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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 64 Documents
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Search results for , issue "Vol 12, No 1: February 2023" : 64 Documents clear
Performance parameters optimization of CMOS analog signal processing circuits based on smart algorithms Rasheed, Israa Mohammed; Motlak, Hassan Jasim
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4128

Abstract

Designing ideal analogue circuits has become difficult due to extremely large-scale integration. The complementary metal oxide semiconductor (CMOS) analog integrated circuits (IC) could use an evolutionary method to figure out the size of each device. The CMOS operational transconductance amplifier (CMOS OTA) and the CMOS current conveyor second generation (CMOS CCII) are designed using advanced nanometer transistor technology (180 nm). Both CMOS OTA and CMOS CCII have high performance, such as a wide frequency, voltage gain, slew rate, and phase margin, to include very wide applications in signal processing, such as active filters and oscillators. The optimization approach is an iterative procedure that uses an optimization algorithm to change design variables until the optimal solution is identified. In this study, different sorts of algorithms the genetic algorithm (GA), particle swarm optimization (PSO), and cuckoo search (CS) are employed to boost and enhance the performance parameters. While decreasing the time required to develop a conventional operation amplifier's settling time. Some studies decrease the value of the power utilized at various frequencies. Others operate at extremely high frequencies, but their power consumption is greater than that of those operating at lower frequencies.
Real-time military person detection and classification system using deep metric learning with electrostatic loss Suprayitno, Suprayitno; Fauzi, Willy Achmat; Ain, Khusnul; Yasin, Moh.
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4284

Abstract

This study addressed a system design to detect the presence of military personnel (combatants or non-combatants) and civilians in real-time using the convolutional neural network (CNN) and a new loss function called electrostatic loss. The basis of the proposed electrostatic loss is the triplet loss algorithm. Triplet loss’ input is a triplet image consisting of an anchor image (xa), a positive image (xp), and a negative image (xn). In triplet loss, xn will be moved away from xa but not far from both xa and xp. It is possible to create clusters where the intra-class distance becomes large and does not determine the magnitude and direction of xn displacement. As a result, the convergence condition is more difficult to achieve. Meanwhile, in electrostatic loss, some of these problems are solved by approaching the electrostatic force on charged particles as described in Coulomb's law. With the inception ResNet-v2 128-dimensional vectors network within electrostatic loss, the system was able to produce accuracy values of 0.994681, mean average precision (mAP) of 0.994385, R-precision of 0.992908, adjusted mutual information (AMI) of 0.964917, and normalized mutual information (NMI) of 0.965031.
Character level vehicle license detection using multi layered feed forward back propagation neural network Ummadisetti, Ganesh Naidu; Thiruvengatanadhan, R.; Narayana, Satyala; Dhanalakshmi, P.
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4010

Abstract

Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.
Fingerprint-based indoor positioning system using BLE: real deployment study Safwat, Rokaya; Shaaban, Eman; Al-Tabbakh, Shahinaz M.; Emara, Karim
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3798

Abstract

There are a myriad of applications where the localization of interior surroundings is vital in the era of smart cities Bluetooth low energy (BLE) technology is designed for short-range wireless communication, low energy consumption, low cost hardware design and simple deployment with respect to other technologies. This paper presents a low cost BLE fingerprint-based indoor positioning system, where a minimum number of Beacons are deployed in different test bed subareas with different conditions. Collected measured received signal strength indicator (RSSI) signals received from all beacons in each grid cell of all areas of interest are stored. We experimented two deterministic matching algorithms: k-nearest neighbors (KNN) and weighted algorithm (WKNN), to match previously collected RSSI readings with the RSSI at mobile unknown location, to determine where the user is. Experiments results show that WKNN algorithm manages to obtain less mean and standard deviation positioning error for all subareas, that experiencing different conditions of obstructions, reflections, and interferences.
Cluster-based segmentation for tobacco plant detection and classification Thimmegowda, Thirthe Gowda Mallinathapura; Jayaramaiah, Chandrika
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4388

Abstract

Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.
Multimodal music emotion recognition in Indonesian songs based on CNN-LSTM, XLNet transformers Sams, Andrew Steven; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4231

Abstract

Music carries emotional information and allows the listener to feel the emotions contained in the music. This study proposes a multimodal music emotion recognition (MER) system using Indonesian song and lyrics data. In the proposed multimodal system, the audio data will use the mel spectrogram feature, and the lyrics feature will be extracted by going through the tokenizing process from XLNet. Convolutional long short term memory network (CNN-LSTM) performs the audio classification task, while XLNet transformers performs the lyrics classification task. The outputs of the two classification tasks are probability weight and actual prediction with the value of positive, neutral, and negative emotions, which are then combined using the stacking ensemble method. The combined output will be trained into an artificial neural network (ANN) model to get the best probability weight output. The multimodal system achieves the best performance with an accuracy of 80.56%. The results showed that the multimodal method of recognizing musical emotions gave better performance than the single modal method. In addition, hyperparameter tuning can affect the performance of multimodal systems.
Automated water quality monitoring and regression-based forecasting system for aquaculture Wei, Toh Yin; Tindik, Emmanuel Steward; Fui, Ching Fui; Haviluddin, Haviluddin; Hijazi, Mohd Hanafi Ahmad
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4464

Abstract

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.
Design and analysis several band antenna for wireless communication Nghaimesh, ِAbeer Khalid; Jassim, Ali Khalid; Abid Ali, Waleed Khalid
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4238

Abstract

This article describes the construction of a dual-band planar monopole antenna. A microstrip patch antenna with a feedline impedance of 50 ohm and a patch composed of G-shaped and inverted L-shaped strips is used to make the suggested antenna ultra wideband for frequencies ranging from 3.1 to 10.6 GHz. In order to design the antennas, we need to know the dimensions 40x40x1.6 mm3 and the thickness of the ground plane (0.035 mm) (5.2 GHz). There is a method of altering the present distribution by introducing slots. the proposed worldwide interoperability for microwave access (WiMAX) and wireless local area network (WLAN) bands, with a peak gain of 5.2% and an omnidirectional radiation pattern, suitable for ultra wide band (UWB) were shown to be viable.
A comparative study on channel coding scheme for underwater acoustic communication Ahmed, Mustafa Sami; Shah, Nor Shahida Mohd; Al-Aboosi, Yasin Yousif; Ghaleb, Ali Nadhim
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.3827

Abstract

There are various challenges in underwater acoustic communication (UWA) however bit error rate (BER) is considered as the main challenge as it significantly affects the UWA communication. In this paper, different coding schemes such as convolution, turbo, low density parity check (LDPC), and polar coding based on the t-distribution noise channel are investigated, and binary phase-shift keying (BPSK) modulation with a code rate of 1/2 has considered in the evaluation and analyses. The evaluation of these channel coding schemes is performed based on BER, computational complexity as well as latency. The results have shown the outperform of polar coding in UWA over other channel coding schemes as it has lower BER and lower computational complexity.
Classifying thai news headlines using an artificial neural network Chanakot, Benjamin; Sanrach, Charun
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4228

Abstract

This research aimed to measure the effectiveness of Thai news headlines classification using an artificial neural network (ANN). The headlines consisted of i) political news, ii) sports news, iii) economic news, and iv) crime news, 1,200 headlines in total. The distribution of headlines was measured by using chi-square, information gain, and term frequency inverse class frequency (TFICF). Threshold default value was set in relation to terms of headlines before cross-validation was employed to categorize the data to examine the efficiency of the model using a neural network algorithm in classifying the headlines. The investigation of the news headline classification efficiency revealed that the 15-fold data division using TFICF was the most accurate in classifying headlines, with the accuracy rate of 99.60% and F-measure rate of 99.05%. Moreover, it was found that when more news headlines were provided as the learning data, the news headline classification became more accurate. Likewise, appropriate threshold value determination facilitated the selection of appropriate features in the headlines and resulted in more effective and accurate classification. Hence, it can be concluded that headline classification will be more accurate if the appropriate amount of learning data exists, and appropriate threshold value was set.

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