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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 112 Documents
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Search results for , issue "Vol 13, No 1: February 2023" : 112 Documents clear
Meta-surface (frequency selective surface) loaded high gain directional antenna systems for ultra-wideband applications Mohammed Amer Hamed AL-shamili; Madan Kumar Sharma
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp511-521

Abstract

In this work, a meta-surface (frequency selective surface) loaded high gain directional antenna system is presented. The antenna system is developed using ultra-wideband (UWB) antenna element and meta-surface reflector. The UWB antenna element is designed and simulated without meta-surface reflector. The UWB antenna element has poor impedance bandwidth and directivity. A meta-surface is created using unit cell and equal in the size of the antenna substrate. The meta-surface is placed over the UWB antenna element at optimized height (H=30 mm). The impedance bandwidth, directivity and gain of the proposed antenna are improved by the meta-surface reflector. The proposed antenna is fabricated and experimentally validated by the comparison of the simulated and measured results. The antenna has 3 to 6 GHz wide impedance bandwidth, more than 5 dBi gain and maximum 4.6 dBi directivity at 3.5 GHz frequency. Performance of the proposed antenna is also compared with existing carried out work. Comparatively, the proposed antenna with high directivity is most suitable for IEEE 802.15.4a UWB wireless sensor network (WSN) security application.
Real-time wireless temperature measurement system of infant incubator Irawan Sukma; Wuwus Ardiatna; Novitasari Novitasari; Vera Permatasari; Siddiq Wahyu Hidayat; Asep Rahmat Hidayat; Khusnul Khotimah; Ihsan Supono
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1152-1160

Abstract

The internet of things (IoT) has allowed for ubiquitous measurement. Infant incubator temperature is one of crucial parts that need to be measured, especially for the stability and uniformity temperature. Based on the interpretation of IEC 60601-2-19, we proposed measurement method using IoT with the message queue telemetry transport (MQTT). In the 10,000 packet, the result shows the quality of service (QoS) level 2 of the system has the highest delay, however it has the lowest packet loss data than the other QoS. For 1 hour, the uniformity result and stability can fulfill the standards. Uniformity of 32°C, the lowest difference is point C with 0.32 °C, and the highest difference is point B with 0.75 °C. Uniformity of 36 °C, the lowest difference is point B with 0.27 °C, and the highest difference is point C with 0.79 °C. The stability of 32 °C and 36 °C is 0.32 °C and 0.44 °C, respectively. Moreover, the Kruskal Wallis test shows the highest difference average from point M is point A and B. It occurred because of the point A and B located far from the heater part, so the point A and B colder than point C.
Few-mode optical fiber surface plasmon resonance sensor with controllable range of measured refractive index Wael Abu Shehab; Ahmad Salah; Wael Al-Sawalmeh; Haitham Alashaary
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp454-464

Abstract

A few-mode optical fiber surface plasmon resonance sensor with graphene layer is investigated, firstly, with the aim of studying the behavior of the guided modes and, secondly, with the aim of determining the range of the measured refractive index for some selected few-mode fibers. The results show that as the number of modes propagated in the fiber increases, the maximum sensitivity of a particular mode decreases while the range of the measured refractive index of that mode increases. Also, it is shown that the range can be easily tuned with sensitivity consideration by only adjusting the operating wavelength without any modification of the sensor, which is desirable from practical point of view. In addition, it is shown that the core diameter of the fiber should be chosen according to sensitivity and range needing, where a compromise between them must be found. The study presented in this paper can significantly help in developing new sensing techniques, such as multi-parameter sensing, by monitoring the various responses of the modes. Also, it can be used to customize the sensor for specific sensing applications in various fields, especially to measure refractive indices in subranges of 1.38 to 1.46.
A comparative analysis of chronic obstructive pulmonary disease using machine learning, and deep learning Ramadoss Ramalingam; Vimala Chinnaiyan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp389-399

Abstract

Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms. 
Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition Ravikumar Hodikehosahally Channegowda; Palani Karthik; Raghavendra Srinivasaiah; Mahadev Shivaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp413-424

Abstract

In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015.
Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset Jinsi Jose; Deepa V. Jose
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1134-1141

Abstract

Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset.
Linear regression models with autoregressive integrated moving average errors for measurements from real time kinematics-global navigation satellite system during dynamic test Kok Mun Ng; Ravenny Sandin Nahar; Mamun IbneReaz
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp770-780

Abstract

The autoregressive integrated moving average (ARIMA) method has been used to model global navigation satellite systems (GNSS) measurement errors. Most ARIMA error models describe time series data of static GNSS receivers. Its application for modeling of GNSS under dynamic tests is not evident. In this paper, we aim to describe real time kinematic-GNSS (RTK-GNSS) errors during dynamic tests using linear regression with ARIMA errors to establish a proof of concept via simulation that measurement errors along a trajectory logged by the RTK-GNSS can be “filtered”, which will result in improved positioning accuracy. Three sets of trajectory data of an RTK-GNSS logged in a multipath location were collected. Preliminary analysis on the data reveals the inability of the RTK-GNSS to achieve fixed integer solution most of the time, along with the presence of correlated noise in the error residuals. The best linear regression models with ARIMA errors for each data set were identified using the Akaike information criterion (AIC). The models were implemented via simulations to predict improved coordinate points. Evaluation on model residuals using autocorrelation, partial correlation, scatter plot, quantile-quantile (QQ) plot and histogram indicated that the models fitted the data well. Mean absolute errors were improved by up to 57.35% using the developed models.
Long term temperature stability of thermal cycler developed using low profile microprocessor cooler Setyawan Purnomo Sakti; Adin Okta Triqadafi; Aldi Dwi Putra; Triswantoro Putro; Dewi Anggraeni
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp278-287

Abstract

Developing a low-cost thermal cycler for a polymerase chain reaction (PCR) is becoming interested in the pandemic era caused by viruses. PCR is the standard gold for the diagnostic. However, in a low-income country, the availability of the device is limited. In this work, the development of a thermal cycler uses electronic modules available in the market. The central part is thermoelectric for heating and cooling, an embedded system to control, and a low-profile cooling fan. The system temperature control used a combination of feedforward, bang-bang, and proportional-integral-derivative (PID) control. The control parameter of the PID was successfully obtained by using Chien servo tuning. The feedforward and bang-bang control are used to optimize the cooling cycle and minimize the rise time. The system shows a well-suited temperature accuracy at the denaturation, annealing, and extension temperature with a temperature deviation of less than 0.5 °C. System performance is maintained even though the system has been running non-stop for 24 hours. The low-profile cooling fan, which is usually used for CPU cooling, shows good results in maintaining temperature stability.
An internet of things enabled framework to monitor the lifecycle of Cordyceps sinensis mushrooms Minakshi Memoria; Sanjeev Kumar Shah; Harishchander Anandaram; Anooja Ali; Kapil Joshi; Parag Verma; Rajesh Singh; Anita Gehlot; Shaik Vaseem Akram
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1142-1151

Abstract

Cordyceps sinensis is an edible mushroom found in high quantities in the regions of the Himalayas and widely considered in traditional systems of medicine. It is a non-toxic remedy mushroom and has a high measure of clinical medical benefits including cancer restraint, high blood pressure, diabetes, asthma, depression, fatigue, immune disorder, and many infections of the upper respiratory tract. The cultivation of this kind of mushroom is limited to the region of the Sikkim and to cultivate in the other regions of the country, they are need of investigation and prediction of cordyceps sinensis mushroom lifecycle. From the studies, it is concluded that the precision-based agriculture techniques are limitedly explored for the prediction and growth of Cordyceps sinensis mushrooms. In this study, an internet of things (IoT) inspired framework is proposed to predict the lifecycle of Cordyceps sinensis mushrooms and also provide alternate substrate to cultivate Cordyceps sinensis mushrooms in other parts of the country. As a part of lifecycle prediction, a framework is proposed in this study. According to the findings, an IoT sensor-based system with the ideal moisture level of the mushroom rack is required for the growth of Cordyceps sinensis mushrooms.
Efficient systematic turbo polar decoding based on optimized scaling factor and early termination mechanism Ahmed A. Hamad; Mohammed Taih Gatte; Laith Ali Abdul-Rahaim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp629-637

Abstract

In this paper, an efficient early termination (ET) mechanism for systematic turbo-polar code (STPC) based on optimal estimation of scaling factor (SF) is proposed. The gradient of the regression line which best fits the distance between a priori and extrinsic information is used to estimate the SF. The multiplication of the extrinsic information by the proposed SF presents effectiveness in resolving the correlation issue between intrinsic and extrinsic reliability information traded between the two typical parallel concatenated soft-cancellation (SCAN) decoders. It is shown that the SF has improved the conventional STPC by about 0.3 dB with an interleaver length of 64 bits, and about 1 dB over the systematic polar code (SPC) at a bit error rate (BER) of . A new scheme is proposed as a stopping criterion, which is mainly based on the estimated value of SF at the second component decoder and the decoded frozen bits for each decoding iteration. It is shown that the proposed ET results in halving the average number of iterations (ANI) without adding considerable complexity. Moreover, the modified codes present comparable results in terms of BER to the codes that utilize fix number of iterations.

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