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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
Analysis of the visualizing changes in radar time series using the REACTIV method through satellite imagery Hamood Shehab Hamid; Raad Farhood Chisab
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.pp3770-3780

Abstract

A visualizing temporal stack of synthetic aperture radar (SAR) images are presented in this work, the method is called REACTIV, which enabled us to highlight color zones that have undergone change over the detected period of time. This work has been widely tested using Google earth engine (GEE) platform, this method depends on the hue-saturation-value (HSV) of visualizing space and supports estimation only in the time domain; the method does not support the spatial estimation. The coefficient of temporal coefficient variation is coded depending on the saturation color, of which several statistical properties are described. The limitations are studied, and some applications are implemented in this study.
Open distance learning simulation-based virtual laboratory experiences during COVID-19 pandemic Iza Sazanita Isa; Hasnain Abdullah; Nazirah Mohamat Kasim; Noor Azila Ismail; Zafirah Faiza
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.pp4042-4053

Abstract

The widespread of coronavirus disease 2019 (COVID-19) pandemic led to a discovery that open distance learning (ODL) has turned out to be the only choice for teaching and learning by most institution (s) of higher learning (IHLs). In Malaysia, ODL is considered a new approach as physical laboratory practice has always been conducted for laboratory courses. This is a quantitative study which explores the perceptions of e-Lab among the students of bachelor’s in electrical and electronic engineering (EE) by focusing on the effectiveness and readiness in conducting the e-Lab. Simulation-based model is proposed for conducting the e-Lab using an interactive media and validated with the final score performance. With the future goals of improving the e-Lab in terms of delivering methods and engaging mediums between students and laboratory instructor, this study also discovered the levels of response from students’ perception to substitute the conventional laboratory by providing an equivalent and comparable learning experiences of the students.
Accelerometer-based elderly fall detection system using edge artificial intelligence architecture Osama Zaid Salah; Sathish Kumar Selvaperumal; Raed Abdulla
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.pp4430-4438

Abstract

Falls have long been one of the most serious threats to elderly people's health. Detecting falls in real-time can reduce the time the elderly remains on the floor after a fall, hence avoiding fall-related medical conditions. Recently, the fall detection problem has been extensively researched. However, the fall detection systems that use a traditional internet of things (IoT) architecture have some limitations such as latency, high power consumption, and poor performance in areas with unstable internet. This paper intends to show the efficacy of detecting falls in a resource-constrained microcontroller at the edge of the network using a wearable accelerometer. Since the hardware resources of microcontrollers are limited, a lightweight fall detection deep learning model was developed to be deployed on a microcontroller with only a few kilobytes of memory. The microcontroller was installed in a low-power wide-area network based on long range (LoRa) communication technology. Through comparative testing of different lightweight neural networks and traditional machine learning algorithms, the convolutional neural network (CNN) has been shown to be the most suited, with 95.55% accuracy. The CNN model reached inference times lower than 37.84 ms with 61.084 kilobytes storage requirements, which implies the capability to detect fall event in real-time in low-power microcontrollers.
Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control Ayachi Chater, El; Housny, Halima; El Fadil, Hassan
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.pp3607-3619

Abstract

When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.
Design and characterization of polarization reconfigurable heart shape monopole antenna for 2.4 GHz application Abu Hena Murshed; Md. Azad Hossain; Muhammad Asad Rahman; Eisuke Nishiyama; Ichihiko Toyoda
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.pp3808-3819

Abstract

This article represented a heart shape reconfigurable monopole antenna with polarization diversity. The proposed antenna is fed by a 50 Ω microstrip feed line that is printed on a flexible FR-4 (εr=4.4) substrate. The antenna comprises a ring-slot, a cross slot and four positive-intrinsic-negative (PIN) diodes that are soldered on ring slot. Four PIN diodes act as a switch and by controlling these PIN diodes effective current direction is changed hence four various states of polarization are achieved. Four states of polarization such as horizontal linear polarization (H-LP), vertical linear polarization (V-LP), right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) can be switched easily with the help of these PIN diodes and achieved an efficiency of more than 90%. Proposed antenna shows voltage standing wave ratio (VSWR)<2 at all working frequency and -10 dB reflection coefficients (RC) bandwidths (BW) (i.e., S11≤-10 dB) about 32.86% for linear polarization (LP) states while RHCP and LHCP states possess BW of about 31.61% and 31.67% respectively. It also shows axial ratio (AR) BW of 3.41% and 2.44% for RHCP and LHCP, respectively. Besides, the antenna has a well-suited omnidirectional pattern with a positive gain of all working frequency of interest where cross-polarization level is much lower than that of antenna gain.
A new method for self-organized dynamic delay loop associated pipeline with reconfigurable computing system Nandigam Suresh Kumar; Dasari Vijaya Lakshmi; Bejjanki Pooja Sree Prasanna; Dodda Venkata Rama Koti Reddy
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.pp3530-3539

Abstract

The minimization of propagation delay between pipeline stages is very important in wave propagation through pipeline-stages. The propagation delay can be minimized by minimizing the number of stages in a pipeline. In the proposed design a dynamic stage control is imparted in the pipeline. The propagation delay can be optimized in any type of pipeline by controlling number of stages dynamically. The pipeline interpretation helps a lot to overcome the flaws due to not ready sequence (NRS) and synchronization problems. It is observed that, in the pipeline design the basic and actively involved pipeline techniques are concerned with different challenges like clock, throughput, cell area, and sizes. As the data throughput increases the number of stages in pipeline also needs to be increased to meet the desired goal. In the case of unpredictable data speed, the definite number of pipeline stages creates severe problems. In this work a dynamic pipeline is integrated where the number of stages is dynamically changing depending up on data speed. In dynamic pipeline technique the circuit cell area of reconfigurable computing system (RCS) will be reduced dynamically at low-speed data transmission. In the high-speed data communication, the data speed is managed and controlled by dynamic delay loops.
Cuckoo algorithm with great deluge local-search for feature selection problems Khalil Alsmadi, Mutasem; Alzaqebah, Malek; Jawarneh, Sana; Brini, Sami; Al-Marashdeh, Ibrahim; Briki, Khaoula; Alrefai, Nashat; Ali Alghamdi, Fahad; Al-Rashdan, Maen T.
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.pp4315-4326

Abstract

Feature selection problem is concerned with searching in a dataset for a set of features aiming to reduce the training time and enhance the accuracy of a classification method. Therefore, feature selection algorithms are proposed to choose important features from large and complex datasets. The cuckoo search (CS) algorithm is a type of natural-inspired optimization algorithms and is widely implemented to find the optimum solution for a specified problem. In this work, the cuckoo search algorithm is hybridized with a local search algorithm to find a satisfactory solution for the problem of feature selection. The great deluge (GD) algorithm is an iterative search procedure, that can accept some worse moves to find better solutions for the problem, also to increase the exploitation ability of CS. The comparison is also provided to examine the performance of the proposed method and the original CS algorithm. As result, using the UCI datasets the proposed algorithm outperforms the original algorithm and produces comparable results compared with some of the results from the literature.
Design, modeling and simulation of perturb and observe maximum power point tracking for a photovoltaic water pumping system Hilali, Abdelilah; Mardoude, Yahya; Ben Akka, Youssef; El Alami, Hassan; Rahali, Abderrafii
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.pp3430-3439

Abstract

Maximum power point tracking (MPPT) is considered one of the important factors in minimizing the installation costs and improving the efficiency of any photovoltaic water pumping system. The MPPT controller is specifically used to extract the maximum available power from the photovoltaic (PV) array. The maximum power can be achieved by using a specific algorithm. This work aims to raise awareness among farmers about the energy benefits available in the region of Meknes in Morocco, the economic gain and the environmental impact applied to the solar pumping system so that it can be generalized. To obtain the maximum power at each moment, a direct current (DC) water pump (SQF 0-6-2) powered by the solar panels (REC_330NP) through a buck converter was adapted. In addition, this study illustrates the theory of operation of the perturb and observe (P&O) algorithm and simulates the evaluation of this algorithm under different operating conditions (temperature and solar irradiation), and showed the advantages of this system that can operate at the optimal power regardless of disturbances.
Analysis and simulation of even-level quasi-Z-source inverter Niltala Sai shanmukha Akshath; Avugaddi Naresh; Matcha Nikesh kumar; Mayur Barman; Durgesh Nandan; Tirupathi Abhilash
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.pp3477-3484

Abstract

This research proposes a seven-level inverter with quasi-Z-source boost converters. The proposed topology employs a packed U-cell asymmetrical type multilevel inverter along with front-end quasi-Z-source networks. The quasi networks provide high gain compared to a conventional boost converter. This topology is the most suitable for photovoltaic multi-string applications. The proposed topology has the potential to supply both the alternating current (AC) and direct current (DC) type load. The inverter structure has a lower number of active switches which helps in the reduction of losses and improvement in efficiency. In this paper, the operation principle of a quasi-network and inverter circuit are explained in detail. In addition, the simulation results for various modulation indices are presented. In the MATLAB/Simulink environment, the architecture is proposed by using gated sinusoidal “Pulse width modulation”.
Real-time eyeglass detection using transfer learning for non-standard facial data Jain, Ritik; Goyal, Aashi; Venkatesan, Kalaichelvi
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.pp3709-3720

Abstract

The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.

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