The fusion of Artificial Neural Networks and Fuzzy Logic Systems allows researchers to model real world problems through the development of intelligent and adaptive systems. Artificial Neural networks are able to adapt and learn by adjusting the interconnections between layers while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as “Neuro-Fuzzy” systems. In this chapter, the basic elements of both approaches are analyzed while neuro-fuzzy networks learning algorithms are presented. Here, we combine the use of neuro-fuzzy algorithms with multimedia-based signals for training. Ultimately this process may be employed for automatic identification of patterns introduced in medical applications and more specifically for analysis of content produced by brain imaging processes.
Risk prevention of historical buildings through sensor network monitoring represents a challenging and together promising task towards an optimized buildings’ protection plan. Due to the significant character that historical buildings have within the history and identity of local and consequently national communities, the monitoring approach above is nowadays considered a fundamental component of cultural preservation. Modern trends on sensor networks nowadays allow the installation of wireless monitoring equipment, able to collect a large volume and variety of data that can be further processed in order to support prevention modelling techniques and strategies. Despite the valuable post-processing outcome of the above modelling techniques, in several real-time monitoring cases, an aspect that may significant impact the data analysis accuracy from the monitoring engineer is to create an audiovisual representation of the gathered data that are easily perceived and directly associated to well-known environmental conditions. Towards this aim, in this work an audio-based representation of the collected data is introduced that achieves high monitoring performance in terms of the perceived nature of the building vibrations themselves. The proposed audio-based representation technique is based on a number of advanced sonification methods, combined with specific human-hearing simulation mechanisms that allow an effective increase of the represented information volume in real-time. A number of subjective tests that were performed demonstrate a significant improvement on the way that the monitoring personnel can efficiently perceive the origin and / or nature of the acquired vibrations, rendering the proposed technique a strong supplement towards efficient historical building risk prevention.
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