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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Joint polar with physical layer network coding and massive MIMO: performance analysis Alza Abduljabbar Mahmood; Abdulkareem Abdulrahman Kadhim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1469-1476

Abstract

Large spectral efficiency, reliability, coverage, and energy efficiency are all major requirements to meet the targets of fifth-generation (5G) and beyond communication networks. The transmitted signal is usually susceptible to errors that reduced reliability and throughput of the system. Physical layer network coding (PLNC) is a promising technology to achieve better throughput, low latency, and high transmission rate. This paper considers the combination of PLNC with polar coding using massive multi-input multi-output (MIMO) system to enhance the transmission reliability by reducing the bit error rate (BER) and improve system reliability. This arrangement is investigated in two-way wireless relay transmission over millimetre wave (mmWave) band channel model. The results of the extensive simulation tests demonstrated improvements in throughput and BER achieved using polar code with PLNC and a sufficient number of antenna elements in the mMIMO system. The BER performance of polar-coded PLNC arrangement outperformed PLNC without coding for 128 and 256 receiving antenna elements in the relay node regardless the number of antenna elements in the user equipment side. The tests showed that the combination of the polar code with PLNC and MIMO system is not encouraging at low Signal-to-noise ratio (SNR).
Electronic datasheet parameter extractor and verifier system for printed circuit board development Ma. Chalina S. Cuntapay; Mark Joseph B. Enojas; Hohn Lois C. Bongao
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp143-151

Abstract

Library components are composed of devices, symbols, and footprints which are the fundamental parts to design and develop printed circuit boards (PCB). In archiving PCB libraries, the creation and checking of the component’s parameters are very important. The checking of footprints and pin configurations from datasheets are manually done which causes delays and downtime when the components do not fit the developed PCB. It was found in a survey that 61.5% of errors come from footprints dimensions. These cause delays in the production and development of PCBs. In this study, a parameter extractor and verifier system were developed to mitigate the error of manual process of archiving library parameters from datasheets. Text recognition and pattern matching algorithms were used for the processing of images from datasheets. The 10-trial tests conducted for each footprint for the extraction of text from the cropped images for standard operating procedures (SOP) series and heat sink thin shrink small outline package (HTSSOP) series were found successful without any error. The checking of footprints and pin configuration was reduced from 45 minutes to 25 minutes. The developed system was evaluated based on user perception by 10 PCB library users which resulted to agree that the system is functional, efficient, and convenient to use.
Vector support machine algorithm applied to the improvement of satisfaction levels in the acquisition of professional skills Omar Chamorro-Atalaya; Orlando Ortega-Galicio; Guillermo Morales-Romero; Darío Villar-Valenzuela; Yeferzon Meza-Chaupis; César León-Velarde; Lourdes Quevedo-Sánchez
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp597-604

Abstract

The study carried out identifies the metricss of the predictive model obtained through the support vector machine (VSM) algorithm, which will be applied in the satisfaction of the acquisition of professional skills of the students of the Professional Engineering Career. As part of the development, the statistical classification tool is used, during the development of the research, it was identified that the predictive model presents as general metrics an accuracy of 82.1%, a precision of 70.72%, a sensitivity of 91.06% and a specificity of 87.60%. Through this model, it contributes significantly to decision-making in relation to improving satisfaction related to the acquisition of professional skills in engineering students, since decision-making by university authorities will have a scientific basis, to take early and timely actions in relation to the predictive elements.
Guided genetic algorithm for solving unrelated parallel machine scheduling problem with additional resources Munther H. Abed; Mohd Nizam Mohmad Kahar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1036-1049

Abstract

This paper solved the unrelated parallel machine scheduling with additional resources (UPMR) problem. The processing time and the number of required resources for each job rely on the machine that does the processing. Each job j needed units of resources (rjm) during its time of processing on a machine m. These additional resources are limited, and this made the UPMR a difficult problem to solve. In this study, the maximum completion time of jobs makespan must be minimized. Here, we proposed genetic algorithm to solve the UPMR problem because of the robustness and the success of genetic algorithm in solving many optimization problems. An enhancement of GA (GGA) was also proposed in this work. Generally, the experiment involves tuning the parameters of GA. Additionally, an appropriate selection of GA operators was also experimented. The GGA is not used to solve the unspecified dynamic UPMR. Besides, the utilization of parameters tuning and operators gave a balance between exploration and exploitation and thus help the search escape the local optimum. Results show that the GGA outperforms the SGA, but it still didn't match the results in the literature. On the other hand, GGA significantly outperforms all methods in terms of CPU time.
Convolutional neural network for the detection of coronavirus based on X-ray images Essam Hammodi Ahmed; Majid Razaq Mohamed Alsemawi; Mohammed Hasan Mutar; Hatem Oday Hanoosh; Ali Hashem Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp37-45

Abstract

Nowadays, the coronavirus disease (COVID-19) is considered an ongoing pandemic that spread quickly in most countries around the world. The COVID-19 causes severe acute respiratory syndrome. Moreover, the technique of chest computed tomography (CT) is a method used in the detection of COVID-19. However, the CT method consumes more time and higher-cost as compared with chest X-ray images. Therefore, this paper presents convolutional neural network (CNN) algorithm in the detection of COVID-19 by using X-ray images. In this method, we have used a balanced image database for the normal (healthy) and COVID-19 subjects. The total number of image database is 188 samples (94 healthy samples and 94 COVID-19 samples). Furthermore, there are several evaluation measurements are used to evaluate the proposed model such as accuracy, precision, specificity, sensitivity, F-measure, G-mean, and others. According to the experimental results, the proposed model obtains 98.68% accuracy, 100% precision, and 100% specificity. Besides, the proposed model achieves 97.37%, 98.67%, and 98.68% for sensitivity, F-measure, and G-mean, respectively. The performance of the proposed model by using CNN algorithm shows promising results in the detection of COVID-19. Also, it has outperformed all its comparatives in terms of detection accuracy.
Traffic sign recognition based color model Majid Dherar Younus; Emad A. Al-Sabawi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1495-1501

Abstract

The main problems that encounter the traffic light detection algorithm have become a handicap to the performance of the algorithms. Problems associated with the change of sign color due to bad weather and illumination changes of sunlight make the detection hard task. In the current work, we discuss these problems and propose a new idea of an efficient real time color sign recognition that relies only on color information. The proposed approach is based on building a red-model in hypothetical red, green, blue (RGB) cube using a large database of traffic signs. The segmentation has been implemented on the traffic signs that hold red color only as an example to illustrate the proposed approach. Results showed that the proposed algorithm is accurate as well as the computational cost is reduced.
Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet Shimaa Janabi; Shaimaa Shukri Abd Alhaleem
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1173-1181

Abstract

Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.
Investigate the optimal power system by using hybrid optimization of multiple energy resources software Ghanim Thiab Hasan; Ali Hlal Mutlaq; Mohammad Omar Salih
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp9-19

Abstract

Increasing the effects of global pollution and the availability of renewable energy sources has push many countries to use reasonable energy sources such as wind and solar energy. This paper presents a case study of evaluating a hybrid renewable energy system by using a hybrid optimization of multiple energy resources (HOMER) software program based on the entered data available from the net for the considered location. The hybrid system consisting of a wind turbine, a photovoltaic system, a battery and a diesel generator. The simulation results are presented in a graphical curves n HOMER software. The obtained results indicate that by using the HOMER simulation program, the optimal design of the hybrid electrical power system for the considered location can be achieved which can help the designer to decide the types and number of the competent required for conducting the intending hybrid electrical power system which results in optimum output power in addition to reducing the overall operating costs.
Teaching through virtual tools and its effect on the perception of student satisfaction Omar Chamorro-Atalaya; Guillermo Morales-Romero; Adrián Quispe-Andía; Darío Villar-Valenzuela; Alicia Jeri-Sandoval; César León-Velarde; Irma Aybar-Bellido
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1599-1606

Abstract

In this context of virtualization, the educational sector has seen the need to make use of technological advances, for this reason it is important to know the perception of students, after having fully adapted distance learning through tools. virtual, which have allowed teachers and students to maintain the pedagogical link at a distance, either through the virtual classroom or through the use of simulation software. In this sense, the objective of this article is to identify the perception of teaching through virtual tools in university students and determine its level of effect or relationship in student satisfaction. This research is approached from a qualitative approach using the Likert measurement method and a content analysis methodology using virtual instruments. The results of the study indicate that 92.9% and 89.3% of the students are satisfied, these results focus on the indicators "absolves the questions asked regarding the use of virtual tools" and "knowledge shown by the teacher in the development of the sessions through virtual tools”. Likewise, the correlational analysis, through Spearman's Chi square test, establishes that there is a high relationship or significant effect of 0.850 between the perception of teaching through virtual tools with student satisfaction.
Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion Mohammed I. Berbek; Ahmed A. Oglah
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp229-242

Abstract

Routers are vital during network congestion. All routers have input and output packet buffers. VVarious congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.

Page 657 of 918 | Total Record : 9174

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