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Design of a contactless body temperature measurement system using Arduino

10.11591/ijeecs.v19.i3.pp1251-1258
Asif A. Rahimoon , Mohd Noor Abdullah , Ishkrizat Taib
The recent advances in electronics and microelectronics devices allow the development of newly low-cost monitoring tools used by peoples for health preventive purposes. Sensors used in medical equipments convert various forms of human body vital signs into electrical signals. Therefore, the healthcare monitoring systems considering non-invasive and wearable sensors with integrated communication mediums allow an efficient solution to live a comfortable home life.  This paper presents the remote monitoring of human body temperature (HBT) wirelessly by means of Arduino controller with different sensors and open source internet connection. The proposed monitoring system uses an internet network via wireless fieldity (wifi) connection to be linked with online portal on smart phone or computer. The proposed system is comprised of an Arduino controller, LM-35 (S1), MLX-90614 (S2) temperature sensors and ESP-wifi shield module. The obtained result has shown that real time temperature monitoring data can be transferred to authentic observer by utilizing internet of things (IoT) applications. The findings from this research indicates that the difference of average temperature in between Sensor S1 and S2 is about 15 0C
Volume: 19
Issue: 3
Page: 1251-1258
Publish at: 2020-09-01

Power density of rectangular microstrip patch antenna arrays for 5G indoor base station

10.11591/ijeecs.v19.i3.pp1367-1374
Nor Adibah Ibrahim , Tharek Abd Rahman , Razali Ngah , Omar Abd Aziz , Olakunle Elijah
The fifth-generation (5G) network has been broadly investigated by many researchers. The capabilities of 5G include massive system capacity, incredibly high data rates everywhere, very low latency and the most important point is that it is exceptionally low device cost and low energy consumption. A key technology of 5G is the millimeter wave operating at 28 GHz and 38 GHz frequency bands which enable massive MIMO and small cell base station densification. However, there has been public concern associated with human exposure to electromagnetic fields (EMF) from 5G communication devices. Hence, this paper studies the power density of a 5G antenna array that can be used for the indoor base station. The power density is the amount of power or signal strength absorbed by a receiver such as the human body located a distance from the base station. To achieve this, the design of array antennas using CST software at 28 GHz, fabrication and measurement were carried out in an indoor and hallway environment. The measurement processes were set up at IC5G at UTM Kuala Lumpur in which the distance of the transmitter to receiver where 1 m, 4 m, 8 m, and 10 m. In this study, the measured power density is found to be below the set limit by ICNIRP and hence no health implication is feared. Regardless, sufficient act of cautionary has to be applied by those staying close to small cell base stations and more studies are still needed to ensure the safety of use of 5G base stations.
Volume: 19
Issue: 3
Page: 1367-1374
Publish at: 2020-09-01

Comparison of daily rainfall forecasting using multilayer perceptron neural network model

10.11591/ijai.v9.i3.pp456-463
Mazwin Arleena Masngut , Shuhaida Ismail , Aida Mustapha , Suhaila Mohd Yasin
Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values.
Volume: 9
Issue: 3
Page: 456-463
Publish at: 2020-09-01

Differential evolution algorithm of soft island model based on K-means clustering

10.11591/ijeecs.v19.i3.pp1548-1555
Xujie Tan , Seong-Yoon Shin
Differential evolution (DE) is a highly effective evolutionary algorithm. However, the performance of DE depends on strategies and control parameters. The combination of many strategies helps balance the exploitation and exploration of DE. In this study, a multi-population based on k-means clustering is proposed to realize an ensemble of multiple strategies, thereby resulting in a new DE variant, namely KSDE, where similar individuals in the population implement the same mutation strategies, and dissimilar subpopulations migrate information through the soft island model (SIM). Firstly, the population is virtually divided into k subpopulations by the k-means clustering algorithm. Secondly, the individual specific mutation scheme is selected from a strategy pool by random method. Finally, the migration of subpopulation information is done using soft island model. The performance of the KSDE algorithm is evaluated on 13 benchmark problems. The experiments show that KSDE algorithm improves the performance of the DE algorithm.
Volume: 19
Issue: 3
Page: 1548-1555
Publish at: 2020-09-01

Investigation of voltage regulation in grid–connected PV system

10.11591/ijeecs.v19.i3.pp1131-1139
Soumen Gorai , Sattianadan D , V. Shanmugasundaram , S. Vidyasagar , G. R. Prudhvi Kumar , M. Sudhakaran
In the present scenario the power demand on the load side is increasing day by day, so to balance the power demand and power supply various renewable energy comes to picture as the additional source of electricity generation. The power generated by various renewable resources such as solar, wind, tidal energy and geothermal sources is environmentally clean and have a less emission impact. Out of which PV system draws more attention because it generates energy with a much lower level of carbon dioxide emissions. In the proposed work the objective is to investigate the synchronisation of the grid-connected PV system in terms of voltage and frequency. It includes the P-V characteristics under the circumstances of MPPT technique such as perturb & observe (P&O) method can able to track the local maximum point. The proposed inverter is a voltage source H-Bridge inverter which is controlled using a Clarke and Park transformation to drive a controlled current into the grid to maintain the THD value within the standards. As the grid frequency is fluctuating between SRF-PLL is generally used to fix the output frequency and phase of the grid. It also includes with the design of a three-phase H-bridge inverter as an interface between PV system and grid system. The proposed work is designed and simulated in MATLAB SIMULINK 2017b environment.
Volume: 19
Issue: 3
Page: 1131-1139
Publish at: 2020-09-01

Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system

10.11591/ijai.v9.i3.pp480-487
Siti Nasuha Zubir , S. Sarifah Radiah Shariff , Siti Meriam Zahari
Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight.
Volume: 9
Issue: 3
Page: 480-487
Publish at: 2020-09-01

Coaxial treatment chamber for liquid food treatment through pulsed electric field

10.11591/ijeecs.v19.i3.pp1169-1176
Rai Naveed Arshad , Zulkurnain Abdul Malek , Mohd. Hafizi Ahmad , Zolkafle Buntat , C L G Pavan Kumara , Asaad Zuhair Abdulameer
Over the last couple of decades, pulsed electric field (PEF) attained substantial consideration in the food industry with an effort to generate replacements to conventional thermal treatment. It is recognised to produce secure and chill-stable liquid food samples with fresh and nutritional qualities. The uniform electric field in the treatment zone has been recognised as the main handling parameter. The design of the treatment chamber shows an essential impact on the effectiveness of the procedure by controlling homogenous treatment. This study revealed the homogeneity of the electric field strength of the different existing treatment chambers through COMSOL Multiphysics. It adopted a study to design a co-axial treatment chamber for continuous treatment of liquid samples. This research helps to take some fundamental designing steps for a new treatment chamber with a uniform distribution of the electric field.
Volume: 19
Issue: 3
Page: 1169-1176
Publish at: 2020-09-01

A self-rectifying memristor model for simulation and ReRAM applications

10.11591/ijeecs.v19.i3.pp1204-1209
Sinan Sabah , Nasri Bin Sulaiman
In this paper, a self-rectifying memristor (SRM) model is proposed for memristive circuit simulations. This model is based on the behavior of voltage controlled, bipolar memristors that exhibit diode-like rectification behavior when reverse biased. Such unique feature can solve the sneak path problem in crossbar memristive memory structures without requiring additional cell selectors. The results show that the proposed model satisfies the basic memristor’s i-v characteristics and fits many different memristor devices adequately. The proposed model is implemented in Verilog-A so that it is conveniently incorporated into various memristor applications with different circuit simulators
Volume: 19
Issue: 3
Page: 1204-1209
Publish at: 2020-09-01

Increasing the efficiency of information transmission in communication channels

10.11591/ijeecs.v19.i3.pp1306-1315
Bohdan Zhurakovskyi , Juliy Boiko , Vladymir Druzhynin , Irina Zeniv , Oleksander Eromenko
This paper discusses compression methods focused on data transmission over communication channels. The characteristics of different algorithms for different types of incoming data are analyzed. The purpose of this study is to evaluate the speed of operation of each of the compression algorithms for different types of information and different compression parameters, on the basis of the obtained results to make recommendations for the application of compression methods in systems critical to the performance of the algorithm. Based on the results of the analysis, the methods of compression that can be used in communication channels are selected: LZW, LZH, Vitter and matrix. The practical research of the selected methods on different information flows (text, graphics, measurement data, combined data) was carried out, their comparative analysis was performed. Research has highlighted compression methods that give the most optimal results in each case. Comparative evaluation of algorithms for different parameters is made, the possibility of data compression implementation in systems running in real time is analyzed. Based on the results of the study, recommendations are made for the application of particular compression methods in specific conditions.
Volume: 19
Issue: 3
Page: 1306-1315
Publish at: 2020-09-01

Dynamic spectrum access using markov chain technique for regional area network

10.11591/ijeecs.v19.i3.pp1420-1427
Jayant P Pawar , Prashant V. Ingole
Cognitive Radio Network is the effective solution to the spectrum scarcity.  Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A Markov Chain is a stochastic model describing a sequence of possible events in which probability of each event depends only on the state attained in the previous event. We model the dynamics of cognitive user with 2-D Markov chain. The resource distribution probability (RDP) verses addition/ elimination rate of the channels in the network is also plotted. The RDP verses utilization factor of the queue, which is the secondary user in the network, is also plotted. This plot helps to maintain the total arrival and departure rate based on the RDP. The base station of the network will use this relation to maintain the proper RDP for the devices. The dynamics of each cognitive user and its correlation with Markov Chain is an interesting approach. Here we considered the DSA at Base Station as a Markov chain and analyzed it. This analysis helps us to determine the behavior of the cognitive radio and also helps to find the fault in the cognitive devices. 
Volume: 19
Issue: 3
Page: 1420-1427
Publish at: 2020-09-01

To the theory of electromagnetic radiation by fast moving body

10.11591/ijeecs.v19.i3.pp1267-1274
Sergey Gladkov
The problem of finding out the connection between the flight speed of a metal object and the length of electromagnetic radiation that accompanies its movement has been solved. The calculations are based on the application of Lienar-Vihert's potential theory. Based on the assumption of the dipole mechanism of radiation, the intensity of electromagnetic radiation was calculated and its distribution by coordinates was found. 
Volume: 19
Issue: 3
Page: 1267-1274
Publish at: 2020-09-01

A distributed trust mechanism for malicious behaviors in VANETs

10.11591/ijeecs.v19.i3.pp1147-1155
Ali Kamil Ahmed , Mohanad Najm Abdulwahed , Behnam Farzaneh
Vehicular ad-hoc networks (VANETs) are one of the most important types of networks which are widely used in recent years. Along with all the benefits of quality of service (QoS) improvements, vulnerability analysis for this type of networks is an important issue. For instance, a gray-hole attack decreases network performance. We proposed a novel solution to help to secure these networks against this vulnerability. The proposed method can detect and prevent the gray-hole attack. Anywhere in the network, each node (vehicle) can distinguish between the gray-hole attack and the failed link. Some topology related information helps us to detect attacks more accurately. Also, the proposed method uses the most reliable path in terms of link failure when there is no malicious node. In this paper, we used the TOPSIS method for choosing the most trusted node for routing intelligently. We validated our proposal using a simulation model in the NS-2 simulator. Simulation results show that the proposed method can prevent Gray-hole attack efficiently with low overhead.
Volume: 19
Issue: 3
Page: 1147-1155
Publish at: 2020-09-01

Optimal economic dispatch of power generation solution using lightning search algorithm

10.11591/ijai.v9.i3.pp371-378
Murad Yahya Nassar , Mohd Noor Abdullah , Asif Ahmed Rahimoon
Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.
Volume: 9
Issue: 3
Page: 371-378
Publish at: 2020-09-01

Intelligent reputation system for safety messages in VANET

10.11591/ijai.v9.i3.pp439-447
Ghassan Samara
Nowadays Vehicle Ad - hoc Nets (VANET) applications have become very important in our lives because VANET provides drivers with safety messages, warnings, and instructions to ensure drivers have a safe and enjoyable journey. VANET Security is one of the hottest topics in computer networks research, Falsifying VANET system information violates VANET safety objectives and may lead to hazardous situations and loss of life. In this paper, an Intelligent Reputation System (IRS) aims to identify attacking vehicles will be proposed; the proposed system will rely on opinion generation, trust value collection, traffic analysis, position based, data collection, and intelligent decision making by utilizing the multi-parameter Greedy Best First algorithm. The results of this research will enhance VANET's safety level and will facilitate the identification of misbehaving vehicles and their messages. The results of the proposed system have also proven to be superior to other reputational systems.
Volume: 9
Issue: 3
Page: 439-447
Publish at: 2020-09-01

Grid search of exponential smoothing method: a case study of Ho Chi Minh City load demand

10.11591/ijeecs.v19.i3.pp1121-1130
Ngoc Thanh Tran , Le Van Dai
The exponential smoothing method is one of the widely used methods for load forecasting. The taxonomy of exponential smoothing method shows that its trend and seasonal component affect the results of exponential smoothing method. This paper proposed a framework for grid search with the optimal model of exponential smoothing method based on math formulas. The training process will specify the optimal models which satisfy requirement of minimum of akaike information criterion, accuracy scores of the root mean square error, mean absolute percentage error, and mean absolute error. The testing process will evaluate the accuracy scores between the optimal models and all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The load demand data collected in Ho Chi Minh City were used to verify the accuracy and reliability of the grid search framework.
Volume: 19
Issue: 3
Page: 1121-1130
Publish at: 2020-09-01
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