Please use this identifier to cite or link to this item: http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1922
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dc.contributor299983es_ES
dc.contributor267233es_ES
dc.contributor.otherhttps://orcid.org/0000-0002-7635-4687-
dc.contributor.otherhttps://orcid.org/0000-0002-9498-6602-
dc.contributor.other0000-0002-9498-6602-
dc.coverage.spatialGlobales_ES
dc.creatorGalván Tejada, Carlos Eric-
dc.creatorZanella Calzada, Laura Alejandra-
dc.creatorGarcía Dominguez, Antonio-
dc.creatorMagallanes Quintanar, Rafael-
dc.creatorLuna García, Huizilopoztli-
dc.creatorCelaya Padilla, José-
dc.creatorGalván Tejada, Jorge-
dc.creatorVélez Rodríguez, Alberto-
dc.creatorGamboa Rosales, Hamurabi-
dc.date.accessioned2020-05-20T18:14:03Z-
dc.date.available2020-05-20T18:14:03Z-
dc.date.issued2020-04-14-
dc.identifierinfo:eu-repo/semantics/publishedVersiones_ES
dc.identifier.issn2220-9964es_ES
dc.identifier.urihttp://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1922-
dc.identifier.urihttps://doi.org/10.48779/4kqm-bw37-
dc.descriptionEstimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.es_ES
dc.description.abstractEstimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationhttps://www.mdpi.com/2220-9964/9/4/226es_ES
dc.relation.urigeneralPublices_ES
dc.sourceISPRS International Journal of Geo-Information, Vol. 9, No. 226. 2020es_ES
dc.subject.classificationINGENIERIA Y TECNOLOGIA [7]es_ES
dc.subject.otherindoor locationes_ES
dc.subject.othermagnetic fieldes_ES
dc.subject.otherconvolutional neural networkes_ES
dc.titleEstimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
Appears in Collections:*Documentos Académicos*-- M. en Ciencias del Proc. de la Info.

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