报告人：Dr. Zhanpeng Jin， Binghamton University, State University of New York (SUNY-Binghamton)
报告题目1：Mobile, Wearable, and Cloud Computing in Smart and Connected Health
The severe challenges of the skyrocketing healthcare expenditure and the fast aging population highlight the needs for innovative solutions supporting more accurate, affordable, flexible, and personalized medical services. Our group has been focusing on the research that provides effective and efficient health care and assisted living solutions, leveraging cyber computing technologies, such as wearable wireless body sensors, mobile and cloud computing, and brain-/human-computer interfaces. For instance, to overcome the limitations of computing power and storage space on mobile devices, we propose a new hybrid mobile-cloud computational solution to enable more effective personalized healthcare and the results show that the proposed approach can significantly enhance the conventional mobile-based medical monitoring in terms of diagnostic accuracy, execution efficiency and energy efficiency. Based upon the mobile platform, our research group also investigates a wearable glass-style, eye-controlled HCI technique that can be used to operate the smartphones and appliances, as well as a novel psychophysiological approach for secure user authentication using the non-volitional brain cognitive responses.
报告题目2：Emerging Neuromorphc Computing and Its Applications
Conventional target tracking solutions on UAVs heavily rely on off-line, computation-intensive analysis or human observation in ground control stations. These approaches are usually vulnerable to issues like limited communication bandwidth, substantial power consumption, and malicious interference. In this project we propose a new approach to enhance UAV-based surveillance capability by employing a fast and autonomous target tracking system capable of online, real-time pattern recognition. Specifically, we will demonstrate a non-conventional computing platform based on emerging concepts of neuromorphic systems, which have been developed to meet the increasing demands of large-scale neural network modeling and simulation, to support computationally expensive target detection and tracking tasks on the fly. Our research can significantly strengthen ongoing research efforts in both defense and civilian domains, towards high-performance, autonomous, and intelligent systems to meet the size, weight, and power (SWaP) constrained environments.
Dr. Zhanpeng Jin is currently an Assistant Professor in the Departments of Electrical and Computer Engineering, and Bioengineering at the Binghamton University, State University of New York (SUNY-Binghamton). Prior to joining SUNY-Binghamton, He was a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign (UIUC) and received his Ph.D. degree in Electrical Engineering from the University of Pittsburgh. His research interests include mobile and wearable computing in health, neural engineering, neuromorphic computing, low-power sensing, and body sensor networks. He was a U.S. Air Force Summer Faculty Fellow and an Air Force Visiting Faculty Fellow. His research has been supported by National Science Foundation (NSF), Air Force Office of the Scientific Research (AFOSR), Air Force Research Laboratory (AFRL), SUNY Research Foundation, and a number of industrial companies including Xerox Research and Analog Devices. He has published over 50 papers in international journals and conferences, as well as served on the editorial boards for four international journals and on the Technical Committees for more than a dozen of conferences. He is a member of Sigma Xi, IEEE, IEEE Computer Society, and IEEE Engineering in Medicine and Biology Society.