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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 6,393 Documents
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High performance modified bit-vector based packet classification module on low-cost FPGA Anita P.; Manju Devi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3855-3863

Abstract

The packet classification plays a significant role in many network systems, which requires the incoming packets to be categorized into different flows and must take specific actions as per functional and application requirements. The network system speed is continuously increasing, so the demand for the packet classifier also increased. Also, the packet classifier's complexity is increased further due to multiple fields should match against a large number of rules. In this manuscript, an efficient and high performance modified bitvector (MBV) based packet classification (PC) is designed and implemented on low-cost Artix-7 FPGA. The proposed MBV based PC employs pipelined architecture, which offers low latency and high throughput for PC. The MBV based PC utilizes <2% slices, operating at 493.102 MHz, and consumes 0.1 W total power on Artix-7 FPGA. The proposed PC considers only 4 clock cycles to classify the incoming packets and provides 74.95 Gbps throughput. The comparative results in terms of hardware utilization and performance efficiency of proposed work with existing similar PC approaches are analyzed with better constraints improvement.
Comparative study between metaheuristic algorithms for internet of things wireless nodes localization Rana Jassim Mohammed; Enas Abbas Abed; Mostafa Mahmoud El-gayar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp660-668

Abstract

Wireless networks are currently used in a wide range of healthcare, military, or environmental applications. Wireless networks contain many nodes and sensors that have many limitations, including limited power, limited processing, and narrow range. Therefore, determining the coordinates of the location of a node of the unknown location at a low cost and a limited treatment is one of the most important challenges facing this field. There are many meta-heuristic algorithms that help in identifying unknown nodes for some known nodes. In this manuscript, hybrid metaheuristic optimization algorithms such as grey wolf optimization and salp swarm algorithm are used to solve localization problem of internet of things (IoT) sensors. Several experiments are conducted on every meta-heuristic optimization algorithm to compare them with the proposed method. The proposed algorithm achieved high accuracy with low error rate (0.001) and low power consumption.
Machine learning model for clinical named entity recognition Ravikumar J.; Ramakanth Kumar P.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1689-1696

Abstract

To extract important concepts (named entities) from clinical notes, most widely used NLP task is named entity recognition (NER). It is found from the literature that several researchers have extensively used machine learning models for clinical NER.The most fundamental tasks among the medical data mining tasks are medical named entity recognition and normalization. Medical named entity recognition is different from general NER in various ways. Huge number of alternate spellings and synonyms create explosion of word vocabulary sizes. This reduces the medicine dictionary efficiency. Entities often consist of long sequences of tokens, making harder to detect boundaries exactly. The notes written by clinicians written notes are less structured and are in minimal grammatical form with cryptic short hand. Because of this, it poses challenges in named entity recognition. Generally, NER systems are either rule based or pattern based. The rules and patterns are not generalizable because of the diverse writing style of clinicians. The systems that use machine learning based approach to resolve these issues focus on choosing effective features for classifier building. In this work, machine learning based approach has been used to extract the clinical data in a required manner
An approximation of balanced score in neutrosophic graphs with weak edge weights V. Srisarkun; C. Jittawiriyanukoon
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5286-5291

Abstract

Neutrosophic concept is known undirected graph theory to involve with complex logistic networks, not clearly given and unpredictable real life situations, where fuzzy logic malfunctions to model. The transportation objective is to ship all logistic nodes in the network. The logistic network mostly experiences in stable condition, but for some edges found to be volatile. The weight of these erratic edges may vary at random (bridge-lifting/bascule, ad hoc accident on road, traffic condition) In this article, we propose an approximation algorithm for solving minimum spanning tree (MST) of an undirected neutrosophic graphs (UNG), in which the edge weights represent neutrosophic values. The approximation upon the balanced score calculation is introduced for all known configurations in alternative MST. As the result, we further compute decisive threshold value for the weak weights amid minimum cost pre-computation. If the threshold triggers then the proper MST can direct the decision and avoid post-computation. The proposed algorithm is also related to other existing approaches and a numerical analysis is presented.
A novel methodology for time-domain characterization of a full anechoic chamber for antennas measurements and exposure evaluation Chakib Taybi; Mohammed Anisse Moutaouekkil; Bachir Elmagroud; Abdelhak Ziyyat
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3285-3292

Abstract

In this paper we present a novel methodology for time-domain characterization of a full anechoic chamber using the finite integral method. This approach is considered fast, accurate and not intensive for computer resources. The validation of this approach is carried out on CST-microwave studio for a full anechoic chamber intended for antennas measurement applications and electromagnetic exposure evaluation for cellular network. Low, medium and high gain sources are used in this study. The simulations are realized on a personal computer of medium performances (i7 CPU and 16 GB of RAM). The stability and the convergence of our approach are obtained thanks to local mesh and auto-regressive linear filtering techniques. The minimization of the simulation time is based on use of the Huygens sources in the place of the antennas. The maximum error of the chamber as well as the wave depolarization into the chamber are at one with the previous work and the catalogs of the principles chambers manufacturers for the proposed tests in this paper. The Full simulations time is about 15 hours in average.
Hybrid solar/wind/diesel water pumping system in Dubai, United Arab Emirates Waleed Obaid; Abdul-Kadir Hamid; Chaouki Ghenai
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2062-2067

Abstract

This paper proposes a hybrid power system design for water pumping system in Dubai (Latitude 25.25 &deg;N and Longitude 55 &deg;E), United Arab Emirates using solar photovoltaic (PV) panels, wind turbines, and diesel generator. The proposed design considers the changes in weather conditions (humidity percentage, temperature in celsius, and wind speed in m/s) that directly affect solar irradiance values which alter the performance of the hybrid system. The proposed design deals with the problem of rare rainy days in Dubai between December and March and the high temperature throughout the year since that makes providing water to rural and isolated zones essential. The proposed system uses voltage regulator to maintain stable DC voltage from the solar power system, battery bank to store the voltage from solar PV panels, three-phase rectifier to convert the AC voltage from wind power system to DC, three-phase step-down transformers to reduce the AC voltage of the wind turbine and diesel generator, and DC electric motor for water pumping output. The system used neural network for solar irradiance forecasting over an interval of 10 years (from 2009 to 2019). The proposed system will be demonstrated using Simulink to show the stability and performance under different weather conditions.
A hybrid algorithm for voltage stability enhancement of distribution systems Hazim Sadeq Mohsin Al-Wazni; Shatha Suhbat Abdulla Al-Kubragyi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp50-61

Abstract

This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature.
Electrical characterization of si nanowire GAA-TFET based on dimensions downscaling Abdul-Kadir, Firas Natheer; Hashim, Yasir; Shakib, Muhammad Nazmus; Taha, Faris Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp780-787

Abstract

This research paper explains the effect of the dimensions of Gate-all-around Si nanowire tunneling field effect transistor (GAA Si-NW TFET) on ON/OFF current ratio, drain induces barrier lowering (DIBL), sub-threshold swing (SS), and threshold voltage (VT). These parameters are critical factors of the characteristics of tunnel field effect transistors. The Silvaco TCAD has been used to study the electrical characteristics of Si-NW TFET. Output (gate voltage-drain current) characteristics with channel dimensions were simulated. Results show that 50nm long nanowires with 9nm-18nm diameter and 3nm oxide thickness tend to have the best nanowire tunnel field effect transistor (Si-NW TFET) characteristics.
Automated object detection of mechanical fasteners using faster region based convolutional neural networks M. Karthikeyan; T. S. Subashini
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5430-5437

Abstract

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detection
IoT based on secure personal healthcare using RFID technology and steganography Khan, Haider Ali; Abdulla, Raed; Selvaperumal, Sathish Kumar; Bathich, Ammar
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3300-3309

Abstract

Internet of things (IoT) makes it attainable for connecting different various smart objects together with the internet. The evolutionary medical model towards medicine can be boosted by IoT with involving sensors such as environmental sensors inside the internal environment of a small room with a specific purpose of monitoring of person's health with a kind of assistance which can be remotely controlled. RF identification (RFID) technology is smart enough to provide personal healthcare providing part of the IoT physical layer through low-cost sensors. Recently researchers have shown more IoT applications in the health service department using RFID technology which also increases real-time data collection. IoT platform which is used in the following research is Blynk and RFID technology for the user's better health analyses and security purposes by developing a two-level secured platform to store the acquired data in the database using RFID and Steganography. Steganography technique is used to make the user data more secure than ever. There were certain privacy concerns which are resolved using this technique. Smart healthcare medical box is designed using SolidWorks health measuring sensors that have been used in the prototype to analyze real-time data.

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