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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 112 Documents
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Search results for , issue "Vol 12, No 4: August 2022" : 112 Documents clear
Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion Mir Suhail Alam; Malik Arman Morshidi; Teddy Surya Gunawan; Rashidah Funke Olanrewaju; Fatchul Arifin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3620-3631

Abstract

Augmented reality (AR) applications have become increasingly ubiquitous as it integrates virtual information such as images, 3D objects, video, and more to the real world, which further enhances the real environment. Many researchers have investigated the augmentation of the 3D object on the digital screen. However, certain loopholes exist in the existing system while estimating the object’s pose, making it inaccurate for mobile augmented reality (MAR) applications. Objects augmented in the current system have much jitter due to frame illumination changes, affecting the accuracy of vision-based pose estimation. This paper proposes to estimate the pose of an object by blending both vision-based techniques and micro electrical mechanical system (MEMS) sensor (gyroscope) to minimize the jitter problem in MAR. The algorithm used for feature detection and description is oriented FAST rotated BRIEF (ORB), whereas to evaluate the homography for pose estimation, random sample consensus (RANSAC) is used. Furthermore, gyroscope sensor data is incorporated with the vision-based pose estimation. We evaluated the performance of augmenting the 3D object using the techniques, vision-based, and incorporating the sensor data using the video data. After extensive experiments, the validity of the proposed method was superior to the existing vision-based pose estimation algorithms.
Programmable timer triggered energy harvesting wireless sensor-node using long range radio access technology Prakash Guragain, Deepesh; Kaji Budhathoki, Ram; Ghimire, Pramod
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3869-3881

Abstract

Despite widespread deployment of wireless sensor networks (WSN) in remote and inapproachable locations, energy consumption/storage of WSN hindered its adoption. Similarly, the battery-powered sensor nodes are of no use once the battery is depleted. To overcome this limitation, energy harvesting is one of the key techniques. In this paper, an almost perpetual self-powered sensor node is proposed. This sensor node uses a solar panel to harvest energy while the entire energy management is accomplished by BQ25570. Similarly, a super-capacitor is used as an energy storage unit with long range radio access (LoRa) as a transceiver unit. We measured the power generated/consumed continuously for 15 days with a transmission interval of 10 minutes. The result shows that this sensor node can potentially last for more than 7 days even at a low illuminance. Considering periodic wakeup at every 10 seconds with a sleep interval of 3 sec, a timer-triggered mechanism saves approximately 595 milliwatts of energy in one day compared to a deep-sleep mechanism. Furthermore, it is found that the application of the novel idea of external timer-driven technology in sensor node reduces energy consumption and provides a much efficient power optimization mechanism compared to the deep sleep mechanism that prevailed in WSNs technology.
Automated machine learning: the new data science challenge Slimani, Ilham; Slimani, Nadia; Achchab, Said; Saber, Mohammed; El Farissi, Ilhame; Sbiti, Nawal; Amghar, Mustapha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4243-4252

Abstract

The world is changing quite rapidly while increasingly tuning into digitalization. However, it is important to note that data science is what most technology is evolving around and data is definitely the future of everything. For industries, adopting a “data science approach” is no longer an option, it becomes an obligation in order to enhance their business rather than survive. This paper offers a roadmap for anyone interested in this research field or getting started with “machine learning” learning while enabling the reader to easily comprehend the key concepts behind. Indeed, it examines the benefits of automated machine learning systems, starting with defining machine learning vocabulary and basic concepts. Then, explaining how to, concretely, build up a machine learning model by highlighting the challenges related to data and algorithms. Finally, exposing a summary of two studies applying machine learning in two different fields, namely transportation for road traffic forecasting and supply chain management for demand prediction where the predictive performance of various models iscompared based on different metrics.
Adoption of serious games by teachers: the analysis method of structure, interface and use Farida Bouroumane; Abderrahim Saaidi; Mustapha Abarkan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4021-4030

Abstract

In this article, we determine how to facilitate the analysis of serious games so that teachers could effectively integrate them in their teaching. The aim is to identify the mechanisms that would make serious games exploitation useful. We propose a method for the analysis of serious games that is based on the separation of their components along three phases. In addition, we set up a platform based on a data analysis process that is composed of six steps that help to set the basis of a verification procedure that targets the content of a game and thus facilitates the work of teachers through effective implementation of serious games as teaching strategies (TIC). The obtained experimental results show that 82.5% of the study participants expressed that the use of the platform has helped them to change their perspective on the need to use serious games as an educational tool.
Effective classification of birds’ species based on transfer learning Mohammed Alswaitti; Liao Zihao; Waleed Alomoush; Ayat Alrosan; Khalid Alissa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4172-4184

Abstract

In recent years, with the deterioration of the earth’s ecological environment, the survival of birds has been more threatened. To protect birds and the diversity of species on earth, it is urgent to build an automatic bird image recognition system. Therefore, this paper assesses the performance of traditional machine learning and deep learning models on image recognition. Also, the help-ability of transfer learning in the field of image recognition is tested to evaluate the best model for bird recognition systems. Three groups of classifiers for bird recognition were constructed, namely, classifiers based on the traditional machine learning algorithms, convolutional neural networks, and transfer learning-based convolutional neural networks. After experiments, these three classifiers showed significant differences in the classification effect on the Kaggle-180-birds dataset. The experimental results finally prove that deep learning is more effective than traditional machine learning algorithms in image recognition as the number of bird species increases. Besides, the obtained results show that when the sample data is small, transfer learning can help the deep neural network classifier to improve classification accuracy.
A novel design of wide and multi-bands 2×2 multiple-input multiple-output antenna for 5G mm-wave applications Shakir Muttair, Karrar; Zuhair Ghazi Zahid, Ali; Ahmed Shareef, Oras; Qasim Kamil, Ahmed Mohammed; Farhan Mosleh, Mahmood
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3882-3890

Abstract

In this paper, we present a new design for a multiple-input multiple-output (MIMO) antenna with four ports operating in wide and multi-millimeter-wave (Mm-Wave) bands for various 5G applications (including the internet of things (IoT), communication devices, and smartphones). The antenna is designed in a rectangular zigzag shape with slots to make the antenna operate at different frequencies. For this, the antenna operates at multiple frequencies from 38 to 62 GHz, so it supports all advanced wireless communication applications. The most important characteristic of the design is its small size and compact structure compared to designs presented by researchers in previous literature so the antenna dimensions for four elements are 29×49 mm2. The antenna performance based on the results obtained from CST Studio Suite is good since the reflection coefficients of the antenna resonate at six main frequencies are 39.128 GHz, 42.992 GHz, 47.384 GHz, 51.536 GHz, 55.472 GHz, and 59.288 GHz. In addition, the isolation value between all antenna elements is ≤30 dB and the diversity gain value for all frequencies is 10 dB. Moreover, a very small value was obtained for the envelope correlation coefficient (ECC) is <4.0576×10−11. Finally, the results indicate a favorable design and potential competitor for all 5G MIMO Mm-Wave applications.
Neural network modeling of agglomeration firing process for polymetallic ores Gulnara Abitova; Vladimir Nikulin; Leila Rzayeva; Tansuly Zadenova; Ali Myrzatay
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4352-4363

Abstract

While processing polymetallic ores at the non-ferrous metallurgy problems arises connecting with the excellence of production and the efficient applying the technological devices-firing furnace and crusher machine. In earlier time, similar questions were solved due to the big practice experiences and using a mathematical modeling method. The mathematical model for optimizing those operating mode is a very complex and hard to calculation. Performing computations is needed too much time and many resources. Because the control of the agglomeration furnaces and other machines are including complex multiparameter processes. The method of the math modeling for optimization the operating mode to the firing furnace is replaced with modeling based on the neural network that is here a new method. The results obtained have shown that proposed methods based on the neural network modeling of metallurgical processes allow determining more accurate and adequate results of calculations than mathematical modeling by the traditional program. The use of new approaches for modeling the technological processes at the non-ferrous metallurgy gives opportunity to enhance an effectiveness of production excellence and to enhance an efficient applying the heat-energy equipment while a firing the sulfide polymetallic ores of non-ferrous metallurgy
An agent-based model to assess coronavirus disease 19 spread and health systems burden Madhavarao Seshadri Narassima; Singallur Palanisamy Anbuudayasankar; Guru Rajesh Jammy; AnanthaPadmanabhan Sankarshana; Rashmi Pant; Lincoln Choudhury; Vijay Yeldandi; Shubham Singh; Denny John
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4118-4128

Abstract

The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.
Microwave characterization of pandanus atrocarpus as potential organic-based dielectric substrate Mohd Aziz Aris; Nurfarahin Miswadi; Suhaila Subahir; Hajar Jaafar; Fatimah Nur Mohd Redzwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3792-3799

Abstract

This study investigated the microwave characterization of a potential organic-based substrate pandanus atrocarpus. Pandanus atrocarpus, also known as "pandan mengkuang", is easily found at riverside and beach areas in Penisular Malaysia. The experiment's objective was to measure dielectric constant and tangent loss using the dielectric coaxial probe method by employing vector network analyzer (VNA) and dielectric probe. Dielectric constant and tangent loss are the crucial parameter in the microwave design. Three different samples of pandanus atrocarpus were measured and analyzed. The result showed that the dielectric constant of the pandanus substrate material depended on the leaves' water content. All experimental results obtained were analyzed, presented, and discussed in this paper.
A novel efficient adaptive-neuro fuzzy inference system control based smart grid to enhance power quality Dharamalla Chandra Sekhar; Pokanati Veera Venkata Rama Rao; Rachamadugu Kiranmayi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3375-3387

Abstract

A novel adaptive-neuro fuzzy inference system (ANFIS) control algorithm-based smart grid to solve power quality issues is investigated in this paper. To improve the steady-state and transient response of the solar-wind and grid integrated system proposed ANFIS controller works very well. Fuzzy maximum power point tracking (MPPT) algorithm-based DC-DC converters are utilized to extract maximum power from solar. A permanent magnet synchronous generator (PMSG) is employed to get maximum power from wind. To maximize both power generations, back-to-back voltage source converters (VSC) are operated with an intelligent ANFIS controller. Optimal power converters are adopted this proposed methodology and improved the overall performance of the system to an acceptable limit. The simulation results are obtained for a different mode of smart grid and non-linear fault conditions and the proven proposed control algorithm works well.

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