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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 3: June 2022" : 112 Documents clear
A 2.45/5.8 GHz high-efficiency dual-band rectifier for low radio frequency input power Sara El Mattar; Abdennaceur Baghdad; Abdelhakim Ballouk
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.pp2169-2176

Abstract

This article proposes a concurrent rectifier for radio frequency (RF) energy harvesting from the popular ambient RF sources wireless fidelity (WiFi) 2.45 and 5.8 GHz bands. A voltage doubler-based converter circuit with the Schottky SMS7630 diode is used, this chosen diode has shown good results for low power levels. To ameliorate the resulting circuit, we used an interdigital capacitor (IDC) instead of a lumped component; and then we added a filter to reject the 3rd harmonics of each operating frequency. A dual-band impedance transformer with a direct current (DC) block function is used and optimized at low input power points for more harvested DC power. The final circuit was, therefore, more efficient and more reliable. The maximum conversion efficiencies obtained from the resulting circuit are about 60.321% for 2.45 GHz and 47.175% for 5.8 GHz at 2 dBm of input power. Compared to other previous rectifiers presented in the literature, our proposed circuit presents high efficiencies at low power levels and at these operating frequencies.
Internet of things based fall detection and heart rate monitoring system for senior citizens Md. Hasib Sarowar; Md. Fazlul karim Khondakar; Himaddri Shakhar Roy; Habib Ullah; Riaj Ahmed; Quazi Delwar Hossain
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.pp3204-3216

Abstract

Falls cause the maximum number of injuries, deaths, and hospitalizations due to injury for senior citizens worldwide. So, fall detection is essential in the health care of senior citizens. Present methods lack either accuracy or comfortability. The design of fall detection and heart rate monitoring system for senior citizens has been presented in this paper. The hardware interface includes wearable monitoring devices based on a tri-axial accelerometer and Bluetooth module that makes a wireless connection by software interface (mobile application) to the caregiver. Global positioning system (GPS) can also track the location of the elder. For detecting falls accurately, an effective fall detection algorithm is developed and used. The performance parameters of the fall detection system are accuracy (97.6%), sensitivity (92.8%), and specificity (100%). A pulse sensor is used for monitoring the heart rate of the elder. The device is put on the hips to increase comfortability. Whenever the elder's fall is detected, the device can send information on fall data and heart rate with location to the respective caregiver successfully. So, this device can minimize the injury and health cost of a fallen person as a victim can get help within a short time.
Peanut leaf spot disease identification using pre-trained deep convolutional neural network Urbano B. Patayon; Renato V. Crisostomo
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.pp3005-3012

Abstract

Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants.
Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method Dina Mohammed Sherif El-Torky; Mohamed Ismail Roushdy; Maryam Nabil Al-Berry; Mohammed Abd El-Mageed Salem
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.pp2553-2563

Abstract

3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.
An authenticated key management scheme for securing big data environment Thoyazan Sultan Algaradi; Boddireddy Rama
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.pp3238-3248

Abstract

If data security issues in a big data environment are considered, then the distribution of keys, their management, and the ability to transfer them between server users in a public channel will be one of the most critical issues that must consider on. In which the importance of keys management may outweigh the importance of the encryption algorithm strength. Therefore, this paper raised a new proposed scheme called (AKMS) that works through two levels of security. First, to concerns how the user communicates with the server with preventing any attempt to penetrate senders/receivers. Second, to make the data sent vague by encrypting it, and unreadable by others except for the concerned receiver, thus the server function be limited only as a passageway for communication between the sender and receiver. In the presented work some concepts discussed related to analysis and evaluation as keys security, data security, public channel transmission, and security isolation inquiry which demonstrated the rich value that AKMS scheme carried. As well, AKMS scheme achieved very satisfactory results about computation cost, communication cost, storage overhead which proved that AKMS scheme is appropriate, secure, and practical to use and protect the user's private data in big data environments.
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.
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.
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.
Ultra low phase noise and high output power monolithic microwave integrated circuit oscillator for 5G applications Hanae El Ftouh; El Bakkali Moustapha; Amar Touhami Naima; Zakriti Alia
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.pp2689-2698

Abstract

novel structure of low phase noise and high output power monolithic microwave integrated circuit (MMIC) oscillator is presented in order to use it in 5G applications. The oscillator is based on the ED02AH process which allows us to design a microwave oscillator keeping a minimum size which is impossible to have it using other technologies since microwave oscillators are sensitive components above 20 GHz. The oscillator is studied, designed, and optimized on a GaAs substrate from the OMMIC foundry using the advanced design system (ADS) simulator in order to obtain a miniaturized oscillator (1.1×1.3 mm2) generating two signals of different frequencies fo1=26 GHz and fo2=30 GHz. The objective is to design an oscillator with high output power and low phase noise while respecting its specifications. The optimization of the proposed microwave oscillator shows satisfying results. Indeed, at 26 GHz and 30 GHz, the output powers are respectively 13.33 dBm and 14.89 dBm. The oscillator produces a sinusoidal signal of 1.5 V and 1.75 V amplitude respectively at 26 GHz and 30 GHz. The oscillator phase noise at fo1 and fo2 resonance frequencies using the liquid crystal (LC) resonator shows respectively -109 dBc/Hz and -110 dBc/Hz at 10 MHz offset.

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