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Showing results 1 to 7 of 7

Thesis
Development of High-Frequency Ultrasound Transducers and Signal Processing Algorithms for Integrated Circuit Inspection
  • Yoonseong Jang
  • 2024
  • Yoonseong Jang. (2024). Development of High-Frequency Ultrasound Transducers and Signal Processing Algorithms for Integrated Circuit Inspection. doi: 10.22677/THESIS.200000724875
  • DGIST
  • View : 111
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Thesis
Medically Applicable Electromagnetic Microrobot Manipulation System with Air and Metal-Cored Coils for Real‐Time Vascular Interventions
  • Latifi Gharamaleki Nader
  • 2025
  • Latifi Gharamaleki Nader. (2025). Medically Applicable Electromagnetic Microrobot Manipulation System with Air and Metal-Cored Coils for Real‐Time Vascular Interventions. doi: 10.22677/THESIS.200000828428
  • DGIST
  • View : 356
  • Download : 0
Thesis
On-chip programmable Mach-Zehnder Interferometer based on silicon photonic MEMS
  • Myeongseok Hong
  • 2024
  • Myeongseok Hong. (2024). On-chip programmable Mach-Zehnder Interferometer based on silicon photonic MEMS. doi: 10.22677/THESIS.200000725378
  • DGIST
  • View : 187
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Thesis
Programmable Photonic Integrated Circuits Using Low Power Consumption MEMS
  • Man J. Her
  • 2023
  • Man J. Her. (2023). Programmable Photonic Integrated Circuits Using Low Power Consumption MEMS. doi: 10.22677/THESIS.200000656269
  • DGIST
  • View : 236
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Thesis
PZT-based flexible 1-3 composite for wearable applications
  • Hyeoncheol Lim
  • 2023
  • Hyeoncheol Lim. (2023). PZT-based flexible 1-3 composite for wearable applications. doi: 10.22677/THESIS.200000656180
  • DGIST
  • View : 163
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Thesis
Scalable Recirculating Programmable Silicon Photonic MEMS
  • Do Y. Kim
  • 2022
  • Do Y. Kim. (2022). Scalable Recirculating Programmable Silicon Photonic MEMS. doi: 10.22677/thesis.200000596371
  • DGIST
  • View : 313
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Thesis
Speckle-to-Speckle: Ultrasound speckle reduction technique using unsupervised deep learning
  • Dongkyu Jung
  • 2022
  • Dongkyu Jung. (2022). Speckle-to-Speckle: Ultrasound speckle reduction technique using unsupervised deep learning. doi: 10.22677/thesis.200000629151
  • DGIST
  • View : 264
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