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29,939 Article Results

A novel design of circularly polarized pentagonal planar antenna for ISM band applications

10.12928/telkomnika.v19i5.19393
F.; Hassan First University of Settat Ouberri , A.; Hassan First University of Settat Tajmouati , J.; University Abdelmalek Essaadi Zbitou , I.; LIMIE laboratory ISGA Casablanca Zahraoui , M.; Microwave Group ESEO Latrach
This paper presents a new circular polarized micro-strip antenna with a pentagonal shape radiator. The proposed antenna is designed to operate in the industrial scientific medical (ISM) band at the frequency of 2.45GHz for Wireless applications. The antenna consists of a radiating pentagon patch with appropriate dimensions on the upper side of a dielectric substrate and a defective ground structure (DGS) on the other side. The pentagon patch is fed through a 50Ω microstrip line. The structure is implemented on an FR-4 substrate with a relative permittivity of 4.4, loss tangent equal to 0.025 and thickness 1.58 mm. The antenna is designed and simulated by using advanced design system (ADS) electromagnetic solver and the achieved results are validated by using another electromagnetic solver. The simulation results indicate that the designed circularly polarized (CP) pentagonal microstrip patch antenna gives good results in terms of the reflection coefficient, voltage standing wave ratio (VSWR) and, axial ratio of 1.025 at 2.45 GHz.
Volume: 19
Issue: 5
Page: 1484-1489
Publish at: 2021-10-01

Classification of heart disease based on PCG signal using CNN

10.12928/telkomnika.v19i5.20486
Aditya Wisnugraha; Universitas Negeri Yogyakarta Sugiyarto , Agus Maman; Universitas Negeri Yogyakarta Abadi , Sumarna; Universitas Negeri Yogyakarta Sumarna
Cardiovascular disease is the leading cause of death in the world, so early detection of heart conditions is very important. Detection related to cardiovascular disease can be conducted through the detection of heart signals interference, one of which is called phonocardiography. This study aims to classify heart disease based on phonocardiogram (PCG) signals using the convolutional neural networks (CNN). The study was initiated with signal preprocessing by cutting and normalizing the signal, followed by a continuous wavelet transformation process using a mother wavelet analytic morlet. The decomposition results are visualized using a scalogram, then the results are used as CNN input. In this study, the PCG signals used were classified into normal, angina pectoris (AP), congestive heart failure (CHF), and hypertensive heart disease (HHD). The total data used, classified into 80 training data and 20 testing data. The obtained model shows the level of accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100% for training data, respectively, while the prediction results for testing data indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and 100%, respectively. This result proved to be better than the mother wavelet or other classifier methods, then the model was deployed into the graphical user interface (GUI).
Volume: 19
Issue: 5
Page: 1697-1706
Publish at: 2021-10-01

Development of smart parking system using internet of things concept

10.11591/ijeecs.v24.i1.pp611-620
Dwi Puspitasari , Noprianto Noprianto , Muhammad Afif Hendrawan , Rosa Andrie Asmara
The growing number of vehicles in developing countries causes a slew of issues, including the parking system.The current parking system is mostly manual, requires human intervention as a security system, and does not provide information about available parking areas.Their problems cause nonoptimal parking management. Furthermore, it can lead to income loss and criminal acts. This study addresses one of the possible solutions by using the internet of things (IoT) concept. The parking system is built by utilizing a smart card, machine-to-machine (M2M) communication, and cloud monitoring. As a result, the smart parking system prototype has been provided. The parking system business process can be done automatically, and it provides a more secure parking security system. The proposed parking system architecture also provides a practical system. The system only took around 1 second to perform the data transmission between nodes.
Volume: 24
Issue: 1
Page: 611-620
Publish at: 2021-10-01

Face spoofing detection using surface and sub-surface reflections analysis

10.11591/ijeecs.v24.i1.pp189-197
Azim Zaliha Abd Aziz , Mohd Rizon Mohamed Juhari
Reflection based analysis has been used in previous research for various objectives. Materials classification is one of them. Basically, each material consists of two types of reflections: surface and sub-surface. To separate these two reflections, polarized light could be applied. Previously, multi-reflections characteristics were analyzed using polarized light to classify objects such as between metals and non-metals. However, no trial has been done using the same method to distinguish real and fake faces that could be used to combat spoofing attempts in face biometric system. Since human skin is multi layers structure, it also produces multi reflections. In this paper, driven by the theory, surface and sub-surface reflections of both genuine human face and paper face mask were statistically examined. In addition, iPad displayed face images were also used as spoofing attempts. Images of genuine and spoofing faces were captured using polarized light under two different polarization angles: 0 and 90 degrees. Each angle captured images with surface and sub-surface reflections, accordingly. Those reflections were analyzed based on the mean, standard deviation, skewness and kurtosis. Modality distribution of each image was also studied using another method called the bimodality coefficient (BC). From the results, it is not possible to distinguish between genuine face and printed photos because of the multi reflections’ similarities. However, iPad displayed face images have been successfully identified as spoofing trials.
Volume: 24
Issue: 1
Page: 189-197
Publish at: 2021-10-01

Early fever detection on COVID-19 infection using thermoelectric module generators

10.11591/ijece.v11i5.pp3828-3837
Daniel Sanin-Villa , Oscar D. Monsalve-Cifuentes , Jorge Sierra del Rio
In 2020 the COVID-19 pandemic has suddenly stopped society and changed human interaction. In this work, a thermoelectric generator wearable device for early fever detection symptoms is presented as a possible solution to avoid higher propagation of this disease. To identify a possible fever symptom, numerical and parametric simulations are developed using a highquality-refined hexahedral mesh. At first, a 2-pair-leg thermoelectric module has undergone simulations to establish temperature conditions, open-circuit voltage, and power output generation; and secondly, these previous results are extrapolated for a larger thermoelectric module containing 28 pair-leg of N-P type material. The numerical study shows that a maximum value of electrical power of 60.70 mW was reached for 28-pair-leg N-P type thermocouples under a constant temperature difference of 20 K.
Volume: 11
Issue: 5
Page: 3828-3837
Publish at: 2021-10-01

Outage probability analysis for hybrid TSR-PSR based SWIPT systems over log-normal fading channels

10.11591/ijece.v11i5.pp4233-4240
Hoang Thien Van , Hoang-Phuong Van , Danh Hong Le , Ma Quoc Phu , Hoang-Sy Nguyen
Employing simultaneous information and power transfer (SWIPT) technology in cooperative relaying networks has drawn considerable attention from the research community. We can find several studies that focus on Rayleigh and Nakagami-m fading channels, which are used to model outdoor scenarios. Differing itself from several existing studies, this study is conducted in the context of indoor scenario modelled by log-normal fading channels. Specifically, we investigate a so-called hybrid time switching relaying (TSR)-power splitting relaying (PSR) protocol in an energy-constrained cooperative amplify-and-forward (AF) relaying network. We evaluate the system performance with outage probability (OP) by analytically expressing and simulating it with Monte Carlo method. The impact of power-splitting (PS), time-switching (TS) and signal-to-noise ratio (SNR) on the OP was as well investigated. Subsequently, the system performance of TSR, PSR and hybrid TSR-PSR schemes were compared. The simulation results are relatively accurate because they align well with the theory.
Volume: 11
Issue: 5
Page: 4233-4240
Publish at: 2021-10-01

Hybrid clustering based on multi-criteria segmentation for higher education marketing

10.12928/telkomnika.v19i5.18965
Hardika; Universitas Sahid Surakarta Khusnuliawati , Dhian Riskiana; Universitas Sahid Surakarta Putri
Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful information about student preference. Data result of the integration will be processed using hybrid clustering using K-means and self organizing map (SOM) algorithm. The hybrid clustering conducted to get promising clustering result along with the visualization of segmentation. This study successfully produces five student segments. It received 1,386 as the Davies-Bouldin index (DBI) value and 2,752 as the quantization error (QE) value which indicates a good clustering result for market segmentation. In addition, the visualization of the clustering result can be seen in a hexagonal map.
Volume: 19
Issue: 5
Page: 1498-1506
Publish at: 2021-10-01

Static hand gesture recognition of Arabic sign language by using deep CNNs

10.11591/ijeecs.v24.i1.pp178-188
Mohammad H. Ismail , Shefa A. Dawwd , Fakhradeen H. Ali
An Arabic sign language recognition using two concatenated deep convolution neural network models DenseNet121 & VGG16 is presented. The pre-trained models are fed with images, and then the system can automatically recognize the Arabic sign language. To evaluate the performance of concatenated two models in the Arabic sign language recognition, the red-green-blue (RGB) images for various static signs are collected in a dataset. The dataset comprises 220,000 images for 44 categories: 32 letters, 11 numbers (0:10), and 1 for none. For each of the static signs, there are 5000 images collected from different volunteers. The pre-trained models were used and trained on prepared Arabic sign language data. These models were used after some modification. Also, an attempt has been made to adopt two models from the previously trained models, where they are trained in parallel deep feature extractions. Then they are combined and prepared for the classification stage. The results demonstrate the comparison between the performance of the single model and multi-model. It appears that most of the multi-model is better in feature extraction and classification than the single models. And also show that when depending on the total number of incorrect recognize sign image in training, validation and testing dataset, the best convolutional neural networks (CNN) model in feature extraction and classification Arabic sign language is the DenseNet121 for a single model using and DenseNet121 & VGG16 for multi-model using.
Volume: 24
Issue: 1
Page: 178-188
Publish at: 2021-10-01

Artificial neural network technique for improving prediction of credit card default: A stacked sparse autoencoder approach

10.11591/ijece.v11i5.pp4392-4402
Sarah A. Ebiaredoh-Mienye , E. Esenogho , Theo G. Swart
Presently, the use of a credit card has become an integral part of contemporary banking and financial system. Predicting potential credit card defaulters or debtors is a crucial business opportunity for financial institutions. For now, some machine learning methods have been applied to achieve this task. However, with the dynamic and imbalanced nature of credit card default data, it is challenging for classical machine learning algorithms to proffer robust models with optimal performance. Research has shown that the performance of machine learning algorithms can be significantly improved when provided with optimal features. In this paper, we propose an unsupervised feature learning method to improve the performance of various classifiers using a stacked sparse autoencoder (SSAE). The SSAE was optimized to achieve improved performance. The proposed SSAE learned excellent feature representations that were used to train the classifiers. The performance of the proposed approach is compared with an instance where the classifiers were trained using the raw data. Also, a comparison is made with previous scholarly works, and the proposed approach showed superior performance over other methods.
Volume: 11
Issue: 5
Page: 4392-4402
Publish at: 2021-10-01

AQUACISION: a multiparameter aquaculture water quality ester and decision support system

10.11591/ijeecs.v24.i1.pp530-537
Mark Anthony A. Lazo , Louise Mark Kit S. Geronimo , Lester John T. Comilang , Kenneth John B. Cayme , Jay M. Ventura , Ertie C. Abana
The paper presents a multiparameter aquaculture water quality tester with a decision support system. A device was developed to aid aquaculture farmers in monitoring water quality parameters and maintaining or achieving optimal levels by suggesting ways on how a farmer can respond to such measurements. The AQUACISION device measures six different water quality parameters; temperature, practical salinity, pH level, total dissolved solid (TDS), oxidation-reduction potential (ORP), and algae density. Measurements were sent to the AQUACISION application where they were processed to determine the course of action that was best to maintain or achieve optimal levels using fuzzy rules. Based on the comparative result, the AQUACISION was accurate in measuring temperature, practical salinity, pH level, TDS, and ORP during the actual testing. The application also received an excellent rating on the ISO/IEC 25010 software quality model standard
Volume: 24
Issue: 1
Page: 530-537
Publish at: 2021-10-01

An improved Kohonen self-organizing map clustering algorithm for high-dimensional data sets

10.11591/ijeecs.v24.i1.pp600-610
Momotaz Begum , Bimal Chandra Das , Md. Zakir Hossain , Antu Saha , Khaleda Akther Papry
Manipulating high-dimensional data is a major research challenge in the field of computer science in recent years. To classify this data, a lot of clustering algorithms have already been proposed. Kohonen self-organizing map (KSOM) is one of them. However, this algorithm has some drawbacks like overlapping clusters and non-linear separability problems. Therefore, in this paper, we propose an improved KSOM (I-KSOM) to reduce the problems that measures distances among objects using EISEN Cosine correlation formula. So far as we know, no previous work has used EISEN Cosine correlation distance measurements to classify high-dimensional data sets. To the robustness of the proposed KSOM, we carry out the experiments on several popular datasets like Iris, Seeds, Glass, Vertebral column, and Wisconsin breast cancer data sets. Our proposed algorithm shows better result compared to the existing original KSOM and another modified KSOM in terms of predictive performance with topographic and quantization error.
Volume: 24
Issue: 1
Page: 600-610
Publish at: 2021-10-01

K-affinity propagation clustering algorithm for the classification of part-time workers using the internet

10.11591/ijeecs.v24.i1.pp464-472
Novendri Isra Asriny , Muhammad Muhajir , Devi Andrian
There has been a significant increase in the number of part-time workers in the last 3 years. Data collected from sakernas BPS showed that the number of part-time workers was 125,443,748 in the second period of 2016. This number rapidly increased in 2017, 2018 and 2019 in the same period, by 128,062,746, 131,005,641, and 133,560,880 workers. Based on the increase in the last 3 years, East Java province has the highest number of part-time workers that use the internet. This research aims to determine the number of part-time workers that use the internet by using the k-affinity propagation (K-AP) clustering. This method is used to produce the optimal number of cluster points (exemplar) is the affinity propagation (AP). Three clusters were used to determine the sum of the smallest value ratio. The result showed that clusters 1, 2, and 3 have 3, 23, and 5 members in Bondowoso, Jombang, and Surabaya districts.
Volume: 24
Issue: 1
Page: 464-472
Publish at: 2021-10-01

Forecasting number of vulnerabilities using long short-term neural memory network

10.11591/ijece.v11i5.pp4381-4391
Mohammad Shamsul Hoque , Norziana Jamil , Nowshad Amin , Azril Azam Abdul Rahim , Razali B. Jidin
Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
Volume: 11
Issue: 5
Page: 4381-4391
Publish at: 2021-10-01

A small footprint printed cross-dipole antenna with wide impedance bandwidth and circular polarization

10.11591/ijeecs.v24.i1.pp347-356
Mustafa Hasan , Nasr Alkhafaji , Hussam AlAnsary , Azhar R. Mohsin
Wideband circularly polarized (CP) cross-dipole antennas with flat, cavity and artificial magnetic conductor (AMC) reflectors are proposed. Each proposed antenna consists of a pair of driven dipoles, a pair of vacant-quarter printed rings, and a 50Ω coaxial probe. The boomerang shape has been adopted in the crossed-dipole. This shape makes the design more compact, so it can be a good candidate in the antenna array because of reducing the mutual coupling. All numerical simulation works have been done using the ANSYS electromagnetic (EM) software based on the finite element method (FEM) algorithm. The presented crossed-dipole with a cavity has the best performance compared to ones with conventional flat and AMC grounds. However, the crossed-dipole with the AMC ground is a low-profile structure. Thus, the paper investigates and discusses the results of the proposed strctures thoroughly. The obtained impedance bandwidth (IBW) is 42% (5.1-7.85 GHz) and the axial-ratio bandwidth (ARBW) is 7.72% (5.86-6.32 GHz) for the crossed-dipole with the conventional flat ground (i.e., reflector). Furthermore, the IBW and ARBW for the antenna with the cavity reflector are 50.37% (5.08-8.5 GHz) and 26.4% (5.72-7.46 GHz), respectively. The antenna with the AMC ground has the characterstics of the IBW and ARBW as 38.16% (5.36-7.89 GHz) and 15.16% (5.79-6.74 GHz), respectively. All structures are designed to operate for the C-band and wireless local area networks (WLAN) applications.
Volume: 24
Issue: 1
Page: 347-356
Publish at: 2021-10-01

An internet of things ecosystem for planting of coriander (Coriandrum sativum L.)

10.11591/ijece.v11i5.pp4568-4576
Panana Tangwannawit , Kanita Saengkrajang
The internet of things (IoT) is a network of physical devices and is becoming a major area of innovation for computer-based systems. Agriculture is one of the areas which could be improved by utilizing this technology ranging from farming techniques to production efficiency. The objective of this research is to design an IoT to monitor local vegetable (Coriander; Coriandrum sativum L.) growth via sensors (light, humidity, temperature, water level) and combine with an automated watering system. This would provide planters with the ability to monitor field conditions from anywhere at any time. In this research, a group of local vegetables including coriander, cilantro, and dill weed were experimented. The prototype system consists of several smart sensors to accurately monitor the mentioned vegetable growth from seedling stage to a fully grown plant which will ensure the highest production levels from any field environment. Three different types coriander were measured under these parameters: height, trunk width, and leaf width. The result showed that IoT ecosystem for planting different types of coriander could produce effective and efficient plant growth and ready for harvest with a shorter time than conventional method.
Volume: 11
Issue: 5
Page: 4568-4576
Publish at: 2021-10-01
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