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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 9,174 Documents
 667   668   669   670   671 
Outlier tolerant adaptive sampling rate approach for wireless sensor node Sunil Kumar Selvaraj; Venkatramana Bhat Pundikai
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp281-289

Abstract

Nowadays the wireless sensor network (WSN) has been used for variety of applications and still lot of research in progress around the corner for the betterment of the wireless sensor network technology. In this paper, one such issues related to energy consumption in sensor node due to fixed sampling interval of sensing unit and its impact on redundant data is discussed with a possible solution. The association of sampling interval and its impact on energy dissipation in sensor node enforces the need for study on energy efficient adaptive sampling interval approach. The lack of serious consideration of outlier in sensor data degrades the performance of the existing adaptive sampling interval approach. The result of the proposed approach of in-network clustering algorithm shows the better efficiency towards detecting the outlier in real time. The results also showcase the better efficiency of proposed approach in terms of rapid optimization of sampling interval compared to simple variance based approach.
Secure cloud adoption model: novel hybrid reference model Aiman Athambawa; Md Gapar Md Johar; Ali Khathibi
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp936-943

Abstract

This article discusses research conducted to conceptualise a secure cloud adoption model. The study surveyed SMEs in the Sri Lankan information technology industry using a questionnaire to determine cloud computing adoption factors. The study used Rogers' diffusion of innovation (DOI), Tornatzky and Fleischer's technology-organization-environment (TOE) framework, Venkatesh and Bala's technology acceptance model 3 (TAM3), and Venkatesh, Thong, and Xu's Unified theory of acceptance and use of technology 2 (UTAUT2) as the theoretical foundation for evaluating the reference model. Two hundred and fifty-six key officials from information technology (IT) organisations in Sri Lanka participated in the survey. The study used quantitative data coding and analysis methods with the SPSS and AMOS softwares. The findings from previous research and existing technology adoption frameworks and models were summarised to support the secure cloud adoption model (SCAM).
Modelling and proportional-integral-derivative controller design for position analysis of the 3-degree of freedom Nur Syahirah Eshah Budin; Khairuddin Osman
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp62-70

Abstract

A closed-loop system or which can also be known as a feedback system helps the system to achieve the desired output by comparing the input and the output values. If any difference is detected, the closed-loop system will create an error signal and automatically responds to it. Other than that, the proportional-integral-derivative (PID) controller has a feedback mechanism. Thus, this creates the curiosity whether the closed-loop system and PID which both have the characteristic of a feedback system, can give the same. In this paper, the comparison of the model of 3 degree of freedom (DOF) Mitsubishi RV2-AJ is being made between two models of a robot arm that has a closed-loop system but only one that is embedded with PID controller while the other one is not, these two are simulated for different positions. The new model is created by using Solidworks which is later exported to Matlab-Simulink. The results from MATLAB-Simulink show that the model which is equipped with a PID controller has better results in terms of the rise time and percentage of overshoot. These results confirm the effectiveness of PID controller in producing smaller errors in the systems even when both models are created together with closed-loop systems.
Color-adjustable phosphors Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ impact on luminescence of white light-emitting diodes My Hanh Nguyen Thi; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp728-734

Abstract

We used the ceramic approach to synthesize a sequence of Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ within our study. Especially, the attributes of Sr4La(PO4)3O that were extensively investigate are luminous characteristics, thermal stableness, and energy transfering from Ce3+ to Tb3 and Mn2+.Through energy transfer, sensitizer Ce3+ ions can considerably boost the fragile green radiation from Tb3+ and red radiation from Mn2+. The color of the radiation can be changed by adjusting the Ce3+/Tb3+ and Ce3+/Mn2+ ion ratios. The Sr4La(PO4)3O: 0.12Ce3+, 0.3Mn2+ specimen produced white light having hue coordinates of (0.3326, 0.3298). According to this result, it shows that Sr4La(PO4)3O:Ce3+,Tb3+,Mn2+ might be used in white light-emitting diodes (WLEDs).
Configuration of an IoT microhydraulic power generation system for education Zaira Pineda-Rico; Pedro Cruz Alcantar; Ulises Pineda-Rico; Francisco Javier Martinez-Lopez
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp44-51

Abstract

Internet of things (IoT) involves the communication of all kinds of things embedded with sensors, electronics, software and people connected to the internet. Knowledge of IoT in the classroom provides an experience for engineering students to explore different career options. Under this scope, an IoT platform on the Arduino UNO and Raspberry Pi 3 development boards was built for academic purposes. The IoT platform was configured to monitor a microhydraulic power generation system used for the study of small-scale hydraulic power production, using a hydraulic head provided by a system of hydraulic pumps in series and/or parallel connection. The platform was designed considering a monitoring station for the acquisition of analog, digital, SPI and PWM data; a control station that receives data from the monitoring station and sends data to the cloud. The communication between modules was established using a publication/subscription system. The platform allows to registrate, visualize and process data directly in the cloud. Meaning that the IoT systems connected to this platform can be monitored from a cell phone, tablet or PC with internet access, promoting immediate access to the emerging information generated in the operating system.
A survey of exact motif finding algorithms Ali Basim Yousif; Hussein Keitan Al-Khafaji; Thekra Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp1109-1118

Abstract

Deoxyribonucleic acid (DNA) motif finding (discovery/mining) in biological chains is the most recent challenging and interesting trend in bioinformatics. It represents a crucial phase in most bioinformatics systems related to unravelling the secrets of gene functions. Despite the efforts made to date to produce robust algorithms, DNA motif finding remains a difficult task for researchers in this field. In general, biological pattern locating algorithms are categorized into two categories: probabilistic and numerical methods. In this paper, we provide a survey of exact DNA motif finding algorithms and their working principles with a suitable comparison among these algorithms to provide an essential step for researchers in this field.
Converting 2D magnetic resource imagining brain tumors to 3D structure using depth map machine learning techniques K. A. Mohamed Riyazudeen; Mohamed Sathik
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp513-520

Abstract

The use of medical imaging technology aids clinicians in recognizing and assessing patient problems, as well as improving treatment procedures. However, while conducting complex procedures such as the excision of brain tumors, the knowledge and biological research gathered from 2D images are insufficient. Converting 2D images to 3D images may assist doctors in determining the size, shape, and sharp area of tumor cells in the brain. The feasibility of translating 2D medical image data to a 3D model is described in this work. A suggested framework for predicting the size, shape, and location of a brain tumor using a minimized genetic machine learning method, and then converting the tumor information into 3D images using a depth map estimation approach after detecting the tumor information. When the tumor is located, the left and right view data are combined to form a 3D magnetic resonance imaging reconstruction. We used mixed reality methods to minimize file size while preserving the greatest quality of the model during a brain surgical operation.
Low feature dimension in image steganographic recognition Ismail Taha Ahmed; Norziana Jamil; Baraa Tareq Hammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp885-891

Abstract

Steganalysis aids in the detection of steganographic data without the need to know the embedding algorithm or the "cover" image. The researcher's major goal was to develop a Steganalysis technique that might improve recognition accuracy while utilizing a minimal feature vector dimension. A number of Steganalysis techniques have been developed to detect steganography in images. However, the steganalysis technique's performance is still limited due to their large feature vector dimension, which takes a long time to compute. The variations of texture and properties of an embedded image are clearly seen. Therefore, in this paper, we proposed Steganalysis recognition based on one of the texture features, such as gray level co-occurrence matrix (GLCM). As a classifier, Ada-Boost and Gaussian discriminant analysis (GDA) are used. In order to evaluate the performance of the proposed method, we use a public database in our proposed and applied it using IStego100K datasets. The results of the experiment show that the proposed can improve accuracy greatly. It also indicates that in terms of accuracy, the Ada-Boost classifier surpassed the GDA. The comparative findings show that the proposed method outperforms other current techniques especially in terms of feature size and recognition accuracy.
Thermodynamic properties in quantum dot nanocomposite for white light-emitted diodes Phuc Dang Huu; Phan Xuan Le
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp632-637

Abstract

For a colour conversion substance, we developed a new nanocomposite containing a CdSe/CdS/ZnS red-emitted quantum dot (QD), a green-emittedd Sr2SiO4:Eu phosphorus, and silicon resins. Regarding QD concentration and ingredients, the heat increase and optic features of the nanocomposite attributable to the QD inclusion were examined. According to the findings, a modest portion of QDs added to a photon converter at the emission wavelength of QD produced a considerable degree of heat. We used 0.2 wt% QDs over an InGaN blue-emitting light-emitted diodes (LED) chip to simulate a thermal increase in a nanocomposite. Consequently, we were able to produce a white-emitted LED module featuring a good 83.2 colour rendered index, an excellent 65.86 lm W-1 brightness, and a reasonable 94 °C thermal rise. The recently founded QD-phosphorus nanocomposite transformed white-emitted LED offers a lot of possibility of modern lighting.
Improving the efficiency of machine learning models for predicting blood glucose levels and diabetes risk Kriengsak Yothapakdee; Sarawoot Charoenkhum; Tanunchai Boonnuk
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 1: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i1.pp555-562

Abstract

Fasting blood glucose is used as an indicator in the process of predicting diabetes risk. This research aims to, i) create a model for predicting blood glucose level using data mining algorithms, ii) a selection algorithm was used to select a feature from the correlation of the data, and iii) to compare the model's performance with the classical methods. All clinical data ware recorded and compiled in a database by hospital staff from 2014-2019. In our previous research, the blood glucose prediction model had an acceptable accuracy where 18 patient features were used as input data to the data mining process. In this research, we demonstrated that the random forest classifier and extra tree classifier algorithms have an outstanding in discarding non-critical attributes. And the process of reducing the number of those features has impacted the glycemic prediction model with higher efficiency. Seventeen machine learning algorithms are used to find the best performance models. Our results clearly show that the improved prediction model is more efficient. This experiment has shown that improvements to our proposed model were able to predict blood glucose levels with 99.69% and 99.63% accuracy for random forest classifier, extra tree classifier, and Gaussian process classifier, respectively.

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