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Your search keyword '"Raquel Diaz Hernandez"' showing total 4 results
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4 results on '"Raquel Diaz Hernandez"'

1. Human Gesture Recognition using Hidden Markov Models and Sensor Fusion

Author

Emmanuel Domínguez Ramón, Raquel Diaz Hernandez, and Leopoldo Altamirano Robles

Subjects
Computational Theory and Mathematics, Artificial Intelligence, Computer Networks and Communications, Modeling and Simulation, Computer Science (miscellaneous), Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design
Abstract

Considering the continued drive of human needs besides the constant improvement of technology, it is convenient to develop techniques that enhance the communication between computers and humans in the most intuitive ways as possible. The possibility to automatically recognize human gestures using artificial vision among other kind sensorsallows to explore a whole range of interaction applications to control and interact with environments. Nowadays, most of approaches for gesture recognition using sensors agree in the use of vision, myography and movement devices applied to robotic, medical and industrial applications. In the context of this work, we study the principles of using both vision andbody contact sensing applied to automatic classification of a human gesture set. For this, two different approaches are evaluated: Feed-forward Neural Networks and Hidden Markov Models. These models are studied and implemented for the recognition up to eight different human hand gestures commonly applied in collaborative robotics tasks. In our tests,we conclude the effectiveness of combining the information of two different sort of devices for human gesture recognition reaching accuracy rates up to 95.05% for a whole proposed Hand-gesture set.

Published
2022
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2. Horizontal muon track identification with neural networks in HAWC

Author

Ian Herzog, Lukas Nellen, Hao Zhou, Ramiro Torres Escobedo, Segev BenZvi, Nicola Omodei, Samridha Kunwar, Arturo Iriarte, Israel Martinez Castellanos, David Kieda, Ke Fang, Hannah Jhee, Josefa Becerra Gonzalez, Harm Schoorlemmer, Jesús Martínez-Castro, Michael Newbold, Francisco Salesa Greus, Fernando Garfias, J. A. Goodman, Joshua Wood, David Berley, Nissim Illich Fraija, Alison Peisker, Thomas Weisgarber, Andrew James Smith, Xiaojie Wang, Henrike Fleischhack, Rishi Babu, Kelly Malone, Jason Fan, Humberto Ibarguen Salazar, Rhiannon Turner, Chad Brisbois, Vikas Joshi, Kristi Engel, Dezhi Huang, Diego Garcia Aguilar, Vincent Marandon, Alberto Carramiñana, Brenda Dingus, Mehr Nisa, Raquel Diaz Hernandez, Ian James Watson, Vardan Baghmanyan, José Roberto Angeles Camacho, Daniel Omar Avila Rojas, Hawc, Miguel Mostafa, Dirk Lennarz, Jorge Luis Flores, Cederik León de León, Ibrahim Torres, Joe Lundeen, Gilgamesh Luis-Raya, Maria Magdalena González, Pedro Miranda-Romagnoli, Kirsten Tollefson, Eucario Gonzalo Pérez Pérez, Andrea Albert, Hugo Alberto Ayala Solares, Eduardo Moreno Barbosa, Roberto Noriega-Papaqui, Eduardo de la Fuente, Gwenael Giacinti, Filiberto Hueyotl-Zahuantitla, Juan Carlos Arteaga Velazquez, Guillermo García-Torales, Arun Babu Kollamparambil, Robert Ellsworth, Meghan Tanner, Gerd J. Kunde, Colas Rivière, Hermes Leon Vargas, Oscar Chaparro-Amaro, Umberto Cotti, Arnulfo Zepeda, Sabrina Casanova, Ruben Lopez-Coto, Brian Fick, Pooja Surajbali, Andrés Sandoval, Lorenzo Diaz, Laura Olivera-Nieto, Yunior Pérez Araujo, Anna Lia Longinotti, Gus Sinnis, Fernando Ureña-Mena, Ernesto Belmont-Moreno, Jim Hinton, María Catalina Espinoza Hernández, Jorge Antonio Morales Soto, Tomás Capistrán, Hazal Goksu, Gaurang Yodh, Sergio Hernández Cadena, Brian Humensky, Jorge Cotzomi, Anushka Udara Abeysekara, Michelle Hui, Elijah Willox, César Alvarez, Jose Luis García-Luna, Ignacio Taboada, José Serna-Franco, Ruben Alfaro, Karen S. Caballero Mora, Jason Lee, J. T. Linnemann, Daniel Rosa-Gonzalez, Michael Schneider, Luis Villaseñor, Felix Werner, Ahron S. Barber, Jose Andres Garcia-Gonzalez, John Matthews, Mora Durocher, Petra Hüntemeyer, Mateo Fernandez Alonso, Oscar Martinez, Wayne Robert Springer, Amid Nayerhoda, James Ryan, Binita Hona, Sara Coutiño de Leon, Alejandro Lara, Michael DuVernois, Juan de Dios Álvarez Romero, William H. Lee, Julie McEnery, Juan Carlos Díaz Vélez, Chang Dong Rho, Armelle Jardin-Blicq, J. Patrick Harding, Humberto Martínez Huerta, Edna Ruiz-Velasco, and HAWC Collaboration

Subjects
HAWC - Abteilung Hinton, Physics - Instrumentation and Detectors, Muon, Artificial neural network, Computer science, business.industry, Track (disk drive), Computer Science::Neural and Evolutionary Computation, FOS: Physical sciences, Instrumentation and Detectors (physics.ins-det), Identification (information), Computer vision, Artificial intelligence, Astrophysics - Instrumentation and Methods for Astrophysics, business, Instrumentation and Methods for Astrophysics (astro-ph.IM)
Abstract

Nowadays the implementation of artificial neural networks in high-energy physics has obtained excellent results on improving signal detection. In this work we propose to use neural networks (NNs) for event discrimination in HAWC. This observatory is a water Cherenkov gamma-ray detector that in recent years has implemented algorithms to identify horizontal muon tracks. However, these algorithms are not very efficient. In this work we describe the implementation of three NNs: two based on image classification and one based on object detection. Using these algorithms we obtain an increase in the number of identified tracks. The results of this study could be used in the future to improve the performance of the Earth-skimming technique for the indirect measurement of neutrinos with HAWC., Comment: 8 pages, 6 figures, Proceedings of the 37th International Cosmic Ray Conference (ICRC 2021)

Published
2022

3. Evaluation of Deformable Convolution: An Investigation in Image and Video Classification

Author

Andrea Burgos Madrigal, Victor Romero Bautista, Raquel Díaz Hernández, and Leopoldo Altamirano Robles

Subjects
computer vision, image/video analysis, deformable neural networks, Mathematics, QA1-939
Abstract

Convolutional Neural Networks (CNNs) present drawbacks for modeling geometric transformations, caused by the convolution operation’s locality. Deformable convolution (DCON) is a mechanism that solves these drawbacks and improves the robustness. In this study, we clarify the optimal way to replace the standard convolution with its deformable counterpart in a CNN model. To this end, we conducted several experiments using DCONs applied in the layers that conform a small four-layer CNN model and on the four-layers of several ResNets with depths 18, 34, 50, and 101. The models were tested in binary balanced classes with 2D and 3D data. If DCON is used on the first layers of the proposal of model, the computational resources will tend to increase and produce bigger misclassification than the standard CNN. However, if the DCON is used at the end layers, the quantity of Flops will decrease, and the classification accuracy will improve by up to 20% about the base model. Moreover, it gains robustness because it can adapt to the object of interest. Also, the best kernel size of the DCON is three. With these results, we propose a guideline and contribute to understanding the impact of DCON on the robustness of CNNs.

Published
2024
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4. A New Algorithm for Detecting GPN Protein Expression and Overexpression of IDC and ILC Her2+ Subtypes on Polyacrylamide Gels Associated with Breast Cancer

Author

Jorge Juarez-Lucero, Maria Guevara-Villa, Anabel Sanchez-Sanchez, Raquel Diaz-Hernandez, and Leopoldo Altamirano-Robles

Subjects
sodium dodecyl sulfate–polyacrylamide gel electrophoresis, image analysis, protein band, molecular weight, image segmentation, binary mask, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95
Abstract

Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) is used to identify protein presence, absence, or overexpression and usually, their interpretation is visual. Some published methods can localize the position of proteins using image analysis on images of SDS-PAGE gels. However, they cannot automatically determine a particular protein band’s concentration or molecular weight. In this article, a new methodology to identify the number of samples present in an SDS-PAGE gel and the molecular weight of the recombinant protein is developed. SDS-PAGE images of different concentrations of pure GPN protein were created to produce homogeneous gels. Then, these images were analyzed using the developed methodology called Image Profile Based on Binarized Image Segmentation (IPBBIS). It is based on detecting the maximum intensity values of the analyzed bands and produces the segmentation of images filtered by a binary mask. The IPBBIS was developed to identify the number of samples in an SDS-PAGE gel and the molecular weight of the recombinant protein of interest, with a margin of error of 3.35%. An accuracy of 0.9850521 was achieved for homogeneous gels and 0.91736 for heterogeneous gels of low quality.

Published
2024
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