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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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Design and implementation of a centralized approach for multi-node localization Hasan, Ola A.; T. Rashid, Abdulmuttalib; S. Ali, Ramzy; Qasim, Hamza H.; A. Al Sibahee, Mustafa; Audah, Lukman
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2477-2487

Abstract

In this paper, a centralized approach for multi nodes localization is introduced. This approach is based on using a beacon fixed at the lower middle edge of the environment. This beacon is provided with a distance sensor and can scan the environment to measure the distance between the detecting node and the beacon. Also, remote control is fixed on the beacon to distinguish the identity of the detecting node. Two nodes are used in this approach, each node contains eight cells, and each cell has a 5 mm IR transmitter and TSOP4P38 IR receiver. If any one of the IR receivers has received the beacon ID, the transmitter which belongs to the same cell will respond by sending the node ID to the beacon. The beacon measurements and the information received from the detected nodes are then used to estimate the location and orientation of the visible nodes and the results will be saved in the main computer. Several experimental results have been tested with different distances from the nodes to the beacon. Also, different rotation angles at the beacon have been experienced to analyze the performance of the introduced approach.
Fault tolerant nine-level inverter topology for solar water pumping applications Narasimha Rao Mucherla; Nagaraj Karthick; Airineni Madhukar Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3485-3493

Abstract

Diminished voltage pressure and occasional-general harmonic distortion are the essential causes for such a ways and extensive usage of multi-level inverters (MLIs) in numerous industrial applications. Nonetheless, unwavering quality is one of the significant worries of MLIs as it utilizes countless switches as contrasted to 2-level inverters. Here, a fault tolerant 9-level inverter setup for the use of photovoltaic (PV) system-water pumping applications is suggested. This fault tolerant 9-level inverter is accomplished by combining a 2-level inverter, a 3-level fault tolerant inverter alongside switches with bidirectional ability. The setup is taken care of with four PV fed sources. The arrangement suggested shows the behavior towards switch fault in at least one inverter legs under open circuit conditions. On account of source failure, it could use the better hotspot for introducing continuous power to the water pumping motor. Meanwhile, the suggested fault-tolerant inverter works as seven-level inverter. The activity related to proposed inverter in the course of various failure modes is mentioned and simulated the usage of MATLAB/Simulink.
Performance and statistical analysis of ant colony route in mobile ad-hoc networks Ibrahim Ahmed Alameri; Jitka Komarkova
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2818-2828

Abstract

Research on mobile ad-hoc networks (MANETs) is increasing in popularity due to its rapid, budget-friendly, and easily altered implementation, and relevance to emergencies such as forest firefighting and health care provisioning. The main concerns that ad-hoc networks face is dynamic topology, energy usage, packet drop rate, and throughput. Routing protocol selection is a critical point to surmount alterations in topology and maintain quality in MANET networks. The effectiveness of any network can be vastly enhanced with a well-designed routing protocol. In recent decades, standard MANET protocols have not been able to keep pace with growing demands for MANET applications. The current study investigates and contrasts ant colony optimization (ACO) with various routing protocols. This paper compares ad-hoc on-demand multi-path distance vector (AOMDV), dynamic source routing protocol (DSR), ad-hoc on-demand distance vector routing (AODV), and AntHocNet protocols regarding the quality of service (QoS) and statistical analysis. The current research aims to study the behavior of the state-of-the-art MANET protocols with the ACO technique. The ACO technique is a hybrid technique, integrating a reactive route maintaining technique with a proactive method. The reason and motivation for including the ACO algorithm in the current study is to improve by using optimization algorithms proved in other domains. The ACO algorithm appears to have substantial use in large-scale MANET simulation.
Stream-keys generation based on graph labeling for strengthening Vigenere encryption Antonius Cahya Prihandoko; Dafik Dafik; Ika Hesti Agustin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3960-3969

Abstract

This paper address the cryptographic keys management problem: how to generate the cryptographic keys and apply them to secure encryption. The purpose of this research was to study on utilizing graph labeling for generating stream-keys and implementing the keys for strengthening Vigenere encryption. To achieve this objective, the research was carried out in four stages: developing an algorithm for generating stream-keys, testing the randomness of the constructed keys, implementing the eligible keys in a modified Vigenere encryption and, finally, analyzing the security of the encryption. As the result, most of stream-keys produced by the algorithm are random, and the implementation of the stream keys to the modified Vigenere cipher achieve good security. The contributions of this research are utilizing graph labeling to generate stream-keys and providing different encryption keys for different blocks in a block based cipher with low storage capacity. The novel technical results yielded from this research are the algorithm of developing the source of the stream-keys based on graph labeling, the algorithm of constructing the initial block keys, and the protocol of a modified Vigenere encryption using stream-keys and operated in cipher block chaining mode.
Optimal state estimation techniques for accurate measurements in internet of things enabled microgrids using deep neural networks Rao Padupanambur, Sudhakar; Riyaz Ahmed, Mohammed; Divakar, Bangalore Prabhakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4288-4301

Abstract

The employment of microgrids in smart cities is not only changing the landscape of power generation, transmission, and distribution but it helps in green alleviation by converting passive consumers into active produces (using renewable energy sources). Real-time monitoring is a crucial factor in the successful adoption of microgrids. Real-time state estimation of a microgrid is possible through internet-of-things (IoT). State estimation can provide the necessary monitoring of grid for many system optimization applications. We will use raw and missing data before we learn from data, the processing must be done. This paper describes various Kalman variants use for preprocessing. In this paper a formulated approach along with algorithms are described for optimal state estimation and forecasting, with weights update using deep neural networks (DNN) is presented to enable accurate measurements at component and system level model analysis in an IoT enabled microgrid. The real load data experiments are carried out on the IEEE 118-bus benchmark system for the power system state estimation and forecasting. This research paves a way for developing a novel DNN based algorithms for a power system under dynamically varying conditions and corresponding time dependencies.
A load balancing strategy for reducing data loss risk on cloud using remodified throttled algorithm Fatema Tuj Johora; Iftakher Ahmed; Md. Ashiqul Islam Shajal; Rony Chowdhory
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3217-3225

Abstract

Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.
Employing deep learning for lung sounds classification Dhari Satea, Huda; Saleem Elameer, Amer; Hussein Salman, Ahmed; Dhari Sateaa, Shahad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4345-4351

Abstract

Respiratory diseases indicate severe medical problems. They cause death for more than three million people annually according to the world health organization (WHO). Recently, with corona virus disease 19 (COVID-19) spreading the situation has become extremely serious. Thus, early detection of infected people is very vital in limiting the spread of respiratory diseases and COVID-19. In this paper, we have examined two different models using convolution neural networks. Firstly, we proposed and build a convolution neural network (CNN) model from scratch for classification the lung breath sounds. Secondly, we employed transfer learning using the pre-trained network AlexNet applying on the similar dataset. Our proposed model achieved an accuracy of 0.91 whereas the transfer learning model performing much better with an accuracy of 0.94.
Hybrid controller design using gain scheduling approach for compressor systems Muddenahalli Narasimhaiah, Divya; Narayanappa, Chikkajala Krishnappa; Lakshmaiah, Gangadharaiah Soralamavu
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3051-3060

Abstract

The automatic control system plays a crucial role in industries for controlling the process operations. The automatic control system provides a safe and proper controlling mechanism to avoid environmental and quality problems. The control system controls pressure flow, mass flow, speed control, and other process metrics and solves robustness and stability issues. In this manuscript, The Hybrid controller approach like proportional integral (PI) and proportional derivative (PD) based fuzzy logic controller (FLC) using with and without gain scheduling approach is modeled for the compressor to improve the robustness and error response control mechanism. The PI/PD-based FLC system includes step input function, the PI/PD controller, FLC with a closed-loop mechanism, and gain scheduler. The error signals and control response outputs are analyzed in detail for PI/PD-based FLC’s and compared with conventional PD/PID controllers. The PD-based FLC with the Gain scheduling approach consumes less overshoot time of 74% than the PD-based FLC without gain scheduling approach. The PD-based FLC with the gain scheduling approach produces less error response in terms of 7.9% in integral time absolute error (ITAE), 7.4% in integral absolute error (IAE), and 16% in integral square error (ISE) than PD based FLC without gain scheduling approach.
Forest quality assessment based on bird sound recognition using convolutional neural networks Nazrul Effendy; Didi Ruhyadi; Rizky Pratama; Dana Fatadilla Rabba; Ananda Fathunnisa Aulia; Anugrah Yuwan Atmadja
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4235-4242

Abstract

Deforestation in Indonesia is in a status that is quite alarming. From year to year, deforestation is still happening. The decline in fauna and the diminishing biodiversity are greatly affected by deforestation. This paper proposes a bioacoustics-based forest quality assessment tool using Nvidia Jetson Nano and convolutional neural networks (CNN). The device, named GamaDet, is a portable physical product based on the microprocessor and equipped with a microphone to record the sounds of birds in the forest and display the results of their analysis. In addition, a Google Collaboratorybased GamaNet digital product is also proposed. GamaNet requires forest recording audio files to be further analyzed into a forest quality index. Testing the forest recording for 60 seconds at an arboretum forest showed that both products could work well. The GamaDet takes 370 seconds, while the GamaNet takes 70 seconds to process the audio data into a forest quality index and a list of detected birds.
Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images Ayman AbuBaker; Yazeed Yasin Ghadi; Nader Santarisi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2564-2570

Abstract

The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions.

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