Mirna Muñoz

Dr. Mirna Muñoz

orcid

Research Focus & Metrics

Academic Career Length

20Years (2007-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 May 2013 in Computational and Mathematical Methods in Medicine
Verified
Journal Article
Open access
PDF file
CMClaudia I. Martínez-Alcalá
Claudia I. Martínez-Alcalá
MMMirna Muñoz
Mirna Muñoz
JFJosep Monguet Fierro
Josep Monguet Fierro
In recent years, the advances in information and communication technology (ICT) have resulted in the development of systems and applications aimed at supporting rehabilitation therapy that contributes to enrich patients’ life quality. This work is focused on the improvement of the telemedicine systems with the purpose of customizing therapies according to the profile and disability of patients. For doing this, as salient contribution, this work proposes the adoption of user-centered design (UCD) methodology for the design and development of telemedicine systems in order to support the rehabilitation of patients with neurological disorders. Finally, some applications of the UCD methodology in the telemedicine field are presented as a proof of concept.

5 December 2019 in Processes
Verified
Journal Article
Open access
PDF file
Miguel De-la-TorreMiguel De-la-Torre
Miguel De-la-Torre
Omar Ali ZatarainOmar Ali Zatarain
Omar Ali Zatarain
Himer Avila-GeorgeHimer Avila-George
Himer Avila-George
MMMirna Muñoz
Mirna Muñoz
Jimy OblitasJimy Oblitas
Jimy Oblitas
WSWilson Manuel Castro Silupu
Wilson Manuel Castro Silupu
This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space.

1 April 2021 in Journal of Software: Evolution and Process
Verified
Journal Article
Closed access
PDF file
MMMirna Muñoz
Mirna Muñoz
MRMario Negrete Rodríguez
Mario Negrete Rodríguez

5 August 2010 in Journal of Software: Evolution and Process
Verified
Journal Article
Closed access
PDF file
JCJose Antonio Calvo-Manzano
Jose Antonio Calvo-Manzano
GCGonzalo Cuevas
Gonzalo Cuevas
GGGerzón Gómez
Gerzón Gómez
JMJezreel Mejia
Jezreel Mejia
MMMirna Muñoz
Mirna Muñoz
TFTomas San Feliu
Tomas San Feliu

11 December 2015 in Science of Computer Programming
Verified
Journal Article
Closed access
JMJezreel Mejia
Jezreel Mejia
Edrisi MuñozEdrisi Muñoz
Edrisi Muñoz
MMMirna Muñoz
Mirna Muñoz

12 August 2017
Verified
Book Chapter
Closed access
PDF file
MMMirna Muñoz
Mirna Muñoz
LHLuis Hernández
Luis Hernández
JMJezreel Mejia
Jezreel Mejia
GGGloria Piedad Gasca-Hurtado
Gloria Piedad Gasca-Hurtado
MGMaría Clara Gómez-Alvarez
María Clara Gómez-Alvarez