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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.
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Articles 64 Documents
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Search results for , issue "Vol 11, No 3: June 2022" : 64 Documents clear
Design and characterization substrate integrated waveguide antenna for WBANS application Mustafa Mohammed Jawad Abed; Nik Noordini Nik Abd Malik; Noor Asniza Murad; Mona Riza Mohd Esa; Mohd Riduan Ahmad; Ola Hussein Abed Al Radh; Ali Abdulateef Abdulbari; Yaqdhan Mahmood Hussein; Fahad Taha Al-Dhief
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3492

Abstract

Millimetre-wave frequencies are defined as one of the front-runner contenders for body-centric wireless communication. In this study, low-profile antenna with the substrate integrated waveguide (SIW) has been proposed that operate in the band of the millimetre-wave frequency that has been centred at 60 GHz. The proposed antenna has been implemented with the use of the FR4 substrate with εr and tangent loss of 4.3 and 0.025, respectively. The substrate height and size are 1.5875 mm and 20 mm x 20 mm, respectively. The performance of the antenna is evaluated in off-body (free space) and on-body (human voxel model), through simulation. The proposed antenna has an ultra-wideband (UWB) and a specific absorption rate the maximal (SAR) for (10 g) is 0.0344815 W/kg and for (1 g) is 0.0184723 W/kg. It achieves 74% and 63% efficiency in the off and on-body scenario, respectively. The small antenna with the exceptional matching of impedance, low SAR, broad bandwidth, and good efficiency, a good voltage standing wave ratio good (VSWR), and good front-to-back ratio (FBR). As a result, its characteristics make it one of the best potential candidates for the simultaneous transmission and reception of data at (mm-wave) band for WBAN applications.
Design and performance analysis of frequency hopping OFDM based noise reduction DCSK system Mokhalad L. Mohammed; Fadhil S. Hasan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3836

Abstract

The reference chaotic signal in the differential chaos shift keying (DCSK) system causes both bit error rate (BER) and security performance degradation. In this paper, a frequency hopping orthogonal frequency division multiplexing based noise reduction DCSK (FH-OFDM-NR-DCSK) system is proposed to enhance both the BER and security performance of the OFDM-DCSK system. This system combines the advantages of both FH-OFDM-DCSK and NR-DCSK. Rather than creating β separate chaotic samples to utilize as a reference sequence, β/P chaotic samples are created and then replicated P times. Moving average filters of size P are applied after the frequency hopper to average the repeated chips of the reference and data-bearing signals. A new frequency hopping pattern is designed depending on the chaotic map on which the matrix pattern is designed efficiently. The performance of the proposed system is examined, and bit error rate analytic equations of the FH-OFDM-NR-DCSK system over an AWGN and two-path Rayleigh fading channel are derived. The simulation results show that the theoretical BER expressions and simulation performance match. The findings show that for the same spreading factor, the proposed system outperforms the DCSK and FH-OFDM-DCSK when P more than 1. This method decreases noise variance and improves BER without adding to the system's complexity.
A smart water grid network for water supply management systems Ali Adil Ali; Saadi Mohammed Saadi; Tameem Mohammed Mahmood; Salama A. Mostafa
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3227

Abstract

This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.
Using liquid phase precursor method to create a high-efficiency Ca2SiO4:Eu2+ green-emitting phosphor Phuc Dang Huu; Dieu An Nguyen Thi
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3767

Abstract

A standard solid-state reaction (SSR), a new fluid phase preparatory method utilizing LPP-SiO2(sol), and a water-based soluble silicone compound were employed to manufacture green Eu2+-based Ca2SiO4 phosphors liquid phase precursor (LPP-WSS). The generated phosphors feature large excitation spectra in the range of 225–450 nm and a strong green emission reaches the peak value at 502 nm owing to a 4f65d1→4f7(8S7/2) transition of Eu2+. These samples burned at 1100 1C produce the highest luminous intensity. The luminous properties of phosphors, which are manufactured by the liquid phase precursor LPP-WSS technique, were investigated at the range of 0.1-5.0 mol percent of Eu2+, with the maximum emission density observed at the value of 3.0 mol percent of Eu2+. The phosphors produced by the LPP-WSS technique exhibited a more uniform phase dispersion and higher luminous strength than those produced using the other procedures, according to a detailed report based on numerous characterizations. As a result, Ca2SiO4:Eu2+ has an indisputable possibility in white light-emitting diodes WLEDs and fluorescent lighting.
Outage performance analysis of NOMA over log-normal fading distribution in presence of CSI and SIC imperfections Chi-Bao Le; Hong-Nhu Nguyen; Huy Hung Nguyen; Thi-Hau Nguyen; Nhan Duc Nguyen
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3395

Abstract

The evolution of wireless communication networks has introduced various applications that require massive device connectivity and high spectral efficiency. Non-orthogonal multiple access (NOMA) technique is one of the most promising technologies to perform efficiently data transmission. The NOMA technique can allocate the same resource block for two users by super-imposing signals. At the receiver, the signals are separated by performing successive interference cancellation (SIC) technique. For efficient data transmission, the fading and shadowing effects of channels also play a pivotal role. Many researches have considered Rayleigh, Rician, Nakagami-m, and other fading channels in various perspectives. In our paper, a system model based on a NOMA network with two users over log-normal fading distribution in the presence of channel estimation errors and SIC imperfections is proposed. The performance is analyzed in terms of outage probability and simulations are performed with the assistance of Monte-Carlo simulations. The obtained results shown the effectiveness in comparison with the traditionally used fading distributions. The same analysis is also performed in various scenarios of power allocation levels, target rates, and imperfections. The transmit SNR and power allocation of the users are important for efficient communication in any fading distribution as shown in this paper.
Burg power spectral density-based characterization of Doppler blood flow sound during hemorrhoidal artery ligation Daniel Santoso; Oyas Wahyunggoro; Prapto Nugroho
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3309

Abstract

Hemorrhoidal artery ligation (HAL) has become universally accepted minimally invasive treatment of hemorrhoids disease. HAL involves precise identification of the superior rectal arteries supplying hemorrhoidal tissues using ultrasonic Doppler principles. During this process, at least there are three distinct sounds may be encountered by the surgeon. Only the pulsing Doppler sound is useful as it indicates the presence of hemorrhoidal artery. The accuracy based on traditional auscultation is commonly affected by surgeon’s hearing sensitivity and clinical experience. Therefore, automatic Doppler blood flow sound will be a great help in locating hemorrhoidal arteries. In this paper, a method based on the center frequency and kurtosis features extracted from Burg’s power spectral density (PSD) to distinguish three different types of Doppler blood flow sound signal during HAL procedure is proposed. Separability measurement was carried out using K– means clustering with the city block distance and three clusters corresponding to different sound types are successfully formed. In terms of arterial sound detection, an accuracy of 94.11% can be achieved. This result suggests that centre frequency, kurtosis, and maybe some other statistical features extracted from Burg PSD have the potential to be utilized as a means in automatic Doppler blood flow sound recognition.
A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image Nezar Ismat Seno; Muntaser Abdul Wahed Salman; Rabah Nory Farhan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3561

Abstract

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.
Facial expression recognition using HOG and LBP features with convolutional neural network Nadia Shamsulddin Abdulsattar; Mohammed Nasser Hussain
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3722

Abstract

In computer vision, automatic facial expression recognition (FER) continued a difficult and interesting topic. The majority of extant techniques are based on traditional features descriptors such as local binary pattern (LBP) and histogram of oriented gradient (HOG), in which the classifier's hyperparameters are tailored to produce the best recognition accuracies across a single database or a small set of similar databases. This paper integrates the power of deep learning techniques with the LBP and HOG. The LBP and HOG are estimated from each image in the dataset. The resulting dataset is applied to a convolutional neural network (CNN). The architecture of this CNN constitutes three convolutional layers and three max-pooling layers. The output layers involve BatchNormalization, three dense layers, and two dropout layers. The proposed architecture is validated on the extended cohn-kanade dataset (CK+). We obtain improvement in the accuracy of the CNN model from 0.9593 to 0.967 and 0.975 after using the LBP and HOG respectively.
Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks Sattar B. Sadkhan; Doaa Jabbar Mardaw Zaidawi
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3811

Abstract

The majority of recently developed approaches require a significant number of labelled samples. The proposed system are dedicated to using less marked samples for automatic modulation detection in the cognitive radio domain. The proposed signal classifier generative adversarial nets (GANs) methodology is a semi-supervised learning framework that focuses on adversarial analysis GANs are a major step forward in the development of competitive generative networks, and they've spawned a slew of apparently unrelated versions. The discovery of a single geometric form in GAN and its derivatives is one of the paper's key contributions. In three geometric stages, by demonstrate how to train an adversarial generative model: updating the discriminator parameter away from the separating hyperplane, looking for the separating hyperplane, and updating the generator along the usual vector route of the separating hyperplane. The shortcomings in current approaches are shown by this geometric intuition, leading us to suggest a new geometric GAN formulation that maximizes the margin using SVM separating hyperplane. An equilibrium is reached between the discriminator and generator in the geometric GAN, according to our theoretical research. Furthermore, detailed computational results showing the superior efficiency of the GAN engineering network were obtained.
Intelligent multiperiod wind power forecast model using statistical and machine learning model Manisha Galphade; Valmik Nikam; Biplab Banerjee; Arvind Kiwelekar
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3756

Abstract

With the rapidly increasing integration of wind energy into the modern energy grid system, wind energy prediction (WPP) is playing an important role in the planning and operation of an electrical distribution system. However, the time series data of wind energy always has nonlinear and non-stationary characteristics, which is still a great challenge to be accurately predicted. This paper proposes the intelligent wind power forecast model and evaluates to forecast long term, short term and medium term wind power. It uses statistical and machine learning approach for finding the best model for multiperiod forecasting. The model has been tested on Sotavento wind farm historical data, located in Galicia, Spain. The experimental results show that random forest has better accuracy than other models for long term, short term and medium term forecasting. The power prediction accuracy of the proposed model has been evaluated on RMSE, and MAE metrics. The proposed model has shown better accuracy for medium term and long term forecast. The accuracy is improved by 72.12% in case of medium term and 50.49% in case of long term.

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