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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 113 Documents
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Search results for , issue "Vol 12, No 6: December 2022" : 113 Documents clear
Optimized reduction approach of congestion in mobile ad hoc network based on Lagrange multiplier Marwa K. Farhan; Muayad S. Croock
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.pp6341-6349

Abstract

Over the past decades, computer networks have experienced an outbreak and with that came severe congestion problems. Congestion is a crucial determinant in the delivery of delay-sensitive applications (voice and video) and the quality of the network. in this paper, the Lagrangian optimization rate, delay, packet loss, and congestion approach (LORDPC) are presented. A congestion avoidance routing method for device-to-device (D2D) nodes in an ad hoc network that addresses the traffic intensity problem. The method of Lagrange multipliers is utilized for active route election to dodge heavy traffic links. To demonstrate the effectiveness of our proposed method, we applied extensive simulation that presents path discovery and selection. Results show that LORDPC decreases delay and traffic intensity while maintaining a high bitrate and low packet loss rate and it outperformed the ad hoc on-demand distance vector (AODV) protocol and the Lagrangian optimization rate, delay, and packet loss, approach (LORDP).
Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques Imad El hajjami; Bachir Benhala
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.pp6320-6331

Abstract

In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
An overview of internet engineering task force mobility management protocols: approaches and its challenges Prabha Mahenthiran; Dinakaran Muruganadam
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.pp6559-6573

Abstract

In recent years, internet protocol mobility management has become one of the most popular research areas in networking. Mobility management protocols are in charge of preserving continuing communications as a user roam between different networks. All existing internet protocols (IP), like MIPv6, and PMIPv6, rely on a centralized mobility anchor to control mobile node traffic and signaling. The disadvantages of centralized mobility management (CMM) include ineffectiveness in handling massive volumes of traffic, poor scalability, wasteful use of network resources, and packet delay. When CMM is required to handle mobile media, which demands a huge amount of information and frequently needs quality of services (QoS) such as session continuance and reduced latency, these difficulties become apparent. It drives the need for distributed mobility management protocol (DMM) systems to manage the growing amount of mobile data, the overwhelming of this is video communication. DMM approaches could be regarded as an innovative and effective method to deal with mobility. An overview of the CMM protocol and its drawbacks are analyzed. This study examines the various DMM protocol techniques and their performance metrics are compared to highlight similarities and differences. The study reveals the network-based DMM protocol improves overall handoff time and packet loss.
A simplified and novel technique to retrieve color images from hand-drawn sketch by human Pavithra Narasimha Murthy; Sharath Kumar Yeliyur Hanumanthaiah
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.pp6140-6148

Abstract

With the increasing adoption of human-computer interaction, there is a growing trend of extracting the image through hand-drawn sketches by humans to find out correlated objects from the storage unit. A review of the existing system shows the dominant use of sophisticated and complex mechanisms where the focus is more on accuracy and less on system efficiency. Hence, this proposed system introduces a simplified extraction of the related image using an attribution clustering process and a cost-effective training scheme. The proposed method uses K-means clustering and bag-of-attributes to extract essential information from the sketch. The proposed system also introduces a unique indexing scheme that makes the retrieval process faster and results in retrieving the highest-ranked images. Implemented in MATLAB, the study outcome shows the proposed system offers better accuracy and processing time than the existing feature extraction technique.
Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic Prisma Megantoro; Hafidz Faqih Aldi Kusuma; Lilik Jamilatul Awalin; Yusrizal Afif; Dimas Febriyan Priambodo; Pandi Vigneshwaran
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.pp5717-5729

Abstract

The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations.
Design of an efficient binary phase-shift keying based IEEE 802.15.4 transceiver architecture and its performance analysis Vivek Raj Kempanna; Dinesha Puttaraje Gowda
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.pp6332-6340

Abstract

The IEEE 802.15.4 physical layer (PHY) standard is one of the communication standards with wireless features by providing low-power and low-data rates in wireless personal area network (WPAN) applications. In this paper, an efficient IEEE 802.15.4 digital transceiver hardware architecture is designed using the binary phase-shift keying (BPSK) technique. The transceiver mainly has transmitter and receiver modules along with the error calculation unit. The BPSK modulation and demodulation are designed using a digital frequency synthesizer (DFS). The DFS is used to generate the in-phase (I) and quadrature-phase (Q) signals and also provides better system performance than the conventional voltage-controlled oscillator (VCO) and look up table (LUT) based memory methods. The differential encoding-decoding mechanism is incorporated to recover the bits effectively and to reduce the hardware complexity. The simulation results are illustrated and used to find the error bits. The design utilizes less chip area, works at 268.2 MHz, and consumes 108 mW of total power. The IEEE 802.15.4 transceiver provides a latency of 3.5 clock cycles and works with a throughput of 76.62 Mbps. The bit error rate (BER) of 2×10-5 is achieved by the proposed digital transceiver and is suitable for real-time applications. The work is compared with existing similar approaches with better improvement in performance parameters.
A survey on bio-signal analysis for human-robot interaction Huda Mustafa Radha; Alia Karim Abdul Hassan
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.pp5998-6009

Abstract

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems.
Optimizing cybersecurity incident response decisions using deep reinforcement learning Hilala Alturkistani; Mohammed A. El-Affendi
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.pp6768-6776

Abstract

The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training.
Cognitive level classification on information communication technology skills for blog Chalothon Chootong; Jirawan Charoensuk
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.pp6387-6396

Abstract

Learners can study and update their knowledge continually due to the rapid growth of online content. The Medium blog is a well-known open platform that encourages authors who want to share their experiences to publish content on various topics in multiple languages. Meanwhile, readers can query interesting content by searching for a related topic. However, finding suitable content is still challenging for learners, especially information communication technology (ICT) content in Thai, and needs to be classified into beginner, intermediate, and advanced cognitive levels. Moreover, ICT blog content is usually a mix of Thai language and technical terms in English. To overcome the challenge of content classification, a deep neural network (DNN) classification model was constructed to classify the ICT content from the Medium blog into three levels based on cognition. We examined and compared the classification results with strong baseline models, including logistic regression, multinomial naïve bayes, support vector machine (SVM), and multilayer perceptron (MLP). The experimental results indicate that the proposed DNN model attained the highest accuracy (0.878), precision (0.882), recall (0.878), and F1-score (0.875).  
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.

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