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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 : -
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Articles 66 Documents
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Search results for , issue "Vol 21, No 3: March 2021" : 66 Documents clear
An ultra-low complexity of 2:1 multiplexer block in QCA technology Ali Hussien Majeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1341-1346

Abstract

The limitations related to CMOS such as power consumption and parasitic capacitance lead scientists to search for new technologies. Quantum-dot cellular automata (QCA) is a CMOS alternative technology that uses charges instead of voltage level for binary representation. In QCA, many metrics are used for circuit differentiation such as delay, complexity and area. In this work, a new simple block of 2:1 QCA-Multiplexer is proposed. The proposed block is more efficient than previous designs by 0.43%, 0.53%, 50% and 0.72 in terms of area, complexity, delay and cost. QCADesigner software is used to design and verify the proposed circuit.
Extracting numerical data from unstructured Arabic texts (ENAT) Abeer K. AL-Mashhadany; Dalal N. Hamood; Ahmed T. Sadiq Al-Obaidi; Waleed K. Al-Mashhsdany
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1759-1770

Abstract

Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The inference engine is based on the Arabic linguistic and morphological rules. The applied method encompasses rules of numerical nouns with Arabic morphological rules, in order to achieve high accurate extraction method. Arithmetic operations are applied to convert the numerical phrase into integer value. The proper operation is determined depending on linguistic and morphological rules. It will be shown that applying Arabic linguistic rules together with arithmetic operations succeeded in extracting numerical data from Arabic unstructured text with high accuracy reaches to 100%.
Key exchange based on Diffie-Hellman protocol and image registration Rachid Rimani; Naima Hadj Said; Adda Ali Pacha; Ozen Ozer
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1751-1758

Abstract

Nowadays, with the advences in ICT and rapid development of mobile internet; media information shared on the various communication networks requires the existence of adequate security measures. Cryptography becoming an effective way to meet these requirements and for maintain the confidentiality. However, communicating with encrypted messages requires secret key exchange, which is a part of a complex protocol. In this paper, we propose a new method for exchanging key based on Diffie-Hellman protocol and image registration with fast fourier transform, the principle of this method consists to concealing the key in a set of transformed images. Therefore, image registration allows finding transformations between images, which become a tool for recovering the key by the receiver.
High modulated soliton power propagation interaction with optical fiber and optical wireless communication channels Mahmoud M. A. Eid; Ashraf S. Seliem; Ahmed Nabih Zaki Rashed; Abd El-Naser A. Mohammed; Mohamed Yassin Ali; Shaimaa S. Abaza
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1575-1583

Abstract

This paper has presented high modulated soliton power transmission interaction with optical fiber and optical wireless communication channels at flow rate of 40 Gbps and 20 km link range. The proposed modulation schemes are continuous phase frequency shift keying (CPFSK), Quadrature amplitude modulation (QAM), differential phase shift keying (DPSK), frequency shift keying (FSK), pulse amplitude modulation (PAM), minimum shift keying (MSK), and optical quadrature phase shift keying (OQPSK). CPFSK has presented better performance than other proposed modulation schemes for both optical fiber and optical wireless communication channels. The enhancement of optical signal/noise ratio at fiber/wireless channel, received electrical power and signal/noise ratio at optical receiver with increase of bits per symbol for different proposed modulation schemes except for CPFSK scheme. Therefore it is evident that CPFSK modulation scheme is more efficient and better performance than other modulation schemes for different communication channels. The obtained results are simulated with optisystem program version 13.
What network simulator questions do users ask? a large-scale study of stack overflow posts Syful Islam; Yusuf Sulistyo Nugroho; Md. Javed Hoss
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1622-1633

Abstract

The use of network simulator as a modern tool in analyzing and predicting the behaviour of computer networks has grown to reduce the complexity of its accuracy measurement. This growth of network simulator implementation have attracted the researchers and practitioners to share problems and discuss to improve the features. To communicate the issues relates to network simulators, users move to an online discussion platform, such as Stack Overflow. Although recent studies have shown the popularity and benefits of adopting network simulation tools, however, the challenges of using network simulator that users face remain mostly unknown. Therefore, in this paper, we examine 2,322 network-simulator-related Stack Overflow posts to provide insights on the topics that users are interested and the challenges they face. We apply the Latent Dirichlet Allocation topic modeling to understand the topics that are being discussed in Stack Overflow. Then, we investigate the popularity and difficulty of each topic. The results of this study show that users use Stack Overflow as an implementation guideline for network simulation model. We determine 8 discussion topics that are merged into 5 major categories. Most of the posts discuss simulation model configuration. We also observe that target network protocol modification and network simulator installation are the most popular topics among the users compared to other topics. Users are specially facing challenges on network simulator installation and target network protocol modification issues.
Design and characteristics assessment of wireless vibration sensor for buildings and houses Suherman Suherman; Fahmi Fahmi; Ulfa Hasnita; Zul Herri
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1381-1388

Abstract

This paper reports the wireless vibration detector design and characterization for practical applications. System is built by using the ATmega microcontrollers, working on a free license 433 MHz frequency. Hardware characteristics are defined through experimental assessments. Assessment mainly on sensor output and sensor installation characteristics. As results, hardware is working as expected, where vibration level achieves at most 13% detection for 12 g vibration source. The vertical axis of the MPU6050 vibration detector results 87.5 times higher detection than in horizontal axis. Detected vibration increases from 1.03 g to 2.61 g when source-sensor distance is shortened from 10 cm to 2 cm. The aluminium sheet as sensor pad causes detection of 8.69 times higher than on ceramic pad. The lower the detection period the better the detection amplitude. However, the lower the period, the higher the consumed power. Microcontroller sleep mode is not suitable for short period detection. The node-based data validation to avoid transmitting false detection is not influencial for short period detection.
Fast learning neural network based on texture for Arabic calligraphy identification Ahmed Kawther Hussein
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1794-1799

Abstract

Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. 
Dorsal hand vein authentication system using artificial neural network Sze Wei Chin; Kim Gaik Tay; Chew Chang Choon; Audrey Huong; Ruzairi Abdul Rahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1837-1846

Abstract

Biometric feature authentication technology had been developed and implemented for the security access system. However, the known biometric features such as fingerprint, face and iris pattern failed to provide ideal security. Dorsal hand vein is the features beneath the skin which makes it not easily be duplicated and forged. It was expected to be used in biometric authentication technology to achieve an ideal accuracy with the uniqueness of its characteristics. In this paper, 240 images of 80 users were obtained from Bosphorus Hand Vein Database. The images were then pre-processed by cropping ROI, mean filtering, CLAHE enhancing and histogram equalizing. The ROI was then segmented by implementing binarization. The local binary pattern (LBP) features were then extracted from the segmented ROI. The extracted features were sent to an artificial neural network (ANN) for the classification of the images. The training result shows that the LBP features and ANN can recognize the dorsal hand vein pattern quite well with 99.86% accuracy. The ANN was then utilized in the MATLAB GUI program for testing 100 images (80 trained images of 80 users and 20 untrained images of 20 users) from the Bosphorus Hand Vein Database. The results revealed 100% accuracy in their matching result.
A new framework based on KNN and DT for speech identification through emphatic letters in Moroccan dialect Bezoui Mouaz; Cherif Walid; Beni-Hssane Abderrahim; Elmoutaouakkil Abdelmajid
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1417-1423

Abstract

Arabic dialects differ substantially from modern standard arabic and each other in terms of phonology, morphology, lexical choice and syntax. This makes the identification of dialects from speeches a very difficult task. In this paper, we introduce a speech recognition system that automatically identifies the gender of speaker, the emphatic letter pronounced and also the diacritic of these emphatic letters given a sample of author’s speeches. Firstly we examined the performance of the single case classifier hidden markov models (HMM) applied to the samples of our data corpus. Then we evaluated our proposed approach KNN-DT which is a hybridization of two classifiers namely decision trees (DT) and k-nearest neighbors (KNN). Both models are singularly applied directly to the data corpus to recognize the emphatic letter of the sound and to the diacritic and the gender of the speaker. This hybridization proved quite interesting; it improved the speech recognition accuracy by more than 10% compared to state-of-the-art approaches.
Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset Hamdalla Fadil Kareem; Muayed S AL-Huseiny; Furat Y. Mohsen; Enam A. Khalil; Zainab S. Hassan
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 3: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i3.pp1731-1738

Abstract

This paper concerns the development/analysis of the IQ-OTH/NCCD lung cancer dataset. This CT-scan dataset includes more than 1100 images of diagnosed healthy and tumorous chest scans collected in two Iraqi hospitals. A computer system is proposed for detecting lung cancer in the dataset by using image-processing/computer-vision techniques. This includes three preprocessing stages: image enhancement, image segmentation, and feature extraction techniques. Then, support vector machine (SVM) is used at the final stage as a classification technique for identifying the cases on the slides as one of three classes: normal, benign, or malignant. Different SVM kernels and feature extraction techniques are evaluated. The best accuracy achieved by applying this procedure on the new dataset was 89.8876%.

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