2023 International Conference on Information Networking (ICOIN)最新文献_第3页

ICOIN 2023 Cover Page ico2023封面

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/icoin56518.2023.10049021

引用次数: 0

ETANet: An Efficient Triple-Attention Network for Salient Object Detection ETANet:一种高效的三注意显著目标检测网络

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048982

Ngo Thien Thu, E. Huh, C. Hong

引用次数: 0

Lower Bound for the Number of Accommodable End-devices in LPWAN with Multiple Interferences 多干扰LPWAN中可容纳终端设备数量的下界

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10049040

Daisuke Kumamoto, S. Narieda, T. Fujii, H. Naruse

{"title":"Lower Bound for the Number of Accommodable End-devices in LPWAN with Multiple Interferences","authors":"Daisuke Kumamoto, S. Narieda, T. Fujii, H. Naruse","doi":"10.1109/ICOIN56518.2023.10049040","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10049040","url":null,"abstract":"Coverage evaluation is necessary to derive the number of end devices that can be accommodated to wireless networks, and it is an evaluation metric for wireless network, such as low power wide area networks (LPWANs). In traditional analysis, conditional coverage probabilities are derived when the desired LoRa signal does not conflict with an interference signal and when the desired LoRa signal conflicts with a single interference signal. However, in the case that network load is 0.45, a probability of interference occurrence from two EDs is approximately 14.7%, whereas the probability from one EDs is approximately 36.1%. The difference between probabilities is approximately 20%, and we believe that it cannot be ignored on derivation of the number of accommodable end devices. In this study, we derive and evaluate the theoretical values of the conditional coverage probability when two interference signals arrive. Based on the probability, we derive a lower bound on the number of end devices that can be accommodated. Numerical examples are shown to validate the effectiveness of the derived lower bound on the number of accommodable end devices.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129194721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security of Energy Harvesting Based D2D Communications in Cognitive Cellular Network 认知蜂窝网络中基于能量采集的D2D通信安全性研究

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048923

Koduru Sree Venkateswara Rao, Ashutosh Kumar Singh

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Advanced Signal Processing of Photo-Excited Current Spectroscopy Based on Trap State Distribution for Photo-Sensor Applications 基于阱态分布的光激发电流光谱在光传感器中的高级信号处理

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048911

Dongwook Kim, Hyunju Lee, Jung-Hyok Kwon, Jaehoon Park, Bokyoung Kim, Eui-Jik Kim

引用次数: 1

Design of a 3D Scene Reconstruction Network Robust to High-Frequency Areas Based on 2.5D Sketches and Encoders 基于2.5D草图和编码器的高频鲁棒三维场景重构网络设计

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048963

Chan-Ho Lee, Jaeseok Yoo, K. Park

{"title":"Design of a 3D Scene Reconstruction Network Robust to High-Frequency Areas Based on 2.5D Sketches and Encoders","authors":"Chan-Ho Lee, Jaeseok Yoo, K. Park","doi":"10.1109/ICOIN56518.2023.10048963","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048963","url":null,"abstract":"In this paper, we propose a new 3D scene reconstruction network that is robust to high-frequency areas by extracting multiple 3D feature volumes with accurate and various 3D information. Previous voxel representation-based methods did not perform well in high-frequency areas such as angled drawer parts and desk corners. In addition, the performance is poor even in the low-frequency areas with few feature points such as walls and floors. To solve this problem, we propose various backbone networks by extracting edge surface normal images from RGB images and constructing new branches. Edge images can provide information in the high-frequency areas, and surface normal images can compensate for the lack of information in edge images. As a result, not only 3D information but also the values of the high-frequency areas may be added. Using this as input for a new branch, various backbone networks such as ConvNeXt and Swin Transformer extract 2D image features that retain accurate 3D information. We designed a network that can represent detailed scenes from the entire scene using the hierarchical structure and unprojection of the backbone network to achieve robust performance in the high-frequency areas. We show that the proposed method outperforms the previous methods in quantitative and stereotyped 3D reconstruction results on the ScanNet dataset.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"456 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115283916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Histopathological Classification of Colorectal Polyps using Deep Learning 基于深度学习的结直肠息肉的组织病理学分类

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048925

M. P. Paing, One-Sun Cho, Jae-Wan Cho

引用次数: 1

Abnormal human behavior detection based on VAE-LSTM hybrid model in WiFi CSI with PCA 基于VAE-LSTM混合模型的PCA WiFi CSI异常行为检测

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048984

Yonghwan Kim, Sang-Chul Kim

引用次数: 0

Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage 基于多臂强盗学习的TDMA传输时隙调度和碎片整理，以提高带宽利用率

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048935

H. Dutta, Amit Kumar Bhuyan, S. Biswas

{"title":"Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage","authors":"H. Dutta, Amit Kumar Bhuyan, S. Biswas","doi":"10.1109/ICOIN56518.2023.10048935","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048935","url":null,"abstract":"This paper proposes a Time Division Multiple Access (TDMA) MAC slot allocation protocol with efficient bandwidth usage in wireless sensor networks and Internet of Things (IoTs). The developed protocol has two primary components: a Multi-Armed Bandits (MAB)-based slot allocation mechanism for collision free transmission, and a Decentralized Defragmented Slot Backshift (DDSB) operation for improving bandwidth usage efficiency. The proposed framework is decentralized in that each node finds its transmission schedule independently without the control of any centralized arbitrator. The developed mechanism is suitable for networks with or without time synchronization, thus, making it suitable for low-complexity wireless transceivers for wireless sensor and IoT nodes. This framework is able to manage the trade-off between learning convergence time and bandwidth. In addition, it allows the nodes to adapt to topological changes while maintaining efficient bandwidth usage. The developed logic is tested for both fully-connected and arbitrary mesh networks with extensive simulation experiments. It is shown how the nodes can learn to select collision-free transmission slots using MAB. Moreover, the nodes learn to self-adjust their transmission schedules using a novel DDSB framework in order to reduce bandwidth usage.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125624375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beam Pattern Estimation of 5G Millimeter-Wave Base Station Based on Radio Map and Multi-Beam Antenna Model at 28GHz 基于无线电波图和多波束天线模型的5G毫米波基站波束方向估计

2023 International Conference on Information Networking (ICOIN) Pub Date : 2023-01-11 DOI: 10.1109/ICOIN56518.2023.10048917

Shougo Matsuo, Sunao Miyamoto, Hirofumi Nakajo, T. Fujii

{"title":"Beam Pattern Estimation of 5G Millimeter-Wave Base Station Based on Radio Map and Multi-Beam Antenna Model at 28GHz","authors":"Shougo Matsuo, Sunao Miyamoto, Hirofumi Nakajo, T. Fujii","doi":"10.1109/ICOIN56518.2023.10048917","DOIUrl":"https://doi.org/10.1109/ICOIN56518.2023.10048917","url":null,"abstract":"Nowadays, radio maps attract attention for estimating tools of accurate radio propagation. Radio maps require interpolation and extrapolation of the average received power because the data can be obtained, based on measurements. When beam patterns with complex geometry are used, it is important to recognize the exact beam patterns for interpolation and extrapolation. In particular, the antenna patterns of 5G millimeter-wave (mmWave) base stations are formed by multiple beams and are complex. The miss estimation of the complex beam patterns has a significant impact on the accuracy of radio maps. Therefore, this paper focuses on the estimation of beam patterns by radio maps and the definition of the radio maps which are suitable for 5G mmWave base stations. The proposed method estimates the beam patterns based on the line-of-sight measured data of the radio map and the multi-beam antenna model. The emulation results show that it is possible to quantify the beam patterns based on actual measurements and estimate the angle of the base station with an accuracy of about 10 degrees.","PeriodicalId":285763,"journal":{"name":"2023 International Conference on Information Networking (ICOIN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131275558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0