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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal 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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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The use of reversible logic gates in the design of residue number systems Ailin Asadpour; Amir Sabbagh Molahosseini; Azadeh Alsadat Emrani Zarandi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2009-2022

Abstract

Reversible computing is an emerging technique to achieve ultra-low-power circuits. Reversible arithmetic circuits allow for achieving energy-efficient high-performance computational systems. Residue number systems (RNS) provide parallel and fault-tolerant additions and multiplications without carry propagation between residue digits. The parallelism and fault-tolerance features of RNS can be leveraged to achieve high-performance reversible computing. This paper proposed RNS full reversible circuits, including forward converters, modular adders and multipliers, and reverse converters used for a class of RNS moduli sets with the composite form {2k, 2p-1}. Modulo 2n-1, 2n, and 2n+1 adders and multipliers were designed using reversible gates. Besides, reversible forward and reverse converters for the 3-moduli set {2n-1, 2n+k, 2n+1} have been designed. The proposed RNS-based reversible computing approach has been applied for consecutive multiplications with an improvement of above 15% in quantum cost after the twelfth iteration, and above 27% in quantum depth after the ninth iteration. The findings show that the use of the proposed RNS-based reversible computing in convolution results in a significant improvement in quantum depth in comparison to conventional methods based on weighted binary adders and multipliers.
Recursive convex approximations for optimal power flow solution in direct current networks Jauder Alexander Ocampo-Toro; Oscar Danilo Montoya; Luis Fernando Grisales-Noreña
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5674-5682

Abstract

The optimal power flow problem in direct current (DC) networks considering dispersal generation is addressed in this paper from the recursive programming point of view. The nonlinear programming model is transformed into two quadratic programming approximations that are convex since the power balance constraint is approximated between affine equivalents. These models are recursively (iteratively) solved from the initial point vt equal to 1.0 pu with t equal to 0, until that the error between both consecutive voltage iterations reaches the desired convergence criteria. The main advantage of the proposed quadratic programming models is that the global optimum finding is ensured due to the convexity of the solution space around vt. Numerical results in the DC version of the IEEE 69-bus system demonstrate the effectiveness and robustness of both proposals when compared with classical metaheuristic approaches such as particle swarm and antlion optimizers, among others. All the numerical validations are carried out in the MATLAB programming environment version 2021b with the software for disciplined convex programming known as CVX tool in conjuction with the Gurobi solver version 9.0; while the metaheuristic optimizers are directly implemented in the MATLAB scripts.
Double sliding window variance detection-based time-of-arrival estimation in ultra-wideband ranging systems Ibrahim Yassine Nouali; Zohra Slimane; Abdelhafid Abdelmalek
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6303-6310

Abstract

Ultra-wideband (UWB) ranging via time-of-arrival (TOA) estimation method has gained a lot of research interests because it can take full advantage of UWB capabilities. Energy detection (ED) based TOA estimation technique is widely used in the area due to its low cost, low complexity and ease of implementation. However, many factors affect the ranging performance of the ED-based methods, especially, non-line-of-sight (NLOS) condition and the integration interval. In this context, a new TOA estimation method is developed in this paper. Firstly, the received signal is denoised using a five-level wavelet decomposition, next, a double sliding window algorithm is applied to detect the change in the variance information of the received signal, the first path (FP) TOA is then calculated according to the first variance sharp increase. The simulation results using the CM1 and CM2 IEEE 802.15.4a channel models, prove that our proposed approach works effectively compared with the conventional ED-based methods.
Discovering the spatial locations of the radio frequency radiations effects around mobile towers Zaid Jabbar Al-Allaq; Haidar Zaeer Dhaam; Mohammed Jawad Al Dujaili Al-Khazraji; Muntadhar Hameed Ismael Al-Khuzaie
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1629-1638

Abstract

Nowadays, smart devices have become a major part of human life, and this need has led to an increase in the demand for these devices, prompting major telecommunications companies to compete with each other to acquire the bulk of this market. This competition led to a significant increase in the number of mobile towers, to expand the coverage area. Each communication tower has transmitters and receivers to connect subscribers within the mobile network and other networks. The receivers and transmitters of each mobile tower operate on radio frequency waves. These waves can cause harm to humans if the body tissues absorb the radiation resulting from these waves. Headache, discomfort, and some other diseases are among the effects resulting from the spatial proximity to the mobile towers. In this paper, a model based on geographic information systems (GIS) software is proposed for the purpose of discovering the area of exposure to radio frequency radiation. This model can assists mitigate the opportunities of exposure to these radiations, thus reducing its danger. Real data of the levels of electromagnetic pollution resulting from mobile towers were analyzed during this study and compared with international safety standards.
Synthesis of new antenna arrays with arbitrary geometries based on the superformula Anas A. Amaireh; Nihad I. Dib; Asem S. Al-Zoubi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6228-6238

Abstract

The synthesis of antenna arrays with low sidelobe levels is needed to enhance the communication systems’ efficiency. In this paper, new arbitrary geometries that improve the ability of the antenna arrays to minimize the sidelobe level, are proposed. We employ the well-known superformula equation in the antenna arrays field by implementing the equation in the general array factor equation. Three metaheuristic optimization algorithms are used to synthesize the antenna arrays and their geometries; antlion optimization (ALO) algorithm, grasshopper optimization algorithm (GOA), and a new hybrid algorithm based on ALO and GOA. All the proposed algorithms are high-performance computational methods, which proved their efficiency for solving different real-world optimization problems. 15 design examples are presented and compared to prove validity with the most general standard geometry: elliptical antenna array (EAA). It is observed that the proposed geometries outperform EAA geometries by 4.5 dB and 10.9 dB in the worst and best scenarios, respectively, which proves the advantage and superiority of our approach.
A conceptual architecture for integrating software defined network and network virtualization with internet of things Ali Haider Shamsan; Arman Rasool Faridi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6777-6784

Abstract

Software defined network (SDN) and network function virtualization (NFV) are new paradigms and technologies of the network which support the best experience of providing functions and services, managing network traffic, and a new way of control. They support virtualization and separating data from control in network devices, as well as provide services in a software-based environment. Internet of things (IoT) is a heterogeneous network with a massive number of connected devices and objects. IoT should be integrated with such technologies for the purpose of providing the capabilities of dynamic reconfiguration with a high level of integration. This paper proposes a conceptual architecture for integrating software defined network (SDN) and NFV with IoT. The proposed work combines the three technologies together in one architecture. It also presents the previous works in this area and takes a look at the theoretical background of those technologies in order to give a complete view of proposed work.
Alpha-divergence two-dimensional nonnegative matrix factorization for biomedical blind source separation Abd Majid Darsono; Toh Cheng Chuan; Nurulfajar Abd Manap; Nik Mohd Zarifie Hashim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1483-1490

Abstract

An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal separation is presented. NMF2D is a popular approach for retrieving low-rank approximations of nonnegative data such as image pixel, audio signal, data mining, pattern recognition and so on. In this paper, we concentrate on biomedical signal separation by using NMF2D with alpha-divergence family which decomposes a mixture into two-dimensional convolution factor matrices that represent temporal code and the spectral basis. The proposed iterative estimation algorithm (alpha-divergence algorithm) is initialized with random values, and it updated using multiplicative update rules until the values converge. Simulation experiments were carried out by comparing the original and estimated signal in term of signal-to-distortion ratio (SDR). The performances have been evaluated by including and excluding the sparseness constraint which sparseness is favored by penalizing nonzero gains. As a result, the proposed algorithm improved the iteration speed and sparseness constraints produce slight improvement of SDR.
Human body blockage effect on wireless network performance for outdoor coverage Karrar Shakir Muttair; Mahmood Farhan Mosleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2340-2349

Abstract

The rapid development in the field of communications and growing numbers of the population every year stimulate telecommunications companies to develop communications systems to accommodate all users. In this paper, we will study the blockage effect of the student body on the propagation of the signals in the external wireless network. We took various numbers of the student density on the campus to know the extent it affects especially in crowded environments. The student body structure and buildings are designed in the college according to the real dimensions by Wireless InSite software. We compared scenarios for the different numbers of student density, we noticed that whenever an increase in the student density in the college will lead to increased path loss and delay spread time. In addition, note there is a gradual decrease in the received power (RP) if there is no student density highest RP is -28.2 dBm, when there are 300 students highest RP is -34.7 dBm, and when there are 600 students highest RP is -36.5 dBm. The reasons are that signals path spread inside the college will be passing through several collisions whether student body blockage or buildings that are built from different materials.
Enhanced convolutional neural network for non-small cell lung cancer classification Yahya Tashtoush; Rasha Obeidat; Abdallah Al-Shorman; Omar Darwish; Mohammad Al-Ramahi; Dirar Darweesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1024-1038

Abstract

Lung cancer is a common type of cancer that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy of the diagnosis relies highly on the doctor's expertise. Recently, clinical decision support systems based on deep learning valuable recommendations to doctors in their diagnoses. In this paper, we present several deep learning models to detect non-small cell lung cancer in CT images and differentiate its main subtypes namely adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. We adopted standard convolutional neural networks (CNN), visual geometry group-16 (VGG16), and VGG19. Besides, we introduce a variant of the CNN that is augmented with convolutional block attention modules (CBAM). CBAM aims to extract informative features by combining cross-channel and spatial information. We also propose variants of VGG16 and VGG19 that utilize a support vector machine (SVM) at the classification layer instead of SoftMax. We validated all models in this study through extensive experiments on a CT lung cancer dataset. Experimental results show that supplementing CNN with CBAM leads to consistent improvements over vanilla CNN. Results also show that the VGG variants that use the SVM classifier outperform the original VGGs by a significant margin.
Simulation and performance analysis of self-powered piezoelectric energy harvesting system for low power applications Mohankumar Venugopal; Govindanayakanapalya Venkatagiriyappa Jayaramaiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5861-5871

Abstract

Energy harvesting is a process of extracting energy from surrounding environments. The extracted energy is stored in the supply power for various applications like wearable, wireless sensor, and internet of thing (IoT) applications. The electricity generation using conventional approaches is very costly and causes more pollution in the environmental surroundings. In this manuscript, an energy-efficient, self-powered battery-less piezoelectric-based energy harvester (PE-EH) system is modeled using maximum power point tracking (MPPT) module. The MPPT is used to track the optimal voltage generated by the piezoelectric (PE) sensor and stored across the capacitor. The proposed PE system is self-operated without additional microarchitecture to harvest the Power. The experimental simulation results for the overall PE-EH systems are analyzed for different frequency ranges with variable input source vibrations. The optimal voltage storage across the storing capacitor varies from 1.12 to 1.6 V. The PE-EH system can harvest power up to 86 µW without using any voltage source and is suitable for low-power applications. The proposed PE-EH module is compared with the existing similar EH system with better improvement in harvested power.

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