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Advanced Bionic Systems

Code 13523
Year 1
Semester S2
ECTS Credits 6
Workload TP(60H)
Scientific area Biomedical Sciences
Entry requirements None
Mode of delivery Face-to-face.
Work placements N/A.
Learning outcomes To teach the main principles of Bionics, review the basics of the bionic systems, study the mimicry, the systems of man-machine interface and the artificial intelligence.
Apply the knowledge about bionic systems.
Understand the human movement patterns and the mimicry, in order to design bioinspired systems.
Know the different types of man-machine interface systems.
Apply different techniques of control systems.
Understand the concepts of artificial intelligence and robotic systems.
Use system simulation software.
Understand the convergence of biological systems and technological systems.
Work individually and within a team.
Develop autonomy and leadership abilities.
Know how to read and write scientific works.
Syllabus Introduction to Bionics.
Revision of the bionic systems: popular and scientific definitions.
Mimecry.
Bio-inspired systems.
Man-machine interface.
Pattern recognition.
Artificial intelligence.
Neural networks.
Fuzzy logics.
Genetic algorithms.
Evolutionary algorithms.
Knowledge representation.
Data mining.
Collective intelligence.
Neuroscience and neurorobotics.
Main Bibliography Russell & Peter Norvig, “Artificial Intelligence, A Modern Approach”, 2nd Ed., Pearson Education Inc., 2003.
Cairo L. Nascimento Jr. & Takashi Yoneyama, “Inteligência Artificial em Controle e Automação”, Editora Edgard Blücher Ltda., 2000.
Norgaard, O. Ravn, N.K. Poulsen & L.K. Hansen, “Neural Networks for Modelling and Control of Dynamic Systems”, Springer-Verlag, 2000.
Shigeo Abe, “Neural Networks and Fuzzy Systems”, Kluwer Academic Publishers, 1997.
Witold Pedrycz and Fernando Gomide, “An Introduction to Fuzzy Sets Analysis and Design”, MIT Press, Hardcover, May 1998.
Nikola K. Kasabov , “Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering”, MIT Press, Cambridge, MA, USA, Hardcover, Oct 1996.
P. K. Simpson, “Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations”, Pergamon Press, 1990.
K.F. Man, K.S. Tang & S. Kwong, “Genetic Algorithms”, Springer-Verlag, 1999.
Teaching Methodologies and Assessment Criteria Every student has to prepare during the semester, under the supervision of the lecturer of the discipline, a work/small project (PR) and make a PowerPoint presentation in class to the other students attending the discipline.

The theoretical classes will cover the topics of the program and the student will have a ‘continuous evaluation’ (AC) where is considered the ‘assiduity’, the ‘punctuality’, the ‘participation’ and the ‘attitude’ of the student in the classroom, as well as by several short tests along of the teaching period.

The practical-laboratory classes (PL) are devoted to performing several experiments/tests and simulations in computer using software such as Matlab, Excel and others, as well as visits to the laboratory for robotics and automation to study the functioning of robots in healthcare, in bionics, and intelligent devices that give human beings an enhanced power.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2020-05-14

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