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Thesis
High-Resolution and Energy-Efficient Bio-Potential Acquisition Systems based on Noise-Shaping SAR
- Geunha Kim
- 2025
- Geunha Kim. (2025). High-Resolution and Energy-Efficient Bio-Potential Acquisition Systems based on Noise-Shaping SAR. doi: 10.22677/THESIS.200000828338
- DGIST
- View : 317
- Download : 0
- Heechang Park
- 2025
- Heechang Park. (2025). The study of ZnO TFT based on ALD system. doi: 10.22677/THESIS.200000841634
- DGIST
- View : 732
- Download : 0
- MINSEO SON
- 2025
- MINSEO SON. (2025). A Joint Source Channel Coding System That Transmits Foveated Images. doi: 10.22677/THESIS.200000841173
- DGIST
- View : 576
- Download : 0
- Wooyub Chun
- 2025
- Wooyub Chun. (2025). A Duty-Cycled CTDSM with Scalable Bandwidth and Power for ExG Biopotential Acquisition. doi: 10.22677/THESIS.200000846193
- DGIST
- View : 567
- Download : 0
Thesis
Hierarchical Network Slicing leveraging Dynamic Optimization and Learning in Open-RAN Architecture
- Han Jong Won
- 2025
- Han Jong Won. (2025). Hierarchical Network Slicing leveraging Dynamic Optimization and Learning in Open-RAN Architecture. doi: 10.22677/THESIS.200000847726
- DGIST
- View : 524
- Download : 0
- Wonhyuk Choi
- 2024
- Wonhyuk Choi. (2024). Improving performance in 3-terminal artificial olfactory sensor system for electrical nose. doi: 10.22677/THESIS.200000804793
- DGIST
- View : 346
- Download : 0
- Seongyeon Yu
- 2024
- Seongyeon Yu. (2024). A Cross-Point PRAM-Based Voltage-Mode Processing-In-Memory Macro with the Gilbert Cell-Based Active Feedback. doi: 10.22677/THESIS.200000804206
- DGIST
- View : 357
- Download : 0
- Taehyun Seo
- 2025
- Taehyun Seo. (2025). Closed-loop System Identification with Varying Feedback Frequency. doi: 10.22677/THESIS.200000843095
- DGIST
- View : 523
- Download : 0
- Sungju Kim
- 2025
- Sungju Kim. (2025). MX format Quantization Optimization for Lightning-NeRF. doi: 10.22677/THESIS.200000828493
- DGIST
- View : 351
- Download : 0
- Jeeyoo Kim
- 2025
- Jeeyoo Kim. (2025). A Deep Reinforcement Learning-Based Policy for Efficient Resource Management in Vehicular Edge Computing. doi: 10.22677/THESIS.200000843414
- DGIST
- View : 330
- Download : 0
