| Original language | English (US) |
|---|---|
| Article number | 9049489 |
| Pages (from-to) | 142-144 |
| Number of pages | 3 |
| Journal | IEEE transactions on biomedical circuits and systems |
| Volume | 14 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2020 |
ASJC Scopus subject areas
- Biomedical Engineering
- Electrical and Electronic Engineering
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Guest Editorial : Special Section on AI-Based Biomedical Circuits and Systems. / Zhou, Jun; Wei, Ying; Wang, Chao; Ren, Fengbo; Huang, Li.
In: IEEE transactions on biomedical circuits and systems, Vol. 14, No. 2, 9049489, 04.2020, p. 142-144.Research output: Contribution to journal › Editorial › peer-review
}
TY - JOUR
T1 - Guest Editorial
T2 - Special Section on AI-Based Biomedical Circuits and Systems
AU - Zhou, Jun
AU - Wei, Ying
AU - Wang, Chao
AU - Ren, Fengbo
AU - Huang, Li
N1 - Funding Information: Ying Wei received the B. S. and M. S. degrees from Xi’an Jiaotong University, Xi’an, China, in 2000 and 2003, respectively. She received the Ph.D. degree from the National University of Singapore, Singapore, in 2008. She is currently working as a Full Professor with the School of Control Science and Engineering, Shandong University, Jinan, China. She is the Director of International Cooperation Research Center of Intelligent Sensing and Information Processing in Shandong Province. She is currently an Associate Editor for the IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS. Her research interests include implementation of high-speed digital systems and biomedical signal processing. In recent years, she has taken charge of various projects including National Key Research and Development Project, National Natural Science Foundation, the Ministry of Science and Technology, and the Ministry of Education. Funding Information: Fengbo Ren (Member, IEEE) received the B.Eng. degree from Zhejiang University, Hangzhou, China, in 2008 and the M.S. and Ph.D. degrees from the University of California, Los Angeles, CA, USA, in 2010 and 2014, respectively, all in electrical engineering. Since 2015, he has been a faculty of the School of Computing, Informatics, and Decision Systems Engineering with Arizona State University (ASU). His Ph.D. research involved in designing energy-efficient VLSI systems, accelerating compressive sensing signal reconstruction, and developing emerging memory technology. His current research interests are focused on hardware acceleration and parallel computing solutions for data analytics and information processing, with emphasis on bringing energy efficiency and signal intelligence into a wide spectrum of today’s computing infrastructures, from data center server systems to wearable and Internet-of-Things devices. He is a member of the Digital Signal Processing Technical Committee and VLSI Systems & Applications Technical Committee of the IEEE Circuits and Systems Society. He is the recipient of the Broadcom Fellowship in 2012, the prestigious National Science Foundation Faculty Early Career Development (CAREER) Award in 2017, and the Google Faculty Research Award in 2018. He is also the recipient of the Top 5% Best Teacher Award from the Fulton Schools of Engineering at ASU from 2016 to 2019.
PY - 2020/4
Y1 - 2020/4
UR - http://www.scopus.com/inward/record.url?scp=85083010443&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083010443&partnerID=8YFLogxK
U2 - 10.1109/TBCAS.2020.2978994
DO - 10.1109/TBCAS.2020.2978994
M3 - Editorial
AN - SCOPUS:85083010443
VL - 14
SP - 142
EP - 144
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
SN - 1932-4545
IS - 2
M1 - 9049489
ER -