Mario-Alberto Ibarra-Manzano
Universidad de Guanajuato, Institut National Polytechnique de Toulouse, Laboratoire d'Analyse et d'Architecture des Systèmes
Papers
11
Total Citations
347
H-Index
7
About
Mario-Alberto Ibarra-Manzano is a leading researcher at the intersection of robotics, computer vision, and brain-computer interfaces, whose work has garnered over 340 citations. His primary contributions span human activity recognition, autonomous robot navigation, and bio-signal processing. Notably, his 2021 paper on "Human activity recognition using temporal convolutional neural network architecture" (109 citations) established new benchmarks for real-time movement analysis. Earlier foundational work on "Detecting objects using color and depth segmentation with Kinect sensor" (94 citations) revolutionized how robots perceive and interact with their environment by fusing visual and depth data. Ibarra-Manzano has also made pioneering advances in biologically-inspired robotics, developing central pattern generator (CPG) systems using spiking neurons for hexapod locomotion (63 citations) and creating recurrent-convolutional architectures for motor imagery classification to control hexapod robots (31 citations). His expertise extends to FPGA-based real-time systems for texture analysis and color segmentation, as well as fiber optic sensor technology for joint angle measurement. Through his work on obstacle detection, probabilistic mapping, and self-parking algorithms, Ibarra-Manzano continues to push the boundaries of autonomous mobile robotics and assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2Detecting objects using color and depth segmentation with Kinect sensor94 citations · 2012
- 3A CPG system based on spiking neurons for hexapod robot locomotion63 citations · 2015
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- 10Intelligent Algorithm for Parallel Self-Parking Assist of a Mobile Robot4 citations · 2012