Articles
<![CDATA[Bio-inspired route estimation in cognitive radio networks ]]>
<![CDATA[Miguel Tuberquia]]>;
<![CDATA[Hans Lopez-Chavez]]>;
<![CDATA[Cesar Hernandez]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 3: June 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v10i3.pp3095-3107
<![CDATA[Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.]]>
<![CDATA[Spectrum sharing in cognitive radio networks ]]>
<![CDATA[Julian Martinez]]>;
<![CDATA[Cesar Hernandez]]>;
<![CDATA[Luis Pedraza]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 6: December 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v10i6.pp6472-6483
<![CDATA[Cognitive radio networks are the next step to tackle scarcity in wireless networks given the increasing demand of radioelectric spectrum where the proposed solution is to share said resource to improve this situation. In the present article, a review of the current state of spectrum sharing in cognitive radio networks. To achieve this purpose, the articles published over the last 4 years on the matter were reviewed including topics such as mobile networks and TV. Some studies and simulations proposed to share the spectrum is shown. The current state of the studies reveals that there has been significant progress in this research area yet it is necessary to continue similar studies and set in motion different schemes.]]>
<![CDATA[Evaluation of deep neural network architectures in the identification of bone fissures ]]>
<![CDATA[Fredy Martinez]]>;
<![CDATA[César Hernández]]>;
<![CDATA[Fernando Martínez]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v18i2.14754
<![CDATA[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.]]>
<![CDATA[An overview of internet of things ]]>
<![CDATA[Sebastian Villamil]]>;
<![CDATA[Cesar Hernandez]]>;
<![CDATA[Giovanny Tarazona]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v18i5.15911
<![CDATA[The internet of things is an emerging technology that is currently present in most processes and devices, allowing to improve the quality of life of people and facilitating the access to specific information and services. The main purpose of the present article is to offer a general overview of internet of things, based on the analysis of recently published work. The added value of this article lies in the analysis of the main recent publications and the diversity of applications of internet of things technology. As a result of the analysis of the current literature, internet of things technology stands out as a facilitator in business and industrial performance but above all in improving the quality of life. As a conclusion to this document, the internet of things is a technology that can overcome the challenges in terms of security, processing capacity and data mobility, as long as the development related to other technologies follows its expected course.]]>
<![CDATA[Identifier of human emotions based on convolutional neural network for assistant robot ]]>
<![CDATA[Fredy Martinez]]>;
<![CDATA[César Hernández]]>;
<![CDATA[Angélica Rendón]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v18i3.14777
<![CDATA[This paper proposes a solution for the problem of continuous prediction in real-time of the emotional state of a human user from the identification of characteristics in facial expressions. In robots whose main task is the care of people (children, sick or elderly people) is important to maintain a close relationship man-machine, anld a rapid response of the robot to the actions of the person under care. We propose to increase the level of intimacy of the robot, and its response to specific situations of the user, identifying in real time the emotion reflected by the person's face. This solution is integrated with algorithms of the research group related to the tracking of people for use on an assistant robot. The strategy used involves two stages of processing, the first involves the detection of faces using HOG and linear SVM, while the second identifies the emotion in the face using a CNN. The strategy was completely tested in the laboratory on our robotic platform, demonstrating high performance with low resource consumption. Through various controlled laboratory tests with different people, which forced a certain emotion on their faces, the scheme was able to identify the emotions with a success rate of 92%.]]>
<![CDATA[Failed handoffs in collaborative Wi-Fi networks ]]>
<![CDATA[Cesar Hernandez]]>;
<![CDATA[Diego Giral]]>;
<![CDATA[C. Salgado]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v18i2.14894
<![CDATA[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.]]>
<![CDATA[Cognitive radio for TVWS usage ]]>
<![CDATA[Diego Pineda]]>;
<![CDATA[Cesar Hernandez]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v17i6.13111
<![CDATA[Spectrum scarcity is an emerging issue in wireless communication systems due to the increasing demand of broadband services like mobile communications, wireless internet access, IoT applications, among others. The migration of analog TV to digital systems (a.k.a. digital TV switchover) has led to the release of a significant spectrum share that can be used to support said additional services. Likewise, TV white spaces emerge as spectral opportunities that can also be explored. Hence, cognitive radio (CR) presents itself as a feasible approach to efficiently use resources and exploit gaps within the spectrum. The goal of this paper is to unveil the state of the art revolving around the usage of TV white spaces, including some of the most important methods developed to exploit such spaces, upcoming opportunities, challenges for future research projects, and suggestions to improve current models.]]>
<![CDATA[Spectral opportunity selection based on the hybrid algorithm AHP-ELECTRE ]]>
<![CDATA[Carlos Perdomo]]>;
<![CDATA[Cesar Hernandez]]>;
<![CDATA[Diego Giral]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v18i6.16372
<![CDATA[Due to an ever-growing demand for spectrum and the fast-paced developmentof wireless applications, technologies such as cognitive radio enablethe efficient use of the spectrum. The objective of the present article is todesign an algorithm capable of choosing the best channel for data transmission.It uses quantitative methods that can modify behavior by changing qualityparameters in the channel. To achieve this task, a hybrid decision-makingalgorithm is designed that combinesanalytical hierarchy process(AHP)algorithms and adjusts the weights of each channel parameter, using a prioritytable. TheElimination Et Choix Tranduisant La Realité(ELECTRE)algorithm processes the information from each channel through a weightmatrix and then delivers the most favorable result for the transmitted data. Theresults reveal that the hybrid AHP-ELECTRE algorithm has a suitableperformance, which improves the throughput rate by 14% compared to similaralternatives.]]>
<![CDATA[Throughput in cooperative wireless networks ]]>
<![CDATA[Diego Armando Giral Ramirez]]>;
<![CDATA[Cesar Hernandez]]>;
<![CDATA[Fredy Martinez]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v9i2.2025
<![CDATA[Cognitive radio networks emerge as a solution to fixed allocation issues and spectrum scarcity through the dynamic access to spectrum. In cognitive networks, users must make intelligent decisions based on spectrum variation and actions taken by other users. Under this dynamic, cooperative systems can significantly improve quality of service parameters. This article presents the comparative study of the multi-criteria decision-making algorithms SAW and FFAHP through four levels of cooperation (10%, 20%, 50%, 80% y 100%) established between secondary users. The results show the performance evaluation obtained through of simulations and experimental measurements. The analysis is carried out based on throughput, depending on the class of service and the type of traffic.]]>
<![CDATA[Analysis and assessment software for multi-user collaborative cognitive radio networks ]]>
<![CDATA[Diego Giral]]>;
<![CDATA[Cesar Hernandez]]>;
<![CDATA[Fredy Martinez]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v12i4.pp4507-4520
<![CDATA[Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features.]]>
