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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 6,301 Documents
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Depth-DensePose: an efficient densely connected deep learning model for camera-based localization Amr Abozeid; Hesham Farouk; Samia Mashali
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2792-2801

Abstract

Camera/image-based localization is important for many emerging applications such as augmented reality (AR), mixed reality, robotics, and self-driving. Camera localization is the problem of estimating both camera position and orientation with respect to an object. Use cases for camera localization depend on two key factors: accuracy and speed (latency). Therefore, this paper proposes Depth-DensePose, an efficient deep learning model for 6-degrees-of-freedom (6-DoF) camera-based localization. The Depth-DensePose utilizes the advantages of both DenseNets and adapted depthwise separable convolution (DS-Conv) to build a deeper and more efficient network. The proposed model consists of iterative depth-dense blocks. Each depth dense block contains two adapted DS-Conv with two kernel sizes 3 and 5, which are useful to retain both low-level as well as high-level features. We evaluate the proposed Depth-DensePose on the Cambridge Landmarks dataset, which shows that the Depth-DensePose outperforms the performance of related deep learning models for camera based localization. Furthermore, extensive experiments were conducted which proven the adapted DS-Conv is more efficient than the standard convolution. Especially, in terms of memory and processing time which is important to real-time and mobile applications.
Optimization of automobile active suspension system using minimal order Sairoel Amertet Finecomes; Fisseha L. Gebre; Abush M. Mesene; Solomon Abebaw
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2378-2392

Abstract

This paper presents an analysis and design of linear quadratic regulator for reduced order full car suspension model incorporating the dynamics of the actuator to improve system performance, aims at benefiting: Ride comfort, long life of vehicle, and stability of vehicle. Vehicle’s road holding or handling and braking for good active safety and driving pleasure, and keeping vehicle occupants comfortable and reasonably well isolated from road noise, bumps, and vibrations are become a key research area conducted by many researchers around the globe. Different researchers were tested effectiveness of different controllers for different vehicle model without considering the actuator dynamics. In this paper full vehicle model was reduced to a minimal order using minimal realization technique. The entire system responses were simulated in MATLAB/Simulink environment. The effectiveness of linear quadratic regulator controller was compared for the system model with and without actuator dynamics for different road profiles. The simulation results were indicated that percentage reduction in the peak value of vertical and horizontal velocity for the linear quadratic regulator with actuator dynamics relative to linear quadratic regulator without actuator dynamics was 28.57%. Overall simulation results were demonstrated that proposed control scheme has able to improve the effectiveness of the car model for both ride comfort and stability.
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.
Increasing electrical grid stability classification performance using ensemble bagging of C4.5 and classification and regression trees Firman Aziz; Armin Lawi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2955-2962

Abstract

The increasing demand for electricity every year makes the electricity infrastructure approach the maximum threshold value, thus affecting the stability of the electricity network. The decentralized smart grid control (DSGC) system has succeeded in maintaining the stability of the electricity network with various assumptions. The data mining approach on the DSGC system shows that the decision tree algorithm provides new knowledge, however, its performance is not yet optimal. This paper poses an ensemble bagging algorithm to reinforce the performance of decision trees C4.5 and classification and regression trees (CART). To evaluate the classification performance, 10-fold cross-validation was used on the grid data. The results showed that the ensemble bagging algorithm succeeded in increasing the performance of both methods in terms of accuracy by 5.6% for C4.5 and 5.3% for CART.
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.
Validity of a graph-based automatic assessment system for programming assignments: human versus automatic grading Zougari, Soundous; Tanana, Mariam; Lyhyaoui, Abdelouahid
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2867-2875

Abstract

Programming is a very complex and challenging subject to teach and learn. A strategy guaranteed to deliver proven results has been intensive and continual training. However, this strategy holds an extra workload for the teachers with huge numbers of programming assignments to evaluate in a fair and timely manner. Furthermore, under the current COVID-19 distance teaching circumstances, regular assessment is a fundamental feedback mechanism. It ensures that students engage in learning as well as determines the extent to which they reached the expected learning goals, in this new learning reality. In sum, automating the assessment process will be particularly appreciated by the instructors and highly beneficial to the students. The purpose of this paper is to investigate the feasibility of automatic assessment in the context of computer programming courses. Thus, a prototype based on merging static and dynamic analysis was developed. Empirical evaluation of the proposed grading tool within an introductory C-language course has been presented and compared to manually assigned marks. The outcomes of the comparative analysis have shown the reliability of the proposed automatic assessment prototype.

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