Mr. Haosheng Liu | Noise and Vibration Awards | Best Researcher Award

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Mr. Haosheng Liu | Noise and Vibration Awards | Best Researcher Award

Mr. Haosheng Liu, Qingdao University of Technology, China

Dr. Liu Haosheng is an accomplished researcher specializing in noise and vibration, with a strong emphasis on sound field synthesis and noise control. He recently published an innovative study, “Experimental Synthesis of Random Pressure Fields Based on Transfer-Matrix Analysis on 1D Arrays,” in the prestigious Journal of Sound and Vibration (February 2025). His research addresses complex challenges in acoustics engineering, environmental noise control, and sound design, bridging the gap between academic inquiry and practical applications. Dr. Liu actively collaborates with leading experts in the field, fostering innovation and contributing significantly to advancements in noise control technologies. His dedication to pushing the boundaries of understanding and technology in his domain has earned him recognition as a leader in his field

Professional Profile:

ORCID

Research Contributions:

Dr. Liu has demonstrated significant contributions to the field of noise and vibration, particularly through his recent publication titled
“Experimental synthesis of random pressure fields based on transfer-matrix analysis on 1D arrays,” published in the Journal of Sound and
Vibration (February 2025). This work reflects his innovative approach to sound field synthesis and showcases his ability to tackle complex
problems within the discipline.

Impact of Research:

The research conducted by Dr. Liu has implications for various practical applications, including acoustics engineering, environmental noise
control, and sound design, making his work valuable to both academic and industrial sectors. His contributions are essential for advancing
the understanding and technology surrounding noise control and sound synthesis.

Collaborative Work:

Dr. Liu has collaborated with notable researchers in the field, enhancing the credibility and impact of his work. This collaborative spirit
fosters innovation and knowledge sharing, which are crucial for a researcher’s growth and influence.

Conclusion:

Given Dr. Liu Haosheng’s notable research output, innovative methodologies, and contributions to the field of noise and vibration, he is
highly suitable for the Best Researcher Award. His work not only pushes the boundaries of current research but also contributes positively
to the community and industry practices.

Publication Top Notes:

Experimental synthesis of random pressure fields based on transfer-matrix analysis on 1D arrays

 

Prof. Shih-Hau Fang | Acoustic Awards | Outstanding Scientist Award

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Prof. Shih-Hau Fang | Acoustic Awards | Outstanding Scientist Award 

Prof. Shih-Hau Fang, National Taiwan Normal University, Taiwan

Shih-Hau Fang is a distinguished professor in the Department of Electrical Engineering at National Taiwan Normal University (NTNU), specializing in AIoT, mm-wave radar applications, and acoustic signal sensing. He holds a Ph.D. in Communication Engineering from National Taiwan University and has a rich academic and industrial career, having previously served as a professor at Yuan-Ze University and a chief scientist at Far EasTone Telecom. Professor Fang’s research is interdisciplinary, focusing on the Internet of Things (IoT), indoor positioning, healthcare applications, and acoustic sensing technologies. He has authored numerous technical papers, holds multiple patents, and has been recognized as one of the top 2% scientists globally in networking and telecommunications. A fellow of the Institution of Engineering and Technology (IET) and a senior member of IEEE, he has received several prestigious awards, including the Outstanding Young Electrical Engineer Award and the Future Technology Award. His research team has made significant contributions to mobile computing, voice enhancement, and pathological voice analysis, with work featured in top IEEE journals and conferences. Prof. Fang’s groundbreaking work has garnered over 4000 citations, establishing him as a leading figure in his field.

Professional Profile:

Summary of Suitability for the Outstanding Scientist Award: Prof. Shih-Hau Fang

Academic and Professional Credentials:

Prof. Shih-Hau Fang is an eminent scientist with a robust academic background and substantial contributions to the fields of AIoT, mm-wave radar applications, and acoustic signal processing. His educational journey, beginning with a Bachelor’s degree in Communication Engineering from National Chiao Tung University (1999), progressing through a Master’s and Ph.D. from National Taiwan University (2001 and 2009), reflects a strong foundation in communication and engineering. Prof. Fang has consistently advanced his career, holding prestigious positions such as Full Professor at National Taiwan Normal University, Chief Scientist for AIoT at Far EasTone Telecom, and Distinguished Professor at Yuan-Ze University.

🎓 EDUCATION

  • National Chiao Tung University | Bachelor (09/1995 – 06/1999) | Communication Engineering
  • National Taiwan University, Taiwan | Master (09/1999 – 06/2001) | Communication Engineering
  • National Taiwan University, Taiwan | Ph.D. (09/2003 – 02/2009) | Communication Engineering

💼 EMPLOYMENT

  • National Taiwan University, Taiwan | Postdoctoral Researcher (02/2009 – 07/2009) | Communication Engineering
  • Yuan-Ze University, Taiwan | Assistant Professor (08/2009 – 01/2013) | Networking
  • Yuan-Ze University, Taiwan | Associate Professor (02/2013 – 07/2016) | Data Science
  • Academia Sinica, Taiwan | Visiting Scholar (01/2015 – 02/2015) | Acoustic Signal Processing
  • Yuan-Ze University, Taiwan | Full Professor (08/2016 – 07/2024) | Data Science
  • Far EasTone Telecom, Taiwan | Chief Scientist (02/2020 – 12/2020) | Cross-domain AI Applications
  • AI Innovation Research Center, Yuan-Ze University, Taiwan | Director (02/2022 – 07/2024) | Cross-domain AI Applications
  • Research and Development Office, Yuan-Ze University, Taiwan | Vice President (02/2022 – 07/2024) | AIoT, Mm-Wave Radar Applications
  • Yuan-Ze University, Taiwan | Distinguished Professor (02/2022 – 07/2024) | AIoT, Mm-Wave Radar Applications
  • National Taiwan Normal University, Taiwan | Full Professor (08/2024 – Present) | AIoT, Mm-Wave Radar Applications

🏅 HONORS

  1. 2024 IET Fellow | [Institution of Engineering and Technology]
  2. 2023-2024 ESI Highly Cited Paper | [Web of Science]
  3. 2020-2024 Top 2% Scientists | [Elsevier]
  4. 2021 Most Cited Paper | [Journal of Voice]
  5. 2021 Top 100 Download Paper | [Scientific Report]

🔬 CONTRIBUTIONS TO SCIENCE

Prof. Fang has authored 2 book chapters, holds 13 patents, and published 60 journal articles. He has 58 international conference papers and has accumulated over 4000 citations on Google Scholar. His research breakthroughs include:

  • Indoor positioning: Eliminating multipath effects, published in IEEE ToWC with 263 citations.
  • Neural networks: Published in IEEE ToNN with 289 citations.
  • Mobile computing: Improving efficiency, published in IEEE ToMC.
  • Voice enhancement technology: Developed a commercially viable offline voice control module.

In 2020, Prof. Fang’s team achieved 3rd place in Track 6 and 1st place in Track 7 at the International Indoor Navigation Competition. Their work in pathological voice analysis ranks among the best globally, with their detection accuracy being one of the highest.

🌍 IMPACT & INNOVATIONS

  • Prof. Fang has pioneered voice enhancement technologies, receiving the Gold Award at the 2021 Taiwan Innovation Expo and the Future Technology Breakthrough Award (2019).
  • His team’s work on AI applications in healthcare has greatly improved pathological voice analysis through a fully annotated voice database in collaboration with Far Eastern Memorial Hospital.

🎯 Research Focus:

  • AIoT
  • mm-Wave Radar
  • Acoustic Signal Processing
  • Indoor Positioning Systems

Publication top Notes:

 

A Lightweight Learning Framework for Packet Loss Concealment and Speech Enhancement

Novel Human-Posture Recognition System Based on Advanced Graph Convolutional Network Using Skeletal Data

Novel Subject-Dependent Human-Posture Recognition Approach Using Tensor Regression

Prediction of Customer Behavior Changing via a Hybrid Approach

Unsupervised Face-Masked Speech Enhancement Using Generative Adversarial Networks With Human-in-the-Loop Assessment Metrics

Improved Speech Authenticity Detection in Chinese–English Bilingual Contexts