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Ernesto Moya-Albor

Dr. Ernesto Moya-Albor

School of Engineering,  Universidad Panamericana

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Diego Renza, an Electronic Engineer by profession and passionate about research, has dedicated his working life to both the public sector and academia, where he is a professor and researcher at the Universidad Militar Nueva Granada. Diego Renza received the M.Sc. degree in Telecommunications Engineering from National University of Colombia in 2010 and PhD degree in 2015, from the Universidad Politécnica de Madrid, Spain. Dr. Ernesto Moya-Albor is a full professor at the School of Engineering at Universidad Panamericana, Mexico. He received his Ph.D. in Electrical Engineering (Digital Signal Processing) from Universidad Nacional Autónoma de México in 2013. He is a member of the National System of Researchers (CONACYT—SNI I). His research focuses on image processing and computer vision using bio-inspired models, machine learning, assisted living, vision-based robotic navigation, and non-contact vital sign monitoring.

Research Keywords & Expertise

Biomedical Engineering
Fuzzy Logic
machine learning
Motion Analysis
Robotics

Fingerprints

29%
Optical Flow
28%
Hermite transform
19%
Robotics
15%
Motion Magnification
11%
machine learning
6%
HVS

Short Biography

Diego Renza, an Electronic Engineer by profession and passionate about research, has dedicated his working life to both the public sector and academia, where he is a professor and researcher at the Universidad Militar Nueva Granada. Diego Renza received the M.Sc. degree in Telecommunications Engineering from National University of Colombia in 2010 and PhD degree in 2015, from the Universidad Politécnica de Madrid, Spain. Dr. Ernesto Moya-Albor is a full professor at the School of Engineering at Universidad Panamericana, Mexico. He received his Ph.D. in Electrical Engineering (Digital Signal Processing) from Universidad Nacional Autónoma de México in 2013. He is a member of the National System of Researchers (CONACYT—SNI I). His research focuses on image processing and computer vision using bio-inspired models, machine learning, assisted living, vision-based robotic navigation, and non-contact vital sign monitoring.