Browsing by Titles

All A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
전체 가 나 다 라 마 바 사 아 자 차 카 타 파 하

Showing results 1 to 19 of 19

A Delete-Aware Compaction Trigger Method for LSM-Tree Based Key-Value Stores

Thesis
A Delete-Aware Compaction Trigger Method for LSM-Tree Based Key-Value Stores
  • Ilseop Lee
  • 2021
  • Ilseop Lee. (2021). A Delete-Aware Compaction Trigger Method for LSM-Tree Based Key-Value Stores. doi: 10.22677/thesis.200000364341
  • DGIST
  • View : 346
  • Download : 341

Age of Information Analysis in Hyperledger Fabric Blockchain-enabled Wireless Monitoring Networks

Thesis
Age of Information Analysis in Hyperledger Fabric Blockchain-enabled Wireless Monitoring Networks
  • Minsu Kim
  • 2021
  • Minsu Kim. (2021). Age of Information Analysis in Hyperledger Fabric Blockchain-enabled Wireless Monitoring Networks. doi: 10.22677/thesis.200000363627
  • DGIST
  • View : 220
  • Download : 156
Thesis
Age of Information Analysis of Short Packet Communications in Cellular Networks
  • Hyeonjun Sung
  • 2021
  • Hyeonjun Sung. (2021). Age of Information Analysis of Short Packet Communications in Cellular Networks. doi: 10.22677/thesis.200000363593
  • DGIST
  • View : 242
  • Download : 0
Thesis
Age-based Optimal Design for Ensuring Data Freshness in Edge Computing-enabled Monitoring Networks
  • Jinwoong Kim
  • 2024
  • Jinwoong Kim. (2024). Age-based Optimal Design for Ensuring Data Freshness in Edge Computing-enabled Monitoring Networks. doi: 10.22677/THESIS.200000802750
  • DGIST
  • View : 173
  • Download : 0
Thesis
Analysis on the Effects of the Interfering Node on the Unmanned Aerial Vehicle Communications
  • Minsu Kim
  • 2019
  • Minsu Kim. (2019). Analysis on the Effects of the Interfering Node on the Unmanned Aerial Vehicle Communications. doi: 10.22689/thesis.200000171472
  • DGIST
  • View : 536
  • Download : 0
Thesis
Blockchain-empowered Wireless Networks: Modeling and Analysis
  • Sungho Lee
  • 2023
  • Sungho Lee. (2023). Blockchain-empowered Wireless Networks: Modeling and Analysis. doi: 10.22677/THESIS.200000686815
  • DGIST
  • View : 102
  • Download : 0
Thesis
Deep Reinforcement Learning for Data Freshness in Blockchain-enabled Wireless Sensor Networks
  • Dongsun Kim
  • 2024
  • Dongsun Kim. (2024). Deep Reinforcement Learning for Data Freshness in Blockchain-enabled Wireless Sensor Networks. doi: 10.22677/THESIS.200000802743
  • DGIST
  • View : 198
  • Download : 0
Thesis
Efficient Design of Secure IoT Data Sharing with Fine-grained Access Control
  • Woojin Jeon
  • 2022
  • Woojin Jeon. (2022). Efficient Design of Secure IoT Data Sharing with Fine-grained Access Control. doi: 10.22677/thesis.200000627132
  • DGIST
  • View : 217
  • Download : 0
Thesis
Efficient Transmission Techniques for Emerging 3D Wireless Networks
  • Chanwon Park
  • 2023
  • Chanwon Park. (2023). Efficient Transmission Techniques for Emerging 3D Wireless Networks. doi: 10.22677/THESIS.200000687229
  • DGIST
  • View : 86
  • Download : 0
Thesis
Energy-efficient Task Offloading and Sensing Decision for Edge Computing-enabled Vehicular Networks
  • Hewon cho
  • 2023
  • Hewon cho. (2023). Energy-efficient Task Offloading and Sensing Decision for Edge Computing-enabled Vehicular Networks. doi: 10.22677/THESIS.200000687729
  • DGIST
  • View : 127
  • Download : 0

Exploiting various patterns for heterogeneous graph attention network

Thesis
Exploiting various patterns for heterogeneous graph attention network
  • Eunjeong Yi
  • 2021
  • Eunjeong Yi. (2021). Exploiting various patterns for heterogeneous graph attention network. doi: 10.22677/thesis.200000497156
  • DGIST
  • View : 229
  • Download : 135
Thesis
Fine-grained Access Control Design for Secure Vehicular Communications
  • Donghyun Yu
  • 2024
  • Donghyun Yu. (2024). Fine-grained Access Control Design for Secure Vehicular Communications. doi: 10.22677/THESIS.200000725989
  • DGIST
  • View : 139
  • Download : 0
Thesis
Improving the Performance of Natural Language Deep Learning Models by Using Dimension Attribute Values
  • Myeonghwa Lee
  • 2021
  • Myeonghwa Lee. (2021). Improving the Performance of Natural Language Deep Learning Models by Using Dimension Attribute Values. doi: 10.22677/thesis.200000363154
  • DGIST
  • View : 219
  • Download : 0
Thesis
Intelligent Decision-Making for Reliable Information Transfer in Next-Generation Wireless Networks
  • Sinwoong Yun
  • 2025
  • Sinwoong Yun. (2025). Intelligent Decision-Making for Reliable Information Transfer in Next-Generation Wireless Networks. doi: 10.22677/THESIS.200000840042
  • DGIST
  • View : 172
  • Download : 0
Thesis
Modeling and Optimization of Network Resource Allocation for Edge Computing-Enabled Integrated Access and Backhaul Networks
  • Mingun Kim
  • 2025
  • Mingun Kim. (2025). Modeling and Optimization of Network Resource Allocation for Edge Computing-Enabled Integrated Access and Backhaul Networks. doi: 10.22677/THESIS.200000840198
  • DGIST
  • View : 206
  • Download : 0
Thesis
Optimal Design for Emerging Networks: Ultra Dense and Edge Computing-Enabled Networks
  • Hewon Cho
  • 2019
  • Hewon Cho. (2019). Optimal Design for Emerging Networks: Ultra Dense and Edge Computing-Enabled Networks. doi: 10.22703/thesis.200000171484
  • DGIST
  • View : 453
  • Download : 0
Thesis
Performance Analysis for Successful Edge Computing in Heterogeneous Networks
  • Chanwon Park
  • 2018
  • Chanwon Park. (2018). Performance Analysis for Successful Edge Computing in Heterogeneous Networks. doi: 10.22677/thesis.200000102654
  • DGIST
  • View : 707
  • Download : 0
Thesis
Precoding Design for Multi-user Multi-antenna Systems in Emerging Networks
  • Minsu Kim
  • 2023
  • Minsu Kim. (2023). Precoding Design for Multi-user Multi-antenna Systems in Emerging Networks. doi: 10.22677/THESIS.200000654685
  • DGIST
  • View : 281
  • Download : 0
Thesis
Toward Connected Sky: Modeling and Performance Analysis for Cellular-connected UAV Communications
  • Seongjun Kim
  • 2025
  • Seongjun Kim. (2025). Toward Connected Sky: Modeling and Performance Analysis for Cellular-connected UAV Communications. doi: 10.22677/THESIS.200000828362
  • DGIST
  • View : 196
  • Download : 0
1