ETRI Journal最新文献

{"title":"2024 Reviewer List","authors":"","doi":"10.4218/etr2.70008","DOIUrl":"https://doi.org/10.4218/etr2.70008","url":null,"abstract":"<p>A, Ashwini, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology</p><p>A, Revathi, SASTRA Deemed University</p><p>A, UMAMAGESWARI, SRM University - Ramapuram Campus</p><p>Abd El-Hafeez, Tarek, Minia University</p><p>Abd Rahman, Mohd Amiruddin, Universiti Putra Malaysia</p><p>Abdi, Asad, University of Derby</p><p>Abdullah, Hadeel, University of Technology</p><p>Abebe, Abiy, Addis Ababa Institute of Technology</p><p>Adewunmi, Mary, National Center for Technology Management</p><p>Afify, Heba M., Higher Inst. of Engineering in Shorouk Academy</p><p>Ahmad, Mushtaq, Nanjing University of Aeronautics and Astronautics</p><p>Ahmed, Suhaib, Baba Ghulam Shah Badshah University</p><p>Ahn, Sungsoo, Gyeongsang National University</p><p>Akbar, Son, Universitas Ahmad Dahlan</p><p>Akhriza, Tubagus, Kampus STIMATA</p><p>Akoushideh, Alireza, Technical and Vocational University</p><p>Al-Araji, Ahmed S., University of technology - Iraq</p><p>Al-Azzoni, Issam, Al Ain University</p><p>Alfaverh, Fayiz, University of Hertfordshire</p><p>alghanimi, abdulhameed, Middle Technical Univ.</p><p>Ali, Dia M, Ninevah University</p><p>ali, Tariq, PMAS Arid Agriculture university</p><p>Alikhani, Nasim,</p><p>Al-Kaltakchi, Musab T. S., Mustansiriyah University</p><p>Al-kaltakchi, Musab, Mustansiriyah University</p><p>Alkinoon, Mohammed, University of Central Florida</p><p>Al-masni, Mohammed A., Sejong University</p><p>Al-Sakkaf, Ahmed Gaafar, Universidad Carlos III de Madrid Escuela Politécnica Superior</p><p>Ansarian, Sasan,</p><p>Arora, Shashank, SUNY</p><p>Asgher, Umer, National University of Sciences and Technology</p><p>Ashraf, Umer, NIT Srinagar</p><p>atashbar, mahmoud, Azarbaijan Shahid Madani University,</p><p>Atrey, Pradeep, State University of New York</p><p>Azim, Rezaul, University of Chittagong</p><p>B, Srinivas, Maharaj Vijayaram Gajapathi Ram College of Engineering</p><p>Baek, Donghyun, Chung-Ang University</p><p>Baek, Hoki, Kyungpook National University</p><p>Balbinot, Alexandre, Universidade Federal do Rio Grande do Sul</p><p>BANDI, SUDHEER, Panimalar Engineering College</p><p>Baranwal, Alok, NIT-Durgapur</p><p>Baydargil, Husnu Baris, Institute for Basic Science</p><p>Beniwal, Ruby, Jaypee Institute of Information Technology</p><p>Benrabah, Abdeldjabar,</p><p>Bhattacharya, Ratnadeep, The George Washington University</p><p>Bhowmik, Showmik, Ghani Khan Choudhury Institute of Engineering and Technology</p><p>Bonthagorla, Praveen Kumar, National Institute of Technology Goa</p><p>Byun, Gangil, UNIST</p><p>Byun, Hayoung, Myongji University</p><p>C, Arunkumar Madhuvappan, Vinayaka Mission's Kirupananda Variyar Engineering College</p><p>Callou, G., UFRPE</p><p>Cammarasana, Simone, CNR-IMATI</p><p>Castillo-Soria, Francisco, Universidad Autónoma de San Luis Potosí</p><p>Ceberio, Josu, University of the Basque Country</p><p>Cha, Ho-Young, Hongik University</p><p>Chabir, Karim, ENIG</p><p>Chaudhary, Girdhari, Jeonbuk National University</p><p>Che, Ren","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 1","pages":"167-171"},"PeriodicalIF":1.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of IPv6 routing attacks using ANN and a novel IoT dataset","authors":"Murat Emeç","doi":"10.4218/etrij.2023-0506","DOIUrl":"https://doi.org/10.4218/etrij.2023-0506","url":null,"abstract":"<p>The Internet of Things (IoT) is an intelligent network paradigm created by interconnected device networks. Although the importance of IoT systems has increased in various applications, the increasing number of connected devices has made security even more critical. This study presents the ROUT-4-2023 dataset, which represents a step toward the security of IoT networks. This dataset simulates potential attacks on RPL-based IoT networks and provides a new platform for researchers in this field. Using artificial intelligence and machine-learning techniques, a performance evaluation was performed on four different artificial neural network models (convolutional neural network, deep neural network, multilayer perceptron structure, and routing attack detection-fed forward neural network [RaD-FFNN]). The results show that the RaD-FFNN model has high accuracy, precision, and retrieval rates, indicating that it can be used as an effective tool for the security of IoT networks. This study contributes to the protection of IoT networks from potential attacks by presenting ROUT-4-2023 and RaD-FFNN models, which will lead to further research on IoT security.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"350-361"},"PeriodicalIF":1.3,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model","authors":"Vandana Tripathi,&nbsp;Prabhat Patel,&nbsp;Prashant Kumar Jain,&nbsp;Shailja Shukla","doi":"10.4218/etrij.2023-0347","DOIUrl":"https://doi.org/10.4218/etrij.2023-0347","url":null,"abstract":"<p>Several peak-to-average power ratio (PAPR) reduction methods have been used in orthogonal frequency division multiplexing (OFDM) applications. Among the available methods, partial transmit sequence (PTS) is an efficient PAPR reduction method but can be computationally expensive while determining optimal phase factors (OPFs). Therefore, an optimization algorithm, namely, the improved salp swarm optimization algorithm (ISSA), is incorporated with the PTS to reduce the PAPR of the OFDM signals with limited computational cost. The ISSA includes a dynamic weight element and Lévy flight process to improve the global exploration ability of the optimization algorithm and to control the global and local search ability of the population with a better convergence rate. Three evaluation measures, namely, the complementary cumulative distribution function (CCDF), bit error rate (BER), and symbol error rate (SER), demonstrate the efficacy of the PTS-ISSA model, which achieves a lower PAPR of 3.47 dB and is superior to other optimization algorithms using the PTS method.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"256-269"},"PeriodicalIF":1.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data","authors":"Mardi Hardjianto,&nbsp;Jazi Eko Istiyanto,&nbsp;A. Min Tjoa,&nbsp;Arfa Shaha Syahrulfath,&nbsp;Satriawan Rasyid Purnama,&nbsp;Rifda Hakima Sari,&nbsp;Zaidan Hakim,&nbsp;M. Ridho Fuadin,&nbsp;Nias Ananto","doi":"10.4218/etrij.2023-0190","DOIUrl":"https://doi.org/10.4218/etrij.2023-0190","url":null,"abstract":"<p>The prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture the complexity of human movements or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs) for detecting prolonged sitting periods using point cloud data from smartphone sensors. Unlike conventional methods, our GNN model excels at processing the unordered, three-dimensional structure of sensor data, enabling more accurate classification of sedentary activities. The effectiveness of our approach is demonstrated by its superior ability to identify sitting, standing, and walking activities—critical for assessing health risks associated with prolonged sitting. By providing real-time activity recognition, our model offers a promising tool for healthcare professionals to mitigate the adverse effects of sedentary behavior.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"290-302"},"PeriodicalIF":1.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of wireless-powered cell-free massive multiple-input multiple-output system with spatial correlation in Internet of Things network","authors":"Haiyan Wang,&nbsp;Xinmin Li,&nbsp;Yuan Fang,&nbsp;Xiaoqiang Zhang","doi":"10.4218/etrij.2023-0216","DOIUrl":"https://doi.org/10.4218/etrij.2023-0216","url":null,"abstract":"<p>The massive multiple-input multiple-output (mMIMO) approach is promising for the Internet of Things (IoT) owing to its massive connectivity and high data rate. We introduce a wireless-powered cell-free mMIMO system, in which ground IoT devices transmit pilot and uplink information by harvesting downlink power from multiantenna access points. Considering the spatial correlation, we derive closed-form expressions for the average harvested power with a nonlinear energy-harvesting model per IoT device and achievable data rate according to the random matrix theory. The analytical expressions show that spatial correlation has a negative effect on the data rate owing to the increasing interference power. In contrast, the average received power improves with increasing spatial correlation. Simulation results demonstrate that the derived analytical expressions are consistent with results from the Monte Carlo method.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"208-215"},"PeriodicalIF":1.3,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network function parallelism configuration with segment routing over IPv6 based on deep reinforcement learning","authors":"Seokwon Jang,&nbsp;Namseok Ko,&nbsp;Yeunwoong Kyung,&nbsp;Haneul Ko,&nbsp;Jaewook Lee,&nbsp;Sangheon Pack","doi":"10.4218/etrij.2023-0511","DOIUrl":"https://doi.org/10.4218/etrij.2023-0511","url":null,"abstract":"<p>Network function parallelism (NFP) has gained attention for processing packets in parallel through service functions arranged in the required service function chain. While parallel processing efficiently reduces the service function chaining (SFC) completion time, it incurs a higher network overhead (e.g., network congestion) to replicate various packets for processing. To reduce the SFC completion time while maintaining a low network overhead, we propose a deep-reinforcement-learning-based NFP algorithm (DeepNFP) that provides an SFC processing policy to determine the sequential or parallel processing of every service function. In DeepNFP, deep reinforcement learning captures the network dynamics and service function conditions and iteratively finds the SFC processing policy in the network environment. Furthermore, an SFC data plane protocol based on segment routing over IPv6 configures and operates the policy in the SFC data network. Evaluation results show that DeepNFP can achieve 46% of the SFC completion time and 66% of the network overhead compared with conventional SFC and NFP, respectively.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"278-289"},"PeriodicalIF":1.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UP-Net: A multi-head architecture for reading and efficiently segmenting distorted QR codes","authors":"Ebrahim Parcham,&nbsp;Mahdi Ilbeygi,&nbsp;Vahid Hajipour,&nbsp;Ali Gharaei,&nbsp;Mahdi Mokhtari,&nbsp;Mostafa Foroutan","doi":"10.4218/etrij.2023-0540","DOIUrl":"https://doi.org/10.4218/etrij.2023-0540","url":null,"abstract":"<p>Semantic segmentation is essential in machine vision but susceptible to noise and distortions that often appear in real-world images. We propose UPlus-Net (UP-Net), a deep-learning architecture based on the U-Net encoder–decoder architecture. We address the limitations of U-Net by introducing a multi-head architecture in UP-Net to properly handle segmentation challenges. In addition, we evaluate UP-Net for decoding distorted quick-response (QR) codes heavily polluted by noise. Experimental results confirm that UP-Net outperforms existing QR reader mobile applications, highlighting the UP-Net ability to handle challenging images. Unlike existing methods focused solely on QR code reading or segmentation, UP-Net offers a combined solution, efficiently and accurately reading distorted QR codes while performing high-quality semantic segmentation. These unique characteristics render UP-Net promising for applications demanding robust image analysis in challenging environments.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 3","pages":"527-544"},"PeriodicalIF":1.3,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators”","authors":"Jeman Park,&nbsp;Misun Yu,&nbsp;Jinse Kwon,&nbsp;Junmo Park,&nbsp;Jemin Lee,&nbsp;Yongin Kwon","doi":"10.4218/etr2.12748","DOIUrl":"https://doi.org/10.4218/etr2.12748","url":null,"abstract":"<p>NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators</p><p>https://doi.org/10.4218/etrij.2024-0139</p><p>ETRI Journal, Volume 46, Issue 5, October 2024, pp. 851–864.</p><p>In the article entitled “NEST-C: A deep learning compiler framework for heterogeneous computing systems with artificial intelligence accelerators,” the authors would like to correct the funding information of their article. It should be written as follows:</p><p><b>Funding information</b> This study is supported by a grant from the Institute of Information &amp; Communications Technology Planning &amp; Evaluation (IITP), funded by the Korean government (MSIT) (No. RS-2023-00277060, Development of OpenEdge AI SoC hardware and software platform and No. 2018-0-00769, Neuromorphic Computing Software Platform for Artificial Intelligence Systems).</p><p>The authors would like to apologize for the inconvenience caused.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1126"},"PeriodicalIF":1.3,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Low-complexity patch projection method for efficient and lightweight point-cloud compression”","authors":"","doi":"10.4218/etr2.12746","DOIUrl":"https://doi.org/10.4218/etr2.12746","url":null,"abstract":"<p><b>Sungryeul Rhyu</b> | <b>Junsik Kim | Gwang Hoon Park | Kyuheon Kim</b></p><p>Low-complexity patch projection method for efficient and lightweight point-cloud compression</p><p>https://doi.org/10.4218/etrij.2023-0242</p><p><i>ETRI Journal</i>, Volume 46, Issue 4, August 2024, pp. 683–696.</p><p>In the article entitled “Low-complexity patch projection method for efficient and lightweight point-cloud compression”, the authors would like to correct the funding information of their article. It should be written as follows:</p><p><b>Funding information</b></p><p>This study was supported by the Information Technology Research Center of the Ministry of Science and ICT, Korea (grant number: IITP-2024-2021-0-02046) and the Institute of Information &amp; Communications Technology Planning &amp; Evaluation, Korea (grant number: RS-2023-00227431, Development of 3D space digital media standard technology).</p><p>The authors would like to apologize for the inconvenience caused.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1125"},"PeriodicalIF":1.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etr2.12746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bi-stream transformer for single-image dehazing","authors":"Mingrui Wang,&nbsp;Jinqiang Yan,&nbsp;Chaoying Wan,&nbsp;Guowei Yang,&nbsp;Teng Yu","doi":"10.4218/etrij.2024-0037","DOIUrl":"https://doi.org/10.4218/etrij.2024-0037","url":null,"abstract":"<p>Deep-learning methods, such as encoder–decoder networks, have achieved impressive results in image dehazing. However, these methods often rely only on synthesized data for training that limits their generalizability to hazy, real-world images. To leverage prior knowledge of haze properties, we propose a bi-encoder structure that integrates a prior-based encoder into a traditional encoder–decoder network. The features from both encoders were fused using a feature enhancement module. We adopted transformer blocks instead of convolutions to model local feature associations. Experimental results demonstrate that our method surpasses state-of-the-art methods for synthesized and actual hazy scenes. Therefore, we believe that our method will be a useful supplement to the collection of current artificial intelligence models and will benefit engineering applications in computer vision.</p>","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 3","pages":"545-558"},"PeriodicalIF":1.3,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2024-0037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}