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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 66 Documents
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Search results for , issue "Vol 24, No 1: October 2021" : 66 Documents clear
Face spoofing detection using surface and sub-surface reflections analysis Azim Zaliha Abd Aziz; Mohd Rizon Mohamed Juhari
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp189-197

Abstract

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.
A new framework for utilizing side information in sparse representation Seyed Hadi Hashemi Rafsanjani; Saeed Ghazi Maghrebi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp403-409

Abstract

An underdetermined system of linear equation has infinitely number of answers. To find a specific solution, regularization method is used. For this propose, we define a cost function based on desired features of the solution and that answer with the best matches to these function is selected as the desired solution. In case of sparse solution, zero-norm function is selected as the cost function. In many engineering cases, there is side information which are omitted because of the zero-norm function. Finding a way to conquer zero-norm function limitation, will help to improve estimation of the desired parameter. In this regard, we utilize maximum a posterior (MAP) estimation and modify the prior information such that both sparsity and side information are utilized. As a consequence, a framework to utilize side information into sparse representation algorithms is proposed. We also test our proposed framework in orthogonal frequency division multiplexing (OFDM) sparse channel estimation problem which indicates, by utilizing our proposed system, the performance of the system improves and fewer resources are required for estimating the channel.
Multi-label classification approach for quranic verses labeling Abdullahi Adeleke; Noor Azah Samsudin; Mohd Hisyam Abdul Rahim; Shamsul Kamal Ahmad Khalid; Riswan Efendi
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp484-490

Abstract

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.
Current mismatch reduction in charge pumps using regulated current stealing-injecting transistors for PLLs Mohd Khairi Zulkalnain; Yan Chiew Wong
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp61-69

Abstract

A charge pump for phase locked loops (PLL) with a novel current mismatch compensation technique is proposed. The proposed circuit uses a simple yet effective current stealing-injecting (CSI) technique and feedback to reduce mismatch between the negative-channel-metal-oxide (NMOS) and positive-channel-metal-oxide (PMOS) transistors. The current stealing transistor steals the current from a replica branch and mirrors it to the output where it is added to the output branch by the injecting transistor. A feedback mechanism is used to set the drain voltages of both branches to be equal and mitigate channel length modulation and ensure high accuracy. The proposed circuit was designed on Silterra 130nm technology and simulated using Cadence Spectre. The simulation results show that the proposed circuit yields a maximum of 0.107% and minimum of 0.00465% current mismatch while operating at a low supply voltage of 800mV for a range of 100mV to 700mV. The proposed design uses only one rail-to-rail op amp for compensating the mismatch and an addition of 4 transistors and utilizing 75% of the supply voltage for high voltage controlled oscillator (VCO) tuning range.
High precision brain tumor classification model based on deep transfer learning and stacking concepts Halima El Hamdaoui; Anass Benfares; Saïd Boujraf; Nour El Houda Chaoui; Badreddine Alami; Mustapha Maaroufi; Hassan Qjidaa
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp167-177

Abstract

In this article, we proposed an intelligent clinical decision support system for the detection and classification of brain tumor from risk of malignancy index (RMI) images. To overcome the lack of labeled training data needed to train convolutional neural networks, we have used a deep transfer learning and stacking concepts. For this, we choosed seven convolutional neural networks (CNN) architectures already pre-trained on an ImageNet dataset that we precisely fit on magnetic resonance imaging (MRI) of brain tumors collected from the brain tumor segmentation (BraTS) 19 database. To improve the accuracy of our global model, we only predict as output the prediction that obtained the maximum score among the predictions of the seven pre-trained CNNs. We used a 10-way cross-validation approach to assess the performance of our main 2-class model: low-grade glioma (LGG) and high-grade glioma (HGG) brain tumors. A comparison of the results of our proposed model with those published in the literature, shows that our proposed model is more efficient than those published with an average test precision of 98.67%, an average f1 score of 98.62%, a test precision average of 98.06% and an average test sensitivity of 98.33%.
Artificial intelligence techniques over the fifth generation mobile networks: a review Ashwaq N. Hassan; Sarab Al-Chlaihawi; Ahlam R. Khekan
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp317-328

Abstract

A well Fifth generation (5G) mobile networks have been a common phrase in recent years. We have all heard this phrase and know its importance. By 2025, the number of devices based on the fifth generation of mobile networks will reach about 100 billion devices. By then, about 2.5 billion users are expected to consume more than a gigabyte of streamed data per month. 5G will play important roles in a variety of new areas, from smart homes and cars to smart cities, virtual reality and mobile augmented reality, and 4K video streaming. Bandwidth much higher than the fourth generation, more reliability and less latency are some of the features that distinguish this generation of mobile networks from previous generations.  Clearly, at first glance, these features may seem very impressive and useful to a mobile network, but these features will pose serious challenges for operators and communications companies. All of these features will lead to considerable complexity. Managing this network, preventing errors, and minimizing latency are some of the challenges that the 5th generation of mobile networks will bring. Therefore, the use of artificial intelligence and machine learning is a good way to solve these challenges. in other say, in such a situation, proper management of the 5G network must be done using powerful tools such as artificial intelligence. Various researches in this field are currently being carried out. Research that enables automated management and servicing and reduces human error as much as possible. In this paper, we will review the artificial intelligence techniques used in communications networks. Creating a robust and efficient communications network using artificial intelligence techniques is a great incentive for future research. The importance of this issue is such that the sixth generation (6G) of cellular communications; There is a lot of emphasis on the use of artificial intelligence.
Detection of cardiac arrhythmia using deep CNN and optimized SVM Mohebbanaaz Mohebbanaaz; Y. Padma Sai; L. V. Rajani Kumari
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp217-225

Abstract

Deep learning (DL) has become a topic of study in various applications, including healthcare. Detection of abnormalities in an electrocardiogram (ECG) plays a significant role in patient monitoring. It is noted that a deep neural network when trained on huge data, can easily detect cardiac arrhythmia. This may help cardiologists to start treatment as early as possible. This paper proposes a new deep learning model adapting the concept of transfer learning to extract deep-CNN features and facilitates automated classification of electrocardiogram (ECG) into sixteen types of ECG beats using an optimized support vector machine (SVM). The proposed strategy begins with gathering ECG datasets, removal of noise from ECG signals, and extracting beats from denoised ECG signals. Feature extraction is done using ResNet18 via concept of transfer learning. These extracted features are classified using optimized SVM. These methods are evaluated and tested on the MIT-BIH arrhythmia database. Our proposed model is effective compared to all State of Art Techniques with an accuracy of 98.70%.
Attentional bias during public speaking anxiety revealed using event-related potentials Farah Shahnaz Feroz; Ahmad Rifhan Salman; Muhammad Hairulnizam Mat Ali; Afiq Idzudden Ismail; S. Indra Devi; S. K. Subramaniam
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp253-259

Abstract

Analysis of brain signals and their properties provides valuable information regarding the underlying neural deficiencies and enables the diagnosis of attention bias related to public speaking anxiety (PSA). Although 25% people around the world suffer from PSA, currently, there exists a lack of standard assessment in diagnosing the severity of attention bias in individuals with PSA. This study aims to distinguish behavioral and neural abnormalities related to attentional bias during PSA by comparing reaction time (RT) and event-related potential (ERP) correlates of high (H) PSA and low (L) PSA individuals. 12 individuals suffering from HPSA and 12 individuals with LPSA participated in the modified emotional Stroop experiment. Electroencephalography (EEG) was recorded with the low cost, 14-channel Emotiv Epoc+. RT showed slower responses, linked to attentional deficits in HPSA individuals. ERP results revealed the P200 emotional Stroop biomarker, found to be linked to attentional bias in HPSA, but not in LPSA individuals. These results revealed significant RT and P200 ERP abnormalities related to attentional bias in HPSA individuals using the low-cost Emotiv Epoc+.
Development of smart parking system using internet of things concept Dwi Puspitasari; Noprianto Noprianto; Muhammad Afif Hendrawan; Rosa Andrie Asmara
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp611-620

Abstract

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.
Impact of optical current transformer on protection scheme of hybrid transmission line Zainal Arifin; Muhammad Zulham; Eko Prasetyo
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp1-11

Abstract

Continuity of power transmission is important to ensure the reliability of the electricity supply. As most system faults are temporary, the auto reclose (AR) scheme has been used extensively to minimise the outage duration, prevent widespread outages, thus increase system stability. Meanwhile, the hybrid transmission line (HTL) combining overhead line (OHL) and high voltage cable has been introduced to provide an inexpensive solution for an urban power grid. Protecting HTL with a conventional protection system would forbid the operation of the AR scheme due to difficulty to ensure whether the fault occurred on the OHL or cable section. Therefore, the circulating current protection (CCP) scheme is used in the cable section to ensure the fault location and block the AR scheme. The technology of an optical current transformer (OCT) as one of the non-conventional instrument transformers (NCIT) has emerged to provide a solution to drawbacks on the conventional current transformer (CCT). Consequently, this paper investigated the impact of using OCT over the CCT for CCP of the HTL. The result shows that OCT could be used for CCP on much longer cable sections thus increase its reliability as the AR scheme can be used on longer or multiple cable section.

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