ETRI Journal最新文献_第6页

Hybrid intelligent reflective surfaces and relay assisted secure transmission scheme with power allocation 具有功率分配功能的混合智能反射面和中继辅助安全传输方案

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-20 DOI: 10.4218/etrij.2023-0401

Yan Shi, Dongqing Zhao, Yue Wu

引用次数: 0

Improved contrastive learning model via identification of false-negatives in self-supervised learning 通过识别自我监督学习中的假阴性来改进对比学习模型

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-16 DOI: 10.4218/etrij.2023-0285

Joonsun Auh, Changsik Cho, Seon-tae Kim

引用次数: 0

Background music monitoring framework and dataset for TV broadcast audio 电视广播音频背景音乐监测框架和数据集

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-12 DOI: 10.4218/etrij.2023-0249

Hyemi Kim, Junghyun Kim, Jihyun Park, Seongwoo Kim, Chanjin Park, Wonyoung Yoo

{"title":"Background music monitoring framework and dataset for TV broadcast audio","authors":"Hyemi Kim, Junghyun Kim, Jihyun Park, Seongwoo Kim, Chanjin Park, Wonyoung Yoo","doi":"10.4218/etrij.2023-0249","DOIUrl":"10.4218/etrij.2023-0249","url":null,"abstract":"Music identification is widely regarded as a solved problem for music searching in quiet environments, but its performance tends to degrade in TV broadcast audio owing to the presence of dialogue or sound effects. In addition, constructing an accurate dataset for measuring the performance of background music monitoring in TV broadcast audio is challenging. We propose a framework for monitoring background music by automatic identification and introduce a background music cue sheet. The framework comprises three main components: music identification, music–speech separation, and music detection. In addition, we introduce the Cue-K-Drama dataset, which includes reference songs, audio tracks from 60 episodes of five Korean TV drama series, and corresponding cue sheets that provide the start and end timestamps of background music. Experimental results on the constructed and existing datasets demonstrate that the proposed framework, which incorporates music identification with music–speech separation and music detection, effectively enhances TV broadcast audio monitoring.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 4","pages":"697-707"},"PeriodicalIF":1.3,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596641","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}

引用次数: 0

Generative autoencoder to prevent overregularization of variational autoencoder 防止变分自动编码器过度规则化的生成自动编码器

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-12 DOI: 10.4218/etrij.2023-0375

YoungMin Ko, SunWoo Ko, YoungSoo Kim

引用次数: 0

A neural network framework based on ConvNeXt for side-channel hardware Trojan detection 基于 ConvNeXt 的侧信道硬件木马检测神经网络框架

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-08 DOI: 10.4218/etrij.2023-0448

Yuchan Gao, Jing Su, Jia Li, Shenglong Wang, Chao Li

{"title":"A neural network framework based on ConvNeXt for side-channel hardware Trojan detection","authors":"Yuchan Gao, Jing Su, Jia Li, Shenglong Wang, Chao Li","doi":"10.4218/etrij.2023-0448","DOIUrl":"10.4218/etrij.2023-0448","url":null,"abstract":"Researchers in the field of hardware security have been dedicated to the study of hardware Trojan detection. Among the various approaches, side-channel detection methods are widely used because of their high detection accuracy and fewer constraints. However, most side-channel detection methods cannot make full use of side-channel information. In this paper, we propose a framework that utilizes the continuous wavelet transform to convert time-series information and employs an improved ConvNeXt network to detect hardware Trojans. This detection framework first converts one-dimensional time-series information into a two-dimensional time–frequency map using the continuous wavelet transform to leverage frequency information in electromagnetic side-channel signals. Then, the two-dimensional time–frequency map is fed into the improved ConvNeXt network, which increases the weight of the informative parts in the two-dimensional time–frequency map and enhances detection efficiency. The results indicate that the method proposed in this paper significantly improves the accuracy of hardware Trojan detection.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 2","pages":"338-349"},"PeriodicalIF":1.3,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596752","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}

引用次数: 0

Effects of parasitic gate capacitance and gate resistance on radiofrequency performance in LG = 0.15 μm GaN high-electron-mobility transistors for X-band applications 用于 X 波段应用的 LG = 0.15 μm 氮化镓高电子迁移率晶体管中寄生栅极电容和栅极电阻对射频性能的影响

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-05 DOI: 10.4218/etrij.2023-0250

Sung-Jae Chang, Hyeon-Seok Jeong, Hyun-Wook Jung, Su-Min Choi, Il-Gyu Choi, Youn-Sub Noh, Seong-Il Kim, Sang-Heung Lee, Ho-Kyun Ahn, Dong Min Kang, Dae-Hyun Kim, Jong-Won Lim

{"title":"Effects of parasitic gate capacitance and gate resistance on radiofrequency performance in LG = 0.15 μm GaN high-electron-mobility transistors for X-band applications","authors":"Sung-Jae Chang, Hyeon-Seok Jeong, Hyun-Wook Jung, Su-Min Choi, Il-Gyu Choi, Youn-Sub Noh, Seong-Il Kim, Sang-Heung Lee, Ho-Kyun Ahn, Dong Min Kang, Dae-Hyun Kim, Jong-Won Lim","doi":"10.4218/etrij.2023-0250","DOIUrl":"10.4218/etrij.2023-0250","url":null,"abstract":"The effects of the parasitic gate capacitance and gate resistance (Rg) on the radiofrequency (RF) performance are investigated in LG = 0.15 μm GaN high-electron-mobility transistors with T-gate head size ranging from 0.83 to 1.08 μm. When the device characteristics are compared, the difference in DC characteristics is negligible. The RF performance in terms of the current-gain cut-off frequency (fT) and maximum oscillation frequency (fmax) substantially depend on the T-gate head size. For clarifying the T-gate head size dependence, small-signal modeling is conducted to extract the parasitic gate capacitance and Rg. When the T-gate head size is reduced from 1.08 to 0.83 μm, Rg increases by 82%, while fT and fmax improve by 27% and 26%, respectively, because the parasitic gate–source and gate–drain capacitances reduce by 19% and 43%, respectively. Therefore, minimizing the parasitic gate capacitance is more effective that reducing Rg in our transistor design and fabrication, leading to improved RF performance when reducing the T-gate head size.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1090-1102"},"PeriodicalIF":1.3,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596732","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}

引用次数: 0

Small dataset augmentation with radial basis function approximation for causal discovery using constraint-based method 使用基于约束的方法，用径向基函数近似法增加小数据集以发现因果关系

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-04-02 DOI: 10.4218/etrij.2023-0397

Chan Young Jung, Yun Jang

{"title":"Small dataset augmentation with radial basis function approximation for causal discovery using constraint-based method","authors":"Chan Young Jung, Yun Jang","doi":"10.4218/etrij.2023-0397","DOIUrl":"10.4218/etrij.2023-0397","url":null,"abstract":"Causal analysis involves analysis and discovery. We consider causal discovery, which implies learning and discovering causal structures from available data, owing to the significance of interpreting causal relationships in various fields. Research on causal discovery has been primarily focused on constraint- and score-based interpretable methods rather than on methods based on complex deep learning models. However, identifying causal relationships in real-world datasets remains challenging. Numerous studies have been conducted using small datasets with established ground truths. Moreover, constraint-based methods are based on conditional independence tests. However, such tests have a lower statistical power when applied to small datasets. To solve the small sample size problem, we propose a model that generates a continuous function from available samples using radial basis function approximation. We address the problem by extracting data from the generated continuous function and evaluate the proposed method on both real and synthetic datasets generated by structural equation modeling. The proposed method outperforms constraint-based methods using only small datasets.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"47 1","pages":"90-101"},"PeriodicalIF":1.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596642","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}

引用次数: 0

Performance analysis of transmit antenna selection and maximum-ratio combining in overlay networks powered by harvested energy 利用采集能量供电的重叠网络中发射天线选择和最大比率组合的性能分析

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-03-22 DOI: 10.4218/etrij.2023-0389

Khuong Ho-Van

引用次数: 0

Enhancing spectral efficiency with low complexity filtered-orthogonal frequency division multiplexing in visible light communication system 在可见光通信系统中利用低复杂度滤波正交频分复用技术提高频谱效率

IF 1.3 4区计算机科学

ETRI Journal Pub Date : 2024-03-22 DOI: 10.4218/etrij.2023-0300

Hayder S. R. Hujijo, Muhammad Ilyas

{"title":"Enhancing spectral efficiency with low complexity filtered-orthogonal frequency division multiplexing in visible light communication system","authors":"Hayder S. R. Hujijo, Muhammad Ilyas","doi":"10.4218/etrij.2023-0300","DOIUrl":"10.4218/etrij.2023-0300","url":null,"abstract":"The filtered-orthogonal frequency division multiplexing (F-OFDM) scheme has gained attention as a promising solution in the field of visible light communication (VLC) systems. One crucial aspect in VLC is the conversion of the complex F-OFDM signal into a real signal that corresponds with direct detection and intensity modulation. Traditionally, achieving a real F-OFDM signal has involved imposing Hermitian symmetry (HS) on the samples of the Inverse Fast Fourier transform (IFFT), which requires 2N-point IFFT and obtains an N-point FFT, thus adding complexity. In this study, a novel approach is presented and implemented, aiming to enhance spectral efficiency and reduce system complexity by generating a real F-OFDM signal without relying on HS. This approach is then compared with HS-free (HSF)-OFDM, direct current biased optical OFDM, and asymmetrically clipped optical OFDM. The suggested method offers a remarkable improvement of ~50% in the required IFFT/FFT volume. Consequently, this method reduces hardware complexity and power usage compared with the traditional F-OFDM method. Moreover, regarding error rates, the proposed method demonstrates better spectral efficiency than HSF-OFDM.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 6","pages":"1007-1019"},"PeriodicalIF":1.3,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197524","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}

引用次数: 0

Anomaly-based Alzheimer's disease detection using entropy-based probability Positron Emission Tomography images 利用基于熵概率的正电子发射断层扫描图像进行基于异常的阿尔茨海默病检测

IF 1.4 4区计算机科学

ETRI Journal Pub Date : 2024-03-22 DOI: 10.4218/etrij.2023-0123

Husnu Baris Baydargil, Jangsik Park, Ibrahim Furkan Ince

{"title":"Anomaly-based Alzheimer's disease detection using entropy-based probability Positron Emission Tomography images","authors":"Husnu Baris Baydargil, Jangsik Park, Ibrahim Furkan Ince","doi":"10.4218/etrij.2023-0123","DOIUrl":"10.4218/etrij.2023-0123","url":null,"abstract":"Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":"46 3","pages":"513-525"},"PeriodicalIF":1.4,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.4218/etrij.2023-0123","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140197528","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}

引用次数: 0