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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics

Fredy H. Martínez S.

Universidad Distrital Francisco José de Caldas

Author-ID : 2455195

Computer Science & IT Electrical & Electronics Engineering

Published : 5 Documents Claim Missing Document

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Bacterial quorum sensing applied to the coordination of autonomous robot swarms Fredy H. Martinez S.; Fernando Martinez S.; Holman Montiel A.
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

This paper proposes a strategy for the coordination of a swarm of robots in an unknown environment. The basic idea is to achieve the autonomous movement of the group from an initial region to a target region avoiding obstacles. We use a behavior model similar to bacterial Quorum Sensing (QS) as a technique for the coordination of robots. This behavior has been described as a key element in the interaction between bacteria, and we use it as a basic tool for local interaction, both between the robot and between the robot and the environment. The movement of the swarm of robots, or multi-agent robotic system, is shown as an emerging behavior resulting from the interaction of agents (in the context of artificial intelligence) from basic rules of behavior. The proposed strategy was successfully evaluated by simulation on a set of robots.

A gesture recognition system for the Colombian sign language based on convolutional neural networks Fredy H. Martínez S.; Faiber Robayo Betancourt; Mario Arbulú
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Sign languages (or signed languages) are languages that use visual techniques, primarily with the hands, to transmit information and enable communication with deaf-mutes people. This language is traditionally only learned by people with this limitation, which is why communication between deaf and non-deaf people is difficult. To solve this problem we propose an autonomous model based on convolutional networks to translate the Colombian Sign Language (CSL) into normal Spanish text. The scheme uses characteristic images of each static sign of the language within a base of 24000 images (1000 images per category, with 24 categories) to train a deep convolutional network of the NASNet type (Neural Architecture Search Network). The images in each category were taken from different people with positional variations to cover any angle of view. The performance evaluation showed that the system is capable of recognizing all 24 signs used with an 88% recognition rate.

Co-Authors Faiber Robayo Betancourt Fernando Martínez S. Fernando Martínez Santa Holman Montiel A. Holman Montiel Ariza Mario Arbulú

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Journal : Bulletin of Electrical Engineering and Informatics