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In this work a modified Resonant Controller is proposed to deal with the tracking/rejection problem of periodic signals robust to period variations and parametric uncertainties in the plant. The control strategy is based on a resonant structure in series with a notch filter, which will be responsible to improve the robustness to period variation. A robust state feedback controller is designed by solving a linear matrix inequality (LMI) optimization problem guaranteeing the robust stability of the closed loop system. A numerical example is presented to illustrate the method.

The aim is to develop a method for obtaining anthropometric parameters using a low cost three-dimensional digitizer. The research methodology is divided in five steps: literature review; collection and analysis of anthropometric data using the direct method and also the development of the indirect method for obtaining anthropometric measurements; comparison and data analysis; discussion of results and the completion of the research. The digitization process developed it is based on Microsoft Kinect, a low-cost device, and also on the software kscan3D. For the anthropometric collection from the three-dimensional model is used Autodesk 3D Studio Max. This research presents requirements and constraints for generate the three-dimensional model in order to obtain a mesh with satisfactory precision. It is presented a flowchart to guide the implementation of the developed method in the design process as well as a summary table containing guidelines for this application. The developed method achieved 97.96% of compatibility considering the results of the measured variables in relation to the direct method. These results were obtained with an exposure time of the individual of only 3 minutes and 28 seconds, which is less than the time required in the direct method – 1 hour and 12 minutes. This demonstrates one major contribution of the developed method.

Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroscedastic (FIEGARCH) models using Markov chain Monte Carlo (MCMC) methods is described. A simulation study is presented to assess the performance of the procedure, under the presence of long-memory in the volatility. Samples from FIEGARCH processes are obtained upon considering the generalized error distribution (GED) for the innovation process. Different values for the tail-thickness parameter ν are considered covering both scenarios, innovation processes with lighter (ν > 2) and heavier (ν < 2) tails than the Gaussian distribution (ν = 2). A comparison between the performance of quasi-maximum likelihood (QML) and MCMC procedures is also discussed. An application of the MCMC procedure to estimate the parameters of a FIEGARCH model for the daily log-returns of the S&P500 U.S. stock market index is provided.