall region SC-LDPC codes of the same rate and better asymptotic thresholds.},
keywords = {Analytical models, capacity-achieving codes, Complexity theory, Couplings, Decoding, Encoding, finite-length analysis, Iterative decoding, Low-density parity-check (LDPC) codes, spatially coupled LDPC codes constructed from prot},
pubstate = {published},
tppubtype = {article}
}
An analysis of spatially coupled low-density parity-check (SC-LDPC) codes constructed from protographs is proposed. Given the protograph used to generate the SC-LDPC code ensemble, a set of scaling parameters to characterize the average finite-length performance in the waterfall region is computed. The error performance of structured SC-LDPC code ensembles is shown to follow a scaling law similar to that of unstructured randomly constructed SC-LDPC codes. Under a finite-length perspective, some of the most relevant SC-LDPC protograph structures proposed to date are compared. The analysis reveals significant differences in their finite-length scaling behavior, which is corroborated by simulation. Spatially coupled repeat-accumulate codes present excellent finite-length performance, as they outperform in the waterfall region SC-LDPC codes of the same rate and better asymptotic thresholds.
2013
Alvarez, Mauricio; Luengo, David; Lawrence, Neil D
Linear Latent Force Models Using Gaussian Processes Artículo de revista
En: IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no 11, pp. 2693–2705, 2013.
Resumen | Enlaces | BibTeX | Etiquetas: Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode
@article{Alvarez2013,
title = {Linear Latent Force Models Using Gaussian Processes},
author = {Mauricio Alvarez and David Luengo and Neil D Lawrence},
url = {http://dblp.uni-trier.de/db/journals/pami/pami35.html#AlvarezLL13 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514873},
year = {2013},
date = {2013-01-01},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
volume = {35},
number = {11},
pages = {2693--2705},
abstract = {Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.},
keywords = {Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode},
pubstate = {published},
tppubtype = {article}
}
Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.