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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) 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.
Failed handoffs in collaborative Wi-Fi networks Cesar Hernandez; Diego Giral; C. Salgado
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14894

Abstract

Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels.
Evaluation of deep neural network architectures in the identification of bone fissures Fredy Martinez; César Hernández; Fernando Martínez
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14754

Abstract

Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model.
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