Support Vector Machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks. These techniques have been proved to have superior performances in a large variety of real world applications due to their generalization abilities and robustness against noise and interferences. This book introduces a set of novel techniques based on SVM that are applied to antenna array processing and electromagnetics. In particular, it introduces methods for linear and nonlinear beamforming and parameter design for arrays and electromagnetic applications.

This lecture discusses the use of graph models to represent reconfigurable antennas. The rise of antennas that adapt to their environment and change their operation based on the user's request hasn't been met with clear design guidelines. There is a need to propose some rules for the optimization of any reconfigurable antenna design and performance. Since reconfigurable antennas are seen as a collection of self-organizing parts, graph models can be introduced to relate each possible topology to a corresponding electromagnetic performance in terms of achieving a characteristic frequency of...