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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 66 Documents
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Search results for , issue "Vol 24, No 1: October 2021" : 66 Documents clear
Hybrid basis vector based underdetermined beamforming algorithm in optimized antenna reconfiguration Krupa Prasad K. R.; H. D. Maheshappa
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp367-375

Abstract

Optimized positioning of antenna to obtain the best beam forming solution is adopted in this research. Non-uniform linear array-based beamforming algorithms have the challenge of placing the array of antennas in positions that would implement best beamforming outputs. This paper attempts to obtain the optimized beam forming by tuning the sparse Bayesian learning based algorithm. The parameters used for tuning involve choosing the hybrid basis vector for creating the steering vector while at the same time developing the optimized position of the antennas. Basis vectors are the building blocks of the steering vector developed for the beamforming algorithm that finds the angle of arrival in antennas. Reconfiguration of antennas is carried out using particle swarm optimization (PSO) algorithm and the basis vectors are generated using two different ways. One by cumulating similar basis vectors and another by cumulating two different basis vectors. The performance of accurate detection of angle of arrival in the beamforming algorithm is analyzed and results are discussed. This basis vector and antenna distance optimization is adopted on the sparse Bayesian learning paradigm. Performance evaluation of these optimizations in the algorithm is realised by validating the mean square error (MSE) versus signal to noise ratio (SNR) graphs for both the cumulative basis vector and hybrid basis vector cases.
Machine learning based outlier detection for medical data R. Vijaya Kumar Reddy; Shaik Subhani; B. Srinivasa Rao; N. Lakshmipathi Anantha
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp564-569

Abstract

The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.
Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco Loubna Rabhi; Noureddine Falih; Lekbir Afraites; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp581-589

Abstract

Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.
A novel secure biomedical data aggregation using fully homomorphic encryption in WSN Chethana G.; Padmaja K. V.
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp428-443

Abstract

A new method of secure data aggregation for decimal data having integer as well as fractional part using homomorphic encryption is described. The proposed homomorphic encryption provides addition, subtraction, multiplication, division and averaging operations in the cipher domain for both positive and negative numbers. The scheme uses integer matrices in finite field Zp as encryption and decryption keys. An embedded Digital signature along with data provides data integrity and authentication by signature verification at the receiving end. The proposed scheme is immune to chosen plaintext and chosen ciphertext attacks. In the case of homomorphic multiplication, the ciphertext expansion ratio grows linearly with the data size. The computational complexity of the proposed method for multiplication and division is relatively less by 22.87% compared to Brakerski and Vaikantanathan method when the size of the plaintext data is ten decimal digits.
Development of mobile and desktop applications for a fingerprint-based attendance management system Olubunmi Adewale Akinola; Sikiru Olatunde Olopade; Akindele Segun Afolabi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp570-580

Abstract

Mobile application technology has been at the forefront of technological advancement in recent years. This has made life easier, and tasks that were considered herculean have been made easier and executable in a much shorter time than ever. One of such tasks is the process of taking attendance during events (such as lectures and conferences) by scribbling one’s signature and other personal details on a central register. This manual process is cumbersome and inconvenient, especially when a large number of participants are involved. To address this problem, this paper presents an automated solution in which a Java-based mobile application was developed and connected wirelessly to a central database that was created using My structured query language (MySQL) application whose task, among others, was to record attendance information. The database was connected to the backend of the web-based software program which was coded in hypertext pre-processor (PHP) programming language. Authentication was achieved through username, password, and fingerprint information. The system was deployedin a university to log students’ details, time absent, time present and cumulative attendances per month and it was realised that the system was highly effective, efficient and 5 times faster than the conventional paper-based attendance logging technique.
Self-doped carrier as a performance limiting factor of perovskite solar cells: study on tandem-junction cells with SCAPS Md. Sazzadur Rahman; Md. Samiur Rahman; Al Jaber; Suman Miah
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp81-89

Abstract

Doping concentration of the absorber layer plays a vital role in the performance of perovskite solar cells, because not only it has a direct impact on the collection efficiency of the photo generated carriers, but it can also be considered as an indicator of the film quality and aging process for so-called self-doped (unintentionally doped) perovskite absorbers, where the carriers are induced from structural imperfections. To observe its influence on the efficiency of perovskite solar cell, a two-junction solar cell structure MAPbBr3/MAPbI3 is analyzed in this study, employing a novel optoelectrical simulation approach with finite-difference time-domain (FDTD) analysis and solar cell capacitance simulation (SCAPS) program. It is found that, the efficiency of the tandem cell falls from ∼22% to ∼12% as front-cell absorber film degrades from single-crystal quality with low self-doped carrier concentration of the order of 1010cm−3 , to degraded film quality with very high carrier concentration of the order of 1018cm−3 . In contrast, the self-doped carrier concentration of the back-cell absorber illustrates less impact on the efficiency of the cell, especially for thicker front-cell absorber. Thus, this case study gives a simpler but novel insight into the long-term stability of the efficiency of high-performance perovskite solar cells establishing a link between the solar cell performance and the self-doped carrier concentration (doping concentration) of the absorber film.

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