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A Programmable look-up table-based interpolator with nonuniform sampling scheme

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A Programmable look-up table-based interpolator with nonuniform sampling scheme. / Dutra e Silva Junior, Elvio Carlos ; Soares Indrusiak, Leandro; Finamore, W.A.; Glesner, Manfred.

In: International Journal of Reconfigurable Computing, Vol. 2012, 647805, 2012.

Research output: Contribution to journalArticlepeer-review

Harvard

Dutra e Silva Junior, EC, Soares Indrusiak, L, Finamore, WA & Glesner, M 2012, 'A Programmable look-up table-based interpolator with nonuniform sampling scheme', International Journal of Reconfigurable Computing, vol. 2012, 647805. https://doi.org/10.1155/2012/647805

APA

Dutra e Silva Junior, E. C., Soares Indrusiak, L., Finamore, W. A., & Glesner, M. (2012). A Programmable look-up table-based interpolator with nonuniform sampling scheme. International Journal of Reconfigurable Computing, 2012, [647805]. https://doi.org/10.1155/2012/647805

Vancouver

Dutra e Silva Junior EC, Soares Indrusiak L, Finamore WA, Glesner M. A Programmable look-up table-based interpolator with nonuniform sampling scheme. International Journal of Reconfigurable Computing. 2012;2012. 647805. https://doi.org/10.1155/2012/647805

Author

Dutra e Silva Junior, Elvio Carlos ; Soares Indrusiak, Leandro ; Finamore, W.A. ; Glesner, Manfred. / A Programmable look-up table-based interpolator with nonuniform sampling scheme. In: International Journal of Reconfigurable Computing. 2012 ; Vol. 2012.

Bibtex - Download

@article{178c730ca3dc4673bc79e0172462ad9b,
title = "A Programmable look-up table-based interpolator with nonuniform sampling scheme",
abstract = "Interpolation is a useful technique for storage of complex functions on limited memory space: some few sampling values are stored on a memory bank, and the function values in between are calculated by interpolation. This paper presents a programmable Look-Up Table-based interpolator, which uses a reconfigurable nonuniform sampling scheme: the sampled points are not uniformly spaced. Their distribution can also be reconfigured to minimize the approximation error on specific portions of the interpolated function's domain. Switching from one set of configuration parameters to another set, selected on the fly from a variety of precomputed parameters, and using different sampling schemes allow for the interpolation of a plethora of functions, achieving memory saving and minimum approximation error. As a study case, the proposed interpolator was used as the core of a programmable noise generatoroutput signals drawn from different Probability Density Functions were produced for testing FPGA implementations of chaotic encryption algorithms. As a result of the proposed method, the interpolation of a specific transformation function on a Gaussian noise generator reduced the memory usage to 2.71% when compared to the traditional uniform sampling scheme method, while keeping the approximation error below a threshold equal to 0.000030518.",
author = "{Dutra e Silva Junior}, {Elvio Carlos} and {Soares Indrusiak}, Leandro and W.A. Finamore and Manfred Glesner",
note = "{\textcopyright}2012, {\'E}lvio Carlos Dutra e Silva J{\'u}nior et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.",
year = "2012",
doi = "10.1155/2012/647805",
language = "English",
volume = "2012",
journal = "International Journal of Reconfigurable Computing",
issn = "1687-7195",
publisher = "Hindawi Publishing Corporation",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - A Programmable look-up table-based interpolator with nonuniform sampling scheme

AU - Dutra e Silva Junior, Elvio Carlos

AU - Soares Indrusiak, Leandro

AU - Finamore, W.A.

AU - Glesner, Manfred

N1 - ©2012, Élvio Carlos Dutra e Silva Júnior et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PY - 2012

Y1 - 2012

N2 - Interpolation is a useful technique for storage of complex functions on limited memory space: some few sampling values are stored on a memory bank, and the function values in between are calculated by interpolation. This paper presents a programmable Look-Up Table-based interpolator, which uses a reconfigurable nonuniform sampling scheme: the sampled points are not uniformly spaced. Their distribution can also be reconfigured to minimize the approximation error on specific portions of the interpolated function's domain. Switching from one set of configuration parameters to another set, selected on the fly from a variety of precomputed parameters, and using different sampling schemes allow for the interpolation of a plethora of functions, achieving memory saving and minimum approximation error. As a study case, the proposed interpolator was used as the core of a programmable noise generatoroutput signals drawn from different Probability Density Functions were produced for testing FPGA implementations of chaotic encryption algorithms. As a result of the proposed method, the interpolation of a specific transformation function on a Gaussian noise generator reduced the memory usage to 2.71% when compared to the traditional uniform sampling scheme method, while keeping the approximation error below a threshold equal to 0.000030518.

AB - Interpolation is a useful technique for storage of complex functions on limited memory space: some few sampling values are stored on a memory bank, and the function values in between are calculated by interpolation. This paper presents a programmable Look-Up Table-based interpolator, which uses a reconfigurable nonuniform sampling scheme: the sampled points are not uniformly spaced. Their distribution can also be reconfigured to minimize the approximation error on specific portions of the interpolated function's domain. Switching from one set of configuration parameters to another set, selected on the fly from a variety of precomputed parameters, and using different sampling schemes allow for the interpolation of a plethora of functions, achieving memory saving and minimum approximation error. As a study case, the proposed interpolator was used as the core of a programmable noise generatoroutput signals drawn from different Probability Density Functions were produced for testing FPGA implementations of chaotic encryption algorithms. As a result of the proposed method, the interpolation of a specific transformation function on a Gaussian noise generator reduced the memory usage to 2.71% when compared to the traditional uniform sampling scheme method, while keeping the approximation error below a threshold equal to 0.000030518.

UR - http://www.scopus.com/inward/record.url?scp=84871826562&partnerID=8YFLogxK

U2 - 10.1155/2012/647805

DO - 10.1155/2012/647805

M3 - Article

VL - 2012

JO - International Journal of Reconfigurable Computing

JF - International Journal of Reconfigurable Computing

SN - 1687-7195

M1 - 647805

ER -