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A methodology to approach the automatic monitoring and prognosis of diseases evolution is proposed. We define a multilevel system architecture capable to process multi-source biomedical data according to a coarse-to-fine paradigm. An application regarding neuro-signals and image categorization is also considered as a case study. The proposed methodology even preliminary has shown to be a possible approach to prognosis activity, mainly if suitably integrated into a hybrid system for medical decision support.

This paper describes a research activity carried on in the frame of a collaboration among ISTI-CNR, leader of the 'Representation and Communication of Data and Metadata' Workpackage in the European Project MUSCLE-NoE('Multimedia Understanding through Semantics, Computation and Learning - Network of Excellence') and the Dept. of Electronics, A.I. & Telecommunications of the Polytechnic University of Marche. In particular, an original approach towards multimedia metadata handling and a solution for massive metadata processing, based on distributed computing, are presented. Besides, a tool kit that glues low level metadata with higherlevel representation capabilities of RDF is also approached.

A methodology for the automatic monitoring of diseases using biomedical data in multimedia is proposed. We adopt a computational intelligence approach mainly based on a multilevel neural network architecture. This approach has been employed in applications for neurosignal and image categorisation.

An image processing procedure is proposed to detect porosity defects in composite materials, analyzing C-scan images obtained by ultrasound inspection techniques. An image described by a set of features is analyzed in order to evaluate its similarity with a reference set. A 2D wavelet transform is applied to the input image and then a feature extraction based on statistics of the detailed images produced by the transform itself is performed. The principal component analysis technique (PCA) is then applied in order to map input features into an output plane maximizing data variance. Finally the image is classified considering the distance between points in the PCA plane. This procedure is also applied for the analysis of a single image. Preliminary results on simulation images and real C-scan maps show that the procedure is able to detect defects.

The real-time detection and tracking of moving objects is a challenging task and automatic tools to identify and follow them are often subjected to constraints regarding the environment under investigation or the full visibility of the targeted object. Exploiting the possibility of a multi-source acquisition in the targeted scene, firstly detection is performed by means of characteristic features extraction and storing in a database; secondly, the tracking task is approached using algorithms, where automatic search involves occluded or masked targets in the scene. This latter problem is solved through database retrieval, based on well-defined multi-modal features. The method has been tested on case studies regarding the identification and tracking of animals moving at night in an open environment (i.e. natural reserves or parks), and the surveillance of known scenes for unauthorized access control.

In recent years the increasing role of Multimedia (MM) data, in the form of still pictures, graphics, 3D models, audio, speech, video or their combination (eg MM presentations), in the real world, has lead to a demand for better procedures for the automatic generation and extraction of both low level and semantic features from multi-source data in order to enhance their potential for computational interpretation and processing.

In the context of the Information System for Italian Research in Antarctica (SIRIA) project, the operating unit of the Institute of Information Science and Technologies has to study and realize a web system for the collection and the management of the metadata on Italian campaigns in Antarctica. The aim of this project report is to illustrate the realization and the possibilities provided by the module of Metadata Entry of the system Geo-Metadata Server (GMDS), that allows to all registered users belonging to PNRA research sectors, to store information on their own Antarctic data.

In the context of the Information System for Italian Research in Antarctica (SIRIA) project, the operating unit of the Institute of Information Science and Technologies has to study and realize a web system for the collection and the management of the metadata on Italian campaigns in Antarctica. The aim of this project report is to illustrate the realization of the Geo-Metadata Server (GMDS), the web system that allows the PNRA researchers to discover all the information about specific data, through a methodology based on an "Incremental Resource Discovery" mechanism.

A research has been carried out finalized to the definition of a methodology useful for the diagnosis and prediction of the correct evolution state of physical systems. In this paper we present a related model and a specific network topology for the considered problem. In particular, the prediction procedure is based on a 'Self Organizing Map'(SOM) and an 'Error Back-Propagation'(EBP) networks combined to form a hierarchical architecture. The network system has been developed and tested using data furnished by Alenia and consisting in sensorial data (FBG, Fiber Bragg Grating) and multi-format descriptive data regarding evaluation (SB). The obtained results have shown that the developed methodology is a promising tool for the diagnosis activity.

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