| Master Session |
To take advantage of the course, the student should spend approximately 120 hours (10 hours per week for 12 weeks). Student with good bases on deterministic signal processing, linear algebra and statistics should be able to follow most of the content with a commitment of 120 hours.
For each chapter several materials will be made available to the students, such as notes in PDF or examples of programs in MATLAB.
This course deals with statistical signal processing techniques from the point of view of theory and practice. The main educational objectives are that students acquire competences related to: modeling real signals by using stochastic processes modeling techniques to model; perform spectral estimation using nonparametric methods and parametric methods based on signal modeling; perform time-frequency non-parametric and parametric estimates; apply bootstrap methodology applied to compute estimation errors, creating non-parametric confidence intervals and its application to solving problems in the field of signal theory. In addition to the knowledge and application of methods of statistical signal processing, students learn how to write scientific papers, practice the peer review process and will write a scientific paper, proving to have acquired the knowledge and skills specified. |
| Forum Index |
Students' participation in the forums on the website of the course will be assessed. |
| Integrated methodologies |
Students will prepare a final project in which they must demonstrate their mastery in the contents of the subject. This final project will follow the format of a scientific paper in any of the conferences of reference in the field.
This article shall employ any of the methods presented in the course to solve a problem of interest to the student. Items will be evaluated by a peer review process similar to that used by IEEE journals. |
