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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Cesar Hernandez
Universidad Distrital Francisco Jose de Caldas
Computer Science & IT Electrical & Electronics Engineering

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Spectrum allocation model for cognitive wireless networks based on the artificial bee colony algorithm Cesar Hernandez; Jorge Rodriguez; Diego Giral
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp257-266

Abstract

Cognitive radio through dynamic spectrum allocation allows an efficient use of the radio-electric spectrum. It is a key subject for the performance of cognitive radio networks. The purpose of the present article is to develop a spectrum allocation model for cognitive wireless networks based on the Artificial Bee Colony algorithm and assess its performance in spectrum occupancy traces obtained from monitoring the spectrum using the energy detection technique. Results show a reduction in the number of spectral handoff with no excessive execution times.
Acoustic event characterization for service robot using convolutional networks Fernando Martinez; Fredy Martinez; Cesar Hernandez
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6684-6696

Abstract

This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
Co-Authors Angélica Rendón C. Salgado Carlos Perdomo Diego Armando Giral Ramirez Diego Giral Diego Pineda Fernando Martinez Fredy Martinez Giovanny Tarazona Hans Lopez-Chavez Jorge Rodriguez Julian Martinez Luis Pedraza Miguel Tuberquia Sebastian Villamil