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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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Analysis of student sentiment during video class with multi-layer deep learning approach Imrus Salehin; Nazmun Nessa Moon; Iftakhar Mohammad Talha; Md. Mehedi Hasan; Farnaz Narin Nur Hasan; Md. Azizul Hakim; A S M Farhan Al Haque
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.pp3981-3993

Abstract

The modern education system is an essential part of the rise of technology. The E-learning education system is not just an experimental system; it is a vital learning system for the whole world over the last few months. In our research, we have developed our learning method in a more effective and modern way for students and teachers. For significant implementation, we are implementing convolutions neural networks and advanced data classifiers. The expression and mood analysis of a student during the onlineclass is the main focus of our study. For output measure, we divide the final output result as attentive, inattentive, understand, and neutral. Showing the output in real-time online class and for sensory analysis, we have used support vector machine (SVM) and OpenCV. The level of 5*4 neural network is created for this work. An advanced learning medium is proposed through our study. Teachers can monitor the live class and different feelings of a student during the class period through this system.
A remote-controlled global navigation satellite system based rover for accurate video-assisted cadastral surveys Paolo Visconti; Marzia Luceri; Ramiro Velazquez; De Fazio Roberto
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.pp3551-3563

Abstract

One of the main tasks of a cadastral surveyor is to accurately determine property boundaries by measuring control points and calculating their coordinates. This paper proposes the development of a remotely-controlled tracking system to perform cadastral measurements. A Bluetooth-controlled rover was developed, including a Raspberry Pi Zero W module that acquires position data from a VBOX 3iSR global navigation satellite system (GNSS) receiver, equipped with a specific modem to download real-time kinematic (RTK) corrections from the internet. Besides, the Raspberry board measures the rover speed with a hall sensor mounted on a track, adjusting the acquisition rate to collect data at a fixed distance. Position and inertial data are shared with a cloud platform, enabling their remote monitoring and storing. Besides, the power supply section was designed to power the different components included in the acquisition section, ensuring 2 hours of energy autonomy. Finally, a mobile application was developed to drive the rover and real-time monitor the travelled path. The tests indicated a good agreement between rover measurements and those obtained by a Trimble R10 GNSS receiver (+0.25% mean error) and proved the superiority of the presented system over a traditional metric wheel.
Extraction of image resampling using correlation aware convolution neural networks for image tampering detection Manjunatha Shivanandappa; Malini M. Patil
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.pp3033-3043

Abstract

Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware convolution neural network (CA-CNN) for extracting resampling features for detecting hybrid tampering attacks. Here the image is resized for detecting tampering under a small-smooth region. The CA-CNN is composed of a three-layer horizontal, vertical, and correlated layer. The correlated layer is used for obtaining correlated resampling feature among horizontal sequence and vertical sequence. Then feature is aggregated and the descriptor is built. An experiment is conducted to evaluate the performance of the CA-CNN model over existing tampering detection methodologies considering the various datasets. From the result achieved it can be seen the CA-CNN is efficient considering various distortions and post-processing attacks such joint photographic expert group (JPEG) compression, and scaling. This model achieves much better accuracies, recall, precision, false positive rate (FPR), and F-measure compared existing methodologies.
Data detection method for uplink massive MIMO systems based on the long recurrence enlarged conjugate gradient Jawarneh, Ahlam; Albataineh, Zaid; Kadoch, Michel
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.pp3911-3921

Abstract

Although the mean square error (MMSE) approach is recognized to be near optimal for uplinking large-scale multiple-input-multiple-output (MIMO) systems, there are certain difficulties in the procedure related to matrix inversion. The long recurrence enlarged conjugate gradient (LRE-CG) approach is proposed in this study as a way to iteratively realize the MMMS algorithm while avoiding the complications of matrix inversion. In addition, a diagonal-approximate starting solution to the LRE-CG approach was used to speed up the conversion rate and reduce the complications required. It has been discovered that the LRE-CG-based approach has the ability to significantly reduce computational complexity. By comparing simulation results, it is clear that this new methodology surpasses well-established wayslike the Neumann series approximation-based method and the Gauss-Siedel iterative method. With a small number of iterations, the suggested approach achieves near-optimal performance of a standard MMSE algorithm.
An efficient application of particle swarm optimization in model predictive control of constrained two-tank system Ahmad Kia Kojouri; Javad Mashayekhi Fard
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.pp3540-3550

Abstract

Despite all the model predictive control (MPC) based solution advantages such as a guarantee of stability, the main disadvantage such as an exponential growth of the number of the polyhedral region by increasing the prediction horizon exists. This causes the increment in computation complexity of control law. In this paper, we present the efficiency of particle swarm optimization (PSO) in optimal control of a two-tank system modeled as piecewise affine. The solution of the constrained final time-optimal control problem (CFTOC) is derived, and then the PSO algorithm is used to reduce the computational complexity of the control law and set the physical parameters of the system to improve performance simultaneously. On the other hand, a new combined algorithm based on PSO is going to be used to reduce the complexity of explicit MPC-based solution CFTOC of the two-tank system; consequently, that the number of polyhedral is minimized, and system performance is more desirable simultaneously. The proposed algorithm is applied in simulation and our desired subjects are reached. The number of control law polyhedral reduces from 42 to 10 and the liquid height in both tanks reaches the desired certain value in 189 seconds. Search time and apply control law in 25 seconds.
Improving the iterative back projection estimation through Lorentzian sharp infinite symmetrical filter Amir Nazren Abdul Rahim; Shahrul Nizam Yaakob; Lee Yeng Seng; Mohd Wafi Nasrudin; Iszaidy Ismail
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.pp2539-2552

Abstract

This study proposed an enhancement technique for improvising the estimation technique in iterative back projection (IBP) by using the Lorentzian error function with a sharp infinite symmetrical filter (SISEF). The IBP estimation is an iteratively based error correction that can minimize the error reconstruction significantly. However, the IBP has a drawback in that it suffers from jaggy and ringing artifacts as a result of the iterative reconstruction method and the absence of edge guidance. Furthermore, because the IBP estimator tended to oscillate at the same solution frequently, numerous iterations were required. Therefore, this study proposed edge enhancement to enhance the estimator by using the combination of the IBP with Lorentzian SISEF to produce a finer high-resolution output image. As a result, the SISEF is used to improvise the estimator by providing high accuracy of edge detail information for enhancing the edge image. At the same time, the Lorentzian error norm helps to increase the robustness of the IBP algorithm from contamination of additional noise and the ringing artifacts.
Joint digital pre-distortion model based on Chebyshev expansion Elham Majdinasab; Abumoslem Jannesari
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.pp3781-3791

Abstract

In this paper, a new low complexity model is proposed for the joint digital pre-distortion of in-phase/quadrature-phase (I/Q) imbalance, local oscillator (LO) leakage, and power amplifier nonlinearity in direct-conversion transmitters (DCTs). In this structure, we proposed a set of orthogonal basis functions based on Chebyshev expansion to attenuate the problem of numerical instability created during the conventional model identification method. This robust joint digital pre-distortion (DPD) utilized the indirect learning architecture and updated the coefficients vector based on the recursive least square (RLS) algorithm. To verify the operation and efficiency of the proposed model, an extensive simulation in MATLAB was carried out. The results showed a significant reduction in the conditional number and the coefficient dispersion of the observation matrix. Furthermore, the power of the signal in the adjacent channel decreased by more than 16 dB for the orthogonal frequency division multiplexing (OFDM), 16 QAM input signal. In comparison to the previous digital pre-distorter models, the proposed DPD builds strong numerical stability with the least coefficients.
Fish classification using extraction of appropriate feature set Usama A. Badawi
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.pp2488-2500

Abstract

The field of wild fish classification faces many challenges such as the amount of training data, pose variation and uncontrolled environmental settings. This research work introduces a hybrid genetic algorithm (GA) that integrates the simulated annealing (SA) algorithm with a back-propagation algorithm (GSB classifier) to make the classification process. The algorithm is based on determining the suitable set of extracted features using color signature and color texture features as well as shape features. Four main classes of fish images have been classified, namely, food, garden, poison, and predatory. The proposed GSB classifier has been tested using 24 fish families with different species in each. Compared to the back-propagation (BP) algorithm, the proposed classifier has achieved a rate of 87.7% while the elder rate is 82.9%.
A novel multi-resonant and wideband fractal antenna for telecommunication applications Ibrahime Hassan Nejdi; Youssef Rhazi; Mustapha Ait Lafkih; Seddik Bri; Lamsalli Mohammed
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.pp3850-3858

Abstract

This letter presents the design, simulation, and measurement of a novel multiband fractal circular antenna for wireless applications. In the antenna design, we used a circular antenna where we took a ring. Then, in the first iteration, we added a new ring divided into two of the same size. For the second iteration, we added a ring of the same size after dividing it into two halves. In the third iteration, we added the third ring of the same size after dividing it into four. Due to the resonator defection, we were able to reduce the size of the starting antenna from 60×70×2 mm3 to 50×50×1.6 mm3, to get the frequency of 2.48 GHz, and we generated new bandwidths with a high gain that reaches 5.02 dB. The proposed antenna radiation characteristics, such as the impedance matching, the gain, the radiation pattern, and the surface current distribution are presented and discussed. We find that the simulated and measured results are in acceptable agreement and affirm the good performance of the proposed antenna. The results obtained affirm that the proposed fractal antenna is a better candidate for integration into wireless communication circuits.
Design and implementation of an oil leakage monitoring system based on wireless network Jamal A. Hameed; Amer T. Saeed; Mohammed M. Sultan; Musa A. Hameed
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.pp2626-2635

Abstract

Monitoring pipeline leaks is one of the recent hot studies. Leakage may occur because of time corrosion in the tube raw materials. To reduce the negative consequences of this leak, an effective leak detection system is used to prevent serious leakage accidents and damage in oil pipelines. Buildings, ecosystems, air pollution, and human life are all at risk in case of leakage occurs which could lead to fires. This paper introduces one of the research methods for the detection of pipeline leaks with a particular focus on software-based methods. The computer board interface (CBI) and wireless sensor networks have been used beside Arduino as a micro-monitor for the entire system. ZigBee is also utilized to send read data from sensors to the monitoring system displayed on the LabVIEW graphical user interface (GUI). The operator can take direct action when a leak occurs. The effectiveness of the leakage monitoring process and its practical use are demonstrated by the introduction of computerized techniques based on pressure gauge analysis on a specific pipeline in the laboratory. The result showed that the system is widely covered, accurate data transmission and robust real-time performance which reduces economic losses and environmental pollution.

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