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Service landscape for private universities in indonesia based on service oriented architecture and cloud technology

10.11591/ijeecs.v22.i1.pp497-506
Faiza Renaldi , Irma Santikarama , Esmeralda C. Djamal , Agya Java Maulidin
Information technology (IT) has been widely adopted and is believed to improve academic processes’ efficiency and run private universities’ academic functions (PTSs) in Indonesia. Nonetheless, adopting diverse technologies for them will also create many challenges. PTSs are struggling to survive in terms of technological implementation, in the sense that the investment and implementation rate in the PTSs just cannot catch up with the technological advancement rate. Even when more PTSs are trying to transform into digital entities, the next problem will be system integration and flexibility. This study aims to overcome this problem by implementing a framework that can be both integrated and flexible while also serving the efficiency of investments. Many studies already suggested that service oriented architecture (SOA) and cloud technology are the solutions. Nevertheless, none has been able to define what standard services can be applied within those platforms. To determine this, we use the BIAN service landscape, which was translated from the banking industry, offering a comprehensive view of the business domain and business capabilities alongside its service functions. While BIAN offers common services throughout the same platform, we modify the framework using the OASIS model from SOA, which allows the framework to be flexible in complying with many platforms of databases, programming languages, and network infrastructures. We completed our study by defining one business area: academic processes, three business domains, 19 business capabilities, and 84 service functions. We are strongly confident that our findings and study results will act as a reference in creating a cloud-based platform for Indonesia’s higher education academic systems.
Volume: 22
Issue: 1
Page: 497-506
Publish at: 2021-04-01

Monitoring of solenoid parameters based on neural networks and optical fiber squeezer for solenoid valves diagnosis

10.11591/ijece.v11i2.pp1697-1708
Abdallah Zahidi , Said Amrane , Nawfel Azami , Naoual Nasser
As crucial parts of various engineering systems, solenoid valves (SVs) operated by electromagnetic solenoid (EMS) are of great importance and their failure may lead to cause unexpected casualties. This failure, characterized by a degradation of the performances of the SVs, could be due to a fluctuations in the EMS parameters. These fluctuations are essentially attributed to the changes in the spring constant, coefficient of friction, inductance, and the resistance of the coil. Preventive maintenance by controlling and monitoring these parameters is necessary to avoid eventual failure of these actuators. The authors propose a new methodology for the functional diagnosis of electromagnetic solenoids (EMS) used in hydraulic systems. The proposed method monitors online the electrical and mechanical parameters varying over time by using artificial neural networks algorithm coupled with an optical fiber polarization squeezer based on EMS for polarization scrambling. First, the MATLAB/Simulink model is proposed to analyze the effect of the parameters on the dynamic EMS model. The result of this simulation is used for training the neural network, then a simulation is proposed using the neural net fitting toolbox to determine the solenoid parameters (Resistance of the coil R, stiffness K and coefficient of friction B of the spring) from the coefficients of the transfer function, established from the model step response. Future work will include not only diagnosing failure modes, but also predicting the remaining life based on the results of monitoring.
Volume: 11
Issue: 2
Page: 1697-1708
Publish at: 2021-04-01

Cloud-based platforms for LoRa internet of things: a survey

10.11591/ijict.v10i1.pp54-64
Mouna Gassara , Manel Elleuchi , Mohamed Abid
Internet of Things (IoT) establishes a smart communication system between the human being and the surrounding objects (or “things”) while using the internet as the backbone of this system. Cloud is the fundamental constituent of IoT. It offers important application services in numerous application domains. Indeed, many IoT cloud platforms are competing to provide appropriate and very specific IoT services. The LoRa platform in particular presents a long-range, low-bit rate, low-power, wireless telecommunication protocol for IoT. As such, end-devices make use of LoRa through a sole wireless hop in order to communicate to the gateway(s) that is (are) considered as relay messages and transparent bridges between these end-devices and an IoT cloud while connecting to the Internet. This article surveys the most used IoT cloud platforms suitable for the LoRa communication protocol through a deep and detailed study of previously published works related to this topic. Moreover, a comparison is made between these IoT cloud platforms according to their characteristics and application domains. The overall aim of the present paper is to provide researchers in the field with detailed knowledge concerning the IoT cloud platforms appropriate to the LoRa communication system including the advantages and drawbacks of each one of them.
Volume: 10
Issue: 1
Page: 54-64
Publish at: 2021-04-01

Air temperature prediction using different machine learning models

10.11591/ijeecs.v22.i1.pp534-541
Rana Muhammad Adnan , Zhongmin Liang , Alban Kuriqi , Ozgur Kisi , Anurag Malik , Binquan Li , Fatemehsadat Mortazavizadeh
Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.
Volume: 22
Issue: 1
Page: 534-541
Publish at: 2021-04-01

Algorithmic TCAM on FPGA with data collision approach

10.11591/ijeecs.v22.i1.pp89-96
Nguyen Trinh , Anh Le Thi Kim , Hung Nguyen , Linh Tran
Content addressable memory (CAM) and ternary content addressable memory (TCAM) are specialized high-speed memories for data searching. CAM and TCAM have many applications in network routing, packet forwarding and Internet data centers. These types of memories have drawbacks on power dissipation and area. As field-programmable gate array (FPGA) is recently being used for network acceleration applications, the demand to integrate TCAM and CAM on FPGA is increasing. Because most FPGAs do not support native TCAM and CAM hardware, methods of implementing algorithmic TCAM using FPGA resources have been proposed through recent years. Algorithmic TCAM on FPGA have the advantages of FPGAs low power consumption and high intergration scalability. This paper proposes a scaleable algorithmic TCAM design on FPGA. The design uses memory blocks to negate power dissipation issue and data collision to save area. The paper also presents a design of a 256 x 104-bit algorithmic TCAM on Intel FPGA Cyclone V, evaluates the performance and application ability of the design on large scale and in future developments.
Volume: 22
Issue: 1
Page: 89-96
Publish at: 2021-04-01

Speed control of an SPMSM using a tracking differentiator-PID controller scheme with a genetic algorithm

10.11591/ijece.v11i2.pp1728-1741
Noor Hameed Hadi , Ibraheem Kasim Ibraheem
In this paper, a tracking differentiator-proportional integral and derivative (TD-PID) control scheme is proposed to control the speed of a surface mount permanent magnet synchronous motor (SPMSM). The TD is used to generate the necessary transient profile for both the reference and the output speed, which are compared with each other to produce the error signals that feed into the PID controller. In addition to the TD unit parameters, the PID controller’s parameters are tuned to achieve the optimum new multi-objective performance index, comprised of the integral of the time absolute error (ITAE), the absolute square of the control energy signal (USQR), and the absolute value of the control energy signal (UABS) and utilizing a genetic algorithm (GA). A nonlinear model of the SPMSM is considered in the design and the performance of the proposed TD-PID scheme was validated by comparing its performance with that of a traditional PI controller in a MATLAB environment. Different case studies were tested to show the effectiveness of the proposed scheme, results including peak overshoot, energy consumption, control signal chatter, and 30% improvement in the OPI, with variable reference speeds, load torque, and parameters uncertainties. Illustrate the proposed scheme's success compared with PI controller.
Volume: 11
Issue: 2
Page: 1728-1741
Publish at: 2021-04-01

A composite controller based on nonlinear H_∞ and nonlinear disturbance observer for attitude stabilization of a flying robot

10.11591/ijeecs.v22.i1.pp270-276
Vahid Razmavar , Heidar Ali Talebi , Farzaneh Abdollahi
In this article a novel composite control technique is introduced. We added a nonlinear disturbance observer to a nonlinear H_∞ control to form this composite controller. The quadrotor kinematics and dynamics is formulated using euler angles and parameters. After that, this nonlinear robust controller is developed for this flying robot attitude control for the outdoor conditions. Because under these conditions the flying robot, experiences both external disturbance and parametric uncertainty. Stability analysis is also presented to show the global asymptotical stability using a Lyapunov function. The simulation results showed that the suggested composite controller had a better performance in comparison with a nonlinear H_∞ control scheme.
Volume: 22
Issue: 1
Page: 270-276
Publish at: 2021-04-01

A 5G mm-wave compact voltage-controlled oscillator in 0.25 µm pHEMT technology

10.11591/ijece.v11i2.pp1036-1042
Abdelhafid Es-saqy , Maryam Abata , Mahmoud Mehdi , Mohammed Fattah , Said Mazer , Moulhime El Bekkali , Catherine Algani
A 5G mm-wave monolithic microwave integrated circuit (MMIC) voltage-controlled oscillator (VCO) is presented in this paper. It is designed on GaAs substrate and with 0.25 µm-pHEMT technology from UMS foundry and it is based on pHEMT varactors in order to achieve a very small chip size. A 0dBm-output power over the entire tuning range from 27.67 GHz to 28.91 GHz, a phase noise of -96.274 dBc/Hz and -116.24 dBc/Hz at 1 and 10 MHz offset frequency from the carrier respectively are obtained on simulation. A power consumption of 111 mW is obtained for a chip size of 0.268 mm2. According to our knowledge, this circuit occupies the smallest surface area compared to pHEMTs oscillators published in the literature.
Volume: 11
Issue: 2
Page: 1036-1042
Publish at: 2021-04-01

A review of remote health monitoring based on internet of things

10.11591/ijeecs.v22.i1.pp297-306
Omar AlShorman , Buthaynah Alshorman , Mahmoud Masadeh , Fahad Alkahtani , Basim Al-Absi
Managing, diagnosis, prognosis, continuous monitoring, early detection, and preventing chronic diseases for patients and elderly people have been gained a crucial role nowadays. However, elderly people with chronic health conditions such as diabetes, cardiovascular disease, and mental diseases, need special health care. With the help of the internet of things (IoT) technologies, remote health monitoring (RHM) helps patients, caregivers, and countries for improving healthcare services, such as medical files services, mobile healthcare (mhealth), telemedicine services, and sensing technology. Moreover, RHM aims to reduce hospitalized demands and costs. The main contribution of the proposed study is to review RHM studies based on IoT technologies. Moreover, the challenges and possible future trends of RMH are highlighted.
Volume: 22
Issue: 1
Page: 297-306
Publish at: 2021-04-01

Delineation of electrocardiogram morphologies by using discrete wavelet transforms

10.11591/ijeecs.v22.i1.pp159-167
Annisa Darmawahyuni , Siti Nurmaini , Hanif Habibie Supriansyah , Muhammad Irham Rizki Fauzi , Muhammad Naufal Rachmatullah , Firdaus Firdaus , Bambang Tutuko
The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. This paper proposes a discrete wavelet transform (DWT), non-stationary signal analysis for noise removal, and onset-offset of PQRST feature extraction. A well-known database from Physionet: QT database (QTDB) is used to validate the DWT function for detecting the onset and offset of P-wave, QRS-complex, and T-wave localization. From the results, P-peak detection gets the highest result that achieves 2.19 and 13.62 milliseconds of mean error and standard deviation, respectively. In contrast, Toff has obtained the highest error value due to differences in the T-wave morphology. It can be affected by inverted or biphasic T-waves and others.
Volume: 22
Issue: 1
Page: 159-167
Publish at: 2021-04-01

Power dissipation analysis of PV module under partial shading

10.11591/ijece.v11i2.pp1029-1035
Byunggyu Yu , Seok-Cheol Ko
Photovoltaic (PV) generation has been growing dramatically over the last years and it ranges from small, rooftop-mounted or building integrated systems, to large utility scale power stations. Especially for rooftop-mounted PV system, PV modules are serially connected to match with PV inverter input voltage specification. For serially connected PV system, shading is a problem since the shaded PV module reduces the output whole string of PV modules. The excess power from the unshaded PV module is dissipated in the shaded PV module. In this paper, power dissipation of PV module under partial shading is analyzed with circuit analysis for series connected PV modules. The specific current and voltage operating point of the shaded PV module are analyzed under shading. PSIM simulation tool is used to verify the power dissipation analysis. When there is no bypass diode and three solar modules are connected in series, upto 39.1% of the total maximum PV power is dissipated in the shaded PV module. On the other hand, when the bypass is attached, 0.3% of the total maximum power is generated as a loss in the shaded PV module. The proposed analysis technique of shaded PV module could be used in PV system performance analysis, especially for maximum power point tracking (MPPT) performance.
Volume: 11
Issue: 2
Page: 1029-1035
Publish at: 2021-04-01

ECG signal denoising using a novel approach of adaptive filters for real-time processing

10.11591/ijece.v11i2.pp1243-1249
Amean Al-Safi
Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.
Volume: 11
Issue: 2
Page: 1243-1249
Publish at: 2021-04-01

Estimation of posture and prediction of the elderly getting out of bed using a body pressure sensor

10.11591/ijece.v11i2.pp1208-1222
Atsushi Hagihara , Fumiko Harada , Hiromitsu Shimakawa
We propose an IoT support system for estimating the posture of the care recipient on the bed from the body pressure of the care recipient measured by a sheet-type body pressure sensor, and detecting the posture related to leaving the bed in real time. In addition, we propose a method that predicts getting out of the bed before the care recipient takes a posture related to getting out of the bed by considering the state transition. Intervention experiment showed that using body pressure features as an explanatory variable and applying machine learning, 16 types of postures on the bed of care recipients with an F value of 0.7 or more could be identified. From the experiment without intervention, by applying the hidden Markov model, we calculated the transition probability to each hidden state when the care recipient getting out of the bed and the transition probability to each hidden state when the care recipient not getting out of the bed. As a result, there was a difference of about 0.1 in the transition probability of the state related to raising upper body.
Volume: 11
Issue: 2
Page: 1208-1222
Publish at: 2021-04-01

Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

10.11591/ijeecs.v22.i1.pp476-484
Rini Sovia , Abulwafa Muhammad , Syafri Arlis , Guslendra Guslendra , Sarjon Defit
This research was conducted to analyze the level of sales of pharmaceutical products at a Pharmacy. This is done to find out the types of products that have high and low sales levels. This study uses the C45 data mining algorithm concept that will produce a conclusion on the prediction of sales of pharmaceutical products through data processing obtained from sales transactions at pharmacies. This C45 algorithm will form a decision tree that provides users with knowledge about products that are in great demand by consumers based on sales data and predetermined variables. The final result of the C45 algorithm produces a number of rules that can identify the inheritance of a type of medicinal product. C45 algorithm is able to produce 20 types of categories that will be labeled goals based on the number of pharmaceutical products, since it can be concluded that C45 successfully defines 55% of the existing objective categories.
Volume: 22
Issue: 1
Page: 476-484
Publish at: 2021-04-01

Real-time Arabic scene text detection using fully convolutional neural networks

10.11591/ijece.v11i2.pp1634-1640
Rajae Moumen , Raddouane Chiheb , Rdouan Faizi
The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.
Volume: 11
Issue: 2
Page: 1634-1640
Publish at: 2021-04-01
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