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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 26, No 2: May 2022" : 64 Documents clear
Inert and mobile agents navigation interaction using reciprocal velocity obstacles for collisions avoidance Susi Juniastuti; Moch Fachri; Supeno Mardi Susiki Nugroho; Mochamad Hariadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1116-1124

Abstract

Reciprocal velocity obstacles (RVO) is a method used for multiagents navigation that enables collision and oscillation-free avoidance against other mobile agents. Despite its ability in collision avoidance between agents, RVO has a hard time dealing with static obstacle avoidance. This problem has led to a tendency to use RVO only for agents avoidance and use other methods to handle static obstacles avoidance. In this paper, we present our new approach for interaction between mobile agents against static obstacles in the RVO based collision avoidance. We propose a concept called inert agents that interact as static obstacles. This inert agent is stand firm as static obstacles should be, while the inert agent also able to satisfy reactive collision avoidance nature of RVO to produce better avoidance result. We conduct an experiment to compare the performance of avoidance in a certain scenario. Our method shows better results when compared with generic static obstacles.
Recognition of Arabic handwritten words using convolutional neural network Asmae Lamsaf; Mounir Ait Kerroum; Siham Boulaknadel; Youssef Fakhri
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1148-1155

Abstract

A new method for recognizing automatically Arabic handwritten words was presented using convolutional neural network architecture. The proposed method is based on global approaches, which consists of recognizing all the words without segmenting into the characters in order to recognize them separately. Convolutional neural network (CNN) is a particular supervised type of neural network based on multilayer principle; our method needs a big dataset of word images to obtain the best result. To optimize our system, a new database was collected from the benchmarking Arabic handwriting database using the pre-processing such as rotation transformation, which is applied on the images of the database to create new images with different features. The convolutional neural network applied on our database that contains 40320 of Arabic handwritten words (26880 images for training set and 13440 for test set). Thus, different configurations on a public benchmark database were evaluated and compared with previous methods. Consequently, it is demonstrated a recognition rate with a success of 96.76%.
A new topology of multilevel inverter with switches count reducing at symmetrical/asymmetrical mode Mohammed D. Albakhait; Arwa Amer Abdulkareem
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp656-666

Abstract

The multi-level inverter (MLI) has an important role in modern technologies due to its advantages. On the other hand, its circuits need a large number of switches, capacitors and direct current (DC) sources. This paper introduces a new topology for a MLI with a reduction in number of switches, no need for capacitors in exchange for an increase in number of levels in the output. The proposed model is operated in symmetric and asymmetric modes with the presence of resistive and inductive loads. Whereas, (5 and 9) output levels were obtained in symmetric and asymmetric modes, respectively. In contrast, the number of switches was halved and without need for capacitors, compared to the conventional MLI topologies not a secret that reducing the number of switches has the effect of reducing cost and complexity, in addition to the problems of balancing the voltage on capacitors. The programming environment used to build the proposed model of the MLI was MATLAB/Simulink, where the validity of the hypotheses contained in this paper were proved and the obtained results are identical to what was planned under different loads and different operation modes. In addition, the paper included a comparison study among the proposed topology and conventional topologies in terms of the number of switches, capacitors and sources.
An approach to analysis of arabic text documents into text lines, words, and characters Hakim A. Abdo; Ahmed Abdu; Ramesh Manza; Shobha Bawiskar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp754-763

Abstract

Text line extraction from a text document image and segmenting it into isolate words and segmenting these words into individual characters are considered as one of the most critical processes in OCR systems development and turning the document into a searchable electronic representation, this paper presents a new approach to analyze the Arabic text documents, the proposed approach contains four steps, preprocessing, text line segmentation, word segmentation, character segmentation. The horizontal projection method are used to detect and extract the text line from preprocessed text documents image, in word segmentation step The space threshold are computed to determine the spaces among connected components in text line as within-word space or between-words space for segmenting the text line into isolate words, finally thinning method applied to find the skeleton of segmented word and analyses geometric characteristics of the characters to detect ligatures and characters. The proposed approach was tested and evaluated on a set of 115 text images, this set contains images from the KFUPM Handwritten Arabic TexT (KHATT) database and some images produced by the authors. The experiment results are extremely encouraging, with a success rate of 98.6% for lines segmentation, 96% for words segmentation, and 87.1% for characters segmentation.
Large-scale parameter modelling for millimeter-wave multiple-input multiple-output channel in 5G ultra-dense network Olabode Idowu-Bismark; Francis Idachaba; Aderemi A. Atayero
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp794-807

Abstract

Network densification (ND) in 5G has been suggested as a solution to improve network capacity. ND has small cell backhaul as its bottleneck in the ensuing ultra-dense network (UDN). Due to the new deployment scenarios of small cells, it becomes necessary to thoroughly investigate the radio-propagation characteristics of the new transmission path between the base station and the small cells. The problem of the impact of small cell height on the backhaul large-scale parameters under typical outdoor-to-indoor (high-rise) and outdoor-to-outdoor (street canyon) scenarios was first investigated. Next, the probability distribution functions of the various parameters were investigated and modeled. Novel use of 5G NR air interface using a deterministic ray-tracing engine to characterize the backhaul at 28 GHz center frequency and 100 MHz bandwidth using 4x4 cross-polarized uniform planar array (UPA) at the base station and 2x2 multiple input, multiple output (MIMO) antennas at the small cells was proposed. New sets of models for root mean square (RMS) delay spread and RMS angular spread suitable for predicting network deployment in the two scenarios and similar environments were presented.
A single port frequency reconfigurable antenna for underlay/interweave cognitive radio Laith Wajeeh Abdullah; Adheed H. Saloomi; Ali Khalid Jassim
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp859-868

Abstract

A frequency reconfigurable antenna is presented in this paper as a novel single port system gathering the functionalities of both underlay and interweave cognitive radio. This 25×30×0.8 mm3 system involves a wide slot antenna to cover the ultra wide band (UWB) range with resonating stubs that are used to prohibit/work-only-in specific bands within the UWB frequencies. Here, six positive intrinsic negative (PIN) diodes are used to decide the active sections of the antenna that leads to select its operation as UWB/filtering/multiband antenna. Diodes' configuration results in eight useful operation modes that include a scanning mode, four single band-notch modes and three dual band communication modes. The scanning mode covers the entire UWB range while one of the bands allocated for WiMax, Cband, WLAN or Xband is to be excluded in each of the band-notch modes. On the other hand, each communaication mode is able to work in one of the ranges that cover WiMax/Cband, Cband/WLAN or Xband/international telecommunication union (ITU). S11, realized gain, voltage standing wave ratio (VSWR) outcomes of this design that is simulated by computer simulation technology (CST) v.10 all confirms the proposed system's ability to work in the intended modes. Its novelity to work as interweave/underlay cognitive radio system, highly candidates this design to address many of the UWB communication issues related interference and multiband operation
Absorption performance of biomass hollow pyramidal microwave absorber using multi-slot array technique Mas Izzati Fazin; Ahmad Rashidy Razali; Mohd Nasir Taib; Norhayati Mohamad Noor; Linda Mohd Kasim; Nazirah Mohamat Kasim; Hasnain Abdullah Idris
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp895-902

Abstract

Electromagnetic interference (EMI) is an undesired electromagnetic (EM) wave by nearby electronic devices, process equipment, and measuring instruments. In this work, a novel multi-slot technique is applied to the hollow biomass pyramidal microwave absorber to study its absorption properties thoroughly. Two different slot arrangements in horizontal and vertical configuration are designed for the proposed microwave absorbers. Both slot design concepts have identical shape and size. This work aims to study, compare and analyze the absorption performance of the proposed designs at L, S, C and X frequency bands. The biomass material is used to form as absorbent material. The characteristics performance of the multi-slot design on biomass hollow pyramidal microwave absorbers are measured by using naval research laboratory (NRL) Arch space-free method. The frequency range set up for the measurement is in between 1 GHz to 12 GHz. The multi-vertical slots design exhibits better absorption performance at C-band and X-band which is -63.67 dB and -46.78 dB respectively while the multi-horizontal slots design provides better absorption performance at S-band which is -16.92 dB. The results shows that both design performances are frequency-dependent since horizontal slots design improve maximum absorption performance at low frequency while vertical slots design delivers better performance at high frequency. 
Clustering similar time series data for the prediction the patients with heart disease Raid Luaibi Lafta; Mohanad S. AL-Musaylh; Qahtan Makki Shallal
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp947-954

Abstract

Developed intelligent technologies are become play a promising role in providing better decision-making and improving the medical services provided to the patients. A risk prediction task for short-term is big challenge task; however, it is a great importance for recommendation systems in health care field to provide patients with accurate and reliable recommendations. In this work, clustering method and least square support vector machine are used for prediction a short-term disease risk prediction. The clustering similar method is based on euclidean Distance which used to identify the similar sliding windows. The proposed model is trained by using the slide windows samples. Finally, the appropriate recommendations are generated for heart diseases patients who need to take a medical test or not for following day using least square support vector machine. A real dataset which collected from heart diseases patient is used for evaluation. The proposed method yields a good results related by the recommendations accuracy generated to chronicle heart patients and reduce the risk of incorrect recommendations.
Design and analysis of wide and multi-bands multi-input multi-output antenna for 5G communications Karrar Shakir Muttair; Ali Zuhair Ghazi Zahid; Oras Ahmed Shareef; Raed Hameed Chyad Alfilh; Ahmed Mohammed Qasim Kamil; Mahmood Farhan Mosleh
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp903-914

Abstract

A good antenna design has played an essential role in the design of wireless communication systems, international companies are looking for the best design that suits their products in terms of size, bandwidth, gain, cost, and performance. In this paper, three antenna models are designed for fifth-generation (5G) communications, the first model is a single antenna, the second model is a two-ports multi-input multi-output (MIMO) antenna, and the third model is a four-ports MIMO antenna. The geometric dimensions of a single antenna are 20×37×1.6 mm3, the two-ports antenna dimensions are 44×37×1.6 mm3, while the four-ports antenna dimensions are 74×44×1.6 mm3. The design of these antennas was based on the latest strategies in terms of their small sizes and operating from 13.5 to 20 GHz in wide and multiple bands to be compatible with all advanced communication devices. Based on the results that emerged, it was noted that the reflection coefficient (S11) < -10 dB and has better isolation between the ports is < -26 dB. While the envelope correlation coefficient (ECC) value is < 1.036×10-9, and the diversity gain (DG) value is 10 dB. All antennas proposed operate in ultra-wideband (UWB) which is very necessary for 5G communications devices.
Sentiment analysis system for COVID-19 vaccinations using data of Twitter Eman Thabet Khalid; Entesar B. Talal; Methaq K. Khamees; Ali A. Yassin
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1156-1164

Abstract

COVID-19 vaccination topic has been a hot topic of discussions on social media platforms wondering its effectiveness against the SARS-COV-2 virus. Twitter is one of the social media platforms that people widely lunched to express and share their thoughts about different issues touching their daily life. Though many studies have been undertaken for COVID-19 vaccine sentiment analysis, they are still limited and need to be updated constantly. This paper conducts a system for COVID-19 vaccine sentiment analysis based on data extracted from Twitter platform for the time interval from 1st of January till the 3rd of Sep. 2021, and by using deep learning techniques. The introduced system proposes to develop a model architecture based on a deep bidirectional long short-term memory (LSTM) neural network, to analyze tweets data in the form of positive, neutral, and negative. As a result, the overall accuracy of the developed model based on validation data is 74.92%. The obtained outcomes from the sentiment analysis system on collected tweets-data of COVID-19 vaccine revealed that neutral is the prominent sentiment with a rate of 69.5%, and negative sentiment has less rate of tweets reached 20.75% while the positive sentiment has a lesser rate of tweets reached of 9.67%.

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