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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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Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language Zul Imran Azhari; Samsul Setumin; Emilia Noorsal; Mohd Hanapiah Abdullah
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.pp1346-1357

Abstract

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.
Acoustic event characterization for service robot using convolutional networks Fernando Martinez; Fredy Martinez; Cesar Hernandez
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.pp6684-6696

Abstract

This paper presents and discusses the creation of a sound event classification model using deep learning. In the design of service robots, it is necessary to include routines that improve the response of both the robot and the human being throughout the interaction. These types of tasks are critical when the robot is taking care of children, the elderly, or people in vulnerable situations. Certain dangerous situations are difficult to identify and assess by an autonomous system, and yet, the life of the users may depend on these robots. Acoustic signals correspond to events that can be detected at a great distance, are usually present in risky situations, and can be continuously sensed without incurring privacy risks. For the creation of the model, a customized database is structured with seven categories that allow to categorize a problem, and eventually allow the robot to provide the necessary help. These audio signals are processed to produce graphical representations consistent with human acoustic identification. These images are then used to train three convolutional models identified as high-performing in this type of problem. The three models are evaluated with specific metrics to identify the best-performing model. Finally, the results of this evaluation are discussed and analyzed.
Conducted emission investigation of infant incubator heating control mode Khusnul Khotimah; Yoppy Yoppy; Muhammad Imam Sudrajat; Vera Permatasari; Elvina Trivida; Tyas Ari Wahyu Wijarnoko
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.pp5900-5910

Abstract

This paper investigates the effect of two different heating power control systems of infant incubators on their conducted emissions. Two infant incubators which respectively employ zero-crossing control mode and phase angle control mode are observed. The research was conducted by measuring conducted electromagnetic interference (EMI) from each infant incubator's power input. Measurements are conducted both during full power condition, while the incubator's compartment temperature is far away from the temperature setpoint, and during power chopping condition, while the compartment temperature reaches the steady-state set point. Method and limit of the measurement refer to CISPR 11. It is found that conducted emission higher than the standard CISPR 11 limit occurs during power chopping on phase angle control mode. This results from the sharp rise time of voltage delivered to the heater, around 220 ns for each chopping cycle.
Automatic modulation classification based deep learning with mixed feature Ali H. Shah; Abbas Hussien Miry; Tariq M. Salman
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.pp1647-1653

Abstract

The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB.
An efficient reconfigurable peak cancellation model for peak to average power ratio reduction in orthogonal frequency division multiplexing communication system Shivaji Rathod; Nataraj Kanathur Ramaswamy; Mallikarjunaswamy Srikantaswamy; Rekha Kanathur Ramaswamy
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.pp6239-6247

Abstract

The peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) communication system will be reduced using reconfigurable peak cancellation (RPC). RPC will also aid in improves the error vector magnitude (EVM) and reduces adjacent channel leakage ratio (ACLR) in OFDM communication system. The proposed RPC design methodology and practical implementation using field programmable gate array (FPGA) are discussed. The proposed RPC has been demonstrated using VIRTEX-7 XC7Z100 dual-core FPGA device with less hardware difficulty and minimum utilization of FPGA resources. The proposed RPC improves the efficiency of OFDM communication process by reducing complementary cumulative distribution function (CCDF) with respect to instantaneous power in dB. A comparison analysis was done between the existing selective mapping (SLM) method with proposed RPS method with respect FPGA resource utilization. The proposed RPC is implemented using VIRTEX-7 XC7Z100 dual-core FPGA device. Its effectively utilizing sub-carriers, fast Fourier transform (FFT) filter, bandwidth, and sampling frequency. Due to parallel switching operation, it reduces the PAPR, ACLR and improves EVM in OFDM signal with less hardware complexity.
Q-learning based distributed denial of service detection Hiba Salah Yaseen; Ahmed Al-Saadi
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.pp972-986

Abstract

Distributed denial of service (DDoS) attacks the target service providers by sending a huge amount of traffic to prevent legitimate users from getting the service. These attacks become more challenging in the software-defined network paradigm, due to the separation of the control plane from the data plane. Centralized software defined networks are more vulnerable to DDoS attacks that may cause the failure of all networks. In this work, a new approach is proposed based on q-learning to enhance the detection of DDoS attacks and reduce false positives and false negatives. The results of this work are compared with entropy detection in terms of the number of received packets to detect the attack and also the continuity of service for legitimate users. Moreover, these results indicate that the proposed system detects the DDoS attack from flash crowds and redirects the traffic to the edge of the data center. A second controller is used to redirect traffic to a honeypot server that works as a mirror server. This guarantees the continuity of service for both normal and suspected traffic until further analysis is done. The results indicate an increase of up to 50% in the throughput compared to other approaches.
Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification Ehsan Sadeghi Pour; Mahdi Esmaeili; Morteza Romoozi
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.pp6397-6409

Abstract

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.
Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system Nurnajmin Qasrina Ann; Dwi Pebrianti; Mohd Fadhil Abas; Luhur Bayuaji
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.pp2167-2176

Abstract

Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.
Avatar design types and user engagement in digital educational games during evaluation phase Dinna N. Mohd Nizam; Dylia Nursakinaz Rudiyansah; Nooralisa Mohd Tuah; Zaidatol Haslinda Abdullah Sani; Kornchulee Sungkaew
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.pp6449-6460

Abstract

Avatar design types can range from human representations to abstract representations. In digital educational games (DEGs), avatars are frequently used to encourage users to play the game. However, the role of avatar design types and their engagement in digital games are still unclear and empirically under research. Therefore, a bespoke digital educational game in geography was developed and validated by six expert users. Then forty-five users participated in the evaluation phase to investigate engagement and avatar types on digital educational games using the user engagement scale (UES). The results reported aesthetics and satisfaction factors somehow influenced the avatar design types, but none of the UES subscales was influenced by preferred avatar design types. Moreover, the human-cartoon avatar, which was not entirely human and cartoonish, was the most popular avatar design type among young adults. Other issues discussed for future developers and research included incorporating more avatar design selections into the study, integrating social interaction features into the game, using the same drawing style for avatars and provide easy access to the bespoke game during data collection.
An approach for radiation dose reduction in computerized tomography Shama Bekal Narayan; Savitha Halkare Mahabaleshwara
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.pp1169-1179

Abstract

Minimization of radiation dose plays an important role in human wellbeing. Excess of radiation dose leads to cancer. Radiation greatly affects young children less than 10 years of age as their life span is longer. Radiation can be reduced by hardware and/or by software techniques. Hardware methods deal with variation of parameters such as tube voltage, tube current, exposure time, focal distance and filter type. Software techniques include image processing methods. The originally acquired X-ray images may be contaminated with noise due to the fact of instability in the case of sensors, electrical power or X-ray source, that is responsible for the degradation of the image attributes. An enhanced image denoising algorithm has been proposed which decreases Gaussian noise combined with salt and pepper noise that retains most information particulars.

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