Video transmission over wireless channel

This work has been realized by the author in his PhD course in Electrical, Computer Science and Telecommunication at the University of Bologna, Faculty of Engineering, Italy. All the documentation here reported is a summary of years of work, under the supervision of Prof. Oreste Andrisano, coordinator of Wireless Communication Laboratory - WiLab, in Bologna. The subject of this thesis is the transmission of video in a context of heterogeneous network, and in particular, using a wireless channel. All the instrumentation that has been used for the characterization of the telecommunication systems belongs to CNR (National Research Council), CNIT (Italian Inter- University Center), and DEIS (Dept. of Electrical, Computer Science, and Systems). From November 2009 to July 2010, the author spent his time abroad, working in collaboration with DLR - German Aerospace Center in Munich, Germany, on channel coding area, developing a general purpose decoder machine to decode a huge family of iterative codes. A patent concerning Doubly Generalized-Low Density Parity Check codes has been produced by the author as well as some important scientic papers, published on IEEE journals and conferences.

Neural network modelling of the wave-structure interaction processes

This thesis presents a new Artificial Neural Network (ANN) able to predict at once the main parameters representative of the wave-structure interaction processes, i.e. the wave overtopping discharge, the wave transmission coefficient and the wave reflection coefficient. The new ANN has been specifically developed in order to provide managers and scientists with a tool that can be efficiently used for design purposes. The development of this ANN started with the preparation of a new extended and homogeneous database that collects all the available tests reporting at least one of the three parameters, for a total amount of 16’165 data. The variety of structure types and wave attack conditions in the database includes smooth, rock and armour unit slopes, berm breakwaters, vertical walls, low crested structures, oblique wave attacks. Some of the existing ANNs were compared and improved, leading to the selection of a final ANN, whose architecture was optimized through an in-depth sensitivity analysis to the training parameters of the ANN. Each of the selected 15 input parameters represents a physical aspect of the wave-structure interaction process, describing the wave attack (wave steepness and obliquity, breaking and shoaling factors), the structure geometry (submergence, straight or non-straight slope, with or without berm or toe, presence or not of a crown wall), or the structure type (smooth or covered by an armour layer, with permeable or impermeable core). The advanced ANN here proposed provides accurate predictions for all the three parameters, and demonstrates to overcome the limits imposed by the traditional formulae and approach adopted so far by some of the existing ANNs. The possibility to adopt just one model to obtain a handy and accurate evaluation of the overall performance of a coastal or harbor structure represents the most important and exportable result of the work.