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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 on the performance of reconfigurable intelligent surface-aided free-space optical link under atmospheric turbulence and pointing errors Duong Huu Ai; Dai Tho Dang; Nguyen Vu Anh Quang; Van Loi Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4204-4211

Abstract

Free-space optical (FSO) communication can provide the cost-efficient, secure, high data-rate communication links required for applications. For example, it provides broadband internet access and backhauling for the fifth-generation (5G) and the sixth-generation (6G) communication networks. However, previous solutions to deal with signal loss caused by obstructions and atmospheric turbulence. In these solutions, reconfigurable intelligent surfaces (RISs) are considered hardware technology to improve the performance of optical wireless communication systems. This study investigates the pointing error effects for RIS-aided FSO links under atmospheric turbulence channels. We analyze the performance of RIS-aided FSO links influenced by pointing errors, atmospheric attenuation, and turbulence for the subcarrier quadrature amplitude modulation (SC-QAM) technique. Atmospheric turbulence is modeled using log-normal distribution for weak atmospheric turbulence. Several numerical outcomes obtained for different transmitter beam waist radius and pointing error displacement standard deviation are shown to quantitatively illustrate the average symbol error rate (ASER).
Adiabatic technique based low power synchronous counter design Minakshi Sanadhya; Devendra Kumar Sharma; Alfilh Raed Hameed Chyad
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3770-3777

Abstract

The performance of integrated circuits is evaluated by their design architecture, which ensures high reliability and optimizes energy. The majority of the system-level architectures consist of sequential circuits. Counters are fundamental blocks in numerous very large-scale integration (VLSI) applications. The T-flip-flop is an important block in synchronous counters, and its high-power consumption impacts the overall effectiveness of the system. This paper calculates the power dissipation (PD), power delay product (PDP), and latency of the presented T flip-flop. To create a 2-bit synchronous counter based on the novel T flip-flops, a performance matrix such as PD, latency, and PDP is analyzed. The analysis is carried out at 100 and 10 MHz frequencies with varying temperatures and operating voltages. It is observed that the presented counter design has a lesser power requirement and PDP compared to the existing counter architectures. The proposed T-flip-flop design at the 45 nm technology node shows an improvement of 30%, 76%, and 85% in latency, PD, and PDP respectively to the 180 nm node at 10 MHz frequency. Similarly, the proposed counter at the 45 nm technology node shows 96% and 97% improvement in power dissipation, delay, and PDP respectively compared to the 180 nm at 10 MHz frequency.
Realtime human face tracking and recognition system on uncontrolled environment Assad H. Thary Al-Ghrairi; Noor Hussein Fallooh Al-Anbari; Esraa Zuhair Sameen
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4246-4255

Abstract

Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer.
Bayes model for assessing the reading difficulty of English text for English education in Jordan Yasser Qawasmeh; Qasem Al-Radaideh; Addy AlQuraan; Ahmed Fawzi Otoom
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4441-4451

Abstract

Predicting the reading difficulty level of English texts is a critical process for second language education and assessment. Reading difficulty level is concerned with the problem of matching a reader’s proficiency and the appropriate text. The reading difficulty level or readability assessment is the process for predicting the reading grade level required from an input text or document, which corresponds to the reader and to the materials. Students in Jordan at their academic levels find obstacles in finding relevant readable data for any subject at their levels. This paper is intended to introduce a model that foretells the reading difficulty level of a given text in terms of a student's ability to read and understand English as a non-native English speaker in Jordanian schools. In this paper, Jordanian students were classified into four categories according to their knowledge of English. The prediction of the reading difficulty level is achieved by using a modern statistical model that is situated on the Bayes model. The model compares the given text with some standard predefined text that strongly reflects the ability to read and understand English text. The accuracy of the proposed model was tested using the hold-out method. The overall prediction accuracy was 75.9%.
Speech emotion recognition with light gradient boosting decision trees machine Kah Liang Ong; Chin Poo Lee; Heng Siong Lim; Kian Ming Lim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4020-4028

Abstract

Speech emotion recognition aims to identify the emotion expressed in the speech by analyzing the audio signals. In this work, data augmentation is first performed on the audio samples to increase the number of samples for better model learning. The audio samples are comprehensively encoded as the frequency and temporal domain features. In the classification, a light gradient boosting machine is leveraged. The hyperparameter tuning of the light gradient boosting machine is performed to determine the optimal hyperparameter settings. As the speech emotion recognition datasets are imbalanced, the class weights are regulated to be inversely proportional to the sample distribution where minority classes are assigned higher class weights. The experimental results demonstrate that the proposed method outshines the state-of-the-art methods with 84.91% accuracy on the emo-DB dataset, 67.72% on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset, and 62.94% on the interactive emotional dyadic motion capture (IEMOCAP) dataset.
Data driven algorithm selection to predict agriculture commodities price Girish Hegde; Vishwanath R. Hulipalled; Jay B. Simha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4671-4682

Abstract

Price prediction and forecasting are common in the agriculture sector. The previous research shows that the advancement in prediction and forecasting algorithms will help farmers to get a better return for their produce. The selection of the best fitting algorithm for the given data set and the commodity is crucial. The historical experimental results show that the performance of the algorithms varies with the input data. Our main objective was to develop a model in which the best-performing prediction algorithm gets selected for the given data set. For the experiment, we have used seasonal autoregressive integrated moving average (SARIMA) stack ensemble and gradient boosting algorithms for the commodities Tomato and Potato with monthly and weekly average prices. The experimental results show that no algorithm is consistent with the given commodities and price data. Using the proposed model for the monthly forecasting and Tomato, stack ensemble is a better choice for Karnataka and Madhya Pradesh states with 59% and 61% accuracy. For Potatoes with the monthly price for Karnataka and Maharashtra, the stack ensemble model gave 60% and 85% accuracy. For weekly prediction, the accuracy of gradient boosting is better compared to other models.
Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced Herlambang Setiadi; Muhammad Abdillah; Yusrizal Afif; Rezi Delfianti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3651-3659

Abstract

The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5).
New microstrip patch antenna array design at 28 GHz millimeter-wave for fifth-generation application Salah-Eddine Didi; Imane Halkhams; Abdelhafid Es-Saqy; Mohammed Fattah; Younes Balboul; Said Mazer; Moulhime El Bekkali
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4184-4193

Abstract

This paper presents a study and an array design consisting of two microstrip patch antennas connected in series in a 2×1 form. This antenna provides better performance for the fifth-generation (5G) wireless communication system. The microstrip line feeding technique realizes the design of this antenna. This feed offers the best bandwidth, is easy to model, and has low spurious radiation. The distance between the feed line and the patch can adapt to the antenna’s impedance. In addition, the antenna array proposed in this paper is designed and simulated using the high frequency structure simulator (HFSS) simulation software at the operating frequency of 28 GHz for the 5G band. The support material used is Rogers RT/duroid® 5880, with relative permittivity of 2.2, a thickness of h=0.5 mm, and a loss tangent of 0.0009. The simulation results obtained in this research paper are as: reflection coefficient: -35.91 dB, standing wave ratio (SWR): 1.032, bandwidth: 1.43 GHz, gain: 9.42 dB, directivity: 9.47 dB, radiated power: 29.94 dBm, accepted the power: 29.99 dBm, radiation efficiency: 29.95, efficiency: 99.83%. This proposed antenna array has achieved better performance than other antenna arrays recently published in scientific journals regarding bandwidth, beam gain, reflection coefficient, SWR, radiated power, accepted power, and efficiency. Therefore, this antenna array will likely become an important competitor for many uses within the 5G wireless applications.
MF-RALU: design of an efficient multi-functional reversible arithmetic and logic unit for processor design on field programmable gate array platform Girija Sanjeevaiah; Sangeetha Bhandari Gajanan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3756-3769

Abstract

Most modern computer applications use reversible logic gates to solve power dissipation issues. This manuscript uses an efficient multi-functional reversible arithmetic and logical unit (MF-RALU) to perform 30 operations. The 32-bit MF-RALU includes arithmetic, logical, complement, shifters, multiplexers, different adders, and multipliers. The multi-bit reversible multiplexers are used to construct the MF-RALU structure. The Reduced instruction set computer (RISC) processor is designed to realize the functionality of the MF-RALU. The MF-RALU can perform its operation in a single clock cycle. The 1-bit RALU is developed and compared with existing approaches with improvements in performance metrics. The 32-bit reversible arithmetic units (RAUs) and reversible logical units (RLUs) are constructed using 1-bit RALU. The MF-RALU and RISC processor are synthesized individually in the Vivado environment using Verilog-HDL and implemented on Artix-7 field programmable gate array (FPGA). The MF-RALU utilizes a <11% chip area and consumes 332 mW total power. The RISC processor utilizes a <3% chip area and works at 483 MHZ frequency by consuming 159 mW of total power on Artix-7 FPGA.
Performance analysis of change detection techniques for land use land cover Aarti Karandikar; Avinash Agrawal
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4339-4346

Abstract

Remotely sensed satellite images have become essential to observe the spatial and temporal changes occurring due to either natural phenomenon or man-induced changes on the earth’s surface. Real time monitoring of this data provides useful information related to changes in extent of urbanization, environmental changes, water bodies, and forest. Through the use of remote sensing technology and geographic information system tools, it has become easier to monitor changes from past to present. In the present scenario, choosing a suitable change detection method plays a pivotal role in any remote sensing project. Previously, digital change detection was a tedious task. With the advent of machine learning techniques, it has become comparatively easier to detect changes in the digital images. The study gives a brief account of the main techniques of change detection related to land use land cover information. An effort is made to compare widely used change detection methods used to identify changes and discuss the need for development of enhanced change detection methods.

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