International Journal of Electrical and Computer Engineering (IJECE)

Journal Information
ISSN / EISSN : 2088-8708 / 2088-8708
Total articles ≅ 4,048
Current Coverage
Archived in

Latest articles in this journal

Muhammad Fauzan Edy Purnomo, Vita Kusumasari, Rudy Yuwono, Rahmadwati Rahmadwati, Rakhmad Romadhoni, Azizurrahman Rafli, Yuyu Wahyu, Akio Kitagawa
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 4125-4134;

In this paper, we acquire the configuration of the left-hand circular polarization (LHCP) array four patches stack triangular truncated microstrip antenna. This construction use the basic corporate feed microstrip-line with modified lossless T-junction power divider on radiating patch for circularly polarized-synthetic aperture radar (CP-SAR) sensor embedded on unmanned aerial vehicle (UAV) with compact, small, and simple configuration. The design of circular polarization (CP) is realized by truncating the whole three tips and adjusting the parameters of antenna at the target frequency, f = 5.2 GHz. The results of characteristic performance and S-parameter for the LHCP array four patches stack antenna at the target frequency show successively about 9.74 dBic of gain, 2.89 dB of axial ratio (Ar), and -10.91 dB of S-parameter. Moreover, the impedance bandwidth and the 3 dB-Ar bandwidth of this antenna are around 410 MHz (7.89%) and 100 MHz (1.92%), respectively.
Vincy Devi V. K, Rajesh R.
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 4281-4288;

In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for down syndrome prevention. In this work, recognition of Down syndrome utilizes a set of facial expression images. Solid geometric descriptor is employed for extracting the facial features from the image set. An AdaBoost method is practiced to gather the required data sets and for the categorization. The extracted information is then assigned and used to instruct the Neural Network using Backpropagation algorithm. This work recorded that the presented model meets the requirement with 98.67% accuracy.
Boumaraf Farid, Boutabba Tarek, Belkacem Sebti
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 3782-3790;

This paper proposes the fundamental aspects of hybrid nonlinear control which is composed of the super twisting algorithm (STA) based second order sliding mode control applying fuzzy logic method (FSOSMC), with pertinent simulation results for a doubly fed induction machine (DFIM) drive. To minimize chattering effect phenomenon due to Signum function employed in sliding mode algorithm, a new method is proposed. This technique consists in replacing the signum function by fuzzy switching function in the SOSMC to minimize flux and torque ripples. This FSOSMC is associated to the double direct torque control DDTC of the doubly fed induction machine (DFIM) by combining the advantages of fuzzy logic (FL) and the advantages of super-twisting sliding mode. The FSOSMC-DDTC strategy is compared with a PI-DDTC and SOSMC-DDTC. Simulation results demonstrate good efficiency and excellent robustness of the hybrid nonlinear controller.
A. K. Kirgizov, S. A. Dmitriev, M. Kh. Safaraliev, D. A. Pavlyuchenko, A. H. Ghulomzoda, J. S. Ahyoev
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 3682-3691;

Effective electricity use can be an option which enables to achieve significant economy while generating and transmitting of electricity. One of the most important things is to improve the electricity quality through reactive power correction up to optimum values. The current article presents the solution to compensate the reactive power in the distribution networks, in GornoBadakhshan Autonomous Oblast (GBAO) with the use of the advanced technologies based on the data collection within real time. The article describes the methodology of fuzzy logic application and bio-heuristic algorithms for the suggested solution effectiveness to be determined. Fuzzy logic application to specify the node priority for compensating devices based on the linguistic matrix power loss and voltage gives the possibility to the expert to take appropriate solutions for compensating devices installation location to be determined. The appropriate (correct) determination of the compensating devices installation location in the electric power system ensures the effective regulation of the reactive power with the least economic costs. Optimization problems related to the active power loss minimization are solved as well as the cost minimization with compensating devices to ensure the values tan(φ) not exceeding 0.35 through reducing multi-objective problem to the single-objective one using linear convolution.
Hoang Thien Van, Vo Tien Anh, Danh Hong Le, Ma Quoc Phu, Hoang-Sy Nguyen
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 4135-4142;

Non-orthogonal multiple access (NOMA) has drawn enormous attention from the research community as a promising technology for future wireless communications with increasing demands of capacity and throughput. Especially, in the light of fifth-generation (5G) communication where multiple internet-of-things (IoT) devices are connected, the application of NOMA to indoor wireless networks has become more interesting to study. In view of this, we investigate the NOMA technique in energy harvesting (EH) half-duplex (HD) decode-and-forward (DF) power-splitting relaying (PSR) networks over indoor scenarios which are characterized by log-normal fading channels. The system performance of such networks is evaluated in terms of outage probability (OP) and total throughput for delay-limited transmission mode whose expressions are derived herein. In general, we can see in details how different system parameters affect such networks thanks to the results from Monte Carlo simulations. For illustrating the accuracy of our analytical results, we plot them along with the theoretical ones for comparison.
Prakash Tunga P., VipulA Singh
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 3964-3976;

In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes.
İlyas Khelafa, Abdelhakim Ballouk, Abdenaceur Baghdad
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 3934-3942;

Many types of research have been interesting by real-time control of urban networks. This paper, basing on a simplified urban traffic model, proposes a novel control approach based on model predictive control concept to reduce congestion and improve the safety of cars on the roads. The contributions of this paper are: First, we consider vehicle heterogeneity, represented by a mathematical model called “S Model” and integrate it with a realtime simulator to evaluate the performance of controllers on real traffic conditions. Second, in order to assess each controller's success under particular circumstances, the structured network-wide traffic controller based on model predictive control (MPC) theory is compared to a fixed time controller (FTC). Using two scenarios, different indicators are tested, i.e total time spent, vehicle number, queue length. The results show that the model predictive control quickly converges, with the different scenarios, and further improves social welfare.
Haider Abdulkarim, Mohammed Z. Al-Faiz
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 4016-4026;

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy
Mostafa Al Gabalawy, Ramy M. Hossam, Shimaa A. Hussien, Nesreen S. Hosny
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 3772-3781;

To link DC power sources to an AC grid, converters are needed. Inverters are the power electronic devices, which are used for this purpose. Conventional inverters employ harmonic filters and transformers that are lossy and expensive. Multilevel inverters (MLIs) are an alternative to conventional ones, proposing reduced total harmonic distortion (THD), increased range of control, and inductor-less design. They generate a stepped waveform, with close similarity to a sine wave. Many distributed sources may be employed in a smart grid. If those sources have minimal THD, the filtering process could be reduced at the point of common coupling. This paper presents two switched capacitor based MLIs, proposing boost capability and low THD. Inverters have inherent charge balancing capability, which eliminates the need for auxiliary circuits and voltage sensors. Inverters switches are modulated using phase opposition disposition pulse-width modulation (PODPWM) method that ease the balancing of the voltage and decrease the losses of switching. Designs were verified by simulation and the output waveforms were introduced.
May Kyi Nyein, Khin Mar Soe
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 4513-4521;

Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus.
Back to Top Top