<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822022000300073</article-id>
<article-id pub-id-type="doi">10.18845/tm.v35i3.5718</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Perfilado de rendimiento de FPS para múltiples arquitecturas computacionales usando el algoritmo de reducción de neblina DCP]]></article-title>
<article-title xml:lang="en"><![CDATA[FPS performance profiling for multiple computational architectures using the DCP dehazing algorithm]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Navarro-Brenes]]></surname>
<given-names><![CDATA[Allan Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chavarría-Zamora]]></surname>
<given-names><![CDATA[Luis Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2022</year>
</pub-date>
<volume>35</volume>
<numero>3</numero>
<fpage>73</fpage>
<lpage>81</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822022000300073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822022000300073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822022000300073&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este documento presenta una prueba de rendimiento creada para evaluar el desempeño de diferentes plataformas en la ejecución de un algoritmo de reducción de niebla basado en el &#8220;Dark Channel Prior&#8221; (DCP) (1). El parámetro utilizado para la evaluación fue el número de cuadros por segundo (FPS, por sus siglas en inglés) que el dispositivo es capaz de procesar. Con esta herramienta se logra determinar aquellas arquitecturas que son aptas para ejecutar el algoritmo en tiempo real. El ambiente de pruebas se ejecutó en cuatro plataformas, un Google Pixel 3a, una Raspberry Pi 3B+, una GPU de NVIDIA y un procesador Intel x86. Se usaron los siguientes kits de desarrollo de software (SDK, por sus siglas en inglés) según la plataforma: Android NDK, Yocto Poky, CUDA y la cadena de herramientas GCC. La herramienta permitió recopilar, para cada plataforma, los FPS para distintos tamaños de imagen, con estos resultados se pueden escoger la arquitectura más idónea según el área de implementación (e.g., bajo consumo o HPC).]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this document we present a benchmark to evaluate the performance of different platforms in the execution of a dehazing algorithm based on the Dark Channel Prior (DCP) (1). The parameter used for the evaluation was the number of frames per second (FPS) that the device was able to process. This tool allows to determine which architectures can execute the algorithm in real time. The testing environment was executing in four platforms, a Google Pixel 3a, a Raspberry Pi 3B+, a GPU by NVIDIA, and an Intel x86 processor. The following software development kits (SDK&#8217;s) where used for each of the platforms: Android NDK, Yocto Poky, CUDA, and the GCC toolchain. The tool allowed us to collect, for each platform, the FPS for different image sizes, these results allow the selection of an ideal architecture depending on a specific application (e.g., low power, HPC).]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Benchmark de FPS]]></kwd>
<kwd lng="es"><![CDATA[procesamiento de imágenes]]></kwd>
<kwd lng="es"><![CDATA[procesamiento en tiempo real]]></kwd>
<kwd lng="es"><![CDATA[sistemas empotrados]]></kwd>
<kwd lng="es"><![CDATA[arquitectura de computadores]]></kwd>
<kwd lng="es"><![CDATA[reducción de neblina]]></kwd>
<kwd lng="es"><![CDATA[restauración de imagen]]></kwd>
<kwd lng="en"><![CDATA[FPS benchmark]]></kwd>
<kwd lng="en"><![CDATA[image processing]]></kwd>
<kwd lng="en"><![CDATA[real-time processing]]></kwd>
<kwd lng="en"><![CDATA[embedded systems]]></kwd>
<kwd lng="en"><![CDATA[computer architecture]]></kwd>
<kwd lng="en"><![CDATA[image dehazing]]></kwd>
<kwd lng="en"><![CDATA[image restoration]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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