Abstract: Precision, robustness, dexterity, and intelligence are the design indices for current generation surgical robotics. To augment the required precision and dexterity into microsurgical work-flow, hand-held robotic instruments are developed to compensate physiological tremor in real-time. The active compensation is challenging due to the time-varying unknown delay introduced by hardware (sensors) and software (causal linear filters) that adversely affects the device real-time performance. The current techniques for 3D tip position control rely on modeling and canceling the tremor in 3-axes separately. Our recent findings show significant correlations in tremor across the three dimensions and the grip force. This talk will first introduce the challenges and then present our research efforts on multi-dimensional (3D and 4D) tremor modeling using machine learning approaches that show improved performance.
Biography: Dr. Kalyana C. Veluvolu is a Professor at the College of IT Engineering, Kyungpook National University, South Korea. He heads an interdisciplinary research group where research interests lie at the intersection of intelligence, robotics, sensing systems and health-care. Specifically, my interests are on developing technology for biomedical, health-care related fields, with primary focus on smart medical robotics, and EEG based human/brain computer interface applications.
Since 2009, he has been with the College of IT Engineering, Kyungpook National University, Daegu, South Korea. He received the B.Tech degree in electrical and electronic engineering from Nagarjuna University, Guntur, India, in 2002, and the Ph.D. in electrical engineering from Nanyang Technological University, Singapore, in 2006. From 2006 to 2009, he was a Research Fellow with the Biorobotics Group, Robotics Research Center, Nanyang Technological University. He was attached to the School of Mechanical and Aerospace Engineering, Nanyang Technological University, as a Visiting Professor during 2016–2017. He has also held several visiting positions in University of Newcastle, UK, and University of Valenciennes, France.
He has been a Principal Investigator or a Co-Investigator on a number of research projects funded by government, industry, and universities with a total budget of over 2.5 Million US$. He also received several awards for research excellence including the 2020 KNU Academic Award and 2017 Excellent Research Award awarded by Ministry of Education, South Korea in 2018. He has authored or co-authored more than 120 journal articles and conference proceedings. He is currently the Associate Editor for Journal of The Franklin Institute, Systems Science and Control Engineering Journals. He has been on technical/program committee’s of several international conferences. Dr. Veluvolu is also a senior member of IEEE.
Abstract: Recent advancement in communication technologies especially in the field of hardware and software has helped the emergence of Internet-connected sensory devices that provide visibility and data measurements in the mobile world. Scientists have projected the by 2025, the total number of devices connected to the Internet will be between 25 billion and 50 billion. Since the number of devices increasing, the volume of published data is also increasing leaps and bound. This is going to be a mammoth task to manage these data. To extract the significant data from this huge database in a real time system is not an easy task. As a solution to this problem, the future generation advanced IOT systems will be incorporated with machine learning based software. Therefore, intelligent processing and analysis of this big data is the key to making Future Generation smart IoT applications. It needs different types of electronic learning approaches that address the challenges posed by IoT data by viewing smart cities and other use cases. Various advance algorithms in Machine learning are also necessary to extract high-quality data. Therefore, in the new generation IOT systems, machine learning will play a major role in this automated world. The future generation IOT would get a revolutionary change due to advancement of Artificial Intelligence and machine learning in recent times.
Biography: Dr. Ankush Ghosh is a Fellow of IETE and Senior Member of IEEE. He has outstanding research experiences and published edited books, patents and more than 100 research papers. He was a research fellow of the Advanced Technology Cell- Defence Research & Development Organization (DRDO), Govt. of India. He was awarded National Scholarship by HRD, Govt. of India. He received his Ph.D. (Engineering) degree from prestigious Jadavpur University, West Bengal in 2010. He has fifteen years of teaching experience in the Institutes, The Neotia University and Jadavpur University. His research areas of interest include AI/ML, Nanotechnology, Renewable Energy Embedded and Smart Devices etc. He has delivered Keynote and Invited talk in more than 50 Seminars, workshops and Refreshers courses. He has guided a large number of B.Tech and M.Tech thesis and Ph.D. students. Dr. Ghosh has successfully executed his duty in various positions like, Dean of Faculty, HOD, Governing body member, Centre in Charge, NBA Coordinator etc. He is Editorial Board Member of seven International Journals. Dr. Ghosh is an active member of IEEE Kolkata section. He has organized a number of International conferences as Technical chair/Co-chair in association with IEEE/Springer. He is a member of National Entrepreneurship Network- Mentor Group. He has received award for contributing in Innovate India from AICTE- DST, Govt. of India in 2019.