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                  <text>VOL. 18, NO. 6 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
New prediction method for data spreading in social networks based on machine learning algorithm </text>
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                <text>Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring &#13;
vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices by active vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of the next article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBLP and Pubmed datasets, respectively. </text>
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                <text>Maytham N. Meqdad, Rawya Al-Akam, Seifedine Kadry</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16300</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                  <text>VOL. 18, NO. 6 2020</text>
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An improved swarm intelligence algorithms-based nonlinear fractional order-PID controller for a trajectory tracking of underwater vehicles </text>
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                <text>Improved GWOA, Improved WOA, Nonlinear-FOPID, PID controller, Swarm intelligence algorithm, Underwater vehicles</text>
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                <text>This paper presents a nonlinear fractional order proportional integral&#13;
derivative (NL-FOPID) for autonomous underwater vehicle (AUV) to solve the path tracking problem under the unknown disturbances (model uncertainty or external disturbances). The considered controller schemes are tuned by two improved swarm intelligence optimization algorithms, the first on is the hybrid grey wolf optimization with simulated annealing (HGWO-SA) algorithm and an improved whale optimization algorithm (IWOA). The developed algorithms are assessed using a set of benchmark function (unimodal, multimodal, and fixed dimension multimodal functions) to guarantee the effectiveness of both proposed swarm algorithms. The HGWO-SA algorithm is used as a tuning method for the AUV system controlled by NL-FOPID scheme, and the IWOA is used as a tuning algorithm to obtain the PID controller’s parameters. The evaluation results show that the HGWO-SA algorithm improved the minimal point of the tested benchmark functions by 1-200 order, while the IWOA improved the minimum point by (1-50) order. Finally, the obtained simulation results from the system operated with NL-FOPID shows the competence in terms of the path tracking by 1-15% as compared to the PID method. </text>
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                <text>Mustafa Wassef Hasan, Nizar Hadi Abbas</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16282</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
High frequency signal injection method for sensorless permanent magnet synchronous motor drives </text>
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                <text>High frequency signal injection, Permanent magnet synchronous motor, Sensorless drives, Speed estimator</text>
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                <text>The objective of this project is to design a high frequency signal injection&#13;
method for sensorless control of permanent magnet synchronous motor&#13;
(PMSM) drives. Generally, the PMSM drives control requires the appearance of speed and positon sensor to measure the motor speed hence to feedback the information for variable speed drives operation. The usage of the sensor will increase the size, cost, extra hardwire and feedback devices. Therefore, there is motivation to eliminate this type of sensor by injecting high frequency signal and utilizing the electrical parameter from the motor so that the speed and positon of rotor can be estimated. The proposed position and speed sensorless control method using high frequency signal injection together with all the power electronic circuit are modelled using Simulink. PMSM sensorless drive&#13;
is simulated and the results are analyzed in terms of speed, torque and stator current response without load disturbance but under the specification of varying speed, forward to reverse operation, reverse to forward operation and step change in reference speed. The results show that the signal injection method performs well during start-up and low speed operation. </text>
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                <text>Tan Jiunn Lin, Jurifa Mat Lazi, Zulkifilie Ibrahim, Md Hairul Nizam Talib, Azrita Alias, Ahmad Shukri Abu Hasim</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16280</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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A novel two-polynomial criteria for higher-order systems stability boundaries detection and control </text>
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                <text>There are many methods for identifying the stability of complex dynamic&#13;
systems. Routh and Hurwitz’s criterion is one of the earliest and commonly used analytical tools analysing the stability of dynamic systems. However, it requires tedious and lengthy derivations of all components of the Routh array to solve the stability problem. Therefore, it is not a simple method to define analytically, stability boundaries for the coefficients of the system characteristic equation. The proposed brand-new criterion is an effective alternative technique in identifying stability higher-order linear time-invariant dynamic system that binds the coefficients of the system characteristic polynomial at the stability boundaries by means of an additional single constant k. It defines the necessary and sufficient conditions for the absolute stability of higher-order dynamic systems. It also allows the analysing of the system’s precise marginal stability or marginal instability condition when the&#13;
roots are relocated on imaginary jω-axis of s-plane. The criterion proposed by the authors, in contrast to Routh criteria, simplifies the identification of maximum and minimum stability limits for any coefficient of the higher-order characteristic equation significantly. The derived in the paper stability boundary formulas for the polynomial coefficients are successfully used for the proportional integral derivative (PID) controller with single or multiple gains selections in closed-loop control systems. </text>
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                <text>N. Mir-Nasiri, Md. Hazrat Ali</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16256</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 6 2020</text>
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Reconfigurable ultra wideband to narrowband antenna for cognitive radio applications using PIN diode </text>
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                <text>Frequency reconfigurable antennas are very attractive for many wireless applications. They offer many advantages such as simplicity and compactness. In this electronic paper, we propose a reconfigurable antenna operating in the S and C bands. The proposed antenna uses a BAP65-02 RF diode to switch between the ultra wideband from 2.92 to 6.19 GHz to the narrowband from 2.92 to 3.93 GHz. The ultra wideband&#13;
is obtained by a partial rectangular ground plane with a symmetrical rectangular slot and the narrowband is obtained by adding a parasitic element electrically connected to the ground plane by the PIN diode when it is positively biased. This patch antenna operates in the Federal Communications Commission band (FCC) and can be used for biomedical applications such as radiometry imaging. The numerical simulation results based on the finite element method and the finite integral method show a very good agreement between them. </text>
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                <text>Aziz El Fatimi, Seddik Bri, Adil Saadi</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16242</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 6 2020</text>
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Raised cosine/NRZ line coding techniques for upgrading free space optical communication systems through various levels of fog </text>
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                <text>Bit stream sequence, Fog weather, FSO channel, NRZ/RZ coding, Optical amplifiers </text>
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                <text>This study examines raised cosine/NRZ line coding techniques for upgrading free space optical (FSO) communication systems through various levels of fog. The max. Q factors are simulated and estimated for clear air, light fog, and moderate fog weather conditions at a data rate of 20 Gb/s. The optical signal to noise ratio is also measured for different weather conditions. The total power after both FSO channel and avalanche photodiode (APD) photo-detector receiver is estimated by an optical power meter for max. distances. The study shows that the max. propagation distance is extended to 50 km through clear air, 2.25 km through light fog, and 1.6 km through moderate fog with acceptable max. Q factor. </text>
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                <text>Aadel M. Alatwi, Ahmed Nabih Zaki Rashed</text>
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              <elementText elementTextId="44318">
                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16236</text>
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              <elementText elementTextId="44319">
                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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              <elementText elementTextId="44322">
                <text>ISSN: 1693-6930</text>
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                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 6 2020</text>
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Discrete wavelet transform recursive inverse algorithm using second-order estimation of the autocorrelation matrix </text>
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                <text>Impulsive noise, Noise cancellation, RI algorithm, RLS algorithm, Wavelet transform</text>
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                <text>The recursive least squares (RLS) algorithm was introduced as an alternative to least mean square (LMS) algorithm with enhanced performance. Computational complexity and instability in updating the autocolleltion matrix are some of the drawbacks of the RLS algorithm that were among the reasons for the introduction of the second-order recursive inverse (RI) adaptive algorithm. The 2nd order RI adaptive algorithm suffered from low convergence rate in certain scenarios that required a relatively small initial step-size. In this paper, we propose a new second-order RI algorithm that projects the input signal to a new domain namely discrete wavelet transform (DWT) as pre step before performing the agorthim. This transformation overcomes the low convergence rate of the second-order RI algorithm by reducing the self-&#13;
correlation of the input signal in the mentioned scenatios. Expeirments are conducted using the noise cancellation setting. The performance of the proposed algorithm is compared to those of the RI, original second-order RI and RLS algorithms in different Gaussian and impulsive noise environments. Simulations demonstrate the superiority of the proposed algorithm in terms of convergence rate compared to those algorithms.</text>
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                <text>Mohammad Shukri Salman, Alaa Eleyan, Bahaa Al-Sheikh</text>
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            <elementTextContainer>
              <elementText elementTextId="44304">
                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16191</text>
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              <elementText elementTextId="44305">
                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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Quantitative analysis of Mouza map image to estimate land area using zooming and Canny edge detection</text>
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inspired us to develop a system that requires only mouza map image. Once image acquisition is done, we applied the curvature interpolation technique to zoom the map that helps to select any area by the Area Selection Method. The selected area is then segmented by employing the Canny edge detection method. Finally, the area is calculated from the segmented image and extracted features of the selected location. Compared to the field measurement, the system gave the accuracy of&#13;
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                <text>Sri Wahyuni</text>
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The detection of handguns from live-video in real-time based on deep learning </text>
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                <text>Many people have been killed indiscriminately by the use of handguns in&#13;
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disturbed people are considered the common reasons for these crimes. A real-time handguns detection surveillance system is built to overcome these bad acts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue, the designed detection system sends an alert message to the supervisor when a weapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgun detection operation. This model has been selected because it is fast and accurate in infering to integrate network for detecting and classifying weapons in images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model both enhance the accuracy level in identifying the real time handguns detection. </text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16174</text>
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Using position control to improve the efficiency of wind turbine</text>
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                <text>Wind energy is one of the renewable energies that can be using to generate electricity. Increasing demand for this type of renewable energy for sustainability and accessibility. Environmentally as it does not cause any pollution in addition to the abundance of required equipment and less maintenance and long operation life of its parts despite the high cost of the system at its installation but at long term, become cheaper. Wind power generators depend on their operation on wind speed and direction. Therefore, it should be installing in places where the wind speed is adequate and sufficient to rotate its rotor, it knows that wind speed is variable in its speed and direction they change every hour and every season. In this design, many practical and theoretical (simulation) experiments have been done which will be mentioned and explained in details in this research shows that this mechanism raises the efficiency of wind power generators by 80% when the rotor of the wind turbine&#13;
directed towards the wind than if they were fixed direction. </text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i6.16171</text>
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