Autonomous & Adaptive Communication Lab
Research Field
Ying-Ren Chien (Senior Member, IEEE) received the B.S. degree in electronic engineering from the National Yunlin University of Science and Technol ogy, Douliu, Taiwan, in 1999, and the M.S. degree in electrical engineering and the Ph.D. degree in communication engineering from National Taiwan University, Taipei, Taiwan, in 2001 and 2009, respectively.
He joined the Department of Electrical Engineer ing, National Ilan University (NIU), Yilan City, Taiwan, from 2012 to 2025. He has been promoted to Full Professor since 2018; he served as the Chair at NIU from 2018 to 2025. Since 2025, he has been with the Department of Electronic Engineering, National Taipei University of Technology (NTUT), Taipei, where he is currently a Full Professor. His research interests are consumer electronics, multimedia denoising algorithms, adaptive signal processing theory, active noise control, machine learning, the Internet of Things, and interference cancellation.
Dr. Chien received best paper awards, including ICCCAS 2007, ROCKLING 2017, and IEEE ISPACS 2021. He was presented with the IEEE CESoc/CTSoc Service Awards in 2019, NSC/MOST Special Outstanding Talent Award in 2021, 2023, and 2024, Excellent Research-Teacher Award in 2018 and 2022, and Excellent Teaching Award in 2021. From 2023 to 2024, he was the Vice Chair of IEEE Consumer Technology Society (CTSoc) Virtual Reality, Augmented Reality, and Metaverse (VAM) Technical Committee (TC). Since 2025, he has been the Secretary of IEEE CTSoc Audio/Video Sytems and Signal Processing (AVS) TC. He is currently an Associate Editor of IEEE TRANSACTIONS ON CONSUMER ELECTRONICS.
The Autonomous & Adaptive Communication Lab (AACL) at NTUT is at the forefront of research in signal processing and modern communication technologies. Situated at the intersection of theoretical innovation and practical application, the lab dedicates its efforts to advancing five key areas of study: Machine Learning Applications, Internet of Things (IoT) Technology, Adaptive Signal Processing Algorithms, Millimeter-wave Radar Signal Processing, and Cross-domain Cooperation Applications.
At AACL, researchers explore the potential of artificial intelligence to enhance communication systems, delve into the intricacies of IoT data management, and develop sophisticated signal filtering techniques. The lab's work in millimeter-wave radar technology contributes to the evolution of 5G networks and autonomous vehicle systems. Furthermore, the lab's commitment to interdisciplinary research is evident in its collaborations with other departments, such as its joint project with the Agricultural Department on high-throughput screening models.
AACL places a strong emphasis on nurturing the next generation of communication engineers and researchers. Undergraduate students are offered unique opportunities to engage in hands-on research projects, ranging from IoT implementations and green energy applications to software-defined radio systems. This practical experience allows students to apply theoretical knowledge to real-world communication challenges, preparing them for future careers in this rapidly evolving field.
Through its comprehensive approach to research and education, the Autonomous & Adaptive Communication Lab continues to push the boundaries of communication technology, fostering innovation and interdisciplinary collaboration while equipping students with the skills and knowledge needed to address the complex challenges of tomorrow's communication landscape.
The lab primarily focuses on adaptive signal processing (ASP)-related topics, with five main research directions:
- Machine Learning Applications
- Internet of Things (IoT) Technology and Applications
- Adaptive Signal Processing Algorithms, including:
- Selective adaptive filtering algorithm design
- Power line communication impulse interference cancellation
- Anti-interference design for Global Navigation Satellite System (GNSS) receivers [Interference identification and localization
4. Millimeter-wave Radar Signal Processing and Applications
5. Cross-domain Cooperation Applications
For undergraduate research projects, the lab emphasizes practical and highly applicable topics such as:
- IoT and green energy technology applications
- Arduino-related applications
- Machine learning for physiological feature signal recognition
- Implementation of digital signal processing algorithms on TI DSP 6713/6416
- Indoor positioning technology for energy-saving design applications
- GPS signal measurement and anti-interference implementation
- Software-defined radio applications in digital communication
- Power line communication implementation
This research agenda demonstrates the lab's focus on cutting-edge signal processing techniques and their practical applications in various fields, including communications, positioning systems, and interdisciplinary collaborations.
- (Agricultural Department Research Project) Establishment of high-throughput screening models
- Selective adaptive filtering algorithm design
- Power line communication impulse interference cancellation
- Anti-interference design for Global Navigation Satellite System (GNSS) receivers [Interference identification and localization]
Research
- 2023: Distinguished Professor at NIU (3 years: 2023-2025)
- 2020-2022: World’s Top 2% Scientists (2022, 2021, 2020) in Networking & Telecommunications Category
- 2023, 2021: NSC/MOST University Research Award
- 2022, 2019: Outstanding Research Award (University-level award)
Service
- 2025-present: Secretary: CTSoc AVS Technical Committee
- 2023: Outstanding Class-Advisor Award (University-level award)
- 2019-2024: Chairman for NIU-EE
- 2024-2026: Associate Editor: IEEE TCE
- 2023-2024: Vicechair: CTSoc VAM Technical Committee
Teaching
- 2022: Outstanding Teaching Award
- Teaching Practice Research/Innovative Teaching (Design Thinking)
- EMI Teaching Competency Development
- 2009 Ph.D., Graduate Institute of Communication Engineering, National Taiwan University, Taiwan
- 2001 M.S., Graduate Institute of Electrical Engineering, National Taiwan University, Taiwan
- 1999 B.S., Department of Electronic Engineering, National Yunlin University of Science and Technology, Taiwan
1 Vacancy
Job Description
Requirements
- Enrolled in a Bachelor's or Master's program or above in Electrical Engineering, Computer Science, or a related field.
- Knowledge of adaptive filtering, distributed signal processing, and sensor networks.
- Proficiency in MATLAB or Python for algorithm development and simulation.
- Strong understanding of probability, stochastic processes, and optimization techniques.
- Ability to work both independently and in a collaborative research environment.
Preferred Intern Education Level
- Enrolled in a Bachelor's or Master's program in Electrical Engineering, Computer Science, or a related field.
Skill sets or Qualities
- Experience with diffusion-based algorithms in distributed networks.
- Knowledge of social network models and misinformation mitigation techniques.
- Prior research experience in adaptive filtering, wireless sensor networks, or distributed learning.
- Knowledge of adaptive filtering, distributed signal processing, and sensor networks.
- Proficiency in MATLAB or Python for algorithm development and simulation.
- Strong understanding of probability, stochastic processes, and optimization techniques.
- Ability to work both independently and in a collaborative research environment.
- Academic paper preparation
- Communication in English or Chinese.
1 Vacancy
Job Description
- Familiarity with adaptive signal processing and kernel-based filtering techniques.
- Proficiency in MATLAB, Python, or Julia for numerical simulations.
- Strong analytical skills and ability to interpret simulation results.
- Ability to document research findings systematically.
Preferred Intern Education Level
Enrolled in a Bachelor's or Master's program or above in Electrical Engineering, Computer Science, or a related field.
Skill sets or Qualities
- Knowledge of statistical signal processing and machine learning fundamentals.
- Experience with Monte Carlo simulations and stochastic optimization.
- Interest in academic research and potential for future graduate studies
- Academic paper preparation
- Communication in English or Chinese.