Authors:
Wilver Auccahuasi,Percy Castro,Orlando Aiquipa,Edward Flores,Fernando Sernaqué,Felipe Gutarra,NabiltMoggiano,DOI NO:
https://doi.org/10.26782/jmcms.2020.04.00027Keywords:
Vector,Biometeorological,Bodies of Water,Temperature,Humidity,Abstract
The analysis and processing of data is important in different areas, and we must pay more attention when it comes to the health of people, in the development of the protocol to prevent the outbreak of vectors transmitting tropical diseases with an emphasis on the mosquito “ AedesAegypti ”, being able to control its reproduction is of vital importance, and is one of the objectives of the protocol, understanding the reproduction times corresponds to the times where we must take necessary actions to be able to cut its reproduction cycle, within the mechanisms Technological we indicate the use of meteorological information to be able to analyze and predict the favorable conditions so that the mosquito can reproduce, added to the valuable information provided by earth observation satellites, in their access to satellite images, which will provide us with Current images of the area of interest, for rapid detection of bodies of water that will be the future nests of the mosquitoes, the heterogeneous processing is characterized by the analysis of the meteorological data in the CPU and the processing of the satellite images in the GPU both running in parallel processes in the same computer, with which we optimize the use of resources available in applications dedicated to health care.Refference:
I. GPGPU General-Purpose computation on Graphics Processing Units Web Site. http://gpgpu.org
II. http://www.nvidia.es/page/gpu_computing.html
III. https://es.windfinder.com/#10/-4.4477/-81.1189/temp
IV. https://www.senamhi.gob.pe/?p=datos-historicos
V. https://www.who.int/denguecontrol/mosquito/es/
VI. https://www.windy.com/es/-Mostrar-a%C3%B1adir-m%C3%A1s-capas/overlays?rh,-5.102,-80.706,7,m:dvkadSD
VII. https://www.windy.com/es/-Mostrar-a%C3%B1adir-m%C3%A1s-capas/overlays?temp,-5.173,-80.739,8,m:dxradTR
VIII. Jason Sanders, Edward Kandrot, CUDA by Example An Introduction to General – Purpose GPU Programming, Nvidia, Addison Wesley, Ann Arbor, Michigan, United States, First printing July 2010.
IX. Lillesand, T., Kiefer, R., Chipman, J., 2004. Remote Sensing and Image Interpretation. fifthed Willey & Sons, New York.
X. Lunetta, R.S., Lyon, J.G., 2004. Remote Sensing and GIS Accuracy Assessment. CRC press,
XI. Nvidia Web Site. http://www.nvidia.com
XII. Nvidia. GPU Computing.
XIII. Pal, M., Mather, P.M., 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 86, 554–565.
XIV. Russ, J.C., 1999. The Image Processing Handbook. third ed. CRC Press, Boca Raton, FL.
XV. Saha, S., Bandyopadhyay, S., 2010. Application of a multiseed-based clustering technique for automatic satellite image segmentation. IEEE Geosci. Remote Sens. Lett. 7, 306–308.
XVI. Sridhar, P.N., Surendran, A., Ramana, I.V., 2008. Auto-extraction technique-based digital classification of saltpans and aquaculture plots using satellite data. Int. J. Remote Sens. 29, 313–323.
XVII. Wilkinson, G.G., 2005. Results and implications of a study of fifteen years of satellite image classification experiments. IEEE Trans. Geosci. Remote Sens. 43, 433–440.