Professor Vincenzo Piuri

Università degli Studi di Milano (Italy)

Keynote: “Biometrics Technologies for Ambient Intelligence and Smart Living”

Professor Vincenzo Piuri has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA. He is Full Professor in computer engineering at the Università degli Studi di Milano, Italy (since 2000).
His main research interests are: intelligent systems, signal and image processing, machine learning, pattern analysis and recognition, theory and industrial applications of neural networks, biometrics, intelligent measurement systems, industrial applications, fault tolerance, digital processing architectures, embedded systems, and arithmetic architectures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters.
He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS.
He has been IEEE Past Vice President for Technical Activities (2016), IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19) and Associate Editor of the IEEE Transactions on Computers, the IEEE Transactions on Cloud Computing and IEEE Access, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.
He received the IEEE Instrumentation and Measurement Society Technical Award (2002) for the contributions to the advancement of theory and practice of computational intelligence in measurement systems and industrial applications. He is Honorary Professor at the Obuda University, Budapest, Hungary (since 2014), Guangdong University of Petrochemical Technology, China (since 2014), the Muroran Institute of Technology, Japan (since 2016), and the Amity University, India (since 2017).


Professor Jacob Scharcanski

Universidade Federal do Rio Grande do Sul (Brazil) and Instrumentation and Measurement Society

Keynote:Computer Vision in Medical Imaging and Measurements: Making Sense of Visual Data

Jacob Scharcanski is a Professor (Full) in Computer Science at the Federal University of Rio Grande do Sul (UFRGS), Brazil.

He holds a cross appointment with the Department of Electrical Engineering at UFRGS, and also is an Adjunct Professor with the Department of Systems Design Engineering, University of Waterloo, Canada.

He authored and co- authored over 150 refereed journal and conference papers, book chapters and books, and delivered over 30 invited presentations worldwide.

He serves as an Associate Editor for two journals, and has served on dozens of International Conference Committees.

In addition to his academic activities, he has several technology transfers to the private sector.

Professor Scharcanski is a licensed Professional Engineer (PEO, Canada), Senior Member of the IEEE, Member of SPIE, and serves as Co-Chair of the Technical Committee IEEE IMS TC-19 (Imaging Measurements and Systems).


Professor Paul C.-P. Chao

National Chiao Tung University (Taiwan, ROC) and Sensors Council

Keynote:A New Cuffless Photoplethysmography (PPG) Sensor Device for Continuous Blood Flow Volume and Pressures Measurement

Paul C.-P. Chao received his Ph.D. degree from Michigan State University, USA, and then with Chrysler Corp in Auburn Hill, Detroit, USA before he joined National Chiao Tung University (NCTU), Taiwan. He is currently University Distinguished Professor of the electrical engineering department at NCTU. His research interests focus on sensors, actuators and their interface circuitry. Dr. Chao has published more than 270 peer-reviewed papers (books, journal papers, conferences, reports) and 38 patents.

Dr. Chao was the recipient of the 1999 Arch T. Colwell Merit Award from Society of Automotive Engineering, Detroit, USA; the 2004 Long-Wen Tsai Best Paper Award from National Society of Machine Theory and Mechanism, Taiwan; the 2005 Best Paper Award from National Society of Engineers, Taiwan; the 2007 Acer Long-Term Award; the 2009 Best Paper Award from the Symposium on Nano-Device Technology; the 2010/2014 Best Paper Award from the Annual ASME Conference on Information Storage and Processing Systems (ISPS); the second most downloaded paper in IEEE Sensors Journal in 2011; the Best Poster Paper award of IDMC 2015; the prestigious Outstanding Research Award from National Association of Automatic Control in Taiwan in 2015; the prestigious National Innovation Award of Taiwan government 2016; The 2017  Best Industrial Project Award by Ministry of Science and technology, Taiwan government; The 2017 Presidential Outstanding Professor of Engineering in Nation (Taiwan) (awarded by the president of the nation in the Presidential House of Taiwan, ROC).

Dr. Chao has served as University Associate Vice Presidents of NCTU for academic affairs (2009-2010) and research and development (2015); the Secretary General, IEEE Taipei Section, 2009-2010; the founding chair of Taipei chapter for the IEEE Sensor Council; Member-at-Large for IEEE Sensors Council, 2012-2014. Dr. Chao received major IEEE awards for this service: The IEEE Large Section Award from IEEE Head Quarter for the outstanding service as the Secretary for 2009-2010, and The IEEE MGA Award from IEEE Region 10 for outstanding service as the Secretary for IEEE Taipei Section, 2009-2010; The 2017 Best Topical Editor, Runner up, IEEE Sensors Journal. He was the General Chair of the 2016 ASME ISPS and IoT conference in Santa Clara, CA, USA; chairs and co-chairs of major conferences. For editorial services, he is currently Topical Editors of IEEE Sensors Journal and IEEE IoT Journal; the Associate Editors of ASME Journal of Vibration and Acoustics and Journal of Circuit, System and Computer; guest editors of special journal issues. He is an IEEE Distinguished Lecturer, a senior member of IEEE and Fellow of ASME.

The most significant technical contributions to date by Dr. Chao are on automatic ball balancers (ABB) and the cuffless blood pressure/flow monitor. Dr. Chao is the true initiator of the research on ABB in the world, while the first scholar developed successfully a cuffless blood pressure/flow monitor using only a single photoplethysmography (PPG) sensor.



Professor Ved Ram Singh

National Physical Laboratory (India) and Engineering in Medicine and Biology Society

Keynote:Advanced Sensors for U-Health Care

Prof. (Dr) V.R.Singh, Ph.D. (Electrical Engg), IIT-Delhi and Life Fellow- IEEE and LF-IETE, LF-IE-I, LF-ASI/USI and LF-IFUMB/WFUMB, has over 37 years of research-cum-teaching experience in India and abroad (Univ of Toronto-Canada, KU Leuven- Belgium, Korea Univ, South Korea, TU-Delft, Netherlands, Univ of Surrey, UK, and others). He has been at National Physical Laboratory (NPL), New Delhi, as a Director-grade-Scientist/Distinguished Professor and Head, Instrumentation, Sensors and Biomedical Measurements and Standards.

He has over 350 papers, 250 talks, 260 conf papers, 4 books, 14 patents and 30 consultancies to his credit. Under his guidance, 30 PhD scholars have earned PhD degree while others are working with him.

Dr. Singh has been the Associate Editor of IEEE Int Sensor Journal (2010-2016), Associate Editor of IEEE Transactions on Instrumentation and Measurements and Regional Editor of Int Journal of Biomedical Engineering and Technology (IJBET). Apart from this, he is on Editorial/Reviwer Boards of other journals. like Sensors & Actuators (Switzerland), IEEE Trans on Engg in Med and Biology , J Computers in Electrical Engg (USA), J.Instn Electr Telecom Engrs, J.Instn Engrs -India, Ind J Pure & Appl Physics, J.of Instrm Soc Ind, J. Pure & Appl Ultrasonics, J. Life Science Engg, etc.

He is the recipient of awards by INSA (Ind Natnl Sci Academy)1974, NPL 1973, Thapar Trust 1983, ICMR (Ind Council of Med Res) 1984; Japan Soc. Ultr in Medicine 1985, Asian Federation of Societies of Ultasound in Medicine & Biology 1987, IE-I(Institution of Engineers- India) 1988/ 1991, IEEE-EMBS 1999 and IEEE-2010/2011/2014, for his outstanding contributions. He has served as Guest Editor of Special Issues of JASI on Physical Acoustics and Utrasonics (2016-17) and Medical Acoustics (2017-18) as well ason IETE Technical Review journal on Transducers(2002).

He is the Chair of IEEE-EMBS/IMS-Delhi Chapter, President of Acoustical Society of India and Vice President of Ultrasonic Society of India and has been theVice President of Instrumentation Soc of India, Vice-President of IFSUMB, Secretary of IEEE India Council and the Chairman of IEEE-Delhi Section. Dr. Singh is a Member of IEEE Standards Association. He was also Council Member of WFUMB (Australia) Ultrasound Safety and Standards. He has served as the Chair or a Member of BIS Committee on Elctro-Medical Committee in the past and presently, he is the Chairman of BIS-MHD-15 Committee. He has been the session chair, plenary/keynote/invited speaker and on advisory boards of world congresses and national/international conferences, world over. He is the Conf Organiser of WESPAC-2018, Nov 10 to 15, New Delhi.

He has been Distinguished Professor at NPL-India and Thapar University, and is working as a Director/Advisor of PDM University, Delhi-NCR.

His main areas of interest are biomedical instrumentation, biomedical standards, computer modeling and simulation, sensors and transducers, biomedical ultrasonics/medical acoustics, POCT devices, neuro-sensors/implants, nano-cancer-technology, cancer hyperthermia, tissue characterisation, lithotripsy, WSN and u-health care.



UBIsym titles

A new sensor device capable of measuring blood-flow volume (BFV) and blood pressure (BP) is successfully designed and prototyped. With this device, the measurement on BFV and BP is non-invasively, and cam be continuously collected over more than 24 hours, resulting in valuable long-time monitoring data for effective medical diagnosis. The long-time BVF measurements are particularly suited to assess quality of arteriovenous fistula in hemodialysis patients. Of particular interests is that BFV is nowadays in clinic practices evaluated using an ultrasound Doppler monitor, which is expensive, bulky, and can only be operated by well-trained medical personnels. The sensor device developed by this study is a low-cost, small-sized, portable, and easy-to-use PPG sensor that is capable of continuous measurement of BFV and BP. New designs of front-end analog circuits, signal processing, and an intelligent neural network calibration method are employed to finally achieve high correlations of R2 = 0.88 for BFV and R2 = 0.85 for BP, as opposed to their gold standard counterpart monitors.
In this talk, computer vision is proposed as a way to facilitate the interpretation of phenomena in medical imaging, and to make measurements or inferences based on models of such phenomena. Actually, this is an ill-posed problem that humans can learn to solve effortlessly, but computer algorithms often are prone to errors. Nevertheless, in some cases computers can surpass humans and help interpret medical imagery more accurately, as we will discuss in this talk. Medical imaging measurements often are indirect and involve errors. For example, estimating tumor growth (or shrinkage) in response to treatment requires measuring the tumor size, modeling the tumor shape, and making accurate predictions to evaluate the treatment effectiveness, which can be challenging in practice. These issues are closely related to machine learning and pattern recognition, and in this talk we discuss some cases that illustrate how techniques of these areas can be adapted to solve problems in medical imaging measurements. In order to illustrate this presentation, several issues in medical imaging and measurements are discussed and illustrated using case studies and examples.
With the advancement in technology, newer and newer sensors and systems are being developed, day by day, for various engineering, scientific and biomedical applications. However, new sensor systems are still required to be developed further for reliable diagnosis of a particular disease/abnormality and for its therapeutic treatment well in time. Here, advanced nano-sensors for diagnostic imaging, point-of-care-devices and nao-instrumentation systems for therapeutic treatment of different types of abnormalities and diseases are discussed for health care applications. Main emphasis is placed on the development of bio-chip based sensors and other biologically inspired systems. Design and fabrication aspects of these devices are described in detail. The sensors and instrumentation systems developed here have direct applications in ubiquitous health care, particularly in monitoring and control of health of old age patients living in isolated/hilly areas. WSN (Wireless Sensor Networking) technology is used as an integral part of u-health care systems. The case study of nano-cancer technology and lithotripsy is presented here as a practical clinical example of the present research. This research would contribute to scientific advancement of biomedical engineering for better health care, at low cost in an effective and reliable manner.
Adaptability and advanced services for ambient intelligence require an intelligent technological support for understanding the current needs and the desires of users in the interactions with the environment for their daily use, as well as for understanding the current status of the environment also in complex situations. This infrastructure constitutes an essential base for smart living. Various technologies are nowadays converging to support the creation of efficient and effective infrastructures for ambient intelligence. Artificial intelligence can provide flexible techniques for designing and implementing monitoring and control systems, which can be configured from behavioral examples or by mimicking approximate reasoning processes to achieve adaptable systems. Machine learning can be effective in extracting knowledge form data and learn the actual and desired behaviors and needs of individuals as well as the environment to support informed decisions in managing the environment itself and its adaptation to the people’s needs. Biometrics can help in identifying individuals or groups: their profiles can be used for adjusting the behavior of the environment. Machine learning can be exploited for dynamically learning the preferences and needs of individuals and enrich/update the profile associated either to such individual or to the group. Biometrics can also be used to create advanced human-computer interaction frameworks. Cloud computing environments will be instrumental in allowing for world-wide availability of knowledge about the preferences and needs of individuals as well as services for ambient intelligence to build applications easily. This talk will analyze the opportunities offered by these technologies to support the realization of adaptable operations and intelligent services for smart living in an ambient intelligent infrastructures.