Yunjung Jeong 정윤중

I am a B.S. candidate at DGIST, double-majoring in Electrical Engineering and Computer Science. I am currently an undergraduate researcher at the Intelligent Radio Sensing Lab (IRS Lab), advised by Prof. Jae-Ho Choi. My interests lie at the intersection of wireless communications and signal processing — particularly in techniques that bridge the two, such as RF / passive sensing, communication-signal-based sensing, and integrated sensing and communications (ISAC).

Email · GitHub · CV

DGIST · Daegu, Republic of Korea

Now

Starting work on drone detection via passive sensing with a USRP X410 SDR platform at IRS Lab.

News

Jan. 2026: Joined the Intelligent Radio Sensing Lab (IRS Lab) at DGIST as an undergraduate researcher.
Apr. 2025: Co-authored paper on real-time video over 5G C-V2X sidelink presented at JCCI 2025.
Feb. 2025: Received the Participation Prize at the KICS 2025 Winter Conference Idea Contest.
Jan. 2024: Discharged from the Republic of Korea Army after completing mandatory military service.
Mar. 2021: Entered DGIST as an undergraduate student.

Research

I am broadly interested in how signals originally designed for communication can also be used to sense the physical world, and in methods that sit naturally in both domains. My current work at IRS Lab explores passive radar sensing with software-defined radios (USRP-2921), using existing RF signals to monitor vital signs and detect moving targets such as drones. I am also curious about how learning-based methods can improve detection and classification of RF signals in complex environments.

Wireless signal processing & RF sensing — passive sensing, SDR/USRP-based sensing, vital-sign sensing, drone sensing.
Communication-signal-based sensing — 5G/6G waveforms, C-V2X sidelink, ISAC.
AI for wireless sensing and communications — deep learning for RF signal representation, detection and classification.

Education

Daegu Gyeongbuk Institute of Science & Technology (DGIST)
B.S. Candidate, Double Major in Electrical Engineering and Computer Science
School of Undergraduate Studies · Daegu, Republic of Korea
Expected graduation: Aug. 2027 (military leave Jul. 2022 – Jan. 2024)
Feb. 2021 – Present

Research Experience

Undergraduate Researcher, Intelligent Radio Sensing Lab (IRS Lab), DGIST
Advisor: Prof. Jae-Ho Choi
Jan. 2026 – Present
- Conducted passive sensing experiments using a USRP-2921 SDR platform.
- Investigated RF sensing methods for vital-sign monitoring (respiration, heartbeat).
- Studied drone-related sensing problems using wireless signal measurements.
- Worked with reference/surveillance-channel architectures to analyze target-induced signal variations.
- Studied Cross-Ambiguity Function (CAF) and Range-Doppler map interpretation for passive radar.
Undergraduate Researcher, Communication & Signal Processing Lab (CSP Lab), DGIST
Advisor: Prof. Ji-Woong Choi
Jan. 2025 – Apr. 2025
- Empirical study of real-time video transmission over 5G C-V2X sidelink in a V2I scenario using OBU and RSU devices.
- Analyzed the trade-off between Block Error Rate (BLER) and average throughput across MCS indices.
- Evaluated wireless link performance via RSSI, estimated SNR, BLER, and throughput.
- Co-authored a domestic conference paper presented at JCCI 2025.

Publications

Domestic Conference

D. Lee, M. Kim, Y. Jeong, Y. Jang, Y. Kang, T. Lee, and J.-W. Choi. “Real-Time Video Transmission in 5G C-V2X Sidelink: An Empirical Study (5G C-V2X Sidelink 기반 실시간 영상 전송 성능에 대한 실증 연구).” JCCI, 2025.
PDF

Awards

Participation Prize (장려상)
2025 Winter Conference Academic Presentation — Idea Contest, The Korean Institute of Communications and Information Sciences (KICS)
Feb. 2025

Selected Projects

CNN Implementation from Scratch
Python · NumPy

Implemented convolution, pooling, fully connected layers, cross-entropy loss, and backpropagation in NumPy without any deep learning framework. Built the full pipeline — data preprocessing, training, evaluation, and model saving — to understand the mathematical foundations of CNNs.
Network-based AI Game Client with RGB LED Matrix
C · TCP/IP · Minimax / Alpha-Beta pruning
2nd of 40 teams

TCP/IP socket-based game client in C using Minimax with Alpha-Beta pruning for real-time decision-making, visualized on a 64×64 RGB LED matrix. Placed 2nd of 40 teams in the course competition.

Teaching

Teaching Assistant, Linear Algebra, DGIST
Feb. 2026 – Present

Skills

Programming Python, C/C++, Git, Linux.
Wireless & RF passive radar sensing, RF measurement, C-V2X sidelink analysis.
Equipment SDR (USRP-2921).

Service

Mandatory Military Service
Republic of Korea Army
Jul. 2022 – Jan. 2024