C
Carlos E. Galván-Tejada
Researcher at Autonomous University of Zacatecas
Publications - 110
Citations - 1357
Carlos E. Galván-Tejada is an academic researcher from Autonomous University of Zacatecas. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 14, co-authored 85 publications receiving 831 citations. Previous affiliations of Carlos E. Galván-Tejada include Monterrey Institute of Technology and Higher Education.
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Journal ArticleDOI
Evolution of Indoor Positioning Technologies: A Survey
Ramón F. Brena,Juan-Pablo García-Vázquez,Carlos E. Galván-Tejada,David Munoz-Rodriguez,Cesar Vargas-Rosales,James Fangmeyer +5 more
TL;DR: A technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches is provided, and the existing approaches are classified in a structure in order to guide the review and discussion of the different approaches.
Journal ArticleDOI
Comparison of Convolutional Neural Network Architectures for Classification of Tomato Plant Diseases
Valeria Maeda-Gutiérrez,Carlos E. Galván-Tejada,Laura A. Zanella-Calzada,José M. Celaya-Padilla,Jorge I. Galván-Tejada,Hamurabi Gamboa-Rosales,Huizilopoztli Luna-García,Rafael Magallanes-Quintanar,Carlos A. Guerrero Méndez,Carlos Olvera-Olvera +9 more
TL;DR: This work focused on fine-tuning based on the comparison of the state-of-the-art architectures: AlexNet, GoogleNet, Inception V3, Residual Network (ResNet) 18, and ResNet 50, and concluded that this significantly success rate makes the GoogleNet model a useful tool for farmers in helping to identify and protect tomatoes from the diseases mentioned.
Journal ArticleDOI
Multi-view stacking for activity recognition with sound and accelerometer data
TL;DR: This work proposes the use of a multi-view stacking method to fuse the data from heterogeneous types of sensors for activity recognition, and uses sound and accelerometer data collected with a smartphone and a wrist-band while performing home task activities.
Journal ArticleDOI
Magnetic Field Feature Extraction and Selection for Indoor Location Estimation
TL;DR: An extension and improvement of the current indoor localization model based on the feature extraction of 46 magnetic field signal features is presented and it is verified that reducing the number of features increases the probability of the estimator correctly detecting the user's location and its capacity to detect false positives in both scenarios.
Journal ArticleDOI
Image Edge Detection Using Fractional Calculus with Feature and Contrast Enhancement
Amita Nandal,Hamurabi Gamboa-Rosales,Arvind Dhaka,José M. Celaya-Padilla,Jorge I. Galván-Tejada,Carlos E. Galván-Tejada,Francisco J. Martinez-Ruiz,Cesar H. Guzmán-Valdivia +7 more
TL;DR: By theoretical and experimental results, it is observed that the proposed feature and contrast enhancement of image using Riemann–Liouville fractional differential operator outperforms the existing methods under comparison.