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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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Proposed system for data security in distributed computing in using ‎triple data encryption standard and ‎Rivest Shamir ‎Adlemen Shihab A. Shawkat; Bilal A. Tuama; Israa Al_Barazanchi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6496-6505

Abstract

Cloud computing is considered a distributed computing paradigm in which resources ‎are ‎provided as services. In cloud computing, the ‎applications do not run ‎from a user’s personal computer but are run and stored on distributed ‎servers on the Internet. The ‎resources of the cloud infrastructures are shared on cloud ‎computing on the Internet in the open ‎environment. This increases the security problems in ‎security such as data confidentiality, data ‎integrity and data availability, so the solution of such ‎problems are conducted by adopting data ‎encryption is very important for securing users data. ‎In this paper, a comparative ‎study is done between the two security algorithms on a cloud ‎platform called eyeOS. From the ‎comparative study it was found that the Rivest Shamir ‎Adlemen ‎(3kRSA) algorithm ‎outperforms that triple data encryption standard (3DES) algorithm with ‎respect to the complexity, and output bytes. The main ‎drawback of the 3kRSA algorithm is its ‎computation time, while 3DES is faster than that ‎‎3kRSA. This is useful for storing large amounts of ‎data used in the cloud computing, the key ‎distribution and authentication of the asymmetric ‎encryption, speed, data integrity and data ‎confidentiality of the symmetric encryption are also ‎important also it enables to execute ‎required computations on this encrypted data.‎
Empirical analysis of polarization division multiplexing-dense wavelength division multiplexing hybrid multiplexing techniques for channel capacity enhancement Sabiqun Nahar; Md. Redowan Mahmud Arnob; Mohammad Nasir Uddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp590-600

Abstract

This paper exemplifies dense wavelength division multiplexing combined with polarization division multiplexing with C-band frequency range-based single-mode fiber. In the proposed link, 32 independent channels with 16 individual wavelengths are multiplexed with two different angles of polarization. Each carrying 130 Gbps dual-polarization data with 200 GHz channel spacing claiming a net transmission rate of 4.16 Tbits/s with spectral efficiency of 69% with 20% side-mode-suppression-ratio (SMSR) and optical signal to noise ratio (OSNR) 40.7. The performance of the proposed techniques has been analyzed using optimized system parameters securing a minimum bit error rate (BER) 10-9 at a transmission distance up to 50 km.
Development of a microcontroller based automobile speed limiting device and alarm control system Oluwaseun Ibrahim Adebisi; Isaiah Adediji Adejumobi; Folasade Olayinka Durodola; Haastrup Ayobami Jim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp195-206

Abstract

Road accident due to overspeeding is a common occurrence in a developing nation such as Nigeria. Therefore, the need for a device capable of notifying a vehicle driver when the allowed speed limit of an area is exceeded arises. In this work, a microcontroller based automobile speed limiting device and alarm control system was designed and developed. The core components employed for the system design include Arduino Nano microcontroller, 1602 liquid crystal displays (LCD) module, light-emitting diodes (LEDs), buzzer, 18650 battery, I2C, infrared detectors and push buttons. Data gathering and circuit designs were implemented with microcontroller as focal point using suitable design models. Performance test was carried out on the developed system and the device’s reading error was determined. The developed automobile speed limiting device and alarm control system was functional and performed satisfactorily during testing. The reading error of the device was evaluated as 5.83%. The developed speed limiting device, apart from being suitable and efficient for vehicle speed measurement, could also be deployed for general applications requiring speed measurement.
Speech emotion recognition using 2D-convolutional neural network Fauzivy Reggiswarashari; Sari Widya Sihwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6594-6601

Abstract

This research proposes a speech emotion recognition model to predict human emotions using the convolutional neural network (CNN) by learning segmented audio of specific emotions. Speech emotion recognition utilizes the extracted features of audio waves to learn speech emotion characteristics; one of them is mel frequency cepstral coefficient (MFCC). Dataset takes a vital role to obtain valuable results in model learning. Hence this research provides the leverage of dataset combination implementation. The model learns a combined dataset with audio segmentation and zero padding using 2D-CNN. Audio segmentation and zero padding equalize the extracted audio features to learn the characteristics. The model results in 83.69% accuracy to predict seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise from the combined dataset with the segmentation of the audio files.
Keratoviz-A multistage keratoconus severity analysis and visualization using deep learning and class activated maps Priya Dhinakaran; Mamatha Gowdra Shivanandappa; Srijan Devnath
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp920-935

Abstract

The detection of keratoconus has been a difficult and arduous process over the years for ophthalmologists who have devised traditional approaches of diagnosis including the slit-lamp examination and observation of thinning of the corneal. The main contribution of this paper is using deep learning models namely Resnet50 and EfficientNet to not just detect whether an eye has been infected with keratoconus or not but also accurately detect the stages of infection namely mild, moderate, and advanced. The dataset used consists of corneal topographic maps and pentacam images. Individually the models achieved 97% and 94% accuracy on the dataset. We have also employed class activated maps (CAM) to observe and help visualize which areas of the images are utilized when making classifications for the different stages of keratoconus. Using deep learning models to predict the detection and severity of the infection can drastically speed up and provide accurate results at the same time.
Fabrication and experimental study of transformer 400 V with a simple rectifier circuit design Fitri Puspasari; Sismanto Sismanto; Ahmad Ashari
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1320-1328

Abstract

The demand for increased voltage in renewable energy sources is relatively high. This study examines the rapid development of technology considering the use of voltage-increasing transformers. Voltage regulator circuits are generally used to stabilize the output voltage of the rectifier according to the amount of input from the transformer. However, components for high-voltage stabilizer circuits are rare, which becomes an obstacle to the stabilization of the rectifier output. This study aimed to determine the performance of the designed rectifier circuit against a non-center tap step-up direct current (DC) 400 V transformer and compare the measurement results to manual calculations. The research method is a direct comparison between the input and output voltage values of the transformer after going through a rectifier circuit. This experiment was conducted using the repeatability method three to five times for each voltage variation on the transformer. The voltage variations successfully created are 0 to 50, 0 to 100, 0 to 200, and 0 to 400 V. The output test results from the DC transformer and rectifier circuit show linear results and an increase in peak-to-peak voltage data between the transformer and rectifier outputs by 3.8%.
Hybrid features and ensembles of convolution neural networks for weed detection Sandeep Kumar Kempegowda; Rajeswari Rajeswari; Lakshmikanth Satyanarayana; Siddesh Matada Basavarajaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6756-6767

Abstract

Weeds compete with plants for sunlight, nutrients and water. Conventional weed management involves spraying of herbicides to the entire crop which increases the cost of cultivation, decreasing the quality of the crop, in turn affecting human health. Precise automatic spraying of the herbicides on weeds has been in research and use. This paper discusses automatic weed detection using hybrid features which is generated by extracting the deep features from convolutional neural network (CNN) along with the texture and color features. The color and texture features are extracted by color moments, gray level co-occurrence matrix (GLCM) and Gabor wavelet transform. The proposed hybrid features are classified by Bayesian optimized support vector machine (BO-SVM) classifier. The experimental results read that the proposed hybrid features yield a maximum accuracy of 95.83%, higher precision, sensitivity and F-score. A performance analysis of the proposed hybrid features with BO-SVM classifier in terms of the evaluation parameters is made using the images from crop weed field image dataset.
High directivity microstrip antenna with stopband and passband frequency selective surfaces for 6G at low-THz Uri Nissanov; Ghanshyam Singh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6272-6283

Abstract

There is still no high-directivity microstrip antenna with directivity beyond 25 dBi, bandwidth (BW) of more than 24%, which can be used for 6G cellular communication at low-THz at a resonance frequency of 144 GHz. So, duo broadband microstrip antennas have been designed at a resonance frequency of 144 GHz with the Taconic TLY-5 laminate in this work. These designs were carried out with the computer simulation technology microwave studio (CST MWS) software. The first antenna simulation results were compared within an Ansys high-frequency structure simulator (HFSS) software, and the obtained simulation results from both software were in fair consent, supporting the proposed designs. The peak directivity, peak gain, total peak efficiency, and BW obtained for the proposed THz microstrip antennas were 27.01 dBi, 25.3 dB, 78.96%, and 34.21 GHz (24.93%), respectively. Therefore, these antennas can be a base for 6G at low-THz.
Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis Bhavana Valsala; Krishnappa Honnamachanahalli Kariputtaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1491-1498

Abstract

In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.
Characterization of cadmium sulfide light dependent resistors sensors for optical solar trackers Youssef Boukdir; Hamid El Omari
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp184-194

Abstract

The aim of this paper is to study the effect of dissimilarity of the intrinsic characteristics of the light dependent resistor (LDRs) on optical sun tracking systems, designed for solar power concentration applications such as parabolic trough collectors, Fresnel mirrors concentrators, and concentrated photovoltaic, a comparative study was done between a sun tracker based on LDRs chosen randomly with and without an initial calibration of the offsets, and a sun tracker based on LDRs selected meticulously thanks to a black box test bench, developed especially for this purpose. By choosing two light dependent resistors randomly, the dissimilarity between them can reach 23.2%, which cause a bad sun tracking even with initial offset calibration, in the other hand, and by the use of selected LDRs using the test bench, the dissimilarity drops to 0.06%, which meets requirements of solar power concentration systems.

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