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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 11, No 4: August 2022" : 64 Documents clear
A modified artificial bee colony based fuzzy motion tracking scheme for mobile robot Abdulkareem Younis Abdalla; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3739

Abstract

In this study a new modified artificial bee colony algorithm for the optimization of the fuzzy control scheme for motion tracking of mobile robot is developed. The modification is based on using some features from the particle swarm optimization algorithm to improve solution quality. The modified artificialbee colony (MABC) balance the exploration and exploitation of the original one. This balancing results in going through the global search space and increases the convergence speed and solution accuracy. MABC is then used for the design of an efficient fuzzy system that perform motion tracking for mobile robot more accurate through minimizing a suitable selected objective function. Results illustrate the high quality of the proposed method.
Simulation and performance evaluation of IEEE802.11 WLAN under different operating conditions Sara Khalaf; Hamid M. Hasan
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3804

Abstract

A wireless local area network (WLAN) based on IEEE802.11 protocol is the essential part for connection to the internet .and it is widely used in our life today and tomorrow. The growth in the number of nodes per small area likes home, office, and so on. This increase in density led to the degradation in performance in terms of throughput and delay experienced by the end-users. So many amendments proposed in order to improve the performance. Then the performance evaluation of WLAN is an important research topic. In this paper, the performance of WLAN working at 2.4GHz and 5 GHz assessed in a home-like area under the widely used applications the assessment is in terms of throughput and delay. The evaluation conducted by using the optimum networks (OPNET) 14.5 modeler simulator. In this work, we concluded that: using a single AP showed degradation in performance due to congestion. Because all nodes use the same transmission channel which showed clearly in the case of the data rate of 6 Mbps in both (a and g) standards. The effectiveness is about (1.1%, 1.3%) respectively. While using two APs improves the performance for the same case to about (28% and 65%) since it mitigates the congestion because each AP uses a different communication channel.
Solid waste recycling and management cost optimization algorithm Mustafa Abdulsatar Noori; Thair Abed Al-Janabi; Sura Abed Sarab Hussien
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3855

Abstract

Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, to find the optimal solution. This method also included multiple adaptive and random variables to guarantee that the solution space was explored and used in various optimization tasks. When all criteria are considered, the results of this study show that the SSA is efficient for least-distance path allocation. The simulation findings reveal a significant improvement over the well-known particle swarm optimization (PSO) algorithm, with recycling and disposal costs decreasing by 10% to 30%.
Car license plate segmentation and recognition system based on deep learning Ghida Yousif Abbass; Ali Fadhil Marhoon
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3434

Abstract

Artificial intelligence techniques and computer vision techniques dealt with the issue of automatic license plate recognition (ALPR) that has many applications in important research field. In this paper, the method of recognizing the license plates of Iraqi cars was applied based on deep learning techniques convolutional neural network (CNN). The two database built to identifying Iraqi car plates. First database includes 2000 images of Arabic numbers and Arabic letters. Second database conations 1200 images of the Arabic names for Iraqi governorates. This paper used image-processing techniques to segmenting the numbers, letters and words from the car license plate images and then convert them into two databases that used to train the two CNN. These training CNN used to recognizing the vocabulary of the car license plate. The success rate of the numbers, letters and words recognition was 98%. The overall rate of success of this proposed system in all stages was 97%.
Crowd evacuation navigation for evasive maneuver of brownian based dynamic obstacles using reciprocal velocity obstacles Susi Juniastuti; Moch Fachri; Fresy Nugroho; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3806

Abstract

This paper presents an approach for evasive maneuver against dynamic obstacles in multi-agent navigation in a crowd evacuation scenario. Our proposed approach is based on reciprocal velocity obstacles (RVO) with a different manner to treat the obstacles. We treat all possible hindrances in velocity space reciprocally thus all collision cones generated by other agents and obstacles are treated in the same RVO manner with the key difference in the effort of avoidance. Our approach assumes that dynamic obstacles bear no awareness of navigation space unlike agents thus the avoidance effort lies on behalf of the mobile agents, creating unmutual effort in an evasive maneuver. We display our approach in an evacuation scenario where a crowd of agents must navigate through an evacuation area trespassing zone filled with dynamic obstacles. These dynamic obstacles consist of random motion built based on Brownian motion thus posses an immense challenge for the mobile agent in order to overcome this hindrance and safely navigate to their evacuation area. Our experimentation shows that 51.1% fewer collisions occurred which is denote safer navigation for agents in approaching their evacuation point.
Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone Annisa Istiqomah Arrahmah; Rissa Rahmania; Dany Eka Saputra
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3784

Abstract

In autonomous drones, the drone’s ability to move depends on the drone’s capacity to know its position, either in relative or absolute position. The Pinhole model is one of the methods to calculate a drone’s relative position based on the triangle similarity concept using a single camera. This method utilizes bounding box information generated from an object detection algorithm. Thus, accuracy of the generated bounding box is crucial, and selection of object detection algorithm is necessary. This paper compares and evaluates machine learning and deep learning object detection methods to determine which method is suitable for distance measurement using a single camera for autonomous drone’s controller based on pinhole model. A novel K-nearest neighbours-based (KNN-based) object detection is constructed to represent the machine learning method while you only look once version 5 (YOLOv5) convolutional neural network (CNN) architecture is selected to represent the deep learning method. A dataset consists of two different classes, with a total of 1520 images, collected from the unmanned aerial vehicle (UAV) camera for training and evaluation purposes. Confusion matrix and intersection over union (IoU)/generalized intersection of union (GIoU) matrix are used to evaluate the performance of both methods. The result of this paper shows the performance of each system and concludes the suitable type of object detection algorithm for the autonomous UAV purpose.
Design of triple band antenna for energy harvesting application Siti Nur Illia Abdullah; Mohd Muzafar Ismail; Jeefferie Abd Razak; Zahriladha Zakaria; Siti Rosmaniza Ab Rashid; Nor Hadzfizah Mohd Radi
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3686

Abstract

Energy harvesting is a fast-expanding topic in many scientific and engineering-related disciplines due to the extreme necessity to discover answers to the world's power challenges. This paper focuses on the design of a novel antenna that will operate at frequencies of 2.45 GHz, 4.5 GHz, and 5.725 GHz. The study shows the improvement of the current triband antenna for energy harvesting applications by varying the antenna parameters. The return loss, the voltage standing wave ratio (VSWR) and the radiation pattern for the antenna at all frequencies 2.45 GHz, 4.5 GHz, and 5.725 GHz have been compared in the results. The scope of this project development comprises the antenna design utilizing simulation software, computer simulation technology (CST) 2019 and vector network analyzer (VNA) test instrument for physical testing. During the design process, fundamental antenna characteristics were estimated and validated to determine optimal performance of an antenna for this project.
Transformer induced enhanced feature engineering for contextual similarity detection in text Dakshinamoorthy Meenakshi; Abdul Rahim Mohamed Shanavas
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3284

Abstract

Availability of large data storage systems has resulted in digitization of information. Question and answering communities like Quora and stack overflow take advantage of such systems to provide information to users. However, as the amount of information stored gets larger, it becomes difficult to keep track of the existing information, especially information duplication. This work presents a similarity detection technique that can be used to identify similarity levels in textual data based on the context in which the information was provided. This work presents the transformer based contextual similarity detection (TCSD), which uses a combination of bidirectional encoder representations from transformers (BERT) and similarity metrics to derive features from the data. The derived features are used to train the ensemble model for similarity detection. Experiments were performed using the Quora question similarity data set. Results and comparisons indicate that the proposed model exhibits similarity detection with an accuracy of 92.5%, representing high efficiency.
SiO2@LaOF:Eu3+ in white light emitting diodes optic efficiency enhancement Phuc Dang Huu; Guo Feng Luo; Minh Pham Quang
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.4065

Abstract

The earliest intense red hue compound of SiO2@LaOF:Eu3+ core-shell nanostructures (NS) was created utilizing a basic solvothermal technique and heat processing. The produced core-shell particles are spherical, non-agglomerated, and have restricted size dispersion. Photoluminescence (PL) radiation spectra exhibit sharp maximums in 593, 611, and 650 nm, corresponding with 5D0 -- more than 7FJ (J=0, 1, 2) Eu3+ conversions. The Judd-Ofelt (J-O) hypothesis helps determine the spectrum strength indices and Eu-O ligand activities. The CIE coordinates are x=0.63, y=0.36, nearly equal the NTSC coordinates which are x=0.67, y=0.33. Because of the CCT level of 3475 K, which is lower than 5000 K, this phosphor is appropriate for warm light-emitting diodes. To visualize latent fingermarks both porous and non-porous substrates, the fluorescent labeling marker adapted core-cover SiO2 (coat III)@LaOF:Eu3+ (5 mol%) was utilized. With no background influence, the fingermarks obtained are exceedingly sensible and exclusive, permitting for fingerprint ridge features ranging from level-I to level-III. The findings indicate the significant enhancement in the illumination of corecover NS as a responsive operational nanoparticle for increased forensics and firm status illuminating implementations.
Compact automatic modulation recognition using over-the-air signals and FOS features Emmanuel Adetiba; Folarin Joseph Olaloye; Abdultaofeek Abayomi; Nasir Faruk; Sibusiso Moyo; Obiseye Obiyemi; Surendra Thakur
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.4119

Abstract

The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second-generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration.

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