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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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Auto tuning of frequency on wireless power transfer for an electric vehicle Kazuya Yamaguchi; Kenichi Iida
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1147-1152

Abstract

In these days, electric vehicles are enthusiastically researched as a countermeasure to air pollution, although these do not have practicality compared to gasoline-powered vehicles. The aim of this study is to transport energy wirelessly and efficiently to an electric vehicle. To accomplish this, we focused on frequency of an alternating current (AC) power supply, and suggested a method which determined the value of it constantly. In particular, a wireless power transfer circuit and a lithium-ion battery in an electric vehicle were expressed with an equivalent circuit, and efficiency of energy transfer was calculated. Furthermore, the optimal frequency which maximizes efficiency was found, and the behavior of voltage was demonstrated on a secondary circuit. Finally, we could obtain the larger electromotive force at the secondary inductor than an input voltage.
Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal Sameh El-Sharo; Amani Al-Ghraibah; Jamal Al-Nabulsi; Mustafa Muhammad Matalgah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1456-1467

Abstract

The use of pulse wave analysis may assist cardiologists in diagnosing patients with vascular diseases. However, it is not common in clinical practice to interpret and analyze pulse wave data and utilize them to detect the abnormalities of the signal. This paper presents a novel approach to the clinical application of pulse waveform analysis using the wavelet technique by decomposing the normal and pathology signal into many levels. The discrete wavelet transform (DWT) decomposes the carotid arterial pulse wave (CAPW) signal, and the continuous wavelet transform (CWT) creates images of the decomposed signal. The wavelet analysis technique in this work aims to strengthen the medical benefits of the pulse wave. The obtained results show a clear difference between the signal and the images of the arterial pathologies in comparison with normal ones. The certain distinct that were achieved are promising but further improvement may be required in the future.
Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering Ahmad Alzu'bi; Maysarah Barham
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1498-1507

Abstract

Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.
The voltage-mode first order universal filter using single voltage differencing differential input buffered amplifier with electronic controllability Danupat Duangmalai; Peerawut Suwanjan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1308-1323

Abstract

In this research contribution, the electronically tunable first-order universal filter employing a single voltage differencing differential input buffered amplifier (VD-DIBA) (constructed from two commercially available integrated circuit (IC): the operational transconductance amplifier, IC number LT1228, and the differential voltage input buffer, IC number AD830), one capacitor and two resistors. The features of the designed first order universal filter are as follows. Three voltage-mode first-order functions, low-pass (LP), all-pass (AP) and high-pass (HP) responses are given. The natural frequency (????0) of the presented configuration can be electronically adjusted by setting the DC bias current. Moreover, the voltage gain of the LP and HP filters can be controllable. The phase responses of an AP configuration can be varied from 00 to −1800 and 1800 to 00. The power supply voltages were set at ±5 ????. Verification of the theoretically described performances of the introduced electronically tunable universal filter was proved by the PSpice simulation and experiment.
Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model Kamilia Hosny; Abeer El-korany
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2014-2025

Abstract

Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.
Development and testing of braking and acceleration features for vehicle advanced driver assistance system Johann Carlo Marasigan; Gian Paolo Mayuga; Elmer Magsino
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2047-2057

Abstract

Traffic congestion is a constant problem for cities worldwide. The human driving inefficiency and poor urban planning and development contribute to traffic buildup and travel discomfort. An example of human inefficiency is the phantom traffic jam, which is caused by unnecessary braking, causing traffic to slow down, and eventually coming to a stop. In this study, a brake and acceleration feature (BAF) for the advanced driver assistance system (ADAS) is proposed to mitigate the effects of the phantom traffic phenomenon. In its initial stage, the BAF provides a heads-up display that gives information on how much braking and acceleration input is needed to maintain smooth driving conditions, i.e., without sudden acceleration or deceleration, while observing a safe distance from the vehicle in front. BAF employs a fuzzy logic controller that takes distance information from a light detection and ranging (LIDAR) sensor and the vehicle’s instantaneous speed from the engine control unit (ECU). It then calculates the corresponding percentage value of needed acceleration and braking in order to maintain travel objectives of smooth and safe-distance travel. Empirical results show that the system suggests acceleration and braking values slightly higher than the driver’s actual inputs and can achieve 90% accuracy overall.
A hybrid method for traumatic brain injury lesion segmentation Ahmad Yahya Dawod; Aniwat Phaphuangwittayakul; Salita Angkurawaranon
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1437-1448

Abstract

Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain.
Ultra-optical characterization of thin film solar cells materials using core/shell absorber layer Ahmed Thabet; Safaa Abdelhady; Youssef Mobarak
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1377-1384

Abstract

This paper investigates on new design of heterojunction quantum dot (HJQD) photovoltaics solar cells CdS/PbS that is based on quantum dot metallics PbS core/shell absorber layer and quantum dot window layer. It has been enhanced the performance of traditional HJQD thin film solar cells model based on quantum dot absorber layer and bulk window layer. The new design has been used sub-micro absorber layer thickness to achieve high efficiency with material reduction, low cost, and time. Metallics-semiconductor core/shell absorber layer has been succeeded for improving the optical characteristics such energy band gap and the absorption of absorber layer materials, also enhancing the performance of HJQD ITO/CdS/QDPbS/Au, sub micro thin film solar cells. Finally, it has been formulating the quantum dot (QD) metallic cores concentration effect on the absorption, energy band gap and electron-hole generation rate in absorber layers, external quantum efficiency, energy conversion efficiency, fill factor of the innovative design of HJQD cells.
Operational transconductance amplifier-based comparator for high frequency applications using 22 nm FinFET technology Vasudeva Gowdagere; Uma Bidikinamane Venkataramanaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2158-2168

Abstract

Fin field-effect transistor (FinFET) based analog circuits are gaining importance over metal oxide semiconductor field effect transistor (MOSFET) based circuits with stability and high frequency operations. Comparator that forms the sub block of most of the analog circuits is designed using operational transconductance amplifier (OTA). The OTA is designed using new design procedures and the comparator circuit is designed integrating the sub circuits with OTA. The building blocks of the comparator design such as input level shifter, differential pair with cascode stage and class AB amplifier for output swing are designed and integrated. Folded cascode circuit is used in the feedback path to maintain the common mode input value to a constant, so that the differential pair amplifies the differential signal. The gain of the comparator is achieved to be greater than 100 dB, with phase margin of 65°, common mode rejection ratio (CMRR) of above 70 dB and output swing from rail to rail. The circuit provides unity gain bandwidth of 5 GHz and is suitable for high sampling rate data converter circuits.
Binary classification of rainfall time-series using machine learning algorithms Shilpa Hudnurkar; Neela Rayavarapu
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1945-1954

Abstract

Summer monsoon rainfall contributes more than 75% of the annual rainfall in India. For the state of Maharashtra, India, this is more than 80% for almost all regions of the state. The high variability of rainfall during this period necessitates the classification of rainy and non-rainy days. While there are various approaches to rainfall classification, this paper proposes rainfall classification based on weather variables. This paper explores the use of support vector machine (SVM) and artificial neural network (ANN) algorithms for the binary classification of summer monsoon rainfall using common weather variables such as relative humidity, temperature, pressure. The daily data, for the summer monsoon months, for nineteen years, was collected for the Shivajinagar station of Pune in the state of Maharashtra, India. Classification accuracy of 82.1 and 82.8%, respectively, was achieved with SVM and ANN algorithms, for an imbalanced dataset. While performance parameters such as misclassification rate, F1 score indicate that better results were achieved with ANN, model parameter selection for SVM was less involved than ANN. Domain adaptation technique was used for rainfall classification at the other two stations of Maharashtra with the network trained for the Shivajinagar station. Satisfactory results for these two stations were obtained only after changing the training method for SVM and ANN.

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