International Journal of Electrical and Computer Engineering (IJECE)

Journal Information
ISSN / EISSN : 2088-8708 / 2088-8708
Current Publisher: Institute of Advanced Engineering and Science (10.11591)
Total articles ≅ 3,222
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Latest articles in this journal

Chokri Baccouch, Chayma Bahhar, Hèdi Sakli, Nizar Sakli
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1405-1413; doi:10.11591/ijece.v11i2.pp1405-1413

A novel optical rectenna design is presented in this paper to operate in S-band communication. We propose a new method of combining antennas and solar cells to collect and transmit optical and radio frequency signals respectively. In this work, we determined the electrical power collected, it can be used for the polarization of a diode or a low-noise amplifier in a receiver block thus simulation results provides a gain of 6.74 dBi at 2.9 GHz with an effective return loss of -33.62 dB and radiated power of 7.08 mW. These good results make it possible to use the antenna particularly in point-to-point communication systems. A three topologies of rectifying circuits are proposed in the present work. The parametric study has been shown that the efficiency RF/DC conversion can reach 66% for an input power of 10 dBm and a load resistance of 3 kΩ.
Badr-Eddine Boudriki Semlali, Chaker El Amrani
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1521-1530; doi:10.11591/ijece.v11i2.pp1521-1530

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.
Tran Thanh Ngoc, Le Van Dai, Dang Thi Phuc
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1742-1751; doi:10.11591/ijece.v11i2.pp1742-1751

Multilayer perceptron neural network is one of the widely used method for load forecasting. There are hyperparameters which can be used to determine the network structure and used to train the multilayer perceptron neural network model. This paper aims to propose a framework for grid search model based on the walk-forward validation methodology. The training process will specify the optimal models which satisfy requirement for minimum of accuracy scores of root mean square error, mean absolute percentage error and mean absolute error. The testing process will evaluate the optimal models along with the other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The US airline passenger and Ho Chi Minh city load demand data were used to verify the accuracy and reliability of the grid search framework.
Hafsa Hamidane, Samira El Faiz, Mohammed Guerbaoui, Abdelali Ed-Dahhak, Abdeslam Lachhab, Benachir Bouchikhi
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1223-1234; doi:10.11591/ijece.v11i2.pp1223-1234

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives.
Sagor Saha, Farhan Hossain Shakal, Mufrath Mahmood
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1276-1283; doi:10.11591/ijece.v11i2.pp1276-1283

The loss of vision restrained the visually impaired people from performing their daily task. This issue has impeded their free-movement and turned them into dependent a person. People in this sector did not face technologies revamping their situations. With the advent of computer vision, artificial intelligence, the situation improved to a great extent. The propounded design is an implementation of a wearable device which is capable of performing a lot of features. It is employed to provide visual instinct by recognizing objects, identifying the face of choices. The device runs a pre-trained model to classify common objects from household items to automobiles items. Optical character recognition and Google translate were executed to read any text from image and convert speech of the user to text respectively. Besides, the user can search for an interesting topic by the command in the form of speech. Additionally, ultrasonic sensors were kept fixed at three positions to sense the obstacle during navigation. The display attached help in communication with deaf person and GPS and GSM module aid in tracing the user. All these features run by voice commands which are passed through the microphone of any earphone. The visual input is received through the camera and the computation task is processed in the raspberry pi board. However, the device seemed to be effective during the test and validation.
Khaled Alrifai, Ghaida Rebdawi, Nada Ghneim
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1627-1633; doi:10.11591/ijece.v11i2.pp1627-1633

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.
Abdelladim Hadioui, Yassine Benjelloun Touimi, Nour-Eddine El Faddouli, Samir Bennani
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1675-1688; doi:10.11591/ijece.v11i2.pp1675-1688

Higher education is increasingly integrating free learning management systems (LMS). The main objective underlying such systems integration is the automatization of online educational processes for the benefit of all the involved actors who use these systems. The said processes are developed through the integration and implementation of learning scenarios similar to traditional learning systems. LMS produce big data traces emerging from actors’ interactions in online learning. However, we note the absence of instruments adequate for representing knowledge extracted from big traces. In this context, the research at hand is aimed at transforming the big data produced via interactions into big knowledge that can be used in MOOCs by actors falling within a given learning level within a given learning domain, be it formal or informal. In order to achieve such an objective, ontological approaches are taken, namely: mapping, learning and enrichment, in addition to artificial intelligence-based approaches which are relevant in our research context. In this paper, we propose three interconnected algorithms for a better ontological representation of learning actors’ knowledge, while premising heavily on artificial intelligence approaches throughout the stages of this work. For verifying the validity of our contribution, we will implement an experiment about knowledge sources example.
Duaa Mohammad, Inad Aljarrah, Moath Jarrah
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1656-1665; doi:10.11591/ijece.v11i2.pp1656-1665

Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.
Abdullah Mahmoud Almasri, Luis Borges Gouveia
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1599-1612; doi:10.11591/ijece.v11i2.pp1599-1612

The tremendous increase in smartphone usage is accompanied by an increase in the need for more energy. This preoperational relationship between modern technology and energy generates energy-greedy apps, and therefore power-hungry end users. With many apps falling under the same category in an app store, these apps usually share similar functionality. Because developers follow different design and development schools, each app has its energy-consumption habits. Since apps share similar features, an end-user with limited access to recharging resources would prefer an energy-friendly app rather than a popular energy-greedy app. However, app stores do not indicate the energy behavior of the apps they offer, which causes users to randomly choose apps without understanding their energy-consumption behavior. A review of the relevant literature was provided covering various energy-saving techniques. The results gave an initial impression about the popularity of the usage of two power-saving modes where the average usage of these modes did not exceed 31% among the total 443 Android users. To address this issue, we propose a star-rating evaluation model (SREM), an approach that generates a tentative energy rating label for each app. The model was tested on 7 open-source apps to act as a primary evaluation sample. To that end, SREM adapts current energy-aware refactoring tools to demonstrate the level of energy consumption of an app and presents it in a star-rating schema similar to the Ecolabels used on electrical home appliances. As per our results, SREM helped in saving 35% of smartphone energy.
Ahmed J. Ali, Laith A. Khalaf, Ahmed H. Ahmed
International Journal of Electrical and Computer Engineering (IJECE), Volume 11, pp 1105-1113; doi:10.11591/ijece.v11i2.pp1105-1113

This paper has been proposed to simulate the transient model of 3-Ф cage rotor induction motor based on winding function approach (WFA). According to this method the motor is assumed to be consist of an electrical circuits on both stator and rotor. The magneto motive forces (MMF) that have been generated by these circuits play a role for coupling them together. Then mutual and self-inductances will be easily computed using WFA. Two types of WFA have been used to build and simulate the model of the induction motor. In the one part type, it’s assumed that the coupling MMF between stator and rotor have a non-sinusoidal shapes according to the actual windings distribution over the motor slots. While in second part type the generated MMF in are assumed to have sinusoidal waveform. The suggested models may be used to simulate the dynamic as well as steady state performance of a faulty and non-faulty motor. A simulation of the suggested models that consists of m-rotor bars and n-stator phases multiple coupled circuit-based has been performed using matlab m.file and the results of the motor current have been proved in its nonlinear way by using WFA.
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