The objective of this thesis is to develop reduced models for the numerical solution of optimal control, shape optimization and inverse problems. In all these cases suitable functionals of state variables have to be minimized. State variables are solutions ...
Understanding how organisms deal with probabilistic stimulus-reward associations has been advanced by a convergence between reinforcement learning models and primate physiology, which demonstrated that the brain encodes a reward prediction error signal. Ho ...
Over the last decades, calibration techniques have been widely used in robotics since they represent a cost-effective solution for improving the accuracy of robots and machine-tools. They only involve software modification without the necessity of revising ...
The medical image registration problem can be formulated as a nonlinear programming problem. To identify appropriate algorithms to solve it, it is critical to analyze the properties of the problem. In this paper, we consider the image registration problem ...
In this paper, we propose an analytical low-level representation of images, obtained by a decomposition process, namely the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invaria ...
This work follows from a research project, in which we investigate the underlying mechanisms of human imitation and develop a neural model of its core neural circuits. The present paper presents a model of a neural mechanism by which an imitator agent can ...
The present work has been focused on the development of analytical and numerical techniques for the analysis of highly convoluted antennas and microwave devices including fractal shaped antennas. The accurate prediction of the frequency response of a high- ...