Resumen. En este artículo se realiza un análisis de la correlación entre el crecimiento de la mancha urbana y el cambio de temperaturas de la ciudad de Mérida, Yucatán, México, mediante la implementación de técnicas de inteligencia artificial enfocadas a la segmentación de imágenes. Partiendo de una secuencia multitemporal de imágenes satelitales registradas por Landsat en formato RGB ocupando un rango de los años 2001 al 2016, se realiza la segmentación de la mancha urbana utilizando una técnica de inteligencia artificial, particularmente optimización por enjambre de partículas, una implementación de inteligencia de enjambres. La segmentación de los datos nos permite estimar el historial de crecimiento del área de suelo construido en la ciudad. Posteriormente los datos históricos de temperaturas registradas en ese mismo periodo son analizados con el método de descomposición modal empírica. El análisis preliminar de la correlación positiva entre los datos de área construida y temperatura como funciones numéricas nos permiten concluir que puede existir una estrecha relación entre ambos indicadores.Palabras clave: expansión urbana, descomposición modal empírica, incremento de temperatura, optimización por enjambre de partículas.Abstract. In this article an analysis of the correlation between the growth of urban sprawl and the change of temperatures of the city of Merida, Yucatan,
Nowadays, remote sensing data taken from artificial satellites require high space com- munications bandwidths as well as high computational processing burdens due to the vertiginous development and specialisation of on-board payloads specifically designed for remote sensing purposes. Nevertheless, these factors become a severe problem when con- sidering nanosatellites, particularly those based in the CubeSat standard, due to the strong limitations that it imposes in volume, power and mass. Thus, the applications of remote sensing in this class of satellites, widely sought due to their affordable cost and easiness of construction and deployment, are very restricted due to their very limited on-board computer power, notwithstanding their Low Earth Orbits (LEO) which make them ideal for Earth’s remote sensing. In this work we present the feasibility of the integration of an NVIDIA GPU of low mass and power as the on-board computer for 1-3U CubeSats. From the remote sensing point of view, we present nine processing-intensive algorithms very commonly used for the processing of remote sensing data which can be executed on-board on this platform. In this sense, we present the performance of these algorithms on the proposed on-board computer with respect with a typical on-board computer for CubeSats (ARM Cortex-A57 MP Core Processor), showing that they have acceleration factors of average of 14.04× ∼14.72× in average. This study sets the precedent to perform satellite on-board high performance computing so to widen the remote sensing capabilities of CubeSats.
Melanoma is the most deadly form of skin cancer in human in all over the world with an increase number of victims yearly. One traditional form of diagnosis melanoma is by using the so called ABCDE rule which stands for Asymmetry, Border, Color, Diameter and Evolution of the lesion. For melanoma lesions, the color as a descriptor exhibits heterogeneous values, ranging from light brown to dark brown (sometimes blue reddish or even white). Therefore, investigating on color features from digital melanoma images could provide insights for developing automated algorithms for melanoma discrimination from common nevus. In this research work, an algorithm is proposed and tested to characterize the color in a pigmented lesion. The developed algorithm measures the hue of different sites in the same pigmented area from a digital image using the HSI color space. The algorithm was applied to 40 digital images of unequivocal melanomas and 40 images of common nevus, which were taken from several data bases. Preliminary results indicate that visible color changes of melanoma sites are well accounted by the proposed algorithm. Other factors, such as quality of images and the influence of the shiny areas on the results obtained with the proposed algorithm are discussed.
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