UBIsym in Healthcare Engineering

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The 2018 edition of UBIsym, an IEEE symposium at Universidade da Beira Interior, Covilhã Portugal, will take place on July 24, 2018 with the theme "Healthcare Engineering". This will consist in a day of presentations led by distinguished lecturers, inserted in several sections of engineering linked to health and social sciences. The event will also have one special session with the confirmed presence of the IEEE 2020 presidential candidate, Professor Vincenzo Piuri.



  Date and Time

  Location

  Contact

  Registration


  • Calçada Fonte Lameiro
  • Covilhã, Centro
  • Portugal 6200-358
  • Building: Edificio 1 das Engenharias
  • Room Number: 8.01
  • Click here for Map

Staticmap?size=250x200&sensor=false&zoom=14&markers=40.27843%2c 7
  • This event is organized by IEEE Universidade da Beira Interior Student Branch.

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  Speakers

Prof. Vincenzo Piuri of University of Milan, Italy

Topic:

Adaptivity by using Artificial Intelligence, Machine Learning, and Biometrics in Worldwide Cloud-based Environments

Biography:

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).

Address:Lombardia, Italy

Prof. Jacob Scharcanski of Federal University of Rio Grande do Sul (UFRGS), Brazil

Topic:

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

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.

Biography:

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).

Address:Rio Grande do Sul, Brazil