Núcleo de estudos-Online
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Núcleo de Estudos
Center for Studies in Computational Intelligence
- NIC
Person in Charge:
Professor Bruno Henrique Groenner Barbosa
brunohb@deg.ufla.br
Institutional Information:
www.deg.ufla.br
Background:
The Center for Studies in Computational Intelligence
(NIC) was created on August 5, 2011 as NEMECA
(Center for Studies in Mechatronics). On that date, in
a General Assembly with all students from the Control
and Automation Engineering course, the study center
was created for students to interact and spread
knowledge in the area of mechatronics. Therefore,
subgroups were created in charge of different areas
related to electronics, computing and mechanics.
Over time, several of those subgroups developed
an identity of their own, forming new groups such
as TROIA (called NEMECA-Botz while still part
of NEMECA) and the Center for Studies in UAV
(formerly called NEMECA-UAV). Thus, the remaining
members of NEMECA decided to modify the center’s
objectives to develop activities exclusively in the area
of computational intelligence. Consequently, its name
was changed and it is now called NIC - Center for
Studies in Computational Intelligence.
Overall Objectives:
To study numerical methods and software used in
computer simulations of the most diverse industrial
problems and other problems of public and/or
scientific interest. The center also aims to organize
scientific, social and cultural debates and events.
Areas of Expertise:
1. Soft Sensor: Implementation of virtual sensors to
replace high cost sensors or to measure quantities that
cannot be measured with physical sensors.
2. Biomedical Engineering: Application of CI
techniques in biomedical signal analysis to assist
health specialists in the diagnosis of pathologies.
3. Pattern Recognition: Use of CI to classify patterns
in various applications such as biomedics and smart
grids.
4. Optimization: Use of evolutionary computating
techniques for single- and multi-objective optimization
of real systems.
33