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International Journal of Electrical and Computer Engineering

IJECE
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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

295 296 297 298 299

Analysis of a framework implementation of the transceiver performances for integrating optical technologies and wireless LAN based on OFDM-RoF Adnan Hussein Ali; Alaa Desher Farhood; Maham Kamil Naji
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The greatest advantages of optical fibers are the possibility of extending data rate transmission and propagation distances. Being a multi-carrier technique, the orthogonal frequency division multiplexing (OFDM) can be applicable in hybrid optical-wireless systems design owing to its best spectral efficiency for the interferences of radio frequency (RF) and minor multi-path distortion. An optical OFDM-RoF-based wireless local area network (W-LAN) system has been studied and evaluated in this work. The outline for integrating an optical technology and wireless in a single system was provided with the existence of OFDM-RoF technology and the microstrip patch antenna; these were applied in the Optisystem communication tool. The design of the proposed OFDM-RoF system is aimed at supporting mm-wave services and multi-standard operations. The proposed system can operate on different RF bands using different modulation schemes like 4,16 and 64QAM, that may be associated to OFDM and multidata rates up to 5 Gbps. The results demonstrate the robustness of the integrated optical wireless link in propagating OFDM-RoF-based WLAN signals across optical fibers.

Noise uncertainty effect on multi-channel cognitive radio networks Amira Osama; Heba A. Tag El-Dien; Ahmad A. Aziz El-Banna; Adly S. Tag El-Dien
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.

Development and testing of a thai website accessibility evaluation tool Kewalin Angkananon; Mike Wald; Piyabud Ploadaksorn
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

This paper deals with the important problem that there is no help with the accessibility evaluations of Thailand’s web by developing and evaluating a new method and tool online, WebThai2Access, with experts, developers, and users with disabilities. This tool was evaluated by 30 developers, 30 hearing impaired people, 30 visually impaired people, and 30 elderly people. The developers evaluated the websites whereas experimental tasks were given to each disabled group based on the problems they had accessing web information. The developers found WebThai2Access very usable and the 15 test criteria were reliable for evaluating websites. The lower and upper 95% limits for confidence ratings of developers were minus or plus 10% for YouTube and Pantip websites and minus or plus 3% with the blind association website. The 95% lower and upper limits of confidence were minus or plus 5% for hearing impaired users, minus or plus 2% for elderly users and minus or plus 0% for visually impaired users. The results therefore showed WebThai2Access was reliable and accessible for developers whose evaluations reasonably well predicted website accessibility for users with disabilities.

Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm Thanh Long Duong; Thuan Thanh Nguyen; Van-Duc Phan; Thang Trung Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

In this study, the problem of optimal capacitor location and size determination (OCLSD) in radial distribution networks for reducing losses is unraveled by moth swarm algorithm (MSA). MSA is one of the most powerful meta-heuristic algorithm that is taken from the inspiration of the food source finding behavior of moths. Four study cases of installing different numbers of capacitors in the 15-bus radial distribution test system including two, three, four and five capacitors areemployed to run the applied MSA for an investigation of behavior and assessment of performances. Power loss and the improvement of voltage profile obtained by MSA are compared with those fromother methods. As a result, it can be concluded that MSA can give a good truthful and effective solution method for OCLSD problem.

Design and analysis of optimized CORDIC based GMSK system on FPGA platform Renuka Kajur; K. V. Prasad
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

The Gaussian minimum shift keying (GMSK) is one of the best suited digital modulation schemes in the global system for mobile communication (GSM) because of its constant envelop and spectral efficiency characteristics. Most of the conventional GMSK approaches failed to balance the digital modulation with efficient usage of spectrum. In this article, the hardware architecture of the optimized CORDIC-based GMSK system is designed, which includes GMSK Modulation with the channel and GMSK Demodulation. The modulation consists of non-return zero (NRZ) encoder, an integrator followed by Gaussian filtering and frequency modulation (FM). The GMSK demodulation consists of FM demodulator, followed by differentiation and NRZ decoder. The FM Modulation and demodulation use the optimized CORDIC model for an In-phase (I) and quadrature (Q) phase generation. The optimized CORDIC is designed by using quadrant mapping and pipelined structure to improve the hardware and computational complexity in GMSK systems. The GMSK system is designed on the Xilinx platform and implemented on Artix-7 and Spartan-3EFPGA. The hardware constraints like area, power, and timing utilization are summarized. The comparison of the optimized CORDIC model with similar CORDIC approaches is tabulated with improvements.

Adaptive management of technical condition of power transformers Vladimir M. Levin; Ammar A. Yahya
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Ensuring reliable operation of power transformers as part of electric power facilities is assigned to the maintenance and repair system, whose important components are diagnostics and monitoring of the technical condition. Monitoring allows you to answer the question of whether the transformer abnormalities and how to do they manifest, while diagnostics allow determining the nature, the severity of the problem, determine the cause and possible consequences. The article presents the results of the authors ' research on creating an algorithm for adaptive control of the technical condition of power transformers using diagnostic and monitoring data. The developed algorithm implements the decision-making procedure for ensuring the reliable operation of oil-filled transformer equipment as part of the substations of electric power facilities. The decision-making procedure is based on the method of statistical Bayesian identification the states of a transformer based on the results of dissolved gas analysis (DGA) in oil. The method is characterized by high reliability of recognizing defects in the transformer and the ability to adapt the probabilities of the obtained solutions to the newly received diagnostic information. These results illustrate the effectiveness of the developed approach and the possibility of its application in the operation of oil-filled transformer equipment.

Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network Md. Kamrul Hossain; Md. Mokammel Haque
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Burst header packet flooding is an attack on optical burst switching (OBS) network which may cause denial of service. Application of machine learning technique to detect malicious nodes in OBS network is relatively new. As finding sufficient amount of labeled data to perform supervised learning is difficult, semi-supervised method of learning (SSML) can be leveraged. In this paper, we studied the classical self-training algorithm (ST) which uses SSML paradigm. Generally, in ST, the available true-labeled data (L) is used to train a base classifier. Then it predicts the labels of unlabeled data (U). A portion from the newly labeled data is removed from U based on prediction confidence and combined with L. The resulting data is then used to re-train the classifier. This process is repeated until convergence. This paper proposes a modified self-training method (MST). We trained multiple classifiers on L in two stages and leveraged agreement among those classifiers to determine labels. The performance of MST was compared with ST on several datasets and significant improvement was found. We applied the MST on a simulated OBS network dataset and found very high accuracy with a small number of labeled data. Finally we compared this work with some related works.

Novel holistic architecture for analytical operation on sensory data relayed as cloud services Manujakshi B. C; K. B. Ramesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

Random forest application on cognitive level classification of E-learning content Benny Thomas; Chandra J.
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with Random Forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.

Campus realities: Forecasting user bandwidth utilization using monte carlo simulation Haruna Bege; Aminu Yusuf Zubairu
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Adequate network design, planning, and improvement are pertinent in a campus network as the use of smart devices is escalating. Underinvesting and overinvesting in campus network devices lead to low network performance and low resource utilization respectively. Due to this fact, it becomes very necessary to ascertain if the current network capacity satisfies the available bandwidth requirement. The bandwidth demand varies from different times and periods as the number of connected devices is on the increase. Thus, emphasizing the need for adequate bandwidth forecast. This paper presents a Monte Carlo simulation model that forecast user bandwidth utilization in a campus network. This helps in planning campus network design and upgrade to deliver available content in a period of high and normal traffic load.

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Tole Sutikno

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ijece@iaesjournal.com

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Location
Kota yogyakarta,

Daerah istimewa yogyakarta

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Tole Sutikno

Contact Email ijece@iaesjournal.com

Phone -

Journal Mail Official ijece@iaesjournal.com

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

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Abstract

Abstract

Abstract

Abstract

Contact Name
Tole Sutikno

Contact Email
ijece@iaesjournal.com

Phone
-

Journal Mail Official
ijece@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA