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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 64 Documents
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Search results for , issue "Vol 31, No 1: July 2023" : 64 Documents clear
Architectural framework and register-transfer level design synthesis for cost-effective smart eyewear Kashish Malhotra; Revathi M. S.; Uma B. V.; Ajay K. M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp88-97

Abstract

In today’s time more than 70% of the world’s population suffer from eye disnormalities leading to the usage of eyewear or spectacles. Integrating profound technologies with daily utilities could serve some of the issues improving and optimizing our lifestyle to the most. One such way is to infuse nanosized chip in eyewear i.e., powered spectacles or shades to detect the location of the spectacles whenever it is necessary. The nanosized chip proposed has features including self-designed Bluetooth operating digital circuit, timer logic, clock generation using astable multivibrator circuit, emergency button, beep alarm and impact sensor. The values of resistance and capacitace is calculated to be 18 K ohm and 47 uF to obtain 1 Hz frequency. An optimal pin placement arrangement is analyzed, and the timing waveform is simulated using Verilog as proof of logical working of the chip. 13 D flipflops have been calculated to refrain from eye related strains. This paper suggests a bottom-up approach and develops the architectural framework of the chip, its working flow, system on chip top-view, digital logic description of each block and its implementation using Verilog hardware description language (HDL). The complexity and computational cost of the designed chip is minimal thus being commercially viable.
Fault tolerant and load balancing model for software defined networking controllers Ihssane Choukri; Mohammed Ouzzif; Khalid Bouragba
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp378-385

Abstract

Previous years have seen increased interest in a new network paradigm, Software defined networking (SDN). The basic idea behind this new concept consists of removing the smart parts of the connectivity components and moving them to a seul control point known as a controller. This centralized network view makes the network maintenance and management easier and facilitates the creation of new services. Despite many advantages of SDN, the concentration of network intelligence in a single controller raises serious challenges that impact SDN scalability, performance, and fault tolerance. One of the main problems in SDN is controller failure. In this article, we develop a fault tolerant model called fault-tolerant load balancing (FTLBC) for SDN controllers. To reduce the cascading failure problem, the proposed model requires the load of the failing controller to be shared among other controllers. In the case of a controller failure, The FTLBC model concentrates on distributing the load among the remaining controllers based on the load of the orphans' switches and the load of the remaining controllers.
Steering angle prediction via neural networks Fayez Saeed Faizi; Ahmed Khorsheed Alsulaifanie
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp392-399

Abstract

Methods to calculate the correct steering angle are an important aspect of developing self-driving vehicles. Recently, steering angle prediction methods based on deep neural networks have achieved accurate results that outperform other methods over a range of road types. This paper investigates steering angle prediction for an autonomous vehicle as a regression problem and solves it using deep neural networks. The proposed approach obtains data in the form of the coordinates of lane lines and examines if they belong to one or more lines. Based on that, the method locates the path line that the vehicle is moving on. It then determines the equation that represents that path. Finally, the coefficients of this equation are fed into a trained neural network, which predicts the steering angle for that video frame. Extensive tests were performed on the Comma.ai and Udacity benchmarks to evaluate the performance of the approach. State-of-the-art results were achieved, with mean absolute error 0.64 and root mean square error 0.87 for the Comma.ai dataset, and mean absolute error 1.04 and root mean square error 2.33 for the Udacity dataset.
An improvement of the computational effective diameter measurement in thoracic computed tomography examinations Choirul Anam; Riska Amilia; Wahyu S. Budi; Heri Sutanto; Zaenul Muhlisin; Ariij Naufal; Geoff Dougherty
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp189-196

Abstract

A method to calculate a corrected effective diameter (DMIL) to more accurately estimate the dose received by a patient in chest computed tomography (CT) examination had been previously proposed. However, the discrepancy between DMIL and water-equivalent diameter (Dw) is still relatively high (i.e. about 6%). Furthermore, the method is still performed manually, so it is laborious and time-consuming. This study aims to improve the corrected effective diameter with bone correction (Deffcorr) and to automatically calculate it. The automated Deffcorr was calculated as the square root of the product of these corrected AP and LAT diameters. The approach was implemented on 30 patients who had undergone chest CT examination with the standard protocol. The results show that the correlation between the Deffcorr and Dw is R2=0.93 with no statistical difference (p>0.05). The automated Deffcorr is 3.1% lower than Dw. While the DMIL is 10.5% higher than Dw and both are statistically different (p<0.05). In conclusion, the new Deffcorr was introduced and the result obtained was closer to Dw than DMIL. This method is simple enough to be used as an alternative method to accurately estimate Dw for radiation dose estimation in clinical chest CT scanning.
Aura detection using thermal camera with convolutional neural network method for mental health diagnosis Her Gumiwang Ariswati; Liliek Soetjiatie
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp553-561

Abstract

Mental health is an important aspect in realizing overall health. For this reason, non-invasive medical equipment is needed for people with mental disorders. This study aimed to create a psychological health diagnostic tool by detecting auras using a thermal camera from facial objects. The contribution of this study is that the tool can detect the patient's aura without physical contact so that the patient is more comfortable and does not feel invaded. This research designed a system for detecting electromagnetic wave radiation energy emitted by the body using a thermal camera. Face detection in the input image was performed using a convolutional neural network (CNN) model single shot multibox detector (SSD), which is one of the CNN models that implements a bounding box to estimate the localization of detected objects. In this case, system testing was used to evaluate the performance of the CNN system algorithm for aura detection in terms of color (main color or average color). The results obtained were detectibility by 80%, selectivity by 88.88%, precision by 70%, sensitivity by 87.5%, and accuracy by 63.63%. The design of the aura detection system in this study will make it easier for psychiatrists and psychologists to help make a noninvasive diagnosis.
Detecting man-in-the-middle attacks via hybrid quantumclassical protocol in software-defined network Thakwan A. Jawad; Awan Nahel Mahmood; Abdulhameed N. Hameed
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp205-211

Abstract

Man-in-the-middle (MitM) attacks became one of the most risk attacks on OpenFlow communication channel in software-defined networking, its detection is a very hard task due there is no authentication in OpenFlow protocol. This channel is the most important in the network and is responsible for sending the control commands from the controller to the switches, so once the OpenFlow channel is hacked, the entire network is controlled by the attacker. Therefore, we propose a complementary solution to transport layer security protocol to detect man-in-the-middle attacks based on hybrid quantum-classical protocol. Based on the hybrid protocol, an easy-toimplement authentication between controller and switches depends on quantum and classical security layers. Also, detect eavesdropping on channel depending on quantum parameters. In this paper, we implement a simulation of hybrid protocol using a software-defined networking emulator for monitoring the OpenFlow channel to detect attacks, and the results showed the ease of detecting the eavesdrop and verifying the authentication of the other party with a hybrid method to get a high level of authentication.
Energy efficient data fusion approach using squirrel search optimization and recurrent neural network Arulkumar Varatharajan; Poonkodi Ramasamy; Suguna Marappan; Devipriya Ananthavadivel; Chemmalar Selvi Govardanan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp480-490

Abstract

Sensor networks have helped wireless communication systems. Over the last decade, researchers have focused on energy efficiency in wireless sensor networks. Energy-efficient routing remains unsolved. Because energyconstrained sensors have limited computing capabilities, extending their lifespan is difficult. This work offers a simple, energy-efficient data fusion technique employing zonal node information. Using the witness-based data fusion technique, the evaluated network lifetime, energy consumption, communication overhead, end-to-end delay, and data delivery ratio. Energyefficient data fusion optimizes energy utilization using squirrel search optimization and a recurrent neural network. The method allows the system to recognize a sensor with excessive energy dissipation and relocate data fusion to a more energy-efficient node. The proposed model was compared against artificial neural network-particle swarm optimization (ANN-PSO), cuckoo optimization algorithm-back propagation neural network (COABPNN), Elman neural network-whale optimization algorithm (ENN-WOA), and extreme learning machine-particle swarm optimization (ELM-PSO). The model achieved 94.50% network lifetime, 26.63% communication overhead, 93.85% data delivery ratio, 10.50 ms end-to-end delay, and 282 J energy usage.
Content-based image retrieval using integrated dual deep convolutional neural network Feroza D. Mirajkar; Ruksar Fatima; Shaik A. Qadeer
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp77-87

Abstract

The image retrieval focuses on finding images that are similar from a dataset that is of a large scale against an image of a query. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as their shape, colour, and texture. used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deepconvolutional neural networks (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e., learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets of Paris and the oxford dataset considering metrics; also, image retrieval and re-ranking is carried out against the given query. Comparative analysis of various difficulty levels against the different CNN models suggests that IDD-CNN simply outperforms the existing model.
Age and gender classification with bone images using deep learning algorithms Sathyavathi Sundarasamy; Baskaran Kuttuva Rajendran
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp359-368

Abstract

In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.
Sales forecasting of marketing using adaptive response rate single exponential smoothing algorithm Tegar Arifin Prasetyo; Evan Richardo Sianipar; Poibe Leny Naomi; Ester Saulina Hutabarat; Rudy Chandra; Wesly Mailander Siagian; Goklas Henry Agus Panjaitan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp423-432

Abstract

Micro, small and medium enterprises (UMKM) is one of the important aspects to support the improvement of the economy in Indonesia. Zee Mart’s business is one of the UMKM shop in Pematang Siantar City with sales and purchase transaction activities for supplies. The purpose of this study is to predict the sales of Zee Mart store goods in the coming month using the adaptive response rate single exponential smoothing (ARRSES) method. ARRSES is a method with the advantage of having two parameters, alpha and beta, where alpha will change every period when the data pattern changes. The dataset obtained will be pre-processed through data selection, cleaning, and transformation. The best beta is determined based on the level of accuracy calculated using the mean absolute percentage error (MAPE). Model development using the ARRSES method will produce forecasting percentages and errors for each product using MAPE. The number of sales data is 23,092 before preprocessing and 23,021 after pre-processing, with the total quantity of goods sold being 149,764 of 1,492 products. The results obtained using sales data 23,021 show the lowest MAPE value of 9.85 at the best beta of 0.6 with the highest accuracy of 90.15% and the model is implemented into a web interface.

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