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Dong-Seob Lee (Integrated PhD Program) Wins Grand Prize at the 9th KIEES Electromagnetic Measurement Paper Competition

Dong-Seob Lee, an integrated PhD student from POSTECH’s Department of Electrical Engineering (advisor: Professor Won-Bin Hong), was awarded the Grand Prize (최우수상) at the 9th Electromagnetic Measurement Paper Competition hosted by the Korean Institute of Electromagnetic Engineering and Science (KIEES). His paper, titled “Development of a Novel Multiphysics Evaluation Metric for Precise Measurement of FOWLP Antenna Performance: Equivalent Loss Modeling, Die Shift Tolerance, and Thermal Analysis,” received top honors for its innovation and academic excellence.

About the Research

Lee’s research introduces a novel multiphysics evaluation metric for accurately measuring the performance of Fan-Out Wafer-Level Package (FOWLP) antennas. By integrating thermodynamics, electromagnetics, and nanotechnology, the proposed methodology offers precise measurements applicable to advanced technologies in 5G/6G communications, radar, sensing semiconductors, and wireless communication systems.

The study’s emphasis on equivalent loss modeling, die shift tolerance, and thermal analysis provides a comprehensive framework that enhances the accuracy and reliability of antenna performance evaluations. Its potential impact on future applications across multiple disciplines was a key factor in being selected for the Grand Prize.

Significance of the Competition

The Electromagnetic Measurement Paper Competition is organized to highlight the importance of electromagnetic measurement in research and to promote accurate measurement techniques based on established standards. The event aims to nurture talent in the field by engaging undergraduate and graduate students specializing in electromagnetics across Korea.

This award highlights POSTECH’s leadership in interdisciplinary research and its contribution to advancing cutting-edge technologies in electromagnetic measurement and related fields.

Beom-Hyun Kim (Integrated PhD Program) Wins “Best Student Paper Award” at IEEE EuCAP 2024

Beom-Hyun Kim, an integrated PhD student from POSTECH’s Department of Electrical Engineering (advisor: Professor Won-Bin Hong), received the prestigious Best Student Paper Award at the IEEE European Conference on Antennas and Propagation (EuCAP) 2024. His paper, titled “A Completely Overlapped Ku- and Ka-Band Dual-Polarized Phased Array for Simultaneous Terrestrial and Satellite Communications,” was recognized as the best among 987 submissions.

This marks the second Best Student Paper Award for Professor Hong’s research group, following their groundbreaking win at EuCAP 2020 as the first-ever Asian recipient, setting a new record by achieving this distinction twice within four years.

About the Research

The paper proposed a novel dual-band, dual-polarized phased array antenna design based on the Shared Aperture concept and cavity model analysis. This innovative approach maximizes spatial efficiency and antenna performance while offering advantages in compactness and scalability for new frequency bands. The method has been highly praised for its potential to expand applications in simultaneous terrestrial and satellite communications.

About EuCAP

The IEEE European Conference on Antennas and Propagation (EuCAP) is the largest conference in Europe dedicated to antennas and propagation. It is one of the three flagship conferences of the IEEE Antennas and Propagation Society (AP-S) and was held this year in Glasgow, Scotland. The conference is renowned for its rigorous selection process, with only one paper selected for the Best Student Paper Award after a four-stage evaluation process.

This recognition underscores POSTECH’s leadership in antenna technology and its continued excellence in cutting-edge research on the global stage.

Professor Tae-Hyun Oh Appointed as Editorial Board Member of International Journal of Computer Vision (IJCV)

Professor Tae-Hyun Oh of POSTECH’s Department of Electrical Engineering has been appointed as an Editorial Board Member (equivalent to Associate Editor) of the International Journal of Computer Vision (IJCV), one of the most prestigious journals in computer vision. Published by Springer Nature, IJCV is a top-tier Q1 journal with an impact factor of 19.5, ranked 3rd out of 145 journals in the Computer Science/Artificial Intelligence category.

Significance of the Appointment

Editorial board members of IJCV are selected based on their research expertise, impact, and leadership in the field. As an Editorial Board Member, Professor Oh will oversee the review process for submissions related to computer vision, graphics, artificial intelligence, and machine learning, ensuring the publication of high-quality and impactful research.

Contributions to the Research Community

Professor Oh actively contributes to the global AI and computer vision community. He has served as an Area Chair and Session Chair at leading conferences such as ICCV 2023, NeurIPS 2023, ICLR 2024, and CVPR 2024. Additionally, he has participated as a Senior Program Committee Member for AAAI, IJCAI, and SIGGRAPH Asia. He also holds editorial roles as an Associate Editor for The Visual Computer (TVCJ) (Springer) and IEIE Transactions on Smart Processing & Computing.

Through his leadership and editorial work, Professor Oh continues to elevate the standards of research and foster innovation in the fields of AI and computer vision.

Professor Soo-Seok Choi’s Research Team Publishes Cover Article in Advanced Materials Technologies on Stretchable Multi-Pixel Color Matrix Driving

A research team led by Professor Soo-Seok Choi from POSTECH’s Department of Electrical Engineering, comprising PhD candidates Jun-Hyuk Shin (first author), Seung-Min Nam, and Sang-Hyun Han, as well as master’s students Ji-Yoon Park and Chae-Hyun Kwon, has been recognized for their groundbreaking study on stretchable color devices. Their research has been selected as an Inside Back Cover article in the interdisciplinary journal Advanced Materials Technologies and will be officially published on December 21.

Research Highlights

The study extends the concept of intrinsic stretchable color-changing devices from single-pixel designs to a multi-pixel system capable of uniform and precise color control. The team successfully implemented Segmented Matrix Driving technology, enabling display functionality in stretchable systems.

This innovative technology represents the first demonstration of a stretchable multi-pixel system with intrinsic color changes under mechanical deformation, providing a significant step forward in developing stretchable displays and flexible electronics.

Significance

This achievement highlights a major advancement in stretchable display technology, addressing the challenges of uniform control and scalability. The proposed system opens up new possibilities for applications in wearable electronics, electronic skins, and next-generation flexible displays.

Being featured as a cover article in Advanced Materials Technologies further underscores the importance and potential impact of this research, solidifying POSTECH’s position as a global leader in cutting-edge materials and device engineering.

Jae-Won Yoon (Integrated PhD Program) and Yong-Jung Oh (PhD Candidate) Win “Outstanding Paper Award” at the 2023 KICS Summer Conference

Jae-Won Yoon, an integrated PhD program student, and Yong-Jung Oh, a PhD candidate from POSTECH’s Department of Electrical Engineering (advisor: Professor Yosep Jeon), were awarded the Outstanding Paper Award at the 2023 Summer Conference of the Korean Institute of Communications and Information Sciences (KICS). Their paper, titled “Sparsification-Based Compression Techniques for Communication-Efficient Federated Learning,” was recognized for its innovative contribution to the field.

Research Highlights

The study addresses the communication overhead problem in federated learning by proposing a novel compression method using sparsification and quantization for model updates. Additionally, it introduces recovery techniques leveraging the Bussgang method and Linear Minimum Mean Squared Error (LMMSE) detection.

The results demonstrate that the proposed compression and recovery methods significantly reduce communication overhead while maintaining high performance in federated learning systems. This breakthrough has the potential to enhance the scalability and efficiency of federated learning in real-world applications.