Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Evaluation of the H.264 scalable video coding in error prone IP networks

The Joint Video Team, composed by the ISO/IEC Moving Picture Experts Group (MPEG) and the ITU-T Video Coding Experts Group (VCEG), has standardized a scalable extension of the H.264/AVC video coding standard called Scalable Video Coding (SVC). H.264/SVC provides scalable video streams which are composed by a base layer and one or more enhancement layers. Enhancement layers may improve the temporal, the spatial or the signal-to-noise ratio resolutions of the content represented by the lower layers. One of the applications, of this standard is related to video transmission in both wired and wireless communication systems, and it is therefore important to analyze in which way packet losses contribute to the degradation of quality, and which mechanisms could be used to improve that quality. This paper provides an analysis and evaluation of H.264/SVC in error prone environments, quantifying the degradation caused by packet losses in the decoded video. It also proposes and analyzes the consequences of QoS-based discarding of packets through different marking solutions.

Monitoring of urban transportation networks using wireless sensor networks

Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015