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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 9,174 Documents
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An investigation of low-density parity-check codes and polar codes for future communication systems Layla Mahdi Salih; Thuraya Mahmood Al-Qaradaghi; Jalal Jamal Hamad Ameen
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp110-120

Abstract

In the fifth-generation (5G) era and future, mobile internet and internet of things (IoT) are the driving forces for mobile communications’ development. The three main 5G usage scenarios: enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communications (URLLC), require improvement in throughput, reliability, and latency as compared with the previous fourth generation (4G) system. In this paper, an investigation is done on the coding part of the wireless communication systems. Two channel coding types; low-density parity-check (LDPC) code which is used as the coding scheme for data transmission, and Polar code which is utilized for control in 5G are discussed. Moreover, simulations are performed to assess their performance. The simulation results revealed the superiority of polar code for transmitting short information messages and LDPC for transmitting long data messages. The use of LDPC and polar codes in 5G communication systems is justified by their ability to accommodate a wide range of data lengths and code rates, as well as their good bit error rate (BER) performance. Furthermore, the effect of the number of iterations on the BER performance of LDPC code and different decoding algorithms of polar code are considered.
Prototype and monitoring system of phasor measurement unit based on the internet of things Riny Sulistyowati; Hari Agus Sujono; Dedet Chandra Riawan; Rony Seto Wibowo; Mochamad Ashari
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp14-23

Abstract

This research resulting the method to reduce phasor measurement unit (PMU) amount and optimization of PMU replacement using a combination of Integer linear k-means. The first step of modeling is using a lot of PMUs that are optimized at Bendul Merisi network using integer linear k-means clustering for achieving an optimum solution of amount and replacement of PMU to be installed. The second step is estimating the uninstalled bus's power and voltage. PMU is using modified adaptive neuro-fuzzy inference system (ANFIS) of hybrid particle swarm optimization (PSO)-genetic algorithm (GA). The third step is to test and simulate the hardware design of the research for offline and online data. Research also tested network transmission of Java–Bali 500 kV. Designed simulation can calculate the active and reactive power of each bus in clusters so the total active and reactive power of each cluster can be known. Device tests to transmit data using internet of things (IoT) from a laboratory scale during 7 days have an average of 2.8 seconds while the field test required an average of 10.416 seconds during 24 hours.
Deriving equivalent structure of elements for low density parity check codes construction Jenjen Ahmad Zaeni; Fandy Ali Muzhofi; Cecep Solehudin; Khoirul Anwar; Nanang Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp144-156

Abstract

This paper proposes a method to derive equivalent coding structures of elements to construct low density parity check (LDPC) codes. We propose stairs LDPC (SLDPC) codes to demonstrate the effectiveness of the proposed method, which is expected to be beneficial for short block-length transmissions, but providing high coding rate. The equivalent coding structures are both for transmitter and receiver to: (i) reduce the encoding and decoding computational complexity, and (ii) search possibility of finding new coding scheme and observe their performances. We evaluate the validity of the method by confirming the equality in performances of the SLDPC codes in terms of bit-error-rate (BER) followed by investigation on their performance gaps to the Shannon limit via a series of computer simulations. The results show that the SLDPC codes have the same BER performance with that of the low density generator matrix (LDGM) codes confirming the validity of the proposed equivalent matrix derivation. This result indicates that different graphs can provide the same performances, because their equivalent matrices are the same. This result is expected to open new insight for the designing simple channel coding for short block-length LDPC codes having high coding rate for future less power consumption applications.
Street-crimes modelled arms recognition technique employing deep learning and quantum deep learning (SMARTED) Syed Atif Ali Shah; Ahmed Abdel-Wahab; Nasir Ageelani; Najeeb Najeeb
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp528-544

Abstract

An increase in population causes loopholes in controlling law and order situations. One of the threatening aspects of peace is the availability of weapons to the general public. As a result, many dangerous situations arise, most notably street crimes. Traditional methods are not sufficient to deal with such situations. Consequently, the police and other security concerns need serious technological reforms to prevent such situations. In modern technology tools, deep learning has made great improvements in various areas of daily life, especially in object detection. This paper presents an efficient technique for detecting weapons from closed-circuit television (CCTV) cameras, videos, or images. Upon the detection of the weapon, the concerned person is automatically informed to take the necessary action; without human intervention. For the first time, RetinaNet has been employed to detect weapons in real-time scenarios. RetinaNet has shown remarkable improvement in this domain, by achieving an average of 90% accuracy in real-time scenarios. With the emergence of quantum computing, many computer environments saw a revolution. Thus, we have also utilized quantum computing technology for real-time weapons detection using quantum deep learning. In this paper, quantum inspired RetinaNet (QIR-Net) is developed for weapons detection and amazing results are observed.
Identification scheme of false data injection attack based on deep learning algorithms for smart grids Marwah Ezzulddin Merza; Shamil H. Hussein; Qutaiba I. Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp219-228

Abstract

This paper presents the artificial intelligence (AI) techniques based on the deep learning algorithms to diagnose false data injection (FDI) attacks to smart grids with the measurement data in real-time. The power and data flow between end-user consumers and all components of the advanced metering infrastructure (AMI) and supervisory control and data acquisition (SCADA) system in the SG is bidirectional flow by advanced communication networks. For all the advantages of the SG come with, they remain at risk to a series of many potential threats and ongoing attacks. The conditional-deep-belief-network (CDBN) architecture is employed to un-observable FDI attacks which pass the state-vector-estimator (SVE) mechanisms. The IEEE 118 bus, and IEEE 300 bus power system have been used to evaluate our detection scheme. Finally, the suggested CDBN scheme is compared with other detection such as artificial neural network (ANN) and support vector machine (SVM). It is observed that the simulation result shows that suggested detection methods can attain a high accuracy of unobservable FDI attacks.
IoT cloud based noise intensity monitoring system Tashfat Fatema; Md. Azizul Hakim; Tanjila Khan Mim; Mushrat Jahan Mitu; Bijan Paul
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp289-298

Abstract

According to a survey published by the United Nations Environment Program (UNEP) last month, Dhaka, Bangladesh’s capital, is the world’s noisiest metropolis. Dhaka is consistently ranked as one of the most polluted cities in the world. The World Health Organization (WHO) has set a noise intensity limit of 55 decibels for a certain region. Dhaka, on the other hand, was determined to be twice as loud, at 110 to 132 decibels. As many as 66 percent of traffic cops work on the road to control traffic, and this high noise intensity noise is causing them hearing and sleeping problems on a daily basis. Furthermore, loud noises can increase blood pressure and pulse rates, produce mental stress, and interfere with amusement and personal interactions. Keeping all of this in mind, we designed a real-time noise pollution monitoring system that will assist in conducting experiments in this type of environment by evaluating noise intensity and automatically sending data to a IoT cloud-based platform.
Web service discovery approach: application in e-healing domain Mohamed Halim; Nouha Adadi; Mohammed Berrada; Driss Chenouni
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp557-566

Abstract

The main objective of today's companies is to cope with rapid changes in the environment. For this, they must ensure the integration and interoperability of their applications. To manage and automate the life cycle of these applications, companies are adopting web services technology. The current problem is that the content of these web services cannot be processed automatically. Only humans can interpret its contents. The semantic web is a new vision of the web that promises to overcome this difficulty. The goal of this technology is to automate the retrieval, assembly, and selection of web services. In this post, we are interested in semantic detection of web services. The main problem is automatically discovering web services on request from clients. Against this background, we first describe the principle of the proposed detection mechanism and then present the designed matchmaking algorithm. Finally, we implement our proposed method. To verify our work, we run tests against various user requests and web service panels. As part of a case study, we consider an online hospital problem. This problem is a typical web service discovery scenario to which the concepts of our method are applied.
An abbreviated review of deep learning-based image classification models Zaman Talal Abbood; Mohammed Nasser Al-Turfi; Layth Kamil Adday Almajmaie
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp491-500

Abstract

Image classification is an extensively researched sub-fields of computer vision implemented in face recognition, self-driving, medical image segmentation, biological identification, and others. Traditional models of image classification require manual construction of feature extraction techniques and classification accuracy which are closely associated with these utilized techniques. During the rapid progress of multimedia technologies, the number of images that require classification got bigger, and this led to making image classification more complicated, hence, the manual construction of feature extraction techniques consumes more time and provides lower accuracy. In the recent decade, deep learning-based models have appeared in various applications. These models hold the merits of an effective extraction of image features, low-weight features filtering, a large capacity for processing, and higher classification speed and accuracy. Thus, lots of researchers have attempted to utilize deep learning algorithms, especially convolutional neural networks (CNNs) for image classification. Therefore, this paper concentrates on providing an abbreviated review of deep learning-based image classification models, by covering the recently utilized deep learning algorithms, comparing various related works and benchmark datasets mentioned in this paper, and summarizing the fundamental analysis and discussion.
NOVA-a virtual nursing assistant Vijaykumar Bidve; Amit Virkar; Prajakta Raut; Samruddhi Velapurkar
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp307-315

Abstract

The majority of people are medically unqualified to research or comprehend the severity of their ailments or symptoms. Natural language processing plays a critical role in healthcare in this area. These chatbots collect patient health data and, based on that data, provide more relevant information to patients about their physical ailments, as well as advise next steps. Artificial intelligence (AI)-powered healthcare chatbots are useful in the medical industry for supporting patients and directing them to the most appropriate resources. Chatbots are more useful for online searches that users or patients conduct when they are searching for answers to their health-related questions. With this application, a user can make health requests via text message and might also get relevant health suggestions/recommendations through it. This Chatbot is developed to be both educational and conversational. Chatbot delivers medical information, such as symptoms and remedies for diseases. Patients’ personal and medical information is stored in a database for further study, and patients receive real-time advice from experts. AI-powered apps in healthcare have experienced a significant increase in recent days. As a result, office wait times are reduced, saving money and energy. Patients may be learning medical information and assisting at their own pace and location.
Weed detection by using image processing Vijaykumar Bidve; Sulakshana Mane; Pradip Tamkhade; Ganesh Pakle
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp341-349

Abstract

In agricultural regions, the procedure of weed removal is crucial. Weed removal in the classic way, takes longer and requires greater physical effort. The idea is to eliminate weeds from agricultural fields automatically. The proposed study uses a deep learning algorithm to detect weeds growing between crops. Deep learning method also known as deep learning is used to analyse the main properties of agricultural photographs. Weeds and crops have been identified using the dataset. Convolutional neural network (CNN) uses a completely attached surface with rectified linear units (RELU) to differentiate weed and crop. It extracts features of crop using deep learning. The CNN uses features of proceeded image to extract region of interest (ROI). A deep learning network features are used to identify crop. In total of 1280 images are used for testing the system, and 10 images are used to find the confidence score.

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