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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 63 Documents
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Search results for , issue "Vol 11, No 2: April 2022" : 63 Documents clear
Comparative study of BER With NOMA system in different fading channels Roselin Suganthi Jesudoss; Rajeswari Kaleeswaran; Manjunathan Alagarsamy; Dineshkumar Thangaraju; Dinesh Paramathi Mani; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3311

Abstract

In today's world, cellular communication is rapidly expanding. One of the most common strategies for assigning the spectrum of users in cellular communication is the multiple access strategy. Because the number of people using cellular communication is continually expanding, spectrum allotment is an important factor to consider. To access the channel in fifth-generation mobile communication, a method known as non-orthogonal multiple access (NOMA) is used. NOMA is a promising method for improving sum rate and spectral efficiency. In this research, we used the NOMA approach to compare the bit error rate (BER) versus signal to noise ratio (SNR) of two users in rayleigh, rician, and nakagami fading channels. A single antenna with two users is used in this NOMA system. Two users can tolerate the same frequency with differing power levels in the power domain using 5G NOMA technology. Non-orthogonality ensures that NOMA users are treated equally to OMA users. According to the MATLAB simulation findings, the BER vs. SNR of two user NOMA in the Nakagami channel is substantially better than the rayleigh and rician channels.
Review on facial expression modeling Noor Adibah Najihah Mat Noor; Norhaida Mohd Suaib
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3558

Abstract

Facial modeling has been an ongoing research for many years and still shows research trend due to its relevance to current technology. Many applications incorporate facial expression modeling with the help of facial tracking, facial animation and facial recognition. The existing performance of the modeling method faces the challenges to perform well due to many factors. Currently, the use of 2D images and videos as inputs for modeling process are gaining popularity. However, current technologies and development had extended the trends towards acquiring 3D human data. This paper provides an overview on variety of modeling techniques based on human facial model that can lead to future research.
Performance improvement of stand-alone induction generator using distribution SSC for wind power application Mahmood T. Alkhayyat; Ziad Saeed Mohammed; Ahmed J. Ali
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.2730

Abstract

Self-excited induction generators (SEIGs) are used in wind turbine system because of high reliability, rigidity, simple structure, and capability to work under severe badly operating conditions. This type of generator has a poor terminal voltage and frequency regulation during changing the connected loads due to the absence of constant excitation current. Therefore, it is essential to stabilize the generated voltage and frequency besides suppress the injected harmonic current components. In this work, the dynamic performance of SEIG with distribution static series compensator (DSSC) is analyzed. The DSSC based on neuro-fuzzy controlled (NFC) is applied to control both voltage and frequency to enhance the regulation of SEIG. The NFC is used to control the DSSC which leads to balance the requirement of the reactive and active power of stand-alone grid under load variation and attempts to obtain a constant terminal voltage. The model is simulated using MATLAB/Simulink. The NFC structure designed to regulate and control the output voltage of the SEIG driven by a wind turbine to feed a consumer in remote and rural places. Furthermore, the power system parameters calculated depending on the d-q theory. Modeling results explained that the suggested controller is consistent and tough related to the conventional types.
Improved modified a multi-level inverter with a minimum total harmonic distortion Khalid M. Abdulhassan; Osama Yaseen Khudair Al-Atbee
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3466

Abstract

Renewable energy sources are developed as a result of the increased demand for electrical power. The nature of the solar energy source is DC. The DC source for many applications needs to be converted to AC. The inverter is used to convert the power from DC to AC. Total harmonic distortion (THD) is a significant concern with inverters. Multi-level inverters are used to reduce the THD. The stair output voltage of the multi-level inverter not only reduces the THD but also reduces the switches' stresses, so a low voltage rating can be used for the switches. In this paper, a modified inverter topology is introduced in which the number of switches is reduced for the same number of output voltage levels, which leads to reducing the losses and the cost. To reduce the THD, different amplitudes for the carrier signals that control the switches in each level are suggested. Another method to reduce the THD by using different capacitor values across the input DC source is presented. The MATLAB/Simulink is used to show the validity of the suggested modified topology and the modifications.
Surveillance system of mask detection with infrared temperature sensor on Jetson Nano Kit Noor Faleh Abdul Hassan; Ali A. Abed; Turki Y. Abdalla
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3369

Abstract

Coronavirus desease-19 (COVID-19) has made it mandatory for people to cover their faces in public areas since the right use of a mask is effective at protecting people from viral infection. It has been also shown that body temperature may indicate an individual's health state. Deep learning is being used in this work to construct an actual strategy to meet the current demand for mask-wearing status and facial temperature detection before entering a public venue. For the mask detection service provider, a surveillance system is constructed utilizing a deep learning technique employing a Jetson Nano. The alert is triggered by an infrared temperature sensor and a buzzer. AMG8833 and C920e camera are used to take input images and measure a person's body temperature at the same time. A warning sound is produced when the temperature of a person's face exceeds the normal range for human beings during these tests, which result in a live video showing the right information on whether the individual is wearing a mask correctly and how hot his or her face is. The model is light and fast, with a 99% accuracy rate for training and a 100% accuracy rate while testing.
Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading Adewale Adeyinka Ajao; Ben Obaje Abraham; Etinosa Noma Osaghae; Okesola Olatunji; Edikan Ekong; Abdulkareem Ademola
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.2575

Abstract

Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.
Feature selection for urban land cover classification employing genetic algorithm Ali Alzahrani; Md. Al-Amin Bhuiyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3399

Abstract

Feature selection has attained substantial research interest in image processing, computer vision, pattern recognition and so on due to tremendous dimensional reduction in image analysis. This research addresses a genetic algorithm based feature selection strategy for urban land cover classification. The principal purpose of this research is to monitor the land cover alterations in satellite imagery for urban planning. The method is based on object based classification by detecting the object area of a given image with the knowledge of visual information of the object from remote sensing images. The classification system is organized through a multilayer perceptron with genetic algorithm (MLPGA). Experimental results explicitly indicate that this MLPGA based hybrid feature selection procedure performs classification with sensitivity 94%, specificity 90% and precision 89%, respectively. This MLPGA centered hybrid feature selection scheme attains better performance than the counterpart methods in terms of classification accuracy.
Cloud resources modelling using smart cloud management Haitham Salman Chyad; Raniah Ali Mustafa; Dena Nadir George
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3286

Abstract

Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).
Comparison study of channel coding on non-orthogonal multiple access techniques Sarmad Khaleel Ibrahim; Nooruldeen Q. Ismaeel; Saif A. Abdulhussien
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3348

Abstract

Some of the benefits of fifth-generation (5G) mobile communications include low latency, fast data rates, and increased amount of perceived service quality of users and base station capacity. The purpose of this paper is to solve some of the problems in the traditional mobile system by increasing the channel capacity, non-orthogonal multiple access (NOMA), has a chance of winning the race, power-domain NOMA (PD-NOMA) is widely used in but it requires a large power imbalance between the signals allocated to various users to work. This paper also proposes an improved mobile system model and compares it with a traditional mobile system, then evaluates the effect of channel coding types on the spectrum efficiency performance. A proposed mobile system relied on increasing the number of users as well as increasing the frequency spectrum and is also proposed to improve the error rate, which is incorporated into NOMA and orthogonal frequency division multiplexing (OFDM) schemes at the same time to provide great flexibility and compatibility with other services, such as the 5G and sixth-generation (6G) systems. The mobile gully system (MGS) system is compared to a traditional system, the result is demonstrated that the proposed outperforms the orthogonal multiple access (OMA) system in terms of sum-rate capacity, and bit error rate (BER) performance.
Multimodal deep learning model for human handover classification Islam A Monir; Mohamed W. Fakhr; Nashwa El-Bendary
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3690

Abstract

Giving and receiving objects between humans and robots is a critical task which collaborative robots must be able to do. In order for robots to achieve that, they must be able to classify different types of human handover motions. Previous works did not mainly focus on classifying the motion type from both giver and receiver perspectives. However, they solely focused on object grasping, handover detection, and handover classification from one side only (giver/receiver). This paper discusses the design and implementation of different deep learning architectures with long short term memory (LSTM) network; and different feature selection techniques for human handover classification from both giver and receiver perspectives. Classification performance while using unimodal and multimodal deep learning models is investigated. The data used for evaluation is a publicly available dataset with four different modalities: motion tracking sensors readings, Kinect readings for 15 joints positions, 6-axis inertial sensor readings, and video recordings. The multimodality added a huge boost in the classification performance; achieving 96% accuracy with the feature selection based deep learning architecture.

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